system

Abstract

The system according to this embodiment aims to reduce the long working hours of delivery drivers and achieve efficient delivery. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a planning unit. The collection unit collects data on the experience of area drivers, redelivery records of delivery destinations, records of time when delivery destinations are home, traffic conditions, data on the timing of traffic light changes in the delivery area, information on the day's delivery schedule, and time-specified information. The analysis unit analyzes the data collected by the collection unit and calculates the shortest route considering fuel efficiency and reduction of the number of redeliveries. The planning unit plans an efficient delivery route based on the shortest route calculated by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the long working hours of delivery drivers and the reduction in efficiency due to redelivery have been problems, and there is room for improvement.

[0005] The system according to the embodiment aims to reduce the long working hours of delivery drivers and achieve efficient delivery.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, and a planning unit. The data collection unit collects data on the experience level of area drivers, redelivery records of delivery destinations, actual time slots when delivery destinations are home, traffic conditions, traffic light change timing data in the delivery area, information on the day's delivery schedule, and time-specific information. The analysis unit analyzes the data collected by the data collection unit and calculates the shortest route considering fuel efficiency and reduction of the number of redeliveries. The planning unit plans an efficient delivery route based on the shortest route calculated by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can reduce the long working hours of delivery drivers and enable efficient delivery. [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 a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The delivery navigator system according to an embodiment of the present invention is a system for solving problems in the delivery industry such as labor shortages, redeliveries due to recipient absence, overwork, and increased costs due to fuel costs and wage increases. This delivery navigator system collects area driver experience data, redelivery records of delivery destinations, records of recipients' availability times, traffic conditions, traffic light change timing data in the delivery area, information on the day's delivery schedule, and time-specified delivery information. The collected data is analyzed by AI to calculate the shortest route considering fuel efficiency and reduction of the number of redeliveries. Based on this shortest route, the system plans an efficient delivery route, thereby improving the problem of long working hours for delivery drivers. First, the delivery navigator system collects area driver experience data, redelivery records of delivery destinations, records of recipients' availability times, traffic conditions, traffic light change timing data in the delivery area, information on the day's delivery schedule, and time-specified delivery information. In this process, each data is automatically collected by AI. For example, area driver experience data is collected from past delivery history and driver feedback. Redelivery records of delivery destinations are collected from past redelivery records. The system collects data on the time recipients are available at the delivery location from past delivery time data. Traffic conditions are collected from real-time traffic information. Data on traffic light change timings in the delivery area is collected from the traffic light control system. Information on the day's scheduled deliveries and time specifications is collected from the delivery schedule. Next, the delivery navigator system uses AI to analyze the collected data. Based on the collected data, the AI ​​calculates the shortest route considering fuel efficiency and reducing the number of redeliveries. For example, the AI ​​identifies the most efficient route based on the experience data of area drivers. Based on the history of redeliveries at the delivery location, it identifies routes that avoid delivery locations with a high probability of redelivery. Based on the time recipients are available at the delivery location, it identifies routes that match the time recipients are available. Based on traffic conditions, it identifies routes that avoid congestion. Based on data on traffic light change timings in the delivery area, it identifies routes that minimize waiting time at traffic lights. Based on the day's scheduled deliveries and time specifications, it identifies routes that match the delivery schedule.Finally, the delivery navigator system plans efficient delivery routes based on the shortest route calculated by AI. This can improve the problem of long working hours for delivery drivers. For example, by delivering according to the shortest route calculated by AI, travel time is reduced and the number of redeliveries decreases. This reduces the working hours of delivery drivers and suppresses overtime. In addition, fuel costs are reduced through improved fuel efficiency. This results in cost reductions for delivery companies. In summary, the delivery navigator system can improve the problem of long working hours for delivery drivers and achieve improved fuel efficiency and a reduction in the number of redeliveries.

[0029] The delivery navigator system according to the embodiment comprises a collection unit, an analysis unit, and a planning unit. The collection unit collects area driver experience data, redelivery records for delivery destinations, records of recipients' availability times, traffic conditions, traffic light change timing data for the delivery area, information on the day's delivery schedule, and time-specified information. For example, the collection unit collects area driver experience data. Area driver experience data includes the number of deliveries, delivery success rate, and delivery time. For example, the collection unit collects redelivery records for delivery destinations. Redelivery records for delivery destinations include the number of redeliveries and reasons for redelivery. For example, the collection unit collects records of recipients' availability times. Records of recipients' availability times include patterns of availability and past availability data. For example, the collection unit collects traffic conditions. Traffic conditions include congestion information and traffic accident information. For example, the collection unit collects traffic light change timing data for the delivery area. The data on traffic light change timings in the delivery area includes the location of the traffic lights and the changeover time. The collection unit collects information on the day's delivery schedule, for example. This information includes a list of delivery destinations and delivery time slots. The collection unit also collects information on time specifications, for example. This information includes the specified delivery time and priority. The analysis unit calculates the shortest route based on the collected data, taking into account fuel efficiency and reducing the number of redeliveries. The analysis unit calculates the shortest route based on the collected data, for example, taking into account fuel efficiency and reducing the number of redeliveries. The analysis unit identifies the most efficient route based on the experience data of area drivers, for example. The analysis unit identifies routes that avoid delivery destinations with a high probability of redelivery, for example, based on the history of redeliveries to delivery destinations. The analysis unit identifies routes that match the time when delivery destinations are likely to be at home, for example, based on the history of time when delivery destinations are likely to be at home. The analysis unit identifies routes that avoid congestion based on traffic conditions, for example. The analysis unit identifies routes that minimize waiting time at traffic lights, for example, based on data on the timing of traffic light changeovers in the delivery area. The analysis unit, for example, identifies a route that matches the delivery schedule based on information about the day's scheduled delivery and the time slot.The planning unit plans an efficient delivery route based on the calculated shortest route. The planning unit plans an efficient delivery route based on the calculated shortest route. The planning unit, for example, delivers according to the shortest route calculated by the AI, thereby reducing travel time and decreasing the number of redeliveries. The planning unit plans an efficient delivery route based on the shortest route calculated by the AI. As a result, the delivery navigator system according to this embodiment can improve the problem of long working hours for delivery drivers, improve fuel efficiency, and reduce the number of redeliveries.

[0030] The data collection unit collects area driver experience data, delivery destination redelivery records, delivery destination availability times, traffic conditions, traffic light timing data for the delivery area, information on the day's delivery schedule, and time-specific delivery information. Specifically, area driver experience data includes the number of deliveries, delivery success rate, and delivery time, and this data is collected from the driver's past delivery history. For example, it records how often a driver has made deliveries in the past, what their success rate was, and the average time required for each delivery. This allows for the evaluation of the driver's skill level and efficiency. Delivery destination redelivery records include the number of redeliveries and the reasons for redelivery, and this data is collected from past delivery history. For example, if a particular delivery destination experiences frequent redeliveries, it is possible to determine whether the reason is absence or other factors such as an incorrect address. Delivery destination availability times include patterns of availability and past availability data, and this data is collected based on past delivery history and information provided by customers. For example, if a particular delivery destination is often home on weekday mornings, the delivery time can be adjusted based on this information. Traffic information includes congestion and accident data, collected from real-time traffic information services. For example, information on congestion on major roads and accident locations is collected and used to select delivery routes. Traffic light timing data in the delivery area includes the location and timing of traffic lights, collected from traffic management systems. For example, knowing how often traffic lights at a particular intersection change allows for the selection of routes that minimize waiting times. Daily delivery schedule information includes delivery destination lists and delivery time slots, collected from delivery management systems. For example, knowing the list of packages to be delivered that day and the preferred delivery time slots for each destination allows for the creation of efficient delivery schedules. Time-specific information includes delivery time specifications and priorities, collected from customer order information. For example, if a particular package should be delivered in the morning, the delivery route is adjusted based on that information. This allows the data collection unit to centrally collect diverse data and provide a foundation for efficient delivery planning.

[0031] The analysis unit calculates the shortest route based on collected data, taking into account fuel efficiency and reducing the number of redeliveries. Specifically, it identifies the most efficient route based on the experience data of area drivers. For example, it calculates the optimal route under similar conditions by referring to routes that experienced drivers have successfully completed in the past. Based on the redelivery history of delivery destinations, it identifies routes that avoid destinations with a high probability of redelivery. For example, by avoiding delivery destinations where redeliveries occur frequently, it reduces wasted time and effort. Based on the time periods when delivery destinations are likely to be at home, it identifies routes that match those times. For example, by prioritizing visits to destinations where people are often at home during specific times, it improves the success rate of deliveries. Based on traffic conditions, it identifies routes that avoid congestion. For example, it selects routes that avoid congestion by utilizing real-time traffic information. Based on data on the timing of traffic light changes in the delivery area, it identifies routes that minimize waiting time at traffic lights. For example, it selects routes that reduce waiting time at traffic lights by considering the timing of traffic light changes. Based on information on the day's delivery schedule and time-specified information, it identifies routes that match the delivery schedule. For example, it selects routes that prioritize the delivery of time-specified packages. This allows the analysis unit to comprehensively analyze the collected data and calculate efficient delivery routes. Furthermore, the analysis unit uses AI to process the data in real time and continuously update the optimal route. For example, if traffic conditions or the availability of recipients at delivery locations change, it immediately incorporates the new data and recalculates the route. As a result, the analysis unit can always provide the optimal delivery route based on the latest information, enabling efficient deliveries.

[0032] The planning department plans efficient delivery routes based on the shortest route calculated by the AI. Specifically, by delivering according to the shortest route calculated by the AI, travel time is reduced and the number of redeliveries decreases. For example, based on the route calculated by the AI, the order in which each delivery destination is visited is optimized to reduce unnecessary travel. This allows drivers to deliver efficiently and reduce fuel consumption. Furthermore, the planning department also considers reducing the burden on drivers when planning delivery routes. For example, it plans routes that include appropriate rest periods to avoid long hours of driving. The planning department also considers improving customer satisfaction when planning delivery routes. For example, it improves customer satisfaction by delivering at the time requested by the customer. In this way, the planning department can not only plan efficient delivery routes but also reduce the burden on drivers and improve customer satisfaction. Furthermore, the planning department also considers countermeasures for foreseeable problems when planning delivery routes. For example, it prepares alternative routes for foreseeable problems such as changes in weather or traffic accidents. This allows the planning unit to respond quickly to anticipated problems and prevent delivery delays. As a result, the delivery navigator system according to this embodiment can improve the problem of long working hours for delivery drivers, improve fuel efficiency, and reduce the number of redeliveries.

[0033] The data collection unit can collect experience data of area drivers. For example, the data collection unit collects experience data of area drivers. This experience data includes the number of deliveries, delivery success rate, and delivery time. The data collection unit can also collect experience data based on the delivery history of area drivers. For example, the data collection unit can also collect experience data based on feedback from area drivers. By collecting experience data of area drivers, efficient delivery routes can be identified. 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 delivery history data of area drivers into a generating AI and have the generating AI perform the collection of experience data.

[0034] The collection unit can collect redelivery records for delivery destinations. For example, the collection unit collects redelivery records for delivery destinations. These redelivery records include the number of redeliveries and the reasons for redelivery. The collection unit can also collect redelivery records based on past redelivery records. For example, the collection unit can analyze the causes of redeliveries and collect redelivery records. By collecting redelivery records for delivery destinations, it is possible to identify delivery destinations that are likely to require redelivery. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input past redelivery record data into a generating AI and have the generating AI perform the collection of redelivery records.

[0035] The collection unit can collect data on the time when recipients are home during delivery. For example, the collection unit collects data on the time when recipients are home during delivery. This data includes patterns of time spent at home and past data on time spent at home. The collection unit can also collect data on time spent at home based on past delivery time data. For example, the collection unit can analyze patterns of time spent at home during delivery and collect data on time spent at home. By collecting data on time spent at home during delivery, it is possible to identify delivery routes that are tailored to the recipients' availability. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input past delivery time data into a generating AI and have the generating AI collect data on time spent at home.

[0036] The collection unit can collect traffic information. For example, the collection unit collects traffic information, which includes congestion information, traffic accident information, etc. The collection unit can also collect traffic information based on real-time traffic information, for example. The collection unit can also analyze changes in traffic conditions and collect traffic information, for example. By collecting traffic information, it is possible to identify routes that avoid congestion. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input real-time traffic information data into a generating AI and have the generating AI perform the collection of traffic information.

[0037] The data collection unit can collect data on the timing of traffic light changes in the delivery area. For example, the data collection unit collects data on the timing of traffic light changes in the delivery area. This data on the timing of traffic light changes in the delivery area includes the location of the traffic lights and the timing of the changes. The data collection unit can also collect data from, for example, the traffic light control system. The data collection unit can also analyze patterns in the timing of traffic light changes and collect data. By collecting data on the timing of traffic light changes, it is possible to identify routes that minimize waiting time at traffic lights. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input data obtained from the traffic light control system into a generating AI and have the generating AI perform the collection of data on the timing of traffic light changes.

[0038] The collection unit can collect information on the day's delivery schedule. For example, the collection unit collects information on the day's delivery schedule. This information includes a list of delivery destinations and delivery time slots. The collection unit can also collect information from delivery schedules, for example. The collection unit can also reflect changes to the delivery schedule in real time and collect that information, for example. By collecting information on the day's delivery schedule, it is possible to identify routes that match the delivery schedule. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input delivery schedule data into a generating AI and have the generating AI collect information on the day's delivery schedule.

[0039] The collection unit can collect time-specific information. For example, the collection unit collects time-specific information. This time-specific information includes the specified delivery time and priority. The collection unit can also collect information from the delivery schedule, for example. The collection unit can also reflect changes in time specifications in real time and collect the information. By collecting time-specific information, it is possible to identify routes that match the time specifications. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input delivery schedule data into a generating AI and have the generating AI collect time-specific information.

[0040] The analysis unit calculates the shortest route based on collected data, taking into account fuel efficiency and reducing the number of redeliveries. For example, the analysis unit calculates the shortest route based on collected data, taking into account fuel efficiency and reducing the number of redeliveries. For example, the analysis unit identifies the most efficient route based on the experience data of area drivers. For example, the analysis unit identifies routes that avoid delivery destinations with a high probability of redelivery based on the history of redeliveries to the delivery destinations. For example, the analysis unit identifies routes that match the time when the delivery destination is at home, based on the history of time when the delivery destination is at home. For example, the analysis unit identifies routes that avoid congestion based on traffic conditions. For example, the analysis unit identifies routes that minimize waiting time at traffic lights based on data on the timing of traffic light changes in the delivery area. For example, the analysis unit identifies routes that match the delivery schedule based on the information of the day's scheduled deliveries and time-specified deliveries. In this way, by calculating the shortest route based on collected data, it is possible to achieve fuel efficiency and reduce the number of redeliveries. Some or all of the above processing in the analysis unit is performed using AI, for example. For example, the analysis unit can input the collected data into the generating AI and have the generating AI calculate the shortest route.

[0041] The planning unit can plan an efficient delivery route based on the calculated shortest route. For example, the planning unit plans an efficient delivery route based on the calculated shortest route. For example, by making deliveries according to the shortest route calculated by the AI, the planning unit can reduce travel time and decrease the number of redeliveries. For example, the planning unit plans an efficient delivery route based on the shortest route calculated by the AI. This can improve the problem of long working hours for delivery drivers by planning an efficient delivery route based on the calculated shortest route. Some or all of the above processing in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can have the generating AI plan an efficient delivery route based on the shortest route calculated by the generating AI.

[0042] The data collection unit can analyze the past delivery performance of area drivers and select the optimal data collection method. For example, the data collection unit analyzes the past delivery performance of area drivers and selects the optimal data collection method. For example, the data collection unit prioritizes collecting data from drivers with high past delivery performance. For example, the data collection unit collects additional data to supplement the data of drivers with low delivery performance. For example, the data collection unit customizes the data collection method based on the driver's delivery performance. This allows the optimal data collection method to be selected by analyzing the past delivery performance of area drivers. 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 past delivery performance data of area drivers into a generating AI and have the generating AI select the optimal data collection method.

[0043] The data collection unit can update the actual time slots when recipients are home at the delivery destination in real time and collect the latest data. For example, the data collection unit updates the actual time slots when recipients are home at the delivery destination in real time and collects the latest data. For example, the data collection unit predicts the optimal delivery time based on the actual time slots when recipients are home at the delivery destination. For example, the data collection unit analyzes the actual time slots when recipients are home at the delivery destination to reduce the possibility of redelivery. This allows the collection of the latest data by updating the actual time slots when recipients are home at the delivery destination in real time. 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 actual time slot data when recipients are home at the delivery destination into a generating AI and have the generating AI perform real-time updates.

[0044] The data collection unit can collect weather information for the delivery area and use it to optimize the delivery route. For example, the data collection unit collects weather information for the delivery area and uses it to optimize the delivery route. For example, the data collection unit collects weather information for the delivery area in real time and uses it to optimize the delivery route. For example, the data collection unit proposes changes to the delivery route based on the weather information. For example, the data collection unit adjusts the delivery schedule taking weather information into consideration. In this way, by collecting weather information for the delivery area, it can be used to optimize the delivery route. 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 weather information data for the delivery area into a generating AI and have the generating AI perform the collection of weather information.

[0045] The collection unit can collect security information about delivery destinations and ensure safe delivery routes. The collection unit can, for example, collect security information about delivery destinations and ensure safe delivery routes. The collection unit can, for example, collect security information about delivery destinations and ensure safe delivery routes. The collection unit can, for example, propose routes that avoid dangerous areas based on security information. The collection unit can, for example, adjust delivery schedules taking security information into consideration. In this way, safe delivery routes can be ensured by collecting security information about delivery destinations. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input delivery destination security information data into a generating AI and have the generating AI perform the collection of security information.

[0046] The analysis unit can perform a risk assessment of delivery routes based on the collected data and calculate low-risk routes. For example, the analysis unit can perform a risk assessment of delivery routes based on the collected data and calculate low-risk routes. For example, the analysis unit can propose changes to delivery routes based on the results of the risk assessment. For example, the analysis unit can adjust the delivery schedule taking into account the results of the risk assessment. In this way, by performing a risk assessment of delivery routes based on the collected data, it is possible to calculate low-risk routes. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the risk assessment.

[0047] The analysis unit can prioritize analyzing delivery destinations that are likely to require redelivery based on their redelivery history. For example, the analysis unit prioritizes analyzing delivery destinations that are likely to require redelivery based on their redelivery history. For example, the analysis unit proposes routes that avoid delivery destinations that are likely to require redelivery. For example, the analysis unit adjusts the delivery schedule considering delivery destinations that are likely to require redelivery. In this way, the number of redeliveries can be reduced by prioritizing the analysis of delivery destinations that are likely to require redelivery based on their redelivery history. 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 redelivery history data of delivery destinations into a generating AI and have the generating AI perform an analysis of delivery destinations that are likely to require redelivery.

[0048] The analysis unit can analyze crime rate data for delivery areas and calculate safe delivery routes. For example, the analysis unit can analyze crime rate data for delivery areas and calculate safe delivery routes. For example, the analysis unit can propose routes that avoid areas with high crime rates. For example, the analysis unit can adjust delivery schedules considering crime rate data. In this way, safe delivery routes can be calculated by analyzing crime rate data for delivery areas. 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 crime rate data for delivery areas into a generating AI and have the generating AI calculate safe delivery routes.

[0049] The analysis unit can analyze building structure information of the delivery destination and calculate an efficient delivery route. For example, the analysis unit analyzes building structure information of the delivery destination and calculates an efficient delivery route. For example, the analysis unit proposes changes to the delivery route based on the building structure information. For example, the analysis unit adjusts the delivery schedule taking the building structure information into consideration. In this way, an efficient delivery route can be calculated by analyzing the building structure information of the delivery destination. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input building structure information data of the delivery destination into a generating AI and have the generating AI perform the calculation of an efficient delivery route.

[0050] The planning unit can predict delivery times and create an optimal delivery schedule based on the shortest route calculated by the analysis unit. For example, the planning unit predicts delivery times and creates an optimal delivery schedule based on the shortest route calculated by the analysis unit. For example, the planning unit proposes changes to the delivery schedule based on the predicted delivery times. For example, the planning unit adjusts the delivery schedule considering the predicted delivery times. In this way, an optimal delivery schedule can be created by predicting delivery times based on the shortest route calculated by the analysis unit. Some or all of the above processes in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can have the generating AI perform delivery time prediction and create an optimal delivery schedule based on the shortest route calculated by the generating AI.

[0051] The planning unit can plan delivery routes that match the time recipients are home, based on actual data on when recipients are home. For example, the planning unit plans delivery routes that match the time recipients are home, based on actual data on when recipients are home. For example, the planning unit proposes changes to delivery routes based on actual data on when recipients are home. For example, the planning unit adjusts the delivery schedule, taking into account actual data on when recipients are home. By planning delivery routes based on actual data on when recipients are home, the number of redeliveries can be reduced. Some or all of the above processes in the planning unit may be performed using AI, or not. For example, the planning unit can input actual data on when recipients are home into a generating AI and have the generating AI plan delivery routes that match the time recipients are home.

[0052] The planning unit can plan efficient delivery routes by taking into account the topographic information of the delivery area. For example, the planning unit plans efficient delivery routes by taking into account the topographic information of the delivery area. For example, the planning unit proposes changes to the delivery route based on the topographic information. For example, the planning unit adjusts the delivery schedule by taking into account the topographic information. In this way, by taking into account the topographic information of the delivery area, an efficient delivery route can be planned. Some or all of the above processes in the planning unit may be performed using AI, for example, or without using AI. For example, the planning unit can input topographic information data of the delivery area into a generating AI and have the generating AI perform the planning of an efficient delivery route.

[0053] The planning unit can plan delivery routes tailored to specific events at delivery destinations, taking into account event information at those destinations. For example, the planning unit plans delivery routes tailored to events, taking into account specific event information at delivery destinations. For example, the planning unit proposes changes to delivery routes based on event information. For example, the planning unit adjusts delivery schedules, taking event information into account. In this way, by taking into account specific event information at delivery destinations, it is possible to plan delivery routes tailored to events. Some or all of the above processes in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can input specific event information data at delivery destinations into a generating AI and have the generating AI execute the planning of delivery routes tailored to events.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The data collection unit can collect information about the presence of pets at delivery locations and, if so, provide information to remind delivery drivers to take precautions. For example, the unit can collect information about the type and number of pets at delivery locations and notify delivery drivers of their presence. The unit can also collect information about pet behavior patterns and provide instructions to prevent pets from getting outside during delivery. Furthermore, the unit can collect instructions from pet owners and provide special consideration for pets during delivery. By collecting information about pets at delivery locations, delivery drivers can ensure safe deliveries.

[0056] The analysis unit can analyze the operating status of elevators in the delivery building and calculate routes using stairs if elevators are unavailable. For example, the analysis unit can analyze elevator maintenance information and suggest routes that avoid elevators undergoing maintenance. The analysis unit can also analyze elevator congestion and suggest optimal delivery times to avoid congestion. Furthermore, the analysis unit can analyze elevator malfunction information and suggest routes that avoid malfunctioning elevators. By analyzing elevator operating status, delivery drivers can perform deliveries more efficiently.

[0057] The planning department can plan delivery routes that take into account the health condition of the residents at the delivery destination. For example, if the resident is elderly, the planning department will suggest a route that avoids stairs and uses the elevator. If the resident is ill, the planning department will suggest the shortest route to reduce delivery time. If the resident has a disability, the planning department will suggest a barrier-free route. In this way, by considering the health condition of the residents, delivery drivers can make deliveries with consideration for the residents.

[0058] The collection unit can gather information about the hobbies and preferences of residents at delivery locations and provide relevant information during delivery. For example, if a resident enjoys gardening, the collection unit can provide information about gardening during delivery. If a resident owns a pet, the collection unit can provide information about pets. If a resident enjoys cooking, the collection unit can provide information about cooking. By providing information tailored to the hobbies and preferences of residents, delivery drivers can facilitate smoother communication with residents.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The data collection unit collects area driver experience data, delivery destination redelivery records, delivery destination availability times, traffic conditions, traffic light change timing data for the delivery area, information on the day's delivery schedule, and time-specified information. For example, area driver experience data includes the number of deliveries, delivery success rate, and delivery time. Delivery destination redelivery records include the number of redeliveries and reasons for redelivery. Delivery destination availability times include patterns of availability and past availability data. Traffic conditions include congestion information and traffic accident information. Traffic light change timing data for the delivery area includes traffic light locations and change times. Information on the day's delivery schedule includes delivery destination lists and delivery time slots. Time-specified information includes specified delivery times and priorities. Step 2: The analysis unit calculates the shortest route based on the collected data, taking into account fuel efficiency and the reduction of redeliveries. For example, it identifies the most efficient route based on the experience data of area drivers, and identifies routes that avoid delivery destinations with a high probability of redelivery based on the redelivery history of those destinations. It identifies routes that match the time when recipients are home based on the time when they are home, and identifies routes that avoid congestion based on traffic conditions. It identifies routes that minimize waiting time at traffic lights based on data on the timing of traffic light changes in the delivery area, and identifies routes that match the delivery schedule based on the day's delivery schedule and time-specified information. Step 3: The planning unit plans efficient delivery routes based on the calculated shortest route. For example, by delivering according to the shortest route calculated by AI, travel time is reduced and the number of redeliveries decreases. This can improve the problem of long working hours for delivery drivers and achieve fuel efficiency and a reduction in the number of redeliveries.

[0061] (Example of form 2) The delivery navigator system according to an embodiment of the present invention is a system for solving problems in the delivery industry such as labor shortages, redeliveries due to recipient absence, overwork, and increased costs due to fuel costs and wage increases. This delivery navigator system collects area driver experience data, redelivery records of delivery destinations, records of recipients' availability times, traffic conditions, traffic light change timing data in the delivery area, information on the day's delivery schedule, and time-specified delivery information. The collected data is analyzed by AI to calculate the shortest route considering fuel efficiency and reduction of the number of redeliveries. Based on this shortest route, the system plans an efficient delivery route, thereby improving the problem of long working hours for delivery drivers. First, the delivery navigator system collects area driver experience data, redelivery records of delivery destinations, records of recipients' availability times, traffic conditions, traffic light change timing data in the delivery area, information on the day's delivery schedule, and time-specified delivery information. In this process, each data is automatically collected by AI. For example, area driver experience data is collected from past delivery history and driver feedback. Redelivery records of delivery destinations are collected from past redelivery records. The system collects data on the time recipients are available at the delivery location from past delivery time data. Traffic conditions are collected from real-time traffic information. Data on traffic light change timings in the delivery area is collected from the traffic light control system. Information on the day's scheduled deliveries and time specifications is collected from the delivery schedule. Next, the delivery navigator system uses AI to analyze the collected data. Based on the collected data, the AI ​​calculates the shortest route considering fuel efficiency and reducing the number of redeliveries. For example, the AI ​​identifies the most efficient route based on the experience data of area drivers. Based on the history of redeliveries at the delivery location, it identifies routes that avoid delivery locations with a high probability of redelivery. Based on the time recipients are available at the delivery location, it identifies routes that match the time recipients are available. Based on traffic conditions, it identifies routes that avoid congestion. Based on data on traffic light change timings in the delivery area, it identifies routes that minimize waiting time at traffic lights. Based on the day's scheduled deliveries and time specifications, it identifies routes that match the delivery schedule.Finally, the delivery navigator system plans efficient delivery routes based on the shortest route calculated by AI. This can improve the problem of long working hours for delivery drivers. For example, by delivering according to the shortest route calculated by AI, travel time is reduced and the number of redeliveries decreases. This reduces the working hours of delivery drivers and suppresses overtime. In addition, fuel costs are reduced through improved fuel efficiency. This results in cost reductions for delivery companies. In summary, the delivery navigator system can improve the problem of long working hours for delivery drivers and achieve improved fuel efficiency and a reduction in the number of redeliveries.

[0062] The delivery navigator system according to the embodiment comprises a collection unit, an analysis unit, and a planning unit. The collection unit collects area driver experience data, redelivery records for delivery destinations, records of recipients' availability times, traffic conditions, traffic light change timing data for the delivery area, information on the day's delivery schedule, and time-specified information. For example, the collection unit collects area driver experience data. Area driver experience data includes the number of deliveries, delivery success rate, and delivery time. For example, the collection unit collects redelivery records for delivery destinations. Redelivery records for delivery destinations include the number of redeliveries and reasons for redelivery. For example, the collection unit collects records of recipients' availability times. Records of recipients' availability times include patterns of availability and past availability data. For example, the collection unit collects traffic conditions. Traffic conditions include congestion information and traffic accident information. For example, the collection unit collects traffic light change timing data for the delivery area. The data on traffic light change timings in the delivery area includes the location of the traffic lights and the changeover time. The collection unit collects information on the day's delivery schedule, for example. This information includes a list of delivery destinations and delivery time slots. The collection unit also collects information on time specifications, for example. This information includes the specified delivery time and priority. The analysis unit calculates the shortest route based on the collected data, taking into account fuel efficiency and reducing the number of redeliveries. The analysis unit calculates the shortest route based on the collected data, for example, taking into account fuel efficiency and reducing the number of redeliveries. The analysis unit identifies the most efficient route based on the experience data of area drivers, for example. The analysis unit identifies routes that avoid delivery destinations with a high probability of redelivery, for example, based on the history of redeliveries to delivery destinations. The analysis unit identifies routes that match the time when delivery destinations are likely to be at home, for example, based on the history of time when delivery destinations are likely to be at home. The analysis unit identifies routes that avoid congestion based on traffic conditions, for example. The analysis unit identifies routes that minimize waiting time at traffic lights, for example, based on data on the timing of traffic light changeovers in the delivery area. The analysis unit, for example, identifies a route that matches the delivery schedule based on information about the day's scheduled delivery and the time slot.The planning unit plans an efficient delivery route based on the calculated shortest route. The planning unit plans an efficient delivery route based on the calculated shortest route. The planning unit, for example, delivers according to the shortest route calculated by the AI, thereby reducing travel time and decreasing the number of redeliveries. The planning unit plans an efficient delivery route based on the shortest route calculated by the AI. As a result, the delivery navigator system according to this embodiment can improve the problem of long working hours for delivery drivers, improve fuel efficiency, and reduce the number of redeliveries.

[0063] The data collection unit collects area driver experience data, delivery destination redelivery records, delivery destination availability times, traffic conditions, traffic light timing data for the delivery area, information on the day's delivery schedule, and time-specific delivery information. Specifically, area driver experience data includes the number of deliveries, delivery success rate, and delivery time, and this data is collected from the driver's past delivery history. For example, it records how often a driver has made deliveries in the past, what their success rate was, and the average time required for each delivery. This allows for the evaluation of the driver's skill level and efficiency. Delivery destination redelivery records include the number of redeliveries and the reasons for redelivery, and this data is collected from past delivery history. For example, if a particular delivery destination experiences frequent redeliveries, it is possible to determine whether the reason is absence or other factors such as an incorrect address. Delivery destination availability times include patterns of availability and past availability data, and this data is collected based on past delivery history and information provided by customers. For example, if a particular delivery destination is often home on weekday mornings, the delivery time can be adjusted based on this information. Traffic information includes congestion and accident data, collected from real-time traffic information services. For example, information on congestion on major roads and accident locations is collected and used to select delivery routes. Traffic light timing data in the delivery area includes the location and timing of traffic lights, collected from traffic management systems. For example, knowing how often traffic lights at a particular intersection change allows for the selection of routes that minimize waiting times. Daily delivery schedule information includes delivery destination lists and delivery time slots, collected from delivery management systems. For example, knowing the list of packages to be delivered that day and the preferred delivery time slots for each destination allows for the creation of efficient delivery schedules. Time-specific information includes delivery time specifications and priorities, collected from customer order information. For example, if a particular package should be delivered in the morning, the delivery route is adjusted based on that information. This allows the data collection unit to centrally collect diverse data and provide a foundation for efficient delivery planning.

[0064] The analysis unit calculates the shortest route based on collected data, taking into account fuel efficiency and reducing the number of redeliveries. Specifically, it identifies the most efficient route based on the experience data of area drivers. For example, it calculates the optimal route under similar conditions by referring to routes that experienced drivers have successfully completed in the past. Based on the redelivery history of delivery destinations, it identifies routes that avoid destinations with a high probability of redelivery. For example, by avoiding delivery destinations where redeliveries occur frequently, it reduces wasted time and effort. Based on the time periods when delivery destinations are likely to be at home, it identifies routes that match those times. For example, by prioritizing visits to destinations where people are often at home during specific times, it improves the success rate of deliveries. Based on traffic conditions, it identifies routes that avoid congestion. For example, it selects routes that avoid congestion by utilizing real-time traffic information. Based on data on the timing of traffic light changes in the delivery area, it identifies routes that minimize waiting time at traffic lights. For example, it selects routes that reduce waiting time at traffic lights by considering the timing of traffic light changes. Based on information on the day's delivery schedule and time-specified information, it identifies routes that match the delivery schedule. For example, it selects routes that prioritize the delivery of time-specified packages. This allows the analysis unit to comprehensively analyze the collected data and calculate efficient delivery routes. Furthermore, the analysis unit uses AI to process the data in real time and continuously update the optimal route. For example, if traffic conditions or the availability of recipients at delivery locations change, it immediately incorporates the new data and recalculates the route. As a result, the analysis unit can always provide the optimal delivery route based on the latest information, enabling efficient deliveries.

[0065] The planning department plans efficient delivery routes based on the shortest route calculated by the AI. Specifically, by delivering according to the shortest route calculated by the AI, travel time is reduced and the number of redeliveries decreases. For example, based on the route calculated by the AI, the order in which each delivery destination is visited is optimized to reduce unnecessary travel. This allows drivers to deliver efficiently and reduce fuel consumption. Furthermore, the planning department also considers reducing the burden on drivers when planning delivery routes. For example, it plans routes that include appropriate rest periods to avoid long hours of driving. The planning department also considers improving customer satisfaction when planning delivery routes. For example, it improves customer satisfaction by delivering at the time requested by the customer. In this way, the planning department can not only plan efficient delivery routes but also reduce the burden on drivers and improve customer satisfaction. Furthermore, the planning department also considers countermeasures for foreseeable problems when planning delivery routes. For example, it prepares alternative routes for foreseeable problems such as changes in weather or traffic accidents. This allows the planning unit to respond quickly to anticipated problems and prevent delivery delays. As a result, the delivery navigator system according to this embodiment can improve the problem of long working hours for delivery drivers, improve fuel efficiency, and reduce the number of redeliveries.

[0066] The data collection unit can collect experience data of area drivers. For example, the data collection unit collects experience data of area drivers. This experience data includes the number of deliveries, delivery success rate, and delivery time. The data collection unit can also collect experience data based on the delivery history of area drivers. For example, the data collection unit can also collect experience data based on feedback from area drivers. By collecting experience data of area drivers, efficient delivery routes can be identified. 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 delivery history data of area drivers into a generating AI and have the generating AI perform the collection of experience data.

[0067] The collection unit can collect redelivery records for delivery destinations. For example, the collection unit collects redelivery records for delivery destinations. These redelivery records include the number of redeliveries and the reasons for redelivery. The collection unit can also collect redelivery records based on past redelivery records. For example, the collection unit can analyze the causes of redeliveries and collect redelivery records. By collecting redelivery records for delivery destinations, it is possible to identify delivery destinations that are likely to require redelivery. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input past redelivery record data into a generating AI and have the generating AI perform the collection of redelivery records.

[0068] The collection unit can collect data on the time when recipients are home during delivery. For example, the collection unit collects data on the time when recipients are home during delivery. This data includes patterns of time spent at home and past data on time spent at home. The collection unit can also collect data on time spent at home based on past delivery time data. For example, the collection unit can analyze patterns of time spent at home during delivery and collect data on time spent at home. By collecting data on time spent at home during delivery, it is possible to identify delivery routes that are tailored to the recipients' availability. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input past delivery time data into a generating AI and have the generating AI collect data on time spent at home.

[0069] The collection unit can collect traffic information. For example, the collection unit collects traffic information, which includes congestion information, traffic accident information, etc. The collection unit can also collect traffic information based on real-time traffic information, for example. The collection unit can also analyze changes in traffic conditions and collect traffic information, for example. By collecting traffic information, it is possible to identify routes that avoid congestion. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input real-time traffic information data into a generating AI and have the generating AI perform the collection of traffic information.

[0070] The data collection unit can collect data on the timing of traffic light changes in the delivery area. For example, the data collection unit collects data on the timing of traffic light changes in the delivery area. This data on the timing of traffic light changes in the delivery area includes the location of the traffic lights and the timing of the changes. The data collection unit can also collect data from, for example, the traffic light control system. The data collection unit can also analyze patterns in the timing of traffic light changes and collect data. By collecting data on the timing of traffic light changes, it is possible to identify routes that minimize waiting time at traffic lights. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input data obtained from the traffic light control system into a generating AI and have the generating AI perform the collection of data on the timing of traffic light changes.

[0071] The collection unit can collect information on the day's delivery schedule. For example, the collection unit collects information on the day's delivery schedule. This information includes a list of delivery destinations and delivery time slots. The collection unit can also collect information from delivery schedules, for example. The collection unit can also reflect changes to the delivery schedule in real time and collect that information, for example. By collecting information on the day's delivery schedule, it is possible to identify routes that match the delivery schedule. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input delivery schedule data into a generating AI and have the generating AI collect information on the day's delivery schedule.

[0072] The collection unit can collect time-specific information. For example, the collection unit collects time-specific information. This time-specific information includes the specified delivery time and priority. The collection unit can also collect information from the delivery schedule, for example. The collection unit can also reflect changes in time specifications in real time and collect the information. By collecting time-specific information, it is possible to identify routes that match the time specifications. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input delivery schedule data into a generating AI and have the generating AI collect time-specific information.

[0073] The analysis unit calculates the shortest route based on collected data, taking into account fuel efficiency and reducing the number of redeliveries. For example, the analysis unit calculates the shortest route based on collected data, taking into account fuel efficiency and reducing the number of redeliveries. For example, the analysis unit identifies the most efficient route based on the experience data of area drivers. For example, the analysis unit identifies routes that avoid delivery destinations with a high probability of redelivery based on the history of redeliveries to the delivery destinations. For example, the analysis unit identifies routes that match the time when the delivery destination is at home, based on the history of time when the delivery destination is at home. For example, the analysis unit identifies routes that avoid congestion based on traffic conditions. For example, the analysis unit identifies routes that minimize waiting time at traffic lights based on data on the timing of traffic light changes in the delivery area. For example, the analysis unit identifies routes that match the delivery schedule based on the information of the day's scheduled deliveries and time-specified deliveries. In this way, by calculating the shortest route based on collected data, it is possible to achieve fuel efficiency and reduce the number of redeliveries. Some or all of the above processing in the analysis unit is performed using AI, for example. For example, the analysis unit can input the collected data into the generating AI and have the generating AI calculate the shortest route.

[0074] The planning unit can plan an efficient delivery route based on the calculated shortest route. For example, the planning unit plans an efficient delivery route based on the calculated shortest route. For example, by making deliveries according to the shortest route calculated by the AI, the planning unit can reduce travel time and decrease the number of redeliveries. For example, the planning unit plans an efficient delivery route based on the shortest route calculated by the AI. This can improve the problem of long working hours for delivery drivers by planning an efficient delivery route based on the calculated shortest route. Some or all of the above processing in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can have the generating AI plan an efficient delivery route based on the shortest route calculated by the generating AI.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed information. For example, if the user is in a hurry, the data collection unit can speed up the timing of data collection to immediately obtain the necessary information. In this way, the user's burden can be reduced by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the 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 the generative AI and have the generative AI adjust the timing of data collection.

[0076] The data collection unit can analyze the past delivery performance of area drivers and select the optimal data collection method. For example, the data collection unit analyzes the past delivery performance of area drivers and selects the optimal data collection method. For example, the data collection unit prioritizes collecting data from drivers with high past delivery performance. For example, the data collection unit collects additional data to supplement the data of drivers with low delivery performance. For example, the data collection unit customizes the data collection method based on the driver's delivery performance. This allows the optimal data collection method to be selected by analyzing the past delivery performance of area drivers. 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 past delivery performance data of area drivers into a generating AI and have the generating AI select the optimal data collection method.

[0077] The data collection unit can update the actual time slots when recipients are home at the delivery destination in real time and collect the latest data. For example, the data collection unit updates the actual time slots when recipients are home at the delivery destination in real time and collects the latest data. For example, the data collection unit predicts the optimal delivery time based on the actual time slots when recipients are home at the delivery destination. For example, the data collection unit analyzes the actual time slots when recipients are home at the delivery destination to reduce the possibility of redelivery. This allows the collection of the latest data by updating the actual time slots when recipients are home at the delivery destination in real time. 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 actual time slot data when recipients are home at the delivery destination into a generating AI and have the generating AI perform real-time updates.

[0078] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only important data. If the user is relaxed, the data collection unit will prioritize collecting detailed data. If the user is in a hurry, the data collection unit will prioritize collecting data that can be collected quickly. This allows for the priority collection of important data by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0079] The data collection unit can collect weather information for the delivery area and use it to optimize the delivery route. For example, the data collection unit collects weather information for the delivery area and uses it to optimize the delivery route. For example, the data collection unit collects weather information for the delivery area in real time and uses it to optimize the delivery route. For example, the data collection unit proposes changes to the delivery route based on the weather information. For example, the data collection unit adjusts the delivery schedule taking weather information into consideration. In this way, by collecting weather information for the delivery area, it can be used to optimize the delivery route. 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 weather information data for the delivery area into a generating AI and have the generating AI perform the collection of weather information.

[0080] The collection unit can collect security information about delivery destinations and ensure safe delivery routes. The collection unit can, for example, collect security information about delivery destinations and ensure safe delivery routes. The collection unit can, for example, collect security information about delivery destinations and ensure safe delivery routes. The collection unit can, for example, propose routes that avoid dangerous areas based on security information. The collection unit can, for example, adjust delivery schedules taking security information into consideration. In this way, safe delivery routes can be ensured by collecting security information about delivery destinations. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input delivery destination security information data into a generating AI and have the generating AI perform the collection of security information.

[0081] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit simplifies the analysis algorithm to provide quick results. For example, if the user is relaxed, the analysis unit performs a detailed analysis to provide highly accurate results. For example, if the user is in a hurry, the analysis unit speeds up the analysis algorithm to provide quick results. In this way, by adjusting the analysis algorithm according to the user's emotions, quick and highly accurate results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the analysis algorithm.

[0082] The analysis unit can perform a risk assessment of delivery routes based on the collected data and calculate low-risk routes. For example, the analysis unit can perform a risk assessment of delivery routes based on the collected data and calculate low-risk routes. For example, the analysis unit can propose changes to delivery routes based on the results of the risk assessment. For example, the analysis unit can adjust the delivery schedule taking into account the results of the risk assessment. In this way, by performing a risk assessment of delivery routes based on the collected data, it is possible to calculate low-risk routes. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the risk assessment.

[0083] The analysis unit can prioritize analyzing delivery destinations that are likely to require redelivery based on their redelivery history. For example, the analysis unit prioritizes analyzing delivery destinations that are likely to require redelivery based on their redelivery history. For example, the analysis unit proposes routes that avoid delivery destinations that are likely to require redelivery. For example, the analysis unit adjusts the delivery schedule considering delivery destinations that are likely to require redelivery. In this way, the number of redeliveries can be reduced by prioritizing the analysis of delivery destinations that are likely to require redelivery based on their redelivery history. 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 redelivery history data of delivery destinations into a generating AI and have the generating AI perform an analysis of delivery destinations that are likely to require redelivery.

[0084] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, a display that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of the display method of the analysis results.

[0085] The analysis unit can analyze crime rate data for delivery areas and calculate safe delivery routes. For example, the analysis unit can analyze crime rate data for delivery areas and calculate safe delivery routes. For example, the analysis unit can propose routes that avoid areas with high crime rates. For example, the analysis unit can adjust delivery schedules considering crime rate data. In this way, safe delivery routes can be calculated by analyzing crime rate data for delivery areas. 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 crime rate data for delivery areas into a generating AI and have the generating AI calculate safe delivery routes.

[0086] The analysis unit can analyze building structure information of the delivery destination and calculate an efficient delivery route. For example, the analysis unit analyzes building structure information of the delivery destination and calculates an efficient delivery route. For example, the analysis unit proposes changes to the delivery route based on the building structure information. For example, the analysis unit adjusts the delivery schedule taking the building structure information into consideration. In this way, an efficient delivery route can be calculated by analyzing the building structure information of the delivery destination. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input building structure information data of the delivery destination into a generating AI and have the generating AI perform the calculation of an efficient delivery route.

[0087] The planning unit can estimate the user's emotions and adjust the delivery route plan based on the estimated emotions. For example, if the user is stressed, the planning unit will plan a simple and efficient delivery route. For example, if the user is relaxed, the planning unit will plan a delivery route that includes detailed information. For example, if the user is in a hurry, the planning unit will plan a route that allows for quick delivery. This reduces the user's burden by adjusting the delivery route plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the planning unit may be performed using AI or not. For example, the planning unit can input user emotion data into a generative AI and have the generative AI adjust the delivery route plan.

[0088] The planning unit can predict delivery times and create an optimal delivery schedule based on the shortest route calculated by the analysis unit. For example, the planning unit predicts delivery times and creates an optimal delivery schedule based on the shortest route calculated by the analysis unit. For example, the planning unit proposes changes to the delivery schedule based on the predicted delivery times. For example, the planning unit adjusts the delivery schedule considering the predicted delivery times. In this way, an optimal delivery schedule can be created by predicting delivery times based on the shortest route calculated by the analysis unit. Some or all of the above processes in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can have the generating AI perform delivery time prediction and create an optimal delivery schedule based on the shortest route calculated by the generating AI.

[0089] The planning unit can plan delivery routes that match the time recipients are home, based on actual data on when recipients are home. For example, the planning unit plans delivery routes that match the time recipients are home, based on actual data on when recipients are home. For example, the planning unit proposes changes to delivery routes based on actual data on when recipients are home. For example, the planning unit adjusts the delivery schedule, taking into account actual data on when recipients are home. By planning delivery routes based on actual data on when recipients are home, the number of redeliveries can be reduced. Some or all of the above processes in the planning unit may be performed using AI, or not. For example, the planning unit can input actual data on when recipients are home into a generating AI and have the generating AI plan delivery routes that match the time recipients are home.

[0090] The planning unit can estimate the user's emotions and determine the priority of delivery routes based on the estimated emotions. For example, if the user is stressed, the planning unit will plan a route that prioritizes important delivery destinations. For example, if the user is relaxed, the planning unit will plan a delivery route that includes detailed information. For example, if the user is in a hurry, the planning unit will plan a route that allows for quick delivery. This allows for prioritizing important delivery destinations by determining the priority of delivery routes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the planning unit may be performed using AI or not using AI. For example, the planning unit can input user emotion data into a generative AI and have the generative AI determine the priority of delivery routes.

[0091] The planning unit can plan efficient delivery routes by taking into account the topographic information of the delivery area. For example, the planning unit plans efficient delivery routes by taking into account the topographic information of the delivery area. For example, the planning unit proposes changes to the delivery route based on the topographic information. For example, the planning unit adjusts the delivery schedule by taking into account the topographic information. In this way, by taking into account the topographic information of the delivery area, an efficient delivery route can be planned. Some or all of the above processes in the planning unit may be performed using AI, for example, or without using AI. For example, the planning unit can input topographic information data of the delivery area into a generating AI and have the generating AI perform the planning of an efficient delivery route.

[0092] The planning unit can plan delivery routes tailored to specific events at delivery destinations, taking into account event information at those destinations. For example, the planning unit plans delivery routes tailored to events, taking into account specific event information at delivery destinations. For example, the planning unit proposes changes to delivery routes based on event information. For example, the planning unit adjusts delivery schedules, taking event information into account. In this way, by taking into account specific event information at delivery destinations, it is possible to plan delivery routes tailored to events. Some or all of the above processes in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can input specific event information data at delivery destinations into a generating AI and have the generating AI execute the planning of delivery routes tailored to events.

[0093] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0094] The data collection unit can collect information about the presence of pets at delivery locations and, if so, provide information to remind delivery drivers to take precautions. For example, the unit can collect information about the type and number of pets at delivery locations and notify delivery drivers of their presence. The unit can also collect information about pet behavior patterns and provide instructions to prevent pets from getting outside during delivery. Furthermore, the unit can collect instructions from pet owners and provide special consideration for pets during delivery. By collecting information about pets at delivery locations, delivery drivers can ensure safe deliveries.

[0095] The analysis unit can analyze the operating status of elevators in the delivery building and calculate routes using stairs if elevators are unavailable. For example, the analysis unit can analyze elevator maintenance information and suggest routes that avoid elevators undergoing maintenance. The analysis unit can also analyze elevator congestion and suggest optimal delivery times to avoid congestion. Furthermore, the analysis unit can analyze elevator malfunction information and suggest routes that avoid malfunctioning elevators. By analyzing elevator operating status, delivery drivers can perform deliveries more efficiently.

[0096] The planning department can plan delivery routes that take into account the health condition of the residents at the delivery destination. For example, if the resident is elderly, the planning department will suggest a route that avoids stairs and uses the elevator. If the resident is ill, the planning department will suggest the shortest route to reduce delivery time. If the resident has a disability, the planning department will suggest a barrier-free route. In this way, by considering the health condition of the residents, delivery drivers can make deliveries with consideration for the residents.

[0097] The collection unit can gather information about the hobbies and preferences of residents at delivery locations and provide relevant information during delivery. For example, if a resident enjoys gardening, the collection unit can provide information about gardening during delivery. If a resident owns a pet, the collection unit can provide information about pets. If a resident enjoys cooking, the collection unit can provide information about cooking. By providing information tailored to the hobbies and preferences of residents, delivery drivers can facilitate smoother communication with residents.

[0098] The analysis unit can estimate the emotions of the resident at the delivery destination and adjust the delivery response based on the estimated emotions. For example, if the analysis unit is stressed, it will deliver quickly to reduce the resident's burden. If the resident is relaxed, it will deliver carefully and communicate with the resident. If the resident is in a hurry, it will deliver quickly to save the resident's time. In this way, by responding according to the resident's emotions, delivery drivers can provide considerate deliveries.

[0099] The collection unit can estimate the emotions of the residents at the delivery destination and adjust its delivery approach based on those estimates. For example, if the resident is stressed, the collection unit will deliver quickly to reduce the resident's burden. If the resident is relaxed, the collection unit will deliver carefully and communicate with the resident. If the resident is in a hurry, the collection unit will deliver quickly to save the resident's time. This allows delivery drivers to provide considerate deliveries by responding according to the resident's emotions.

[0100] The planning department can estimate the emotions of residents at delivery destinations and prioritize delivery routes based on those estimates. For example, if a resident is stressed, the planning department will plan a route that prioritizes important delivery destinations. If a resident is relaxed, the planning department will plan a delivery route that includes detailed information. If a resident is in a hurry, the planning department will plan a route that allows for quick delivery. This allows for prioritizing important delivery destinations by determining delivery route priorities according to the resident's emotions.

[0101] The analysis unit can estimate the emotions of the resident at the delivery destination and adjust the display method of the analysis results based on the estimated emotions. For example, if the resident is feeling stressed, the analysis unit provides a simple and highly visible display method. If the resident is relaxed, the analysis unit provides a display method that includes detailed information. If the resident is in a hurry, the analysis unit provides a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the resident's emotions, it is possible to provide a display that is easy for the resident to understand.

[0102] The data collection unit can estimate the emotions of the residents at the delivery destination and prioritize the data to be collected based on those estimated emotions. For example, if the resident is stressed, the unit will prioritize collecting only the most important data. If the resident is relaxed, the unit will prioritize collecting detailed data. If the resident is in a hurry, the unit will prioritize collecting data that can be collected quickly. In this way, by prioritizing the data to be collected according to the resident's emotions, it is possible to prioritize the collection of important data.

[0103] The data collection unit can estimate the emotions of the residents at the delivery destination and adjust the timing of data collection based on the estimated emotions. For example, if the resident is stressed, the data collection unit reduces the frequency of data collection to alleviate the resident's burden. If the resident is relaxed, the data collection unit increases the frequency of data collection to gather more detailed information. If the resident is in a hurry, the data collection unit speeds up the timing of data collection to obtain the necessary information immediately. In this way, by adjusting the timing of data collection according to the resident's emotions, the burden on the resident can be reduced.

[0104] The following briefly describes the processing flow for example form 2.

[0105] Step 1: The data collection unit collects area driver experience data, delivery destination redelivery records, delivery destination availability times, traffic conditions, traffic light change timing data for the delivery area, information on the day's delivery schedule, and time-specified information. For example, area driver experience data includes the number of deliveries, delivery success rate, and delivery time. Delivery destination redelivery records include the number of redeliveries and reasons for redelivery. Delivery destination availability times include patterns of availability and past availability data. Traffic conditions include congestion information and traffic accident information. Traffic light change timing data for the delivery area includes traffic light locations and change times. Information on the day's delivery schedule includes delivery destination lists and delivery time slots. Time-specified information includes specified delivery times and priorities. Step 2: The analysis unit calculates the shortest route based on the collected data, taking into account fuel efficiency and the reduction of redeliveries. For example, it identifies the most efficient route based on the experience data of area drivers, and identifies routes that avoid delivery destinations with a high probability of redelivery based on the redelivery history of those destinations. It identifies routes that match the time when recipients are home based on the time when they are home, and identifies routes that avoid congestion based on traffic conditions. It identifies routes that minimize waiting time at traffic lights based on data on the timing of traffic light changes in the delivery area, and identifies routes that match the delivery schedule based on the day's delivery schedule and time-specified information. Step 3: The planning unit plans efficient delivery routes based on the calculated shortest route. For example, by delivering according to the shortest route calculated by AI, travel time is reduced and the number of redeliveries decreases. This can improve the problem of long working hours for delivery drivers and achieve fuel efficiency and a reduction in the number of redeliveries.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] Each of the multiple elements described above, including the data collection unit, analysis unit, planning unit, and emotion estimation function, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and collects information such as area driver experience data and traffic conditions. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and calculates the shortest route based on the collected data. The planning unit is implemented by the control unit 46A of the smart device 14 and plans an efficient delivery route based on the calculated shortest route. The emotion estimation function is implemented by the specific processing unit 290 of the data processing device 12 and estimates the user's emotions and adjusts the timing of data collection. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0110] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the data collection unit, analysis unit, planning unit, and emotion estimation function, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects information such as area driver experience data and traffic conditions. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and calculates the shortest route based on the collected data. The planning unit is implemented by the control unit 46A of the smart glasses 214 and plans an efficient delivery route based on the calculated shortest route. The emotion estimation function is implemented by the identification processing unit 290 of the data processing unit 12 and estimates the user's emotions and adjusts the timing of data collection. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0126] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the data collection unit, analysis unit, planning unit, and emotion estimation function, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and collects information such as area driver experience data and traffic conditions. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and calculates the shortest route based on the collected data. The planning unit is implemented by the control unit 46A of the headset terminal 314 and plans an efficient delivery route based on the calculated shortest route. The emotion estimation function is implemented by the identification processing unit 290 of the data processing unit 12 and estimates the user's emotions and adjusts the timing of data collection. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0142] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the data collection unit, analysis unit, planning unit, and emotion estimation function, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and collects information such as area driver experience data and traffic conditions. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and calculates the shortest route based on the collected data. The planning unit is implemented by the control unit 46A of the robot 414 and plans an efficient delivery route based on the calculated shortest route. The emotion estimation function is implemented by the specific processing unit 290 of the data processing unit 12 and estimates the user's emotions and adjusts the timing of data collection. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] (Note 1) Area driver experience data, Redelivery record for delivery destinations, Actual time slots when recipients are home during delivery, Traffic conditions, Data on the timing of traffic light changes in the delivery area. Information regarding the delivery schedule for the day, A collection unit that collects time-specified information, An analysis unit analyzes the data collected by the aforementioned collection unit and calculates the shortest route considering fuel efficiency and reduction of the number of redeliveries, The system includes a planning unit that plans an efficient delivery route based on the shortest route calculated by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect experience data of area drivers. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Collect records of redelivery attempts for delivery destinations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Collect data on the time when recipients were home during delivery. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collect traffic information The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Collect data on the timing of traffic light changes in the delivery area. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Gather information on the delivery schedule for the day. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Collect time-specific information The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, Based on the collected data, the shortest route is calculated, taking into account fuel efficiency and the reduction of redeliveries. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned planning unit, Plan efficient delivery routes based on the calculated shortest route. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is Analyze the past delivery performance of area drivers and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is The system updates the actual time slots when recipients are home during delivery and collects the latest data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is We collect weather information for the delivery area and use it to optimize delivery routes. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is We collect security information about delivery destinations and ensure safe delivery routes. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, Based on the collected data, a risk assessment of delivery routes is performed, and low-risk routes are calculated. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, Based on past redelivery records for delivery destinations, the system prioritizes analyzing destinations with a high probability of requiring redelivery. 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 how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, We analyze crime rate data in the delivery area to calculate safe delivery routes. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, The system analyzes the building structure information of the delivery destination to calculate the most efficient delivery route. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned planning unit, The system estimates the user's emotions and adjusts delivery route planning based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned planning unit, Based on the shortest route calculated by the analysis unit, delivery times are predicted and an optimal delivery schedule is created. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned planning unit, Based on past data regarding when recipients are home during delivery times, delivery routes are planned to match those times. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned planning unit, The system estimates the user's emotions and prioritizes delivery routes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned planning unit, Plan efficient delivery routes by taking into account the topography of the delivery area. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned planning unit, Considering specific event information at the delivery destination, plan delivery routes tailored to the event. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0178] 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. Area driver experience data, Redelivery record for delivery destinations, Actual time slots when recipients are home during delivery, Traffic conditions, Data on the timing of traffic light changes in the delivery area. Information regarding the delivery schedule for the day, A collection unit that collects time-specified information, An analysis unit analyzes the data collected by the aforementioned collection unit and calculates the shortest route considering fuel efficiency and reduction of the number of redeliveries, The system includes a planning unit that plans an efficient delivery route based on the shortest route calculated by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect experience data of area drivers. The system according to feature 1.

3. The aforementioned collection unit is Collect records of redelivery attempts for delivery destinations. The system according to feature 1.

4. The aforementioned collection unit is Collect data on the time when recipients were home during delivery. The system according to feature 1.

5. The aforementioned collection unit is Collect traffic information The system according to feature 1.

6. The aforementioned collection unit is Collect data on the timing of traffic light changes in the delivery area. The system according to feature 1.

7. The aforementioned collection unit is Gather information on the delivery schedule for the day. The system according to feature 1.

8. The aforementioned collection unit is Collect time-specific information The system according to feature 1.

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