System

Abstract

A system is provided.SOLUTION: A system, comprising: means for collecting behavioral history information of a general consumer; means for analyzing the collected behavioral history information and predicting a time at home; means for suggesting an optimal date and time of delivery based on the predicted time at home; means for calculating an optimal delivery route using generated AI and map information; means for sending the delivery route information to a delivery person and performing an optimal delivery; means for completing a final delivery with minimal effort in cooperation with the delivery person; and means for notifying a user of an estimated delivery time and a real-time delivery status.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In the logistics industry, the application of caps on overtime work has led to reduced working hours, raising concerns about a shortage of transportation capacity. Furthermore, given the difficulty of increasing the number of truck drivers, new solutions are needed to maintain current transportation. Furthermore, it may become difficult to provide fast and reliable home delivery services to general consumers, and the costs of redelivery are also becoming an issue. The present invention aims to solve these issues and provide an efficient and economical home delivery service. [Means for solving the problem]

[0005] This invention provides a means for collecting consumer behavior history data and analyzing it using a generation API. This allows for predicting when consumers will be at home and proposing optimal delivery dates and times. It also provides a means for calculating optimal delivery routes based on the prediction results using generation AI and map information. It also provides a means for truck drivers to deliver packages to the nearest collection and distribution station, from which delivery personnel handle the last mile of delivery. This reduces redelivery costs and alleviates labor shortages. Furthermore, it provides a means for users to be notified of the scheduled delivery time and real-time delivery status, enabling a fast and reliable service. It also provides a means for users to submit a request to change the delivery date and time based on the scheduled delivery date and time, and a means for calculating a new delivery schedule and recalculating the optimal delivery route based on the change request.

[0006] "Behavioral history data" refers to data relating to the everyday behavior of general consumers, and specifically includes information such as the time spent outside, the time returned home, and activities on holidays.

[0007] A "generation API" is an application programming interface that has the functionality to analyze behavioral history data and generate a specific result.

[0008] "Home time" refers to the time when the average consumer is at home and is likely to be able to receive deliveries.

[0009] A "delivery route" refers to the order or route that should be taken to deliver a package, and is optimized based on map and traffic information.

[0010] A "delivery person" is a person who is responsible for ultimately delivering packages from a collection station or convenience store to the recipient.

[0011] "Redelivery costs" refer to the expenses and labor incurred when redelivering a package that was not received due to the recipient's absence.

[0012] A "collection and delivery station" is a delivery base where packages are received from truck drivers and handed over to delivery personnel.

[0013] The "optimal delivery date and time" refers to the most suitable delivery timing, taking into account the user's home time and desired delivery time.

[0014] "User" means a consumer who uses the system to receive delivery services.

[0015] "Notification means" refers to a method or device for notifying the user of the estimated delivery time and real-time delivery status. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] This invention is a system that collects behavioral history data of general consumers, analyzes the data using a generation API, and predicts the time when they will be at home. This can reduce redelivery costs in the logistics industry and optimize delivery routes. Furthermore, by collaborating with truck drivers and delivery personnel, it can solve the problem of labor shortages and build an efficient delivery system. This system is implemented by the following means.

[0038] Collecting and predicting user behavior data

[0039] The server collects user behavior history data, including the time spent outside the home, the time returned home, and activities on holidays.

[0040] The server inputs the collected data into the generation API and analyzes it to predict the time the customer will be at home. The predicted time is saved in the delivery management system and used to optimize future deliveries.

[0041] Delivery route optimization

[0042] The server then suggests the optimal delivery date and time based on the predicted user's home time, thereby reducing the risk of redelivery.

[0043] The server combines map information and traffic information to calculate the optimal delivery route, which is then sent to the delivery person's terminal.

[0044] Collaboration with delivery staff

[0045] The terminal (the delivery driver's terminal) takes the package to the nearest collection and distribution station.

[0046] The delivery person will pick up the package at the station and ultimately deliver it to the user's home.

[0047] User Notification

[0048] The server notifies the user of the estimated delivery time, and the user can check this notification and submit a request to change the delivery date and time if necessary.

[0049] Specific examples

[0050] Example 1: Predicting user's time at home

[0051] The server collects user behavior history data from the past six months and analyzes it using the generation API. If the prediction result shows that the user is often at home on weekday nights, the scheduled delivery time is set to a weekday night.

[0052] Example 2: Delivery route optimization

[0053] The server predicts the user's expected time of home and determines the optimal delivery date and time to be between 10:00 AM and noon on Saturday. Using map and traffic information, the server calculates the shortest route from the nearest collection and delivery station to the user's home. The route information is then sent to the delivery person's device.

[0054] Example 3: Collaboration with delivery staff and user notification

[0055] The terminal (the driver's terminal) will deliver the package to the collection and delivery station at 8:00 AM on Saturday. The delivery person will pick up the package at the station and complete the delivery between 10:00 AM and noon, when the user is expected to be home.

[0056] The server notifies the user that the package is scheduled to be delivered between 10:00 AM and 12:00 PM on Saturday. If the user cannot receive the package during this time, they submit a request to change the delivery time to the system. Based on this request, the server recalculates a new delivery schedule and route and issues instructions to the delivery person.

[0057] In this way, the present invention can predict user behavior patterns and propose optimal delivery dates, times, and routes, thereby reducing redelivery costs and resolving labor shortages. Furthermore, the real-time notification function makes it possible to provide users with a fast and reliable home delivery service.

[0058] The processing flow will be explained below.

[0059] Step 1:

[0060] Users provide their behavioral history data, which is sent through a dedicated application or retrieved from an existing database.

[0061] Step 2:

[0062] The server collects the provided user behavioral history data, specifically, data on the time spent outside the home, the time returned home, and activities on holidays.

[0063] Step 3:

[0064] The server calls the generation API based on the collected behavioral history data and predicts the user's home time using a machine learning algorithm.

[0065] Step 4:

[0066] The server stores the predicted user's home time in the delivery management system, which then uses it to optimize future delivery dates and times.

[0067] Step 5:

[0068] The server calculates the optimal delivery time based on the predicted time the customer will be at home, taking into account the customer's preferred time and delivery priority.

[0069] Step 6:

[0070] The server uses map and traffic information to generate optimal delivery routes, taking into account distances between delivery points and traffic conditions.

[0071] Step 7:

[0072] The server then sends the generated optimal delivery route and delivery date and time to the delivery person's terminal, enabling the delivery person to make deliveries along an efficient route.

[0073] Step 8:

[0074] The terminal (delivery driver's terminal) displays the route information received from the server and delivers the package to the nearest collection and distribution station.

[0075] Step 9:

[0076] The delivery person will pick up the package at the collection and delivery station and finally deliver it to the user's home. The delivery status will be updated on the terminal in real time.

[0077] Step 10:

[0078] The server notifies the user of the final estimated delivery time, including the specific delivery date and time and real-time delivery status.

[0079] Step 11:

[0080] The user checks the estimated delivery time and, if necessary, submits a request to change the delivery date and time to the system.

[0081] Step 12:

[0082] The server receives the change request from the user, calculates a new delivery schedule, and generates the optimal delivery route again.

[0083] Step 13:

[0084] The server sends the new delivery schedule and route information to the delivery person's terminal and notifies the user of the changed delivery schedule.

[0085] In this way, the system of the present invention can efficiently carry out a series of processes, from collecting user behavior data, to predicting the time the user will be at home, proposing the optimal delivery date and route, collaborating with delivery personnel, and notifying the user.

[0086] Example 1

[0087] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0088] In modern logistics operations, redelivery is common, resulting in significant costs for the logistics industry. Furthermore, delivery routes are not adequately optimized, resulting in inefficient deliveries. Furthermore, labor shortages are a serious problem, necessitating efficient labor allocation. To solve these issues, a system is needed that can predict when users will be at home and propose optimal delivery dates, times, and routes.

[0089] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0090] In this invention, the server includes a means for collecting activity history data of general users, a means for analyzing the collected activity history data and predicting the time the user will be at home, and a means for proposing the optimal date and time for delivery based on the predicted time the user will be at home, thereby reducing redelivery costs and improving delivery efficiency.

[0091] "General user" refers to an individual or household who uses the system.

[0092] "Activity history data" refers to information related to a user's behavior, such as the time they spend outside, the time they return home, and their activities on holidays.

[0093] "Analysis" refers to the processing that a generative AI model performs on collected data to understand the data and extract meaningful information.

[0094] "Home time" refers to the time period when the user is at home.

[0095] "Delivery route" refers to the route a delivery company takes to deliver a package.

[0096] "Generative AI" refers to artificial intelligence technology that uses generative AI models to analyze data and make predictions.

[0097] "Map Information" refers to map data and related location information.

[0098] "Delivery Company" refers to the person or shipping company that ultimately delivers the package to the User.

[0099] "Delivery route information" refers to information regarding the optimal delivery route.

[0100] "Real-time delivery status" refers to information that displays the current delivery progress status in real time.

[0101] "Delivery Time Change Request" means a request submitted by a User to change an already set delivery time.

[0102] "Transport vehicle operator" refers to the person or transport driver who transports goods to the nearest collection and delivery station.

[0103] A "collection station" is a location where packages are temporarily collected and received by the delivery company for final delivery.

[0104] "Final delivery" refers to the process by which a package is delivered from the collection and distribution station to the user.

[0105] This invention is a system that collects activity history data of general users, analyzes the data using a generative AI model, and predicts the time they will be at home. This can reduce redelivery costs in the logistics industry and optimize delivery routes. Furthermore, by collaborating with delivery companies, it can solve the problem of labor shortages and build an efficient delivery system. This system is implemented by the following means.

[0106] Collecting and predicting user behavior data

[0107] The server collects data on the user's online behavior history from their smartphone or PC, including the time the user leaves the house, the time they return home, and their activities on holidays. The collected data is stored in a database on the server.

[0108] Next, the server inputs this collected data into a generative AI model API (e.g., generative AI). The prompt used here is something like, "Please predict the amount of time the user will be at home based on the user's behavioral history data." The generative AI model analyzes the data and predicts the user's amount of time at home.

[0109] Save to delivery management system

[0110] Based on the analysis results, the server predicts when the user will be at home and stores this data in the delivery management system database, which is used to plan future deliveries.

[0111] Delivery route optimization

[0112] The server proposes the date and time with the highest probability of successful delivery based on the predicted user's home time. Based on the proposed date and time, the server calculates the optimal delivery route using map information (e.g., map API) and real-time traffic information (e.g., traffic conditions API). This allows the delivery company to deliver the package using the most efficient route.

[0113] Collaboration with delivery staff

[0114] The server then sends the calculated delivery date and time and the optimal route information to the delivery company's terminal. The delivery company will then pick up the package at the specified date and time and deliver it according to the user's home time.

[0115] User Notification

[0116] Once the delivery date and time is determined, the server sends a notification to the user saying, "The package is scheduled to be delivered between 2:00 PM and 4:00 PM on the XXth of the month." The user can check this notification and request a change in the delivery time if they cannot receive the package during this time period.

[0117] Handling delivery time changes

[0118] When a user submits a delivery time change request, the server accepts the request, recalculates a new delivery schedule and route, and sends this new information back to the delivery company.

[0119] Specific examples

[0120] Example 1: Predicting user's time at home

[0121] The server collects user behavioral history data from the past six months and analyzes it with the generation AI. For example, if the user is often at home on weekday nights, the scheduled delivery time will be set to a weekday night.

[0122] Example 2: Delivery route optimization

[0123] The server predicts the user's expected time of home delivery and determines the optimal delivery date and time to be between 10:00 AM and noon on Saturdays. It then uses map and traffic information to calculate the shortest route from the nearest collection and delivery station to the user's home. This route information is then sent to the delivery company's terminal.

[0124] Example 3: Collaboration with delivery staff and user notification

[0125] The terminal (the driver's terminal) will deliver the package to the collection and delivery station at 8:00 AM on Saturday. The delivery company will pick up the package at the station and complete the delivery between 10:00 AM and noon, when the user is expected to be home.

[0126] The server notifies the user that the package is scheduled to be delivered between 10:00 AM and 12:00 PM on Saturday. If the user cannot receive the package during this time, they can submit a request to change the delivery time to the system. Based on this request, the server recalculates a new delivery schedule and route and sends instructions to the delivery company.

[0127] In this way, the present invention can predict user behavior patterns and propose optimal delivery dates, times, and routes, thereby reducing redelivery costs and resolving labor shortages. Furthermore, the real-time notification function makes it possible to provide users with a fast and reliable home delivery service.

[0128] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0129] Step 1:

[0130] (Collection of user behavior data)

[0131] The server collects data on the user's online behavior history through a dedicated application installed on the user's smartphone or computer. The collected data includes the time of leaving and returning home, holiday activities, and other location information. This data is sent to and stored in the server's database in real time.

[0132] Input: Behavioral history data from the user's smartphone or computer

[0133] Output: Behavioral history data stored in the server database

[0134] Step 2:

[0135] (Analysis of behavioral history data)

[0136] The server inputs the collected behavioral history data into the generation AI model API. The prompt used at this time is "Please predict the time the user will be at home based on the user behavioral history data." The generation AI model analyzes the input data and returns a prediction of the time the user will be at home.

[0137] Input: Collected behavioral history data, prompt text

[0138] Output: Predicted time at home

[0139] Step 3:

[0140] (Saving prediction results)

[0141] The server stores the predictions returned by the generative AI model in the delivery management system's database, which is used to plan future deliveries.

[0142] Input: Prediction results returned by the generative AI model

[0143] Output: Prediction results stored in the delivery management system database

[0144] Step 4:

[0145] (Proposal of optimal delivery date and time)

[0146] The server calculates the optimal delivery date and time based on the predicted user's home time, minimizing the risk of re-delivery. This calculation takes into account the days of the week and times when the user is likely to be at home.

[0147] Input: Predicted user home time

[0148] Output: Suggested best delivery date and time

[0149] Step 5:

[0150] (Delivery route calculation)

[0151] The server calculates the optimal delivery route based on the proposed delivery date and time, combining map information and real-time traffic information. This calculation uses map APIs and traffic condition APIs. The calculated route information is sent to the delivery company's device.

[0152] Input: Suggested optimal delivery date and time, map information, traffic information

[0153] Output: Calculated optimal delivery route

[0154] Step 6:

[0155] (Collaboration with delivery companies)

[0156] The server then sends the calculated delivery date and time and the optimal route information to the delivery company's terminal. The delivery company then picks up the user's package according to the specified date, time and route, and makes the final delivery.

[0157] Input: Calculated optimal delivery route, delivery date and time information

[0158] Output: Route and delivery information sent to the delivery company's terminal

[0159] Step 7:

[0160] (User Notification)

[0161] The server then sends the user a notification of the scheduled delivery date and time. For example, a notification saying, "Your package is scheduled to be delivered between 2:00 PM and 4:00 PM on a certain date" is sent. Notifications are sent via smartphone push notifications or email.

[0162] Input: Scheduled delivery date and time information

[0163] Output: Estimated delivery notification sent to user

[0164] Step 8:

[0165] (Processing delivery time change requests)

[0166] The user checks the notification and, if a change in delivery time is necessary, sends a change request to the system. The server accepts this request and recalculates a new delivery schedule and route. The recalculated schedule is then sent to the delivery company.

[0167] Input: Delivery time change request from user

[0168] Output: New recalculated delivery schedule and route

[0169] Step 9:

[0170] (Pick up your parcel at the collection and delivery station)

[0171] The terminal (driver's terminal) delivers the package to the nearest collection and delivery station at the specified time, where the delivery company receives the package and prepares it for delivery at the specified delivery date and time.

[0172] Input: Packages received at the collection and delivery station

[0173] Output: Package received by the delivery company

[0174] Step 10:

[0175] (Final delivery completion)

[0176] The delivery company will head to the user's home at the specified delivery date and time to deliver the package safely. Once the delivery is complete, the status is updated in the system and the server sends a notification to the user that the delivery has been completed.

[0177] Input: Packages being delivered to the user's home

[0178] Output: Delivered package, delivery completion notification to the user

[0179] Through the above processing steps, the present invention predicts user behavior patterns and proposes optimal delivery dates, times, and routes, thereby reducing redelivery costs and resolving labor shortages. Furthermore, the real-time notification function makes it possible to provide users with a fast and reliable home delivery service.

[0180] (Application example 1)

[0181] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0182] In today's logistics industry, no-shows and redeliveries are major issues. This reduces delivery efficiency and increases the costs associated with redeliveries. Furthermore, there is a significant labor shortage of delivery personnel, increasing the burden on delivery work. Furthermore, optimizing deliveries based on traffic congestion and predicted times when customers will be at home is difficult, creating a need for optimized transportation routes.

[0183] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0184] In this invention, the server includes means for collecting behavioral history data of general consumers, means for analyzing the collected behavioral history data and predicting the time when consumers will be at home, means for installing a delivery management system in an autonomous vehicle and proposing the optimal date and time for delivery based on the predicted time when consumers will be at home, means for calculating the optimal delivery route using generation AI and map information, means for transmitting delivery route information to the autonomous vehicle and making the optimal delivery, means for the autonomous vehicle to deliver the package to the nearest station based on the delivery route and prediction data and complete the final delivery with minimal effort, and means for notifying the user of the estimated delivery time and real-time delivery status, which makes it possible to reduce redelivery costs, optimize delivery routes, eliminate labor shortages, and provide real-time delivery notifications.

[0185] "General consumer behavior history data" refers to records of past behavior of consumers, including the time spent outside the home, the time returned home, and activities on holidays.

[0186] "Predicting time spent at home" refers to predicting the amount of time a consumer will be at home based on collected behavioral history data.

[0187] "Means of installing a delivery management system on an autonomous vehicle" means installing software or hardware on an autonomous vehicle to manage deliveries.

[0188] The "means for proposing the optimal delivery date and time" is a technology that calculates and presents the optimal date and time for package delivery based on the predicted time the customer will be at home.

[0189] "Generative AI" is a type of artificial intelligence technology that predicts future events and situations based on collected data.

[0190] "Map information" is data that includes geographical information, such as roads, buildings, topography, and traffic conditions.

[0191] The "means for calculating the optimal delivery route" is a system that uses map information and generation AI to calculate the optimal delivery route to maximize delivery efficiency.

[0192] "Means for transmitting delivery route information to an autonomous vehicle" refers to a device or method for sending the calculated optimal delivery route information as instructions to an autonomous vehicle.

[0193] "Nearest station" refers to the nearest package storage or relay station set up by the delivery company.

[0194] "Means for notifying real-time delivery status" refers to a communication method for instantly informing users of the current delivery status of their packages.

[0195] "Reducing redelivery costs" refers to aiming for successful delivery the first time and reducing the unnecessary expenses associated with repeat deliveries.

[0196] "Delivery route optimization" is the process of planning the best route to maximize delivery efficiency and reduce delivery time and costs.

[0197] "Resolving labor shortages" means trying to solve the labor shortage problem by introducing an efficient delivery system.

[0198] "Real-time delivery notifications" means instantly providing users with details of ongoing deliveries and keeping them updated on the progress of their deliveries.

[0199] This invention relates to a system that collects consumer behavior history data and delivers packages efficiently in cooperation with a delivery management system installed in an autonomous driving vehicle. Specific embodiments of this system are described below.

[0200] Collecting and predicting user behavior data

[0201] The server collects consumer behavior history data, including information on when they leave the home, when they return home, and their activities on holidays. The collected data is analyzed using a generative AI model to predict when consumers will be at home.

[0202] Delivery planning based on historical forecasts

[0203] Based on the predicted time at home, the server will suggest the optimal delivery date and time to the consumer through the delivery management system installed in the autonomous vehicle. This suggestion is set during the time when the consumer is predicted to be at home to reduce the risk of redelivery.

[0204] Delivery route optimization

[0205] The server uses generative AI and map information, including Google Maps API and traffic data, to calculate the optimal delivery route. The calculated route information is sent to the autonomous vehicle, which instructs it to maximize delivery efficiency.

[0206] Delivery execution by autonomous vehicles

[0207] The autonomous vehicle will then calculate an optimal route to deliver the package to the nearest station, where a delivery person will complete the last mile if necessary, resulting in more efficient delivery and reduced labor.

[0208] User Notification

[0209] The server notifies the consumer of the estimated delivery time and real-time delivery status. Notifications are sent to the consumer's smartphone or other device, allowing the consumer to check the delivery progress in real time. If the estimated delivery date and time can be changed, the consumer can send a request to adjust the delivery date and time.

[0210] Specific examples

[0211] For example, let's say that behavioral data from the past six months predicts that consumers often return home between 6:00 PM and 10:00 PM on weekdays. Based on this information, autonomous vehicles can deliver packages during those times, reducing the risk of having to redeliver packages due to consumers being absent.

[0212] Using data from the user's behavior over the past six months, it is predicted that the user will typically return home between 6:00 PM and 10:00 PM on weekdays. Based on this information, the autonomous vehicle will calculate the optimal delivery route and notify the user.

[0213] The present invention aims to reduce redelivery costs and resolve labor shortages by analyzing consumer behavior data and providing efficient delivery plans and routes based on predicted times when consumers will be at home.

[0214] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0215] Step 1:

[0216] The server collects data on the behavioral history of general consumers. Specifically, data on time spent outside the home, time returned home, and activities on holidays is obtained from APIs and databases. The collected data is stored for analysis.

[0217] Input: Behavioral history data obtained from APIs and databases

[0218] Output: Behavioral history data saved for analysis

[0219] Step 2:

[0220] The server analyzes the collected behavioral history data using a generative AI model to predict the amount of time the consumer will be at home. For example, the model is trained based on data from the past six months to generate a predictive model. By inputting the latest data into this model, the amount of time the consumer will be at home can be predicted.

[0221] Input: Collected behavioral history data

[0222] Output: Predicted time at home

[0223] Step 3:

[0224] The server then suggests the best delivery time based on the predicted time the consumer will be at home. This suggestion is calculated taking into account the time of day the consumer is expected to be at home. For example, if a consumer is usually at home between 6:00 PM and 10:00 PM on weekdays, the server will suggest that time as the delivery time.

[0225] Input: Predicted time at home

[0226] Output: Suggested best delivery date and time

[0227] Step 4:

[0228] The server uses the generative AI and map information to calculate the optimal delivery route. This uses Google Maps API and traffic information data to calculate the optimal route taking into account current traffic conditions. The calculated route information is sent to the autonomous vehicle.

[0229] Input: Proposed delivery date and time, map information, and traffic information

[0230] Output: Calculated optimal delivery route

[0231] Step 5:

[0232] The server then sends the calculated delivery route information to the autonomous vehicle and instructs the vehicle to follow the route, which the autonomous vehicle then follows to deliver the package to the nearest station.

[0233] Input: Calculated delivery route information

[0234] Output: Route instructions for the autonomous vehicle

[0235] Step 6:

[0236] The autonomous vehicle will then arrive at the nearest station, where a delivery person will handle the last mile of delivery, making the final delivery from the station to the consumer's home.

[0237] Input: A package transported by an autonomous vehicle

[0238] Output: Final delivery to the consumer's home

[0239] Step 7:

[0240] The server notifies the consumer of the estimated delivery time and real-time delivery status. Notifications are sent to the consumer's smartphone, allowing the consumer to check the delivery progress in real time. In addition, if there is a request to change the delivery date and time, the server accepts it and applies the recalculated new schedule.

[0241] Input: Estimated delivery time, real-time delivery status

[0242] Output: Notified estimated delivery time and current delivery status

[0243] Through these steps, the invention can achieve an efficient delivery system, reduce the risk of redelivery, and eliminate the problem of labor shortages.

[0244] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0245] The present invention is a system that combines a system that collects consumer behavior history data, analyzes it using a generation API, and predicts the amount of time consumers will be at home with an emotion engine that recognizes user emotions. This makes it possible to reduce redelivery costs, optimize delivery routes, and provide flexible delivery support based on user emotions. Specific embodiments are described in detail below.

[0246] Collecting and predicting user behavior data

[0247] The server collects user behavior history data, such as the time spent outside, the time returned home, and activities on holidays.

[0248] The server inputs the collected data into the generation API and analyzes it to predict the user's home time. This predicted data is stored in the delivery management system database.

[0249] Delivery route optimization

[0250] The server calculates the optimal delivery time based on the predicted time the customer will be at home, taking into account the customer's preferred time and delivery priority.

[0251] The server uses map information and traffic information to generate an optimal delivery route, which is then sent to the delivery person's terminal.

[0252] Collaboration with delivery staff

[0253] The terminal (the delivery driver's terminal) takes the package to the nearest collection and distribution station according to the predicted route.

[0254] The delivery person will pick up the package from the collection and delivery station and deliver it to the user's home.

[0255] User support with an emotional engine

[0256] The server uses an emotion engine to collect and analyze the user's emotion data, including the user's voice and text data.

[0257] The server uses an emotion engine to analyze the user's emotional state and adjusts the content and timing of notifications based on the results. For example, if the user is feeling anxious or dissatisfied, the server provides an option to easily change the delivery schedule.

[0258] The server stores the collected emotional data and analyzes long-term emotional trends, enabling it to provide a flexible delivery service tailored to the user's emotional state.

[0259] User Notification

[0260] The server notifies the user of the estimated delivery time and real-time delivery status, and the content of the notification is adjusted based on the analysis results of the emotion engine.

[0261] Specific examples

[0262] Example 1: Predicting user's time at home

[0263] The server collects user behavior history data from the past few months and analyzes it using a generation API. Based on this, if it predicts that the user is likely to be at home during weekday nights, it will schedule deliveries for those times.

[0264] Example 2: Delivery route optimization

[0265] The server predicts the user's expected time of home and determines the optimal delivery date and time to be between 10:00 a.m. and noon on Saturdays. Using map and traffic information, the server calculates the shortest route from the nearest delivery station to the user's home.

[0266] Example 3: Supported by an Emotion Engine

[0267] The server uses an emotion engine to analyze the user's recent text messages and recognize that they are stressed, which results in the delivery notification being more subdued and adding a link to easily reschedule the delivery.

[0268] Example 4: Collaboration with delivery personnel and user notification

[0269] The terminal (the delivery driver's terminal) will deliver the package to the collection and distribution station at 8:00 AM on Saturday, and the delivery person will complete the delivery to the user's home between 10:00 AM and noon on Saturday. The server will notify the user of this schedule, and the user will confirm the notification. The real-time status of the delivery will also be updated as it progresses.

[0270] In this way, the present invention can provide an effective and economical delivery service by predicting the user's behavioral patterns and emotions and calculating the optimal delivery date and route. Furthermore, by utilizing an emotion engine, it is possible to respond flexibly to the user's emotional state, providing a higher level of satisfaction.

[0271] The processing flow will be explained below.

[0272] Step 1:

[0273] Users provide their own behavioral history data, including the time they spend outside, the time they return home, and their activities on holidays.

[0274] Step 2:

[0275] The server collects the provided user behavioral history data, which is either sent through a dedicated application or retrieved from an existing database.

[0276] Step 3:

[0277] The server inputs the collected behavioral history data into a generation API and analyzes it to predict the user's time at home, using a machine learning algorithm.

[0278] Step 4:

[0279] The server stores the prediction results in the delivery management system database, which can then be used for future delivery planning.

[0280] Step 5:

[0281] The server calculates the optimal delivery date and time based on the predicted time the customer will be at home, taking into account the customer's preferred time and delivery priority.

[0282] Step 6:

[0283] The server uses map and traffic information to calculate the optimal delivery route, enabling efficient delivery.

[0284] Step 7:

[0285] The server sends the calculated delivery route and delivery date and time to the delivery person's terminal, and the delivery person makes the delivery based on the received route.

[0286] Step 8:

[0287] The terminal (the delivery driver's terminal) takes the package to the nearest collection and distribution station, thereby assisting with the last mile of delivery.

[0288] Step 9:

[0289] The delivery person will collect the package at the collection and delivery station and finally deliver it to the user's home, updating the delivery status on the terminal in real time.

[0290] Step 10:

[0291] The server notifies the user of the estimated delivery time and real-time delivery status, including the specific delivery date and time.

[0292] Step 11:

[0293] The server uses an emotion engine to collect and analyze the user's emotional data, and uses the user's voice and text data as analysis data.

[0294] Step 12:

[0295] The server adjusts the content and timing of notifications based on the results of the emotion engine analysis. For example, if the user is feeling stressed, the delivery notification will be more subdued in tone.

[0296] Step 13:

[0297] The server accumulates user emotional data and analyzes long-term emotional trends, thereby improving the quality of service for users.

[0298] Step 14:

[0299] Users can check the estimated delivery time and, if necessary, submit a request to change the delivery date and time to the system.

[0300] Step 15:

[0301] The server receives change requests from users and recalculates new delivery schedules and optimal delivery routes.

[0302] Step 16:

[0303] The server sends the new delivery schedule and route information to the delivery person's terminal and notifies the user of the changed delivery schedule.

[0304] In this way, the present invention analyzes user behavior data and emotion data to propose optimal delivery schedules and routes, thereby achieving efficient deliveries and improving user satisfaction.

[0305] Example 2

[0306] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0307] With the current delivery system, it is difficult to simultaneously optimize delivery and improve user satisfaction. In particular, there are many cases where redelivery is required when the user is not at home, which increases delivery costs and reduces efficiency. In addition, there is a lack of flexibility in responding to user emotions and requests, which can easily lead to user dissatisfaction. By solving these issues, it is necessary to reduce delivery costs and improve user satisfaction.

[0308] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting behavioral history data of general consumers, means for analyzing the collected behavioral history data and predicting the time the consumer will be at home, means for proposing the optimal date and time for delivery based on the predicted time the consumer will be at home, means for calculating the optimal delivery route using the generative AI model and location information, means for sending delivery route information to a delivery employee and making the optimal delivery, means for completing the final delivery with minimal effort by cooperating with the delivery employee, means for collecting and analyzing user emotion data, means for adjusting notification content and delivery options based on the emotion analysis results, and means for notifying the user of the estimated delivery time and real-time delivery status. This enables delivery optimization and flexible response according to the user's emotions.

[0309] "General consumers" refer to individuals who do not have a specific industry or specialized knowledge and who engage in general consumer activities.

[0310] "Behavioral history data" refers to past data regarding a user's movements, schedules, activities, etc.

[0311] "Home time" refers to the time period when the user is at home.

[0312] A "generative AI model" refers to a system that uses machine learning and artificial intelligence algorithms to analyze data and make predictions.

[0313] "Location information" refers to data that indicates the physical location of a user or object, such as geographic coordinates or address information.

[0314] "Delivery route" refers to the optimal route for delivering a package.

[0315] "Delivery employee" refers to a person whose job is to deliver packages to a user's home or designated location.

[0316] "Emotional data" refers to data that indicates a user's emotions and psychological state. Specifically, it includes information obtained from voice, text, facial expressions, etc.

[0317] "Notification" means a message or alert intended to provide information to the User.

[0318] The system of this invention combines a system that collects user behavior history data, analyzes it using a generative AI model, and predicts the time the user will be at home with an emotion engine that recognizes the user's emotions. This not only reduces redelivery costs and optimizes delivery routes, but also provides flexible delivery support based on the user's emotions.

[0319] Collecting and predicting user behavior data

[0320] The server collects the user's behavioral history data. This collection includes data acquisition from the user's smartphone and wearable device. Specific data includes GPS data, calendar appointments, and motion sensor data. For example, the server periodically sends the location information set by the user on the smartphone to the server, recording the user's time out and time back home.

[0321] The collected data is analyzed using a generative AI model (e.g., a machine learning algorithm). This analysis extracts user behavioral patterns and predicts how long the user will be at home. The prediction results are stored in the delivery management system's database.

[0322] Delivery route optimization

[0323] The server calculates the optimal delivery date and time based on the predicted time the delivery person will be at home, taking into account the user's preferred time and delivery priority. It then calculates the optimal delivery route using map information (e.g., Google Maps API) and traffic information. The calculated route information is sent to the delivery person's device.

[0324] Collaboration with delivery staff

[0325] The terminal (the delivery person's device) will then follow the predicted route and deliver the package to the nearest collection and delivery station, where the delivery person will pick up the package and deliver it to the user's home.

[0326] User support with an emotional engine

[0327] The server uses a voice recognition system and text analysis tools to collect user emotion data, for example, from voice data or text messages when a user contacts customer support.

[0328] The emotion engine analyzes this data to identify the user's emotional state and adjusts the notification content and delivery options accordingly. For example, if the user is stressed, the notification will be sent in a more gentle tone and offer the option to reschedule the delivery.

[0329] User Notification

[0330] The server notifies the user of the estimated delivery time based on the determined delivery date and time and the optimal route. Notifications are sent via smartphone app, email, or SMS. Real-time delivery status information is also provided as appropriate.

[0331] Specific examples

[0332] Example 1: Predicting user's time at home

[0333] The server collects user behavior history data from the past few months and analyzes it with a generative AI model. For example, if it predicts that the user is likely to be at home during weekday nights, it will schedule deliveries for those times.

[0334] Example 2: Delivery route optimization

[0335] The server predicts the user's expected time of home and determines the optimal delivery date and time between 10:00 a.m. and noon on Saturdays. It also uses map and traffic information to calculate the shortest route from the nearest delivery station to the user's home.

[0336] Example 3: Supported by an Emotion Engine

[0337] The server uses an emotion engine to analyze the user's recent text messages and recognize that they are stressed, which results in the delivery notification being more subdued and includes a link to easily reschedule the delivery.

[0338] Example 4: Collaboration with delivery personnel and user notification

[0339] The terminal (delivery worker's device) will deliver the package to the collection and delivery station at 8:00 AM on Saturday, and the delivery worker will complete the delivery to the user's home between 10:00 AM and noon on Saturday. The server will notify the user of this schedule, and the user will confirm the notification. The real-time status of the delivery will also be updated as it progresses.

[0340] An example of a prompt is, "Analyze the user's behavioral history data from the past few months and predict when the user will be at home. Calculate the optimal delivery date, time, and route based on the prediction results. Also, adjust the notification content taking into account the user's emotional state."

[0341] In this way, the system of the present invention calculates the optimal delivery date and route based on the user's behavioral data and emotional data, and provides an effective and flexible delivery service.

[0342] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0343] Step 1:

[0344] The server collects user behavior history data. The collected data is obtained from the user's smartphone or wearable device. Specifically, it includes GPS data, calendar events, and motion sensor data. For example, the user's behavior history while out and the time of return home are recorded. The input data is the behavior history data sent from the user's device, and the output is the collected dataset.

[0345] Step 2:

[0346] The server preprocesses the collected behavioral history data, complementing missing values ​​and excluding outliers. Specifically, it detects and excludes data containing sudden movements, and complements missing data using the mean or median. The input is the collected behavioral history data, and the output is the preprocessed data.

[0347] Step 3:

[0348] The server inputs the preprocessed data into a generative AI model to predict the user's home time. The generative AI model uses a time-series data prediction algorithm. For example, it predicts the home time on weekday nights and weekends based on past behavioral patterns. The input is the preprocessed behavioral history data, and the output is the predicted home time data.

[0349] Step 4:

[0350] The server determines the optimal delivery date and time based on the predicted at-home time data, taking into account the user's desired time and delivery priority. The input is the predicted at-home time data and the user's desired time, and the output is the optimal delivery date and time.

[0351] Step 5:

[0352] The server calculates the optimal delivery route based on the determined delivery date and time using map and traffic information. For map information, it uses Google Maps API and public transportation API to calculate the shortest route from the delivery center to the user's address. The input is the optimal delivery date and time, map information, and traffic information, and the output is the optimal delivery route.

[0353] Step 6:

[0354] The server sends the calculated delivery route information to the delivery person's terminal. The delivery person makes the delivery based on this route information. The input is the optimal delivery route information, and the output is the route information sent to the delivery person's terminal.

[0355] Step 7:

[0356] The terminal (the delivery person's device) carries the package to the nearest collection / distribution station according to the predicted route. Specifically, it follows the route from the delivery center to the collection / distribution station. The input is the package from the delivery center and the optimal delivery route information, and the output is the state of the package arriving at the collection / distribution station.

[0357] Step 8:

[0358] The delivery person delivers the package from the collection and delivery station to the user's home at the delivery date and time specified by the user. The input is the package from the collection and delivery station and the optimal delivery route information, and the output is the package delivered to the user's home.

[0359] Step 9:

[0360] The server uses a voice recognition system and text analysis tools to collect user emotion data. Specifically, it obtains emotion information from voice data and text messages when a user contacts customer support. The input is the user's inquiry data, and the output is the collected emotion data.

[0361] Step 10:

[0362] The server uses an emotion engine to analyze the collected emotion data and identify the user's emotional state. For example, it determines whether the user is "angry" or "anxious" based on the content of their voice or text message. The input is the collected emotion data, and the output is the analyzed emotion state.

[0363] Step 11:

[0364] The server adjusts the notification content and delivery options based on the analysis results of the emotion engine. For example, if the user is stressed, the server may send a softer notification and offer the option to reschedule the delivery. The input is the analyzed emotional state, and the output is the adjusted notification content.

[0365] Step 12:

[0366] The server notifies the user of the estimated delivery time and real-time delivery status via a smartphone app, email, or SMS. For example, a notification is sent when the package leaves the delivery center or arrives in the delivery area. The input is delivery status data and the adjusted notification content, and the output is the notification sent to the user.

[0367] In this way, the system calculates the optimal delivery date and route based on the user's behavioral history and emotional data, providing an efficient and flexible delivery service.

[0368] (Application example 2)

[0369] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0370] Conventional delivery systems are unable to accurately predict when users will be at home, resulting in frequent redelivery and delays. Furthermore, because they do not take user emotions into consideration, delivery notifications and responses are mechanical, leading to lower user satisfaction. To solve these problems, a new system is needed that analyzes user behavioral history data and emotional data to provide optimal delivery dates, times, and routes.

[0371] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting behavioral history data of general consumers, means for analyzing the collected behavioral history data and predicting the time the consumer will be at home, means for proposing the optimal date and time for delivery based on the predicted time the consumer will be at home, means for calculating the optimal delivery route using generation AI and map information, means for sending delivery route information to a delivery person and making the optimal delivery, means for completing the final delivery with minimal effort by cooperating with the delivery person, means for notifying the user of the estimated delivery time and real-time delivery status, and means for recognizing the user's emotions and adjusting the content and timing of notifications based on the emotions. This not only improves delivery efficiency but also enables flexible responses according to the user's emotions, providing a high level of satisfaction.

[0372] "General consumer" refers to an individual who purchases goods or uses services.

[0373] "Behavioral history data" refers to data related to a user's behavioral patterns, such as the time they spend outside the home, the time they return home, and their activities on holidays.

[0374] "Forecasting" refers to estimating future events or conditions based on collected data.

[0375] "Delivery route" refers to the route taken to deliver a package to a user.

[0376] "Generative AI" refers to artificial intelligence technology that generates and analyzes information based on large amounts of data.

[0377] "Map information" refers to data relating to geographical locations and routes.

[0378] "Delivery Person" refers to an individual or team whose role is to receive packages and deliver them to users.

[0379] "Optimization" refers to maximizing efficiency and effectiveness.

[0380] "Emotion recognition" refers to the technology of analyzing and understanding a user's emotional state from their voice, text, etc.

[0381] The "last mile" refers to the final stage of delivery, delivering the package from the collection and distribution station to the user's home.

[0382] "Notification" refers to the act of conveying information from the system to the user or delivery person.

[0383] This invention is a system that collects and analyzes user behavior history data and emotion data to provide optimal delivery dates and routes. A detailed description of the system based on the present invention is given below.

[0384] First, the server collects the user's behavioral history data, specifically the time the user leaves the house, the time they return home, their activities on holidays, etc. This data is collected using the smartphone's GPS data and calendar information.

[0385] The server then analyzes the collected data using generative AI to predict when the user will be at home, using techniques such as linear regression. The predicted time is then used in the next step to suggest the optimal delivery date and time.

[0386] The server also calculates the optimal delivery route based on map and traffic information, allowing delivery personnel to deliver efficiently. The route information is sent to the delivery personnel's terminal, and delivery is carried out according to the optimal route.

[0387] Furthermore, the server uses an emotion engine to recognize the user's emotions and adjust the content and timing of notifications accordingly. The server understands the user's emotional state by analyzing the user's voice input and text messages. This emotional data is used to adjust the wording and timing of delivery notifications, ensuring that users do not feel stressed.

[0388] Additionally, the server provides users with notifications of estimated delivery times and real-time delivery status, which are tailored based on the collected emotion data.

[0389] For example, if a user is often at home on weekday evenings, delivery can be scheduled for that time. Also, if the system determines that the user is stressed, delivery notifications will be changed to gentler language and delivery will be handled more quickly.

[0390] For example, the server might use the following prompt:

[0391] "Using the user's behavioral history data and calendar information, the system predicts when they will be at home and creates an ideal delivery plan based on their emotional data. Delivery routes are optimized using map and traffic information. If the user is under stress, the system changes the delivery time and notification content."

[0392] This system will enable flexible delivery services based on the user's behavioral patterns and emotional state, which is expected to reduce redelivery and delays and improve user satisfaction.

[0393] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0394] Step 1:

[0395] Users use their smartphones to collect behavioral history data, specifically GPS data and calendar information. This data includes detailed information such as the user's time out, the time they return home, and their activities on holidays.

[0396] Input: Smartphone GPS data, calendar information

[0397] Output: Behavioral history data

[0398] Step 2:

[0399] The server inputs the behavioral history data collected in step 1 into the generative AI model for analysis. The generative AI model learns past patterns and predicts the user's time at home. This prediction is made using techniques such as linear regression.

[0400] Input: Behavioral history data

[0401] Output: Predicted time spent at home

[0402] Step 3:

[0403] The server proposes the optimal delivery date and time based on the predicted time at home data, and calculates the optimal delivery time taking into account the user's desired time and delivery priority.

[0404] Input: Predicted time at home, user's desired time, delivery priority

[0405] Output: Best delivery date and time

[0406] Step 4:

[0407] The server uses map and traffic information to calculate the optimal delivery route, taking into account predicted home time and priority, allowing delivery personnel to make deliveries efficiently.

[0408] Input: Best delivery date and time, map information, traffic information

[0409] Output: Optimal delivery route

[0410] Step 5:

[0411] The terminal sends the delivery route information generated in step 4 to the delivery person. Based on this information, the delivery person picks up the package and begins delivering it to the user's home.

[0412] Input: Delivery route information

[0413] Output: Instructions to the delivery person

[0414] Step 6:

[0415] The server uses an emotion engine to collect and analyze the user's emotional data. Specifically, it recognizes the user's emotional state from their voice input or text message. The recognized emotional state is used to adjust the content and timing of delivery notifications.

[0416] Input: Voice input, text message

[0417] Output: Emotional state data

[0418] Step 7:

[0419] The server notifies the user of the estimated delivery time and real-time delivery status based on the emotional state data. The server adjusts the wording and timing of the notification depending on the user's emotional state. For example, if the user is feeling stressed, the server will send a gentler notification.

[0420] Input: Emotional state data, optimal delivery route, real-time delivery status

[0421] Output: User notification

[0422] Step 8:

[0423] The terminal (delivery person) reports the real-time delivery status to the server. The user can check the progress of the delivery based on this information. The server uses this information to notify the user of the latest delivery status.

[0424] Input: Real-time delivery status

[0425] Output: Report to server, latest notifications to user

[0426] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0427] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0428] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0429] [Second embodiment]

[0430] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0431] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0432] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0433] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0434] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0435] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0436] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0437] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0438] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0439] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0440] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0441] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0442] This invention is a system that collects behavioral history data of general consumers, analyzes the data using a generation API, and predicts the time when they will be at home. This can reduce redelivery costs in the logistics industry and optimize delivery routes. Furthermore, by collaborating with truck drivers and delivery personnel, it can solve the problem of labor shortages and build an efficient delivery system. This system is implemented by the following means.

[0443] Collecting and predicting user behavior data

[0444] The server collects user behavior history data, including the time spent outside the home, the time returned home, and activities on holidays.

[0445] The server inputs the collected data into the generation API and analyzes it to predict the time the customer will be at home. The predicted time is saved in the delivery management system and used to optimize future deliveries.

[0446] Delivery route optimization

[0447] The server then suggests the optimal delivery date and time based on the predicted user's home time, thereby reducing the risk of redelivery.

[0448] The server combines map information and traffic information to calculate the optimal delivery route, which is then sent to the delivery person's terminal.

[0449] Collaboration with delivery staff

[0450] The terminal (the delivery driver's terminal) takes the package to the nearest collection and distribution station.

[0451] The delivery person will pick up the package at the station and ultimately deliver it to the user's home.

[0452] User Notification

[0453] The server notifies the user of the estimated delivery time, and the user can check this notification and submit a request to change the delivery date and time if necessary.

[0454] Specific examples

[0455] Example 1: Predicting user's time at home

[0456] The server collects user behavior history data from the past six months and analyzes it using the generation API. If the prediction result shows that the user is often at home on weekday nights, the scheduled delivery time is set to a weekday night.

[0457] Example 2: Delivery route optimization

[0458] The server predicts the user's expected time of home and determines the optimal delivery date and time to be between 10:00 AM and noon on Saturday. Using map and traffic information, the server calculates the shortest route from the nearest collection and delivery station to the user's home. The route information is then sent to the delivery person's device.

[0459] Example 3: Collaboration with delivery staff and user notification

[0460] The terminal (the driver's terminal) will deliver the package to the collection and delivery station at 8:00 AM on Saturday. The delivery person will pick up the package at the station and complete the delivery between 10:00 AM and noon, when the user is expected to be home.

[0461] The server notifies the user that the package is scheduled to be delivered between 10:00 AM and 12:00 PM on Saturday. If the user cannot receive the package during this time, they submit a request to change the delivery time to the system. Based on this request, the server recalculates a new delivery schedule and route and issues instructions to the delivery person.

[0462] In this way, the present invention can predict user behavior patterns and propose optimal delivery dates, times, and routes, thereby reducing redelivery costs and resolving labor shortages. Furthermore, the real-time notification function makes it possible to provide users with a fast and reliable home delivery service.

[0463] The processing flow will be explained below.

[0464] Step 1:

[0465] Users provide their behavioral history data, which is sent through a dedicated application or retrieved from an existing database.

[0466] Step 2:

[0467] The server collects the provided user behavioral history data, specifically, data on the time spent outside the home, the time returned home, and activities on holidays.

[0468] Step 3:

[0469] The server calls the generation API based on the collected behavioral history data and predicts the user's home time using a machine learning algorithm.

[0470] Step 4:

[0471] The server stores the predicted user's home time in the delivery management system, which then uses it to optimize future delivery dates and times.

[0472] Step 5:

[0473] The server calculates the optimal delivery time based on the predicted time the customer will be at home, taking into account the customer's preferred time and delivery priority.

[0474] Step 6:

[0475] The server uses map and traffic information to generate optimal delivery routes, taking into account distances between delivery points and traffic conditions.

[0476] Step 7:

[0477] The server then sends the generated optimal delivery route and delivery date and time to the delivery person's terminal, enabling the delivery person to make deliveries along an efficient route.

[0478] Step 8:

[0479] The terminal (delivery driver's terminal) displays the route information received from the server and delivers the package to the nearest collection and distribution station.

[0480] Step 9:

[0481] The delivery person will pick up the package at the collection and delivery station and finally deliver it to the user's home. The delivery status will be updated on the terminal in real time.

[0482] Step 10:

[0483] The server notifies the user of the final estimated delivery time, including the specific delivery date and time and real-time delivery status.

[0484] Step 11:

[0485] The user checks the estimated delivery time and, if necessary, submits a request to change the delivery date and time to the system.

[0486] Step 12:

[0487] The server receives the change request from the user, calculates a new delivery schedule, and generates the optimal delivery route again.

[0488] Step 13:

[0489] The server sends the new delivery schedule and route information to the delivery person's terminal and notifies the user of the changed delivery schedule.

[0490] In this way, the system of the present invention can efficiently carry out a series of processes, from collecting user behavior data, to predicting the time the user will be at home, proposing the optimal delivery date and route, collaborating with delivery personnel, and notifying the user.

[0491] Example 1

[0492] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0493] In modern logistics operations, redelivery is common, resulting in significant costs for the logistics industry. Furthermore, delivery routes are not adequately optimized, resulting in inefficient deliveries. Furthermore, labor shortages are a serious problem, necessitating efficient labor allocation. To solve these issues, a system is needed that can predict when users will be at home and propose optimal delivery dates, times, and routes.

[0494] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0495] In this invention, the server includes a means for collecting activity history data of general users, a means for analyzing the collected activity history data and predicting the time the user will be at home, and a means for proposing the optimal date and time for delivery based on the predicted time the user will be at home, thereby reducing redelivery costs and improving delivery efficiency.

[0496] "General user" refers to an individual or household who uses the system.

[0497] "Activity history data" refers to information related to a user's behavior, such as the time they spend outside, the time they return home, and their activities on holidays.

[0498] "Analysis" refers to the processing that a generative AI model performs on collected data to understand the data and extract meaningful information.

[0499] "Home time" refers to the time period when the user is at home.

[0500] "Delivery route" refers to the route a delivery company takes to deliver a package.

[0501] "Generative AI" refers to artificial intelligence technology that uses generative AI models to analyze data and make predictions.

[0502] "Map Information" refers to map data and related location information.

[0503] "Delivery Company" refers to the person or shipping company that ultimately delivers the package to the User.

[0504] "Delivery route information" refers to information regarding the optimal delivery route.

[0505] "Real-time delivery status" refers to information that displays the current delivery progress status in real time.

[0506] "Delivery Time Change Request" means a request submitted by a User to change an already set delivery time.

[0507] "Transport vehicle operator" refers to the person or transport driver who transports goods to the nearest collection and delivery station.

[0508] A "collection station" is a location where packages are temporarily collected and received by the delivery company for final delivery.

[0509] "Final delivery" refers to the process by which a package is delivered from the collection and distribution station to the user.

[0510] This invention is a system that collects activity history data of general users, analyzes the data using a generative AI model, and predicts the time they will be at home. This can reduce redelivery costs in the logistics industry and optimize delivery routes. Furthermore, by collaborating with delivery companies, it can solve the problem of labor shortages and build an efficient delivery system. This system is implemented by the following means.

[0511] Collecting and predicting user behavior data

[0512] The server collects data on the user's online behavior history from their smartphone or PC, including the time the user leaves the house, the time they return home, and their activities on holidays. The collected data is stored in a database on the server.

[0513] Next, the server inputs this collected data into a generative AI model API (e.g., generative AI). The prompt used here is something like, "Please predict the amount of time the user will be at home based on the user's behavioral history data." The generative AI model analyzes the data and predicts the user's amount of time at home.

[0514] Save to delivery management system

[0515] Based on the analysis results, the server predicts when the user will be at home and stores this data in the delivery management system database, which is used to plan future deliveries.

[0516] Delivery route optimization

[0517] The server proposes the date and time with the highest probability of successful delivery based on the predicted user's home time. Based on the proposed date and time, the server calculates the optimal delivery route using map information (e.g., map API) and real-time traffic information (e.g., traffic conditions API). This allows the delivery company to deliver the package using the most efficient route.

[0518] Collaboration with delivery staff

[0519] The server then sends the calculated delivery date and time and the optimal route information to the delivery company's terminal. The delivery company will then pick up the package at the specified date and time and deliver it according to the user's home time.

[0520] User Notification

[0521] Once the delivery date and time is determined, the server sends a notification to the user saying, "The package is scheduled to be delivered between 2:00 PM and 4:00 PM on the XXth of the month." The user can check this notification and request a change in the delivery time if they cannot receive the package during this time period.

[0522] Handling delivery time changes

[0523] When a user submits a delivery time change request, the server accepts the request, recalculates a new delivery schedule and route, and sends this new information back to the delivery company.

[0524] Specific examples

[0525] Example 1: Predicting user's time at home

[0526] The server collects user behavioral history data from the past six months and analyzes it with the generation AI. For example, if the user is often at home on weekday nights, the scheduled delivery time will be set to a weekday night.

[0527] Example 2: Delivery route optimization

[0528] The server predicts the user's expected time of home delivery and determines the optimal delivery date and time to be between 10:00 AM and noon on Saturdays. It then uses map and traffic information to calculate the shortest route from the nearest collection and delivery station to the user's home. This route information is then sent to the delivery company's terminal.

[0529] Example 3: Collaboration with delivery staff and user notification

[0530] The terminal (the driver's terminal) will deliver the package to the collection and delivery station at 8:00 AM on Saturday. The delivery company will pick up the package at the station and complete the delivery between 10:00 AM and noon, when the user is expected to be home.

[0531] The server notifies the user that the package is scheduled to be delivered between 10:00 AM and 12:00 PM on Saturday. If the user cannot receive the package during this time, they can submit a request to change the delivery time to the system. Based on this request, the server recalculates a new delivery schedule and route and sends instructions to the delivery company.

[0532] In this way, the present invention can predict user behavior patterns and propose optimal delivery dates, times, and routes, thereby reducing redelivery costs and resolving labor shortages. Furthermore, the real-time notification function makes it possible to provide users with a fast and reliable home delivery service.

[0533] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0534] Step 1:

[0535] (Collection of user behavior data)

[0536] The server collects data on the user's online behavior history through a dedicated application installed on the user's smartphone or computer. The collected data includes the time of leaving and returning home, holiday activities, and other location information. This data is sent to and stored in the server's database in real time.

[0537] Input: Behavioral history data from the user's smartphone or computer

[0538] Output: Behavioral history data stored in the server database

[0539] Step 2:

[0540] (Analysis of behavioral history data)

[0541] The server inputs the collected behavioral history data into the generation AI model API. The prompt used at this time is "Please predict the time the user will be at home based on the user behavioral history data." The generation AI model analyzes the input data and returns a prediction of the time the user will be at home.

[0542] Input: Collected behavioral history data, prompt text

[0543] Output: Predicted time at home

[0544] Step 3:

[0545] (Saving prediction results)

[0546] The server stores the predictions returned by the generative AI model in the delivery management system's database, which is used to plan future deliveries.

[0547] Input: Prediction results returned by the generative AI model

[0548] Output: Prediction results stored in the delivery management system database

[0549] Step 4:

[0550] (Proposal of optimal delivery date and time)

[0551] The server calculates the optimal delivery date and time based on the predicted user's home time, minimizing the risk of re-delivery. This calculation takes into account the days of the week and times when the user is likely to be at home.

[0552] Input: Predicted user home time

[0553] Output: Suggested best delivery date and time

[0554] Step 5:

[0555] (Delivery route calculation)

[0556] The server calculates the optimal delivery route based on the proposed delivery date and time, combining map information and real-time traffic information. This calculation uses map APIs and traffic condition APIs. The calculated route information is sent to the delivery company's device.

[0557] Input: Suggested optimal delivery date and time, map information, traffic information

[0558] Output: Calculated optimal delivery route

[0559] Step 6:

[0560] (Collaboration with delivery companies)

[0561] The server then sends the calculated delivery date and time and the optimal route information to the delivery company's terminal. The delivery company then picks up the user's package according to the specified date, time and route, and makes the final delivery.

[0562] Input: Calculated optimal delivery route, delivery date and time information

[0563] Output: Route and delivery information sent to the delivery company's terminal

[0564] Step 7:

[0565] (User Notification)

[0566] The server then sends the user a notification of the scheduled delivery date and time. For example, a notification saying, "Your package is scheduled to be delivered between 2:00 PM and 4:00 PM on a certain date" is sent. Notifications are sent via smartphone push notifications or email.

[0567] Input: Scheduled delivery date and time information

[0568] Output: Estimated delivery notification sent to user

[0569] Step 8:

[0570] (Processing delivery time change requests)

[0571] The user checks the notification and, if a change in delivery time is necessary, sends a change request to the system. The server accepts this request and recalculates a new delivery schedule and route. The recalculated schedule is then sent to the delivery company.

[0572] Input: Delivery time change request from user

[0573] Output: New recalculated delivery schedule and route

[0574] Step 9:

[0575] (Pick up your parcel at the collection and delivery station)

[0576] The terminal (driver's terminal) delivers the package to the nearest collection and delivery station at the specified time, where the delivery company receives the package and prepares it for delivery at the specified delivery date and time.

[0577] Input: Packages received at the collection and delivery station

[0578] Output: Package received by the delivery company

[0579] Step 10:

[0580] (Final delivery completion)

[0581] The delivery company will head to the user's home at the specified delivery date and time to deliver the package safely. Once the delivery is complete, the status is updated in the system and the server sends a notification to the user that the delivery has been completed.

[0582] Input: Packages being delivered to the user's home

[0583] Output: Delivered package, delivery completion notification to the user

[0584] Through the above processing steps, the present invention predicts user behavior patterns and proposes optimal delivery dates, times, and routes, thereby reducing redelivery costs and resolving labor shortages. Furthermore, the real-time notification function makes it possible to provide users with a fast and reliable home delivery service.

[0585] (Application example 1)

[0586] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0587] In today's logistics industry, no-shows and redeliveries are major issues. This reduces delivery efficiency and increases the costs associated with redeliveries. Furthermore, there is a significant labor shortage of delivery personnel, increasing the burden on delivery work. Furthermore, optimizing deliveries based on traffic congestion and predicted times when customers will be at home is difficult, creating a need for optimized transportation routes.

[0588] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0589] In this invention, the server includes means for collecting behavioral history data of general consumers, means for analyzing the collected behavioral history data and predicting the time when consumers will be at home, means for installing a delivery management system in an autonomous vehicle and proposing the optimal date and time for delivery based on the predicted time when consumers will be at home, means for calculating the optimal delivery route using generation AI and map information, means for transmitting delivery route information to the autonomous vehicle and making the optimal delivery, means for the autonomous vehicle to deliver the package to the nearest station based on the delivery route and prediction data and complete the final delivery with minimal effort, and means for notifying the user of the estimated delivery time and real-time delivery status, which makes it possible to reduce redelivery costs, optimize delivery routes, eliminate labor shortages, and provide real-time delivery notifications.

[0590] "General consumer behavior history data" refers to records of past behavior of consumers, including the time spent outside the home, the time returned home, and activities on holidays.

[0591] "Predicting time spent at home" refers to predicting the amount of time a consumer will be at home based on collected behavioral history data.

[0592] "Means of installing a delivery management system on an autonomous vehicle" means installing software or hardware on an autonomous vehicle to manage deliveries.

[0593] The "means for proposing the optimal delivery date and time" is a technology that calculates and presents the optimal date and time for package delivery based on the predicted time the customer will be at home.

[0594] "Generative AI" is a type of artificial intelligence technology that predicts future events and situations based on collected data.

[0595] "Map information" is data that includes geographical information, such as roads, buildings, topography, and traffic conditions.

[0596] The "means for calculating the optimal delivery route" is a system that uses map information and generation AI to calculate the optimal delivery route to maximize delivery efficiency.

[0597] "Means for transmitting delivery route information to an autonomous vehicle" refers to a device or method for sending the calculated optimal delivery route information as instructions to an autonomous vehicle.

[0598] "Nearest station" refers to the nearest package storage or relay station set up by the delivery company.

[0599] "Means for notifying real-time delivery status" refers to a communication method for instantly informing users of the current delivery status of their packages.

[0600] "Reducing redelivery costs" refers to aiming for successful delivery the first time and reducing the unnecessary expenses associated with repeat deliveries.

[0601] "Delivery route optimization" is the process of planning the best route to maximize delivery efficiency and reduce delivery time and costs.

[0602] "Resolving labor shortages" means trying to solve the labor shortage problem by introducing an efficient delivery system.

[0603] "Real-time delivery notifications" means instantly providing users with details of ongoing deliveries and keeping them updated on the progress of their deliveries.

[0604] This invention relates to a system that collects consumer behavior history data and delivers packages efficiently in cooperation with a delivery management system installed in an autonomous driving vehicle. Specific embodiments of this system are described below.

[0605] Collecting and predicting user behavior data

[0606] The server collects consumer behavior history data, including information on when they leave the home, when they return home, and their activities on holidays. The collected data is analyzed using a generative AI model to predict when consumers will be at home.

[0607] Delivery planning based on historical forecasts

[0608] Based on the predicted time at home, the server will suggest the optimal delivery date and time to the consumer through the delivery management system installed in the autonomous vehicle. This suggestion is set during the time when the consumer is predicted to be at home to reduce the risk of redelivery.

[0609] Delivery route optimization

[0610] The server uses generative AI and map information, including Google Maps API and traffic data, to calculate the optimal delivery route. The calculated route information is sent to the autonomous vehicle, which instructs it to maximize delivery efficiency.

[0611] Delivery execution by autonomous vehicles

[0612] The autonomous vehicle will then calculate an optimal route to deliver the package to the nearest station, where a delivery person will complete the last mile if necessary, resulting in more efficient delivery and reduced labor.

[0613] User Notification

[0614] The server notifies the consumer of the estimated delivery time and real-time delivery status. Notifications are sent to the consumer's smartphone or other device, allowing the consumer to check the delivery progress in real time. If the estimated delivery date and time can be changed, the consumer can send a request to adjust the delivery date and time.

[0615] Specific examples

[0616] For example, let's say that behavioral data from the past six months predicts that consumers often return home between 6:00 PM and 10:00 PM on weekdays. Based on this information, autonomous vehicles can deliver packages during those times, reducing the risk of having to redeliver packages due to consumers being absent.

[0617] Using data from the user's behavior over the past six months, it is predicted that the user will typically return home between 6:00 PM and 10:00 PM on weekdays. Based on this information, the autonomous vehicle will calculate the optimal delivery route and notify the user.

[0618] The present invention aims to reduce redelivery costs and resolve labor shortages by analyzing consumer behavior data and providing efficient delivery plans and routes based on predicted times when consumers will be at home.

[0619] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0620] Step 1:

[0621] The server collects data on the behavioral history of general consumers. Specifically, data on time spent outside the home, time returned home, and activities on holidays is obtained from APIs and databases. The collected data is stored for analysis.

[0622] Input: Behavioral history data obtained from APIs and databases

[0623] Output: Behavioral history data saved for analysis

[0624] Step 2:

[0625] The server analyzes the collected behavioral history data using a generative AI model to predict the amount of time the consumer will be at home. For example, the model is trained based on data from the past six months to generate a predictive model. By inputting the latest data into this model, the amount of time the consumer will be at home can be predicted.

[0626] Input: Collected behavioral history data

[0627] Output: Predicted time at home

[0628] Step 3:

[0629] The server then suggests the best delivery time based on the predicted time the consumer will be at home. This suggestion is calculated taking into account the time of day the consumer is expected to be at home. For example, if a consumer is usually at home between 6:00 PM and 10:00 PM on weekdays, the server will suggest that time as the delivery time.

[0630] Input: Predicted time at home

[0631] Output: Suggested best delivery date and time

[0632] Step 4:

[0633] The server uses the generative AI and map information to calculate the optimal delivery route. This uses Google Maps API and traffic information data to calculate the optimal route taking into account current traffic conditions. The calculated route information is sent to the autonomous vehicle.

[0634] Input: Proposed delivery date and time, map information, and traffic information

[0635] Output: Calculated optimal delivery route

[0636] Step 5:

[0637] The server then sends the calculated delivery route information to the autonomous vehicle and instructs the vehicle to follow the route, which the autonomous vehicle then follows to deliver the package to the nearest station.

[0638] Input: Calculated delivery route information

[0639] Output: Route instructions for the autonomous vehicle

[0640] Step 6:

[0641] The autonomous vehicle will then arrive at the nearest station, where a delivery person will handle the last mile of delivery, making the final delivery from the station to the consumer's home.

[0642] Input: A package transported by an autonomous vehicle

[0643] Output: Final delivery to the consumer's home

[0644] Step 7:

[0645] The server notifies the consumer of the estimated delivery time and real-time delivery status. Notifications are sent to the consumer's smartphone, allowing the consumer to check the delivery progress in real time. In addition, if there is a request to change the delivery date and time, the server accepts it and applies the recalculated new schedule.

[0646] Input: Estimated delivery time, real-time delivery status

[0647] Output: Notified estimated delivery time and current delivery status

[0648] Through these steps, the invention can achieve an efficient delivery system, reduce the risk of redelivery, and eliminate the problem of labor shortages.

[0649] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0650] The present invention is a system that combines a system that collects consumer behavior history data, analyzes it using a generation API, and predicts the amount of time consumers will be at home with an emotion engine that recognizes user emotions. This makes it possible to reduce redelivery costs, optimize delivery routes, and provide flexible delivery support based on user emotions. Specific embodiments are described in detail below.

[0651] Collecting and predicting user behavior data

[0652] The server collects user behavior history data, such as the time spent outside, the time returned home, and activities on holidays.

[0653] The server inputs the collected data into the generation API and analyzes it to predict the user's home time. This predicted data is stored in the delivery management system database.

[0654] Delivery route optimization

[0655] The server calculates the optimal delivery time based on the predicted time the customer will be at home, taking into account the customer's preferred time and delivery priority.

[0656] The server uses map information and traffic information to generate an optimal delivery route, which is then sent to the delivery person's terminal.

[0657] Collaboration with delivery staff

[0658] The terminal (the delivery driver's terminal) takes the package to the nearest collection and distribution station according to the predicted route.

[0659] The delivery person will pick up the package from the collection and delivery station and deliver it to the user's home.

[0660] User support with an emotional engine

[0661] The server uses an emotion engine to collect and analyze the user's emotion data, including the user's voice and text data.

[0662] The server uses an emotion engine to analyze the user's emotional state and adjusts the content and timing of notifications based on the results. For example, if the user is feeling anxious or dissatisfied, the server provides an option to easily change the delivery schedule.

[0663] The server stores the collected emotional data and analyzes long-term emotional trends, enabling it to provide a flexible delivery service tailored to the user's emotional state.

[0664] User Notification

[0665] The server notifies the user of the estimated delivery time and real-time delivery status, and the content of the notification is adjusted based on the analysis results of the emotion engine.

[0666] Specific examples

[0667] Example 1: Predicting user's time at home

[0668] The server collects user behavior history data from the past few months and analyzes it using a generation API. Based on this, if it predicts that the user is likely to be at home during weekday nights, it will schedule deliveries for those times.

[0669] Example 2: Delivery route optimization

[0670] The server predicts the user's expected time of home and determines the optimal delivery date and time to be between 10:00 a.m. and noon on Saturdays. Using map and traffic information, the server calculates the shortest route from the nearest delivery station to the user's home.

[0671] Example 3: Supported by an Emotion Engine

[0672] The server uses an emotion engine to analyze the user's recent text messages and recognize that they are stressed, which results in the delivery notification being more subdued and adding a link to easily reschedule the delivery.

[0673] Example 4: Collaboration with delivery personnel and user notification

[0674] The terminal (the delivery driver's terminal) will deliver the package to the collection and distribution station at 8:00 AM on Saturday, and the delivery person will complete the delivery to the user's home between 10:00 AM and noon on Saturday. The server will notify the user of this schedule, and the user will confirm the notification. The real-time status of the delivery will also be updated as it progresses.

[0675] In this way, the present invention can provide an effective and economical delivery service by predicting the user's behavioral patterns and emotions and calculating the optimal delivery date and route. Furthermore, by utilizing an emotion engine, it is possible to respond flexibly to the user's emotional state, providing a higher level of satisfaction.

[0676] The processing flow will be explained below.

[0677] Step 1:

[0678] Users provide their own behavioral history data, including the time they spend outside, the time they return home, and their activities on holidays.

[0679] Step 2:

[0680] The server collects the provided user behavioral history data, which is either sent through a dedicated application or retrieved from an existing database.

[0681] Step 3:

[0682] The server inputs the collected behavioral history data into a generation API and analyzes it to predict the user's time at home, using a machine learning algorithm.

[0683] Step 4:

[0684] The server stores the prediction results in the delivery management system database, which can then be used for future delivery planning.

[0685] Step 5:

[0686] The server calculates the optimal delivery date and time based on the predicted time the customer will be at home, taking into account the customer's preferred time and delivery priority.

[0687] Step 6:

[0688] The server uses map and traffic information to calculate the optimal delivery route, enabling efficient delivery.

[0689] Step 7:

[0690] The server sends the calculated delivery route and delivery date and time to the delivery person's terminal, and the delivery person makes the delivery based on the received route.

[0691] Step 8:

[0692] The terminal (the delivery driver's terminal) takes the package to the nearest collection and distribution station, thereby assisting with the last mile of delivery.

[0693] Step 9:

[0694] The delivery person will collect the package at the collection and delivery station and finally deliver it to the user's home, updating the delivery status on the terminal in real time.

[0695] Step 10:

[0696] The server notifies the user of the estimated delivery time and real-time delivery status, including the specific delivery date and time.

[0697] Step 11:

[0698] The server uses an emotion engine to collect and analyze the user's emotional data, and uses the user's voice and text data as analysis data.

[0699] Step 12:

[0700] The server adjusts the content and timing of notifications based on the results of the emotion engine analysis. For example, if the user is feeling stressed, the delivery notification will be more subdued in tone.

[0701] Step 13:

[0702] The server accumulates user emotional data and analyzes long-term emotional trends, thereby improving the quality of service for users.

[0703] Step 14:

[0704] Users can check the estimated delivery time and, if necessary, submit a request to change the delivery date and time to the system.

[0705] Step 15:

[0706] The server receives change requests from users and recalculates new delivery schedules and optimal delivery routes.

[0707] Step 16:

[0708] The server sends the new delivery schedule and route information to the delivery person's terminal and notifies the user of the changed delivery schedule.

[0709] In this way, the present invention analyzes user behavior data and emotion data to propose optimal delivery schedules and routes, thereby achieving efficient deliveries and improving user satisfaction.

[0710] Example 2

[0711] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0712] With the current delivery system, it is difficult to simultaneously optimize delivery and improve user satisfaction. In particular, there are many cases where redelivery is required when the user is not at home, which increases delivery costs and reduces efficiency. In addition, there is a lack of flexibility in responding to user emotions and requests, which can easily lead to user dissatisfaction. By solving these issues, it is necessary to reduce delivery costs and improve user satisfaction.

[0713] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting behavioral history data of general consumers, means for analyzing the collected behavioral history data and predicting the time the consumer will be at home, means for proposing the optimal date and time for delivery based on the predicted time the consumer will be at home, means for calculating the optimal delivery route using the generative AI model and location information, means for sending delivery route information to a delivery employee and making the optimal delivery, means for completing the final delivery with minimal effort by cooperating with the delivery employee, means for collecting and analyzing user emotion data, means for adjusting notification content and delivery options based on the emotion analysis results, and means for notifying the user of the estimated delivery time and real-time delivery status. This enables delivery optimization and flexible response according to the user's emotions.

[0714] "General consumers" refer to individuals who do not have a specific industry or specialized knowledge and who engage in general consumer activities.

[0715] "Behavioral history data" refers to past data regarding a user's movements, schedules, activities, etc.

[0716] "Home time" refers to the time period when the user is at home.

[0717] A "generative AI model" refers to a system that uses machine learning and artificial intelligence algorithms to analyze data and make predictions.

[0718] "Location information" refers to data that indicates the physical location of a user or object, such as geographic coordinates or address information.

[0719] "Delivery route" refers to the optimal route for delivering a package.

[0720] "Delivery employee" refers to a person whose job is to deliver packages to a user's home or designated location.

[0721] "Emotional data" refers to data that indicates a user's emotions and psychological state. Specifically, it includes information obtained from voice, text, facial expressions, etc.

[0722] "Notification" means a message or alert intended to provide information to the User.

[0723] The system of this invention combines a system that collects user behavior history data, analyzes it using a generative AI model, and predicts the time the user will be at home with an emotion engine that recognizes the user's emotions. This not only reduces redelivery costs and optimizes delivery routes, but also provides flexible delivery support based on the user's emotions.

[0724] Collecting and predicting user behavior data

[0725] The server collects the user's behavioral history data. This collection includes data acquisition from the user's smartphone and wearable device. Specific data includes GPS data, calendar appointments, and motion sensor data. For example, the server periodically sends the location information set by the user on the smartphone to the server, recording the user's time out and time back home.

[0726] The collected data is analyzed using a generative AI model (e.g., a machine learning algorithm). This analysis extracts user behavioral patterns and predicts how long the user will be at home. The prediction results are stored in the delivery management system's database.

[0727] Delivery route optimization

[0728] The server calculates the optimal delivery date and time based on the predicted time the delivery person will be at home, taking into account the user's preferred time and delivery priority. It then calculates the optimal delivery route using map information (e.g., Google Maps API) and traffic information. The calculated route information is sent to the delivery person's device.

[0729] Collaboration with delivery staff

[0730] The terminal (the delivery person's device) will then follow the predicted route and deliver the package to the nearest collection and delivery station, where the delivery person will pick up the package and deliver it to the user's home.

[0731] User support with an emotional engine

[0732] The server uses a voice recognition system and text analysis tools to collect user emotion data, for example, from voice data or text messages when a user contacts customer support.

[0733] The emotion engine analyzes this data to identify the user's emotional state and adjusts the notification content and delivery options accordingly. For example, if the user is stressed, the notification will be sent in a more gentle tone and offer the option to reschedule the delivery.

[0734] User Notification

[0735] The server notifies the user of the estimated delivery time based on the determined delivery date and time and the optimal route. Notifications are sent via smartphone app, email, or SMS. Real-time delivery status information is also provided as appropriate.

[0736] Specific examples

[0737] Example 1: Predicting user's time at home

[0738] The server collects user behavior history data from the past few months and analyzes it with a generative AI model. For example, if it predicts that the user is likely to be at home during weekday nights, it will schedule deliveries for those times.

[0739] Example 2: Delivery route optimization

[0740] The server predicts the user's expected time of home and determines the optimal delivery date and time between 10:00 a.m. and noon on Saturdays. It also uses map and traffic information to calculate the shortest route from the nearest delivery station to the user's home.

[0741] Example 3: Supported by an Emotion Engine

[0742] The server uses an emotion engine to analyze the user's recent text messages and recognize that they are stressed, which results in the delivery notification being more subdued and includes a link to easily reschedule the delivery.

[0743] Example 4: Collaboration with delivery personnel and user notification

[0744] The terminal (delivery worker's device) will deliver the package to the collection and delivery station at 8:00 AM on Saturday, and the delivery worker will complete the delivery to the user's home between 10:00 AM and noon on Saturday. The server will notify the user of this schedule, and the user will confirm the notification. The real-time status of the delivery will also be updated as it progresses.

[0745] An example of a prompt is, "Analyze the user's behavioral history data from the past few months and predict when the user will be at home. Calculate the optimal delivery date, time, and route based on the prediction results. Also, adjust the notification content taking into account the user's emotional state."

[0746] In this way, the system of the present invention calculates the optimal delivery date and route based on the user's behavioral data and emotional data, and provides an effective and flexible delivery service.

[0747] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0748] Step 1:

[0749] The server collects user behavior history data. The collected data is obtained from the user's smartphone or wearable device. Specifically, it includes GPS data, calendar events, and motion sensor data. For example, the user's behavior history while out and the time of return home are recorded. The input data is the behavior history data sent from the user's device, and the output is the collected dataset.

[0750] Step 2:

[0751] The server preprocesses the collected behavioral history data, complementing missing values ​​and excluding outliers. Specifically, it detects and excludes data containing sudden movements, and complements missing data using the mean or median. The input is the collected behavioral history data, and the output is the preprocessed data.

[0752] Step 3:

[0753] The server inputs the preprocessed data into a generative AI model to predict the user's home time. The generative AI model uses a time-series data prediction algorithm. For example, it predicts the home time on weekday nights and weekends based on past behavioral patterns. The input is the preprocessed behavioral history data, and the output is the predicted home time data.

[0754] Step 4:

[0755] The server determines the optimal delivery date and time based on the predicted at-home time data, taking into account the user's desired time and delivery priority. The input is the predicted at-home time data and the user's desired time, and the output is the optimal delivery date and time.

[0756] Step 5:

[0757] The server calculates the optimal delivery route based on the determined delivery date and time using map and traffic information. For map information, it uses Google Maps API and public transportation API to calculate the shortest route from the delivery center to the user's address. The input is the optimal delivery date and time, map information, and traffic information, and the output is the optimal delivery route.

[0758] Step 6:

[0759] The server sends the calculated delivery route information to the delivery person's terminal. The delivery person makes the delivery based on this route information. The input is the optimal delivery route information, and the output is the route information sent to the delivery person's terminal.

[0760] Step 7:

[0761] The terminal (the delivery person's device) carries the package to the nearest collection / distribution station according to the predicted route. Specifically, it follows the route from the delivery center to the collection / distribution station. The input is the package from the delivery center and the optimal delivery route information, and the output is the state of the package arriving at the collection / distribution station.

[0762] Step 8:

[0763] The delivery person delivers the package from the collection and delivery station to the user's home at the delivery date and time specified by the user. The input is the package from the collection and delivery station and the optimal delivery route information, and the output is the package delivered to the user's home.

[0764] Step 9:

[0765] The server uses a voice recognition system and text analysis tools to collect user emotion data. Specifically, it obtains emotion information from voice data and text messages when a user contacts customer support. The input is the user's inquiry data, and the output is the collected emotion data.

[0766] Step 10:

[0767] The server uses an emotion engine to analyze the collected emotion data and identify the user's emotional state. For example, it determines whether the user is "angry" or "anxious" based on the content of their voice or text message. The input is the collected emotion data, and the output is the analyzed emotion state.

[0768] Step 11:

[0769] The server adjusts the notification content and delivery options based on the analysis results of the emotion engine. For example, if the user is stressed, the server may send a softer notification and offer the option to reschedule the delivery. The input is the analyzed emotional state, and the output is the adjusted notification content.

[0770] Step 12:

[0771] The server notifies the user of the estimated delivery time and real-time delivery status via a smartphone app, email, or SMS. For example, a notification is sent when the package leaves the delivery center or arrives in the delivery area. The input is delivery status data and the adjusted notification content, and the output is the notification sent to the user.

[0772] In this way, the system calculates the optimal delivery date and route based on the user's behavioral history and emotional data, providing an efficient and flexible delivery service.

[0773] (Application example 2)

[0774] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0775] Conventional delivery systems are unable to accurately predict when users will be at home, resulting in frequent redelivery and delays. Furthermore, because they do not take user emotions into consideration, delivery notifications and responses are mechanical, leading to lower user satisfaction. To solve these problems, a new system is needed that analyzes user behavioral history data and emotional data to provide optimal delivery dates, times, and routes.

[0776] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting behavioral history data of general consumers, means for analyzing the collected behavioral history data and predicting the time the consumer will be at home, means for proposing the optimal date and time for delivery based on the predicted time the consumer will be at home, means for calculating the optimal delivery route using generation AI and map information, means for sending delivery route information to a delivery person and making the optimal delivery, means for completing the final delivery with minimal effort by cooperating with the delivery person, means for notifying the user of the estimated delivery time and real-time delivery status, and means for recognizing the user's emotions and adjusting the content and timing of notifications based on the emotions. This not only improves delivery efficiency but also enables flexible responses according to the user's emotions, providing a high level of satisfaction.

[0777] "General consumer" refers to an individual who purchases goods or uses services.

[0778] "Behavioral history data" refers to data related to a user's behavioral patterns, such as the time they spend outside the home, the time they return home, and their activities on holidays.

[0779] "Forecasting" refers to estimating future events or conditions based on collected data.

[0780] "Delivery route" refers to the route taken to deliver a package to a user.

[0781] "Generative AI" refers to artificial intelligence technology that generates and analyzes information based on large amounts of data.

[0782] "Map information" refers to data relating to geographical locations and routes.

[0783] "Delivery Person" refers to an individual or team whose role is to receive packages and deliver them to users.

[0784] "Optimization" refers to maximizing efficiency and effectiveness.

[0785] "Emotion recognition" refers to the technology of analyzing and understanding a user's emotional state from their voice, text, etc.

[0786] The "last mile" refers to the final stage of delivery, delivering the package from the collection and distribution station to the user's home.

[0787] "Notification" refers to the act of conveying information from the system to the user or delivery person.

[0788] This invention is a system that collects and analyzes user behavior history data and emotion data to provide optimal delivery dates and routes. A detailed description of the system based on the present invention is given below.

[0789] First, the server collects the user's behavioral history data, specifically the time the user leaves the house, the time they return home, their activities on holidays, etc. This data is collected using the smartphone's GPS data and calendar information.

[0790] The server then analyzes the collected data using generative AI to predict when the user will be at home, using techniques such as linear regression. The predicted time is then used in the next step to suggest the optimal delivery date and time.

[0791] The server also calculates the optimal delivery route based on map and traffic information, allowing delivery personnel to deliver efficiently. The route information is sent to the delivery personnel's terminal, and delivery is carried out according to the optimal route.

[0792] Furthermore, the server uses an emotion engine to recognize the user's emotions and adjust the content and timing of notifications accordingly. The server understands the user's emotional state by analyzing the user's voice input and text messages. This emotional data is used to adjust the wording and timing of delivery notifications, ensuring that users do not feel stressed.

[0793] Additionally, the server provides users with notifications of estimated delivery times and real-time delivery status, which are tailored based on the collected emotion data.

[0794] For example, if a user is often at home on weekday evenings, delivery can be scheduled for that time. Also, if the system determines that the user is stressed, delivery notifications will be changed to gentler language and delivery will be handled more quickly.

[0795] For example, the server might use the following prompt:

[0796] "Using the user's behavioral history data and calendar information, the system predicts when they will be at home and creates an ideal delivery plan based on their emotional data. Delivery routes are optimized using map and traffic information. If the user is under stress, the system changes the delivery time and notification content."

[0797] This system will enable flexible delivery services based on the user's behavioral patterns and emotional state, which is expected to reduce redelivery and delays and improve user satisfaction.

[0798] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0799] Step 1:

[0800] Users use their smartphones to collect behavioral history data, specifically GPS data and calendar information. This data includes detailed information such as the user's time out, the time they return home, and their activities on holidays.

[0801] Input: Smartphone GPS data, calendar information

[0802] Output: Behavioral history data

[0803] Step 2:

[0804] The server inputs the behavioral history data collected in step 1 into the generative AI model for analysis. The generative AI model learns past patterns and predicts the user's time at home. This prediction is made using techniques such as linear regression.

[0805] Input: Behavioral history data

[0806] Output: Predicted time spent at home

[0807] Step 3:

[0808] The server proposes the optimal delivery date and time based on the predicted time at home data, and calculates the optimal delivery time taking into account the user's desired time and delivery priority.

[0809] Input: Predicted time at home, user's desired time, delivery priority

[0810] Output: Best delivery date and time

[0811] Step 4:

[0812] The server uses map and traffic information to calculate the optimal delivery route, taking into account predicted home time and priority, allowing delivery personnel to make deliveries efficiently.

[0813] Input: Best delivery date and time, map information, traffic information

[0814] Output: Optimal delivery route

[0815] Step 5:

[0816] The terminal sends the delivery route information generated in step 4 to the delivery person. Based on this information, the delivery person picks up the package and begins delivering it to the user's home.

[0817] Input: Delivery route information

[0818] Output: Instructions to the delivery person

[0819] Step 6:

[0820] The server uses an emotion engine to collect and analyze the user's emotional data. Specifically, it recognizes the user's emotional state from their voice input or text message. The recognized emotional state is used to adjust the content and timing of delivery notifications.

[0821] Input: Voice input, text message

[0822] Output: Emotional state data

[0823] Step 7:

[0824] The server notifies the user of the estimated delivery time and real-time delivery status based on the emotional state data. The server adjusts the wording and timing of the notification depending on the user's emotional state. For example, if the user is feeling stressed, the server will send a gentler notification.

[0825] Input: Emotional state data, optimal delivery route, real-time delivery status

[0826] Output: User notification

[0827] Step 8:

[0828] The terminal (delivery person) reports the real-time delivery status to the server. The user can check the progress of the delivery based on this information. The server uses this information to notify the user of the latest delivery status.

[0829] Input: Real-time delivery status

[0830] Output: Report to server, latest notifications to user

[0831] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0832] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0833] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0834] [Third embodiment]

[0835] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0836] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0837] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0838] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0839] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0840] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0841] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0842] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0843] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0844] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0845] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0846] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0847] This invention is a system that collects behavioral history data of general consumers, analyzes the data using a generation API, and predicts the time when they will be at home. This can reduce redelivery costs in the logistics industry and optimize delivery routes. Furthermore, by collaborating with truck drivers and delivery personnel, it can solve the problem of labor shortages and build an efficient delivery system. This system is implemented by the following means.

[0848] Collecting and predicting user behavior data

[0849] The server collects user behavior history data, including the time spent outside the home, the time returned home, and activities on holidays.

[0850] The server inputs the collected data into the generation API and analyzes it to predict the time the customer will be at home. The predicted time is saved in the delivery management system and used to optimize future deliveries.

[0851] Delivery route optimization

[0852] The server then suggests the optimal delivery date and time based on the predicted user's home time, thereby reducing the risk of redelivery.

[0853] The server combines map information and traffic information to calculate the optimal delivery route, which is then sent to the delivery person's terminal.

[0854] Collaboration with delivery staff

[0855] The terminal (the delivery driver's terminal) takes the package to the nearest collection and distribution station.

[0856] The delivery person will pick up the package at the station and ultimately deliver it to the user's home.

[0857] User Notification

[0858] The server notifies the user of the estimated delivery time, and the user can check this notification and submit a request to change the delivery date and time if necessary.

[0859] Specific examples

[0860] Example 1: Predicting user's time at home

[0861] The server collects user behavior history data from the past six months and analyzes it using the generation API. If the prediction result shows that the user is often at home on weekday nights, the scheduled delivery time is set to a weekday night.

[0862] Example 2: Delivery route optimization

[0863] The server predicts the user's expected time of home and determines the optimal delivery date and time to be between 10:00 AM and noon on Saturday. Using map and traffic information, the server calculates the shortest route from the nearest collection and delivery station to the user's home. The route information is then sent to the delivery person's device.

[0864] Example 3: Collaboration with delivery staff and user notification

[0865] The terminal (the driver's terminal) will deliver the package to the collection and delivery station at 8:00 AM on Saturday. The delivery person will pick up the package at the station and complete the delivery between 10:00 AM and noon, when the user is expected to be home.

[0866] The server notifies the user that the package is scheduled to be delivered between 10:00 AM and 12:00 PM on Saturday. If the user cannot receive the package during this time, they submit a request to change the delivery time to the system. Based on this request, the server recalculates a new delivery schedule and route and issues instructions to the delivery person.

[0867] In this way, the present invention can predict user behavior patterns and propose optimal delivery dates, times, and routes, thereby reducing redelivery costs and resolving labor shortages. Furthermore, the real-time notification function makes it possible to provide users with a fast and reliable home delivery service.

[0868] The processing flow will be explained below.

[0869] Step 1:

[0870] Users provide their behavioral history data, which is sent through a dedicated application or retrieved from an existing database.

[0871] Step 2:

[0872] The server collects the provided user behavioral history data, specifically, data on the time spent outside the home, the time returned home, and activities on holidays.

[0873] Step 3:

[0874] The server calls the generation API based on the collected behavioral history data and predicts the user's home time using a machine learning algorithm.

[0875] Step 4:

[0876] The server stores the predicted user's home time in the delivery management system, which then uses it to optimize future delivery dates and times.

[0877] Step 5:

[0878] The server calculates the optimal delivery time based on the predicted time the customer will be at home, taking into account the customer's preferred time and delivery priority.

[0879] Step 6:

[0880] The server uses map and traffic information to generate optimal delivery routes, taking into account distances between delivery points and traffic conditions.

[0881] Step 7:

[0882] The server then sends the generated optimal delivery route and delivery date and time to the delivery person's terminal, enabling the delivery person to make deliveries along an efficient route.

[0883] Step 8:

[0884] The terminal (delivery driver's terminal) displays the route information received from the server and delivers the package to the nearest collection and distribution station.

[0885] Step 9:

[0886] The delivery person will pick up the package at the collection and delivery station and finally deliver it to the user's home. The delivery status will be updated on the terminal in real time.

[0887] Step 10:

[0888] The server notifies the user of the final estimated delivery time, including the specific delivery date and time and real-time delivery status.

[0889] Step 11:

[0890] The user checks the estimated delivery time and, if necessary, submits a request to change the delivery date and time to the system.

[0891] Step 12:

[0892] The server receives the change request from the user, calculates a new delivery schedule, and generates the optimal delivery route again.

[0893] Step 13:

[0894] The server sends the new delivery schedule and route information to the delivery person's terminal and notifies the user of the changed delivery schedule.

[0895] In this way, the system of the present invention can efficiently carry out a series of processes, from collecting user behavior data, to predicting the time the user will be at home, proposing the optimal delivery date and route, collaborating with delivery personnel, and notifying the user.

[0896] Example 1

[0897] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0898] In modern logistics operations, redelivery is common, resulting in significant costs for the logistics industry. Furthermore, delivery routes are not adequately optimized, resulting in inefficient deliveries. Furthermore, labor shortages are a serious problem, necessitating efficient labor allocation. To solve these issues, a system is needed that can predict when users will be at home and propose optimal delivery dates, times, and routes.

[0899] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0900] In this invention, the server includes a means for collecting activity history data of general users, a means for analyzing the collected activity history data and predicting the time the user will be at home, and a means for proposing the optimal date and time for delivery based on the predicted time the user will be at home, thereby reducing redelivery costs and improving delivery efficiency.

[0901] "General user" refers to an individual or household who uses the system.

[0902] "Activity history data" refers to information related to a user's behavior, such as the time they spend outside, the time they return home, and their activities on holidays.

[0903] "Analysis" refers to the processing that a generative AI model performs on collected data to understand the data and extract meaningful information.

[0904] "Home time" refers to the time period when the user is at home.

[0905] "Delivery route" refers to the route a delivery company takes to deliver a package.

[0906] "Generative AI" refers to artificial intelligence technology that uses generative AI models to analyze data and make predictions.

[0907] "Map Information" refers to map data and related location information.

[0908] "Delivery Company" refers to the person or shipping company that ultimately delivers the package to the User.

[0909] "Delivery route information" refers to information regarding the optimal delivery route.

[0910] "Real-time delivery status" refers to information that displays the current delivery progress status in real time.

[0911] "Delivery Time Change Request" means a request submitted by a User to change an already set delivery time.

[0912] "Transport vehicle operator" refers to the person or transport driver who transports goods to the nearest collection and delivery station.

[0913] A "collection station" is a location where packages are temporarily collected and received by the delivery company for final delivery.

[0914] "Final delivery" refers to the process by which a package is delivered from the collection and distribution station to the user.

[0915] This invention is a system that collects activity history data of general users, analyzes the data using a generative AI model, and predicts the time they will be at home. This can reduce redelivery costs in the logistics industry and optimize delivery routes. Furthermore, by collaborating with delivery companies, it can solve the problem of labor shortages and build an efficient delivery system. This system is implemented by the following means.

[0916] Collecting and predicting user behavior data

[0917] The server collects data on the user's online behavior history from their smartphone or PC, including the time the user leaves the house, the time they return home, and their activities on holidays. The collected data is stored in a database on the server.

[0918] Next, the server inputs this collected data into a generative AI model API (e.g., generative AI). The prompt used here is something like, "Please predict the amount of time the user will be at home based on the user's behavioral history data." The generative AI model analyzes the data and predicts the user's amount of time at home.

[0919] Save to delivery management system

[0920] Based on the analysis results, the server predicts when the user will be at home and stores this data in the delivery management system database, which is used to plan future deliveries.

[0921] Delivery route optimization

[0922] The server proposes the date and time with the highest probability of successful delivery based on the predicted user's home time. Based on the proposed date and time, the server calculates the optimal delivery route using map information (e.g., map API) and real-time traffic information (e.g., traffic conditions API). This allows the delivery company to deliver the package using the most efficient route.

[0923] Collaboration with delivery staff

[0924] The server then sends the calculated delivery date and time and the optimal route information to the delivery company's terminal. The delivery company will then pick up the package at the specified date and time and deliver it according to the user's home time.

[0925] User Notification

[0926] Once the delivery date and time is determined, the server sends a notification to the user saying, "The package is scheduled to be delivered between 2:00 PM and 4:00 PM on the XXth of the month." The user can check this notification and request a change in the delivery time if they cannot receive the package during this time period.

[0927] Handling delivery time changes

[0928] When a user submits a delivery time change request, the server accepts the request, recalculates a new delivery schedule and route, and sends this new information back to the delivery company.

[0929] Specific examples

[0930] Example 1: Predicting user's time at home

[0931] The server collects user behavioral history data from the past six months and analyzes it with the generation AI. For example, if the user is often at home on weekday nights, the scheduled delivery time will be set to a weekday night.

[0932] Example 2: Delivery route optimization

[0933] The server predicts the user's expected time of home delivery and determines the optimal delivery date and time to be between 10:00 AM and noon on Saturdays. It then uses map and traffic information to calculate the shortest route from the nearest collection and delivery station to the user's home. This route information is then sent to the delivery company's terminal.

[0934] Example 3: Collaboration with delivery staff and user notification

[0935] The terminal (the driver's terminal) will deliver the package to the collection and delivery station at 8:00 AM on Saturday. The delivery company will pick up the package at the station and complete the delivery between 10:00 AM and noon, when the user is expected to be home.

[0936] The server notifies the user that the package is scheduled to be delivered between 10:00 AM and 12:00 PM on Saturday. If the user cannot receive the package during this time, they can submit a request to change the delivery time to the system. Based on this request, the server recalculates a new delivery schedule and route and sends instructions to the delivery company.

[0937] In this way, the present invention can predict user behavior patterns and propose optimal delivery dates, times, and routes, thereby reducing redelivery costs and resolving labor shortages. Furthermore, the real-time notification function makes it possible to provide users with a fast and reliable home delivery service.

[0938] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0939] Step 1:

[0940] (Collection of user behavior data)

[0941] The server collects data on the user's online behavior history through a dedicated application installed on the user's smartphone or computer. The collected data includes the time of leaving and returning home, holiday activities, and other location information. This data is sent to and stored in the server's database in real time.

[0942] Input: Behavioral history data from the user's smartphone or computer

[0943] Output: Behavioral history data stored in the server database

[0944] Step 2:

[0945] (Analysis of behavioral history data)

[0946] The server inputs the collected behavioral history data into the generation AI model API. The prompt used at this time is "Please predict the time the user will be at home based on the user behavioral history data." The generation AI model analyzes the input data and returns a prediction of the time the user will be at home.

[0947] Input: Collected behavioral history data, prompt text

[0948] Output: Predicted time at home

[0949] Step 3:

[0950] (Saving prediction results)

[0951] The server stores the predictions returned by the generative AI model in the delivery management system's database, which is used to plan future deliveries.

[0952] Input: Prediction results returned by the generative AI model

[0953] Output: Prediction results stored in the delivery management system database

[0954] Step 4:

[0955] (Proposal of optimal delivery date and time)

[0956] The server calculates the optimal delivery date and time based on the predicted user's home time, minimizing the risk of re-delivery. This calculation takes into account the days of the week and times when the user is likely to be at home.

[0957] Input: Predicted user home time

[0958] Output: Suggested best delivery date and time

[0959] Step 5:

[0960] (Delivery route calculation)

[0961] The server calculates the optimal delivery route based on the proposed delivery date and time, combining map information and real-time traffic information. This calculation uses map APIs and traffic condition APIs. The calculated route information is sent to the delivery company's device.

[0962] Input: Suggested optimal delivery date and time, map information, traffic information

[0963] Output: Calculated optimal delivery route

[0964] Step 6:

[0965] (Collaboration with delivery companies)

[0966] The server then sends the calculated delivery date and time and the optimal route information to the delivery company's terminal. The delivery company then picks up the user's package according to the specified date, time and route, and makes the final delivery.

[0967] Input: Calculated optimal delivery route, delivery date and time information

[0968] Output: Route and delivery information sent to the delivery company's terminal

[0969] Step 7:

[0970] (User Notification)

[0971] The server then sends the user a notification of the scheduled delivery date and time. For example, a notification saying, "Your package is scheduled to be delivered between 2:00 PM and 4:00 PM on a certain date" is sent. Notifications are sent via smartphone push notifications or email.

[0972] Input: Scheduled delivery date and time information

[0973] Output: Estimated delivery notification sent to user

[0974] Step 8:

[0975] (Processing delivery time change requests)

[0976] The user checks the notification and, if a change in delivery time is necessary, sends a change request to the system. The server accepts this request and recalculates a new delivery schedule and route. The recalculated schedule is then sent to the delivery company.

[0977] Input: Delivery time change request from user

[0978] Output: New recalculated delivery schedule and route

[0979] Step 9:

[0980] (Pick up your parcel at the collection and delivery station)

[0981] The terminal (driver's terminal) delivers the package to the nearest collection and delivery station at the specified time, where the delivery company receives the package and prepares it for delivery at the specified delivery date and time.

[0982] Input: Packages received at the collection and delivery station

[0983] Output: Package received by the delivery company

[0984] Step 10:

[0985] (Final delivery completion)

[0986] The delivery company will head to the user's home at the specified delivery date and time to deliver the package safely. Once the delivery is complete, the status is updated in the system and the server sends a notification to the user that the delivery has been completed.

[0987] Input: Packages being delivered to the user's home

[0988] Output: Delivered package, delivery completion notification to the user

[0989] Through the above processing steps, the present invention predicts user behavior patterns and proposes optimal delivery dates, times, and routes, thereby reducing redelivery costs and resolving labor shortages. Furthermore, the real-time notification function makes it possible to provide users with a fast and reliable home delivery service.

[0990] (Application example 1)

[0991] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0992] In today's logistics industry, no-shows and redeliveries are major issues. This reduces delivery efficiency and increases the costs associated with redeliveries. Furthermore, there is a significant labor shortage of delivery personnel, increasing the burden on delivery work. Furthermore, optimizing deliveries based on traffic congestion and predicted times when customers will be at home is difficult, creating a need for optimized transportation routes.

[0993] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0994] In this invention, the server includes means for collecting behavioral history data of general consumers, means for analyzing the collected behavioral history data and predicting the time when consumers will be at home, means for installing a delivery management system in an autonomous vehicle and proposing the optimal date and time for delivery based on the predicted time when consumers will be at home, means for calculating the optimal delivery route using generation AI and map information, means for transmitting delivery route information to the autonomous vehicle and making the optimal delivery, means for the autonomous vehicle to deliver the package to the nearest station based on the delivery route and prediction data and complete the final delivery with minimal effort, and means for notifying the user of the estimated delivery time and real-time delivery status, which makes it possible to reduce redelivery costs, optimize delivery routes, eliminate labor shortages, and provide real-time delivery notifications.

[0995] "General consumer behavior history data" refers to records of past behavior of consumers, including the time spent outside the home, the time returned home, and activities on holidays.

[0996] "Predicting time spent at home" refers to predicting the amount of time a consumer will be at home based on collected behavioral history data.

[0997] "Means of installing a delivery management system on an autonomous vehicle" means installing software or hardware on an autonomous vehicle to manage deliveries.

[0998] The "means for proposing the optimal delivery date and time" is a technology that calculates and presents the optimal date and time for package delivery based on the predicted time the customer will be at home.

[0999] "Generative AI" is a type of artificial intelligence technology that predicts future events and situations based on collected data.

[1000] "Map information" is data that includes geographical information, such as roads, buildings, topography, and traffic conditions.

[1001] The "means for calculating the optimal delivery route" is a system that uses map information and generation AI to calculate the optimal delivery route to maximize delivery efficiency.

[1002] "Means for transmitting delivery route information to an autonomous vehicle" refers to a device or method for sending the calculated optimal delivery route information as instructions to an autonomous vehicle.

[1003] "Nearest station" refers to the nearest package storage or relay station set up by the delivery company.

[1004] "Means for notifying real-time delivery status" refers to a communication method for instantly informing users of the current delivery status of their packages.

[1005] "Reducing redelivery costs" refers to aiming for successful delivery the first time and reducing the unnecessary expenses associated with repeat deliveries.

[1006] "Delivery route optimization" is the process of planning the best route to maximize delivery efficiency and reduce delivery time and costs.

[1007] "Resolving labor shortages" means trying to solve the labor shortage problem by introducing an efficient delivery system.

[1008] "Real-time delivery notifications" means instantly providing users with details of ongoing deliveries and keeping them updated on the progress of their deliveries.

[1009] This invention relates to a system that collects consumer behavior history data and delivers packages efficiently in cooperation with a delivery management system installed in an autonomous driving vehicle. Specific embodiments of this system are described below.

[1010] Collecting and predicting user behavior data

[1011] The server collects consumer behavior history data, including information on when they leave the home, when they return home, and their activities on holidays. The collected data is analyzed using a generative AI model to predict when consumers will be at home.

[1012] Delivery planning based on historical forecasts

[1013] Based on the predicted time at home, the server will suggest the optimal delivery date and time to the consumer through the delivery management system installed in the autonomous vehicle. This suggestion is set during the time when the consumer is predicted to be at home to reduce the risk of redelivery.

[1014] Delivery route optimization

[1015] The server uses generative AI and map information, including Google Maps API and traffic data, to calculate the optimal delivery route. The calculated route information is sent to the autonomous vehicle, which instructs it to maximize delivery efficiency.

[1016] Delivery execution by autonomous vehicles

[1017] The autonomous vehicle will then calculate an optimal route to deliver the package to the nearest station, where a delivery person will complete the last mile if necessary, resulting in more efficient delivery and reduced labor.

[1018] User Notification

[1019] The server notifies the consumer of the estimated delivery time and real-time delivery status. Notifications are sent to the consumer's smartphone or other device, allowing the consumer to check the delivery progress in real time. If the estimated delivery date and time can be changed, the consumer can send a request to adjust the delivery date and time.

[1020] Specific examples

[1021] For example, let's say that behavioral data from the past six months predicts that consumers often return home between 6:00 PM and 10:00 PM on weekdays. Based on this information, autonomous vehicles can deliver packages during those times, reducing the risk of having to redeliver packages due to consumers being absent.

[1022] Using data from the user's behavior over the past six months, it is predicted that the user will typically return home between 6:00 PM and 10:00 PM on weekdays. Based on this information, the autonomous vehicle will calculate the optimal delivery route and notify the user.

[1023] The present invention aims to reduce redelivery costs and resolve labor shortages by analyzing consumer behavior data and providing efficient delivery plans and routes based on predicted times when consumers will be at home.

[1024] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1025] Step 1:

[1026] The server collects data on the behavioral history of general consumers. Specifically, data on time spent outside the home, time returned home, and activities on holidays is obtained from APIs and databases. The collected data is stored for analysis.

[1027] Input: Behavioral history data obtained from APIs and databases

[1028] Output: Behavioral history data saved for analysis

[1029] Step 2:

[1030] The server analyzes the collected behavioral history data using a generative AI model to predict the amount of time the consumer will be at home. For example, the model is trained based on data from the past six months to generate a predictive model. By inputting the latest data into this model, the amount of time the consumer will be at home can be predicted.

[1031] Input: Collected behavioral history data

[1032] Output: Predicted time at home

[1033] Step 3:

[1034] The server then suggests the best delivery time based on the predicted time the consumer will be at home. This suggestion is calculated taking into account the time of day the consumer is expected to be at home. For example, if a consumer is usually at home between 6:00 PM and 10:00 PM on weekdays, the server will suggest that time as the delivery time.

[1035] Input: Predicted time at home

[1036] Output: Suggested best delivery date and time

[1037] Step 4:

[1038] The server uses the generative AI and map information to calculate the optimal delivery route. This uses Google Maps API and traffic information data to calculate the optimal route taking into account current traffic conditions. The calculated route information is sent to the autonomous vehicle.

[1039] Input: Proposed delivery date and time, map information, and traffic information

[1040] Output: Calculated optimal delivery route

[1041] Step 5:

[1042] The server then sends the calculated delivery route information to the autonomous vehicle and instructs the vehicle to follow the route, which the autonomous vehicle then follows to deliver the package to the nearest station.

[1043] Input: Calculated delivery route information

[1044] Output: Route instructions for the autonomous vehicle

[1045] Step 6:

[1046] The autonomous vehicle will then arrive at the nearest station, where a delivery person will handle the last mile of delivery, making the final delivery from the station to the consumer's home.

[1047] Input: A package transported by an autonomous vehicle

[1048] Output: Final delivery to the consumer's home

[1049] Step 7:

[1050] The server notifies the consumer of the estimated delivery time and real-time delivery status. Notifications are sent to the consumer's smartphone, allowing the consumer to check the delivery progress in real time. In addition, if there is a request to change the delivery date and time, the server accepts it and applies the recalculated new schedule.

[1051] Input: Estimated delivery time, real-time delivery status

[1052] Output: Notified estimated delivery time and current delivery status

[1053] Through these steps, the invention can achieve an efficient delivery system, reduce the risk of redelivery, and eliminate the problem of labor shortages.

[1054] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1055] The present invention is a system that combines a system that collects consumer behavior history data, analyzes it using a generation API, and predicts the amount of time consumers will be at home with an emotion engine that recognizes user emotions. This makes it possible to reduce redelivery costs, optimize delivery routes, and provide flexible delivery support based on user emotions. Specific embodiments are described in detail below.

[1056] Collecting and predicting user behavior data

[1057] The server collects user behavior history data, such as the time spent outside, the time returned home, and activities on holidays.

[1058] The server inputs the collected data into the generation API and analyzes it to predict the user's home time. This predicted data is stored in the delivery management system database.

[1059] Delivery route optimization

[1060] The server calculates the optimal delivery time based on the predicted time the customer will be at home, taking into account the customer's preferred time and delivery priority.

[1061] The server uses map information and traffic information to generate an optimal delivery route, which is then sent to the delivery person's terminal.

[1062] Collaboration with delivery staff

[1063] The terminal (the delivery driver's terminal) takes the package to the nearest collection and distribution station according to the predicted route.

[1064] The delivery person will pick up the package from the collection and delivery station and deliver it to the user's home.

[1065] User support with an emotional engine

[1066] The server uses an emotion engine to collect and analyze the user's emotion data, including the user's voice and text data.

[1067] The server uses an emotion engine to analyze the user's emotional state and adjusts the content and timing of notifications based on the results. For example, if the user is feeling anxious or dissatisfied, the server provides an option to easily change the delivery schedule.

[1068] The server stores the collected emotional data and analyzes long-term emotional trends, enabling it to provide a flexible delivery service tailored to the user's emotional state.

[1069] User Notification

[1070] The server notifies the user of the estimated delivery time and real-time delivery status, and the content of the notification is adjusted based on the analysis results of the emotion engine.

[1071] Specific examples

[1072] Example 1: Predicting user's time at home

[1073] The server collects user behavior history data from the past few months and analyzes it using a generation API. Based on this, if it predicts that the user is likely to be at home during weekday nights, it will schedule deliveries for those times.

[1074] Example 2: Delivery route optimization

[1075] The server predicts the user's expected time of home and determines the optimal delivery date and time to be between 10:00 a.m. and noon on Saturdays. Using map and traffic information, the server calculates the shortest route from the nearest delivery station to the user's home.

[1076] Example 3: Supported by an Emotion Engine

[1077] The server uses an emotion engine to analyze the user's recent text messages and recognize that they are stressed, which results in the delivery notification being more subdued and adding a link to easily reschedule the delivery.

[1078] Example 4: Collaboration with delivery personnel and user notification

[1079] The terminal (the delivery driver's terminal) will deliver the package to the collection and distribution station at 8:00 AM on Saturday, and the delivery person will complete the delivery to the user's home between 10:00 AM and noon on Saturday. The server will notify the user of this schedule, and the user will confirm the notification. The real-time status of the delivery will also be updated as it progresses.

[1080] In this way, the present invention can provide an effective and economical delivery service by predicting the user's behavioral patterns and emotions and calculating the optimal delivery date and route. Furthermore, by utilizing an emotion engine, it is possible to respond flexibly to the user's emotional state, providing a higher level of satisfaction.

[1081] The processing flow will be explained below.

[1082] Step 1:

[1083] Users provide their own behavioral history data, including the time they spend outside, the time they return home, and their activities on holidays.

[1084] Step 2:

[1085] The server collects the provided user behavioral history data, which is either sent through a dedicated application or retrieved from an existing database.

[1086] Step 3:

[1087] The server inputs the collected behavioral history data into a generation API and analyzes it to predict the user's time at home, using a machine learning algorithm.

[1088] Step 4:

[1089] The server stores the prediction results in the delivery management system database, which can then be used for future delivery planning.

[1090] Step 5:

[1091] The server calculates the optimal delivery date and time based on the predicted time the customer will be at home, taking into account the customer's preferred time and delivery priority.

[1092] Step 6:

[1093] The server uses map and traffic information to calculate the optimal delivery route, enabling efficient delivery.

[1094] Step 7:

[1095] The server sends the calculated delivery route and delivery date and time to the delivery person's terminal, and the delivery person makes the delivery based on the received route.

[1096] Step 8:

[1097] The terminal (the delivery driver's terminal) takes the package to the nearest collection and distribution station, thereby assisting with the last mile of delivery.

[1098] Step 9:

[1099] The delivery person will collect the package at the collection and delivery station and finally deliver it to the user's home, updating the delivery status on the terminal in real time.

[1100] Step 10:

[1101] The server notifies the user of the estimated delivery time and real-time delivery status, including the specific delivery date and time.

[1102] Step 11:

[1103] The server uses an emotion engine to collect and analyze the user's emotional data, and uses the user's voice and text data as analysis data.

[1104] Step 12:

[1105] The server adjusts the content and timing of notifications based on the results of the emotion engine analysis. For example, if the user is feeling stressed, the delivery notification will be more subdued in tone.

[1106] Step 13:

[1107] The server accumulates user emotional data and analyzes long-term emotional trends, thereby improving the quality of service for users.

[1108] Step 14:

[1109] Users can check the estimated delivery time and, if necessary, submit a request to change the delivery date and time to the system.

[1110] Step 15:

[1111] The server receives change requests from users and recalculates new delivery schedules and optimal delivery routes.

[1112] Step 16:

[1113] The server sends the new delivery schedule and route information to the delivery person's terminal and notifies the user of the changed delivery schedule.

[1114] In this way, the present invention analyzes user behavior data and emotion data to propose optimal delivery schedules and routes, thereby achieving efficient deliveries and improving user satisfaction.

[1115] Example 2

[1116] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1117] With the current delivery system, it is difficult to simultaneously optimize delivery and improve user satisfaction. In particular, there are many cases where redelivery is required when the user is not at home, which increases delivery costs and reduces efficiency. In addition, there is a lack of flexibility in responding to user emotions and requests, which can easily lead to user dissatisfaction. By solving these issues, it is necessary to reduce delivery costs and improve user satisfaction.

[1118] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting behavioral history data of general consumers, means for analyzing the collected behavioral history data and predicting the time the consumer will be at home, means for proposing the optimal date and time for delivery based on the predicted time the consumer will be at home, means for calculating the optimal delivery route using the generative AI model and location information, means for sending delivery route information to a delivery employee and making the optimal delivery, means for completing the final delivery with minimal effort by cooperating with the delivery employee, means for collecting and analyzing user emotion data, means for adjusting notification content and delivery options based on the emotion analysis results, and means for notifying the user of the estimated delivery time and real-time delivery status. This enables delivery optimization and flexible response according to the user's emotions.

[1119] "General consumers" refer to individuals who do not have a specific industry or specialized knowledge and who engage in general consumer activities.

[1120] "Behavioral history data" refers to past data regarding a user's movements, schedules, activities, etc.

[1121] "Home time" refers to the time period when the user is at home.

[1122] A "generative AI model" refers to a system that uses machine learning and artificial intelligence algorithms to analyze data and make predictions.

[1123] "Location information" refers to data that indicates the physical location of a user or object, such as geographic coordinates or address information.

[1124] "Delivery route" refers to the optimal route for delivering a package.

[1125] "Delivery employee" refers to a person whose job is to deliver packages to a user's home or designated location.

[1126] "Emotional data" refers to data that indicates a user's emotions and psychological state. Specifically, it includes information obtained from voice, text, facial expressions, etc.

[1127] "Notification" means a message or alert intended to provide information to the User.

[1128] The system of this invention combines a system that collects user behavior history data, analyzes it using a generative AI model, and predicts the time the user will be at home with an emotion engine that recognizes the user's emotions. This not only reduces redelivery costs and optimizes delivery routes, but also provides flexible delivery support based on the user's emotions.

[1129] Collecting and predicting user behavior data

[1130] The server collects the user's behavioral history data. This collection includes data acquisition from the user's smartphone and wearable device. Specific data includes GPS data, calendar appointments, and motion sensor data. For example, the server periodically sends the location information set by the user on the smartphone to the server, recording the user's time out and time back home.

[1131] The collected data is analyzed using a generative AI model (e.g., a machine learning algorithm). This analysis extracts user behavioral patterns and predicts how long the user will be at home. The prediction results are stored in the delivery management system's database.

[1132] Delivery route optimization

[1133] The server calculates the optimal delivery date and time based on the predicted time the delivery person will be at home, taking into account the user's preferred time and delivery priority. It then calculates the optimal delivery route using map information (e.g., Google Maps API) and traffic information. The calculated route information is sent to the delivery person's device.

[1134] Collaboration with delivery staff

[1135] The terminal (the delivery person's device) will then follow the predicted route and deliver the package to the nearest collection and delivery station, where the delivery person will pick up the package and deliver it to the user's home.

[1136] User support with an emotional engine

[1137] The server uses a voice recognition system and text analysis tools to collect user emotion data, for example, from voice data or text messages when a user contacts customer support.

[1138] The emotion engine analyzes this data to identify the user's emotional state and adjusts the notification content and delivery options accordingly. For example, if the user is stressed, the notification will be sent in a more gentle tone and offer the option to reschedule the delivery.

[1139] User Notification

[1140] The server notifies the user of the estimated delivery time based on the determined delivery date and time and the optimal route. Notifications are sent via smartphone app, email, or SMS. Real-time delivery status information is also provided as appropriate.

[1141] Specific examples

[1142] Example 1: Predicting user's time at home

[1143] The server collects user behavior history data from the past few months and analyzes it with a generative AI model. For example, if it predicts that the user is likely to be at home during weekday nights, it will schedule deliveries for those times.

[1144] Example 2: Delivery route optimization

[1145] The server predicts the user's expected time of home and determines the optimal delivery date and time between 10:00 a.m. and noon on Saturdays. It also uses map and traffic information to calculate the shortest route from the nearest delivery station to the user's home.

[1146] Example 3: Supported by an Emotion Engine

[1147] The server uses an emotion engine to analyze the user's recent text messages and recognize that they are stressed, which results in the delivery notification being more subdued and includes a link to easily reschedule the delivery.

[1148] Example 4: Collaboration with delivery personnel and user notification

[1149] The terminal (delivery worker's device) will deliver the package to the collection and delivery station at 8:00 AM on Saturday, and the delivery worker will complete the delivery to the user's home between 10:00 AM and noon on Saturday. The server will notify the user of this schedule, and the user will confirm the notification. The real-time status of the delivery will also be updated as it progresses.

[1150] An example of a prompt is, "Analyze the user's behavioral history data from the past few months and predict when the user will be at home. Calculate the optimal delivery date, time, and route based on the prediction results. Also, adjust the notification content taking into account the user's emotional state."

[1151] In this way, the system of the present invention calculates the optimal delivery date and route based on the user's behavioral data and emotional data, and provides an effective and flexible delivery service.

[1152] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1153] Step 1:

[1154] The server collects user behavior history data. The collected data is obtained from the user's smartphone or wearable device. Specifically, it includes GPS data, calendar events, and motion sensor data. For example, the user's behavior history while out and the time of return home are recorded. The input data is the behavior history data sent from the user's device, and the output is the collected dataset.

[1155] Step 2:

[1156] The server preprocesses the collected behavioral history data, complementing missing values ​​and excluding outliers. Specifically, it detects and excludes data containing sudden movements, and complements missing data using the mean or median. The input is the collected behavioral history data, and the output is the preprocessed data.

[1157] Step 3:

[1158] The server inputs the preprocessed data into a generative AI model to predict the user's home time. The generative AI model uses a time-series data prediction algorithm. For example, it predicts the home time on weekday nights and weekends based on past behavioral patterns. The input is the preprocessed behavioral history data, and the output is the predicted home time data.

[1159] Step 4:

[1160] The server determines the optimal delivery date and time based on the predicted at-home time data, taking into account the user's desired time and delivery priority. The input is the predicted at-home time data and the user's desired time, and the output is the optimal delivery date and time.

[1161] Step 5:

[1162] The server calculates the optimal delivery route based on the determined delivery date and time using map and traffic information. For map information, it uses Google Maps API and public transportation API to calculate the shortest route from the delivery center to the user's address. The input is the optimal delivery date and time, map information, and traffic information, and the output is the optimal delivery route.

[1163] Step 6:

[1164] The server sends the calculated delivery route information to the delivery person's terminal. The delivery person makes the delivery based on this route information. The input is the optimal delivery route information, and the output is the route information sent to the delivery person's terminal.

[1165] Step 7:

[1166] The terminal (the delivery person's device) carries the package to the nearest collection / distribution station according to the predicted route. Specifically, it follows the route from the delivery center to the collection / distribution station. The input is the package from the delivery center and the optimal delivery route information, and the output is the state of the package arriving at the collection / distribution station.

[1167] Step 8:

[1168] The delivery person delivers the package from the collection and delivery station to the user's home at the delivery date and time specified by the user. The input is the package from the collection and delivery station and the optimal delivery route information, and the output is the package delivered to the user's home.

[1169] Step 9:

[1170] The server uses a voice recognition system and text analysis tools to collect user emotion data. Specifically, it obtains emotion information from voice data and text messages when a user contacts customer support. The input is the user's inquiry data, and the output is the collected emotion data.

[1171] Step 10:

[1172] The server uses an emotion engine to analyze the collected emotion data and identify the user's emotional state. For example, it determines whether the user is "angry" or "anxious" based on the content of their voice or text message. The input is the collected emotion data, and the output is the analyzed emotion state.

[1173] Step 11:

[1174] The server adjusts the notification content and delivery options based on the analysis results of the emotion engine. For example, if the user is stressed, the server may send a softer notification and offer the option to reschedule the delivery. The input is the analyzed emotional state, and the output is the adjusted notification content.

[1175] Step 12:

[1176] The server notifies the user of the estimated delivery time and real-time delivery status via a smartphone app, email, or SMS. For example, a notification is sent when the package leaves the delivery center or arrives in the delivery area. The input is delivery status data and the adjusted notification content, and the output is the notification sent to the user.

[1177] In this way, the system calculates the optimal delivery date and route based on the user's behavioral history and emotional data, providing an efficient and flexible delivery service.

[1178] (Application example 2)

[1179] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1180] Conventional delivery systems are unable to accurately predict when users will be at home, resulting in frequent redelivery and delays. Furthermore, because they do not take user emotions into consideration, delivery notifications and responses are mechanical, leading to lower user satisfaction. To solve these problems, a new system is needed that analyzes user behavioral history data and emotional data to provide optimal delivery dates, times, and routes.

[1181] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting behavioral history data of general consumers, means for analyzing the collected behavioral history data and predicting the time the consumer will be at home, means for proposing the optimal date and time for delivery based on the predicted time the consumer will be at home, means for calculating the optimal delivery route using generation AI and map information, means for sending delivery route information to a delivery person and making the optimal delivery, means for completing the final delivery with minimal effort by cooperating with the delivery person, means for notifying the user of the estimated delivery time and real-time delivery status, and means for recognizing the user's emotions and adjusting the content and timing of notifications based on the emotions. This not only improves delivery efficiency but also enables flexible responses according to the user's emotions, providing a high level of satisfaction.

[1182] "General consumer" refers to an individual who purchases goods or uses services.

[1183] "Behavioral history data" refers to data related to a user's behavioral patterns, such as the time they spend outside the home, the time they return home, and their activities on holidays.

[1184] "Forecasting" refers to estimating future events or conditions based on collected data.

[1185] "Delivery route" refers to the route taken to deliver a package to a user.

[1186] "Generative AI" refers to artificial intelligence technology that generates and analyzes information based on large amounts of data.

[1187] "Map information" refers to data relating to geographical locations and routes.

[1188] "Delivery Person" refers to an individual or team whose role is to receive packages and deliver them to users.

[1189] "Optimization" refers to maximizing efficiency and effectiveness.

[1190] "Emotion recognition" refers to the technology of analyzing and understanding a user's emotional state from their voice, text, etc.

[1191] The "last mile" refers to the final stage of delivery, delivering the package from the collection and distribution station to the user's home.

[1192] "Notification" refers to the act of conveying information from the system to the user or delivery person.

[1193] This invention is a system that collects and analyzes user behavior history data and emotion data to provide optimal delivery dates and routes. A detailed description of the system based on the present invention is given below.

[1194] First, the server collects the user's behavioral history data, specifically the time the user leaves the house, the time they return home, their activities on holidays, etc. This data is collected using the smartphone's GPS data and calendar information.

[1195] The server then analyzes the collected data using generative AI to predict when the user will be at home, using techniques such as linear regression. The predicted time is then used in the next step to suggest the optimal delivery date and time.

[1196] The server also calculates the optimal delivery route based on map and traffic information, allowing delivery personnel to deliver efficiently. The route information is sent to the delivery personnel's terminal, and delivery is carried out according to the optimal route.

[1197] Furthermore, the server uses an emotion engine to recognize the user's emotions and adjust the content and timing of notifications accordingly. The server understands the user's emotional state by analyzing the user's voice input and text messages. This emotional data is used to adjust the wording and timing of delivery notifications, ensuring that users do not feel stressed.

[1198] Additionally, the server provides users with notifications of estimated delivery times and real-time delivery status, which are tailored based on the collected emotion data.

[1199] For example, if a user is often at home on weekday evenings, delivery can be scheduled for that time. Also, if the system determines that the user is stressed, delivery notifications will be changed to gentler language and delivery will be handled more quickly.

[1200] For example, the server might use the following prompt:

[1201] "Using the user's behavioral history data and calendar information, the system predicts when they will be at home and creates an ideal delivery plan based on their emotional data. Delivery routes are optimized using map and traffic information. If the user is under stress, the system changes the delivery time and notification content."

[1202] This system will enable flexible delivery services based on the user's behavioral patterns and emotional state, which is expected to reduce redelivery and delays and improve user satisfaction.

[1203] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1204] Step 1:

[1205] Users use their smartphones to collect behavioral history data, specifically GPS data and calendar information. This data includes detailed information such as the user's time out, the time they return home, and their activities on holidays.

[1206] Input: Smartphone GPS data, calendar information

[1207] Output: Behavioral history data

[1208] Step 2:

[1209] The server inputs the behavioral history data collected in step 1 into the generative AI model for analysis. The generative AI model learns past patterns and predicts the user's time at home. This prediction is made using techniques such as linear regression.

[1210] Input: Behavioral history data

[1211] Output: Predicted time spent at home

[1212] Step 3:

[1213] The server proposes the optimal delivery date and time based on the predicted time at home data, and calculates the optimal delivery time taking into account the user's desired time and delivery priority.

[1214] Input: Predicted time at home, user's desired time, delivery priority

[1215] Output: Best delivery date and time

[1216] Step 4:

[1217] The server uses map and traffic information to calculate the optimal delivery route, taking into account predicted home time and priority, allowing delivery personnel to make deliveries efficiently.

[1218] Input: Best delivery date and time, map information, traffic information

[1219] Output: Optimal delivery route

[1220] Step 5:

[1221] The terminal sends the delivery route information generated in step 4 to the delivery person. Based on this information, the delivery person picks up the package and begins delivering it to the user's home.

[1222] Input: Delivery route information

[1223] Output: Instructions to the delivery person

[1224] Step 6:

[1225] The server uses an emotion engine to collect and analyze the user's emotional data. Specifically, it recognizes the user's emotional state from their voice input or text message. The recognized emotional state is used to adjust the content and timing of delivery notifications.

[1226] Input: Voice input, text message

[1227] Output: Emotional state data

[1228] Step 7:

[1229] The server notifies the user of the estimated delivery time and real-time delivery status based on the emotional state data. The server adjusts the wording and timing of the notification depending on the user's emotional state. For example, if the user is feeling stressed, the server will send a gentler notification.

[1230] Input: Emotional state data, optimal delivery route, real-time delivery status

[1231] Output: User notification

[1232] Step 8:

[1233] The terminal (delivery person) reports the real-time delivery status to the server. The user can check the progress of the delivery based on this information. The server uses this information to notify the user of the latest delivery status.

[1234] Input: Real-time delivery status

[1235] Output: Report to server, latest notifications to user

[1236] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1237] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1238] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1239] [Fourth embodiment]

[1240] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1241] 7, a 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.

[1242] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1243] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1244] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1245] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1246] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1247] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1248] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1249] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1250] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1251] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1252] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1253] This invention is a system that collects behavioral history data of general consumers, analyzes the data using a generation API, and predicts the time when they will be at home. This can reduce redelivery costs in the logistics industry and optimize delivery routes. Furthermore, by collaborating with truck drivers and delivery personnel, it can solve the problem of labor shortages and build an efficient delivery system. This system is implemented by the following means.

[1254] Collecting and predicting user behavior data

[1255] The server collects user behavior history data, including the time spent outside the home, the time returned home, and activities on holidays.

[1256] The server inputs the collected data into the generation API and analyzes it to predict the time the customer will be at home. The predicted time is saved in the delivery management system and used to optimize future deliveries.

[1257] Delivery route optimization

[1258] The server then suggests the optimal delivery date and time based on the predicted user's home time, thereby reducing the risk of redelivery.

[1259] The server combines map information and traffic information to calculate the optimal delivery route, which is then sent to the delivery person's terminal.

[1260] Collaboration with delivery staff

[1261] The terminal (the delivery driver's terminal) takes the package to the nearest collection and distribution station.

[1262] The delivery person will pick up the package at the station and ultimately deliver it to the user's home.

[1263] User Notification

[1264] The server notifies the user of the estimated delivery time, and the user can check this notification and submit a request to change the delivery date and time if necessary.

[1265] Specific examples

[1266] Example 1: Predicting user's time at home

[1267] The server collects user behavior history data from the past six months and analyzes it using the generation API. If the prediction result shows that the user is often at home on weekday nights, the scheduled delivery time is set to a weekday night.

[1268] Example 2: Delivery route optimization

[1269] The server predicts the user's expected time of home and determines the optimal delivery date and time to be between 10:00 AM and noon on Saturday. Using map and traffic information, the server calculates the shortest route from the nearest collection and delivery station to the user's home. The route information is then sent to the delivery person's device.

[1270] Example 3: Collaboration with delivery staff and user notification

[1271] The terminal (the driver's terminal) will deliver the package to the collection and delivery station at 8:00 AM on Saturday. The delivery person will pick up the package at the station and complete the delivery between 10:00 AM and noon, when the user is expected to be home.

[1272] The server notifies the user that the package is scheduled to be delivered between 10:00 AM and 12:00 PM on Saturday. If the user cannot receive the package during this time, they submit a request to change the delivery time to the system. Based on this request, the server recalculates a new delivery schedule and route and issues instructions to the delivery person.

[1273] In this way, the present invention can predict user behavior patterns and propose optimal delivery dates, times, and routes, thereby reducing redelivery costs and resolving labor shortages. Furthermore, the real-time notification function makes it possible to provide users with a fast and reliable home delivery service.

[1274] The processing flow will be explained below.

[1275] Step 1:

[1276] Users provide their behavioral history data, which is sent through a dedicated application or retrieved from an existing database.

[1277] Step 2:

[1278] The server collects the provided user behavioral history data, specifically, data on the time spent outside the home, the time returned home, and activities on holidays.

[1279] Step 3:

[1280] The server calls the generation API based on the collected behavioral history data and predicts the user's home time using a machine learning algorithm.

[1281] Step 4:

[1282] The server stores the predicted user's home time in the delivery management system, which then uses it to optimize future delivery dates and times.

[1283] Step 5:

[1284] The server calculates the optimal delivery time based on the predicted time the customer will be at home, taking into account the customer's preferred time and delivery priority.

[1285] Step 6:

[1286] The server uses map and traffic information to generate optimal delivery routes, taking into account distances between delivery points and traffic conditions.

[1287] Step 7:

[1288] The server then sends the generated optimal delivery route and delivery date and time to the delivery person's terminal, enabling the delivery person to make deliveries along an efficient route.

[1289] Step 8:

[1290] The terminal (delivery driver's terminal) displays the route information received from the server and delivers the package to the nearest collection and distribution station.

[1291] Step 9:

[1292] The delivery person will pick up the package at the collection and delivery station and finally deliver it to the user's home. The delivery status will be updated on the terminal in real time.

[1293] Step 10:

[1294] The server notifies the user of the final estimated delivery time, including the specific delivery date and time and real-time delivery status.

[1295] Step 11:

[1296] The user checks the estimated delivery time and, if necessary, submits a request to change the delivery date and time to the system.

[1297] Step 12:

[1298] The server receives the change request from the user, calculates a new delivery schedule, and generates the optimal delivery route again.

[1299] Step 13:

[1300] The server sends the new delivery schedule and route information to the delivery person's terminal and notifies the user of the changed delivery schedule.

[1301] In this way, the system of the present invention can efficiently carry out a series of processes, from collecting user behavior data, to predicting the time the user will be at home, proposing the optimal delivery date and route, collaborating with delivery personnel, and notifying the user.

[1302] Example 1

[1303] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1304] In modern logistics operations, redelivery is common, resulting in significant costs for the logistics industry. Furthermore, delivery routes are not adequately optimized, resulting in inefficient deliveries. Furthermore, labor shortages are a serious problem, necessitating efficient labor allocation. To solve these issues, a system is needed that can predict when users will be at home and propose optimal delivery dates, times, and routes.

[1305] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1306] In this invention, the server includes a means for collecting activity history data of general users, a means for analyzing the collected activity history data and predicting the time the user will be at home, and a means for proposing the optimal date and time for delivery based on the predicted time the user will be at home, thereby reducing redelivery costs and improving delivery efficiency.

[1307] "General user" refers to an individual or household who uses the system.

[1308] "Activity history data" refers to information related to a user's behavior, such as the time they spend outside, the time they return home, and their activities on holidays.

[1309] "Analysis" refers to the processing that a generative AI model performs on collected data to understand the data and extract meaningful information.

[1310] "Home time" refers to the time period when the user is at home.

[1311] "Delivery route" refers to the route a delivery company takes to deliver a package.

[1312] "Generative AI" refers to artificial intelligence technology that uses generative AI models to analyze data and make predictions.

[1313] "Map Information" refers to map data and related location information.

[1314] "Delivery Company" refers to the person or shipping company that ultimately delivers the package to the User.

[1315] "Delivery route information" refers to information regarding the optimal delivery route.

[1316] "Real-time delivery status" refers to information that displays the current delivery progress status in real time.

[1317] "Delivery Time Change Request" means a request submitted by a User to change an already set delivery time.

[1318] "Transport vehicle operator" refers to the person or transport driver who transports goods to the nearest collection and delivery station.

[1319] A "collection station" is a location where packages are temporarily collected and received by the delivery company for final delivery.

[1320] "Final delivery" refers to the process by which a package is delivered from the collection and distribution station to the user.

[1321] This invention is a system that collects activity history data of general users, analyzes the data using a generative AI model, and predicts the time they will be at home. This can reduce redelivery costs in the logistics industry and optimize delivery routes. Furthermore, by collaborating with delivery companies, it can solve the problem of labor shortages and build an efficient delivery system. This system is implemented by the following means.

[1322] Collecting and predicting user behavior data

[1323] The server collects data on the user's online behavior history from their smartphone or PC, including the time the user leaves the house, the time they return home, and their activities on holidays. The collected data is stored in a database on the server.

[1324] Next, the server inputs this collected data into a generative AI model API (e.g., generative AI). The prompt used here is something like, "Please predict the amount of time the user will be at home based on the user's behavioral history data." The generative AI model analyzes the data and predicts the user's amount of time at home.

[1325] Save to delivery management system

[1326] Based on the analysis results, the server predicts when the user will be at home and stores this data in the delivery management system database, which is used to plan future deliveries.

[1327] Delivery route optimization

[1328] The server proposes the date and time with the highest probability of successful delivery based on the predicted user's home time. Based on the proposed date and time, the server calculates the optimal delivery route using map information (e.g., map API) and real-time traffic information (e.g., traffic conditions API). This allows the delivery company to deliver the package using the most efficient route.

[1329] Collaboration with delivery staff

[1330] The server then sends the calculated delivery date and time and the optimal route information to the delivery company's terminal. The delivery company will then pick up the package at the specified date and time and deliver it according to the user's home time.

[1331] User Notification

[1332] Once the delivery date and time is determined, the server sends a notification to the user saying, "The package is scheduled to be delivered between 2:00 PM and 4:00 PM on the XXth of the month." The user can check this notification and request a change in the delivery time if they cannot receive the package during this time period.

[1333] Handling delivery time changes

[1334] When a user submits a delivery time change request, the server accepts the request, recalculates a new delivery schedule and route, and sends this new information back to the delivery company.

[1335] Specific examples

[1336] Example 1: Predicting user's time at home

[1337] The server collects user behavioral history data from the past six months and analyzes it with the generation AI. For example, if the user is often at home on weekday nights, the scheduled delivery time will be set to a weekday night.

[1338] Example 2: Delivery route optimization

[1339] The server predicts the user's expected time of home delivery and determines the optimal delivery date and time to be between 10:00 AM and noon on Saturdays. It then uses map and traffic information to calculate the shortest route from the nearest collection and delivery station to the user's home. This route information is then sent to the delivery company's terminal.

[1340] Example 3: Collaboration with delivery staff and user notification

[1341] The terminal (the driver's terminal) will deliver the package to the collection and delivery station at 8:00 AM on Saturday. The delivery company will pick up the package at the station and complete the delivery between 10:00 AM and noon, when the user is expected to be home.

[1342] The server notifies the user that the package is scheduled to be delivered between 10:00 AM and 12:00 PM on Saturday. If the user cannot receive the package during this time, they can submit a request to change the delivery time to the system. Based on this request, the server recalculates a new delivery schedule and route and sends instructions to the delivery company.

[1343] In this way, the present invention can predict user behavior patterns and propose optimal delivery dates, times, and routes, thereby reducing redelivery costs and resolving labor shortages. Furthermore, the real-time notification function makes it possible to provide users with a fast and reliable home delivery service.

[1344] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1345] Step 1:

[1346] (Collection of user behavior data)

[1347] The server collects data on the user's online behavior history through a dedicated application installed on the user's smartphone or computer. The collected data includes the time of leaving and returning home, holiday activities, and other location information. This data is sent to and stored in the server's database in real time.

[1348] Input: Behavioral history data from the user's smartphone or computer

[1349] Output: Behavioral history data stored in the server database

[1350] Step 2:

[1351] (Analysis of behavioral history data)

[1352] The server inputs the collected behavioral history data into the generation AI model API. The prompt used at this time is "Please predict the time the user will be at home based on the user behavioral history data." The generation AI model analyzes the input data and returns a prediction of the time the user will be at home.

[1353] Input: Collected behavioral history data, prompt text

[1354] Output: Predicted time at home

[1355] Step 3:

[1356] (Saving prediction results)

[1357] The server stores the predictions returned by the generative AI model in the delivery management system's database, which is used to plan future deliveries.

[1358] Input: Prediction results returned by the generative AI model

[1359] Output: Prediction results stored in the delivery management system database

[1360] Step 4:

[1361] (Proposal of optimal delivery date and time)

[1362] The server calculates the optimal delivery date and time based on the predicted user's home time, minimizing the risk of re-delivery. This calculation takes into account the days of the week and times when the user is likely to be at home.

[1363] Input: Predicted user home time

[1364] Output: Suggested best delivery date and time

[1365] Step 5:

[1366] (Delivery route calculation)

[1367] The server calculates the optimal delivery route based on the proposed delivery date and time, combining map information and real-time traffic information. This calculation uses map APIs and traffic condition APIs. The calculated route information is sent to the delivery company's device.

[1368] Input: Suggested optimal delivery date and time, map information, traffic information

[1369] Output: Calculated optimal delivery route

[1370] Step 6:

[1371] (Collaboration with delivery companies)

[1372] The server then sends the calculated delivery date and time and the optimal route information to the delivery company's terminal. The delivery company then picks up the user's package according to the specified date, time and route, and makes the final delivery.

[1373] Input: Calculated optimal delivery route, delivery date and time information

[1374] Output: Route and delivery information sent to the delivery company's terminal

[1375] Step 7:

[1376] (User Notification)

[1377] The server then sends the user a notification of the scheduled delivery date and time. For example, a notification saying, "Your package is scheduled to be delivered between 2:00 PM and 4:00 PM on a certain date" is sent. Notifications are sent via smartphone push notifications or email.

[1378] Input: Scheduled delivery date and time information

[1379] Output: Estimated delivery notification sent to user

[1380] Step 8:

[1381] (Processing delivery time change requests)

[1382] The user checks the notification and, if a change in delivery time is necessary, sends a change request to the system. The server accepts this request and recalculates a new delivery schedule and route. The recalculated schedule is then sent to the delivery company.

[1383] Input: Delivery time change request from user

[1384] Output: New recalculated delivery schedule and route

[1385] Step 9:

[1386] (Pick up your parcel at the collection and delivery station)

[1387] The terminal (driver's terminal) delivers the package to the nearest collection and delivery station at the specified time, where the delivery company receives the package and prepares it for delivery at the specified delivery date and time.

[1388] Input: Packages received at the collection and delivery station

[1389] Output: Package received by the delivery company

[1390] Step 10:

[1391] (Final delivery completion)

[1392] The delivery company will head to the user's home at the specified delivery date and time to deliver the package safely. Once the delivery is complete, the status is updated in the system and the server sends a notification to the user that the delivery has been completed.

[1393] Input: Packages being delivered to the user's home

[1394] Output: Delivered package, delivery completion notification to the user

[1395] Through the above processing steps, the present invention predicts user behavior patterns and proposes optimal delivery dates, times, and routes, thereby reducing redelivery costs and resolving labor shortages. Furthermore, the real-time notification function makes it possible to provide users with a fast and reliable home delivery service.

[1396] (Application example 1)

[1397] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1398] In today's logistics industry, no-shows and redeliveries are major issues. This reduces delivery efficiency and increases the costs associated with redeliveries. Furthermore, there is a significant labor shortage of delivery personnel, increasing the burden on delivery work. Furthermore, optimizing deliveries based on traffic congestion and predicted times when customers will be at home is difficult, creating a need for optimized transportation routes.

[1399] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1400] In this invention, the server includes means for collecting behavioral history data of general consumers, means for analyzing the collected behavioral history data and predicting the time when consumers will be at home, means for installing a delivery management system in an autonomous vehicle and proposing the optimal date and time for delivery based on the predicted time when consumers will be at home, means for calculating the optimal delivery route using generation AI and map information, means for transmitting delivery route information to the autonomous vehicle and making the optimal delivery, means for the autonomous vehicle to deliver the package to the nearest station based on the delivery route and prediction data and complete the final delivery with minimal effort, and means for notifying the user of the estimated delivery time and real-time delivery status, which makes it possible to reduce redelivery costs, optimize delivery routes, eliminate labor shortages, and provide real-time delivery notifications.

[1401] "General consumer behavior history data" refers to records of past behavior of consumers, including the time spent outside the home, the time returned home, and activities on holidays.

[1402] "Predicting time spent at home" refers to predicting the amount of time a consumer will be at home based on collected behavioral history data.

[1403] "Means of installing a delivery management system on an autonomous vehicle" means installing software or hardware on an autonomous vehicle to manage deliveries.

[1404] The "means for proposing the optimal delivery date and time" is a technology that calculates and presents the optimal date and time for package delivery based on the predicted time the customer will be at home.

[1405] "Generative AI" is a type of artificial intelligence technology that predicts future events and situations based on collected data.

[1406] "Map information" is data that includes geographical information, such as roads, buildings, topography, and traffic conditions.

[1407] The "means for calculating the optimal delivery route" is a system that uses map information and generation AI to calculate the optimal delivery route to maximize delivery efficiency.

[1408] "Means for transmitting delivery route information to an autonomous vehicle" refers to a device or method for sending the calculated optimal delivery route information as instructions to an autonomous vehicle.

[1409] "Nearest station" refers to the nearest package storage or relay station set up by the delivery company.

[1410] "Means for notifying real-time delivery status" refers to a communication method for instantly informing users of the current delivery status of their packages.

[1411] "Reducing redelivery costs" refers to aiming for successful delivery the first time and reducing the unnecessary expenses associated with repeat deliveries.

[1412] "Delivery route optimization" is the process of planning the best route to maximize delivery efficiency and reduce delivery time and costs.

[1413] "Resolving labor shortages" means trying to solve the labor shortage problem by introducing an efficient delivery system.

[1414] "Real-time delivery notifications" means instantly providing users with details of ongoing deliveries and keeping them updated on the progress of their deliveries.

[1415] This invention relates to a system that collects consumer behavior history data and delivers packages efficiently in cooperation with a delivery management system installed in an autonomous driving vehicle. Specific embodiments of this system are described below.

[1416] Collecting and predicting user behavior data

[1417] The server collects consumer behavior history data, including information on when they leave the home, when they return home, and their activities on holidays. The collected data is analyzed using a generative AI model to predict when consumers will be at home.

[1418] Delivery planning based on historical forecasts

[1419] Based on the predicted time at home, the server will suggest the optimal delivery date and time to the consumer through the delivery management system installed in the autonomous vehicle. This suggestion is set during the time when the consumer is predicted to be at home to reduce the risk of redelivery.

[1420] Delivery route optimization

[1421] The server uses generative AI and map information, including Google Maps API and traffic data, to calculate the optimal delivery route. The calculated route information is sent to the autonomous vehicle, which instructs it to maximize delivery efficiency.

[1422] Delivery execution by autonomous vehicles

[1423] The autonomous vehicle will then calculate an optimal route to deliver the package to the nearest station, where a delivery person will complete the last mile if necessary, resulting in more efficient delivery and reduced labor.

[1424] User Notification

[1425] The server notifies the consumer of the estimated delivery time and real-time delivery status. Notifications are sent to the consumer's smartphone or other device, allowing the consumer to check the delivery progress in real time. If the estimated delivery date and time can be changed, the consumer can send a request to adjust the delivery date and time.

[1426] Specific examples

[1427] For example, let's say that behavioral data from the past six months predicts that consumers often return home between 6:00 PM and 10:00 PM on weekdays. Based on this information, autonomous vehicles can deliver packages during those times, reducing the risk of having to redeliver packages due to consumers being absent.

[1428] Using data from the user's behavior over the past six months, it is predicted that the user will typically return home between 6:00 PM and 10:00 PM on weekdays. Based on this information, the autonomous vehicle will calculate the optimal delivery route and notify the user.

[1429] The present invention aims to reduce redelivery costs and resolve labor shortages by analyzing consumer behavior data and providing efficient delivery plans and routes based on predicted times when consumers will be at home.

[1430] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1431] Step 1:

[1432] The server collects data on the behavioral history of general consumers. Specifically, data on time spent outside the home, time returned home, and activities on holidays is obtained from APIs and databases. The collected data is stored for analysis.

[1433] Input: Behavioral history data obtained from APIs and databases

[1434] Output: Behavioral history data saved for analysis

[1435] Step 2:

[1436] The server analyzes the collected behavioral history data using a generative AI model to predict the amount of time the consumer will be at home. For example, the model is trained based on data from the past six months to generate a predictive model. By inputting the latest data into this model, the amount of time the consumer will be at home can be predicted.

[1437] Input: Collected behavioral history data

[1438] Output: Predicted time at home

[1439] Step 3:

[1440] The server then suggests the best delivery time based on the predicted time the consumer will be at home. This suggestion is calculated taking into account the time of day the consumer is expected to be at home. For example, if a consumer is usually at home between 6:00 PM and 10:00 PM on weekdays, the server will suggest that time as the delivery time.

[1441] Input: Predicted time at home

[1442] Output: Suggested best delivery date and time

[1443] Step 4:

[1444] The server uses the generative AI and map information to calculate the optimal delivery route. This uses Google Maps API and traffic information data to calculate the optimal route taking into account current traffic conditions. The calculated route information is sent to the autonomous vehicle.

[1445] Input: Proposed delivery date and time, map information, and traffic information

[1446] Output: Calculated optimal delivery route

[1447] Step 5:

[1448] The server then sends the calculated delivery route information to the autonomous vehicle and instructs the vehicle to follow the route, which the autonomous vehicle then follows to deliver the package to the nearest station.

[1449] Input: Calculated delivery route information

[1450] Output: Route instructions for the autonomous vehicle

[1451] Step 6:

[1452] The autonomous vehicle will then arrive at the nearest station, where a delivery person will handle the last mile of delivery, making the final delivery from the station to the consumer's home.

[1453] Input: A package transported by an autonomous vehicle

[1454] Output: Final delivery to the consumer's home

[1455] Step 7:

[1456] The server notifies the consumer of the estimated delivery time and real-time delivery status. Notifications are sent to the consumer's smartphone, allowing the consumer to check the delivery progress in real time. In addition, if there is a request to change the delivery date and time, the server accepts it and applies the recalculated new schedule.

[1457] Input: Estimated delivery time, real-time delivery status

[1458] Output: Notified estimated delivery time and current delivery status

[1459] Through these steps, the invention can achieve an efficient delivery system, reduce the risk of redelivery, and eliminate the problem of labor shortages.

[1460] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1461] The present invention is a system that combines a system that collects consumer behavior history data, analyzes it using a generation API, and predicts the amount of time consumers will be at home with an emotion engine that recognizes user emotions. This makes it possible to reduce redelivery costs, optimize delivery routes, and provide flexible delivery support based on user emotions. Specific embodiments are described in detail below.

[1462] Collecting and predicting user behavior data

[1463] The server collects user behavior history data, such as the time spent outside, the time returned home, and activities on holidays.

[1464] The server inputs the collected data into the generation API and analyzes it to predict the user's home time. This predicted data is stored in the delivery management system database.

[1465] Delivery route optimization

[1466] The server calculates the optimal delivery time based on the predicted time the customer will be at home, taking into account the customer's preferred time and delivery priority.

[1467] The server uses map information and traffic information to generate an optimal delivery route, which is then sent to the delivery person's terminal.

[1468] Collaboration with delivery staff

[1469] The terminal (the delivery driver's terminal) takes the package to the nearest collection and distribution station according to the predicted route.

[1470] The delivery person will pick up the package from the collection and delivery station and deliver it to the user's home.

[1471] User support with an emotional engine

[1472] The server uses an emotion engine to collect and analyze the user's emotion data, including the user's voice and text data.

[1473] The server uses an emotion engine to analyze the user's emotional state and adjusts the content and timing of notifications based on the results. For example, if the user is feeling anxious or dissatisfied, the server provides an option to easily change the delivery schedule.

[1474] The server stores the collected emotional data and analyzes long-term emotional trends, enabling it to provide a flexible delivery service tailored to the user's emotional state.

[1475] User Notification

[1476] The server notifies the user of the estimated delivery time and real-time delivery status, and the content of the notification is adjusted based on the analysis results of the emotion engine.

[1477] Specific examples

[1478] Example 1: Predicting user's time at home

[1479] The server collects user behavior history data from the past few months and analyzes it using a generation API. Based on this, if it predicts that the user is likely to be at home during weekday nights, it will schedule deliveries for those times.

[1480] Example 2: Delivery route optimization

[1481] The server predicts the user's expected time of home and determines the optimal delivery date and time to be between 10:00 a.m. and noon on Saturdays. Using map and traffic information, the server calculates the shortest route from the nearest delivery station to the user's home.

[1482] Example 3: Supported by an Emotion Engine

[1483] The server uses an emotion engine to analyze the user's recent text messages and recognize that they are stressed, which results in the delivery notification being more subdued and adding a link to easily reschedule the delivery.

[1484] Example 4: Collaboration with delivery personnel and user notification

[1485] The terminal (the delivery driver's terminal) will deliver the package to the collection and distribution station at 8:00 AM on Saturday, and the delivery person will complete the delivery to the user's home between 10:00 AM and noon on Saturday. The server will notify the user of this schedule, and the user will confirm the notification. The real-time status of the delivery will also be updated as it progresses.

[1486] In this way, the present invention can provide an effective and economical delivery service by predicting the user's behavioral patterns and emotions and calculating the optimal delivery date and route. Furthermore, by utilizing an emotion engine, it is possible to respond flexibly to the user's emotional state, providing a higher level of satisfaction.

[1487] The processing flow will be explained below.

[1488] Step 1:

[1489] Users provide their own behavioral history data, including the time they spend outside, the time they return home, and their activities on holidays.

[1490] Step 2:

[1491] The server collects the provided user behavioral history data, which is either sent through a dedicated application or retrieved from an existing database.

[1492] Step 3:

[1493] The server inputs the collected behavioral history data into a generation API and analyzes it to predict the user's time at home, using a machine learning algorithm.

[1494] Step 4:

[1495] The server stores the prediction results in the delivery management system database, which can then be used for future delivery planning.

[1496] Step 5:

[1497] The server calculates the optimal delivery date and time based on the predicted time the customer will be at home, taking into account the customer's preferred time and delivery priority.

[1498] Step 6:

[1499] The server uses map and traffic information to calculate the optimal delivery route, enabling efficient delivery.

[1500] Step 7:

[1501] The server sends the calculated delivery route and delivery date and time to the delivery person's terminal, and the delivery person makes the delivery based on the received route.

[1502] Step 8:

[1503] The terminal (the delivery driver's terminal) takes the package to the nearest collection and distribution station, thereby assisting with the last mile of delivery.

[1504] Step 9:

[1505] The delivery person will collect the package at the collection and delivery station and finally deliver it to the user's home, updating the delivery status on the terminal in real time.

[1506] Step 10:

[1507] The server notifies the user of the estimated delivery time and real-time delivery status, including the specific delivery date and time.

[1508] Step 11:

[1509] The server uses an emotion engine to collect and analyze the user's emotional data, and uses the user's voice and text data as analysis data.

[1510] Step 12:

[1511] The server adjusts the content and timing of notifications based on the results of the emotion engine analysis. For example, if the user is feeling stressed, the delivery notification will be more subdued in tone.

[1512] Step 13:

[1513] The server accumulates user emotional data and analyzes long-term emotional trends, thereby improving the quality of service for users.

[1514] Step 14:

[1515] Users can check the estimated delivery time and, if necessary, submit a request to change the delivery date and time to the system.

[1516] Step 15:

[1517] The server receives change requests from users and recalculates new delivery schedules and optimal delivery routes.

[1518] Step 16:

[1519] The server sends the new delivery schedule and route information to the delivery person's terminal and notifies the user of the changed delivery schedule.

[1520] In this way, the present invention analyzes user behavior data and emotion data to propose optimal delivery schedules and routes, thereby achieving efficient deliveries and improving user satisfaction.

[1521] Example 2

[1522] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1523] With the current delivery system, it is difficult to simultaneously optimize delivery and improve user satisfaction. In particular, there are many cases where redelivery is required when the user is not at home, which increases delivery costs and reduces efficiency. In addition, there is a lack of flexibility in responding to user emotions and requests, which can easily lead to user dissatisfaction. By solving these issues, it is necessary to reduce delivery costs and improve user satisfaction.

[1524] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting behavioral history data of general consumers, means for analyzing the collected behavioral history data and predicting the time the consumer will be at home, means for proposing the optimal date and time for delivery based on the predicted time the consumer will be at home, means for calculating the optimal delivery route using the generative AI model and location information, means for sending delivery route information to a delivery employee and making the optimal delivery, means for completing the final delivery with minimal effort by cooperating with the delivery employee, means for collecting and analyzing user emotion data, means for adjusting notification content and delivery options based on the emotion analysis results, and means for notifying the user of the estimated delivery time and real-time delivery status. This enables delivery optimization and flexible response according to the user's emotions.

[1525] "General consumers" refer to individuals who do not have a specific industry or specialized knowledge and who engage in general consumer activities.

[1526] "Behavioral history data" refers to past data regarding a user's movements, schedules, activities, etc.

[1527] "Home time" refers to the time period when the user is at home.

[1528] A "generative AI model" refers to a system that uses machine learning and artificial intelligence algorithms to analyze data and make predictions.

[1529] "Location information" refers to data that indicates the physical location of a user or object, such as geographic coordinates or address information.

[1530] "Delivery route" refers to the optimal route for delivering a package.

[1531] "Delivery employee" refers to a person whose job is to deliver packages to a user's home or designated location.

[1532] "Emotional data" refers to data that indicates a user's emotions and psychological state. Specifically, it includes information obtained from voice, text, facial expressions, etc.

[1533] "Notification" means a message or alert intended to provide information to the User.

[1534] The system of this invention combines a system that collects user behavior history data, analyzes it using a generative AI model, and predicts the time the user will be at home with an emotion engine that recognizes the user's emotions. This not only reduces redelivery costs and optimizes delivery routes, but also provides flexible delivery support based on the user's emotions.

[1535] Collecting and predicting user behavior data

[1536] The server collects the user's behavioral history data. This collection includes data acquisition from the user's smartphone and wearable device. Specific data includes GPS data, calendar appointments, and motion sensor data. For example, the server periodically sends the location information set by the user on the smartphone to the server, recording the user's time out and time back home.

[1537] The collected data is analyzed using a generative AI model (e.g., a machine learning algorithm). This analysis extracts user behavioral patterns and predicts how long the user will be at home. The prediction results are stored in the delivery management system's database.

[1538] Delivery route optimization

[1539] The server calculates the optimal delivery date and time based on the predicted time the delivery person will be at home, taking into account the user's preferred time and delivery priority. It then calculates the optimal delivery route using map information (e.g., Google Maps API) and traffic information. The calculated route information is sent to the delivery person's device.

[1540] Collaboration with delivery staff

[1541] The terminal (the delivery person's device) will then follow the predicted route and deliver the package to the nearest collection and delivery station, where the delivery person will pick up the package and deliver it to the user's home.

[1542] User support with an emotional engine

[1543] The server uses a voice recognition system and text analysis tools to collect user emotion data, for example, from voice data or text messages when a user contacts customer support.

[1544] The emotion engine analyzes this data to identify the user's emotional state and adjusts the notification content and delivery options accordingly. For example, if the user is stressed, the notification will be sent in a more gentle tone and offer the option to reschedule the delivery.

[1545] User Notification

[1546] The server notifies the user of the estimated delivery time based on the determined delivery date and time and the optimal route. Notifications are sent via smartphone app, email, or SMS. Real-time delivery status information is also provided as appropriate.

[1547] Specific examples

[1548] Example 1: Predicting user's time at home

[1549] The server collects user behavior history data from the past few months and analyzes it with a generative AI model. For example, if it predicts that the user is likely to be at home during weekday nights, it will schedule deliveries for those times.

[1550] Example 2: Delivery route optimization

[1551] The server predicts the user's expected time of home and determines the optimal delivery date and time between 10:00 a.m. and noon on Saturdays. It also uses map and traffic information to calculate the shortest route from the nearest delivery station to the user's home.

[1552] Example 3: Supported by an Emotion Engine

[1553] The server uses an emotion engine to analyze the user's recent text messages and recognize that they are stressed, which results in the delivery notification being more subdued and includes a link to easily reschedule the delivery.

[1554] Example 4: Collaboration with delivery personnel and user notification

[1555] The terminal (delivery worker's device) will deliver the package to the collection and delivery station at 8:00 AM on Saturday, and the delivery worker will complete the delivery to the user's home between 10:00 AM and noon on Saturday. The server will notify the user of this schedule, and the user will confirm the notification. The real-time status of the delivery will also be updated as it progresses.

[1556] An example of a prompt is, "Analyze the user's behavioral history data from the past few months and predict when the user will be at home. Calculate the optimal delivery date, time, and route based on the prediction results. Also, adjust the notification content taking into account the user's emotional state."

[1557] In this way, the system of the present invention calculates the optimal delivery date and route based on the user's behavioral data and emotional data, and provides an effective and flexible delivery service.

[1558] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1559] Step 1:

[1560] The server collects user behavior history data. The collected data is obtained from the user's smartphone or wearable device. Specifically, it includes GPS data, calendar events, and motion sensor data. For example, the user's behavior history while out and the time of return home are recorded. The input data is the behavior history data sent from the user's device, and the output is the collected dataset.

[1561] Step 2:

[1562] The server preprocesses the collected behavioral history data, complementing missing values ​​and excluding outliers. Specifically, it detects and excludes data containing sudden movements, and complements missing data using the mean or median. The input is the collected behavioral history data, and the output is the preprocessed data.

[1563] Step 3:

[1564] The server inputs the preprocessed data into a generative AI model to predict the user's home time. The generative AI model uses a time-series data prediction algorithm. For example, it predicts the home time on weekday nights and weekends based on past behavioral patterns. The input is the preprocessed behavioral history data, and the output is the predicted home time data.

[1565] Step 4:

[1566] The server determines the optimal delivery date and time based on the predicted at-home time data, taking into account the user's desired time and delivery priority. The input is the predicted at-home time data and the user's desired time, and the output is the optimal delivery date and time.

[1567] Step 5:

[1568] The server calculates the optimal delivery route based on the determined delivery date and time using map and traffic information. For map information, it uses Google Maps API and public transportation API to calculate the shortest route from the delivery center to the user's address. The input is the optimal delivery date and time, map information, and traffic information, and the output is the optimal delivery route.

[1569] Step 6:

[1570] The server sends the calculated delivery route information to the delivery person's terminal. The delivery person makes the delivery based on this route information. The input is the optimal delivery route information, and the output is the route information sent to the delivery person's terminal.

[1571] Step 7:

[1572] The terminal (the delivery person's device) carries the package to the nearest collection / distribution station according to the predicted route. Specifically, it follows the route from the delivery center to the collection / distribution station. The input is the package from the delivery center and the optimal delivery route information, and the output is the state of the package arriving at the collection / distribution station.

[1573] Step 8:

[1574] The delivery person delivers the package from the collection and delivery station to the user's home at the delivery date and time specified by the user. The input is the package from the collection and delivery station and the optimal delivery route information, and the output is the package delivered to the user's home.

[1575] Step 9:

[1576] The server uses a voice recognition system and text analysis tools to collect user emotion data. Specifically, it obtains emotion information from voice data and text messages when a user contacts customer support. The input is the user's inquiry data, and the output is the collected emotion data.

[1577] Step 10:

[1578] The server uses an emotion engine to analyze the collected emotion data and identify the user's emotional state. For example, it determines whether the user is "angry" or "anxious" based on the content of their voice or text message. The input is the collected emotion data, and the output is the analyzed emotion state.

[1579] Step 11:

[1580] The server adjusts the notification content and delivery options based on the analysis results of the emotion engine. For example, if the user is stressed, the server may send a softer notification and offer the option to reschedule the delivery. The input is the analyzed emotional state, and the output is the adjusted notification content.

[1581] Step 12:

[1582] The server notifies the user of the estimated delivery time and real-time delivery status via a smartphone app, email, or SMS. For example, a notification is sent when the package leaves the delivery center or arrives in the delivery area. The input is delivery status data and the adjusted notification content, and the output is the notification sent to the user.

[1583] In this way, the system calculates the optimal delivery date and route based on the user's behavioral history and emotional data, providing an efficient and flexible delivery service.

[1584] (Application example 2)

[1585] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1586] Conventional delivery systems are unable to accurately predict when users will be at home, resulting in frequent redelivery and delays. Furthermore, because they do not take user emotions into consideration, delivery notifications and responses are mechanical, leading to lower user satisfaction. To solve these problems, a new system is needed that analyzes user behavioral history data and emotional data to provide optimal delivery dates, times, and routes.

[1587] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting behavioral history data of general consumers, means for analyzing the collected behavioral history data and predicting the time the consumer will be at home, means for proposing the optimal date and time for delivery based on the predicted time the consumer will be at home, means for calculating the optimal delivery route using generation AI and map information, means for sending delivery route information to a delivery person and making the optimal delivery, means for completing the final delivery with minimal effort by cooperating with the delivery person, means for notifying the user of the estimated delivery time and real-time delivery status, and means for recognizing the user's emotions and adjusting the content and timing of notifications based on the emotions. This not only improves delivery efficiency but also enables flexible responses according to the user's emotions, providing a high level of satisfaction.

[1588] "General consumer" refers to an individual who purchases goods or uses services.

[1589] "Behavioral history data" refers to data related to a user's behavioral patterns, such as the time they spend outside the home, the time they return home, and their activities on holidays.

[1590] "Forecasting" refers to estimating future events or conditions based on collected data.

[1591] "Delivery route" refers to the route taken to deliver a package to a user.

[1592] "Generative AI" refers to artificial intelligence technology that generates and analyzes information based on large amounts of data.

[1593] "Map information" refers to data relating to geographical locations and routes.

[1594] "Delivery Person" refers to an individual or team whose role is to receive packages and deliver them to users.

[1595] "Optimization" refers to maximizing efficiency and effectiveness.

[1596] "Emotion recognition" refers to the technology of analyzing and understanding a user's emotional state from their voice, text, etc.

[1597] The "last mile" refers to the final stage of delivery, delivering the package from the collection and distribution station to the user's home.

[1598] "Notification" refers to the act of conveying information from the system to the user or delivery person.

[1599] This invention is a system that collects and analyzes user behavior history data and emotion data to provide optimal delivery dates and routes. A detailed description of the system based on the present invention is given below.

[1600] First, the server collects the user's behavioral history data, specifically the time the user leaves the house, the time they return home, their activities on holidays, etc. This data is collected using the smartphone's GPS data and calendar information.

[1601] The server then analyzes the collected data using generative AI to predict when the user will be at home, using techniques such as linear regression. The predicted time is then used in the next step to suggest the optimal delivery date and time.

[1602] The server also calculates the optimal delivery route based on map and traffic information, allowing delivery personnel to deliver efficiently. The route information is sent to the delivery personnel's terminal, and delivery is carried out according to the optimal route.

[1603] Furthermore, the server uses an emotion engine to recognize the user's emotions and adjust the content and timing of notifications accordingly. The server understands the user's emotional state by analyzing the user's voice input and text messages. This emotional data is used to adjust the wording and timing of delivery notifications, ensuring that users do not feel stressed.

[1604] Additionally, the server provides users with notifications of estimated delivery times and real-time delivery status, which are tailored based on the collected emotion data.

[1605] For example, if a user is often at home on weekday evenings, delivery can be scheduled for that time. Also, if the system determines that the user is stressed, delivery notifications will be changed to gentler language and delivery will be handled more quickly.

[1606] For example, the server might use the following prompt:

[1607] "Using the user's behavioral history data and calendar information, the system predicts when they will be at home and creates an ideal delivery plan based on their emotional data. Delivery routes are optimized using map and traffic information. If the user is under stress, the system changes the delivery time and notification content."

[1608] This system will enable flexible delivery services based on the user's behavioral patterns and emotional state, which is expected to reduce redelivery and delays and improve user satisfaction.

[1609] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1610] Step 1:

[1611] Users use their smartphones to collect behavioral history data, specifically GPS data and calendar information. This data includes detailed information such as the user's time out, the time they return home, and their activities on holidays.

[1612] Input: Smartphone GPS data, calendar information

[1613] Output: Behavioral history data

[1614] Step 2:

[1615] The server inputs the behavioral history data collected in step 1 into the generative AI model for analysis. The generative AI model learns past patterns and predicts the user's time at home. This prediction is made using techniques such as linear regression.

[1616] Input: Behavioral history data

[1617] Output: Predicted time spent at home

[1618] Step 3:

[1619] The server proposes the optimal delivery date and time based on the predicted time at home data, and calculates the optimal delivery time taking into account the user's desired time and delivery priority.

[1620] Input: Predicted time at home, user's desired time, delivery priority

[1621] Output: Best delivery date and time

[1622] Step 4:

[1623] The server uses map and traffic information to calculate the optimal delivery route, taking into account predicted home time and priority, allowing delivery personnel to make deliveries efficiently.

[1624] Input: Best delivery date and time, map information, traffic information

[1625] Output: Optimal delivery route

[1626] Step 5:

[1627] The terminal sends the delivery route information generated in step 4 to the delivery person. Based on this information, the delivery person picks up the package and begins delivering it to the user's home.

[1628] Input: Delivery route information

[1629] Output: Instructions to the delivery person

[1630] Step 6:

[1631] The server uses an emotion engine to collect and analyze the user's emotional data. Specifically, it recognizes the user's emotional state from their voice input or text message. The recognized emotional state is used to adjust the content and timing of delivery notifications.

[1632] Input: Voice input, text message

[1633] Output: Emotional state data

[1634] Step 7:

[1635] The server notifies the user of the estimated delivery time and real-time delivery status based on the emotional state data. The server adjusts the wording and timing of the notification depending on the user's emotional state. For example, if the user is feeling stressed, the server will send a gentler notification.

[1636] Input: Emotional state data, optimal delivery route, real-time delivery status

[1637] Output: User notification

[1638] Step 8:

[1639] The terminal (delivery person) reports the real-time delivery status to the server. The user can check the progress of the delivery based on this information. The server uses this information to notify the user of the latest delivery status.

[1640] Input: Real-time delivery status

[1641] Output: Report to server, latest notifications to user

[1642] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1643] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1644] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1645] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1646] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1647] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1648] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1649] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1650] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1651] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1652] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1653] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1654] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1655] 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.

[1656] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1657] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1658] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1659] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1660] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1661] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1662] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1663] The following is further disclosed regarding the above embodiment.

[1664] (Claim 1)

[1665] A means for collecting behavioral history data of general consumers;

[1666] A means for analyzing collected behavioral history data and predicting time spent at home;

[1667] A means for suggesting the best date and time for delivery based on the predicted time at home;

[1668] A means of calculating the optimal delivery route using generative AI and map information;

[1669] A means for transmitting delivery route information to delivery personnel to ensure optimal delivery;

[1670] A means to complete final delivery with minimal effort by coordinating with delivery personnel;

[1671] a means for notifying the user of the estimated delivery time and real-time delivery status;

[1672] A system including:

[1673] (Claim 2)

[1674] 10. The system of claim 1, further comprising means for a truck driver to deliver the package to a nearest collection and distribution station, from which a delivery person handles the last mile of delivery.

[1675] (Claim 3)

[1676] means for allowing a user to submit a request to change the delivery date and time based on the delivery schedule;

[1677] 10. The system of claim 1, further comprising means for calculating a new delivery schedule based on the change request and recalculating an optimal delivery route taking into account the submitted change request.

[1678] "Example 1"

[1679] (Claim 1)

[1680] A means for collecting activity history data of general users;

[1681] A means for analyzing collected activity history data and predicting time spent at home;

[1682] A means for suggesting the best date and time for delivery based on the predicted time at home;

[1683] A means for calculating optimal delivery routes using generative artificial intelligence and map information;

[1684] A means for transmitting delivery route information to a delivery company to perform optimal delivery;

[1685] A means of cooperating with the delivery company to complete the final delivery with minimal effort;

[1686] a means for notifying the user of the estimated delivery time and real-time delivery status;

[1687] A means for accepting and analyzing delivery time change requests from users to generate a new schedule;

[1688] A system including:

[1689] (Claim 2)

[1690] 10. The system of claim 1, further comprising means for a transport vehicle operator to deliver the package to a nearest collection and distribution station from which a delivery company is responsible for final delivery.

[1691] (Claim 3)

[1692] a means for enabling a user to send a request to change the delivery date and time based on the delivery schedule;

[1693] 10. The system of claim 1, further comprising means for calculating a new delivery schedule based on the change request and recalculating an optimal delivery route taking into account the submitted change request.

[1694] "Application Example 1"

[1695] (Claim 1)

[1696] A means for collecting behavioral history data of general consumers;

[1697] A means for analyzing collected behavioral history data and predicting time spent at home;

[1698] A means for installing a delivery management system in the autonomous vehicle and suggesting the best date and time for delivery based on the predicted time at home;

[1699] A means of calculating the optimal delivery route using generative AI and map information;

[1700] A means for transmitting delivery route information to autonomous vehicles to make optimal deliveries;

[1701] A means for autonomous vehicles to deliver packages to the nearest station based on delivery route and predictive data, completing the final delivery with minimal effort;

[1702] a means for notifying the user of the estimated delivery time and real-time delivery status;

[1703] A system including:

[1704] (Claim 2)

[1705] 10. The system of claim 1, further comprising means for a truck driver to deliver the package to a nearest distribution station from which an autonomous vehicle handles the last mile of delivery.

[1706] (Claim 3)

[1707] means for allowing a user to submit a request to change the delivery date and time based on the delivery schedule;

[1708] 10. The system of claim 1, further comprising means for calculating a new delivery schedule based on the change request and for the autonomous vehicle to recalculate an optimal delivery route that takes into account the submitted change request.

[1709] "Example 2: Combining Emotion Engines"

[1710] (Claim 1)

[1711] A means for collecting behavioral history data of general consumers;

[1712] A means for analyzing collected behavioral history data and predicting time spent at home;

[1713] A means for suggesting the best date and time for delivery based on the predicted time at home;

[1714] A means of calculating optimal delivery routes using generative AI models and location information; and

[1715] A means for transmitting delivery route information to delivery employees to make optimal deliveries;

[1716] A means of coordinating with delivery employees to complete final deliveries with minimal effort;

[1717] A means of collecting and analyzing user emotional data;

[1718] A means to tailor notification content and delivery options based on sentiment analysis results; and

[1719] a means for notifying the user of the estimated delivery time and real-time delivery status;

[1720] A system including:

[1721] (Claim 2)

[1722] 10. The system of claim 1, further comprising means for a driver of the delivery vehicle to deliver the package to the nearest collection and distribution station, from which a delivery employee handles the last mile of delivery.

[1723] (Claim 3)

[1724] means for allowing a user to submit a request to change the delivery date and time based on the delivery schedule;

[1725] means for calculating a new delivery schedule based on the change request and recalculating an optimal delivery route taking into account the submitted change request;

[1726] 10. The system of claim 1, further comprising means for analyzing the user's emotional state with an emotion engine and providing an option to easily change the delivery date and time based on the analysis result.

[1727] "Application example 2 when combining emotion engines"

[1728] (Claim 1)

[1729] A means for collecting behavioral history data of general consumers;

[1730] A means for analyzing collected behavioral history data and predicting time spent at home;

[1731] A means for suggesting the best date and time for delivery based on the predicted time at home;

[1732] A means of calculating the optimal delivery route using generative AI and map information;

[1733] A means for transmitting delivery route information to delivery personnel to ensure optimal delivery;

[1734] A means of coordinating with delivery personnel to complete final delivery with minimal effort;

[1735] A means for notifying users of estimated delivery times and real-time delivery status;

[1736] A way to recognize user emotions and tailor the content and timing of notifications accordingly;

[1737] A system including:

[1738] (Claim 2)

[1739] 10. The system of claim 1, further comprising means for a truck driver to deliver the package to a nearest collection and distribution station, from which a delivery person handles the last mile of delivery.

[1740] (Claim 3)

[1741] means for allowing a user to submit a request to change the delivery date and time based on the delivery schedule;

[1742] 10. The system of claim 1, further comprising means for calculating a new delivery schedule based on the change request and recalculating an optimal delivery route taking into account the submitted change request. [Explanation of symbols]

[1743] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for collecting behavioral history data of general consumers; A means for analyzing collected behavioral history data and predicting time spent at home; A means for suggesting the best date and time for delivery based on the predicted time at home; A means of calculating the optimal delivery route using generative AI and map information; A means for transmitting delivery route information to delivery personnel to ensure optimal delivery; A means to complete final delivery with minimal effort by coordinating with delivery personnel; a means for notifying the user of the estimated delivery time and real-time delivery status; A system including:

2. The system of claim 1 further comprising means for a truck driver to deliver the package to the nearest collection and distribution station, from which a delivery person is responsible for the last mile of delivery.

3. means for allowing a user to submit a request to change the delivery date and time based on the delivery schedule; 10. The system of claim 1, further comprising means for calculating a new delivery schedule based on the change request and recalculating an optimal delivery route taking into account the submitted change request.

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