System

Abstract

An object of a system according to an embodiment is to suppress the occurrence of redelivery in a home delivery service.SOLUTION: A system according to an embodiment includes a receivable time confirmation unit and a delivery time suggestion unit. The receivable time period check unit checks in advance a receivable time period for the delivery destination. The delivery time proposing unit proposes an optimal delivery time based on the receivable time period confirmed by the receivable time period confirming unit.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] Conventional technologies have had the problem of frequent redelivery being required in home delivery services.

[0005] The system according to the embodiment aims to reduce the occurrence of redelivery in a home delivery service. [Means for solving the problem]

[0006] The system according to the embodiment includes an available pickup time confirmation unit and a delivery time proposal unit. The available pickup time confirmation unit confirms the available pickup time at the delivery destination in advance. The delivery time proposal unit proposes an optimal delivery time based on the available pickup time confirmed by the available pickup time confirmation unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the occurrence of redelivery in a home delivery service. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The delivery agent AI system according to the embodiment of the present invention is a system that checks the delivery destination's available reception times in advance and proposes the optimal delivery time. This allows the delivery agent AI system to reduce the need for redelivery.

[0029] The delivery agent AI system according to the embodiment includes an available receipt time confirmation unit and a delivery time suggestion unit. The available receipt time confirmation unit confirms the available receipt time at the delivery destination in advance. For example, the available receipt time confirmation unit sends a message to the resident asking about the available receipt time. The available receipt time confirmation unit can also predict the available receipt time by analyzing the resident's past receipt history and behavioral patterns. The available receipt time confirmation unit can also link with the resident's calendar app to automatically obtain the resident's schedule and confirm the available receipt time. The delivery time suggestion unit suggests an optimal delivery time based on the available receipt time confirmed by the available receipt time confirmation unit. For example, the delivery time suggestion unit schedules a delivery time based on the resident's available receipt time. The delivery time suggestion unit can also calculate the optimal delivery time in real time, taking traffic conditions and weather data into consideration. The delivery time suggestion unit can also learn the resident's lifestyle and suggest the most convenient time for delivery. This allows the delivery agent AI system according to the embodiment to reduce the need for redelivery.

[0030] The available receipt time confirmation unit can send a message to the resident asking what time period they can receive the package. For example, the available receipt time confirmation unit can send a message to the resident saying, "You have a package scheduled for delivery. Please tell me what time period you can receive it." The available receipt time confirmation unit can also store the resident's past receipt history in a database, and the generation AI can analyze that data to predict the available receipt time. For example, if the resident has received packages at the same time period many times in the past, that time period will be suggested as a priority. The available receipt time confirmation unit can also analyze the resident's behavioral patterns to predict the available receipt time. For example, if the resident is at home on a specific day of the week every week, that day will be suggested as the available receipt time. This allows the resident's available receipt time to be accurately determined.

[0031] The delivery time suggestion unit can schedule delivery times based on the resident's available pickup times. For example, the delivery time suggestion unit can link with the resident's calendar app to automatically obtain the schedule and check the available pickup times. For example, it can analyze the plans written on the calendar and suggest available pickup times based on the schedule. The delivery time suggestion unit can also use the calendar app's API to obtain the resident's schedule in real time and check the available pickup times. For example, it can respond immediately if the plans change. This makes it possible to deliver the package according to the resident's available pickup times.

[0032] The available receipt time confirmation unit can analyze a resident's past receipt history and behavioral patterns to predict available receipt times. For example, the available receipt time confirmation unit stores the resident's past receipt history in a database, and the generation AI analyzes that data to predict available receipt times. For example, if a resident has received packages at the same time period many times in the past, that time period will be suggested as a priority. The available receipt time confirmation unit can also analyze a resident's behavioral patterns to predict available receipt times. For example, if a resident is at home on a specific day of the week every week, that day will be suggested as an available receipt time. This makes it possible to predict available receipt times based on the resident's past behavioral patterns.

[0033] The available pickup time confirmation unit can work in conjunction with the resident's calendar app to automatically obtain the schedule and confirm the available pickup time. The available pickup time confirmation unit, for example, works in conjunction with the resident's calendar app to automatically obtain the schedule. For example, it analyzes the plans written on the calendar and suggests available time slots as available pickup times. The available pickup time confirmation unit can also use the calendar app's API to obtain the resident's schedule in real time and confirm the available pickup time. For example, it can respond immediately if the plans change. This makes it possible to confirm the available pickup time based on the resident's schedule.

[0034] The available receipt time confirmation unit can cooperate with the resident's smart home devices to automatically detect the time when the resident is at home and confirm the available receipt time. The available receipt time confirmation unit, for example, cooperates with the resident's smart home devices to automatically detect the time when the resident is at home. For example, it analyzes data from a smart doorbell or security camera to identify the time when the resident will be at home. The available receipt time confirmation unit can also analyze data from the smart home devices to predict the time when the resident will be at home. For example, it identifies the time when the resident will be at home based on the usage of a smart thermostat or smart light. This makes it possible to automatically detect the time when the resident is at home and confirm the available receipt time.

[0035] The available pickup time confirmation unit can analyze the resident's social media activity and estimate the available pickup time. The available pickup time confirmation unit, for example, analyzes the resident's social media activity and estimates the available pickup time. For example, based on the time periods when the resident frequently posts, it identifies the time periods when the resident is likely to be at home. The available pickup time confirmation unit can also analyze location information data from social media to understand the resident's behavioral patterns. For example, it estimates the available pickup time based on the time periods when the resident is in a specific location. This makes it possible to estimate the available pickup time based on the resident's social media activity.

[0036] The delivery time proposal unit can calculate the optimal delivery time in real time based on traffic conditions and weather data. For example, the delivery time proposal unit acquires traffic condition data in real time, and the generation AI calculates the optimal delivery time. For example, the delivery time is adjusted taking into account traffic congestion information. The delivery time proposal unit can also analyze weather data, and the generation AI can calculate the optimal delivery time. For example, the delivery time is adjusted to avoid bad weather. This allows the optimal delivery time to be calculated taking into account traffic conditions and weather data.

[0037] The delivery time suggestion unit can learn the resident's lifestyle and suggest the most convenient time for receiving the package. The delivery time suggestion unit, for example, analyzes the lifestyle of the resident at the delivery destination and suggests the most convenient time for receiving the package. For example, if the resident is at home at the same time every day, that time will be suggested as a priority. The delivery time suggestion unit can also analyze the resident's past receiving history and learn the resident's lifestyle. For example, if the resident often receives packages on a specific day of the week or at a specific time, that time will be suggested. This makes it possible to suggest the optimal delivery time based on the resident's lifestyle.

[0038] The delivery time suggestion unit can analyze the resident's health data and suggest the optimal delivery time. The delivery time suggestion unit can, for example, analyze health data obtained from the resident's fitness tracker and suggest the optimal delivery time. For example, the delivery time can be set to avoid times when the resident is exercising. The delivery time suggestion unit can also analyze the resident's lifestyle based on the health data and suggest the optimal delivery time. For example, it can prioritize times when the resident is relaxing. This makes it possible to suggest the optimal delivery time based on the resident's health data.

[0039] The delivery time suggestion unit takes into consideration the schedules of other members in the resident's household and can suggest a time slot that is convenient for everyone to receive the package. For example, the delivery time suggestion unit analyzes the schedules of other members in the resident's household and suggests a time slot that is convenient for everyone to receive the package. For example, it prioritizes suggesting a time slot when all family members are at home. The delivery time suggestion unit can also link with the calendar apps of household members and automatically obtain their schedules. For example, it analyzes everyone's schedule and suggests the optimal delivery time. This makes it possible to suggest a time slot that is convenient for everyone in the household to receive the package.

[0040] The reminder notification unit can analyze the resident's past reminder response patterns and determine the optimal notification timing. The reminder notification unit, for example, analyzes the resident's past reminder response patterns and determines the optimal notification timing. For example, it identifies the time periods when the resident is most likely to respond to reminders. The reminder notification unit can also analyze the reminder notification history and identify the time periods when the resident is most likely to respond. For example, it prioritizes notifications during the time periods when the resident most frequently responds to reminders. This makes it possible to determine the optimal notification timing based on the resident's past response patterns.

[0041] The reminder notification unit can customize the content of reminder notifications to suit the resident's preferences. For example, the reminder notification unit analyzes the resident's past reminder notification history and generates notification content that suits the resident's preferences. For example, the reminder notification unit sends notifications using the resident's preferred language and expressions. The reminder notification unit can also collect the resident's preferences in advance through a questionnaire or settings screen and customize the content of reminder notifications based on that information. For example, the reminder notification unit can set the resident's preferred notification format and frequency. This makes it possible to send reminder notifications that suit the resident's preferences.

[0042] The reminder notification unit can also send reminder notifications to the resident's smartwatch or smart speaker. For example, the reminder notification unit can send reminder notifications to the resident's smartwatch so that they can be viewed at hand. For example, the reminder notification can be notified by vibration or sound. The reminder notification unit can also send reminder notifications to the resident's smart speaker so that the notification can be notified by voice. For example, the voice assistant can read the reminder aloud. This allows the resident to receive reminder notifications on multiple devices.

[0043] The reminder notification unit can automatically add reminder notifications to the resident's calendar app. For example, the reminder notification unit can automatically add reminder notifications to the resident's calendar app and display them as a schedule. For example, the reminder notification unit can register the scheduled delivery time on the calendar. The reminder notification unit can also use the calendar app's API to build a system that automatically adds reminder notifications. For example, the reminder notification unit can automatically register the scheduled delivery time on the calendar. This makes it possible to automatically add reminder notifications to the resident's calendar app.

[0044] The delivery status tracking unit can analyze the location information of the delivery person and traffic conditions in real time to provide an accurate arrival prediction. For example, the delivery status tracking unit obtains the location information of the delivery person in real time, and the generation AI analyzes the traffic conditions to provide an accurate arrival prediction. For example, the arrival time is adjusted taking into account traffic congestion information. The delivery status tracking unit can also integrate the location information of the delivery person with traffic condition data, and the generation AI can provide an accurate arrival prediction. For example, the delivery route is optimized and the arrival time is predicted. This makes it possible to analyze the location information of the delivery person and traffic conditions in real time to provide an accurate arrival prediction.

[0045] The delivery status tracking unit can display the delivery status in cooperation with the resident's smart home device. For example, the delivery status tracking unit can display the delivery status on the resident's smart doorbell and notify the resident that a delivery person is approaching. For example, the delivery status can be displayed in cooperation with the camera image of the smart doorbell. The delivery status tracking unit can also display the delivery status on the resident's smart home device and notify the resident of the delivery person's arrival in real time. For example, a smart speaker can notify the resident of the delivery status by voice. In this way, the delivery status can be displayed in cooperation with the resident's smart home device.

[0046] The delivery status tracking unit can also notify the resident's smart watch or smart speaker of the delivery status. For example, the delivery status tracking unit can notify the resident's smart watch of the delivery status so that the resident can check it at their fingertips. For example, the delivery status can be notified by vibration or sound. The delivery status tracking unit can also notify the resident's smart speaker of the delivery status so that the delivery status can be notified by voice. For example, a voice assistant can read out the delivery status. This allows the resident to check the delivery status on multiple devices.

[0047] The delivery status tracking unit can automatically add the delivery status to the resident's calendar app. For example, the delivery status tracking unit automatically adds the delivery status to the resident's calendar app and displays it as a schedule. For example, the scheduled delivery time is registered in the calendar. The delivery status tracking unit can also use the calendar app's API to build a system that automatically adds the delivery status. For example, the scheduled delivery time is automatically registered in the calendar. This makes it possible to automatically add the delivery status to the resident's calendar app.

[0048] The redelivery scheduling unit can analyze the resident's past redelivery history and propose the optimal redelivery time. The redelivery scheduling unit, for example, analyzes the resident's past redelivery history and proposes the optimal redelivery time. For example, it identifies the time period when the resident is most likely to receive a redelivery. The redelivery scheduling unit can also analyze the resident's lifestyle based on the redelivery history and propose the optimal redelivery time. For example, it prioritizes proposing time periods when the resident is at home. This makes it possible to propose the optimal redelivery time based on the resident's past redelivery history.

[0049] The redelivery scheduling unit can work with the resident's calendar app when redelivery is required, automatically obtain the schedule, and set the redelivery time. The redelivery scheduling unit can work with the resident's calendar app, for example, automatically obtain the schedule, and set the redelivery time. For example, it can analyze the schedule written on the calendar and set an available time slot as the redelivery time. The redelivery scheduling unit can also use the calendar app's API to build a system that automatically sets the redelivery time. For example, it can set redelivery to a time slot that does not overlap with the resident's schedule. This makes it possible to set the redelivery time in cooperation with the resident's calendar app.

[0050] When redelivery is required, the redelivery scheduling unit can work with the resident's smart home devices to automatically detect the time the resident will be at home and set the redelivery time. The redelivery scheduling unit can, for example, work with the resident's smart home devices to automatically detect the time the resident will be at home and set the redelivery time. For example, it can analyze data from smart doorbells or security cameras to identify the time periods when the resident will be at home. The redelivery scheduling unit can also analyze data from smart home devices to predict the time the resident will be at home and set the redelivery time. For example, it can identify the time periods when the resident will be at home based on the usage of smart thermostats or smart lights. This makes it possible to work with the resident's smart home devices to set the redelivery time.

[0051] The redelivery scheduling unit can analyze the resident's social media activity when redelivery is required and propose the optimal redelivery time. The redelivery scheduling unit, for example, analyzes the resident's social media activity and proposes the optimal redelivery time. For example, the redelivery scheduling unit identifies the time periods when the resident is likely to be at home based on the time periods when the resident frequently posts. The redelivery scheduling unit can also analyze social media location data to understand the resident's behavioral patterns and propose a redelivery time. For example, the redelivery time can be set based on the time periods when the resident is in a specific location. This makes it possible to propose the optimal redelivery time based on the resident's social media activity.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The delivery agent AI system can also be equipped with a health data analysis unit that analyzes residents' health data and suggests optimal delivery times. For example, it can analyze data obtained from residents' fitness trackers and schedule delivery times that avoid times when they are exercising. It can also analyze residents' lifestyles based on health data and prioritize times when they are relaxing. This makes it possible to suggest delivery times that take residents' health into consideration.

[0054] The delivery agent AI system can also be equipped with a household schedule adjustment unit that takes into account the schedules of other members of the household and suggests a time that is convenient for everyone to receive the package. For example, it can prioritize suggesting a time when all family members are at home. It can also link with the calendar apps of household members to automatically obtain everyone's schedules and suggest the optimal delivery time. This makes it possible to suggest a time that is convenient for everyone in the household to receive the package.

[0055] The Delivery Agent AI system can also be equipped with a smart home integration unit that connects with residents' smart home devices to automatically detect when they are at home and confirm when they are available to receive deliveries. For example, it can analyze data from smart doorbells and security cameras to identify when residents are at home. It can also predict when residents will be at home based on the usage of smart thermostats and smart lights. This makes it possible to automatically detect when residents are at home and confirm when they are available to receive deliveries.

[0056] The delivery agent AI system can also be equipped with a social media analysis unit that analyzes residents' social media activity and estimates available pickup times. For example, it can identify times when residents are likely to be at home based on the times when they frequently post. It can also analyze location data from social media to understand residents' behavioral patterns. This makes it possible to estimate available pickup times based on residents' social media activity.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The receiving time confirmation unit checks the available receiving times at the delivery destination in advance. For example, it sends a message to the resident asking what time they can receive the package. It can also analyze past receiving history and behavioral patterns to predict the available receiving times. It can also link with a calendar app to automatically retrieve schedules and check the available receiving times. Step 2: The delivery time suggestion unit suggests the optimal delivery time based on the available pickup times confirmed by the available pickup time confirmation unit. For example, it schedules delivery times based on the resident's available pickup times. It can also calculate the optimal delivery time in real time, taking into account traffic conditions and weather data. It can also learn the resident's lifestyle and suggest the most convenient time to pick up the package.

[0059] (Example 2) The delivery agent AI system according to the embodiment of the present invention is a system that checks the delivery destination's available reception times in advance and proposes the optimal delivery time. This allows the delivery agent AI system to reduce the need for redelivery.

[0060] The delivery agent AI system according to the embodiment includes an available receipt time confirmation unit and a delivery time suggestion unit. The available receipt time confirmation unit confirms the available receipt time at the delivery destination in advance. For example, the available receipt time confirmation unit sends a message to the resident asking about the available receipt time. The available receipt time confirmation unit can also predict the available receipt time by analyzing the resident's past receipt history and behavioral patterns. The available receipt time confirmation unit can also link with the resident's calendar app to automatically obtain the resident's schedule and confirm the available receipt time. The delivery time suggestion unit suggests an optimal delivery time based on the available receipt time confirmed by the available receipt time confirmation unit. For example, the delivery time suggestion unit schedules a delivery time based on the resident's available receipt time. The delivery time suggestion unit can also calculate the optimal delivery time in real time, taking traffic conditions and weather data into consideration. The delivery time suggestion unit can also learn the resident's lifestyle and suggest the most convenient time for delivery. This allows the delivery agent AI system according to the embodiment to reduce the need for redelivery.

[0061] The available receipt time confirmation unit can send a message to the resident asking what time period they can receive the package. For example, the available receipt time confirmation unit can send a message to the resident saying, "You have a package scheduled for delivery. Please tell me what time period you can receive it." The available receipt time confirmation unit can also store the resident's past receipt history in a database, and the generation AI can analyze that data to predict the available receipt time. For example, if the resident has received packages at the same time period many times in the past, that time period will be suggested as a priority. The available receipt time confirmation unit can also analyze the resident's behavioral patterns to predict the available receipt time. For example, if the resident is at home on a specific day of the week every week, that day will be suggested as the available receipt time. This allows the resident's available receipt time to be accurately determined.

[0062] The delivery time suggestion unit can schedule delivery times based on the resident's available pickup times. For example, the delivery time suggestion unit can link with the resident's calendar app to automatically obtain the schedule and check the available pickup times. For example, it can analyze the plans written on the calendar and suggest available pickup times based on the schedule. The delivery time suggestion unit can also use the calendar app's API to obtain the resident's schedule in real time and check the available pickup times. For example, it can respond immediately if the plans change. This makes it possible to deliver the package according to the resident's available pickup times.

[0063] The available receipt time confirmation unit can analyze a resident's past receipt history and behavioral patterns to predict available receipt times. For example, the available receipt time confirmation unit stores the resident's past receipt history in a database, and the generation AI analyzes that data to predict available receipt times. For example, if a resident has received packages at the same time period many times in the past, that time period will be suggested as a priority. The available receipt time confirmation unit can also analyze a resident's behavioral patterns to predict available receipt times. For example, if a resident is at home on a specific day of the week every week, that day will be suggested as an available receipt time. This makes it possible to predict available receipt times based on the resident's past behavioral patterns.

[0064] The available pickup time confirmation unit can work in conjunction with the resident's calendar app to automatically obtain the schedule and confirm the available pickup time. The available pickup time confirmation unit, for example, works in conjunction with the resident's calendar app to automatically obtain the schedule. For example, it analyzes the plans written on the calendar and suggests available time slots as available pickup times. The available pickup time confirmation unit can also use the calendar app's API to obtain the resident's schedule in real time and confirm the available pickup time. For example, it can respond immediately if the plans change. This makes it possible to confirm the available pickup time based on the resident's schedule.

[0065] The available receiving time confirmation unit can use the emotion estimation function to estimate the time period when the resident feels the least stress and suggest that time period. The available receiving time confirmation unit can, for example, use the emotion estimation function to analyze the stress level of the resident and suggest the time period when the resident feels the least stress. For example, it can suggest a time period when the resident is relaxed as the available receiving time. The available receiving time confirmation unit can also analyze the resident's past emotion data to identify time periods when the stress level is low. For example, it can preferentially suggest time periods when the resident is relaxed. This makes it possible to suggest time periods that will reduce the resident's stress.

[0066] The available receipt time confirmation unit can cooperate with the resident's smart home devices to automatically detect the time when the resident is at home and confirm the available receipt time. The available receipt time confirmation unit, for example, cooperates with the resident's smart home devices to automatically detect the time when the resident is at home. For example, it analyzes data from a smart doorbell or security camera to identify the time when the resident will be at home. The available receipt time confirmation unit can also analyze data from the smart home devices to predict the time when the resident will be at home. For example, it identifies the time when the resident will be at home based on the usage of a smart thermostat or smart light. This makes it possible to automatically detect the time when the resident is at home and confirm the available receipt time.

[0067] The available pickup time confirmation unit can analyze the resident's social media activity and estimate the available pickup time. The available pickup time confirmation unit, for example, analyzes the resident's social media activity and estimates the available pickup time. For example, based on the time periods when the resident frequently posts, it identifies the time periods when the resident is likely to be at home. The available pickup time confirmation unit can also analyze location information data from social media to understand the resident's behavioral patterns. For example, it estimates the available pickup time based on the time periods when the resident is in a specific location. This makes it possible to estimate the available pickup time based on the resident's social media activity.

[0068] The available receiving time confirmation unit can use the emotion estimation function to estimate the time period when the resident is most relaxed and suggest that time period. The available receiving time confirmation unit can, for example, use the emotion estimation function to analyze the time period when the resident is relaxed and suggest that time period as the available receiving time. For example, it can prioritize suggesting the time period when the resident is relaxed. The available receiving time confirmation unit can also analyze the resident's past emotion data to identify the time period when the resident is relaxed. For example, it can suggest the available receiving time based on the time period when the resident is relaxed. In this way, it can suggest the time period when the resident is relaxed.

[0069] The delivery time proposal unit can calculate the optimal delivery time in real time based on traffic conditions and weather data. For example, the delivery time proposal unit acquires traffic condition data in real time, and the generation AI calculates the optimal delivery time. For example, the delivery time is adjusted taking into account traffic congestion information. The delivery time proposal unit can also analyze weather data, and the generation AI can calculate the optimal delivery time. For example, the delivery time is adjusted to avoid bad weather. This allows the optimal delivery time to be calculated taking into account traffic conditions and weather data.

[0070] The delivery time suggestion unit can learn the resident's lifestyle and suggest the most convenient time for receiving the package. The delivery time suggestion unit, for example, analyzes the lifestyle of the resident at the delivery destination and suggests the most convenient time for receiving the package. For example, if the resident is at home at the same time every day, that time will be suggested as a priority. The delivery time suggestion unit can also analyze the resident's past receiving history and learn the resident's lifestyle. For example, if the resident often receives packages on a specific day of the week or at a specific time, that time will be suggested. This makes it possible to suggest the optimal delivery time based on the resident's lifestyle.

[0071] The delivery time suggestion unit can use the emotion estimation function to estimate the delivery time that will most satisfy the resident and suggest that time slot. The delivery time suggestion unit can, for example, use the emotion estimation function to analyze the delivery time that will most satisfy the resident and suggest that time slot. For example, it can prioritize suggesting time slots when the resident is relaxed. The delivery time suggestion unit can also analyze the resident's past emotion data to identify time slots that are highly satisfying. For example, it can suggest the most satisfying delivery time based on the time slots when the resident is relaxed. This makes it possible to suggest delivery times that will increase the resident's satisfaction.

[0072] The delivery time suggestion unit can analyze the resident's health data and suggest the optimal delivery time. The delivery time suggestion unit can, for example, analyze health data obtained from the resident's fitness tracker and suggest the optimal delivery time. For example, the delivery time can be set to avoid times when the resident is exercising. The delivery time suggestion unit can also analyze the resident's lifestyle based on the health data and suggest the optimal delivery time. For example, it can prioritize times when the resident is relaxing. This makes it possible to suggest the optimal delivery time based on the resident's health data.

[0073] The delivery time suggestion unit takes into consideration the schedules of other members in the resident's household and can suggest a time slot that is convenient for everyone to receive the package. For example, the delivery time suggestion unit analyzes the schedules of other members in the resident's household and suggests a time slot that is convenient for everyone to receive the package. For example, it prioritizes suggesting a time slot when all family members are at home. The delivery time suggestion unit can also link with the calendar apps of household members and automatically obtain their schedules. For example, it analyzes everyone's schedule and suggests the optimal delivery time. This makes it possible to suggest a time slot that is convenient for everyone in the household to receive the package.

[0074] The delivery time proposal unit can use the emotion estimation function to estimate the time period when the resident is most relaxed and propose that time period. The delivery time proposal unit can, for example, use the emotion estimation function to analyze the time period when the resident is relaxed and propose that time period as a time period when the resident is available for pickup. For example, the delivery time proposal unit can prioritize the time period when the resident is relaxed. The delivery time proposal unit can also analyze the resident's past emotion data to identify the time period when the resident is relaxed. For example, the delivery time proposal unit can propose the time period when the resident is relaxed based on the time period when the resident is relaxed. This makes it possible to propose the time period when the resident is relaxed.

[0075] The reminder notification unit can analyze the resident's past reminder response patterns and determine the optimal notification timing. The reminder notification unit, for example, analyzes the resident's past reminder response patterns and determines the optimal notification timing. For example, it identifies the time periods when the resident is most likely to respond to reminders. The reminder notification unit can also analyze the reminder notification history and identify the time periods when the resident is most likely to respond. For example, it prioritizes notifications during the time periods when the resident most frequently responds to reminders. This makes it possible to determine the optimal notification timing based on the resident's past response patterns.

[0076] The reminder notification unit can customize the content of reminder notifications to suit the resident's preferences. For example, the reminder notification unit analyzes the resident's past reminder notification history and generates notification content that suits the resident's preferences. For example, the reminder notification unit sends notifications using the resident's preferred language and expressions. The reminder notification unit can also collect the resident's preferences in advance through a questionnaire or settings screen and customize the content of reminder notifications based on that information. For example, the reminder notification unit can set the resident's preferred notification format and frequency. This makes it possible to send reminder notifications that suit the resident's preferences.

[0077] The reminder notification unit can use the emotion estimation function to send a reminder at the time when the resident is most relaxed. The reminder notification unit, for example, uses the emotion estimation function to analyze the time when the resident is relaxed and sends a reminder at that time. For example, the reminder notification unit prioritizes notifications for time periods when the resident is relaxed. The reminder notification unit can also analyze the resident's past emotion data to identify the time when the resident is relaxed. For example, the reminder notification unit sends a reminder based on the time periods when the resident is relaxed. This allows the reminder to be sent at the time when the resident is relaxed.

[0078] The reminder notification unit can also send reminder notifications to the resident's smartwatch or smart speaker. For example, the reminder notification unit can send reminder notifications to the resident's smartwatch so that they can be viewed at hand. For example, the reminder notification can be notified by vibration or sound. The reminder notification unit can also send reminder notifications to the resident's smart speaker so that the notification can be notified by voice. For example, the voice assistant can read the reminder aloud. This allows the resident to receive reminder notifications on multiple devices.

[0079] The reminder notification unit can automatically add reminder notifications to the resident's calendar app. For example, the reminder notification unit can automatically add reminder notifications to the resident's calendar app and display them as a schedule. For example, the reminder notification unit can register the scheduled delivery time on the calendar. The reminder notification unit can also use the calendar app's API to build a system that automatically adds reminder notifications. For example, the reminder notification unit can automatically register the scheduled delivery time on the calendar. This makes it possible to automatically add reminder notifications to the resident's calendar app.

[0080] The reminder notification unit can use the emotion estimation function to send reminders at times when the resident feels the least stress. The reminder notification unit, for example, uses the emotion estimation function to analyze the stress level of the resident and send reminders at times when the resident feels the least stress. For example, the reminder notification unit prioritizes notifications for times when the resident is relaxed. The reminder notification unit can also analyze the resident's past emotion data and identify times when the stress level is low. For example, the reminder notification unit sends reminders based on times when the resident is relaxed. This allows reminders to be sent at times when the resident feels the least stress.

[0081] The delivery status tracking unit can analyze the location information of the delivery person and traffic conditions in real time to provide an accurate arrival prediction. For example, the delivery status tracking unit obtains the location information of the delivery person in real time, and the generation AI analyzes the traffic conditions to provide an accurate arrival prediction. For example, the arrival time is adjusted taking into account traffic congestion information. The delivery status tracking unit can also integrate the location information of the delivery person with traffic condition data, and the generation AI can provide an accurate arrival prediction. For example, the delivery route is optimized and the arrival time is predicted. This makes it possible to analyze the location information of the delivery person and traffic conditions in real time to provide an accurate arrival prediction.

[0082] The delivery status tracking unit can display the delivery status in cooperation with the resident's smart home device. For example, the delivery status tracking unit can display the delivery status on the resident's smart doorbell and notify the resident that a delivery person is approaching. For example, the delivery status can be displayed in cooperation with the camera image of the smart doorbell. The delivery status tracking unit can also display the delivery status on the resident's smart home device and notify the resident of the delivery person's arrival in real time. For example, a smart speaker can notify the resident of the delivery status by voice. In this way, the delivery status can be displayed in cooperation with the resident's smart home device.

[0083] The delivery status tracking unit can use the emotion estimation function to notify the resident of the delivery status at a time when the resident feels most at ease. For example, the delivery status tracking unit can use the emotion estimation function to analyze the resident's sense of security and notify the resident of the delivery status at a time when the resident feels most at ease. For example, the delivery status tracking unit can prioritize notification of the time period when the resident is relaxed. The delivery status tracking unit can also analyze the resident's past emotion data and identify times when the resident feels most at ease. For example, the delivery status can be notified based on the time period when the resident is relaxed. This allows the delivery status to be notified at a time when the resident feels most at ease.

[0084] The delivery status tracking unit can also notify the resident's smart watch or smart speaker of the delivery status. For example, the delivery status tracking unit can notify the resident's smart watch of the delivery status so that the resident can check it at their fingertips. For example, the delivery status can be notified by vibration or sound. The delivery status tracking unit can also notify the resident's smart speaker of the delivery status so that the delivery status can be notified by voice. For example, a voice assistant can read out the delivery status. This allows the resident to check the delivery status on multiple devices.

[0085] The delivery status tracking unit can automatically add the delivery status to the resident's calendar app. For example, the delivery status tracking unit automatically adds the delivery status to the resident's calendar app and displays it as a schedule. For example, the scheduled delivery time is registered in the calendar. The delivery status tracking unit can also use the calendar app's API to build a system that automatically adds the delivery status. For example, the scheduled delivery time is automatically registered in the calendar. This makes it possible to automatically add the delivery status to the resident's calendar app.

[0086] The delivery status tracking unit can use the emotion estimation function to notify the resident of the delivery status at the time when the resident is most relaxed. For example, the delivery status tracking unit can use the emotion estimation function to analyze the time when the resident is relaxed and notify the resident of the delivery status at that time. For example, the delivery status tracking unit can prioritize notifying the resident of the delivery status during the time period when the resident is relaxed. The delivery status tracking unit can also analyze the resident's past emotion data and identify the time when the resident is relaxed. For example, the delivery status can be notified based on the time period when the resident is relaxed. This allows the delivery status to be notified at the time when the resident is relaxed.

[0087] The redelivery scheduling unit can analyze the resident's past redelivery history and propose the optimal redelivery time. The redelivery scheduling unit, for example, analyzes the resident's past redelivery history and proposes the optimal redelivery time. For example, it identifies the time period when the resident is most likely to receive a redelivery. The redelivery scheduling unit can also analyze the resident's lifestyle based on the redelivery history and propose the optimal redelivery time. For example, it prioritizes proposing time periods when the resident is at home. This makes it possible to propose the optimal redelivery time based on the resident's past redelivery history.

[0088] The redelivery scheduling unit can work with the resident's calendar app when redelivery is required, automatically obtain the schedule, and set the redelivery time. The redelivery scheduling unit can work with the resident's calendar app, for example, automatically obtain the schedule, and set the redelivery time. For example, it can analyze the schedule written on the calendar and set an available time slot as the redelivery time. The redelivery scheduling unit can also use the calendar app's API to build a system that automatically sets the redelivery time. For example, it can set redelivery to a time slot that does not overlap with the resident's schedule. This makes it possible to set the redelivery time in cooperation with the resident's calendar app.

[0089] The redelivery scheduling unit can use the emotion estimation function to estimate the redelivery time that will cause the resident the least stress and suggest that time slot. The redelivery scheduling unit can, for example, use the emotion estimation function to analyze the resident's stress level and suggest the redelivery time that will cause the resident the least stress. For example, it can prioritize suggesting time slots when the resident is relaxed. The redelivery scheduling unit can also analyze the resident's past emotion data and identify time slots when stress levels are low. For example, it can suggest a redelivery time based on time slots when the resident is relaxed. This makes it possible to suggest the redelivery time that will cause the resident the least stress.

[0090] When redelivery is required, the redelivery scheduling unit can work with the resident's smart home devices to automatically detect the time the resident will be at home and set the redelivery time. The redelivery scheduling unit can, for example, work with the resident's smart home devices to automatically detect the time the resident will be at home and set the redelivery time. For example, it can analyze data from smart doorbells or security cameras to identify the time periods when the resident will be at home. The redelivery scheduling unit can also analyze data from smart home devices to predict the time the resident will be at home and set the redelivery time. For example, it can identify the time periods when the resident will be at home based on the usage of smart thermostats or smart lights. This makes it possible to work with the resident's smart home devices to set the redelivery time.

[0091] The redelivery scheduling unit can analyze the resident's social media activity when redelivery is required and propose the optimal redelivery time. The redelivery scheduling unit, for example, analyzes the resident's social media activity and proposes the optimal redelivery time. For example, the redelivery scheduling unit identifies the time periods when the resident is likely to be at home based on the time periods when the resident frequently posts. The redelivery scheduling unit can also analyze social media location data to understand the resident's behavioral patterns and propose a redelivery time. For example, the redelivery time can be set based on the time periods when the resident is in a specific location. This makes it possible to propose the optimal redelivery time based on the resident's social media activity.

[0092] The redelivery scheduling unit can use the emotion estimation function to estimate the time period when the resident is most relaxed and suggest that time period. The redelivery scheduling unit can, for example, use the emotion estimation function to analyze the time period when the resident is relaxed and suggest that time period as the redelivery time. For example, it can prioritize suggesting time periods when the resident is relaxed. The redelivery scheduling unit can also analyze the resident's past emotion data to identify time periods when the resident is relaxed. For example, it can suggest a redelivery time based on the time periods when the resident is relaxed. This makes it possible to suggest time periods when the resident is most relaxed.

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

[0094] The delivery agent AI system can also be equipped with a health data analysis unit that analyzes residents' health data and suggests optimal delivery times. For example, it can analyze data obtained from residents' fitness trackers and schedule delivery times that avoid times when they are exercising. It can also analyze residents' lifestyles based on health data and prioritize times when they are relaxing. This makes it possible to suggest delivery times that take residents' health into consideration.

[0095] The delivery agent AI system can also be equipped with a household schedule adjustment unit that takes into account the schedules of other members of the household and suggests a time that is convenient for everyone to receive the package. For example, it can prioritize suggesting a time when all family members are at home. It can also link with the calendar apps of household members to automatically obtain everyone's schedules and suggest the optimal delivery time. This makes it possible to suggest a time that is convenient for everyone in the household to receive the package.

[0096] The Delivery Agent AI system can also be equipped with a smart home integration unit that connects with residents' smart home devices to automatically detect when they are at home and confirm when they are available to receive deliveries. For example, it can analyze data from smart doorbells and security cameras to identify when residents are at home. It can also predict when residents will be at home based on the usage of smart thermostats and smart lights. This makes it possible to automatically detect when residents are at home and confirm when they are available to receive deliveries.

[0097] The delivery agent AI system can also be equipped with a social media analysis unit that analyzes residents' social media activity and estimates available pickup times. For example, it can identify times when residents are likely to be at home based on the times when they frequently post. It can also analyze location data from social media to understand residents' behavioral patterns. This makes it possible to estimate available pickup times based on residents' social media activity.

[0098] The delivery agent AI system can further include an emotion estimation unit that estimates the resident's emotions and suggests the time period when the resident is most relaxed. For example, it can analyze the time period when the resident is most relaxed and suggest that time period as the time when the resident is most relaxed. It can also analyze the resident's past emotion data to identify the time period when the resident is most relaxed. This makes it possible to suggest the time period when the resident is most relaxed.

[0099] The delivery agent AI system can also be equipped with an emotion satisfaction estimation unit that estimates the resident's emotions and suggests the most satisfying delivery time. For example, it can analyze the time periods when the resident is relaxed and prioritize those time periods. It can also analyze the resident's past emotion data to identify time periods when satisfaction is high. This makes it possible to suggest delivery times that will increase the resident's satisfaction.

[0100] The delivery agent AI system can further include an emotion / stress estimation unit that estimates the resident's emotions and suggests the least stressful time of day. For example, it can analyze the resident's stress level and suggest the least stressful time of day. It can also analyze the resident's past emotion data to identify time periods when stress levels are low. This makes it possible to suggest time periods that will reduce the resident's stress.

[0101] The delivery agent AI system can further include an emotion / security estimation unit that estimates the resident's emotions and notifies the resident of the delivery status at the timing when they feel most at ease. For example, it can analyze the resident's sense of security and notify the resident of the delivery status at the timing when they feel most at ease. It can also analyze the resident's past emotion data and identify the timing when they feel most at ease. This allows the resident to be notified of the delivery status at the timing when they feel most at ease.

[0102] The delivery agent AI system can also be equipped with an emotion relaxation estimation unit that estimates the resident's emotions and notifies the delivery status at the time when the resident is most relaxed. For example, it can analyze the time periods when the resident is most relaxed and notify the delivery status at that time. It can also analyze the resident's past emotion data to identify the times when the resident is most relaxed. This allows the delivery status to be notified at the time when the resident is most relaxed.

[0103] The delivery agent AI system can further include an emotion / stress estimation unit that estimates the resident's emotions and suggests the least stressful redelivery time. For example, it can analyze the resident's stress level and suggest the least stressful redelivery time. It can also analyze the resident's past emotion data and identify time periods when stress levels are low. This makes it possible to suggest the least stressful redelivery time for the resident.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The receiving time confirmation unit checks the available receiving times at the delivery destination in advance. For example, it sends a message to the resident asking what time they can receive the package. It can also analyze past receiving history and behavioral patterns to predict the available receiving times. It can also link with a calendar app to automatically retrieve schedules and check the available receiving times. Step 2: The delivery time suggestion unit suggests the optimal delivery time based on the available pickup times confirmed by the available pickup time confirmation unit. For example, it schedules delivery times based on the resident's available pickup times. It can also calculate the optimal delivery time in real time, taking into account traffic conditions and weather data. It can also learn the resident's lifestyle and suggest the most convenient time to pick up the package.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0107] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] 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.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] 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.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] 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.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] 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.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] 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.

[0148] 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.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] 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.

[0156] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0157] 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.

[0158] 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).

[0159] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0160] 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."

[0161] 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.

[0162] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0167] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0168] 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.

[0169] 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.

[0170] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0171] 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.

[0172] 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. [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a receiving time confirmation unit that confirms in advance the receiving time of the delivery destination; a delivery time proposal unit that proposes an optimal delivery time based on the available receipt time confirmed by the available receipt time confirmation unit. A system characterized by:

2. The receiving time confirmation unit Send a message to the resident asking when they are available for pickup 2. The system of claim 1.

3. The delivery time proposal unit Schedule deliveries based on residents' availability 2. The system of claim 1.

4. The receiving time confirmation unit Analyze residents' past collection history and behavioral patterns to predict when they can receive their parcels 2. The system of claim 1.

5. The receiving time confirmation unit Link with residents' calendar apps to automatically retrieve schedules and check available pickup times 2. The system of claim 1.

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