System

Abstract

An object of a system according to an embodiment is to perform adjustment such that a user is at home at the time of delivery.SOLUTION: A system includes a determination unit, a notification unit, and an adjustment unit. The determination unit determines a scheduled delivery time. The notification unit notifies the user of the expected delivery time determined by the determination unit. The adjustment unit performs adjustment so that the user is at home based on the scheduled delivery time notified by the notification 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] With conventional technology, there was a problem that the user was often absent when the delivery was made, which resulted in the effort and cost of redelivery.

[0005] The system according to the embodiment aims to arrange for the user to be at home at the time of delivery. [Means for solving the problem]

[0006] A system according to an embodiment includes a determination unit, a notification unit, and an adjustment unit. The determination unit determines a scheduled delivery time. The notification unit notifies a user of the scheduled delivery time determined by the determination unit. The adjustment unit adjusts the delivery time so that the user will be at home based on the scheduled delivery time notified by the notification unit. [Effects of the Invention]

[0007] The system according to the embodiment can be arranged so that the user is present at the time of delivery. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A delivery system according to an embodiment of the present invention notifies a user of a scheduled delivery time in advance, thereby increasing the probability that the user will be at home. The delivery system determines a scheduled delivery time, notifies the user of that time, and adjusts the delivery time so that the user will be at home. For example, the delivery system determines the optimal delivery time by taking into account the delivery route, traffic conditions, and past delivery data. The system then notifies the user of the determined scheduled delivery time. The notification method is selected by the user, such as email, SMS, or a dedicated app. The user receives the notification and adjusts the time so that they will be at home. For example, if the user is at work, the user can temporarily return home at the scheduled delivery time. The user can also request a family member or friend to receive the package on their behalf. This reduces the probability of the user being at home at the time of delivery and eliminates the need for redelivery. This reduces the probability of the user being at home at the time of delivery and eliminates the need for redelivery. For example, eliminating the need for redelivery can improve the efficiency of delivery companies and reduce costs. Furthermore, convenience is improved for users, as they no longer have to go through the hassle of requesting redelivery and receiving the package. Furthermore, it is possible to add a function to the notification of the estimated delivery time that allows users to check the delivery person's current location and delivery progress in real time. This allows users to know the delivery person's arrival time more accurately and makes it easier to adjust when they will be at home. In this way, by informing users of the delivery time in advance, the rate at which the delivery person will be at home increases, improving convenience for both the delivery company and the user.

[0029] A delivery system according to an embodiment includes a determination unit, a notification unit, and an adjustment unit. The determination unit determines an estimated delivery time. The determination unit determines the optimal delivery time by taking into account, for example, the delivery route, traffic conditions, and past delivery data. For example, if the determination unit determines from past data that traffic congestion is likely to occur during a specific time period, it sets a delivery time that avoids that time period. The determination unit can also calculate the estimated delivery time using AI. For example, AI may analyze past delivery data and predict the optimal delivery time. The notification unit notifies the user of the estimated delivery time determined by the determination unit. The notification unit notifies the user by a method selected by the user, such as email, SMS, or a dedicated app. For example, if the user is using a dedicated app, the notification unit sends a push notification to the app. The notification unit may also have a function for checking the delivery person's current location and delivery progress in real time. For example, the notification unit may track the delivery person's current location using GPS and notify the user. The adjustment unit adjusts the delivery time so that the user is at home based on the estimated delivery time notified by the notification unit. For example, if the user is at work, the adjustment unit prompts the user to temporarily return home at the scheduled delivery time. The adjustment unit may also have a function to encourage the user to use a proxy delivery service or a delivery locker. For example, the adjustment unit may suggest the user to ask a family member or friend to receive the package on their behalf. In this way, the delivery system according to the embodiment notifies the user of the scheduled delivery time, increases the probability that the user will be at home, and eliminates the need for redelivery.

[0030] The decision unit can use AI to calculate the scheduled delivery time. AI calculates the scheduled delivery time using, for example, a machine learning algorithm or a neural network. For example, AI can learn from past delivery data and predict the optimal delivery time. AI can also obtain traffic and weather information in real time and adjust the delivery time based on that information. For example, AI can set a delivery time that avoids times when traffic congestion is likely to occur. AI can also analyze the user's home history and set the delivery time to the time when the user is most likely to be at home. In this way, the use of AI improves the accuracy of the scheduled delivery time.

[0031] The notification unit may have a function to check the delivery person's current location or delivery progress in real time. "Real time" refers to obtaining information updated, for example, every second or minute. The notification unit may, for example, use GPS to track the delivery person's current location and notify the user. For example, the notification unit may display the delivery person's current location on a map so that the user can understand the progress of the delivery. The notification unit may also display the delivery progress in real time and notify the user. For example, the notification unit may display the delivery person's current location and the time remaining until delivery. This allows the user to easily understand the progress of the delivery by checking the delivery person's current location and delivery progress in real time.

[0032] The adjustment unit can have a function to encourage the use of proxy reception or a delivery box. Proxy reception refers, for example, to the user having a family member or friend receive the package. The adjustment unit, for example, suggests the user to request proxy reception. For example, the adjustment unit notifies the user to have a family member or friend receive the package when the user is not at home. A delivery box refers, for example, to a dedicated box for receiving packages when the user is not at home. The adjustment unit, for example, encourages the user to use the delivery box. For example, the adjustment unit suggests that the user leave the package in the delivery box when the user is not at home. In this way, by encouraging the use of proxy reception or a delivery box, the user can receive packages even when the user is not at home.

[0033] The notification unit may have a security function that includes a facial photo or vehicle information of the delivery person. The security function refers to a function that allows the user to receive the package with peace of mind by notifying the user of the facial photo and vehicle information of the delivery person. The notification unit, for example, notifies the user of the facial photo of the delivery person. For example, the notification unit sends the facial photo of the delivery person by email or app notification. The notification unit can also notify the user of the vehicle information of the delivery person. For example, the notification unit notifies the user of the license plate and model of the delivery person's vehicle. In this way, by notifying the user of the facial photo and vehicle information of the delivery person, the user can receive the package with peace of mind.

[0034] The determination unit can analyze past delivery data and the user's at-home history to calculate an appropriate delivery time. Past delivery data refers to information such as delivery date and time and delivery success rate. The determination unit, for example, identifies a time period when the user is at home from the past delivery data and sets the delivery time to that time period. For example, the determination unit analyzes the user's at-home history and selects a time period with the highest at-home rate. The determination unit can also combine past delivery data and at-home history to calculate the optimal delivery time. In this way, the optimal delivery time can be calculated by analyzing past data and at-home history.

[0035] The determination unit can determine the delivery time by taking into account weather or traffic conditions in real time, in addition to optimizing the delivery route. Weather refers to, for example, weather forecast data or information acquired from a weather sensor. Traffic conditions refer to, for example, traffic congestion information or traffic accident information. The determination unit determines the optimal delivery route and time based on, for example, real-time traffic information. For example, the determination unit takes into account weather information and sets a delivery time that avoids bad weather. The determination unit can also determine the optimal delivery time by comprehensively considering the optimization of the delivery route, weather, and traffic conditions. In this way, delivery delays can be prevented by taking into account weather and traffic conditions.

[0036] When determining the scheduled delivery time, the determination unit can select an appropriate time by referring to the user's schedule information. Schedule information refers to, for example, data from a calendar app or a planner. The determination unit, for example, refers to the user's calendar information and sets the delivery time to a time slot that is available. For example, the determination unit selects a time slot that does not have any important appointments from the user's schedule information. The determination unit can also suggest an optimal delivery time based on the user's schedule information. This makes it possible to make deliveries that suit the user's convenience by referring to the user's schedule information.

[0037] When determining the scheduled delivery time, the determination unit can select an appropriate time by taking into account the user's geographical location information. Geographical location information refers to, for example, GPS data or location information services. The determination unit sets the optimal delivery time based on, for example, the user's current location. For example, the determination unit can select a delivery time that avoids traffic conditions by taking into account the user's geographical location information. The determination unit can also determine the optimal delivery route and time based on the user's geographical location information. This allows the optimal delivery time to be selected by taking into account the user's geographical location information.

[0038] The determination unit can analyze the user's social media activity and reflect related information when determining the scheduled delivery time. Social media activity refers to, for example, posted content and activity logs. The determination unit can, for example, analyze the user's social media posts and set the optimal delivery time. For example, the determination unit can determine the delivery time based on the user's check-in information on social media. The determination unit can also adjust the delivery time by taking into account the activity of the user's friends on social media. In this way, by analyzing social media activity, it is possible to set a delivery time that matches the user's behavior.

[0039] When determining the scheduled delivery time, the determination unit can customize an appropriate time by reflecting the user's past feedback. Past feedback refers to, for example, survey results, review comments, etc. The determination unit sets the optimal delivery time based on, for example, the user's past feedback. For example, the determination unit selects a delivery time that avoids a specific time period based on the user's past feedback. The determination unit can also customize the delivery time by reflecting the user's past feedback. In this way, by reflecting the past feedback, it is possible to set a delivery time that meets the user's wishes.

[0040] When notifying, the notification unit can display the delivery person's current location and delivery progress status in real time, in addition to the scheduled delivery time. "Real time" refers to obtaining information updated, for example, on a second-by-second or minute-by-minute basis. The notification unit, for example, displays the delivery person's current location in real time along with the scheduled delivery time. For example, the notification unit displays the delivery progress status in real time and notifies the user. The notification unit can also combine the delivery person's current location and the delivery progress status and notify the user. In this way, by displaying the delivery person's current location and delivery progress status in real time, the user can more easily grasp the progress of the delivery.

[0041] When notifying, the notification unit can select the optimal notification method by referring to the user's past notification history. Notification history refers to, for example, past notification dates and times and notification contents. The notification unit selects the optimal notification method, for example, based on the user's past notification history. For example, the notification unit prioritizes the notification method that the user has used favorably in the past. The notification unit can also analyze the user's past notification history and suggest the optimal notification method. In this way, the optimal notification method for the user can be selected by referring to the past notification history.

[0042] The notification unit can set an appropriate notification timing by taking into consideration the user's schedule information when sending a notification. Schedule information refers to, for example, data from a calendar app or a notebook. The notification unit, for example, references the user's calendar information and sets the optimal notification timing. For example, the notification unit may send a notification during a time period when the user has no important appointments based on the user's schedule information. The notification unit can also adjust the notification timing based on the user's schedule information. This allows the optimal notification timing to be set by taking into consideration the user's schedule information.

[0043] The notification unit can select an appropriate notification method by taking into account the user's geographical location information when sending a notification. Geographical location information refers to, for example, GPS data or location information services. The notification unit selects the optimal notification method based on, for example, the user's current location. For example, the notification unit sets the optimal notification timing by taking into account the user's geographical location information. The notification unit can also suggest the optimal notification method based on the user's geographical location information. This allows the optimal notification method to be selected by taking into account the user's geographical location information.

[0044] The notification unit can analyze the user's social media activity at the time of notification and include relevant information in the notification. Social media activity refers to, for example, posted content and activity logs. The notification unit can analyze, for example, the user's social media posted content and include relevant information in the notification. For example, the notification unit can customize the notification content based on the user's social media check-in information. The notification unit can also include relevant information in the notification by referring to the activity of the user's friends on social media. In this way, by analyzing social media activity, information related to the user can be included in the notification.

[0045] The notification unit can customize the notification method by reflecting the user's past feedback when notifying the user. Past feedback refers to, for example, survey results, review comments, etc. The notification unit sets the optimal notification method based on the user's past feedback. For example, the notification unit preferentially selects a specific notification method based on the user's past feedback. The notification unit can also customize the notification method by reflecting the user's past feedback. In this way, the optimal notification method can be provided to the user by reflecting the past feedback.

[0046] During adjustment, the adjustment unit can analyze the user's past home-at-home history to select an appropriate adjustment method. Home-at-home history refers to, for example, past home-at-home time periods and number of days at home. The adjustment unit selects the optimal home-at-home adjustment method, for example, based on the user's past home-at-home history. For example, the adjustment unit suggests a method for being at home during a specific time period based on the user's past home-at-home history. The adjustment unit can also analyze the user's past home-at-home history to suggest the optimal home-at-home adjustment method. In this way, the optimal home-at-home adjustment method can be selected by analyzing the past home-at-home history.

[0047] When making adjustments, the adjustment unit can make appropriate at-home adjustments by taking into account the user's current living situation and schedule. Living situation refers to, for example, family composition and daily activity patterns. Schedule refers to, for example, data from a calendar app or a notebook. The adjustment unit, for example, refers to the user's calendar information and proposes an optimal at-home adjustment method. For example, the adjustment unit selects a home adjustment method by taking into account the user's current living situation. The adjustment unit can also propose a home adjustment method based on the user's schedule information. This makes it possible to make optimal at-home adjustments for the user by taking into account the user's current living situation and schedule.

[0048] The adjustment unit can improve the home adjustment method by reflecting user feedback during adjustment. Feedback refers to, for example, survey results, review comments, etc. The adjustment unit improves the home adjustment method based on, for example, user feedback. For example, the adjustment unit selects the optimal home adjustment method from the user's past feedback. The adjustment unit can also customize the home adjustment method by reflecting user feedback. In this way, the home adjustment method can be improved by reflecting feedback.

[0049] During adjustment, the adjustment unit can select an appropriate home adjustment method by taking into account the user's geographical location information. Geographical location information refers to, for example, GPS data or location information services. The adjustment unit selects the optimal home adjustment method, for example, based on the user's current location. For example, the adjustment unit sets the optimal home adjustment timing by taking into account the user's geographical location information. The adjustment unit can also suggest the optimal home adjustment method based on the user's geographical location information. In this way, the optimal home adjustment method can be selected by taking into account the geographical location information.

[0050] During the adjustment, the adjustment unit can analyze the user's social media activity and reflect related information in the at-home adjustment. Social media activity refers to, for example, posted content and activity logs. The adjustment unit, for example, analyzes the user's social media posts and reflects related information in the at-home adjustment. For example, the adjustment unit customizes the at-home adjustment method based on the user's social media check-in information. The adjustment unit can also reflect related information in the at-home adjustment by referring to the activity of the user's friends on social media. In this way, by analyzing social media activity, it is possible to provide the optimal at-home adjustment for the user.

[0051] During adjustment, the adjustment unit can customize the home adjustment method by reflecting the user's past feedback. Feedback refers to, for example, survey results, review comments, etc. The adjustment unit sets the optimal home adjustment method, for example, based on the user's past feedback. For example, the adjustment unit preferentially selects a specific home adjustment method from the user's past feedback. The adjustment unit can also customize the home adjustment method by reflecting the user's past feedback. In this way, by reflecting the past feedback, the optimal home adjustment method can be provided to the user.

[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 notification unit can customize the design and sound of notifications according to the user's preferences. For example, the user can select their favorite color or theme and change the notification design. It is also possible to set the notification sound to music or sound effects selected by the user. Furthermore, the content of notifications can be customized to suit the user's preferences, providing more personalized notifications.

[0054] The adjustment unit can adjust the time at home by taking into account the user's family composition and whether or not they have pets. For example, if the user has small children, the delivery time can be set to avoid times when the children are taking a nap. Also, if the user has pets, the delivery time can be adjusted to take into account the time it takes to walk the pet. Furthermore, by setting the delivery time to prioritize times when all family members are at home, the reliability of receipt can be increased.

[0055] The adjustment unit can analyze the user's past purchase history and propose the optimal at-home adjustment method. For example, if the user frequently purchases a specific product, the delivery time of that product can be adjusted as a priority. It is also possible to select the most convenient time for the user to receive the product by referring to the delivery times of products the user has received in the past. Furthermore, by adjusting the delivery time of related products based on the user's purchase history, convenience for the user can be improved.

[0056] The notification unit can suggest an optimal delivery route based on the user's geographical location information. For example, if the user frequently visits a specific location, the notification unit can set a delivery route taking that location into consideration. Also, if the user is traveling, it can suggest the delivery point closest to the user's current location. Furthermore, by suggesting an optimal delivery route that takes traffic conditions into consideration based on the user's geographical location information, delivery efficiency can be improved.

[0057] The determination unit can analyze the user's social media activity and suggest the optimal delivery time. For example, if the user has plans to attend a specific event, the delivery time can be set to avoid that time period. It can also suggest the optimal delivery time based on the location where the user has checked in on social media. Furthermore, by analyzing the user's social media activity and reflecting related information in determining the delivery time, it is possible to improve user convenience.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The decision unit determines the scheduled delivery time. The decision unit determines the optimal delivery time by taking into account the delivery route, traffic conditions, and past delivery data. For example, if it is known that traffic congestion is likely to occur during a certain time period, the delivery time can be set to avoid that time period. It is also possible to use AI to calculate the scheduled delivery time. Step 2: The notification unit notifies the user of the estimated delivery time determined by the determination unit. The notification unit notifies the user by the method selected by the user, such as email, SMS, or a dedicated app. For example, if a dedicated app is being used, a push notification is sent to the app. The notification unit can also be equipped with a function that allows the user to check the delivery person's current location and delivery progress in real time. Step 3: The adjustment unit adjusts the time so that the user is at home based on the scheduled delivery time notified by the notification unit. If the user is at work, the adjustment unit prompts the user to temporarily return home at the scheduled delivery time. The adjustment unit can also be equipped with a function to encourage the user to use a proxy delivery service or a delivery locker. For example, the adjustment unit suggests that the user ask a family member or friend to receive the package on their behalf.

[0060] (Example 2) A delivery system according to an embodiment of the present invention notifies a user of a scheduled delivery time in advance, thereby increasing the probability that the user will be at home. The delivery system determines a scheduled delivery time, notifies the user of that time, and adjusts the delivery time so that the user will be at home. For example, the delivery system determines the optimal delivery time by taking into account the delivery route, traffic conditions, and past delivery data. The system then notifies the user of the determined scheduled delivery time. The notification method is selected by the user, such as email, SMS, or a dedicated app. The user receives the notification and adjusts the time so that they will be at home. For example, if the user is at work, the user can temporarily return home at the scheduled delivery time. The user can also request a family member or friend to receive the package on their behalf. This reduces the probability of the user being at home at the time of delivery and eliminates the need for redelivery. This reduces the probability of the user being at home at the time of delivery and eliminates the need for redelivery. For example, eliminating the need for redelivery can improve the efficiency of delivery companies and reduce costs. Furthermore, convenience is improved for users, as they no longer have to go through the hassle of requesting redelivery and receiving the package. Furthermore, it is possible to add a function to the notification of the estimated delivery time that allows users to check the delivery person's current location and delivery progress in real time. This allows users to know the delivery person's arrival time more accurately and makes it easier to adjust when they will be at home. In this way, by informing users of the delivery time in advance, the rate at which the delivery person will be at home increases, improving convenience for both the delivery company and the user.

[0061] A delivery system according to an embodiment includes a determination unit, a notification unit, and an adjustment unit. The determination unit determines an estimated delivery time. The determination unit determines the optimal delivery time by taking into account, for example, the delivery route, traffic conditions, and past delivery data. For example, if the determination unit determines from past data that traffic congestion is likely to occur during a specific time period, it sets a delivery time that avoids that time period. The determination unit can also calculate the estimated delivery time using AI. For example, AI may analyze past delivery data and predict the optimal delivery time. The notification unit notifies the user of the estimated delivery time determined by the determination unit. The notification unit notifies the user by a method selected by the user, such as email, SMS, or a dedicated app. For example, if the user is using a dedicated app, the notification unit sends a push notification to the app. The notification unit may also have a function for checking the delivery person's current location and delivery progress in real time. For example, the notification unit may track the delivery person's current location using GPS and notify the user. The adjustment unit adjusts the delivery time so that the user is at home based on the estimated delivery time notified by the notification unit. For example, if the user is at work, the adjustment unit prompts the user to temporarily return home at the scheduled delivery time. The adjustment unit may also have a function to encourage the user to use a proxy delivery service or a delivery locker. For example, the adjustment unit may suggest the user to ask a family member or friend to receive the package on their behalf. In this way, the delivery system according to the embodiment notifies the user of the scheduled delivery time, increases the probability that the user will be at home, and eliminates the need for redelivery.

[0062] The decision unit can use AI to calculate the scheduled delivery time. AI calculates the scheduled delivery time using, for example, a machine learning algorithm or a neural network. For example, AI can learn from past delivery data and predict the optimal delivery time. AI can also obtain traffic and weather information in real time and adjust the delivery time based on that information. For example, AI can set a delivery time that avoids times when traffic congestion is likely to occur. AI can also analyze the user's home history and set the delivery time to the time when the user is most likely to be at home. In this way, the use of AI improves the accuracy of the scheduled delivery time.

[0063] The notification unit may have a function to check the delivery person's current location or delivery progress in real time. "Real time" refers to obtaining information updated, for example, every second or minute. The notification unit may, for example, use GPS to track the delivery person's current location and notify the user. For example, the notification unit may display the delivery person's current location on a map so that the user can understand the progress of the delivery. The notification unit may also display the delivery progress in real time and notify the user. For example, the notification unit may display the delivery person's current location and the time remaining until delivery. This allows the user to easily understand the progress of the delivery by checking the delivery person's current location and delivery progress in real time.

[0064] The adjustment unit can have a function to encourage the use of proxy reception or a delivery box. Proxy reception refers, for example, to the user having a family member or friend receive the package. The adjustment unit, for example, suggests the user to request proxy reception. For example, the adjustment unit notifies the user to have a family member or friend receive the package when the user is not at home. A delivery box refers, for example, to a dedicated box for receiving packages when the user is not at home. The adjustment unit, for example, encourages the user to use the delivery box. For example, the adjustment unit suggests that the user leave the package in the delivery box when the user is not at home. In this way, by encouraging the use of proxy reception or a delivery box, the user can receive packages even when the user is not at home.

[0065] The notification unit may have a security function that includes a facial photo or vehicle information of the delivery person. The security function refers to a function that allows the user to receive the package with peace of mind by notifying the user of the facial photo and vehicle information of the delivery person. The notification unit, for example, notifies the user of the facial photo of the delivery person. For example, the notification unit sends the facial photo of the delivery person by email or app notification. The notification unit can also notify the user of the vehicle information of the delivery person. For example, the notification unit notifies the user of the license plate and model of the delivery person's vehicle. In this way, by notifying the user of the facial photo and vehicle information of the delivery person, the user can receive the package with peace of mind.

[0066] The determination unit can estimate the user's emotions and adjust the scheduled delivery time based on the estimated user emotions. Estimating emotions refers to determining the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the determination unit sets the scheduled delivery time to a time slot when the user can relax. For example, if the user is in a hurry, the determination unit prioritizes setting the earliest delivery time. Furthermore, if the user is relaxed, the determination unit can flexibly adjust the scheduled delivery time to match the user's schedule. In this way, adjusting the scheduled delivery time based on the user's emotions improves user satisfaction.

[0067] The determination unit can analyze past delivery data and the user's at-home history to calculate an appropriate delivery time. Past delivery data refers to information such as delivery date and time and delivery success rate. The determination unit, for example, identifies a time period when the user is at home from the past delivery data and sets the delivery time to that time period. For example, the determination unit analyzes the user's at-home history and selects a time period with the highest at-home rate. The determination unit can also combine past delivery data and at-home history to calculate the optimal delivery time. In this way, the optimal delivery time can be calculated by analyzing past data and at-home history.

[0068] The determination unit can determine the delivery time by taking into account weather or traffic conditions in real time, in addition to optimizing the delivery route. Weather refers to, for example, weather forecast data or information acquired from a weather sensor. Traffic conditions refer to, for example, traffic congestion information or traffic accident information. The determination unit determines the optimal delivery route and time based on, for example, real-time traffic information. For example, the determination unit takes into account weather information and sets a delivery time that avoids bad weather. The determination unit can also determine the optimal delivery time by comprehensively considering the optimization of the delivery route, weather, and traffic conditions. In this way, delivery delays can be prevented by taking into account weather and traffic conditions.

[0069] When determining the scheduled delivery time, the determination unit can select an appropriate time by referring to the user's schedule information. Schedule information refers to, for example, data from a calendar app or a planner. The determination unit, for example, refers to the user's calendar information and sets the delivery time to a time slot that is available. For example, the determination unit selects a time slot that does not have any important appointments from the user's schedule information. The determination unit can also suggest an optimal delivery time based on the user's schedule information. This makes it possible to make deliveries that suit the user's convenience by referring to the user's schedule information.

[0070] The determination unit can estimate the user's emotions and prioritize delivery times based on the estimated user emotions. Estimating emotions refers to determining the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the determination unit sets the delivery time as the highest priority. For example, if the user is relaxed, the determination unit sets the delivery time in accordance with other deliveries. Furthermore, if the user is in a hurry, the determination unit can also prioritize the earliest delivery time. This improves user satisfaction by prioritizing delivery times based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0071] When determining the scheduled delivery time, the determination unit can select an appropriate time by taking into account the user's geographical location information. Geographical location information refers to, for example, GPS data or location information services. The determination unit sets the optimal delivery time based on, for example, the user's current location. For example, the determination unit can select a delivery time that avoids traffic conditions by taking into account the user's geographical location information. The determination unit can also determine the optimal delivery route and time based on the user's geographical location information. This allows the optimal delivery time to be selected by taking into account the user's geographical location information.

[0072] The determination unit can analyze the user's social media activity and reflect related information when determining the scheduled delivery time. Social media activity refers to, for example, posted content and activity logs. The determination unit can, for example, analyze the user's social media posts and set the optimal delivery time. For example, the determination unit can determine the delivery time based on the user's check-in information on social media. The determination unit can also adjust the delivery time by taking into account the activity of the user's friends on social media. In this way, by analyzing social media activity, it is possible to set a delivery time that matches the user's behavior.

[0073] When determining the scheduled delivery time, the determination unit can customize an appropriate time by reflecting the user's past feedback. Past feedback refers to, for example, survey results, review comments, etc. The determination unit sets the optimal delivery time based on, for example, the user's past feedback. For example, the determination unit selects a delivery time that avoids a specific time period based on the user's past feedback. The determination unit can also customize the delivery time by reflecting the user's past feedback. In this way, by reflecting the past feedback, it is possible to set a delivery time that meets the user's wishes.

[0074] The notification unit can estimate the user's emotions and adjust the notification presentation method based on the estimated user emotions. Estimating emotions refers to determining the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the notification unit can send a simple and easy-to-understand notification. For example, if the user is relaxed, the notification unit can send a notification containing detailed information. Furthermore, if the user is in a hurry, the notification unit can send a concise notification that focuses on the main points. This allows the user to better understand the notification by adjusting the notification presentation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] When notifying, the notification unit can display the delivery person's current location and delivery progress status in real time, in addition to the scheduled delivery time. "Real time" refers to obtaining information updated, for example, on a second-by-second or minute-by-minute basis. The notification unit, for example, displays the delivery person's current location in real time along with the scheduled delivery time. For example, the notification unit displays the delivery progress status in real time and notifies the user. The notification unit can also combine the delivery person's current location and the delivery progress status and notify the user. In this way, by displaying the delivery person's current location and delivery progress status in real time, the user can more easily grasp the progress of the delivery.

[0076] When notifying, the notification unit can select the optimal notification method by referring to the user's past notification history. Notification history refers to, for example, past notification dates and times and notification contents. The notification unit selects the optimal notification method, for example, based on the user's past notification history. For example, the notification unit prioritizes the notification method that the user has used favorably in the past. The notification unit can also analyze the user's past notification history and suggest the optimal notification method. In this way, the optimal notification method for the user can be selected by referring to the past notification history.

[0077] The notification unit can set an appropriate notification timing by taking into consideration the user's schedule information when sending a notification. Schedule information refers to, for example, data from a calendar app or a notebook. The notification unit, for example, references the user's calendar information and sets the optimal notification timing. For example, the notification unit may send a notification during a time period when the user has no important appointments based on the user's schedule information. The notification unit can also adjust the notification timing based on the user's schedule information. This allows the optimal notification timing to be set by taking into consideration the user's schedule information.

[0078] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated user emotions. Estimating emotions refers to determining the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the notification unit sets a high priority for notifications when the user is feeling stressed. For example, when the user is relaxed, the notification unit sets the priority in coordination with other notifications. The notification unit can also prioritize sending the most important notifications when the user is in a hurry. In this way, by determining the priority of notifications based on the user's emotions, important notifications can be sent preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] The notification unit can select an appropriate notification method by taking into account the user's geographical location information when sending a notification. Geographical location information refers to, for example, GPS data or location information services. The notification unit selects the optimal notification method based on, for example, the user's current location. For example, the notification unit sets the optimal notification timing by taking into account the user's geographical location information. The notification unit can also suggest the optimal notification method based on the user's geographical location information. This allows the optimal notification method to be selected by taking into account the user's geographical location information.

[0080] The notification unit can analyze the user's social media activity at the time of notification and include relevant information in the notification. Social media activity refers to, for example, posted content and activity logs. The notification unit can analyze, for example, the user's social media posted content and include relevant information in the notification. For example, the notification unit can customize the notification content based on the user's social media check-in information. The notification unit can also include relevant information in the notification by referring to the activity of the user's friends on social media. In this way, by analyzing social media activity, information related to the user can be included in the notification.

[0081] The notification unit can customize the notification method by reflecting the user's past feedback when notifying the user. Past feedback refers to, for example, survey results, review comments, etc. The notification unit sets the optimal notification method based on the user's past feedback. For example, the notification unit preferentially selects a specific notification method based on the user's past feedback. The notification unit can also customize the notification method by reflecting the user's past feedback. In this way, the optimal notification method can be provided to the user by reflecting the past feedback.

[0082] The adjustment unit can estimate the user's emotions and adjust the home adjustment method based on the estimated user emotions. Estimating emotions refers to determining the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the adjustment unit simplifies the home adjustment method when the user is stressed. For example, the adjustment unit provides detailed home adjustment options when the user is relaxed. The adjustment unit can also suggest a method for quickly adjusting the home adjustment method when the user is in a hurry. This improves user satisfaction by adjusting the home adjustment method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] During adjustment, the adjustment unit can analyze the user's past home-at-home history to select an appropriate adjustment method. Home-at-home history refers to, for example, past home-at-home time periods and number of days at home. The adjustment unit selects the optimal home-at-home adjustment method, for example, based on the user's past home-at-home history. For example, the adjustment unit suggests a method for being at home during a specific time period based on the user's past home-at-home history. The adjustment unit can also analyze the user's past home-at-home history to suggest the optimal home-at-home adjustment method. In this way, the optimal home-at-home adjustment method can be selected by analyzing the past home-at-home history.

[0084] When making adjustments, the adjustment unit can make appropriate at-home adjustments by taking into account the user's current living situation and schedule. Living situation refers to, for example, family composition and daily activity patterns. Schedule refers to, for example, data from a calendar app or a notebook. The adjustment unit, for example, refers to the user's calendar information and proposes an optimal at-home adjustment method. For example, the adjustment unit selects a home adjustment method by taking into account the user's current living situation. The adjustment unit can also propose a home adjustment method based on the user's schedule information. This makes it possible to make optimal at-home adjustments for the user by taking into account the user's current living situation and schedule.

[0085] The adjustment unit can improve the home adjustment method by reflecting user feedback during adjustment. Feedback refers to, for example, survey results, review comments, etc. The adjustment unit improves the home adjustment method based on, for example, user feedback. For example, the adjustment unit selects the optimal home adjustment method from the user's past feedback. The adjustment unit can also customize the home adjustment method by reflecting user feedback. In this way, the home adjustment method can be improved by reflecting feedback.

[0086] The adjustment unit can estimate the user's emotions and determine the priority of home adjustments based on the estimated user emotions. Estimating emotions refers to determining the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the adjustment unit sets a high priority for home adjustments. For example, if the user is relaxed, the adjustment unit sets the priority by adjusting it with other adjustments. Furthermore, if the user is in a hurry, the adjustment unit can also prioritize the most important home adjustments. In this way, by determining the priority of home adjustments based on the user's emotions, important home adjustments can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] During adjustment, the adjustment unit can select an appropriate home adjustment method by taking into account the user's geographical location information. Geographical location information refers to, for example, GPS data or location information services. The adjustment unit selects the optimal home adjustment method, for example, based on the user's current location. For example, the adjustment unit sets the optimal home adjustment timing by taking into account the user's geographical location information. The adjustment unit can also suggest the optimal home adjustment method based on the user's geographical location information. In this way, the optimal home adjustment method can be selected by taking into account the geographical location information.

[0088] During the adjustment, the adjustment unit can analyze the user's social media activity and reflect related information in the at-home adjustment. Social media activity refers to, for example, posted content and activity logs. The adjustment unit, for example, analyzes the user's social media posts and reflects related information in the at-home adjustment. For example, the adjustment unit customizes the at-home adjustment method based on the user's social media check-in information. The adjustment unit can also reflect related information in the at-home adjustment by referring to the activity of the user's friends on social media. In this way, by analyzing social media activity, it is possible to provide the optimal at-home adjustment for the user.

[0089] During adjustment, the adjustment unit can customize the home adjustment method by reflecting the user's past feedback. Feedback refers to, for example, survey results, review comments, etc. The adjustment unit sets the optimal home adjustment method, for example, based on the user's past feedback. For example, the adjustment unit preferentially selects a specific home adjustment method from the user's past feedback. The adjustment unit can also customize the home adjustment method by reflecting the user's past feedback. In this way, by reflecting the past feedback, the optimal home adjustment method can be provided to the user. === Hard Collateral 1-1 === Each of the multiple elements including the determination unit, notification unit, and adjustment unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines the optimal delivery time by taking into account the delivery route, traffic conditions, and past delivery data. The notification unit is realized, for example, by the control unit 46A of the smart device 14, and notifies the user of the scheduled delivery time by email, SMS, a dedicated app, or the like. The adjustment unit is realized, for example, by the control unit 46A of the smart device 14, and adjusts the delivery time so that the user is at home. === Hard Collateral 1-2 === Each of the multiple elements including the determination unit, notification unit, and adjustment unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines the optimal delivery time by taking into account the delivery route, traffic conditions, and past delivery data. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214, and notifies the user of the scheduled delivery time by email, SMS, a dedicated app, or the like. The adjustment unit is realized, for example, by the control unit 46A of the smart glasses 214, and adjusts the time so that the user is at home. === Hard Collateral 1-3 === Each of the multiple elements including the determination unit, notification unit, and adjustment unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines the optimal delivery time by taking into account the delivery route, traffic conditions, and past delivery data. The notification unit is realized, for example, by the control unit 46A of the headset type terminal 314, and notifies the user of the scheduled delivery time by email, SMS, a dedicated app, or the like. The adjustment unit is realized, for example, by the control unit 46A of the headset type terminal 314, and adjusts the delivery time so that the user is at home. === Hard Collateral 1-4 === Each of the multiple elements including the determination unit, notification unit, and adjustment unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines the optimal delivery time by taking into account the delivery route, traffic conditions, and past delivery data. The notification unit is realized, for example, by the control unit 46A of the robot 414, and notifies the user of the scheduled delivery time by email, SMS, a dedicated app, or the like. The adjustment unit is realized, for example, by the control unit 46A of the robot 414, and adjusts the delivery time so that the user is at home.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The determination unit can also adjust the scheduled delivery time taking into account the user's health data. For example, it can set the delivery time to a time when the user is most relaxed based on heart rate and sleep data obtained from the user's wearable device. It can also delay the delivery time if data indicates that the user is in poor health. Furthermore, it can support the user's health by analyzing the user's health data and delivering at a time when the user is least stressed.

[0092] The notification unit can customize the design and sound of notifications according to the user's preferences. For example, the user can select their favorite color or theme and change the notification design. It is also possible to set the notification sound to music or sound effects selected by the user. Furthermore, the content of notifications can be customized to suit the user's preferences, providing more personalized notifications.

[0093] The adjustment unit can adjust the time at home by taking into account the user's family composition and whether or not they have pets. For example, if the user has small children, the delivery time can be set to avoid times when the children are taking a nap. Also, if the user has pets, the delivery time can be adjusted to take into account the time it takes to walk the pet. Furthermore, by setting the delivery time to prioritize times when all family members are at home, the reliability of receipt can be increased.

[0094] The determination unit can adjust the scheduled delivery time taking into account the user's hobbies and interests. For example, if the user likes watching sports, the delivery time can be set to avoid game times. Also, if the user enjoys watching movies, the delivery time can be adjusted taking into account movie screening times. Furthermore, by referring to schedules of events and activities related to the user's hobbies and setting the delivery time accordingly, user satisfaction can be improved.

[0095] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated user's emotions. For example, if the user is feeling stressed, the notification content can be simplified and only important information can be provided. Alternatively, if the user is relaxed, it is possible to send a notification containing detailed information. Furthermore, if the user is in a hurry, the notification can be set to a high priority so that the user can respond immediately, thereby improving user convenience.

[0096] The adjustment unit can analyze the user's past purchase history and propose the optimal at-home adjustment method. For example, if the user frequently purchases a specific product, the delivery time of that product can be adjusted as a priority. It is also possible to select the most convenient time for the user to receive the product by referring to the delivery times of products the user has received in the past. Furthermore, by adjusting the delivery time of related products based on the user's purchase history, convenience for the user can be improved.

[0097] The determination unit can estimate the user's emotions and select a delivery time notification method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and easy-to-understand notification method can be selected. Alternatively, if the user is relaxed, a notification method including detailed information can be selected. Furthermore, if the user is in a hurry, a method that delivers a notification immediately can be selected, thereby improving user satisfaction.

[0098] The notification unit can suggest an optimal delivery route based on the user's geographical location information. For example, if the user frequently visits a specific location, the notification unit can set a delivery route taking that location into consideration. Also, if the user is traveling, it can suggest the delivery point closest to the user's current location. Furthermore, by suggesting an optimal delivery route that takes traffic conditions into consideration based on the user's geographical location information, delivery efficiency can be improved.

[0099] The adjustment unit can estimate the user's emotions and select a notification method for home adjustment based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and easy-to-understand notification method can be selected. Also, if the user is relaxed, a notification method including detailed information can be selected. Furthermore, if the user is in a hurry, a method that delivers a notification immediately can be selected, thereby improving user satisfaction.

[0100] The determination unit can analyze the user's social media activity and suggest the optimal delivery time. For example, if the user has plans to attend a specific event, the delivery time can be set to avoid that time period. It can also suggest the optimal delivery time based on the location where the user has checked in on social media. Furthermore, by analyzing the user's social media activity and reflecting related information in determining the delivery time, it is possible to improve user convenience.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The decision unit determines the scheduled delivery time. The decision unit determines the optimal delivery time by taking into account the delivery route, traffic conditions, and past delivery data. For example, if it is known that traffic congestion is likely to occur during a certain time period, the delivery time can be set to avoid that time period. It is also possible to use AI to calculate the scheduled delivery time. Step 2: The notification unit notifies the user of the estimated delivery time determined by the determination unit. The notification unit notifies the user by the method selected by the user, such as email, SMS, or a dedicated app. For example, if a dedicated app is being used, a push notification is sent to the app. The notification unit can also be equipped with a function that allows the user to check the delivery person's current location and delivery progress in real time. Step 3: The adjustment unit adjusts the time so that the user is at home based on the scheduled delivery time notified by the notification unit. If the user is at work, the adjustment unit prompts the user to temporarily return home at the scheduled delivery time. The adjustment unit can also be equipped with a function to encourage the user to use a proxy delivery service or a delivery locker. For example, the adjustment unit suggests that the user ask a family member or friend to receive the package on their behalf.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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 AI 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0131] 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.

[0132] 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.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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 AI 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

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

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

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

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

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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, in order to avoid confusion and to 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 determination unit that determines a scheduled delivery time; a notification unit that notifies a user of the scheduled delivery time determined by the determination unit; an adjustment unit that adjusts the time so that the user is at home based on the scheduled delivery time notified by the notification unit; A system characterized by:

2. The determination unit Calculating estimated delivery times using AI 2. The system of claim 1.

3. The notification unit Equipped with the function to check the delivery person's current location or delivery progress in real time 2. The system of claim 1.

4. The adjustment unit Equipped with a function to encourage the use of a proxy delivery service or a delivery box 2. The system of claim 1.

5. The notification unit Have security features that include a photo of the delivery person or vehicle information 2. The system of claim 1.

6. The determination unit Estimate user emotions and adjust delivery schedules based on the estimated user emotions 2. The system of claim 1.

7. The determination unit Analyzes past delivery data and the user's home time history to calculate the appropriate delivery time 2. The system of claim 1.

8. The determination unit In addition to optimizing delivery routes, delivery times are determined taking into account weather or traffic conditions in real time.

2. The system of claim 1.

Images