system

Abstract

The system according to this embodiment aims to reduce the effort involved by semi-automatically managing the timing and contents of gifts. [Solution] The system according to the embodiment comprises a setting unit, a notification unit, a suggestion unit, a confirmation unit, and a delivery unit. The setting unit sets the time when the gift is to be sent. The notification unit sends a notification based on the time set by the setting unit. The suggestion unit suggests a gift based on the content notified by the notification unit. The confirmation unit confirms the gift based on the content suggested by the suggestion unit. The delivery unit sends the gift based on the content confirmed by the confirmation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is complicated and time-consuming to manage the timing and content of sending gifts.

[0005] The system according to the embodiment aims to semi-automatically manage the timing and content of sending gifts and reduce the labor.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a setting unit, a notification unit, a suggestion unit, a confirmation unit, and a delivery unit. The setting unit sets the time when the gift is to be sent. The notification unit sends a notification based on the time set by the setting unit. The suggestion unit suggests a gift based on the content notified by the notification unit. The confirmation unit confirms the gift based on the content suggested by the suggestion unit. The delivery unit sends the gift based on the content confirmed by the confirmation unit. [Effects of the Invention]

[0007] The system according to this embodiment can semi-automatically manage the timing and contents of gifts, thereby reducing the effort involved. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The gift suggestion system according to an embodiment of the present invention is a system that semi-automates the selection of gifts for family members and those who have helped one another, such as year-end gifts, birthday presents, and gifts for Father's Day and Mother's Day, using an AI agent. This gift suggestion system allows the user to set the time when they want to send a gift, receive a notification via a messaging app when the set time arrives, confirm the gift after receiving a suggestion via message, and send the gift in conjunction with a shopping service. For example, the user sets the time when they want to send a gift. At this time, the user enters the time they want to send the gift via a messaging app. For example, they might set "I want to send a year-end gift in December." This information is entered into the AI ​​agent. Next, when the set time arrives, the AI ​​agent notifies the user via the messaging app with "What would you like to do for this year's year-end gift?" In response to this message, the user can receive gift suggestions. For example, they might enter a wish such as "I would like to give alcohol to someone who has helped me." Based on the user's wish, the AI ​​agent suggests the most suitable gift. For example, it might suggest a particularly popular brand of alcohol, presenting specific products. The user confirms the gift by pressing "Send with this content" for the suggested product. Finally, the AI ​​agent integrates with shopping services to handle the process of sending congratulatory gifts. For example, it can purchase suggested alcoholic beverages from online shopping sites and have them delivered to the specified address. This system allows users to send gifts simply by exchanging messages. This system ensures that even busy professionals don't forget to send seasonal greetings. Furthermore, because the AI ​​agent suggests gifts, users can easily send gifts without worrying about choosing the right one. In addition, by using messaging apps, users can easily operate the system through their familiar messaging apps. As a result, the gift suggestion system reduces the effort required from users and semi-automates the gift-giving process.

[0029] The gift suggestion system according to this embodiment comprises a setting unit, a notification unit, a suggestion unit, a confirmation unit, and a sending unit. The setting unit allows the user to set the time when they want to send a gift. For example, the setting unit allows the user to input the time when they want to send a gift via a messaging app. For example, the user sets "I want to send a year-end gift in December." This information is input to the AI ​​agent. The notification unit sends a notification based on the time set by the setting unit. For example, when the set time arrives, the notification unit notifies the user via a messaging app with "What would you like to do for this year's year-end gift?" The suggestion unit suggests a gift based on the information notified by the notification unit. For example, if the user inputs a wish such as "I want to give alcohol to someone who has helped me," the suggestion unit will suggest the most suitable gift. For example, the suggestion unit will present specific products, such as "We suggest a particularly popular brand of alcohol." The confirmation unit confirms the gift based on the information suggested by the suggestion unit. For example, the user confirms the gift by pressing "Send with this content" for the suggested product. The sending unit sends the gift based on the details confirmed by the confirmation unit. For example, the sending unit purchases the suggested alcoholic beverage from an online shopping site and delivers it to the specified address. This allows the gift suggestion system according to the embodiment to reduce the effort required from the user and semi-automate the sending of gifts.

[0030] The settings unit allows users to specify when they want to send gifts. For example, users can input the desired gift-sending period through a messaging app. Specifically, through the messaging app's interface, users can select their desired date in a calendar format or input a specific time, such as "I want to send a year-end gift in December," via text input. This information is input to the AI ​​agent, which analyzes the user's input and recognizes the appropriate timing. Furthermore, the settings unit also has a function that automatically suggests specific events and anniversaries by referring to the user's past gift-giving history and calendar information. For example, if a user has sent year-end gifts every December in the past, the AI ​​agent learns this pattern and automatically notifies the settings unit when the next year-end gift-giving season approaches. In this way, the settings unit can efficiently manage the user's gift-giving schedule and help them not miss important events.

[0031] The notification unit sends notifications based on the timing set by the settings unit. For example, when the set time arrives, the notification unit will notify the user via the messaging app with a message such as, "What would you like to do about this year's year-end gifts?" Specifically, the notification unit sends push notifications to the user's smartphone or tablet based on the information received from the settings unit. The content of the notifications is customized according to the timing or event set by the user. For example, if the user sets "I want to send year-end gifts in December," a reminder such as "Let's start preparing this year's year-end gifts" will be sent at the beginning of December. The notification unit can also adjust the frequency and timing of notifications according to the user's preferences. For example, notifications can be kept to a minimum during busy periods, and notifications with detailed suggestions can be sent during less busy periods. Furthermore, the notification unit has a reminder function that sends a notification again if the user misses the initial notification, ensuring that important notifications are delivered. In this way, the notification unit can support users in preparing gifts without forgetting and ensure that they do not miss the timing for giving gifts.

[0032] The suggestion department proposes gifts based on information notified by the notification department. For example, if a user enters a wish such as "I want to give alcohol to someone who has helped me," the suggestion department will propose the most suitable gift. Specifically, the suggestion department uses AI to analyze the user's input and selects the best gift considering past gift history, market trends, and the recipient's preferences. For example, if a user enters "I want to give alcohol," the AI ​​will suggest a specific brand or type of alcohol based on past gift history and the recipient's preferences. The suggestion department also provides an interface where users can specify their gift budget, a specific brand, and product characteristics, enabling it to make more specific suggestions. Furthermore, the suggestion department also proposes special gift sets and limited-edition products tailored to the season and events, offering users a variety of options. In this way, the suggestion department can significantly reduce the effort users put into choosing a gift and propose the most suitable gift that will please the recipient.

[0033] The confirmation section finalizes the gift based on the suggestions made by the suggestion section. For example, the user confirms the gift by clicking "Send with this content" for the suggested items. Specifically, the confirmation section displays the suggestions from the suggestion section and provides an interface for the user to confirm the selected items and options. The user can check the details of the suggested items, such as price and delivery address, and make changes or additions as needed. The confirmation section supports the process of confirming the order by having the user make a final confirmation of their selections and clicking the confirmation button. The confirmation section also provides a flexible interface that can accommodate requests from the user to change the order details or make additional requests. For example, if the user wants to add a message card to the gift or specify a delivery date and time, the confirmation section can accept these requests and reflect them in the order details. In this way, the confirmation section supports the user in confirming the gift order with confidence and enables a smooth ordering process.

[0034] The shipping department sends gifts based on the details confirmed by the order confirmation department. For example, the shipping department purchases the suggested alcoholic beverages from an online shopping site and delivers them to the specified address. Specifically, the shipping department arranges the gift delivery in cooperation with online shopping sites and partner delivery companies based on the order details received from the order confirmation department. The shipping department accurately reflects the delivery address and delivery date and time specified by the user and manages the process to ensure that the gift is delivered reliably. The shipping department also has a function to track the delivery status in real time and notify the user of the delivery status. For example, it sends a notification saying "Your gift has been shipped" when the gift is dispatched, and sends notifications such as "Your gift has been delivered" when the delivery status is in progress or when delivery is complete. Furthermore, the shipping department also provides a feedback function to confirm that the gift has been reliably delivered to the recipient. For example, receiving confirmation of receipt or a thank-you message from the recipient can provide the user with peace of mind. In this way, the shipping department can efficiently and reliably manage the gift delivery process and support users in sending gifts with confidence.

[0035] The suggestion function can propose the most suitable gift based on the user's preferences. For example, if a user inputs a wish such as "I want to give alcohol to someone who has helped me," the suggestion function will propose the most suitable gift. For example, the suggestion function may present specific products, such as "We will suggest particularly popular brands of alcohol." The suggestion function can also propose the most suitable gift based on the user's past preferences and current trends. For example, the suggestion function can analyze the trends of gifts the user has given in the past and propose similar gifts. This allows the system to propose the most suitable gift that meets the user's wishes.

[0036] The notification unit can send notifications via messaging apps at a set time. For example, when the set time arrives, the notification unit will notify the user via messaging apps with the message, "What would you like to do about this year's year-end gifts?" The notification unit can also send notifications using means such as email or push notifications. This ensures that users do not forget to send gifts by sending notifications at the set time. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI.

[0037] The confirmation unit can finalize the contents of the proposed gift. For example, the confirmation unit finalizes the contents of the gift when the user presses "Send with these contents" for the proposed product. The confirmation unit can also finalize the contents of the gift using methods such as final confirmation by the user or automatic confirmation by the system. This ensures that the gift is sent by finalizing the contents of the proposed gift. Some or all of the above processes in the confirmation unit may be performed using AI, for example, or without using AI.

[0038] The delivery unit can send gifts in conjunction with shopping services. For example, the delivery unit can purchase suggested alcoholic beverages from an online shopping site and have them delivered to a specified address. The delivery unit can also send gifts using means such as delivery services or electronic gifts. This automates the sending of gifts by integrating with shopping services. Some or all of the above processes in the delivery unit may be performed using AI, for example, or not using AI.

[0039] The settings unit can analyze the user's past gifting history and select the optimal settings method. For example, the settings unit can analyze the trends of gifts the user has given in the past and suggest similar gifts. For example, the settings unit can suggest highly-rated gifts based on the ratings of gifts the user has given in the past. For example, the settings unit can analyze the types of gifts the user has given in the past and suggest different types of gifts. In this way, the optimal settings method can be selected by analyzing the user's past gifting history. Some or all of the above processing in the settings unit may be performed using AI, for example, or without using AI.

[0040] The settings unit can filter the user's current lifestyle and areas of interest when they set the timing for sending a gift. For example, if the user is busy, the settings unit can suggest a gift that is easy to send. For example, if the user is interested in health, the settings unit can suggest a health-related gift. For example, if the user is interested in hobbies, the settings unit can suggest a hobby-related gift. In this way, by filtering based on the user's current lifestyle and areas of interest, a more appropriate gift can be suggested. Some or all of the above processing in the settings unit may be performed using AI, for example, or not using AI.

[0041] The settings unit can prioritize highly relevant times when setting the timing for sending a gift, taking into account the user's geographical location. For example, if the user lives in a specific region, the settings unit can suggest gifts that are appropriate for the customs of that region. For example, if the user is traveling, the settings unit can suggest gifts that can be given at their travel destination. For example, if the user is planning to move, the settings unit can suggest gifts that are appropriate for their new home. By prioritizing highly relevant times while considering the user's geographical location, gifts can be sent at a more appropriate time. Some or all of the above processing in the settings unit may be performed using AI, for example, or not using AI.

[0042] The settings unit can analyze the user's social media activity and set a relevant time when the user wants to send a gift. For example, if the user posts about a specific event on social media, the settings unit can suggest a gift that matches that event. For example, if the user posts about a specific interest on social media, the settings unit can also suggest a gift related to that interest. For example, if the user indicates a relationship with a specific person on social media, the settings unit can also suggest a gift that matches that person. By analyzing the user's social media activity and setting a relevant time, gifts can be sent at a more appropriate time. Some or all of the above processing in the settings unit may be performed using AI, for example, or not using AI.

[0043] The notification unit can adjust the level of detail of a notification based on the importance of the notification content. For example, the notification unit may send a notification with detailed information for important notifications. For example, the notification unit may send a notification with concise information for less important notifications. For example, the notification unit may send a notification with detailed information for notifications of particular interest to the user. This allows for more appropriate notifications to be sent by adjusting the level of detail of notifications based on the importance of the notification content. Some or all of the above processing in the notification unit may be performed using AI, for example, or without using AI.

[0044] The notification unit can apply different notification algorithms depending on the category of the notification content when it sends a notification. For example, in the case of a notification about a gift, the notification unit can send a notification that includes a gift suggestion. For example, in the case of a notification about an event, the notification unit can also send a notification that includes details of the event. For example, in the case of a notification related to the user's interests, the notification unit can also send a notification that includes information related to those interests. By applying different notification algorithms depending on the category of the notification content, more appropriate notifications can be sent. Some or all of the above processing in the notification unit may be performed using AI, for example, or without using AI.

[0045] The notification unit can determine the priority of notifications based on the submission timing of the notification content. For example, the notification unit can prioritize notifications with an approaching submission deadline. For example, the notification unit can postpone notifications with a distant submission deadline. For example, the notification unit can give top priority to notifications with particularly important submission deadlines. This allows for more appropriate notifications to be sent by determining the priority of notifications based on the submission timing of the notification content. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI.

[0046] The notification unit can adjust the order of notifications based on the relevance of their content. For example, it may send notifications that are particularly important to the user first. It may also send notifications that are less relevant to the user later. It may also send notifications that are relevant to the user's interests with priority. By adjusting the order of notifications based on the relevance of their content, more appropriate notifications can be delivered. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI.

[0047] The suggestion function can adjust the level of detail in a suggestion based on the importance of the gift. For example, if the gift is important, the suggestion function will provide a suggestion with detailed information. If the gift is of lower importance, the suggestion function may provide a suggestion with concise information. If the gift is of particular interest to the user, the suggestion function may also provide a suggestion with detailed information. By adjusting the level of detail in the suggestion function based on the importance of the gift, more appropriate suggestions can be made. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not using AI.

[0048] The suggestion unit can apply different suggestion algorithms depending on the gift category when making suggestions. For example, if the gift is food-related, the suggestion unit will suggest food items. If the gift is beverage-related, the suggestion unit can also suggest beverage items. If the gift is hobby-related, the suggestion unit can also suggest hobby-related items. By applying different suggestion algorithms depending on the gift category, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0049] The proposal department can determine the priority of proposals based on the timing of gift submissions. For example, the proposal department will prioritize proposals for gifts with an approaching submission deadline. For example, the proposal department may postpone proposals for gifts with a distant submission deadline. For example, the proposal department may prioritize proposals for gifts with particularly important submission deadlines. This allows for more appropriate proposals to be made by determining the priority of proposals based on the timing of gift submissions. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI.

[0050] The suggestion unit can adjust the order of suggestions based on the relevance of the gifts when making suggestions. For example, the suggestion unit will suggest a gift that is particularly important to the user first. For example, the suggestion unit may suggest a gift that is less relevant to the user later. For example, the suggestion unit may also suggest a gift that is related to the user's interests first. In this way, by adjusting the order of suggestions based on the relevance of the gifts, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI.

[0051] The confirmation unit can analyze the user's past gift history to select the optimal confirmation method at the time of confirmation. For example, the confirmation unit can analyze the trends of gifts the user has given in the past and confirm similar gifts. For example, the confirmation unit can also confirm highly-rated gifts based on the ratings of gifts the user has given in the past. For example, the confirmation unit can analyze the types of gifts the user has given in the past and confirm different types of gifts. In this way, the optimal confirmation method can be selected by analyzing the user's past gift history. Some or all of the above processing in the confirmation unit may be performed using AI, for example, or without using AI.

[0052] The confirmation unit can customize the confirmation process based on the user's current lifestyle. For example, if the user is busy, the confirmation unit can suggest a convenient confirmation method. For example, if the user is interested in health, the confirmation unit can also confirm a health-related gift. For example, if the user is interested in hobbies, the confirmation unit can also confirm a hobby-related gift. By customizing the confirmation process based on the user's current lifestyle, a more appropriate confirmation can be made. Some or all of the above processing in the confirmation unit may be performed using AI, for example, or without using AI.

[0053] The confirmation unit can select the optimal confirmation method at the time of confirmation, taking into account the user's geographical location information. For example, if the user lives in a specific region, the confirmation unit can confirm a gift that is in line with the customs of that region. For example, if the user is traveling, the confirmation unit can also confirm a gift that can be given at the travel destination. For example, if the user is planning to move, the confirmation unit can also confirm a gift that is appropriate for the new residence. By selecting the optimal confirmation method considering the user's geographical location information, a more appropriate confirmation can be made. Some or all of the above processing in the confirmation unit may be performed using AI, for example, or without using AI.

[0054] The confirmation unit can analyze the user's social media activity and propose a confirmation method at the time of confirmation. For example, if the confirmation unit posts about a specific event on social media, it can confirm a gift that matches that event. For example, if the confirmation unit posts about a specific interest on social media, it can also confirm a gift related to that interest. For example, if the confirmation unit indicates a relationship with a specific person on social media, it can also confirm a gift that matches that person. This allows for more appropriate confirmation by analyzing the user's social media activity and proposing a confirmation method. Some or all of the above processing in the confirmation unit may be performed using AI, for example, or without using AI.

[0055] The sending unit can analyze the user's past gift-giving history to select the optimal sending method at the time of sending. For example, the sending unit can analyze the trends of gifts the user has given in the past and send similar gifts. For example, the sending unit can also send highly-rated gifts based on the user's ratings of gifts they have given in the past. For example, the sending unit can analyze the types of gifts the user has given in the past and send different types of gifts. In this way, the optimal sending method can be selected by analyzing the user's past gift-giving history. Some or all of the above processing in the sending unit may be performed using AI, for example, or without using AI.

[0056] The delivery unit can customize the delivery method based on the user's current living situation at the time of delivery. For example, if the user is busy, the delivery unit can suggest a convenient delivery method. For example, if the user is interested in health, the delivery unit can send a health-related gift. For example, if the user is interested in hobbies, the delivery unit can send a hobby-related gift. By customizing the delivery method based on the user's current living situation, more appropriate deliveries can be made. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI.

[0057] The delivery unit can select the optimal delivery method when sending a gift, taking into account the user's geographical location. For example, if the user lives in a specific region, the delivery unit can send a gift that is appropriate for the customs of that region. For example, if the user is traveling, the delivery unit can send a gift that can be sent at the travel destination. For example, if the user is planning to move, the delivery unit can send a gift that is appropriate for the new residence. By selecting the optimal delivery method considering the user's geographical location, more appropriate deliveries can be made. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI.

[0058] The sending unit can analyze the user's social media activity and suggest a suitable method of sending at the time of sending. For example, if the sending unit has posted about a specific event on social media, it can send a gift tailored to that event. For example, if the sending unit has posted about a specific interest on social media, it can also send a gift related to that interest. For example, if the sending unit has indicated a relationship with a specific person on social media, it can also send a gift tailored to that person. This allows for more appropriate sending by analyzing the user's social media activity and suggesting a suitable method of sending. Some or all of the above processing in the sending unit may be performed using AI, for example, or without using AI.

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

[0060] The settings unit can analyze the user's past gift-giving history and suggest the optimal timing for gift-giving. For example, it can analyze the trends of gifts the user has given in the past and suggest gifts at similar times. The settings unit can also suggest times when gifts have received high ratings based on the user's past gift-giving ratings. Furthermore, it can analyze the types of gifts the user has given in the past and suggest different types of gifts. In this way, by analyzing the user's past gift-giving history, the optimal timing for gift-giving can be suggested.

[0061] The suggestion function can propose gifts based on the user's current lifestyle and areas of interest. For example, if the user is busy, it can suggest a gift that is easy to give. If the user is interested in health, it can suggest a health-related gift. If the user is interested in hobbies, it can suggest a gift related to those hobbies. This allows for the suggestion of more appropriate gifts based on the user's current lifestyle and areas of interest.

[0062] The confirmation unit can analyze the user's past gift history to select the optimal confirmation method. For example, it can analyze the trends of gifts the user has given in the past and confirm similar gifts. It can also confirm highly-rated gifts based on the ratings of gifts the user has given in the past. It can also analyze the types of gifts the user has given in the past and confirm different types of gifts. In this way, the optimal confirmation method can be selected by analyzing the user's past gift history.

[0063] The delivery function can customize the delivery method based on the user's current lifestyle. For example, if the user is busy, it can suggest a convenient delivery method. If the user is interested in health, it can send a health-related gift. If the user is interested in hobbies, it can send a gift related to those hobbies. By customizing the delivery method based on the user's current lifestyle, it is possible to deliver more appropriate gifts.

[0064] The delivery unit can select the most suitable delivery method by considering the user's geographical location. For example, if the user lives in a specific region, it can send a gift that is appropriate for the customs of that region. If the user is traveling, it can also send a gift that can be sent at their travel destination. If the user is planning to move, it can also send a gift that is appropriate for their new home. By selecting the most suitable delivery method considering the user's geographical location, more appropriate deliveries can be made.

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

[0066] Step 1: The settings section allows the user to specify when they want to send the gift. For example, the user might type "I want to send a year-end gift in December" via a messaging app. This information is then entered into the AI ​​agent. Step 2: The notification unit sends notifications based on the time set by the settings unit. For example, when the set time arrives, it sends a notification to the user via the messaging app asking, "What would you like to do about this year's year-end gifts?" Step 3: The suggestion department proposes gifts based on the information provided by the notification department. For example, if a user enters a request such as "I want to give alcohol to someone who has helped me," the suggestion department will propose the most suitable gift. Specifically, it will suggest a particular product, such as "We will suggest a particularly popular brand of alcohol." Step 4: The confirmation section confirms the gift based on the content proposed by the proposal section. For example, the user confirms the gift content by pressing "Send with this content" for the proposed product. Step 5: The sending department sends the gift based on the details confirmed by the confirmation department. For example, they purchase the suggested alcoholic beverages from an online shopping site and have them delivered to the specified address.

[0067] (Example of form 2) The gift suggestion system according to an embodiment of the present invention is a system that semi-automates the selection of gifts for family members and those who have helped one another, such as year-end gifts, birthday presents, and gifts for Father's Day and Mother's Day, using an AI agent. This gift suggestion system allows the user to set the time when they want to send a gift, receive a notification via a messaging app when the set time arrives, confirm the gift after receiving a suggestion via message, and send the gift in conjunction with a shopping service. For example, the user sets the time when they want to send a gift. At this time, the user enters the time they want to send the gift via a messaging app. For example, they might set "I want to send a year-end gift in December." This information is entered into the AI ​​agent. Next, when the set time arrives, the AI ​​agent notifies the user via the messaging app with "What would you like to do for this year's year-end gift?" In response to this message, the user can receive gift suggestions. For example, they might enter a wish such as "I would like to give alcohol to someone who has helped me." Based on the user's wish, the AI ​​agent suggests the most suitable gift. For example, it might suggest a particularly popular brand of alcohol, presenting specific products. The user confirms the gift by pressing "Send with this content" for the suggested product. Finally, the AI ​​agent integrates with shopping services to handle the process of sending congratulatory gifts. For example, it can purchase suggested alcoholic beverages from online shopping sites and have them delivered to the specified address. This system allows users to send gifts simply by exchanging messages. This system ensures that even busy professionals don't forget to send seasonal greetings. Furthermore, because the AI ​​agent suggests gifts, users can easily send gifts without worrying about choosing the right one. In addition, by using messaging apps, users can easily operate the system through their familiar messaging apps. As a result, the gift suggestion system reduces the effort required from users and semi-automates the gift-giving process.

[0068] The gift suggestion system according to this embodiment comprises a setting unit, a notification unit, a suggestion unit, a confirmation unit, and a sending unit. The setting unit allows the user to set the time when they want to send a gift. For example, the setting unit allows the user to input the time when they want to send a gift via a messaging app. For example, the user sets "I want to send a year-end gift in December." This information is input to the AI ​​agent. The notification unit sends a notification based on the time set by the setting unit. For example, when the set time arrives, the notification unit notifies the user via a messaging app with "What would you like to do for this year's year-end gift?" The suggestion unit suggests a gift based on the information notified by the notification unit. For example, if the user inputs a wish such as "I want to give alcohol to someone who has helped me," the suggestion unit will suggest the most suitable gift. For example, the suggestion unit will present specific products, such as "We suggest a particularly popular brand of alcohol." The confirmation unit confirms the gift based on the information suggested by the suggestion unit. For example, the user confirms the gift by pressing "Send with this content" for the suggested product. The sending unit sends the gift based on the details confirmed by the confirmation unit. For example, the sending unit purchases the suggested alcoholic beverage from an online shopping site and delivers it to the specified address. This allows the gift suggestion system according to the embodiment to reduce the effort required from the user and semi-automate the sending of gifts.

[0069] The settings unit allows users to specify when they want to send gifts. For example, users can input the desired gift-sending period through a messaging app. Specifically, through the messaging app's interface, users can select their desired date in a calendar format or input a specific time, such as "I want to send a year-end gift in December," via text input. This information is input to the AI ​​agent, which analyzes the user's input and recognizes the appropriate timing. Furthermore, the settings unit also has a function that automatically suggests specific events and anniversaries by referring to the user's past gift-giving history and calendar information. For example, if a user has sent year-end gifts every December in the past, the AI ​​agent learns this pattern and automatically notifies the settings unit when the next year-end gift-giving season approaches. In this way, the settings unit can efficiently manage the user's gift-giving schedule and help them not miss important events.

[0070] The notification unit sends notifications based on the timing set by the settings unit. For example, when the set time arrives, the notification unit will notify the user via the messaging app with a message such as, "What would you like to do about this year's year-end gifts?" Specifically, the notification unit sends push notifications to the user's smartphone or tablet based on the information received from the settings unit. The content of the notifications is customized according to the timing or event set by the user. For example, if the user sets "I want to send year-end gifts in December," a reminder such as "Let's start preparing this year's year-end gifts" will be sent at the beginning of December. The notification unit can also adjust the frequency and timing of notifications according to the user's preferences. For example, notifications can be kept to a minimum during busy periods, and notifications with detailed suggestions can be sent during less busy periods. Furthermore, the notification unit has a reminder function that sends a notification again if the user misses the initial notification, ensuring that important notifications are delivered. In this way, the notification unit can support users in preparing gifts without forgetting and ensure that they do not miss the timing for giving gifts.

[0071] The suggestion department proposes gifts based on information notified by the notification department. For example, if a user enters a wish such as "I want to give alcohol to someone who has helped me," the suggestion department will propose the most suitable gift. Specifically, the suggestion department uses AI to analyze the user's input and selects the best gift considering past gift history, market trends, and the recipient's preferences. For example, if a user enters "I want to give alcohol," the AI ​​will suggest a specific brand or type of alcohol based on past gift history and the recipient's preferences. The suggestion department also provides an interface where users can specify their gift budget, a specific brand, and product characteristics, enabling it to make more specific suggestions. Furthermore, the suggestion department also proposes special gift sets and limited-edition products tailored to the season and events, offering users a variety of options. In this way, the suggestion department can significantly reduce the effort users put into choosing a gift and propose the most suitable gift that will please the recipient.

[0072] The confirmation section finalizes the gift based on the suggestions made by the suggestion section. For example, the user confirms the gift by clicking "Send with this content" for the suggested items. Specifically, the confirmation section displays the suggestions from the suggestion section and provides an interface for the user to confirm the selected items and options. The user can check the details of the suggested items, such as price and delivery address, and make changes or additions as needed. The confirmation section supports the process of confirming the order by having the user make a final confirmation of their selections and clicking the confirmation button. The confirmation section also provides a flexible interface that can accommodate requests from the user to change the order details or make additional requests. For example, if the user wants to add a message card to the gift or specify a delivery date and time, the confirmation section can accept these requests and reflect them in the order details. In this way, the confirmation section supports the user in confirming the gift order with confidence and enables a smooth ordering process.

[0073] The shipping department sends gifts based on the details confirmed by the order confirmation department. For example, the shipping department purchases the suggested alcoholic beverages from an online shopping site and delivers them to the specified address. Specifically, the shipping department arranges the gift delivery in cooperation with online shopping sites and partner delivery companies based on the order details received from the order confirmation department. The shipping department accurately reflects the delivery address and delivery date and time specified by the user and manages the process to ensure that the gift is delivered reliably. The shipping department also has a function to track the delivery status in real time and notify the user of the delivery status. For example, it sends a notification saying "Your gift has been shipped" when the gift is dispatched, and sends notifications such as "Your gift has been delivered" when the delivery status is in progress or when delivery is complete. Furthermore, the shipping department also provides a feedback function to confirm that the gift has been reliably delivered to the recipient. For example, receiving confirmation of receipt or a thank-you message from the recipient can provide the user with peace of mind. In this way, the shipping department can efficiently and reliably manage the gift delivery process and support users in sending gifts with confidence.

[0074] The suggestion function can propose the most suitable gift based on the user's preferences. For example, if a user inputs a wish such as "I want to give alcohol to someone who has helped me," the suggestion function will propose the most suitable gift. For example, the suggestion function may present specific products, such as "We will suggest particularly popular brands of alcohol." The suggestion function can also propose the most suitable gift based on the user's past preferences and current trends. For example, the suggestion function can analyze the trends of gifts the user has given in the past and propose similar gifts. This allows the system to propose the most suitable gift that meets the user's wishes.

[0075] The notification unit can send notifications via messaging apps at a set time. For example, when the set time arrives, the notification unit will notify the user via messaging apps with the message, "What would you like to do about this year's year-end gifts?" The notification unit can also send notifications using means such as email or push notifications. This ensures that users do not forget to send gifts by sending notifications at the set time. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI.

[0076] The confirmation unit can finalize the contents of the proposed gift. For example, the confirmation unit finalizes the contents of the gift when the user presses "Send with these contents" for the proposed product. The confirmation unit can also finalize the contents of the gift using methods such as final confirmation by the user or automatic confirmation by the system. This ensures that the gift is sent by finalizing the contents of the proposed gift. Some or all of the above processes in the confirmation unit may be performed using AI, for example, or without using AI.

[0077] The delivery unit can send gifts in conjunction with shopping services. For example, the delivery unit can purchase suggested alcoholic beverages from an online shopping site and have them delivered to a specified address. The delivery unit can also send gifts using means such as delivery services or electronic gifts. This automates the sending of gifts by integrating with shopping services. Some or all of the above processes in the delivery unit may be performed using AI, for example, or not using AI.

[0078] The settings unit can estimate the user's emotions and suggest a suitable time to send a gift based on those emotions. For example, if the user is feeling very grateful, the settings unit can suggest a gift that expresses that gratitude. For example, if the user is feeling stressed, the settings unit can suggest a gift that helps them relax. For example, if the user is feeling joyful, the settings unit can suggest a gift that allows them to share that joy. By suggesting a suitable time to send a gift based on the user's emotions, gifts can be sent at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the settings unit may be performed using AI or not using AI.

[0079] The settings unit can analyze the user's past gifting history and select the optimal settings method. For example, the settings unit can analyze the trends of gifts the user has given in the past and suggest similar gifts. For example, the settings unit can suggest highly-rated gifts based on the ratings of gifts the user has given in the past. For example, the settings unit can analyze the types of gifts the user has given in the past and suggest different types of gifts. In this way, the optimal settings method can be selected by analyzing the user's past gifting history. Some or all of the above processing in the settings unit may be performed using AI, for example, or without using AI.

[0080] The settings unit can filter the user's current lifestyle and areas of interest when they set the timing for sending a gift. For example, if the user is busy, the settings unit can suggest a gift that is easy to send. For example, if the user is interested in health, the settings unit can suggest a health-related gift. For example, if the user is interested in hobbies, the settings unit can suggest a hobby-related gift. In this way, by filtering based on the user's current lifestyle and areas of interest, a more appropriate gift can be suggested. Some or all of the above processing in the settings unit may be performed using AI, for example, or not using AI.

[0081] The settings unit can estimate the user's emotions and determine the priority of when to set a gift based on the estimated emotions. For example, if the user is feeling very grateful, the settings unit may prioritize setting a gift that expresses gratitude. For example, if the user is feeling stressed, the settings unit may prioritize setting a gift that helps them relax. For example, if the user is feeling joyful, the settings unit may prioritize setting a gift that allows them to share that joy. By determining the priority of when to set a gift based on the user's emotions, gifts can be sent at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the settings unit may be performed using AI, for example, or not using AI.

[0082] The settings unit can prioritize highly relevant times when setting the timing for sending a gift, taking into account the user's geographical location. For example, if the user lives in a specific region, the settings unit can suggest gifts that are appropriate for the customs of that region. For example, if the user is traveling, the settings unit can suggest gifts that can be given at their travel destination. For example, if the user is planning to move, the settings unit can suggest gifts that are appropriate for their new home. By prioritizing highly relevant times while considering the user's geographical location, gifts can be sent at a more appropriate time. Some or all of the above processing in the settings unit may be performed using AI, for example, or not using AI.

[0083] The settings unit can analyze the user's social media activity and set a relevant time when the user wants to send a gift. For example, if the user posts about a specific event on social media, the settings unit can suggest a gift that matches that event. For example, if the user posts about a specific interest on social media, the settings unit can also suggest a gift related to that interest. For example, if the user indicates a relationship with a specific person on social media, the settings unit can also suggest a gift that matches that person. By analyzing the user's social media activity and setting a relevant time, gifts can be sent at a more appropriate time. Some or all of the above processing in the settings unit may be performed using AI, for example, or not using AI.

[0084] The notification unit can estimate the user's emotions and adjust the way notifications are expressed based on the estimated emotions. For example, if the user is feeling very grateful, the notification unit will send a message expressing gratitude. For example, if the user is feeling stressed, the notification unit may also send a message to help them relax. For example, if the user is feeling joyful, the notification unit may also send a message to share that joy. By adjusting the way notifications are expressed based on the user's emotions, more appropriate notifications can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the notification unit may be performed using AI, for example, or without AI.

[0085] The notification unit can adjust the level of detail of a notification based on the importance of the notification content. For example, the notification unit may send a notification with detailed information for important notifications. For example, the notification unit may send a notification with concise information for less important notifications. For example, the notification unit may send a notification with detailed information for notifications of particular interest to the user. This allows for more appropriate notifications to be sent by adjusting the level of detail of notifications based on the importance of the notification content. Some or all of the above processing in the notification unit may be performed using AI, for example, or without using AI.

[0086] The notification unit can apply different notification algorithms depending on the category of the notification content when it sends a notification. For example, in the case of a notification about a gift, the notification unit can send a notification that includes a gift suggestion. For example, in the case of a notification about an event, the notification unit can also send a notification that includes details of the event. For example, in the case of a notification related to the user's interests, the notification unit can also send a notification that includes information related to those interests. By applying different notification algorithms depending on the category of the notification content, more appropriate notifications can be sent. Some or all of the above processing in the notification unit may be performed using AI, for example, or without using AI.

[0087] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is feeling very grateful, the notification unit can send a notification expressing gratitude earlier. For example, if the user is feeling stressed, the notification unit can send a relaxing notification at an appropriate time. For example, if the user is feeling happy, the notification unit can send a notification sharing that happiness at an appropriate time. By adjusting the timing of notifications based on the user's emotions, notifications can be sent at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI.

[0088] The notification unit can determine the priority of notifications based on the submission timing of the notification content. For example, the notification unit can prioritize notifications with an approaching submission deadline. For example, the notification unit can postpone notifications with a distant submission deadline. For example, the notification unit can give top priority to notifications with particularly important submission deadlines. This allows for more appropriate notifications to be sent by determining the priority of notifications based on the submission timing of the notification content. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI.

[0089] The notification unit can adjust the order of notifications based on the relevance of their content. For example, it may send notifications that are particularly important to the user first. It may also send notifications that are less relevant to the user later. It may also send notifications that are relevant to the user's interests with priority. By adjusting the order of notifications based on the relevance of their content, more appropriate notifications can be delivered. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI.

[0090] The suggestion unit can estimate the user's emotions and adjust the way it expresses its suggestions based on those emotions. For example, if the user is feeling very grateful, the suggestion unit can make suggestions that express gratitude. For example, if the user is feeling stressed, the suggestion unit can also make suggestions that help them relax. For example, if the user is feeling joyful, the suggestion unit can also make suggestions that allow them to share that joy. By adjusting the way it expresses its suggestions based on the user's emotions, it is possible to make more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI, for example, or without AI.

[0091] The suggestion function can adjust the level of detail in a suggestion based on the importance of the gift. For example, if the gift is important, the suggestion function will provide a suggestion with detailed information. If the gift is of lower importance, the suggestion function may provide a suggestion with concise information. If the gift is of particular interest to the user, the suggestion function may also provide a suggestion with detailed information. By adjusting the level of detail in the suggestion function based on the importance of the gift, more appropriate suggestions can be made. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not using AI.

[0092] The suggestion unit can apply different suggestion algorithms depending on the gift category when making suggestions. For example, if the gift is food-related, the suggestion unit will suggest food items. If the gift is beverage-related, the suggestion unit can also suggest beverage items. If the gift is hobby-related, the suggestion unit can also suggest hobby-related items. By applying different suggestion algorithms depending on the gift category, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0093] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is feeling very grateful, the suggestion unit can make a longer suggestion that expresses gratitude. For example, if the user is feeling stressed, the suggestion unit can make a shorter suggestion that helps them relax. For example, if the user is feeling joyful, the suggestion unit can make a longer suggestion that allows them to share their joy. By adjusting the length of the suggestion based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not using AI.

[0094] The proposal department can determine the priority of proposals based on the timing of gift submissions. For example, the proposal department will prioritize proposals for gifts with an approaching submission deadline. For example, the proposal department may postpone proposals for gifts with a distant submission deadline. For example, the proposal department may prioritize proposals for gifts with particularly important submission deadlines. This allows for more appropriate proposals to be made by determining the priority of proposals based on the timing of gift submissions. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI.

[0095] The suggestion unit can adjust the order of suggestions based on the relevance of the gifts when making suggestions. For example, the suggestion unit will suggest a gift that is particularly important to the user first. For example, the suggestion unit may suggest a gift that is less relevant to the user later. For example, the suggestion unit may also suggest a gift that is related to the user's interests first. In this way, by adjusting the order of suggestions based on the relevance of the gifts, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI.

[0096] The confirmation unit can estimate the user's emotions and adjust the confirmation method based on the estimated user emotions. For example, if the user feels a strong sense of gratitude, the confirmation unit can suggest a confirmation method that expresses gratitude. For example, if the user is feeling stressed, the confirmation unit can also suggest a confirmation method that helps them relax. For example, if the user is feeling joy, the confirmation unit can also suggest a confirmation method that allows them to share their joy. By adjusting the confirmation method based on the user's emotions, a more appropriate confirmation can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the confirmation unit may be performed using AI, for example, or without AI.

[0097] The confirmation unit can analyze the user's past gift history to select the optimal confirmation method at the time of confirmation. For example, the confirmation unit can analyze the trends of gifts the user has given in the past and confirm similar gifts. For example, the confirmation unit can also confirm highly-rated gifts based on the ratings of gifts the user has given in the past. For example, the confirmation unit can analyze the types of gifts the user has given in the past and confirm different types of gifts. In this way, the optimal confirmation method can be selected by analyzing the user's past gift history. Some or all of the above processing in the confirmation unit may be performed using AI, for example, or without using AI.

[0098] The confirmation unit can customize the confirmation process based on the user's current lifestyle. For example, if the user is busy, the confirmation unit can suggest a convenient confirmation method. For example, if the user is interested in health, the confirmation unit can also confirm a health-related gift. For example, if the user is interested in hobbies, the confirmation unit can also confirm a hobby-related gift. By customizing the confirmation process based on the user's current lifestyle, a more appropriate confirmation can be made. Some or all of the above processing in the confirmation unit may be performed using AI, for example, or without using AI.

[0099] The decision unit can estimate the user's emotions and determine the priority of the decision based on the estimated user emotions. For example, if the user feels a strong sense of gratitude, the decision unit may prioritize the decision of a gift that expresses gratitude. For example, if the user is feeling stressed, the decision unit may also prioritize the decision of a gift that helps them relax. For example, if the user is feeling joyful, the decision unit may also prioritize the decision of a gift that allows them to share that joy. This allows for more appropriate decisions by determining the priority of the decision based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision unit may be performed using AI, for example, or not using AI.

[0100] The confirmation unit can select the optimal confirmation method at the time of confirmation, taking into account the user's geographical location information. For example, if the user lives in a specific region, the confirmation unit can confirm a gift that is in line with the customs of that region. For example, if the user is traveling, the confirmation unit can also confirm a gift that can be given at the travel destination. For example, if the user is planning to move, the confirmation unit can also confirm a gift that is appropriate for the new residence. By selecting the optimal confirmation method considering the user's geographical location information, a more appropriate confirmation can be made. Some or all of the above processing in the confirmation unit may be performed using AI, for example, or without using AI.

[0101] The confirmation unit can analyze the user's social media activity and propose a confirmation method at the time of confirmation. For example, if the confirmation unit posts about a specific event on social media, it can confirm a gift that matches that event. For example, if the confirmation unit posts about a specific interest on social media, it can also confirm a gift related to that interest. For example, if the confirmation unit indicates a relationship with a specific person on social media, it can also confirm a gift that matches that person. This allows for more appropriate confirmation by analyzing the user's social media activity and proposing a confirmation method. Some or all of the above processing in the confirmation unit may be performed using AI, for example, or without using AI.

[0102] The sending unit can estimate the user's emotions and adjust the sending method based on the estimated emotions. For example, if the user feels a strong sense of gratitude, the sending unit can suggest a sending method that expresses gratitude. For example, if the user is feeling stressed, the sending unit can also suggest a sending method that helps them relax. For example, if the user is feeling joy, the sending unit can also suggest a sending method that allows them to share their joy. By adjusting the sending method based on the user's emotions, more appropriate sending can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI.

[0103] The sending unit can analyze the user's past gift-giving history to select the optimal sending method at the time of sending. For example, the sending unit can analyze the trends of gifts the user has given in the past and send similar gifts. For example, the sending unit can also send highly-rated gifts based on the user's ratings of gifts they have given in the past. For example, the sending unit can analyze the types of gifts the user has given in the past and send different types of gifts. In this way, the optimal sending method can be selected by analyzing the user's past gift-giving history. Some or all of the above processing in the sending unit may be performed using AI, for example, or without using AI.

[0104] The delivery unit can customize the delivery method based on the user's current living situation at the time of delivery. For example, if the user is busy, the delivery unit can suggest a convenient delivery method. For example, if the user is interested in health, the delivery unit can send a health-related gift. For example, if the user is interested in hobbies, the delivery unit can send a hobby-related gift. By customizing the delivery method based on the user's current living situation, more appropriate deliveries can be made. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI.

[0105] The sending unit can estimate the user's emotions and determine the priority of gifts based on the estimated emotions. For example, if the user feels a strong sense of gratitude, the sending unit may prioritize sending a gift that expresses gratitude. For example, if the user is feeling stressed, the sending unit may prioritize sending a gift that helps them relax. For example, if the user is feeling joyful, the sending unit may prioritize sending a gift that allows them to share that joy. This allows for more appropriate gifts to be sent by determining the priority of gifts based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the sending unit may be performed using AI, for example, or not using AI.

[0106] The delivery unit can select the optimal delivery method when sending a gift, taking into account the user's geographical location. For example, if the user lives in a specific region, the delivery unit can send a gift that is appropriate for the customs of that region. For example, if the user is traveling, the delivery unit can send a gift that can be sent at the travel destination. For example, if the user is planning to move, the delivery unit can send a gift that is appropriate for the new residence. By selecting the optimal delivery method considering the user's geographical location, more appropriate deliveries can be made. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI.

[0107] The sending unit can analyze the user's social media activity and suggest a suitable method of sending at the time of sending. For example, if the sending unit has posted about a specific event on social media, it can send a gift tailored to that event. For example, if the sending unit has posted about a specific interest on social media, it can also send a gift related to that interest. For example, if the sending unit has indicated a relationship with a specific person on social media, it can also send a gift tailored to that person. This allows for more appropriate sending by analyzing the user's social media activity and suggesting a suitable method of sending. Some or all of the above processing in the sending unit may be performed using AI, for example, or without using AI.

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

[0109] The settings unit can analyze the user's past gift-giving history and suggest the optimal timing for gift-giving. For example, it can analyze the trends of gifts the user has given in the past and suggest gifts at similar times. The settings unit can also suggest times when gifts have received high ratings based on the user's past gift-giving ratings. Furthermore, it can analyze the types of gifts the user has given in the past and suggest different types of gifts. In this way, by analyzing the user's past gift-giving history, the optimal timing for gift-giving can be suggested.

[0110] The notification unit can estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, if the user is feeling very grateful, it can send a notification expressing gratitude earlier. If the user is feeling stressed, it can also send a relaxing notification at the appropriate time. If the user is feeling happy, it can also send a notification sharing that happiness at the appropriate time. In this way, by adjusting the timing of notifications based on the user's emotions, notifications can be delivered at a more appropriate time.

[0111] The suggestion function can propose gifts based on the user's current lifestyle and areas of interest. For example, if the user is busy, it can suggest a gift that is easy to give. If the user is interested in health, it can suggest a health-related gift. If the user is interested in hobbies, it can suggest a gift related to those hobbies. This allows for the suggestion of more appropriate gifts based on the user's current lifestyle and areas of interest.

[0112] The suggestion function can estimate the user's emotions and adjust the way suggestions are expressed based on those emotions. For example, if the user is feeling a strong sense of gratitude, it can make suggestions that express gratitude. If the user is feeling stressed, it can make suggestions that help them relax. If the user is feeling joy, it can make suggestions that allow them to share that joy. In this way, by adjusting the way suggestions are expressed based on the user's emotions, more appropriate suggestions can be made.

[0113] The confirmation unit can analyze the user's past gift history to select the optimal confirmation method. For example, it can analyze the trends of gifts the user has given in the past and confirm similar gifts. It can also confirm highly-rated gifts based on the ratings of gifts the user has given in the past. It can also analyze the types of gifts the user has given in the past and confirm different types of gifts. In this way, the optimal confirmation method can be selected by analyzing the user's past gift history.

[0114] The confirmation unit can estimate the user's emotions and adjust the confirmation method based on those emotions. For example, if the user feels a strong sense of gratitude, it can suggest a confirmation method that expresses that gratitude. If the user is feeling stressed, it can suggest a confirmation method that helps them relax. If the user is feeling joy, it can suggest a confirmation method that allows them to share that joy. By adjusting the confirmation method based on the user's emotions, a more appropriate confirmation can be made.

[0115] The delivery function can customize the delivery method based on the user's current lifestyle. For example, if the user is busy, it can suggest a convenient delivery method. If the user is interested in health, it can send a health-related gift. If the user is interested in hobbies, it can send a gift related to those hobbies. By customizing the delivery method based on the user's current lifestyle, it is possible to deliver more appropriate gifts.

[0116] The sending function can estimate the user's emotions and adjust the sending method based on those emotions. For example, if the user feels a strong sense of gratitude, it can suggest a sending method that expresses that gratitude. If the user is feeling stressed, it can suggest a sending method that helps them relax. If the user is feeling joy, it can suggest a sending method that allows them to share that joy. By adjusting the sending method based on the user's emotions, it is possible to send more appropriate messages.

[0117] The delivery unit can select the most suitable delivery method by considering the user's geographical location. For example, if the user lives in a specific region, it can send a gift that is appropriate for the customs of that region. If the user is traveling, it can also send a gift that can be sent at their travel destination. If the user is planning to move, it can also send a gift that is appropriate for their new home. By selecting the most suitable delivery method considering the user's geographical location, more appropriate deliveries can be made.

[0118] The sending unit can estimate the user's emotions and determine the priority of gifts based on those emotions. For example, if a user feels a strong sense of gratitude, it can prioritize sending gifts that express gratitude. If a user is feeling stressed, it can prioritize sending gifts that help them relax. If a user is feeling joyful, it can prioritize sending gifts that allow them to share that joy. By prioritizing gifts based on the user's emotions, the system can send more appropriate gifts.

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

[0120] Step 1: The settings section allows the user to specify when they want to send the gift. For example, the user might type "I want to send a year-end gift in December" via a messaging app. This information is then entered into the AI ​​agent. Step 2: The notification unit sends notifications based on the time set by the settings unit. For example, when the set time arrives, it sends a notification to the user via the messaging app asking, "What would you like to do about this year's year-end gifts?" Step 3: The suggestion department proposes gifts based on the information provided by the notification department. For example, if a user enters a request such as "I want to give alcohol to someone who has helped me," the suggestion department will propose the most suitable gift. Specifically, it will suggest a particular product, such as "We will suggest a particularly popular brand of alcohol." Step 4: The confirmation section confirms the gift based on the content proposed by the proposal section. For example, the user confirms the gift content by pressing "Send with this content" for the proposed product. Step 5: The sending department sends the gift based on the details confirmed by the confirmation department. For example, they purchase the suggested alcoholic beverages from an online shopping site and have them delivered to the specified address.

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

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

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

[0124] Each of the multiple elements described above, including the setting unit, notification unit, suggestion unit, confirmation unit, and delivery unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the setting unit is implemented by the control unit 46A of the smart device 14, allowing the user to input the time when they want to send a gift via a messaging app. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, and sends a notification via the messaging app when the set time arrives. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, and suggests the most suitable gift based on the user's preferences. The confirmation unit is implemented by the control unit 46A of the smart device 14, for example, and allows the user to confirm the suggested product. The delivery unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, and purchases the suggested gift from an online shopping site and delivers it to the specified address. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0140] Each of the multiple elements described above, including the setting unit, notification unit, suggestion unit, confirmation unit, and delivery unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the setting unit is implemented by the control unit 46A of the smart glasses 214, allowing the user to input the time when they want to send a gift via a messaging app. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, and sends a notification via the messaging app when the set time arrives. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, and suggests the most suitable gift based on the user's preferences. The confirmation unit is implemented by the control unit 46A of the smart glasses 214, for example, and allows the user to confirm the suggested product. The delivery unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, and purchases the suggested gift from an online shopping site and delivers it to the specified address. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0156] Each of the multiple elements described above, including the setting unit, notification unit, suggestion unit, confirmation unit, and delivery unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the setting unit is implemented by the control unit 46A of the headset terminal 314, allowing the user to input the time when they want to send a gift via a messaging app. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, and sends a notification via the messaging app when the set time arrives. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, and suggests the most suitable gift based on the user's preferences. The confirmation unit is implemented by the control unit 46A of the headset terminal 314, for example, and allows the user to confirm the suggested product. The delivery unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, and purchases the suggested gift from an online shopping site and delivers it to the specified address. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0173] Each of the multiple elements described above, including the setting unit, notification unit, suggestion unit, confirmation unit, and delivery unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the setting unit is implemented by the control unit 46A of the robot 414, allowing the user to input the time when they want to send a gift via a messaging app. The notification unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and sends a notification via the messaging app when the set time arrives. The suggestion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and suggests the most suitable gift based on the user's wishes. The confirmation unit is implemented by, for example, the control unit 46A of the robot 414, and the user confirms the suggested product. The delivery unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and purchases the suggested gift from an online shopping site and delivers it to the specified address. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

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

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

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

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

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

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

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

[0183] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

[0192] (Note 1) A setting section for setting the time when you want to send the gift, A notification unit that provides notification based on the time set by the aforementioned setting unit, A proposal unit proposes gifts based on the content notified by the notification unit, A confirmation unit that determines the gift based on the content proposed by the proposal unit, The system includes a delivery unit that sends a gift based on the content determined by the confirmation unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We suggest the perfect gift based on the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned notification unit, Send a notification via the messaging app at the scheduled time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned confirmation unit is, Confirm the contents of the proposed gift. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned transmission unit is Send a gift by linking with a shopping service. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned setting unit is, It estimates the user's emotions and suggests the best time to send a gift based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned setting unit is, Analyze the user's past gift-giving history to select the optimal settings. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned setting unit is, When setting the timing for sending a gift, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned setting unit is, We estimate the user's emotions and determine the priority of the timing of setting based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned setting unit is, When setting the timing for sending a gift, the system prioritizes the most relevant time, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned setting unit is, When setting the timing for sending a gift, the system analyzes the user's social media activity and sets a relevant time. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned notification unit, It estimates the user's emotions and adjusts the way notifications are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned notification unit, When sending a notification, adjust the level of detail based on the importance of the notification content. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned notification unit, When sending notifications, different notification algorithms are applied depending on the category of the notification content. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned notification unit, When notifying, the priority of notifications will be determined based on when the notification content was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned notification unit, When sending notifications, the order of notifications will be adjusted based on the relevance of the notification content. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the gift. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the gift category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the gifts will be submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the gifts. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned confirmation unit is, We estimate the user's emotions and adjust the confirmation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned confirmation unit is, Upon confirmation, the system analyzes the user's past gift history to select the most suitable confirmation method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned confirmation unit is, At the time of confirmation, the confirmation method will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned confirmation unit is, The system estimates the user's emotions and determines confirmation priorities based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned confirmation unit is, When confirming, the system will select the optimal confirmation method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned confirmation unit is, When confirming, we analyze the user's social media activity and propose a confirmation method. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned transmission unit is It estimates the user's emotions and adjusts the delivery method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned transmission unit is When sending a gift, the system analyzes the user's past gift-giving history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned transmission unit is When sending, the method of delivery is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned transmission unit is It estimates the user's emotions and determines the priority of sending messages based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned transmission unit is When sending, the system will select the most suitable shipping method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned transmission unit is When sending, we analyze the user's social media activity and suggest a delivery method. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. A setting section for setting the time when you want to send the gift, A notification unit that provides notification based on the time set by the aforementioned setting unit, A proposal unit proposes gifts based on the content notified by the notification unit, A confirmation unit that determines the gift based on the content proposed by the proposal unit, The system includes a delivery unit that sends a gift based on the content determined by the confirmation unit. A system characterized by the following features.

2. The aforementioned proposal section is, We suggest the perfect gift based on the user's preferences. The system according to feature 1.

3. The aforementioned notification unit, Send a notification via the messaging app at the scheduled time. The system according to feature 1.

4. The aforementioned confirmation unit is, Confirm the contents of the proposed gift. The system according to feature 1.

5. The aforementioned transmission unit is Send a gift by linking with a shopping service. The system according to feature 1.

6. The setting unit is, It estimates the user's emotions and suggests the best time to send a gift based on those emotions. The system according to feature 1.

7. The setting unit is, Analyze the user's past gift-giving history to select the optimal settings. The system according to feature 1.

8. The setting unit is, When setting the timing for sending a gift, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

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