system

Abstract

The system according to this embodiment aims to efficiently create group messages and collect, select, and send gifts. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, a collection unit, a selection unit, and a sending unit. The collection unit collects messages and photos. The generation unit analyzes the messages and photos collected by the collection unit to generate a group message. The collection unit collects the gift money. The selection unit selects a gift based on the gift money collected by the collection unit. The sending unit sends the group message generated by the generation unit and the gift selected by the selection unit together.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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 in 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 was a problem that creating a dedication, collecting, selecting, and sending gifts were laborious, and it was particularly difficult to perform efficiently in a telework environment.

[0005] The system according to the embodiment aims to efficiently create a dedication, collect, select, and send gifts.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a generation unit, a collection unit, a selection unit, and a delivery unit. The collection unit collects messages and photos. The generation unit analyzes the messages and photos collected by the collection unit to generate a group message. The collection unit collects the gift money. The selection unit selects a gift based on the gift money collected by the collection unit. The delivery unit sends the group message generated by the generation unit and the gift selected by the selection unit together. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently create group messages and collect, select, and send gifts. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 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 group message creation system according to an embodiment of the present invention is a system that automates the creation of group messages using generation AI, collects payments through an electronic payment system, and selects gifts via electronic gifting. The group message creation system provides a system that automates the creation of group messages using generation AI, collects payments through an electronic payment system, and selects gifts via electronic gifting. This allows for the sending of group messages and gifts together, and enables the provision of highly original gifts even in a teleworking environment. First, the user inputs messages and photos for the group message. Next, the generation AI organizes these messages and photos and automatically generates a beautiful group message design. The generation AI analyzes the input messages and photos and generates the optimal layout and design. For example, it selects background colors and fonts based on the content of the messages and determines the placement of photos. Next, the gift fees are collected through an electronic payment system. The user can collect gift fees from people participating in the group message using the electronic payment system. This eliminates the hassle of collecting payments. Finally, a gift is selected via electronic gifting and delivered together with the group message. The generation AI considers the collected gift fees and the attributes of the recipient to suggest the most suitable gift. For example, gifts can be selected based on the recipient's preferences and hobbies. This system allows for the simultaneous sending of group messages and gifts, enabling the delivery of highly personalized gifts even in a teleworking environment. Users can easily send group messages and gifts without complex operations. Furthermore, by utilizing generation AI, the system solves the problem of group message designs often becoming templated, providing highly original group messages. As a result, the group message creation system allows for the simultaneous sending of group messages and gifts, enabling the delivery of highly personalized gifts even in a teleworking environment.

[0029] The group message creation system according to this embodiment comprises a collection unit, a generation unit, a collection unit, a selection unit, and a sending unit. The collection unit collects messages and photos. The collection unit allows users to input messages and photos for the group message, for example. The collection unit can also collect messages and photos via email or social media, for example. The collection unit can also collect messages and photos via a dedicated web form, for example. The generation unit analyzes the messages and photos collected by the collection unit to generate the group message. The generation unit analyzes messages and photos using generation AI and automatically generates a beautiful group message design, for example. The generation unit can select background colors and fonts based on the content of the messages, for example. The generation unit can also determine the placement of photos, for example. The collection unit collects the gift money. The collection unit can collect gift money from people participating in the group message using an electronic payment system, for example. The collection unit can also collect money using credit cards or debit cards, for example. The collection unit can also collect money using electronic money or bank transfers, for example. The selection unit selects a gift based on the gift money collected by the collection unit. The selection unit suggests the most suitable gift candidate by considering the recipient's attributes, for example, using a generation AI. The selection unit can select a gift based on the recipient's preferences and hobbies, for example. The selection unit can also list gift candidates and provide the user with choices, for example. The sending unit sends the group message generated by the generation unit and the gift selected by the selection unit all at once. The sending unit can deliver the group message and gift together, for example. The sending unit can also send the group message and gift via email or social media, for example. The sending unit can also send the group message and gift via a dedicated web form, for example. As a result, the group message creation system according to this embodiment can perform everything from collecting messages and photos to generating the group message, collecting gift money, selecting gifts, and sending them all at once.

[0030] The collection unit collects messages and photos. For example, users can input messages and photos for a group message. Specifically, users can access a dedicated web form, enter messages in text boxes, and attach photos using the file upload function. The collection unit can also collect messages and photos via email or social media. Users can easily participate in a group message by sending messages and photos to a designated email address or sending direct messages to a dedicated social media account. The collection unit can also collect messages and photos through a dedicated web form. The web form has a user-friendly interface and is designed to allow for easy confirmation and modification of input. Furthermore, the collection unit centrally manages the collected messages and photos and stores them in a database. This allows the collection unit to efficiently integrate data from multiple channels and quickly provide the information necessary for subsequent processing. The collection unit also has a function to automatically filter out duplicate and inappropriate data, ensuring high-quality data. This allows the collection unit to accommodate diverse input methods from users and collect messages and photos efficiently and effectively.

[0031] The generation unit analyzes messages and photos collected by the collection unit to generate a group message board. For example, the generation unit uses generation AI to analyze messages and photos and automatically generate a beautiful group message board design. Specifically, the generation AI uses natural language processing technology to analyze the content of messages and selects the most suitable design elements based on emotions and themes. For example, if there are many messages of gratitude, it will select a warm background color and font, and if there are many messages of congratulations, it will adopt a bright and cheerful design. The generation unit can, for example, select background colors and fonts based on the content of the messages. Furthermore, the generation AI uses image recognition technology to analyze the content of photos and determine the optimal placement. For example, by placing group photos in the center and arranging individual photos evenly around them, a visually beautiful layout can be achieved. The generation unit can, for example, determine the placement of photos. The generation unit provides the user with a preview of the generated group message board, allowing for corrections and adjustments as needed. This enables the generation unit to automatically generate high-quality group messages that reflect the user's intentions and emotions, thereby increasing user satisfaction. Furthermore, the generation unit can save the generated message board in high resolution, making it suitable for printing and digital distribution. This allows the generation unit to efficiently and effectively generate and provide the message board to users.

[0032] The collection department collects the gift money. For example, the collection department can collect gift money from people participating in the group message using an electronic payment system. Specifically, users can access a dedicated payment page, enter their credit or debit card information, and complete the payment. The collection department can also collect money using credit or debit cards. Furthermore, the collection department can collect money using electronic money or bank transfers. Users can easily pay for gifts by making payments using their electronic money accounts or by transferring money to a designated bank account. The collection department can centrally manage these payment methods and monitor payment status in real time. In addition, the collection department automates payment confirmation and receipt issuance, providing users with a fast and accurate service. This allows the collection department to enhance user convenience and realize a smooth collection process. The collection department also implements robust security measures to safely protect users' personal and payment information. This allows the collection department to provide a reliable collection system and ensure user peace of mind.

[0033] The selection department selects gifts based on the gift money collected by the collection department. The selection department suggests the most suitable gift candidates by considering the recipient's attributes, for example, using a generation AI. Specifically, the generation AI analyzes information such as the recipient's age, gender, hobbies, and past gift history to list the most suitable gift candidates. The selection department can select gifts based on the recipient's preferences and hobbies, for example. Furthermore, the selection department provides the user with a list of gift candidates, allowing the user to make the final selection. This enables the selection department to select gifts that reflect the user's intentions. The selection department also considers information such as gift availability and delivery area to select the most suitable gift. This allows the selection department to provide users with a high-quality gift selection service and increase satisfaction. In addition, the selection department provides users with detailed information and reviews of the selected gifts to help them make their selection. This enables the selection department to provide an environment where users can select gifts with confidence.

[0034] The sending department sends the message board generated by the generation department and the gifts selected by the selection department in a single shipment. The sending department can, for example, deliver the message board and gifts together. Specifically, the sending department works with delivery companies to arrange for the safe and prompt delivery of the message board and gifts. The sending department can also send the message board and gifts via email or social media. Users can send digital versions of the message board and gifts to a designated email address or social media account. The sending department can also send the message board and gifts via a dedicated web form. The web form is designed for easy access by recipients and provides download links and detailed information for the message board and gifts. Furthermore, the sending department can track the delivery status in real time and notify users of the delivery status. This allows the sending department to provide users with peace of mind and ensure a smooth delivery process. The sending department can also respond quickly to any delivery problems or delays, increasing user satisfaction. As a result, the sending department can efficiently and effectively send the message board and gifts and provide users with a high-quality service.

[0035] The generation unit includes a design selection unit that selects background colors and fonts based on the message content. For example, the generation unit can select background colors and fonts based on the message content. For example, the generation unit can select background colors based on the message content. For example, the generation unit can select fonts based on the message content. This allows the generation unit to automatically select a design appropriate to the message content.

[0036] The selection unit includes a gift selection unit that selects a gift based on the recipient's preferences and hobbies. The selection unit can, for example, select a gift based on the recipient's preferences. The selection unit can, for example, select a gift based on the recipient's preferences. The selection unit can, for example, select a gift based on the recipient's hobbies. As a result, the selection unit can automatically select a gift that suits the recipient's preferences and hobbies.

[0037] The sending unit includes a unit for sending the message board and gift together. The sending unit can, for example, send the message board and gift together. The sending unit can, for example, deliver the message board and gift together. The sending unit can, for example, send the message board and gift together via email or social media. This allows the sending unit to send the message board and gift together.

[0038] The collection unit analyzes the user's past participation history in group messages and selects the optimal collection method. For example, the collection unit can analyze the format of group messages the user has frequently participated in in the past and collect messages in a similar format. For example, the collection unit can prioritize suggesting collection methods that the user has preferred in the past (email, social media, etc.). For example, the collection unit can increase participation rates by collecting messages at specific time slots based on the user's past participation history. In this way, the collection unit can select the optimal collection method based on the user's past participation history.

[0039] The collection unit filters messages and photos based on the user's current projects and areas of interest. For example, the collection unit can prioritize collecting messages and photos related to projects the user is currently working on. For example, the collection unit can filter and collect highly relevant messages and photos based on the user's areas of interest. For example, the collection unit can collect messages and photos related to communities and groups the user is participating in. This allows the collection unit to collect highly relevant messages and photos based on the user's current interests.

[0040] The collection unit prioritizes collecting messages and photos that are highly relevant, taking into account the user's geographical location. For example, the collection unit can prioritize collecting messages and photos related to the user's current location. For example, based on the user's geographical location, the collection unit can prioritize collecting messages and photos from nearby friends and family. For example, if the user is traveling, the collection unit can prioritize collecting messages and photos related to their travel destination. In this way, the collection unit can collect messages and photos that are highly relevant based on the user's geographical location.

[0041] The collection unit analyzes the user's social media activity when collecting messages and photos, and collects relevant messages and photos. For example, the collection unit can collect messages and photos related to the user's recent social media posts. For example, the collection unit can prioritize collecting messages and photos from accounts and groups that the user follows. For example, the collection unit can analyze the user's social media activity history and collect highly relevant messages and photos. This allows the collection unit to collect highly relevant messages and photos based on the user's social media activity.

[0042] The generation unit adjusts the level of detail in the group message based on the importance of each message during generation. For example, the generation unit can change the font size and color to highlight important messages. For example, the generation unit can make less important messages less noticeable by blending them with the background color. For example, the generation unit can center important messages and arrange other messages around them. In this way, the generation unit can adjust the level of detail in the group message according to the importance of each message.

[0043] The generation unit applies different generation algorithms depending on the message category during generation. For example, the generation unit can apply a warm design to a message of gratitude. For example, the generation unit can apply a vibrant design to a congratulatory message. For example, the generation unit can apply a powerful design to an encouraging message. This allows the generation unit to apply the most suitable generation algorithm according to the message category.

[0044] The generation unit determines the priority of the group message based on when the messages were submitted. For example, the generation unit can prioritize messages submitted early. For example, the generation unit can postpone messages submitted just before the deadline. For example, the generation unit can adjust the order in which the messages are placed according to the submission time. In this way, the generation unit can determine the priority of the group message based on when the messages were submitted.

[0045] The generation unit adjusts the order of the group messages based on their relevance during generation. For example, the generation unit can place highly relevant messages close together. For example, the generation unit can place less relevant messages far apart. For example, the generation unit can arrange messages in order of relevance based on their content. In this way, the generation unit can adjust the order of the group messages according to their relevance.

[0046] The collection department analyzes the user's past payment history to select the most suitable collection method. For example, the collection department can prioritize suggesting payment methods the user has used in the past. For example, the collection department can select the collection method with the highest success rate based on the user's past payment history. For example, the collection department can analyze the user's payment history and suggest the optimal collection timing. This allows the collection department to select the most suitable collection method based on the user's past payment history.

[0047] The collection department adjusts the collection amount based on the user's current financial situation at the time of collection. For example, the collection department can adjust the collection amount by considering the user's current income. For example, the collection department can analyze the user's spending habits and propose a reasonable collection amount. For example, the collection department can offer installment payment options depending on the user's financial situation. This allows the collection department to set a reasonable collection amount according to the user's financial situation.

[0048] The collection department selects the optimal collection method when collecting payments, taking into account the user's geographical location. For example, the collection department can suggest the optimal collection method based on the user's current location. For example, the collection department can offer nearby payment options based on the user's geographical location. For example, if the user is traveling, the collection department can suggest payment options at their travel destination. This allows the collection department to select the optimal collection method based on the user's geographical location.

[0049] The collection department analyzes the user's social media activity and proposes collection methods during collection. For example, the collection department can suggest payment methods the user uses on social media. For example, the collection department can select the optimal collection method based on the user's social media activity. For example, the collection department can analyze the user's payment history on social media and propose the optimal collection method. This allows the collection department to propose the optimal collection method based on the user's social media activity.

[0050] The selection unit analyzes the user's past gift selection history to select the most suitable gift. For example, the selection unit can analyze trends in gifts the user has selected in the past and suggest similar gifts. For example, the selection unit can select the most suitable gift based on the user's evaluation of gifts they have selected in the past. For example, the selection unit can suggest gifts suitable for a specific event based on the user's past gift selection history. In this way, the selection unit can select the most suitable gift based on the user's past gift selection history.

[0051] The selection unit customizes gift selections based on the user's current life circumstances. For example, if the user has moved into a new house, the selection unit can suggest a housewarming gift. For example, if the user has started a new job, the selection unit can suggest a work-related gift. For example, if the user has recently gotten married, the selection unit can suggest a wedding gift. In this way, the selection unit can select the most suitable gift according to the user's current life circumstances.

[0052] The selection unit selects the most suitable gift by considering the user's geographical location. For example, the selection unit can suggest the most suitable gift based on the user's current location. For example, the selection unit can suggest gifts that can be purchased at nearby stores based on the user's geographical location. For example, if the user is traveling, the selection unit can suggest gifts that can be purchased at their travel destination. In this way, the selection unit can select the most suitable gift based on the user's geographical location.

[0053] The selection unit analyzes the user's social media activity to select gifts. For example, the selection unit can suggest products that the user has shown interest in on social media as gifts. For example, the selection unit can select the most suitable gift based on the user's social media activity. For example, the selection unit can analyze the user's social media activity history and suggest highly relevant gifts. In this way, the selection unit can select the most suitable gift based on the user's social media activity.

[0054] The sending unit analyzes the user's past sending history to select the optimal sending method. For example, the sending unit can prioritize suggesting sending methods the user has used in the past. For example, the sending unit can select the sending method with the highest success rate based on the user's past sending history. For example, the sending unit can analyze the user's sending history and suggest the optimal sending timing. This allows the sending unit to select the optimal sending method based on the user's past sending history.

[0055] The delivery unit customizes the delivery method based on the user's current living situation. For example, if the user has moved to a new house, the delivery unit can send the item to the new address. For example, if the user has started a new job, the delivery unit can send the item to the workplace. For example, if the user has recently gotten married, the delivery unit can send the item as a wedding gift. This allows the delivery unit to select the most suitable delivery method according to the user's current living situation.

[0056] The shipping unit selects the optimal shipping method when shipping, taking into account the user's geographical location. For example, the shipping unit can suggest the optimal shipping method based on the user's current location. For example, the shipping unit can provide nearby delivery options based on the user's geographical location. For example, if the user is traveling, the shipping unit can suggest delivery options at their travel destination. This allows the shipping unit to select the optimal shipping method based on the user's geographical location.

[0057] The delivery unit analyzes the user's social media activity and proposes a delivery method at the time of delivery. For example, the delivery unit can propose a delivery method that the user uses on social media. For example, the delivery unit can select the optimal delivery method based on the user's social media activity. For example, the delivery unit can analyze the user's social media activity history and propose the optimal delivery method. In this way, the delivery unit can propose the optimal delivery method based on the user's social media activity.

[0058] The design selection department selects the most suitable design based on the content of the message. For example, for a message of gratitude, the design selection department can select a warm design. For a congratulatory message, the design selection department can select a vibrant design. For an encouraging message, the design selection department can select a powerful design. In this way, the design selection department can select the most suitable design according to the content of the message.

[0059] The design selection unit customizes the design based on the placement of the photos during the design selection process. For example, if there are many photos, the design selection unit can organize them using a grid layout. For example, if there are few photos, the design selection unit can display them larger to make them stand out. For example, the design selection unit can select the optimal placement based on the content of the photos. In this way, the design selection unit can customize the design to be optimal according to the placement of the photos.

[0060] The design selection department prioritizes designs based on the timing of message submissions. For example, it can prioritize incorporating messages submitted early into the design. For example, it can postpone messages submitted just before the deadline. For example, it can adjust the placement order of designs according to the submission timing. This allows the design selection department to determine the optimal design priority based on the timing of message submissions.

[0061] The design selection unit adjusts the order of designs based on the relevance of the messages during the design selection process. For example, the design selection unit can place highly relevant messages close together. For example, the design selection unit can place less relevant messages far apart. For example, the design selection unit can arrange messages in order of relevance based on their content. In this way, the design selection unit can adjust the optimal order of designs according to the relevance of the messages.

[0062] The gift selection unit analyzes the user's past gift selection history to select the most suitable gift. For example, the gift selection unit can analyze the trends of gifts the user has selected in the past and suggest similar gifts. For example, the gift selection unit can select the most suitable gift based on the user's evaluation of gifts they have selected in the past. For example, the gift selection unit can suggest gifts suitable for a specific event based on the user's past gift selection history. In this way, the gift selection unit can select the most suitable gift based on the user's past gift selection history.

[0063] The gift selection unit customizes gift selections based on the user's current life circumstances. For example, if the user has moved into a new house, the gift selection unit can suggest a housewarming gift. For example, if the user has started a new job, the gift selection unit can suggest a work-related gift. For example, if the user has recently gotten married, the gift selection unit can suggest a wedding gift. In this way, the gift selection unit can select the most suitable gift according to the user's current life circumstances.

[0064] The gift selection unit selects the most suitable gift by considering the user's geographical location. For example, the gift selection unit can suggest the most suitable gift based on the user's current location. For example, the gift selection unit can suggest gifts that can be purchased at nearby stores based on the user's geographical location. For example, if the user is traveling, the gift selection unit can suggest gifts that can be purchased at their travel destination. In this way, the gift selection unit can select the most suitable gift based on the user's geographical location.

[0065] The gift selection unit analyzes the user's social media activity when selecting gifts. For example, the gift selection unit can suggest products that the user has shown interest in on social media as gifts. For example, the gift selection unit can select the most suitable gift based on the user's social media activity. For example, the gift selection unit can analyze the user's social media activity history and suggest highly relevant gifts. In this way, the gift selection unit can select the most suitable gift based on the user's social media activity.

[0066] The bulk mailing unit analyzes the user's past mailing history to select the optimal mailing method during bulk mailing. For example, the bulk mailing unit can prioritize suggesting mailing methods the user has used in the past. For example, the bulk mailing unit can select the mailing method with the highest success rate based on the user's past mailing history. For example, the bulk mailing unit can analyze the user's mailing history and suggest the optimal mailing timing. As a result, the bulk mailing unit can select the optimal mailing method based on the user's past mailing history.

[0067] The bulk delivery unit customizes the delivery method based on the user's current living situation when sending bulk deliveries. For example, if a user has moved to a new house, the bulk delivery unit can send deliveries to the new address. For example, if a user has started a new job, the bulk delivery unit can send deliveries to the workplace. For example, if a user has recently gotten married, the bulk delivery unit can send deliveries as wedding gifts. This allows the bulk delivery unit to select the most suitable delivery method according to the user's current living situation.

[0068] The bulk delivery unit selects the optimal delivery method when sending items in bulk, taking into account the user's geographical location. For example, the bulk delivery unit can suggest the optimal delivery method based on the user's current location. For example, the bulk delivery unit can provide nearby delivery options based on the user's geographical location. For example, if the user is traveling, the bulk delivery unit can suggest delivery options at their travel destination. This allows the bulk delivery unit to select the optimal delivery method based on the user's geographical location.

[0069] The bulk delivery unit analyzes the user's social media activity and proposes a delivery method during bulk delivery. For example, the bulk delivery unit can propose delivery methods used by the user on social media. For example, the bulk delivery unit can select the optimal delivery method based on the user's social media activity. For example, the bulk delivery unit can analyze the user's social media activity history and propose the optimal delivery method. This allows the bulk delivery unit to propose the optimal delivery method based on the user's social media activity.

[0070] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0071] The collection unit can analyze a user's past participation history in group messages and select the optimal collection method. For example, it can analyze the format of group messages a user has frequently participated in in the past and collect messages in a similar format. It can also prioritize suggesting collection methods that the user has preferred in the past (email, social media, etc.). Furthermore, by collecting messages at specific time slots based on the user's past participation history, participation rates can be increased. In this way, the collection unit can select the optimal collection method based on the user's past participation history.

[0072] The collection unit can filter messages and photos based on the user's current projects and areas of interest. For example, it can prioritize collecting messages and photos related to projects the user is currently working on. It can also filter and collect highly relevant messages and photos based on the user's areas of interest. Furthermore, it can collect messages and photos related to communities and groups the user participates in. In this way, the collection unit can collect highly relevant messages and photos based on the user's current interests.

[0073] The data collection unit can prioritize the collection of highly relevant messages and photos by considering the user's geographical location when collecting messages and photos. For example, it can prioritize the collection of messages and photos related to the user's current location. It can also prioritize the collection of messages and photos from nearby friends and family based on the user's geographical location. Furthermore, if the user is traveling, it can prioritize the collection of messages and photos related to their travel destination. In this way, the data collection unit can collect highly relevant messages and photos based on the user's geographical location.

[0074] The generation unit can adjust the level of detail in the group message based on the importance of each message during generation. For example, it can change the font size and color to highlight important messages. Less important messages can be made less noticeable by blending them with the background color. Furthermore, important messages can be placed in the center, with other messages arranged around them. In this way, the generation unit can adjust the level of detail in the group message according to the importance of each message.

[0075] The generation unit can apply different generation algorithms depending on the message category during generation. For example, a warm design can be applied to a message of gratitude, a more elaborate design to a congratulatory message, and a powerful design to an encouraging message. This allows the generation unit to apply the most suitable generation algorithm for each message category.

[0076] The generation unit can determine the priority of the group message based on when the messages were submitted during the generation process. For example, messages submitted early can be given priority. Messages submitted just before the deadline can be postponed. Furthermore, the order in which the messages are placed can be adjusted according to the submission time. In this way, the generation unit can determine the priority of the group message based on when the messages were submitted.

[0077] The following briefly describes the processing flow for example form 1.

[0078] Step 1: The collection unit collects messages and photos. The collection unit allows users to input messages and photos for a group message, and can collect messages and photos via email, social media, or a dedicated web form. Step 2: The generation unit analyzes the messages and photos collected by the collection unit to generate a group message. The generation unit uses generation AI to analyze the messages and photos and automatically generate a beautiful group message design. The generation unit can select background colors and fonts based on the content of the messages and determine the placement of photos. Step 3: The collection team collects the gift money. The collection team can collect gift money from participants using an electronic payment system. The collection team can also collect money using credit cards, debit cards, e-money, or bank transfers. Step 4: The selection unit selects a gift based on the gift money collected by the collection unit. The selection unit uses a generation AI to suggest the most suitable gift candidates, taking into account the recipient's attributes. The selection unit can also select a gift based on the recipient's preferences and hobbies, and provide the user with a list of gift candidates to choose from. Step 5: The sending department sends the message board generated by the generation department and the gifts selected by the selection department all at once. The sending department can deliver the message board and gifts together, and can also send the message board and gifts via email, social media, or a dedicated web form.

[0079] (Example of form 2) The group message creation system according to an embodiment of the present invention is a system that automates the creation of group messages using generation AI, collects payments through an electronic payment system, and selects gifts via electronic gifting. The group message creation system provides a system that automates the creation of group messages using generation AI, collects payments through an electronic payment system, and selects gifts via electronic gifting. This allows for the sending of group messages and gifts together, and enables the provision of highly original gifts even in a teleworking environment. First, the user inputs messages and photos for the group message. Next, the generation AI organizes these messages and photos and automatically generates a beautiful group message design. The generation AI analyzes the input messages and photos and generates the optimal layout and design. For example, it selects background colors and fonts based on the content of the messages and determines the placement of photos. Next, the gift fees are collected through an electronic payment system. The user can collect gift fees from people participating in the group message using the electronic payment system. This eliminates the hassle of collecting payments. Finally, a gift is selected via electronic gifting and delivered together with the group message. The generation AI considers the collected gift fees and the attributes of the recipient to suggest the most suitable gift. For example, gifts can be selected based on the recipient's preferences and hobbies. This system allows for the simultaneous sending of group messages and gifts, enabling the delivery of highly personalized gifts even in a teleworking environment. Users can easily send group messages and gifts without complex operations. Furthermore, by utilizing generation AI, the system solves the problem of group message designs often becoming templated, providing highly original group messages. As a result, the group message creation system allows for the simultaneous sending of group messages and gifts, enabling the delivery of highly personalized gifts even in a teleworking environment.

[0080] The group message creation system according to this embodiment comprises a collection unit, a generation unit, a collection unit, a selection unit, and a sending unit. The collection unit collects messages and photos. The collection unit allows users to input messages and photos for the group message, for example. The collection unit can also collect messages and photos via email or social media, for example. The collection unit can also collect messages and photos via a dedicated web form, for example. The generation unit analyzes the messages and photos collected by the collection unit to generate the group message. The generation unit analyzes messages and photos using generation AI and automatically generates a beautiful group message design, for example. The generation unit can select background colors and fonts based on the content of the messages, for example. The generation unit can also determine the placement of photos, for example. The collection unit collects the gift money. The collection unit can collect gift money from people participating in the group message using an electronic payment system, for example. The collection unit can also collect money using credit cards or debit cards, for example. The collection unit can also collect money using electronic money or bank transfers, for example. The selection unit selects a gift based on the gift money collected by the collection unit. The selection unit suggests the most suitable gift candidate by considering the recipient's attributes, for example, using a generation AI. The selection unit can select a gift based on the recipient's preferences and hobbies, for example. The selection unit can also list gift candidates and provide the user with choices, for example. The sending unit sends the group message generated by the generation unit and the gift selected by the selection unit all at once. The sending unit can deliver the group message and gift together, for example. The sending unit can also send the group message and gift via email or social media, for example. The sending unit can also send the group message and gift via a dedicated web form, for example. As a result, the group message creation system according to this embodiment can perform everything from collecting messages and photos to generating the group message, collecting gift money, selecting gifts, and sending them all at once.

[0081] The collection unit collects messages and photos. For example, users can input messages and photos for a group message. Specifically, users can access a dedicated web form, enter messages in text boxes, and attach photos using the file upload function. The collection unit can also collect messages and photos via email or social media. Users can easily participate in a group message by sending messages and photos to a designated email address or sending direct messages to a dedicated social media account. The collection unit can also collect messages and photos through a dedicated web form. The web form has a user-friendly interface and is designed to allow for easy confirmation and modification of input. Furthermore, the collection unit centrally manages the collected messages and photos and stores them in a database. This allows the collection unit to efficiently integrate data from multiple channels and quickly provide the information necessary for subsequent processing. The collection unit also has a function to automatically filter out duplicate and inappropriate data, ensuring high-quality data. This allows the collection unit to accommodate diverse input methods from users and collect messages and photos efficiently and effectively.

[0082] The generation unit analyzes messages and photos collected by the collection unit to generate a group message board. For example, the generation unit uses generation AI to analyze messages and photos and automatically generate a beautiful group message board design. Specifically, the generation AI uses natural language processing technology to analyze the content of messages and selects the most suitable design elements based on emotions and themes. For example, if there are many messages of gratitude, it will select a warm background color and font, and if there are many messages of congratulations, it will adopt a bright and cheerful design. The generation unit can, for example, select background colors and fonts based on the content of the messages. Furthermore, the generation AI uses image recognition technology to analyze the content of photos and determine the optimal placement. For example, by placing group photos in the center and arranging individual photos evenly around them, a visually beautiful layout can be achieved. The generation unit can, for example, determine the placement of photos. The generation unit provides the user with a preview of the generated group message board, allowing for corrections and adjustments as needed. This enables the generation unit to automatically generate high-quality group messages that reflect the user's intentions and emotions, thereby increasing user satisfaction. Furthermore, the generation unit can save the generated message board in high resolution, making it suitable for printing and digital distribution. This allows the generation unit to efficiently and effectively generate and provide the message board to users.

[0083] The collection department collects the gift money. For example, the collection department can collect gift money from people participating in the group message using an electronic payment system. Specifically, users can access a dedicated payment page, enter their credit or debit card information, and complete the payment. The collection department can also collect money using credit or debit cards. Furthermore, the collection department can collect money using electronic money or bank transfers. Users can easily pay for gifts by making payments using their electronic money accounts or by transferring money to a designated bank account. The collection department can centrally manage these payment methods and monitor payment status in real time. In addition, the collection department automates payment confirmation and receipt issuance, providing users with a fast and accurate service. This allows the collection department to enhance user convenience and realize a smooth collection process. The collection department also implements robust security measures to safely protect users' personal and payment information. This allows the collection department to provide a reliable collection system and ensure user peace of mind.

[0084] The selection department selects gifts based on the gift money collected by the collection department. The selection department suggests the most suitable gift candidates by considering the recipient's attributes, for example, using a generation AI. Specifically, the generation AI analyzes information such as the recipient's age, gender, hobbies, and past gift history to list the most suitable gift candidates. The selection department can select gifts based on the recipient's preferences and hobbies, for example. Furthermore, the selection department provides the user with a list of gift candidates, allowing the user to make the final selection. This enables the selection department to select gifts that reflect the user's intentions. The selection department also considers information such as gift availability and delivery area to select the most suitable gift. This allows the selection department to provide users with a high-quality gift selection service and increase satisfaction. In addition, the selection department provides users with detailed information and reviews of the selected gifts to help them make their selection. This enables the selection department to provide an environment where users can select gifts with confidence.

[0085] The sending department sends the message board generated by the generation department and the gifts selected by the selection department in a single shipment. The sending department can, for example, deliver the message board and gifts together. Specifically, the sending department works with delivery companies to arrange for the safe and prompt delivery of the message board and gifts. The sending department can also send the message board and gifts via email or social media. Users can send digital versions of the message board and gifts to a designated email address or social media account. The sending department can also send the message board and gifts via a dedicated web form. The web form is designed for easy access by recipients and provides download links and detailed information for the message board and gifts. Furthermore, the sending department can track the delivery status in real time and notify users of the delivery status. This allows the sending department to provide users with peace of mind and ensure a smooth delivery process. The sending department can also respond quickly to any delivery problems or delays, increasing user satisfaction. As a result, the sending department can efficiently and effectively send the message board and gifts and provide users with a high-quality service.

[0086] The generation unit includes a design selection unit that selects background colors and fonts based on the message content. For example, the generation unit can select background colors and fonts based on the message content. For example, the generation unit can select background colors based on the message content. For example, the generation unit can select fonts based on the message content. This allows the generation unit to automatically select a design appropriate to the message content.

[0087] The selection unit includes a gift selection unit that selects a gift based on the recipient's preferences and hobbies. The selection unit can, for example, select a gift based on the recipient's preferences. The selection unit can, for example, select a gift based on the recipient's preferences. The selection unit can, for example, select a gift based on the recipient's hobbies. As a result, the selection unit can automatically select a gift that suits the recipient's preferences and hobbies.

[0088] The sending unit includes a unit for sending the message board and gift together. The sending unit can, for example, send the message board and gift together. The sending unit can, for example, deliver the message board and gift together. The sending unit can, for example, send the message board and gift together via email or social media. This allows the sending unit to send the message board and gift together.

[0089] The collection unit estimates the user's emotions and adjusts the timing of message and photo collection based on the estimated emotions. For example, if the user is emotionally depressed, the collection unit can delay collection and wait until the user recovers. If the user is excited, the collection unit can immediately begin collecting messages and photos while their emotions are heightened. If the user is relaxed, the collection unit can flexibly set the collection timing and collect at the moment when the user feels most comfortable. This allows the collection unit to collect messages and photos at the optimal time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The collection unit analyzes the user's past participation history in group messages and selects the optimal collection method. For example, the collection unit can analyze the format of group messages the user has frequently participated in in the past and collect messages in a similar format. For example, the collection unit can prioritize suggesting collection methods that the user has preferred in the past (email, social media, etc.). For example, the collection unit can increase participation rates by collecting messages at specific time slots based on the user's past participation history. In this way, the collection unit can select the optimal collection method based on the user's past participation history.

[0091] The collection unit filters messages and photos based on the user's current projects and areas of interest. For example, the collection unit can prioritize collecting messages and photos related to projects the user is currently working on. For example, the collection unit can filter and collect highly relevant messages and photos based on the user's areas of interest. For example, the collection unit can collect messages and photos related to communities and groups the user is participating in. This allows the collection unit to collect highly relevant messages and photos based on the user's current interests.

[0092] The collection unit estimates the user's emotions and determines the priority of messages and photos to collect based on the estimated emotions. For example, if the user is feeling down, the collection unit can prioritize collecting encouraging messages and cheerful photos. For example, if the user is excited, the collection unit can prioritize collecting messages and photos that allow them to share their emotions. For example, if the user is relaxed, the collection unit can prioritize collecting messages and photos with a relaxed atmosphere. In this way, the collection unit can determine the priority of messages and photos to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The collection unit prioritizes collecting messages and photos that are highly relevant, taking into account the user's geographical location. For example, the collection unit can prioritize collecting messages and photos related to the user's current location. For example, based on the user's geographical location, the collection unit can prioritize collecting messages and photos from nearby friends and family. For example, if the user is traveling, the collection unit can prioritize collecting messages and photos related to their travel destination. In this way, the collection unit can collect messages and photos that are highly relevant based on the user's geographical location.

[0094] The collection unit analyzes the user's social media activity when collecting messages and photos, and collects relevant messages and photos. For example, the collection unit can collect messages and photos related to the user's recent social media posts. For example, the collection unit can prioritize collecting messages and photos from accounts and groups that the user follows. For example, the collection unit can analyze the user's social media activity history and collect highly relevant messages and photos. This allows the collection unit to collect highly relevant messages and photos based on the user's social media activity.

[0095] The generation unit estimates the user's emotions and adjusts the design of the message board based on the estimated emotions. For example, if the user is feeling depressed, the generation unit can generate a design with calm colors. For example, if the user is excited, the generation unit can generate a bright and lively design. For example, if the user is relaxed, the generation unit can generate a design with soft colors. In this way, the generation unit can generate a message board with the optimal design according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0096] The generation unit adjusts the level of detail in the group message based on the importance of each message during generation. For example, the generation unit can change the font size and color to highlight important messages. For example, the generation unit can make less important messages less noticeable by blending them with the background color. For example, the generation unit can center important messages and arrange other messages around them. In this way, the generation unit can adjust the level of detail in the group message according to the importance of each message.

[0097] The generation unit applies different generation algorithms depending on the message category during generation. For example, the generation unit can apply a warm design to a message of gratitude. For example, the generation unit can apply a vibrant design to a congratulatory message. For example, the generation unit can apply a powerful design to an encouraging message. This allows the generation unit to apply the most suitable generation algorithm according to the message category.

[0098] The generation unit estimates the user's emotions and adjusts the length of the message based on the estimated emotions. For example, if the user is emotionally depressed, the generation unit can generate a short, to-the-point message. If the user is excited, for example, the generation unit can generate a longer message with detailed explanations. If the user is relaxed, for example, the generation unit can generate a message of moderate length. In this way, the generation unit can adjust the length of the message according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0099] The generation unit determines the priority of the group message based on when the messages were submitted. For example, the generation unit can prioritize messages submitted early. For example, the generation unit can postpone messages submitted just before the deadline. For example, the generation unit can adjust the order in which the messages are placed according to the submission time. In this way, the generation unit can determine the priority of the group message based on when the messages were submitted.

[0100] The generation unit adjusts the order of the group messages based on their relevance during generation. For example, the generation unit can place highly relevant messages close together. For example, the generation unit can place less relevant messages far apart. For example, the generation unit can arrange messages in order of relevance based on their content. In this way, the generation unit can adjust the order of the group messages according to their relevance.

[0101] The collection unit estimates the user's emotions and adjusts the timing of collection based on the estimated emotions. For example, if the user is emotionally depressed, the collection unit can delay the collection. If the user is excited, the collection unit can start collecting immediately. If the user is relaxed, the collection unit can collect at a flexible time. This allows the collection unit to collect at the optimal time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The collection department analyzes the user's past payment history to select the most suitable collection method. For example, the collection department can prioritize suggesting payment methods the user has used in the past. For example, the collection department can select the collection method with the highest success rate based on the user's past payment history. For example, the collection department can analyze the user's payment history and suggest the optimal collection timing. This allows the collection department to select the most suitable collection method based on the user's past payment history.

[0103] The collection department adjusts the collection amount based on the user's current financial situation at the time of collection. For example, the collection department can adjust the collection amount by considering the user's current income. For example, the collection department can analyze the user's spending habits and propose a reasonable collection amount. For example, the collection department can offer installment payment options depending on the user's financial situation. This allows the collection department to set a reasonable collection amount according to the user's financial situation.

[0104] The collection unit estimates the user's emotions and determines the collection priority based on the estimated emotions. For example, if the user is emotionally depressed, the collection unit can lower the collection priority. For example, if the user is excited, the collection unit can raise the collection priority. For example, if the user is relaxed, the collection unit can collect with flexible priorities. In this way, the collection unit can adjust the collection priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The collection department selects the optimal collection method when collecting payments, taking into account the user's geographical location. For example, the collection department can suggest the optimal collection method based on the user's current location. For example, the collection department can offer nearby payment options based on the user's geographical location. For example, if the user is traveling, the collection department can suggest payment options at their travel destination. This allows the collection department to select the optimal collection method based on the user's geographical location.

[0106] The collection department analyzes the user's social media activity and proposes collection methods during collection. For example, the collection department can suggest payment methods the user uses on social media. For example, the collection department can select the optimal collection method based on the user's social media activity. For example, the collection department can analyze the user's payment history on social media and propose the optimal collection method. This allows the collection department to propose the optimal collection method based on the user's social media activity.

[0107] The selection unit estimates the user's emotions and adjusts the gift selection method based on the estimated emotions. For example, if the user is feeling down, the selection unit can select an encouraging gift. For example, if the user is excited, the selection unit can select a gift that allows them to share their emotions. For example, if the user is relaxed, the selection unit can select a gift that promotes relaxation. In this way, the selection unit can select the most suitable gift according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The selection unit analyzes the user's past gift selection history to select the most suitable gift. For example, the selection unit can analyze trends in gifts the user has selected in the past and suggest similar gifts. For example, the selection unit can select the most suitable gift based on the user's evaluation of gifts they have selected in the past. For example, the selection unit can suggest gifts suitable for a specific event based on the user's past gift selection history. In this way, the selection unit can select the most suitable gift based on the user's past gift selection history.

[0109] The selection unit customizes gift selections based on the user's current life circumstances. For example, if the user has moved into a new house, the selection unit can suggest a housewarming gift. For example, if the user has started a new job, the selection unit can suggest a work-related gift. For example, if the user has recently gotten married, the selection unit can suggest a wedding gift. In this way, the selection unit can select the most suitable gift according to the user's current life circumstances.

[0110] The selection unit estimates the user's emotions and determines the priority of gifts based on the estimated emotions. For example, if the user is feeling down, the selection unit can prioritize encouraging gifts. For example, if the user is excited, the selection unit can prioritize gifts that allow them to share their emotions. For example, if the user is relaxed, the selection unit can prioritize gifts that promote relaxation. In this way, the selection unit can determine the priority of gifts according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The selection unit selects the most suitable gift by considering the user's geographical location. For example, the selection unit can suggest the most suitable gift based on the user's current location. For example, the selection unit can suggest gifts that can be purchased at nearby stores based on the user's geographical location. For example, if the user is traveling, the selection unit can suggest gifts that can be purchased at their travel destination. In this way, the selection unit can select the most suitable gift based on the user's geographical location.

[0112] The selection unit analyzes the user's social media activity to select gifts. For example, the selection unit can suggest products that the user has shown interest in on social media as gifts. For example, the selection unit can select the most suitable gift based on the user's social media activity. For example, the selection unit can analyze the user's social media activity history and suggest highly relevant gifts. In this way, the selection unit can select the most suitable gift based on the user's social media activity.

[0113] The sending unit estimates the user's emotions and adjusts the timing of sending based on the estimated emotions. For example, if the user is emotionally depressed, the sending unit can delay sending. For example, if the user is excited, the sending unit can start sending immediately. For example, if the user is relaxed, the sending unit can send at a flexible timing. This allows the sending unit to send at the optimal time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0114] The sending unit analyzes the user's past sending history to select the optimal sending method. For example, the sending unit can prioritize suggesting sending methods the user has used in the past. For example, the sending unit can select the sending method with the highest success rate based on the user's past sending history. For example, the sending unit can analyze the user's sending history and suggest the optimal sending timing. This allows the sending unit to select the optimal sending method based on the user's past sending history.

[0115] The delivery unit customizes the delivery method based on the user's current living situation. For example, if the user has moved to a new house, the delivery unit can send the item to the new address. For example, if the user has started a new job, the delivery unit can send the item to the workplace. For example, if the user has recently gotten married, the delivery unit can send the item as a wedding gift. This allows the delivery unit to select the most suitable delivery method according to the user's current living situation.

[0116] The sending unit estimates the user's emotions and determines the priority of sending based on the estimated emotions. For example, if the user is emotionally depressed, the sending unit can lower the priority of sending. For example, if the user is excited, the sending unit can raise the priority of sending. For example, if the user is relaxed, the sending unit can send with flexible priorities. In this way, the sending unit can determine the priority of sending according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0117] The shipping unit selects the optimal shipping method when shipping, taking into account the user's geographical location. For example, the shipping unit can suggest the optimal shipping method based on the user's current location. For example, the shipping unit can provide nearby delivery options based on the user's geographical location. For example, if the user is traveling, the shipping unit can suggest delivery options at their travel destination. This allows the shipping unit to select the optimal shipping method based on the user's geographical location.

[0118] The delivery unit analyzes the user's social media activity and proposes a delivery method at the time of delivery. For example, the delivery unit can propose a delivery method that the user uses on social media. For example, the delivery unit can select the optimal delivery method based on the user's social media activity. For example, the delivery unit can analyze the user's social media activity history and propose the optimal delivery method. In this way, the delivery unit can propose the optimal delivery method based on the user's social media activity.

[0119] The design selection unit estimates the user's emotions and adjusts the selection of background colors and fonts based on the estimated emotions. For example, if the user is feeling depressed, the design selection unit can select calm background colors and fonts. For example, if the user is excited, the design selection unit can select bright and lively background colors and fonts. For example, if the user is relaxed, the design selection unit can select soft background colors and fonts. In this way, the design selection unit can select the optimal background colors and fonts according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0120] The design selection department selects the most suitable design based on the content of the message. For example, for a message of gratitude, the design selection department can select a warm design. For a congratulatory message, the design selection department can select a vibrant design. For an encouraging message, the design selection department can select a powerful design. In this way, the design selection department can select the most suitable design according to the content of the message.

[0121] The design selection unit customizes the design based on the placement of the photos during the design selection process. For example, if there are many photos, the design selection unit can organize them using a grid layout. For example, if there are few photos, the design selection unit can display them larger to make them stand out. For example, the design selection unit can select the optimal placement based on the content of the photos. In this way, the design selection unit can customize the design to be optimal according to the placement of the photos.

[0122] The design selection unit estimates the user's emotions and determines design priorities based on those estimated emotions. For example, if the user is emotionally depressed, the design selection unit can prioritize calming designs. For example, if the user is excited, the design selection unit can prioritize energetic designs. For example, if the user is relaxed, the design selection unit can prioritize relaxing designs. In this way, the design selection unit can determine the optimal design priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0123] The design selection department prioritizes designs based on the timing of message submissions. For example, it can prioritize incorporating messages submitted early into the design. For example, it can postpone messages submitted just before the deadline. For example, it can adjust the placement order of designs according to the submission timing. This allows the design selection department to determine the optimal design priority based on the timing of message submissions.

[0124] The design selection unit adjusts the order of designs based on the relevance of the messages during the design selection process. For example, the design selection unit can place highly relevant messages close together. For example, the design selection unit can place less relevant messages far apart. For example, the design selection unit can arrange messages in order of relevance based on their content. In this way, the design selection unit can adjust the optimal order of designs according to the relevance of the messages.

[0125] The gift selection unit estimates the user's emotions and adjusts the gift selection method based on the estimated emotions. For example, if the user is feeling down, the gift selection unit can select an encouraging gift. For example, if the user is excited, the gift selection unit can select a gift that allows them to share their emotions. For example, if the user is relaxed, the gift selection unit can select a gift that promotes relaxation. In this way, the gift selection unit can select the most suitable gift according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0126] The gift selection unit analyzes the user's past gift selection history to select the most suitable gift. For example, the gift selection unit can analyze the trends of gifts the user has selected in the past and suggest similar gifts. For example, the gift selection unit can select the most suitable gift based on the user's evaluation of gifts they have selected in the past. For example, the gift selection unit can suggest gifts suitable for a specific event based on the user's past gift selection history. In this way, the gift selection unit can select the most suitable gift based on the user's past gift selection history.

[0127] The gift selection unit customizes gift selections based on the user's current life circumstances. For example, if the user has moved into a new house, the gift selection unit can suggest a housewarming gift. For example, if the user has started a new job, the gift selection unit can suggest a work-related gift. For example, if the user has recently gotten married, the gift selection unit can suggest a wedding gift. In this way, the gift selection unit can select the most suitable gift according to the user's current life circumstances.

[0128] The gift selection unit estimates the user's emotions and determines the priority of gifts based on the estimated emotions. For example, if the user is feeling down, the gift selection unit can prioritize selecting an encouraging gift. For example, if the user is excited, the gift selection unit can prioritize selecting a gift that allows them to share their emotions. For example, if the user is relaxed, the gift selection unit can prioritize selecting a gift that promotes relaxation. In this way, the gift selection unit can determine the priority of gifts according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0129] The gift selection unit selects the most suitable gift by considering the user's geographical location. For example, the gift selection unit can suggest the most suitable gift based on the user's current location. For example, the gift selection unit can suggest gifts that can be purchased at nearby stores based on the user's geographical location. For example, if the user is traveling, the gift selection unit can suggest gifts that can be purchased at their travel destination. In this way, the gift selection unit can select the most suitable gift based on the user's geographical location.

[0130] The gift selection unit analyzes the user's social media activity when selecting gifts. For example, the gift selection unit can suggest products that the user has shown interest in on social media as gifts. For example, the gift selection unit can select the most suitable gift based on the user's social media activity. For example, the gift selection unit can analyze the user's social media activity history and suggest highly relevant gifts. In this way, the gift selection unit can select the most suitable gift based on the user's social media activity.

[0131] The bulk delivery unit estimates the user's emotions and adjusts the timing of bulk delivery based on the estimated emotions. For example, if the user is emotionally depressed, the bulk delivery unit can delay the timing of bulk delivery. For example, if the user is excited, the bulk delivery unit can start bulk delivery immediately. For example, if the user is relaxed, the bulk delivery unit can deliver bulk delivery at a flexible timing. This allows the bulk delivery unit to deliver bulk delivery at the optimal timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0132] The bulk mailing unit analyzes the user's past mailing history to select the optimal mailing method during bulk mailing. For example, the bulk mailing unit can prioritize suggesting mailing methods the user has used in the past. For example, the bulk mailing unit can select the mailing method with the highest success rate based on the user's past mailing history. For example, the bulk mailing unit can analyze the user's mailing history and suggest the optimal mailing timing. As a result, the bulk mailing unit can select the optimal mailing method based on the user's past mailing history.

[0133] The bulk delivery unit customizes the delivery method based on the user's current living situation when sending bulk deliveries. For example, if a user has moved to a new house, the bulk delivery unit can send deliveries to the new address. For example, if a user has started a new job, the bulk delivery unit can send deliveries to the workplace. For example, if a user has recently gotten married, the bulk delivery unit can send deliveries as wedding gifts. This allows the bulk delivery unit to select the most suitable delivery method according to the user's current living situation.

[0134] The bulk delivery unit estimates the user's emotions and determines the priority of bulk deliveries based on the estimated emotions. For example, if the user is emotionally depressed, the bulk delivery unit can lower the priority of bulk deliveries. For example, if the user is excited, the bulk delivery unit can raise the priority of bulk deliveries. For example, if the user is relaxed, the bulk delivery unit can deliver bulk deliveries with flexible priorities. In this way, the bulk delivery unit can determine the priority of bulk deliveries according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0135] The bulk delivery unit selects the optimal delivery method when sending items in bulk, taking into account the user's geographical location. For example, the bulk delivery unit can suggest the optimal delivery method based on the user's current location. For example, the bulk delivery unit can provide nearby delivery options based on the user's geographical location. For example, if the user is traveling, the bulk delivery unit can suggest delivery options at their travel destination. This allows the bulk delivery unit to select the optimal delivery method based on the user's geographical location.

[0136] The bulk delivery unit analyzes the user's social media activity and proposes a delivery method during bulk delivery. For example, the bulk delivery unit can propose delivery methods used by the user on social media. For example, the bulk delivery unit can select the optimal delivery method based on the user's social media activity. For example, the bulk delivery unit can analyze the user's social media activity history and propose the optimal delivery method. This allows the bulk delivery unit to propose the optimal delivery method based on the user's social media activity.

[0137] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0138] The collection unit can estimate the user's emotions and adjust the timing of message and photo collection based on the estimated emotions. For example, if the user is feeling down, the collection timing can be delayed until the user recovers. If the user is excited, message and photo collection can begin immediately, capturing them while their emotions are heightened. Furthermore, if the user is relaxed, the collection timing can be flexibly set to capture them at the moment the user feels most comfortable. This allows the collection unit to collect messages and photos at the optimal time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0139] The collection unit can analyze a user's past participation history in group messages and select the optimal collection method. For example, it can analyze the format of group messages a user has frequently participated in in the past and collect messages in a similar format. It can also prioritize suggesting collection methods that the user has preferred in the past (email, social media, etc.). Furthermore, by collecting messages at specific time slots based on the user's past participation history, participation rates can be increased. In this way, the collection unit can select the optimal collection method based on the user's past participation history.

[0140] The collection unit can filter messages and photos based on the user's current projects and areas of interest. For example, it can prioritize collecting messages and photos related to projects the user is currently working on. It can also filter and collect highly relevant messages and photos based on the user's areas of interest. Furthermore, it can collect messages and photos related to communities and groups the user participates in. In this way, the collection unit can collect highly relevant messages and photos based on the user's current interests.

[0141] The collection unit can estimate the user's emotions and determine the priority of messages and photos to collect based on the estimated emotions. For example, if the user is feeling down, it can prioritize collecting encouraging messages and cheerful photos. If the user is excited, it can prioritize collecting messages and photos that allow them to share their emotions. Furthermore, if the user is relaxed, it can prioritize collecting messages and photos with a relaxed atmosphere. In this way, the collection unit can determine the priority of messages and photos to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0142] The data collection unit can prioritize the collection of highly relevant messages and photos by considering the user's geographical location when collecting messages and photos. For example, it can prioritize the collection of messages and photos related to the user's current location. It can also prioritize the collection of messages and photos from nearby friends and family based on the user's geographical location. Furthermore, if the user is traveling, it can prioritize the collection of messages and photos related to their travel destination. In this way, the data collection unit can collect highly relevant messages and photos based on the user's geographical location.

[0143] The generation unit can estimate the user's emotions and adjust the design of the message board based on the estimated emotions. For example, if the user is feeling down, it can generate a design with calm colors. If the user is excited, it can generate a bright and lively design. Furthermore, if the user is relaxed, it can generate a design with soft colors. In this way, the generation unit can generate a message board with the optimal design according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0144] The generation unit can adjust the level of detail in the group message based on the importance of each message during generation. For example, it can change the font size and color to highlight important messages. Less important messages can be made less noticeable by blending them with the background color. Furthermore, important messages can be placed in the center, with other messages arranged around them. In this way, the generation unit can adjust the level of detail in the group message according to the importance of each message.

[0145] The generation unit can apply different generation algorithms depending on the message category during generation. For example, a warm design can be applied to a message of gratitude, a more elaborate design to a congratulatory message, and a powerful design to an encouraging message. This allows the generation unit to apply the most suitable generation algorithm for each message category.

[0146] The generation unit can estimate the user's emotions and adjust the length of the message based on the estimated emotions. For example, if the user is emotionally depressed, it can generate a short, concise message. If the user is excited, it can generate a longer message with detailed explanations. Furthermore, if the user is relaxed, it can generate a message of appropriate length. In this way, the generation unit can adjust the length of the message according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0147] The generation unit can determine the priority of the group message based on when the messages were submitted during the generation process. For example, messages submitted early can be given priority. Messages submitted just before the deadline can be postponed. Furthermore, the order in which the messages are placed can be adjusted according to the submission time. In this way, the generation unit can determine the priority of the group message based on when the messages were submitted.

[0148] The following briefly describes the processing flow for example form 2.

[0149] Step 1: The collection unit collects messages and photos. The collection unit allows users to input messages and photos for a group message, and can collect messages and photos via email, social media, or a dedicated web form. Step 2: The generation unit analyzes the messages and photos collected by the collection unit to generate a group message. The generation unit uses generation AI to analyze the messages and photos and automatically generate a beautiful group message design. The generation unit can select background colors and fonts based on the content of the messages and determine the placement of photos. Step 3: The collection team collects the gift money. The collection team can collect gift money from participants using an electronic payment system. The collection team can also collect money using credit cards, debit cards, e-money, or bank transfers. Step 4: The selection unit selects a gift based on the gift money collected by the collection unit. The selection unit uses a generation AI to suggest the most suitable gift candidates, taking into account the recipient's attributes. The selection unit can also select a gift based on the recipient's preferences and hobbies, and provide the user with a list of gift candidates to choose from. Step 5: The sending department sends the message board generated by the generation department and the gifts selected by the selection department all at once. The sending department can deliver the message board and gifts together, and can also send the message board and gifts via email, social media, or a dedicated web form.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Each of the multiple elements described above, including the collection unit, generation unit, collection unit, selection unit, and sending unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit can collect messages and photos using the receiving device 38 of the smart device 14. The generation unit can analyze messages and photos using the specific processing unit 290 of the data processing unit 12 and generate a group message. The collection unit can collect the gift fee using an electronic payment system via the communication I / F 44 of the smart device 14. The selection unit can select a gift considering the attributes of the recipient using the specific processing unit 290 of the data processing unit 12. The sending unit can send the group message and gift together using the output device 40 of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0154] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements described above, including the collection unit, generation unit, collection unit, selection unit, and sending unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit can collect messages and photos using the microphone 238 of the smart glasses 214. The generation unit can analyze messages and photos using the specific processing unit 290 of the data processing unit 12 and generate a group message. The collection unit can collect the gift fee using an electronic payment system via the communication I / F 44 of the smart glasses 214. The selection unit can select a gift considering the attributes of the recipient using the specific processing unit 290 of the data processing unit 12. The sending unit can send the group message and gift together using the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0170] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0175] 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).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] Each of the multiple elements described above, including the collection unit, generation unit, collection unit, selection unit, and sending unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit can collect messages and photos using the microphone 238 of the headset terminal 314. The generation unit can analyze messages and photos using the specific processing unit 290 of the data processing unit 12 and generate a group message. The collection unit can collect the gift fee using an electronic payment system via the communication I / F 44 of the headset terminal 314. The selection unit can select a gift considering the attributes of the recipient using the specific processing unit 290 of the data processing unit 12. The sending unit can send the group message and gift together using the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0186] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0191] 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).

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.).

[0199] 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.

[0200] 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.

[0201] 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.

[0202] Each of the multiple elements described above, including the collection unit, generation unit, money collection unit, selection unit, and sending unit, can be implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit can collect messages and photos using the microphone 238 of the robot 414. The generation unit can analyze messages and photos using the specific processing unit 290 of the data processing unit 12 and generate a group message. The money collection unit can collect the gift money using an electronic payment system via the communication I / F 44 of the robot 414. The selection unit can select a gift considering the attributes of the recipient using the specific processing unit 290 of the data processing unit 12. The sending unit can send the group message and gift together using the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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."

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] (Note 1) The collection department collects messages and photos, A generation unit analyzes the messages and photos collected by the collection unit to generate a group message, The collection department that collects the gift money, A selection department selects gifts based on the gift money collected by the aforementioned collection department, The system includes a sending unit that sends the message board generated by the generation unit and the gift selected by the selection unit together. A system characterized by the following features. (Note 2) The generating unit is It features a design selection section that selects background colors and fonts based on the message content. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned selection unit is It includes a gift selection department that chooses gifts based on the recipient's preferences and hobbies. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned transmission unit is It features a bulk mailing section for sending group messages and gifts together. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of message and photo collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Analyze the user's past participation history in group messages and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting messages and photos, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and determines the priority of messages and photos to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting messages and photos, the system prioritizes collecting the most relevant messages and photos by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting messages and photos, the system analyzes the user's social media activity and collects relevant messages and photos. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is The system estimates the user's emotions and adjusts the design of the message board based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is During generation, the level of detail in the message is adjusted based on the importance of each message. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is During generation, different generation algorithms are applied depending on the message category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and adjusts the length of the message based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, the priority of the group message is determined based on when the message was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During generation, the order of the messages is adjusted based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection department, The system estimates the user's emotions and adjusts the timing of payment collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned collection department, When collecting payments, the system analyzes the user's past payment history to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned collection department, When collecting payments, the amount collected will be adjusted based on the user's current financial situation. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned collection department, The system estimates user sentiment and determines collection priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned collection department, When collecting payments, the system selects the most suitable collection method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned collection department, When collecting payments, we analyze the user's social media activity and suggest payment methods. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned selection unit is The system estimates the user's emotions and adjusts the gift selection method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned selection unit is During the selection process, the system analyzes the user's past gift selection history to choose the most suitable gift. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned selection unit is During the selection process, the gift selection is customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned selection unit is It estimates the user's emotions and determines the priority of gifts based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned selection unit is When selecting a gift, the most suitable gift will be chosen considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned selection unit is During the selection process, we analyze users' social media activity to select the gifts. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned transmission unit is It estimates the user's emotions and adjusts the timing of sending messages based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned transmission unit is When sending a message, the system analyzes the user's past sending history to select the most suitable sending method. The system described in Appendix 1, characterized by the features described herein. (Note 31) 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 32) 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 33) 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 34) 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. (Note 35) The aforementioned design selection unit, It estimates the user's emotions and adjusts the background color and font selection based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned design selection unit, When selecting a design, choose the most suitable design based on the message content. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned design selection unit, When selecting a design, customize the design based on the placement of the photos. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned design selection unit, We estimate user emotions and determine design priorities based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned design selection unit, When selecting a design, prioritize the designs based on when the message will be submitted. The system described in Appendix 2, characterized by the features described herein. (Note 40) The aforementioned design selection unit, When selecting a design, adjust the order of the designs based on the relevance of the messages The system according to appended note 2, characterized in that (Appended note 41) The present selection unit Estimates the user's emotion and adjusts the present selection method based on the estimated user's emotion<​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​(Note 48) The aforementioned bulk sending unit is When sending in bulk, the system analyzes the user's past sending history to select the most suitable sending method. The system described in Appendix 4, characterized by the features described herein. (Note 49) The aforementioned bulk sending unit is When sending items in bulk, the delivery method is customized based on the user's current living situation. The system described in Appendix 4, characterized by the features described herein. (Note 50) The aforementioned bulk sending unit is The system estimates the user's emotions and determines the priority of mass mailings based on the estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 51) The aforementioned bulk sending unit is When sending items in bulk, the system selects the most suitable delivery method by considering the user's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 52) The aforementioned bulk sending unit is When sending out materials in bulk, we analyze users' social media activity and suggest delivery methods. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0222] 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. The collection department collects messages and photos, A generation unit analyzes the messages and photos collected by the collection unit to generate a group message, The collection department that collects the gift money, A selection department selects gifts based on the gift money collected by the aforementioned collection department, The system includes a sending unit that sends the message board generated by the generation unit and the gift selected by the selection unit together. A system characterized by the following features.

2. The generating unit is It features a design selection section that selects background colors and fonts based on the message content. The system according to feature 1.

3. The aforementioned selection unit is It includes a gift selection department that chooses gifts based on the recipient's preferences and hobbies. The system according to feature 1.

4. The aforementioned transmission unit is It features a bulk mailing section for sending group messages and gifts together. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of message and photo collection based on the estimated user emotions. The system according to feature 1.

6. The aforementioned collection unit is Analyze the user's past participation history in group messages and select the optimal collection method. The system according to feature 1.

7. The aforementioned collection unit is When collecting messages and photos, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and determines the priority of messages and photos to collect based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is When collecting messages and photos, the system prioritizes collecting the most relevant messages and photos by considering the user's geographical location. The system according to feature 1.

10. The aforementioned collection unit is When collecting messages and photos, the system analyzes the user's social media activity and collects relevant messages and photos. The system according to feature 1.

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