system

Abstract

The system according to this embodiment aims to streamline photo sharing and life event management among family members, and to support the suggestion and purchase of gifts. [Solution] The system according to the embodiment comprises a sharing unit, a suggestion unit, a provision unit, a generation unit, a notification unit, and a support unit. The sharing unit invites family members to share photos. The suggestion unit suggests gifts based on the information shared by the sharing unit. The provision unit provides a purchase link for the gifts suggested by the suggestion unit. The generation unit generates facial photos using generation AI. The notification unit notifies of life events. The support unit supports the purchase and delivery of celebratory gifts based on the life events notified by the notification unit.

Description

Technical Field

[0004] ,

[0006] , , , ,

[0005] , , , ,

[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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there are problems that photo sharing among family members and management of life events are cumbersome, and present proposals and purchases are not efficiently carried out.

[0005] The system according to the embodiment aims to streamline photo sharing among family members and management of life events and support present proposals and purchases.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a sharing unit, a suggestion unit, a provision unit, a generation unit, a notification unit, and a support unit. The sharing unit invites family members to share photos. The suggestion unit suggests gifts based on the information shared by the sharing unit. The provision unit provides a purchase link for the gifts suggested by the suggestion unit. The generation unit generates facial photos using generation AI. The notification unit notifies of life events. The support unit supports the purchase and delivery of celebratory gifts based on the life events notified by the notification unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline photo sharing and life event management among family members, and support the suggestion and purchase of 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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied 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 system according to an embodiment of the present invention is a system that invites family members to share photos, suggests gifts, provides purchase links, generates facial images, notifies users of life events, and supports the purchase and delivery of celebratory gifts. This system allows users to register necessary information such as the date of birth, facial image, gender, and name of their grandchildren or children when they are born. After registration, it is easy to invite family members and share photos. The AI ​​suggests gifts in image form based on the average interests and preferences of the grandchildren's or children's generation and provides purchase links to e-commerce sites. Furthermore, a function has been added to generate facial images of grandchildren's or children from baby photos, showing them as young adults, middle-aged adults, and elderly adults, using a generation AI. In addition, it includes a life event notification function, notifying users one month in advance of information such as, "What grade are they in now? Are they starting school next year? Is the younger child graduating from kindergarten?" This notification function eliminates the worry of forgetting, and the AI ​​supports the purchase and delivery of celebratory gifts, making it easy to use even for first-time users. For example, a user downloads the app and creates an account. Next, the user enters information about their grandchildren or children and invites family members. Family members can share photos by accepting the invitation. The AI ​​suggests gifts based on the user's input information. For example, the AI ​​analyzes the user's preferences and past purchase history to suggest the most suitable gift. Suggested gifts are displayed with images, and a purchase link is provided. The user can easily purchase the gift by clicking the link. In addition, the generative AI generates portraits of the person in their youth, middle age, and old age based on baby photos uploaded by the user. The generated portraits are saved in the user's album and can be shared with family. Furthermore, a life event notification function allows users to purchase and send celebratory gifts at the appropriate time without forgetting important events. Based on the user's calendar information, the AI ​​sends notifications one month before an event and suggests appropriate gifts. This makes it easy for the user to prepare for celebrations.This allows the system to support family photo sharing, gift suggestions, purchase links, profile picture generation, life event notifications, and the purchase and delivery of celebratory gifts.

[0029] The system according to this embodiment comprises a sharing unit, a suggestion unit, a provision unit, a generation unit, a notification unit, and a support unit. The sharing unit invites family members to share photos. Family members include, but are not limited to, blood relatives, marital relatives, and cohabitants. The sharing unit can, for example, share digital photos in album format. The sharing unit can also share photos using a sharing platform. The suggestion unit suggests gifts based on the information shared by the sharing unit. The suggestion unit suggests gifts based, for example, the user's preferences and past purchase history. The suggestion unit can use AI to analyze the user's interests and preferences and suggest the most suitable gift. For example, the suggestion unit analyzes the user's past purchase history and suggests a highly relevant gift. The suggestion unit can also suggest gifts based on the user's current interests and concerns. The provision unit provides purchase links for the gifts suggested by the suggestion unit. The provision unit provides purchase links to specific e-commerce sites, for example. The provision unit can use AI to provide the most suitable purchase links based on the user's preferences and past purchase history. For example, the provisioning unit analyzes the user's past purchase history and provides highly relevant purchase links. The provisioning unit can also provide purchase links based on the user's current purchasing trends. The generation unit generates facial images using generation AI. For example, the generation unit can generate facial images of adolescence, middle age, and old age based on photos from infancy. The generation unit can use generation AI to analyze the user's past photo history and select the optimal generation method. For example, the generation unit selects a highly relevant generation method based on the user's past photo history. The generation unit can also customize facial images based on the user's current living situation. The notification unit notifies users of life events. For example, the notification unit notifies users of life events such as birthdays, weddings, and graduations one month in advance. The notification unit can use AI to notify users of life events based on their calendar information. For example, the notification unit analyzes the user's calendar information and sends notifications at the appropriate time. The notification unit can also estimate the user's emotions and adjust the timing of notifications based on those emotions.The support department assists with purchasing and shipping celebratory gifts based on life events notified by the notification department. The support department purchases gifts such as gift cards, bouquets, and specific merchandise. Using AI, the support department can suggest the most suitable gifts based on the user's preferences and past purchase history. For example, it can analyze the user's past purchase history and suggest highly relevant gifts. The support department can also customize gifts based on the user's current life circumstances. This allows the system to support family photo sharing, gift suggestions, purchase links, profile picture generation, life event notifications, and the purchase and shipping of celebratory gifts.

[0030] The suggestion department can propose gifts based on the interests and preferences of grandchildren's or children's generations. For example, the suggestion department can identify generational interests and preferences using surveys. For instance, the suggestion department can conduct surveys with users to collect information on their interests and preferences. The suggestion department can also identify generational interests and preferences using statistical data. For example, the suggestion department can analyze statistical data for each generation to identify their interests and preferences. Furthermore, the suggestion department can identify generational interests and preferences using social media analysis. For example, the suggestion department can analyze social media posts to identify their interests and preferences. This allows the suggestion department to propose gifts based on the average interests and preferences of each generation.

[0031] The provider can provide purchase links to e-commerce sites. For example, the provider can provide purchase links to specific e-commerce sites such as Yahoo! Shopping, Amazon, and eBay. The provider can also provide affiliate links. For example, the provider can provide affiliate links to specific products. This enables the provider to provide purchase links to e-commerce sites.

[0032] The generation unit can use generative AI to generate facial images of grandchildren or children from their baby photos, showing them in their youth, middle age, and old age. For example, the generation unit generates facial images of young adults, middle-aged adults, and old age based on baby photos. Baby photos include, for example, photos from a certain number of months to a certain age, but are not limited to this example. The generation unit can use generative AI to analyze the user's past photo history and select the optimal generation method. For example, the generation unit selects a highly relevant generation method based on the user's past photo history. The generation unit can also customize facial images based on the user's current lifestyle. For example, the generation unit generates facial images with clothing and backgrounds that match the user's current lifestyle. This enables the generation unit to generate facial images using generative AI.

[0033] The notification unit can notify users of life events such as "What grade are you in now? Are you starting school next year? Is your younger child graduating from kindergarten?" up to one month in advance. The notification unit can notify users of life events such as birthdays, weddings, and graduations up to one month in advance. The notification unit can use AI to notify users of life events based on their calendar information. For example, the notification unit analyzes the user's calendar information and sends notifications at the appropriate time. The notification unit can also estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, if the notification unit is relaxed, it will send a notification immediately. This enables the notification unit to notify users of life events.

[0034] The support department can assist with the purchase and delivery of celebratory gifts based on notified life events. The support department can purchase celebratory gifts such as gift cards, bouquets, or specific merchandise. Using AI, the support department can suggest the most suitable gifts based on the user's preferences and past purchase history. For example, the support department can analyze the user's past purchase history and suggest highly relevant gifts. Furthermore, the support department can customize gifts based on the user's current life circumstances. For example, the support department can suggest gifts tailored to the user's current life situation. This enables the support department to assist with the purchase and delivery of celebratory gifts.

[0035] The sharing function can analyze a user's past sharing history and select the optimal sharing method. For example, it can prioritize suggesting sharing methods that the user has preferred in the past. The sharing function can use data mining techniques to analyze past sharing history. For example, it can analyze the types and frequency of photos a user has shared in the past and select the optimal sharing method. The sharing function can also suggest sharing at specific times based on the user's past sharing history. For example, it can analyze the times when a user has shared photos in the past and suggest sharing at those times. This allows the sharing function to select the optimal sharing method based on past sharing history.

[0036] The sharing function can filter the photos it shares based on the user's current interests. For example, it can prioritize sharing photos related to themes the user is currently interested in. The sharing function can analyze social media activity to identify the user's current interests. For example, it can analyze the user's recent posts and the number of "likes" to identify areas of interest. It can also identify areas of interest based on the user's recent search history. For example, it can analyze the user's search history and filter related photos. Furthermore, the sharing function can share photos related to groups and communities the user belongs to. For example, it can filter photos based on the themes of groups the user belongs to. This allows the sharing function to filter photos based on the user's current interests.

[0037] The sharing function can prioritize sharing highly relevant photos by considering the user's geographical location information during sharing. For example, it can prioritize sharing photos related to the user's current location. The sharing function can utilize GPS data or IP addresses to obtain geographical location information. For example, it can prioritize sharing relevant photos based on the user's current location information. The sharing function can also share photos related to places the user has visited in the past. For example, it can analyze the user's past location information and share relevant photos. Furthermore, the sharing function can share photos related to nearby events or places based on the user's current location information. For example, it can share photos related to nearby tourist attractions or events based on the user's current location information. This enables the sharing function to prioritize sharing photos based on geographical location information.

[0038] The sharing function can analyze a user's social media activity when sharing and share relevant photos. For example, it can share photos related to posts the user has recently "liked." The sharing function can use data mining techniques to analyze social media activity. For example, it can analyze the content of a user's posts and the number of "likes" and share relevant photos. The sharing function can also share photos related to accounts the user follows. For example, it can share photos based on the themes of the accounts the user follows. Furthermore, the sharing function can share photos related to events the user is attending. For example, it can share photos based on the themes of the events the user is attending. This enables the sharing function to share photos based on social media activity.

[0039] The suggestion function can adjust the level of detail in its suggestions based on the importance of the gift. For example, for important events, the suggestion function can provide detailed descriptions and multiple options. To assess the importance of a gift, the suggestion function can consider the user's past purchase history and the significance of the event. For example, it can analyze the user's past purchase history and suggest gifts related to important events. The suggestion function can also provide concise descriptions and a limited number of options for everyday gifts. For example, it can make concise gift suggestions based on the user's everyday purchase history. Furthermore, for special anniversaries, the suggestion function can offer special options and customizable suggestions. For example, it can make gift suggestions tailored to the user's special anniversary. This allows the suggestion function to adjust the level of detail in its suggestions based on the importance of the gift.

[0040] The suggestion function can apply different suggestion algorithms depending on the gift category. For example, when suggesting toys, it applies an algorithm that considers age-appropriate safety and interests. The suggestion function can analyze the user's past purchase history and interests to identify the gift category. For example, it applies a different suggestion algorithm for each category based on the user's past purchase history. Furthermore, when suggesting clothing, the suggestion function can apply an algorithm that considers the season and trends. For example, it analyzes the user's current season and trends to suggest the most suitable clothing. In addition, when suggesting books, the suggestion function can apply an algorithm that considers the user's reading history and interests. For example, it analyzes the user's reading history to suggest highly relevant books. This allows the suggestion function to apply suggestion algorithms according to the gift category.

[0041] The proposal team can prioritize proposals based on the timing of gift submission. For example, the proposal team might prioritize gifts related to upcoming events. The proposal team can analyze the user's calendar information and event schedules to determine the appropriate time to submit gifts. For example, the proposal team might suggest gifts related to upcoming events based on the user's calendar information. The proposal team can also prioritize seasonal gifts. For example, the proposal team might suggest gifts that are appropriate for the current season. Furthermore, the proposal team can prioritize gifts related to important events based on the user's calendar information. For example, the proposal team might analyze the user's calendar information and suggest gifts related to important events. This allows the proposal team to prioritize proposals based on the timing of gift submission.

[0042] The suggestion function can adjust the order of suggestions based on the relevance of the gifts when making suggestions. For example, the suggestion function can prioritize suggesting highly relevant gifts based on the user's past purchase history. The suggestion function can analyze the user's past purchase history and current interests to evaluate the relevance of gifts. For example, the suggestion function can suggest highly relevant gifts based on the user's past purchase history. The suggestion function can also suggest gifts based on the user's current interests and concerns. For example, the suggestion function can analyze the user's current interests and suggest highly relevant gifts. Furthermore, the suggestion function can suggest gifts based on the user's social media activity. For example, the suggestion function can analyze the user's social media posts and suggest highly relevant gifts. This allows the suggestion function to adjust the order of suggestions based on the relevance of the gifts.

[0043] The service provider can analyze the user's past purchase history at the time of delivery and select the most suitable purchase link. For example, the service provider can prioritize providing links related to products the user has previously purchased. The service provider can use data mining techniques to analyze past purchase history. For example, the service provider can provide highly relevant purchase links based on the user's past purchase history. The service provider can also prioritize providing products of specific brands or categories based on the user's past purchase history. For example, the service provider can analyze the user's past purchase history and provide products of specific brands. This enables the service provider to select the most suitable purchase link based on past purchase history.

[0044] The service provider can filter links based on the user's current purchasing trends at the time of delivery. For example, the service provider may prioritize providing links related to products the user has recently purchased. The service provider can analyze the user's recent purchase and search history to identify current purchasing trends. For example, the service provider may provide highly relevant links based on the user's recent purchase history. The service provider can also provide links based on the user's recent search history. For example, the service provider may analyze the user's search history and provide relevant links. This allows the service provider to filter links based on current purchasing trends.

[0045] The service provider can prioritize providing highly relevant links by considering the user's geographical location information at the time of delivery. For example, the service provider can prioritize providing product links related to the user's current location. The service provider can use GPS data or IP addresses to obtain geographical location information. For example, the service provider can provide relevant product links based on the user's current location information. The service provider can also provide product links related to places the user has visited in the past. For example, the service provider can analyze the user's past location information and provide relevant product links. This enables the service provider to prioritize the provision of links based on geographical location information.

[0046] The service provider can analyze the user's social media activity at the time of delivery and provide relevant links. For example, the service provider can provide product links related to posts the user has recently "liked." The service provider can use data mining techniques to analyze social media activity. For example, the service provider can analyze the content of the user's posts and the number of "likes" and provide relevant product links. The service provider can also provide product links related to accounts the user follows. For example, the service provider can provide product links based on the themes of the accounts the user follows. This enables the service provider to provide links based on social media activity.

[0047] The generation unit can analyze the user's past photo history during generation and select the optimal generation method. For example, the generation unit can generate face photos based on filters and effects that the user has previously preferred to use. The generation unit can use data mining techniques to analyze past photo history. For example, the generation unit can select a highly relevant generation method based on the user's past photo history. The generation unit can also generate face photos based on specific styles or themes from the user's past photo history. For example, the generation unit analyzes the user's past photo history and generates face photos based on specific styles or themes. This allows the generation unit to select the optimal generation method based on past photo history.

[0048] The generation unit can customize the photos it generates based on the user's current lifestyle. For example, it can generate a face photo with clothing and a background that suits the user's current lifestyle. To determine the user's current lifestyle, the generation unit can consider the user's place of residence, occupation, and family structure. For example, it can generate a face photo on a relevant theme based on the user's place of residence and occupation. The generation unit can also generate a face photo based on the user's current interests. For example, it can analyze the user's current interests and generate a face photo on a relevant theme. This allows the generation unit to customize photos based on the user's current lifestyle.

[0049] The generation unit can prioritize generating highly relevant photos by considering the user's geographical location information during generation. For example, the generation unit can generate a face photo with a background related to the user's current location. The generation unit can use GPS data or IP addresses to obtain geographical location information. For example, the generation unit can generate a face photo with a relevant background based on the user's current location information. The generation unit can also generate face photos with themes related to places the user has visited in the past. For example, the generation unit can analyze the user's past location information and generate a face photo with a relevant theme. This enables the generation unit to prioritize the generation of photos based on geographical location information.

[0050] The generation unit can analyze a user's social media activity during generation and generate relevant photos. For example, the generation unit can generate profile pictures on themes related to posts the user has recently "liked." The generation unit can use data mining techniques to analyze social media activity. For example, the generation unit can analyze the content of a user's posts and the number of "likes" and generate profile pictures on relevant themes. The generation unit can also generate profile pictures in styles related to accounts the user follows. For example, the generation unit can generate profile pictures based on the themes of accounts the user follows. Furthermore, the generation unit can generate profile pictures on themes related to events the user is attending. For example, the generation unit can generate profile pictures based on the themes of events the user is attending. This enables the generation unit to generate photos based on social media activity.

[0051] The notification unit can analyze the user's past notification history when sending a notification and select the optimal notification method. For example, the notification unit may prioritize suggesting notification methods that the user has preferred to use in the past. The notification unit can use data mining techniques to analyze past notification history. For example, the notification unit can select the optimal notification method based on the user's past notification history. The notification unit can also select a notification method based on the types of notifications the user has received in the past. For example, the notification unit can analyze the user's past notification history and suggest sending notifications at specific time periods. This allows the notification unit to select the optimal notification method based on past notification history.

[0052] The notification unit can customize notification content based on the user's current living situation. For example, the notification unit can send notifications tailored to the user's current living situation. To identify the current living situation, the notification unit can consider the user's place of residence, occupation, and family structure. For example, the notification unit can send notifications with relevant content based on the user's place of residence and occupation. The notification unit can also customize notification content based on the user's current interests. For example, the notification unit can analyze the user's current interests and send notifications with relevant content. This allows the notification unit to customize notification content based on the user's current living situation.

[0053] The notification unit can prioritize highly relevant notifications by considering the user's geographical location information when sending notifications. For example, the notification unit can send notifications related to the user's current location. The notification unit can use GPS data or IP addresses to obtain geographical location information. For example, the notification unit can send notifications related to the user's current location information. The notification unit can also send notifications related to places the user has visited in the past. For example, the notification unit can analyze the user's past location information and send notifications related to that information. This enables the notification unit to prioritize the execution of notifications based on geographical location information.

[0054] The notification unit can analyze a user's social media activity at the time of notification and send relevant notifications. For example, the notification unit can send notifications related to posts the user has recently "liked". The notification unit can use data mining techniques to analyze social media activity. For example, the notification unit can analyze the content of a user's posts and the number of "likes" and send relevant notifications. The notification unit can also send notifications related to accounts the user follows. For example, the notification unit can send notifications based on the themes of accounts the user follows. This enables the notification unit to execute notifications based on social media activity.

[0055] The support department can analyze a user's past support history during support sessions and select the most appropriate support method. For example, the support department can prioritize suggesting support methods that the user has preferred in the past. The support department can use data mining techniques to analyze past support history. For example, the support department can select the most appropriate support method based on the user's past support history. The support department can also select support methods based on the types of support the user has received in the past. For example, the support department can analyze the user's past support history and suggest providing support during specific time slots. This allows the support department to select the most appropriate support method based on past support history.

[0056] The support department can customize the support provided based on the user's current living situation. For example, the support department can provide support tailored to the user's current living situation. To identify the user's current living situation, the support department can consider the user's place of residence, occupation, and family structure. For example, the support department can provide support based on the user's place of residence and occupation. The support department can also customize the support based on the user's current interests. For example, the support department can analyze the user's current interests and provide support based on those interests. This allows the support department to customize support based on the user's current living situation.

[0057] The support department can select the most appropriate support method by considering the user's geographical location information during support. For example, the support department can provide support related to the user's current location. The support department can use GPS data or IP addresses to obtain geographical location information. For example, the support department can provide support based on the user's current location information. The support department can also provide support related to places the user has visited in the past. For example, the support department can analyze the user's past location information and provide support based on that information. This enables the support department to select the most appropriate support method based on geographical location information.

[0058] The support department can analyze a user's social media activity during support and provide relevant support. For example, the support department can provide support related to posts the user has recently "liked." The support department can use data mining techniques to analyze social media activity. For example, the support department can analyze the content of a user's posts and the number of "likes" and provide relevant support. The support department can also provide support related to accounts the user follows. For example, the support department can provide support based on the themes of the accounts the user follows. Furthermore, the support department can provide support related to events the user is participating in. For example, the support department can provide support based on the themes of events the user is participating in. This enables the support department to provide support based on social media activity.

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

[0060] The suggestion department can adjust the level of detail in its suggestions based on the importance of the gift. For example, for important events, it can provide detailed descriptions and multiple options. To assess the importance of a gift, the suggestion department can consider the user's past purchase history and the significance of events. For example, it can analyze the user's past purchase history and suggest gifts related to important events. For everyday gifts, the suggestion department can also provide concise descriptions and a limited number of options. For example, it can make concise gift suggestions based on the user's everyday purchase history. Furthermore, for special anniversaries, the suggestion department can offer special options and customizable suggestions. For example, it can suggest gifts tailored to the user's special anniversary. This allows the suggestion department to adjust the level of detail in its suggestions based on the importance of the gift.

[0061] The service provider can analyze the user's past purchase history at the time of delivery and select the most relevant purchase links. For example, it can prioritize providing links related to products the user has previously purchased. The service provider can use data mining techniques to analyze past purchase history. For example, it can provide highly relevant purchase links based on the user's past purchase history. The service provider can also prioritize providing products from specific brands or categories based on the user's past purchase history. For example, it can analyze the user's past purchase history and provide products from specific brands. This enables the service provider to select the most relevant purchase links based on past purchase history.

[0062] The generation unit can analyze the user's past photo history during generation and select the optimal generation method. For example, it can generate face photos based on filters and effects the user has previously preferred to use. The generation unit can use data mining techniques to analyze past photo history. For example, it can select a highly relevant generation method based on the user's past photo history. The generation unit can also generate face photos based on specific styles or themes from the user's past photo history. For example, it can analyze the user's past photo history and generate face photos based on specific styles or themes. This allows the generation unit to select the optimal generation method based on past photo history.

[0063] The notification unit can analyze the user's past notification history when sending a notification and select the optimal notification method. For example, it can prioritize suggesting notification methods that the user has preferred in the past. The notification unit can use data mining techniques to analyze past notification history. For example, it can select the optimal notification method based on the user's past notification history. The notification unit can also select a notification method based on the types of notifications the user has received in the past. For example, it can analyze the user's past notification history and suggest sending notifications at specific time periods. This allows the notification unit to select the optimal notification method based on past notification history.

[0064] The support department can analyze a user's past support history during support sessions and select the most suitable support method. For example, it can prioritize suggesting support methods that the user has preferred in the past. The support department can use data mining techniques to analyze past support history. For example, it can select the most suitable support method based on the user's past support history. The support department can also select support methods based on the types of support the user has received in the past. For example, it can analyze the user's past support history and suggest providing support during specific time slots. This allows the support department to select the most suitable support method based on past support history.

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

[0066] Step 1: Share the photos by inviting family members. Family members include, but are not limited to, blood relatives, spouses, and cohabitants. Share the photos in album format, or you can share them using a sharing platform. Step 2: The suggestion department proposes gifts based on the information shared by the sharing department. The suggestion department can propose gifts based on the user's preferences and past purchase history, and can use AI to analyze the user's interests and preferences to propose the most suitable gift. Step 3: The provisioning department provides the purchase link for the gift suggested by the proposaling department. The provisioning department can provide a purchase link for a specific e-commerce site and use AI to provide the most suitable purchase link based on the user's preferences and past purchase history. Step 4: The generation unit generates facial photographs using generation AI. Based on baby photos, the generation unit generates facial photographs for adolescence, middle age, and old age, and can analyze the user's past photo history to select the optimal generation method. Step 5: The notification unit notifies users of life events. The notification unit notifies users of life events such as birthdays, weddings, and graduations one month in advance, and uses AI to send notifications at the appropriate time based on the user's calendar information. Step 6: The support department assists with purchasing and shipping celebratory gifts based on life events notified by the notification department. The support department can purchase celebratory gifts such as gift cards, bouquets, and specific products, and can use AI to suggest the most suitable gifts based on the user's preferences and past purchase history.

[0067] (Example of form 2) The system according to an embodiment of the present invention is a system that invites family members to share photos, suggests gifts, provides purchase links, generates facial images, notifies users of life events, and supports the purchase and delivery of celebratory gifts. This system allows users to register necessary information such as the date of birth, facial image, gender, and name of their grandchildren or children when they are born. After registration, it is easy to invite family members and share photos. The AI ​​suggests gifts in image form based on the average interests and preferences of the grandchildren's or children's generation and provides purchase links to e-commerce sites. Furthermore, a function has been added to generate facial images of grandchildren's or children from baby photos, showing them as young adults, middle-aged adults, and elderly adults, using a generation AI. In addition, it includes a life event notification function, notifying users one month in advance of information such as, "What grade are they in now? Are they starting school next year? Is the younger child graduating from kindergarten?" This notification function eliminates the worry of forgetting, and the AI ​​supports the purchase and delivery of celebratory gifts, making it easy to use even for first-time users. For example, a user downloads the app and creates an account. Next, the user enters information about their grandchildren or children and invites family members. Family members can share photos by accepting the invitation. The AI ​​suggests gifts based on the user's input information. For example, the AI ​​analyzes the user's preferences and past purchase history to suggest the most suitable gift. Suggested gifts are displayed with images, and a purchase link is provided. The user can easily purchase the gift by clicking the link. In addition, the generative AI generates portraits of the person in their youth, middle age, and old age based on baby photos uploaded by the user. The generated portraits are saved in the user's album and can be shared with family. Furthermore, a life event notification function allows users to purchase and send celebratory gifts at the appropriate time without forgetting important events. Based on the user's calendar information, the AI ​​sends notifications one month before an event and suggests appropriate gifts. This makes it easy for the user to prepare for celebrations.This allows the system to support family photo sharing, gift suggestions, purchase links, profile picture generation, life event notifications, and the purchase and delivery of celebratory gifts.

[0068] The system according to this embodiment comprises a sharing unit, a suggestion unit, a provision unit, a generation unit, a notification unit, and a support unit. The sharing unit invites family members to share photos. Family members include, but are not limited to, blood relatives, marital relatives, and cohabitants. The sharing unit can, for example, share digital photos in album format. The sharing unit can also share photos using a sharing platform. The suggestion unit suggests gifts based on the information shared by the sharing unit. The suggestion unit suggests gifts based, for example, the user's preferences and past purchase history. The suggestion unit can use AI to analyze the user's interests and preferences and suggest the most suitable gift. For example, the suggestion unit analyzes the user's past purchase history and suggests a highly relevant gift. The suggestion unit can also suggest gifts based on the user's current interests and concerns. The provision unit provides purchase links for the gifts suggested by the suggestion unit. The provision unit provides purchase links to specific e-commerce sites, for example. The provision unit can use AI to provide the most suitable purchase links based on the user's preferences and past purchase history. For example, the provisioning unit analyzes the user's past purchase history and provides highly relevant purchase links. The provisioning unit can also provide purchase links based on the user's current purchasing trends. The generation unit generates facial images using generation AI. For example, the generation unit can generate facial images of adolescence, middle age, and old age based on photos from infancy. The generation unit can use generation AI to analyze the user's past photo history and select the optimal generation method. For example, the generation unit selects a highly relevant generation method based on the user's past photo history. The generation unit can also customize facial images based on the user's current living situation. The notification unit notifies users of life events. For example, the notification unit notifies users of life events such as birthdays, weddings, and graduations one month in advance. The notification unit can use AI to notify users of life events based on their calendar information. For example, the notification unit analyzes the user's calendar information and sends notifications at the appropriate time. The notification unit can also estimate the user's emotions and adjust the timing of notifications based on those emotions.The support department assists with purchasing and shipping celebratory gifts based on life events notified by the notification department. The support department purchases gifts such as gift cards, bouquets, and specific merchandise. Using AI, the support department can suggest the most suitable gifts based on the user's preferences and past purchase history. For example, it can analyze the user's past purchase history and suggest highly relevant gifts. The support department can also customize gifts based on the user's current life circumstances. This allows the system to support family photo sharing, gift suggestions, purchase links, profile picture generation, life event notifications, and the purchase and shipping of celebratory gifts.

[0069] The suggestion department can propose gifts based on the interests and preferences of grandchildren's or children's generations. For example, the suggestion department can identify generational interests and preferences using surveys. For instance, the suggestion department can conduct surveys with users to collect information on their interests and preferences. The suggestion department can also identify generational interests and preferences using statistical data. For example, the suggestion department can analyze statistical data for each generation to identify their interests and preferences. Furthermore, the suggestion department can identify generational interests and preferences using social media analysis. For example, the suggestion department can analyze social media posts to identify their interests and preferences. This allows the suggestion department to propose gifts based on the average interests and preferences of each generation.

[0070] The provider can provide purchase links to e-commerce sites. For example, the provider can provide purchase links to specific e-commerce sites such as Yahoo! Shopping, Amazon, and eBay. The provider can also provide affiliate links. For example, the provider can provide affiliate links to specific products. This enables the provider to provide purchase links to e-commerce sites.

[0071] The generation unit can use generative AI to generate facial images of grandchildren or children from their baby photos, showing them in their youth, middle age, and old age. For example, the generation unit generates facial images of young adults, middle-aged adults, and old age based on baby photos. Baby photos include, for example, photos from a certain number of months to a certain age, but are not limited to this example. The generation unit can use generative AI to analyze the user's past photo history and select the optimal generation method. For example, the generation unit selects a highly relevant generation method based on the user's past photo history. The generation unit can also customize facial images based on the user's current lifestyle. For example, the generation unit generates facial images with clothing and backgrounds that match the user's current lifestyle. This enables the generation unit to generate facial images using generative AI.

[0072] The notification unit can notify users of life events such as "What grade are you in now? Are you starting school next year? Is your younger child graduating from kindergarten?" up to one month in advance. The notification unit can notify users of life events such as birthdays, weddings, and graduations up to one month in advance. The notification unit can use AI to notify users of life events based on their calendar information. For example, the notification unit analyzes the user's calendar information and sends notifications at the appropriate time. The notification unit can also estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, if the notification unit is relaxed, it will send a notification immediately. This enables the notification unit to notify users of life events.

[0073] The support department can assist with the purchase and delivery of celebratory gifts based on notified life events. The support department can purchase celebratory gifts such as gift cards, bouquets, or specific merchandise. Using AI, the support department can suggest the most suitable gifts based on the user's preferences and past purchase history. For example, the support department can analyze the user's past purchase history and suggest highly relevant gifts. Furthermore, the support department can customize gifts based on the user's current life circumstances. For example, the support department can suggest gifts tailored to the user's current life situation. This enables the support department to assist with the purchase and delivery of celebratory gifts.

[0074] The sharing function can estimate the user's emotions and adjust the timing of photo sharing based on those emotions. For example, if the user is happy, the sharing function will prompt them to share the photo immediately. The sharing function can estimate the user's emotions using facial recognition technology. For example, the sharing function can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The sharing function can also estimate the user's emotions using voice analysis technology. For example, the sharing function can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the sharing function can also estimate the user's emotions using text analysis technology. For example, the sharing function can analyze the content of the user's message to estimate the emotion. This allows the sharing function to adjust the timing of photo sharing based on the user's emotions.

[0075] The sharing function can analyze a user's past sharing history and select the optimal sharing method. For example, it can prioritize suggesting sharing methods that the user has preferred in the past. The sharing function can use data mining techniques to analyze past sharing history. For example, it can analyze the types and frequency of photos a user has shared in the past and select the optimal sharing method. The sharing function can also suggest sharing at specific times based on the user's past sharing history. For example, it can analyze the times when a user has shared photos in the past and suggest sharing at those times. This allows the sharing function to select the optimal sharing method based on past sharing history.

[0076] The sharing function can filter the photos it shares based on the user's current interests. For example, it can prioritize sharing photos related to themes the user is currently interested in. The sharing function can analyze social media activity to identify the user's current interests. For example, it can analyze the user's recent posts and the number of "likes" to identify areas of interest. It can also identify areas of interest based on the user's recent search history. For example, it can analyze the user's search history and filter related photos. Furthermore, the sharing function can share photos related to groups and communities the user belongs to. For example, it can filter photos based on the themes of groups the user belongs to. This allows the sharing function to filter photos based on the user's current interests.

[0077] The sharing function can estimate the user's emotions and prioritize which photos to share based on those emotions. For example, if the user is happy, the sharing function will prioritize sharing happy photos. The sharing function can estimate the user's emotions using facial recognition technology. For example, the sharing function can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The sharing function can also estimate the user's emotions using voice analysis technology. For example, the sharing function can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the sharing function can also estimate the user's emotions using text analysis technology. For example, the sharing function can analyze the content of the user's message to estimate the emotion. This enables the sharing function to prioritize photos based on the user's emotions.

[0078] The sharing function can prioritize sharing highly relevant photos by considering the user's geographical location information during sharing. For example, it can prioritize sharing photos related to the user's current location. The sharing function can utilize GPS data or IP addresses to obtain geographical location information. For example, it can prioritize sharing relevant photos based on the user's current location information. The sharing function can also share photos related to places the user has visited in the past. For example, it can analyze the user's past location information and share relevant photos. Furthermore, the sharing function can share photos related to nearby events or places based on the user's current location information. For example, it can share photos related to nearby tourist attractions or events based on the user's current location information. This enables the sharing function to prioritize sharing photos based on geographical location information.

[0079] The sharing function can analyze a user's social media activity when sharing and share relevant photos. For example, it can share photos related to posts the user has recently "liked." The sharing function can use data mining techniques to analyze social media activity. For example, it can analyze the content of a user's posts and the number of "likes" and share relevant photos. The sharing function can also share photos related to accounts the user follows. For example, it can share photos based on the themes of the accounts the user follows. Furthermore, the sharing function can share photos related to events the user is attending. For example, it can share photos based on the themes of the events the user is attending. This enables the sharing function to share photos based on social media activity.

[0080] The suggestion unit can estimate the user's emotions and adjust the way it presents gift suggestions based on those emotions. For example, if the user is happy, the suggestion unit will present gifts in a bright and cheerful manner. The suggestion unit can estimate the user's emotions using facial recognition technology. For example, it can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The suggestion unit can also estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the suggestion unit can also estimate the user's emotions using text analysis technology. For example, it can analyze the content of the user's message to estimate the emotion. This allows the suggestion unit to adjust the way it presents gift suggestions based on the user's emotions.

[0081] The suggestion function can adjust the level of detail in its suggestions based on the importance of the gift. For example, for important events, the suggestion function can provide detailed descriptions and multiple options. To assess the importance of a gift, the suggestion function can consider the user's past purchase history and the significance of the event. For example, it can analyze the user's past purchase history and suggest gifts related to important events. The suggestion function can also provide concise descriptions and a limited number of options for everyday gifts. For example, it can make concise gift suggestions based on the user's everyday purchase history. Furthermore, for special anniversaries, the suggestion function can offer special options and customizable suggestions. For example, it can make gift suggestions tailored to the user's special anniversary. This allows the suggestion function to adjust the level of detail in its suggestions based on the importance of the gift.

[0082] The suggestion function can apply different suggestion algorithms depending on the gift category. For example, when suggesting toys, it applies an algorithm that considers age-appropriate safety and interests. The suggestion function can analyze the user's past purchase history and interests to identify the gift category. For example, it applies a different suggestion algorithm for each category based on the user's past purchase history. Furthermore, when suggesting clothing, the suggestion function can apply an algorithm that considers the season and trends. For example, it analyzes the user's current season and trends to suggest the most suitable clothing. In addition, when suggesting books, the suggestion function can apply an algorithm that considers the user's reading history and interests. For example, it analyzes the user's reading history to suggest highly relevant books. This allows the suggestion function to apply suggestion algorithms according to the gift category.

[0083] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on those emotions. For example, if the user is in a hurry, the suggestion unit will provide a short, concise suggestion. The suggestion unit can estimate the user's emotions using facial recognition technology. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The suggestion unit can also estimate the user's emotions using voice analysis technology. For example, the suggestion unit can analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the suggestion unit can also estimate the user's emotions using text analysis technology. For example, the suggestion unit can analyze the content of the user's message to estimate their emotions. This allows the suggestion unit to adjust the length of its suggestions based on the user's emotions.

[0084] The proposal team can prioritize proposals based on the timing of gift submission. For example, the proposal team might prioritize gifts related to upcoming events. The proposal team can analyze the user's calendar information and event schedules to determine the appropriate time to submit gifts. For example, the proposal team might suggest gifts related to upcoming events based on the user's calendar information. The proposal team can also prioritize seasonal gifts. For example, the proposal team might suggest gifts that are appropriate for the current season. Furthermore, the proposal team can prioritize gifts related to important events based on the user's calendar information. For example, the proposal team might analyze the user's calendar information and suggest gifts related to important events. This allows the proposal team to prioritize proposals based on the timing of gift submission.

[0085] The suggestion function can adjust the order of suggestions based on the relevance of the gifts when making suggestions. For example, the suggestion function can prioritize suggesting highly relevant gifts based on the user's past purchase history. The suggestion function can analyze the user's past purchase history and current interests to evaluate the relevance of gifts. For example, the suggestion function can suggest highly relevant gifts based on the user's past purchase history. The suggestion function can also suggest gifts based on the user's current interests and concerns. For example, the suggestion function can analyze the user's current interests and suggest highly relevant gifts. Furthermore, the suggestion function can suggest gifts based on the user's social media activity. For example, the suggestion function can analyze the user's social media posts and suggest highly relevant gifts. This allows the suggestion function to adjust the order of suggestions based on the relevance of the gifts.

[0086] The service provider can estimate the user's emotions and adjust how the purchase link is displayed based on those emotions. For example, if the user is happy, the service provider will display the purchase link in a bright and cheerful design. The service provider can estimate the user's emotions using facial recognition technology. For example, the service provider can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The service provider can also estimate the user's emotions using voice analysis technology. For example, the service provider can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the service provider can also estimate the user's emotions using text analysis technology. For example, the service provider can analyze the content of the user's message to estimate the emotion. This allows the service provider to adjust how the purchase link is displayed based on the user's emotions.

[0087] The service provider can analyze the user's past purchase history at the time of delivery and select the most suitable purchase link. For example, the service provider can prioritize providing links related to products the user has previously purchased. The service provider can use data mining techniques to analyze past purchase history. For example, the service provider can provide highly relevant purchase links based on the user's past purchase history. The service provider can also prioritize providing products of specific brands or categories based on the user's past purchase history. For example, the service provider can analyze the user's past purchase history and provide products of specific brands. This enables the service provider to select the most suitable purchase link based on past purchase history.

[0088] The service provider can filter links based on the user's current purchasing trends at the time of delivery. For example, the service provider may prioritize providing links related to products the user has recently purchased. The service provider can analyze the user's recent purchase and search history to identify current purchasing trends. For example, the service provider may provide highly relevant links based on the user's recent purchase history. The service provider can also provide links based on the user's recent search history. For example, the service provider may analyze the user's search history and provide relevant links. This allows the service provider to filter links based on current purchasing trends.

[0089] The service provider can estimate the user's emotions and prioritize purchase links based on those emotions. For example, if the user is happy, the service provider will prioritize offering enjoyable products. The service provider can estimate the user's emotions using facial recognition technology. For example, the service provider can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The service provider can also estimate the user's emotions using voice analysis technology. For example, the service provider can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the service provider can also estimate the user's emotions using text analysis technology. For example, the service provider can analyze the content of the user's message to estimate the emotion. This enables the service provider to prioritize purchase links based on the user's emotions.

[0090] The service provider can prioritize providing highly relevant links by considering the user's geographical location information at the time of delivery. For example, the service provider can prioritize providing product links related to the user's current location. The service provider can use GPS data or IP addresses to obtain geographical location information. For example, the service provider can provide relevant product links based on the user's current location information. The service provider can also provide product links related to places the user has visited in the past. For example, the service provider can analyze the user's past location information and provide relevant product links. This enables the service provider to prioritize the provision of links based on geographical location information.

[0091] The service provider can analyze the user's social media activity at the time of delivery and provide relevant links. For example, the service provider can provide product links related to posts the user has recently "liked." The service provider can use data mining techniques to analyze social media activity. For example, the service provider can analyze the content of the user's posts and the number of "likes" and provide relevant product links. The service provider can also provide product links related to accounts the user follows. For example, the service provider can provide product links based on the themes of the accounts the user follows. This enables the service provider to provide links based on social media activity.

[0092] The generation unit can estimate the user's emotions and adjust the expression of the generated facial image based on the estimated emotions. For example, if the user is happy, the generation unit will generate a facial image with a bright and cheerful expression. The generation unit can estimate the user's emotions using facial recognition technology. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the user's voice and estimate the emotion. Furthermore, the generation unit can also estimate the user's emotions using text analysis technology. For example, the generation unit can analyze the content of the user's message and estimate the emotion. This allows the generation unit to adjust the expression of the facial image based on the user's emotions.

[0093] The generation unit can analyze the user's past photo history during generation and select the optimal generation method. For example, the generation unit can generate face photos based on filters and effects that the user has previously preferred to use. The generation unit can use data mining techniques to analyze past photo history. For example, the generation unit can select a highly relevant generation method based on the user's past photo history. The generation unit can also generate face photos based on specific styles or themes from the user's past photo history. For example, the generation unit analyzes the user's past photo history and generates face photos based on specific styles or themes. This allows the generation unit to select the optimal generation method based on past photo history.

[0094] The generation unit can customize the photos it generates based on the user's current lifestyle. For example, it can generate a face photo with clothing and a background that suits the user's current lifestyle. To determine the user's current lifestyle, the generation unit can consider the user's place of residence, occupation, and family structure. For example, it can generate a face photo on a relevant theme based on the user's place of residence and occupation. The generation unit can also generate a face photo based on the user's current interests. For example, it can analyze the user's current interests and generate a face photo on a relevant theme. This allows the generation unit to customize photos based on the user's current lifestyle.

[0095] The generation unit can estimate the user's emotions and determine the priority of the facial images to generate based on the estimated emotions. For example, if the user is happy, the generation unit will prioritize generating facial images with happy expressions. The generation unit can estimate the user's emotions using facial recognition technology. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the user's voice and estimate the emotion. Furthermore, the generation unit can also estimate the user's emotions using text analysis technology. For example, the generation unit can analyze the content of the user's message and estimate the emotion. This enables the generation unit to prioritize facial images based on the user's emotions.

[0096] The generation unit can prioritize generating highly relevant photos by considering the user's geographical location information during generation. For example, the generation unit can generate a face photo with a background related to the user's current location. The generation unit can use GPS data or IP addresses to obtain geographical location information. For example, the generation unit can generate a face photo with a relevant background based on the user's current location information. The generation unit can also generate face photos with themes related to places the user has visited in the past. For example, the generation unit can analyze the user's past location information and generate a face photo with a relevant theme. This enables the generation unit to prioritize the generation of photos based on geographical location information.

[0097] The generation unit can analyze a user's social media activity during generation and generate relevant photos. For example, the generation unit can generate profile pictures on themes related to posts the user has recently "liked." The generation unit can use data mining techniques to analyze social media activity. For example, the generation unit can analyze the content of a user's posts and the number of "likes" and generate profile pictures on relevant themes. The generation unit can also generate profile pictures in styles related to accounts the user follows. For example, the generation unit can generate profile pictures based on the themes of accounts the user follows. Furthermore, the generation unit can generate profile pictures on themes related to events the user is attending. For example, the generation unit can generate profile pictures based on the themes of events the user is attending. This enables the generation unit to generate photos based on social media activity.

[0098] The notification unit can estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, if the user is relaxed, the notification unit will send a notification immediately. The notification unit can estimate the user's emotions using facial recognition technology. For example, the notification unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The notification unit can also estimate the user's emotions using voice analysis technology. For example, the notification unit can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the notification unit can also estimate the user's emotions using text analysis technology. For example, the notification unit can analyze the content of the user's message to estimate the emotion. This allows the notification unit to adjust the timing of notifications based on the user's emotions.

[0099] The notification unit can analyze the user's past notification history when sending a notification and select the optimal notification method. For example, the notification unit may prioritize suggesting notification methods that the user has preferred to use in the past. The notification unit can use data mining techniques to analyze past notification history. For example, the notification unit can select the optimal notification method based on the user's past notification history. The notification unit can also select a notification method based on the types of notifications the user has received in the past. For example, the notification unit can analyze the user's past notification history and suggest sending notifications at specific time periods. This allows the notification unit to select the optimal notification method based on past notification history.

[0100] The notification unit can customize notification content based on the user's current living situation. For example, the notification unit can send notifications tailored to the user's current living situation. To identify the current living situation, the notification unit can consider the user's place of residence, occupation, and family structure. For example, the notification unit can send notifications with relevant content based on the user's place of residence and occupation. The notification unit can also customize notification content based on the user's current interests. For example, the notification unit can analyze the user's current interests and send notifications with relevant content. This allows the notification unit to customize notification content based on the user's current living situation.

[0101] The notification unit can estimate the user's emotions and determine notification priorities based on those emotions. For example, if the user is happy, the notification unit will prioritize notifications with positive content. The notification unit can estimate the user's emotions using facial recognition technology. For example, the notification unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The notification unit can also estimate the user's emotions using voice analysis technology. For example, the notification unit can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the notification unit can also estimate the user's emotions using text analysis technology. For example, the notification unit can analyze the content of the user's message to estimate the emotion. This enables the notification unit to prioritize notifications based on the user's emotions.

[0102] The notification unit can prioritize highly relevant notifications by considering the user's geographical location information when sending notifications. For example, the notification unit can send notifications related to the user's current location. The notification unit can use GPS data or IP addresses to obtain geographical location information. For example, the notification unit can send notifications related to the user's current location information. The notification unit can also send notifications related to places the user has visited in the past. For example, the notification unit can analyze the user's past location information and send notifications related to that information. This enables the notification unit to prioritize the execution of notifications based on geographical location information.

[0103] The notification unit can analyze a user's social media activity at the time of notification and send relevant notifications. For example, the notification unit can send notifications related to posts the user has recently "liked". The notification unit can use data mining techniques to analyze social media activity. For example, the notification unit can analyze the content of a user's posts and the number of "likes" and send relevant notifications. The notification unit can also send notifications related to accounts the user follows. For example, the notification unit can send notifications based on the themes of accounts the user follows. This enables the notification unit to execute notifications based on social media activity.

[0104] The support unit can estimate the user's emotions and adjust its support methods based on those emotions. For example, if the user is happy, the support unit will provide cheerful and friendly support. The support unit can estimate the user's emotions using facial recognition technology. For example, the support unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The support unit can also estimate the user's emotions using voice analysis technology. For example, the support unit can analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the support unit can also estimate the user's emotions using text analysis technology. For example, the support unit can analyze the content of the user's message to estimate their emotions. This allows the support unit to adjust its support methods based on the user's emotions.

[0105] The support department can analyze a user's past support history during support sessions and select the most appropriate support method. For example, the support department can prioritize suggesting support methods that the user has preferred in the past. The support department can use data mining techniques to analyze past support history. For example, the support department can select the most appropriate support method based on the user's past support history. The support department can also select support methods based on the types of support the user has received in the past. For example, the support department can analyze the user's past support history and suggest providing support during specific time slots. This allows the support department to select the most appropriate support method based on past support history.

[0106] The support department can customize the support provided based on the user's current living situation. For example, the support department can provide support tailored to the user's current living situation. To identify the user's current living situation, the support department can consider the user's place of residence, occupation, and family structure. For example, the support department can provide support based on the user's place of residence and occupation. The support department can also customize the support based on the user's current interests. For example, the support department can analyze the user's current interests and provide support based on those interests. This allows the support department to customize support based on the user's current living situation.

[0107] The support unit can estimate the user's emotions and determine the priority of support based on those emotions. For example, if the user is happy, the support unit will prioritize providing support with positive content. The support unit can estimate the user's emotions using facial recognition technology. For example, the support unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The support unit can also estimate the user's emotions using voice analysis technology. For example, the support unit can analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the support unit can also estimate the user's emotions using text analysis technology. For example, the support unit can analyze the content of the user's message to estimate their emotions. This enables the support unit to prioritize support based on the user's emotions.

[0108] The support department can select the most appropriate support method by considering the user's geographical location information during support. For example, the support department can provide support related to the user's current location. The support department can use GPS data or IP addresses to obtain geographical location information. For example, the support department can provide support based on the user's current location information. The support department can also provide support related to places the user has visited in the past. For example, the support department can analyze the user's past location information and provide support based on that information. This enables the support department to select the most appropriate support method based on geographical location information.

[0109] The support department can analyze a user's social media activity during support and provide relevant support. For example, the support department can provide support related to posts the user has recently "liked." The support department can use data mining techniques to analyze social media activity. For example, the support department can analyze the content of a user's posts and the number of "likes" and provide relevant support. The support department can also provide support related to accounts the user follows. For example, the support department can provide support based on the themes of the accounts the user follows. Furthermore, the support department can provide support related to events the user is participating in. For example, the support department can provide support based on the themes of events the user is participating in. This enables the support department to provide support based on social media activity. === Hard Collateral 1-1 === Each of the multiple elements described above, including the sharing unit, suggestion unit, provision unit, generation unit, notification unit, and support unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the sharing unit is implemented by the control unit 46A of the smart device 14 and invites family members to share photos. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and suggests gifts based on the user's preferences and past purchase history. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 and provides a purchase link for the suggested gift. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a facial photograph using generation AI. The notification unit is implemented, for example, by the control unit 46A of the smart device 14 and notifies of life events. The support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and supports the purchase and delivery of celebratory gifts based on the notified life event. === Hard Collateral 1-2 === Each of the multiple elements described above, including the sharing unit, suggestion unit, provision unit, generation unit, notification unit, and support unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the sharing unit is implemented by the control unit 46A of the smart glasses 214 and invites family members to share photos. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and suggests gifts based on the user's preferences and past purchase history. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides a purchase link for the suggested gift. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a facial photograph using generation AI. The notification unit is implemented, for example, by the control unit 46A of the smart glasses 214 and notifies of life events. The support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and supports the purchase and delivery of celebratory gifts based on the notified life event. === Hard Collateral 1-3 === Each of the multiple elements described above, including the sharing unit, suggestion unit, provision unit, generation unit, notification unit, and support unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the sharing unit is implemented by the control unit 46A of the headset terminal 314 and invites family members to share photos. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and suggests gifts based on the user's preferences and past purchase history. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides a purchase link for the suggested gift. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a facial photograph using generation AI. The notification unit is implemented, for example, by the control unit 46A of the headset terminal 314 and notifies of life events. The support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and supports the purchase and delivery of celebratory gifts based on the notified life event. === Hard Collateral 1-4 === Each of the multiple elements described above, including the sharing unit, suggestion unit, provision unit, generation unit, notification unit, and support unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the sharing unit is implemented by the control unit 46A of the robot 414 and invites family members to share photos. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and suggests gifts based on the user's preferences and past purchase history. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides a purchase link for the suggested gift. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a facial photograph using generation AI. The notification unit is implemented, for example, by the control unit 46A of the robot 414 and notifies of life events. The support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and supports the purchase and delivery of celebratory gifts based on the notified life event.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The suggestion unit can estimate the user's emotions and adjust the way it presents gift suggestions based on those emotions. For example, if the user is happy, it will suggest a gift using a bright and cheerful expression. The suggestion unit can estimate the user's emotions using facial recognition technology. For example, it can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The suggestion unit can also estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the suggestion unit can also estimate the user's emotions using text analysis technology. For example, it can analyze the content of the user's message to estimate the emotion. This allows the suggestion unit to adjust the way it presents gift suggestions based on the user's emotions.

[0112] The service provider can estimate the user's emotions and adjust how the purchase link is displayed based on those emotions. For example, if the user is happy, the purchase link will be displayed in a bright and cheerful design. The service provider can estimate the user's emotions using facial recognition technology. For example, the service provider can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The service provider can also estimate the user's emotions using voice analysis technology. For example, the service provider can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the service provider can also estimate the user's emotions using text analysis technology. For example, the service provider can analyze the content of the user's message to estimate the emotion. This allows the service provider to adjust how the purchase link is displayed based on the user's emotions.

[0113] The generation unit can estimate the user's emotions and adjust the expression of the generated facial image based on the estimated emotions. For example, if the user is happy, it will generate a facial image with a bright and cheerful expression. The generation unit can estimate the user's emotions using facial recognition technology. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the user's voice and estimate the emotion. Furthermore, the generation unit can also estimate the user's emotions using text analysis technology. For example, the generation unit can analyze the content of the user's message and estimate the emotion. This allows the generation unit to adjust the expression of the facial image based on the user's emotions.

[0114] The notification unit can estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, if the user is relaxed, it will send a notification immediately. The notification unit can estimate the user's emotions using facial recognition technology. For example, it can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The notification unit can also estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the notification unit can also estimate the user's emotions using text analysis technology. For example, it can analyze the content of the user's message to estimate their emotions. This allows the notification unit to adjust the timing of notifications based on the user's emotions.

[0115] The support unit can estimate the user's emotions and adjust its support methods based on those emotions. For example, if the user is happy, it will provide cheerful and friendly support. The support unit can estimate the user's emotions using facial recognition technology. For example, it can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The support unit can also estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the support unit can also estimate the user's emotions using text analysis technology. For example, it can analyze the content of the user's message to estimate their emotions. This allows the support unit to adjust its support methods based on the user's emotions.

[0116] The suggestion department can adjust the level of detail in its suggestions based on the importance of the gift. For example, for important events, it can provide detailed descriptions and multiple options. To assess the importance of a gift, the suggestion department can consider the user's past purchase history and the significance of events. For example, it can analyze the user's past purchase history and suggest gifts related to important events. For everyday gifts, the suggestion department can also provide concise descriptions and a limited number of options. For example, it can make concise gift suggestions based on the user's everyday purchase history. Furthermore, for special anniversaries, the suggestion department can offer special options and customizable suggestions. For example, it can suggest gifts tailored to the user's special anniversary. This allows the suggestion department to adjust the level of detail in its suggestions based on the importance of the gift.

[0117] The service provider can analyze the user's past purchase history at the time of delivery and select the most relevant purchase links. For example, it can prioritize providing links related to products the user has previously purchased. The service provider can use data mining techniques to analyze past purchase history. For example, it can provide highly relevant purchase links based on the user's past purchase history. The service provider can also prioritize providing products from specific brands or categories based on the user's past purchase history. For example, it can analyze the user's past purchase history and provide products from specific brands. This enables the service provider to select the most relevant purchase links based on past purchase history.

[0118] The generation unit can analyze the user's past photo history during generation and select the optimal generation method. For example, it can generate face photos based on filters and effects the user has previously preferred to use. The generation unit can use data mining techniques to analyze past photo history. For example, it can select a highly relevant generation method based on the user's past photo history. The generation unit can also generate face photos based on specific styles or themes from the user's past photo history. For example, it can analyze the user's past photo history and generate face photos based on specific styles or themes. This allows the generation unit to select the optimal generation method based on past photo history.

[0119] The notification unit can analyze the user's past notification history when sending a notification and select the optimal notification method. For example, it can prioritize suggesting notification methods that the user has preferred in the past. The notification unit can use data mining techniques to analyze past notification history. For example, it can select the optimal notification method based on the user's past notification history. The notification unit can also select a notification method based on the types of notifications the user has received in the past. For example, it can analyze the user's past notification history and suggest sending notifications at specific time periods. This allows the notification unit to select the optimal notification method based on past notification history.

[0120] The support department can analyze a user's past support history during support sessions and select the most suitable support method. For example, it can prioritize suggesting support methods that the user has preferred in the past. The support department can use data mining techniques to analyze past support history. For example, it can select the most suitable support method based on the user's past support history. The support department can also select support methods based on the types of support the user has received in the past. For example, it can analyze the user's past support history and suggest providing support during specific time slots. This allows the support department to select the most suitable support method based on past support history.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: Share the photos by inviting family members. Family members include, but are not limited to, blood relatives, spouses, and cohabitants. Share the photos in album format, or you can share them using a sharing platform. Step 2: The suggestion department proposes gifts based on the information shared by the sharing department. The suggestion department can propose gifts based on the user's preferences and past purchase history, and can use AI to analyze the user's interests and preferences to propose the most suitable gift. Step 3: The provisioning department provides the purchase link for the gift suggested by the proposaling department. The provisioning department can provide a purchase link for a specific e-commerce site and use AI to provide the most suitable purchase link based on the user's preferences and past purchase history. Step 4: The generation unit generates facial photographs using generation AI. Based on baby photos, the generation unit generates facial photographs for adolescence, middle age, and old age, and can analyze the user's past photo history to select the optimal generation method. Step 5: The notification unit notifies users of life events. The notification unit notifies users of life events such as birthdays, weddings, and graduations one month in advance, and uses AI to send notifications at the appropriate time based on the user's calendar information. Step 6: The support department assists with purchasing and shipping celebratory gifts based on life events notified by the notification department. The support department can purchase celebratory gifts such as gift cards, bouquets, and specific products, and can use AI to suggest the most suitable gifts based on the user's preferences and past purchase history.

[0123] 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.

[0124] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0125] 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.

[0126] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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. 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.

[0141] 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.

[0142] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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. 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.

[0157] 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.

[0158] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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. 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.

[0174] 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.

[0175] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A shared section where you can invite family members and share photos, A suggestion unit that proposes a gift based on the information shared by the aforementioned sharing unit, A provisioning unit that provides a purchase link for the gift proposed by the aforementioned proposal unit, A generation unit that generates facial photographs using generation AI, A notification unit that notifies of life events, A support unit that supports the purchase and delivery of celebratory gifts based on life events notified by the aforementioned notification unit, Equipped with A system characterized by the following features.

2. The aforementioned proposal section is, We suggest gifts based on the interests and preferences of your grandchildren or children. The system according to feature 1.

3. The aforementioned supply unit is, Provides purchase links for e-commerce sites. The system according to feature 1.

4. The generating unit is Using generative AI, we generate facial images of your grandchildren or children from their baby photos, showing them as young adults, middle-aged adults, and elderly adults. The system according to feature 1.

5. The aforementioned support unit is We support the purchase and delivery of celebratory gifts based on notified life events. The system according to feature 1.

6. The aforementioned shared portion is, It estimates the user's emotions and adjusts the timing of photo sharing based on those estimated emotions. The system according to feature 1.

7. The aforementioned shared portion is, When sharing, the system analyzes the user's past sharing history and selects the optimal sharing method. The system according to feature 1.

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