system

Abstract

The system according to this embodiment aims to enable users to easily generate and use original stamps. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives prompts from the user. The generation unit analyzes the prompts received by the reception unit and generates an original stamp. The provision unit provides the stamp generated by the generation unit to the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for a user to create an original stamp by themselves, which takes time and effort.

[0005] The system according to the embodiment aims to enable a user to easily generate and use an original stamp.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives a prompt from a user. The generation unit analyzes the prompt received by the reception unit and generates an original stamp. The provision unit provides the stamp generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment allows users to easily generate and use original stamps. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 original stamp generation system according to an embodiment of the present invention is a system that allows users to easily generate original stamps using an image generation AI. In this system, the user simply sends a prompt in a chat, and the generation AI generates original stamps and presents multiple patterns. The generated stamps are provided free of charge, and users can purchase the ones they like. This lowers the barrier to creating stamps, allowing users to use stamps without going through a review process. Furthermore, the generation AI provides more attractive stamps by referencing data of top-selling stamps. The target audience is high school and university students, and the system solves the difficulties they face in creating their own stamps. This is expected to increase profits from the sale of original stamps. For example, if a user sends a prompt in a chat saying "Make a cat stamp," the generation AI generates multiple cat stamps and presents them to the user. The user can then choose and purchase their favorite stamp. The generation AI also references data of top-selling stamps and incorporates popular designs and themes to create more attractive stamps. This allows users to easily create and sell original stamps. Thus, the original stamp generation system enables users to easily generate and provide original stamps.

[0029] The original stamp generation system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives prompts from the user. Prompts include, but are not limited to, text, voice, and images. For example, the reception unit receives text prompts sent by the user via chat. The reception unit can also use voice recognition technology to convert prompts sent by the user into text and receive them. Furthermore, the reception unit can use image analysis technology to analyze and receive image prompts sent by the user. The generation unit uses generation AI to analyze the prompts received by the reception unit and generate original stamps. For example, the generation unit uses text generation AI (e.g., LLM) to generate stamp designs based on the prompts. The generation unit can also use image generation AI to generate stamp images based on the prompts. Furthermore, the generation unit can use multimodal generation AI to combine text, voice, and image prompts to generate stamps. The provision unit provides the stamps generated by the generation unit to the user. For example, the provision unit displays the generated stamps on the user's device. Furthermore, the provisioning unit can save the generated stamps to the user's account. In addition, the provisioning unit can make the generated stamps available for the user to download. This allows the original stamp generation system according to the embodiment to enable users to easily generate and provide original stamps.

[0030] The reception unit receives prompts from users. Prompts include, but are not limited to, text, audio, and images. For example, the reception unit receives text prompts sent by users via chat. Specifically, it receives text entered by users through chat applications or dedicated interfaces in real time and incorporates it into the system. The reception unit can also use speech recognition technology to convert prompts sent by users via voice into text and receive them. Speech recognition technology employs noise reduction and optimization of the speech model to accurately transcribe user speech into text. Furthermore, the reception unit can use image analysis technology to analyze and receive image prompts sent by users. Image analysis technology utilizes deep learning to recognize objects and text within images and process them appropriately as prompts. This allows the reception unit to flexibly accept various types of prompts, improving user convenience. In addition, the reception unit has an interface for immediately transferring data to the generation unit after receiving a prompt, increasing the overall efficiency of the system. For example, when a prompt is received, the reception unit analyzes its content, converts it into the appropriate format, and sends it to the generation unit. This process is optimized to minimize the time from user input to stamp generation.

[0031] The generation unit uses a generation AI to analyze prompts received by the reception unit and generate original stamps. For example, the generation unit uses a text generation AI (e.g., LLM) to generate stamp designs based on prompts. Specifically, it analyzes the text prompts entered by the user and determines the stamp's theme and design elements based on their content. For example, if the prompt "stamp of a dancing cat" is entered, the generation AI will generate an image of a cat and depict it dancing. The generation unit can also use an image generation AI to generate stamp images based on prompts. The image generation AI has the ability to generate various styles and artworks in response to user prompts, and can be customized to meet user requests. Furthermore, the generation unit can use a multimodal generation AI to combine text, audio, and image prompts to generate stamps. For example, if a user sends the prompt "stamp with a barking dog sound," the generation AI will generate an image of a dog and create a stamp that combines the image with a barking sound. In this way, the generation unit can generate original stamps that meet the diverse needs of users with high accuracy. Furthermore, the generation unit also has a function to evaluate the quality of the generated stamps and make corrections or optimizations as needed. This ensures that the generation unit can always provide users with high-quality stamps.

[0032] The service provider delivers stamps generated by the generation unit to the user. For example, the service provider displays the generated stamps on the user's device. Specifically, it sends the generated stamps to the user's smartphone or tablet in real time and displays them on a dedicated application or messaging platform. The service provider can also save the generated stamps to the user's account, allowing the user to access and reuse them at any time. Furthermore, the service provider can make the generated stamps downloadable to the user, allowing them to save them to their device and use them in other applications and platforms. The service provider supports multiple delivery methods to enhance user convenience. For example, it includes features for sending generated stamps via email or messaging apps, and for saving and sharing them in cloud storage. Additionally, the service provider can collect user feedback to improve the quality and delivery methods of the generated stamps. For instance, users can provide feedback on stamp design and functionality, allowing the system to optimize the stamp generation algorithm and delivery methods based on that feedback. This ensures the service provider consistently provides users with high-quality, user-friendly stamps.

[0033] The generation unit includes a reference unit that references top-selling stamp data. The reference unit, for example, prioritizes referencing the most popular stamp data based on past sales data. The reference unit can also, for example, analyze past sales data and reference stamp data related to specific seasons or events. The reference unit can also, for example, reference stamp data popular with specific user groups based on past sales data. This allows for the generation of more attractive stamps by referencing top-selling stamp data. Some or all of the above processing in the reference unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reference unit can input sales data into the generation AI, which can then select the optimal stamp data.

[0034] The service provider presents the user with multiple generated stamps. The service provider, for example, displays the multiple generated stamps on the user's device. The service provider can also, for example, save the multiple generated stamps to the user's account. The service provider can also, for example, allow the user to download the multiple generated stamps. This increases the user's choices by presenting them with multiple stamps. Some or all of the above processing in the service provider may be performed using a generation AI, or not. For example, the service provider can input the generated stamps into a generation AI, which can then select the optimal stamp.

[0035] The provisioning unit includes a selection unit for the user to select their favorite stamp. The selection unit allows the user to select their favorite stamp from among the generated stamps. The selection unit allows the user to select a stamp by tapping it. The selection unit also allows the user to select a stamp by dragging it. This allows the user to select their favorite stamp. Some or all of the above processing in the selection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the selection unit can input the user's selection data into a generation AI, which can then select the optimal stamp.

[0036] The supply unit includes a purchase unit that allows the user to purchase stamps selected by the user. The purchase unit can, for example, allow the user to purchase stamps using a credit card. The purchase unit can also, for example, allow the user to purchase stamps using electronic money. This allows the user to purchase stamps selected by the user. Some or all of the above processing in the purchase unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the purchase unit can input the user's purchase data into a generation AI, which can then select the optimal purchase method.

[0037] The reception unit can analyze the user's past prompt history and select the optimal reception method. For example, the reception unit may prioritize presenting prompt formats that the user has frequently used in the past. For example, the reception unit may suggest a reception method suitable for a specific time period based on the user's past prompt history. For example, the reception unit may make relevant suggestions based on the prompt content the user has previously entered. This allows the reception unit to select the optimal reception method based on the user's past prompt history. Some or all of the above processing in the reception unit may be performed using a generation AI, or not. For example, the reception unit may input the user's prompt history data into a generation AI, which can then select the optimal reception method.

[0038] The reception unit can filter prompts based on the user's current interests and preferences when receiving them. For example, the reception unit can prioritize relevant prompts based on keywords the user has recently searched for. For example, the reception unit can analyze the user's social media activity and present prompts related to topics of interest. For example, the reception unit can suggest appropriate prompts based on topics in online communities the user participates in. This allows prompts to be filtered based on the user's current interests and preferences. Some or all of the above processing in the reception unit may be performed using or without generative AI. For example, the reception unit can input user interest data into a generative AI, which can then select the most appropriate prompt.

[0039] The reception unit can prioritize receiving highly relevant prompts by considering the user's geographical location when receiving prompts. For example, if the user is in a specific region, the reception unit will prioritize prompts related to that region. For example, if the user is traveling, the reception unit will prioritize prompts related to the travel destination. For example, if the user is at an event venue, the reception unit will prioritize prompts related to that event. This allows the reception unit to receive highly relevant prompts based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception unit can input the user's geographical location information into a generative AI, which can then select the most appropriate prompt.

[0040] The reception unit can analyze the user's social media activity when receiving a prompt and receive relevant prompts. For example, the reception unit may prioritize receiving relevant prompts based on the user's recent posts. For example, the reception unit may suggest appropriate prompts based on the topics of accounts the user follows. For example, the reception unit may receive relevant prompts based on the activities of groups the user participates in. This allows the reception unit to receive relevant prompts based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using a generative AI, or not. For example, the reception unit can input the user's social media data into a generative AI, which can then select the most appropriate prompt.

[0041] The generation unit can select the optimal generation method by referring to the user's past stamp usage history when generating stamps. For example, the generation unit can generate similar stamps based on the style of stamps the user has used in the past. For example, the generation unit can generate related stamps based on themes the user has preferred to use in the past. For example, the generation unit can analyze the user's past stamp usage frequency and prioritize the generation of the most frequently used styles. This allows the generation unit to select the optimal generation method based on the user's past stamp usage history. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's stamp usage history data into a generation AI, which can then select the optimal stamp generation method.

[0042] The generation unit can customize the content generated when creating stamps based on the user's current interests and preferences. For example, the generation unit can generate relevant stamps based on keywords the user has recently searched for. For example, the generation unit can analyze the user's social media activity and generate stamps related to topics of interest. For example, the generation unit can generate appropriate stamps by referring to topics in online communities the user participates in. This allows the content of stamp generation to be customized based on the user's current interests and preferences. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input user interest data into a generation AI, which can then generate the most suitable stamps.

[0043] The generation unit can generate highly relevant stamps by considering the user's geographical location information when generating stamps. For example, if the user is in a specific region, the generation unit will generate stamps related to that region. For example, if the user is traveling, the generation unit will generate stamps related to the travel destination. For example, if the user is at an event venue, the generation unit will generate stamps related to that event. This allows for the generation of highly relevant stamps based on the user's geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI, which can then generate the most suitable stamps.

[0044] The generation unit can analyze the user's social media activity and generate relevant stamps when generating stamps. For example, the generation unit can generate relevant stamps based on the user's recent posts. For example, the generation unit can generate appropriate stamps based on the topics of accounts the user follows. For example, the generation unit can generate relevant stamps based on the activities of groups the user participates in. This allows the generation of relevant stamps to be generated based on the user's social media activity. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's social media data into a generation AI, which can then generate the optimal stamp.

[0045] The distribution unit can select the optimal distribution method by referring to the user's past stamp purchase history when providing stamps. For example, the distribution unit may provide similar stamps based on the style of stamps the user has purchased in the past. For example, the distribution unit may provide related stamps based on themes the user has preferred to purchase in the past. For example, the distribution unit may analyze the user's past stamp purchase frequency and prioritize providing the most frequently purchased style. This allows the distribution unit to select the optimal distribution method based on the user's past stamp purchase history. Some or all of the above processing in the distribution unit may be performed using a generation AI, or not. For example, the distribution unit may input the user's purchase history data into a generation AI, which can then select the optimal distribution method.

[0046] The service provider can customize the content offered based on the user's current interests when providing stamps. For example, the service provider can provide relevant stamps based on keywords the user has recently searched for. For example, the service provider can analyze the user's social media activity and provide stamps related to topics of interest. For example, the service provider can provide appropriate stamps based on topics in online communities the user participates in. This allows the service provider to customize the content offered based on the user's current interests. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input user interest data into a generative AI, which can then provide the most suitable stamps.

[0047] The service provider can provide stamps that are highly relevant to the user, taking into account the user's geographical location information. For example, if the user is in a specific region, the service provider can provide stamps related to that region. For example, if the user is traveling, the service provider can provide stamps related to the travel destination. For example, if the user is at an event venue, the service provider can provide stamps related to that event. This allows the service provider to provide stamps that are highly relevant based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's geographical location information into a generation AI, which can then provide the most suitable stamp.

[0048] The service provider can analyze a user's social media activity and provide relevant stamps when providing stamps. For example, the service provider can provide relevant stamps based on the user's recent posts. For example, the service provider can provide appropriate stamps based on the topics of accounts the user follows. For example, the service provider can provide relevant stamps based on the activities of groups the user participates in. This allows the service provider to provide relevant stamps based on the user's social media activity. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input the user's social media data into a generative AI, which can then provide the most suitable stamps.

[0049] The reference unit can select the most suitable stamp data by referencing past sales data during the reference process. For example, the reference unit may prioritize referencing the most popular stamp data from past sales data. For example, the reference unit may analyze past sales data and reference stamp data related to a specific season or event. For example, the reference unit may reference stamp data popular with a specific user group based on past sales data. This allows for the selection of the most suitable stamp data based on past sales data. Some or all of the above processing in the reference unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reference unit can input sales data into a generation AI, which can then select the most suitable stamp data.

[0050] The reference unit can customize the content it references based on the user's current interests and preferences. For example, it can reference relevant stamp data based on keywords the user has recently searched for. For example, it can analyze the user's social media activity and reference stamp data related to topics of interest. For example, it can reference appropriate stamp data based on topics in online communities the user participates in. This allows the content to be customized based on the user's current interests and preferences. Some or all of the above processing in the reference unit may be performed using or without a generative AI. For example, the reference unit can input the user's interest data into a generative AI, which can then reference the most suitable stamp data.

[0051] The reference unit can retrieve highly relevant stamp data while considering the user's geographical location information. For example, if the user is in a specific region, the reference unit will prioritize retrieving stamp data related to that region. For example, if the user is traveling, the reference unit will prioritize retrieving stamp data related to the travel destination. For example, if the user is at an event venue, the reference unit will prioritize retrieving stamp data related to that event. This allows the reference unit to retrieve highly relevant stamp data based on the user's geographical location information. Some or all of the above processing in the reference unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reference unit can input the user's geographical location information into a generation AI, which can then retrieve the most suitable stamp data.

[0052] The reference unit can analyze the user's social media activity and reference relevant stamp data when referencing data. For example, the reference unit can reference relevant stamp data based on the user's recent posts. For example, the reference unit can reference appropriate stamp data based on the topics of accounts the user follows. For example, the reference unit can reference relevant stamp data based on the activities of groups the user participates in. This allows the reference unit to reference relevant stamp data based on the user's social media activity. Some or all of the above processing in the reference unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the reference unit can input the user's social media data into a generative AI, which can then reference the most suitable stamp data.

[0053] The selection unit can select the optimal selection method by referring to the user's past stamp usage history when selecting stamps. For example, the selection unit may prioritize selecting similar stamps based on the style of stamps the user has used in the past. For example, the selection unit may prioritize selecting related stamps based on themes the user has preferred to use in the past. For example, the selection unit may analyze the user's past stamp usage frequency and prioritize selecting the most frequently used style. This allows the selection unit to determine the optimal selection method based on the user's past stamp usage history. Some or all of the above processing in the selection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the selection unit can input the user's stamp usage history data into a generation AI, which can then select the optimal selection method.

[0054] The selection unit can select stamps that are highly relevant to the user's geographical location when selecting stamps. For example, if the user is in a specific region, the selection unit will prioritize selecting stamps related to that region. For example, if the user is traveling, the selection unit will prioritize selecting stamps related to the travel destination. For example, if the user is at an event venue, the selection unit will prioritize selecting stamps related to that event. This allows for the selection of highly relevant stamps based on the user's geographical location. Some or all of the above processing in the selection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the selection unit can input the user's geographical location information into a generation AI, which can then select the most suitable stamp.

[0055] The purchasing unit can select the optimal purchasing method by referring to the user's past purchase history when purchasing stamps. For example, the purchasing unit may prioritize providing similar stamps based on the style of stamps the user has purchased in the past. For example, the purchasing unit may prioritize providing related stamps based on the themes the user has preferred to purchase in the past. For example, the purchasing unit may analyze the user's past stamp purchase frequency and prioritize providing the most frequently purchased style. This allows the system to select the optimal purchasing method based on the user's past purchase history. Some or all of the above processing in the purchasing unit may be performed using a generative AI, or not. For example, the purchasing unit may input the user's purchase history data into a generative AI, which can then select the optimal purchasing method.

[0056] The purchasing unit can purchase stamps that are highly relevant to the user's geographical location when the user purchases stamps. For example, if the user is in a specific region, the purchasing unit will prioritize providing stamps related to that region. For example, if the user is traveling, the purchasing unit will prioritize providing stamps related to the travel destination. For example, if the user is at an event venue, the purchasing unit will prioritize providing stamps related to that event. This allows the user to purchase stamps that are highly relevant based on their geographical location. Some or all of the above processing in the purchasing unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the purchasing unit can input the user's geographical location information into a generation AI, which can then select the most suitable stamps.

[0057] The purchasing unit can analyze a user's social media activity when they purchase stamps and purchase relevant stamps. For example, the purchasing unit can provide relevant stamps based on the user's recent posts. For example, the purchasing unit can provide appropriate stamps based on the topics of accounts the user follows. For example, the purchasing unit can provide relevant stamps based on the activities of groups the user participates in. This allows users to purchase stamps relevant to their social media activity. Some or all of the above processing in the purchasing unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the purchasing unit can input the user's social media data into a generative AI, which can then select the most suitable stamps.

[0058] The purchasing unit can make suggestions based on the user's schedule by referring to the user's calendar information when purchasing stamps. For example, the purchasing unit can refer to the schedule registered in the user's calendar and provide related stamps. For example, the purchasing unit can suggest stamps related to a specific event from the user's calendar information. For example, the purchasing unit can suggest the most suitable stamps for the schedule based on the user's calendar information. This allows the purchasing unit to suggest stamps related to the schedule based on the user's calendar information. Some or all of the above processing in the purchasing unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the purchasing unit can input the user's calendar information into a generation AI, and the generation AI can select the most suitable stamps.

[0059] The purchasing unit can suggest the most suitable stamps when a user purchases stamps, taking into account their health condition. For example, if the user is tired, the purchasing unit can suggest relaxing stamps. If the user is seeking healthy exercise, the purchasing unit can suggest energizing stamps. If the user is feeling unwell, the purchasing unit can suggest soothing stamps. This allows the system to suggest the most suitable stamps based on the user's health condition. Some or all of the above processing in the purchasing unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the purchasing unit can input the user's health data into a generation AI, which can then select the most suitable stamps.

[0060] The purchasing unit can suggest the most suitable stamps when a user purchases stamps by referring to their past purchase history. For example, the purchasing unit can prioritize providing similar stamps based on the style of stamps the user has purchased in the past. For example, the purchasing unit can prioritize providing related stamps based on the themes the user has preferred to purchase in the past. For example, the purchasing unit can analyze the user's past stamp purchase frequency and prioritize providing the most frequently purchased style. This allows the purchasing unit to suggest the most suitable stamps based on the user's past purchase history. Some or all of the above processing in the purchasing unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the purchasing unit can input the user's purchase history data into a generative AI, which can then select the most suitable stamps.

[0061] The purchasing function can customize the purchase of stamps based on the user's current interests and preferences. For example, the purchasing function can provide relevant stamps based on keywords the user has recently searched for. For example, the purchasing function can analyze the user's social media activity and provide stamps related to topics of interest. For example, the purchasing function can provide appropriate stamps based on topics in online communities the user participates in. This allows the purchase to be customized based on the user's current interests and preferences. Some or all of the above processing in the purchasing function may be performed using or without a generative AI. For example, the purchasing function can input user interest data into a generative AI, which can then select the most suitable stamps.

[0062] The purchase unit can select the optimal display method when a stamp is purchased, taking into account the user's device information. For example, if the user is using a smartphone, the purchase unit provides a display method that matches the screen size. For example, if the user is using a tablet, the purchase unit provides a display method optimized for a larger screen. For example, if the user is using a smartwatch, the purchase unit provides a concise and highly visible display method. This allows the purchase unit to select the optimal display method based on the user's device information. Some or all of the above processing in the purchase unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the purchase unit can input the user's device information into a generation AI, and the generation AI can select the optimal display method.

[0063] The purchase unit can provide a multilingual purchase method when purchasing stamps, depending on the user's language settings. For example, the purchase unit can automatically set the purchase language based on the language settings of the user's device. For example, the purchase unit can provide a language switching function if the user uses multiple languages. For example, the purchase unit can provide the purchase in a specific language if the user selects one. This allows the purchase method to be provided in a multilingual manner based on the user's language settings. Some or all of the above processing in the purchase unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the purchase unit can input the user's language setting data into a generative AI, which can then select the optimal language setting.

[0064] The purchase unit can suggest the most suitable payment method when a stamp is purchased by referring to the user's payment history. For example, the purchase unit may prioritize suggesting the same payment method the user has used in the past. For example, the purchase unit may suggest the most frequently used payment method among those the user has used in the past. For example, the purchase unit may analyze the user's payment history and suggest the most convenient payment method. This allows the purchase unit to suggest the most suitable payment method based on the user's payment history. Some or all of the above processes in the purchase unit may be performed using a generative AI, or not. For example, the purchase unit can input the user's payment history data into a generative AI, which can then select the most suitable payment method.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The reception unit can analyze the user's past prompt history and select the optimal reception method. For example, the reception unit may prioritize presenting prompt formats that the user has frequently used in the past. For example, the reception unit may suggest a reception method suitable for a specific time period based on the user's past prompt history. For example, the reception unit may make relevant suggestions based on the prompt content the user has previously entered. This allows the reception unit to select the optimal reception method based on the user's past prompt history. Some or all of the above processing in the reception unit may be performed using a generation AI, or not. For example, the reception unit may input the user's prompt history data into a generation AI, which can then select the optimal reception method.

[0067] The reception unit can filter prompts based on the user's current interests and preferences when receiving them. For example, the reception unit can prioritize relevant prompts based on keywords the user has recently searched for. For example, the reception unit can analyze the user's social media activity and present prompts related to topics of interest. For example, the reception unit can suggest appropriate prompts based on topics in online communities the user participates in. This allows prompts to be filtered based on the user's current interests and preferences. Some or all of the above processing in the reception unit may be performed using or without generative AI. For example, the reception unit can input user interest data into a generative AI, which can then select the most appropriate prompt.

[0068] The generation unit can select the optimal generation method by referring to the user's past stamp usage history when generating stamps. For example, the generation unit can generate similar stamps based on the style of stamps the user has used in the past. For example, the generation unit can generate related stamps based on themes the user has preferred to use in the past. For example, the generation unit can analyze the user's past stamp usage frequency and prioritize the generation of the most frequently used styles. This allows the generation unit to select the optimal generation method based on the user's past stamp usage history. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's stamp usage history data into a generation AI, which can then select the optimal stamp generation method.

[0069] The generation unit can customize the content generated when creating stamps based on the user's current interests and preferences. For example, the generation unit can generate relevant stamps based on keywords the user has recently searched for. For example, the generation unit can analyze the user's social media activity and generate stamps related to topics of interest. For example, the generation unit can generate appropriate stamps by referring to topics in online communities the user participates in. This allows the content of stamp generation to be customized based on the user's current interests and preferences. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input user interest data into a generation AI, which can then generate the most suitable stamps.

[0070] The generation unit can generate highly relevant stamps by considering the user's geographical location information when generating stamps. For example, if the user is in a specific region, the generation unit will generate stamps related to that region. For example, if the user is traveling, the generation unit will generate stamps related to the travel destination. For example, if the user is at an event venue, the generation unit will generate stamps related to that event. This allows for the generation of highly relevant stamps based on the user's geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI, which can then generate the most suitable stamps.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The reception unit receives prompts from the user. Prompts can include text, voice, and images. For example, it can receive text prompts sent by the user via chat. It can also use speech recognition technology to convert prompts sent by the user via voice into text and receive them. Furthermore, it can use image analysis technology to analyze and receive image prompts sent by the user. Step 2: The generation unit uses a generation AI to analyze the prompt received by the reception unit and generate an original stamp. For example, a text generation AI (e.g., LLM) can be used to generate a stamp design based on the prompt. Alternatively, an image generation AI can be used to generate a stamp image based on the prompt. Furthermore, a multimodal generation AI can be used to combine text, voice, and image prompts to generate a stamp. Step 3: The providing unit provides the stamps generated by the generating unit to the user. For example, it displays the generated stamps on the user's device. It can also save the generated stamps to the user's account. Furthermore, it can make the generated stamps available for the user to download.

[0073] (Example of form 2) The original stamp generation system according to an embodiment of the present invention is a system that allows users to easily generate original stamps using an image generation AI. In this system, the user simply sends a prompt in a chat, and the generation AI generates original stamps and presents multiple patterns. The generated stamps are provided free of charge, and users can purchase the ones they like. This lowers the barrier to creating stamps, allowing users to use stamps without going through a review process. Furthermore, the generation AI provides more attractive stamps by referencing data of top-selling stamps. The target audience is high school and university students, and the system solves the difficulties they face in creating their own stamps. This is expected to increase profits from the sale of original stamps. For example, if a user sends a prompt in a chat saying "Make a cat stamp," the generation AI generates multiple cat stamps and presents them to the user. The user can then choose and purchase their favorite stamp. The generation AI also references data of top-selling stamps and incorporates popular designs and themes to create more attractive stamps. This allows users to easily create and sell original stamps. Thus, the original stamp generation system enables users to easily generate and provide original stamps.

[0074] The original stamp generation system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives prompts from the user. Prompts include, but are not limited to, text, voice, and images. For example, the reception unit receives text prompts sent by the user via chat. The reception unit can also use voice recognition technology to convert prompts sent by the user into text and receive them. Furthermore, the reception unit can use image analysis technology to analyze and receive image prompts sent by the user. The generation unit uses generation AI to analyze the prompts received by the reception unit and generate original stamps. For example, the generation unit uses text generation AI (e.g., LLM) to generate stamp designs based on the prompts. The generation unit can also use image generation AI to generate stamp images based on the prompts. Furthermore, the generation unit can use multimodal generation AI to combine text, voice, and image prompts to generate stamps. The provision unit provides the stamps generated by the generation unit to the user. For example, the provision unit displays the generated stamps on the user's device. Furthermore, the provisioning unit can save the generated stamps to the user's account. In addition, the provisioning unit can make the generated stamps available for the user to download. This allows the original stamp generation system according to the embodiment to enable users to easily generate and provide original stamps.

[0075] The reception unit receives prompts from users. Prompts include, but are not limited to, text, audio, and images. For example, the reception unit receives text prompts sent by users via chat. Specifically, it receives text entered by users through chat applications or dedicated interfaces in real time and incorporates it into the system. The reception unit can also use speech recognition technology to convert prompts sent by users via voice into text and receive them. Speech recognition technology employs noise reduction and optimization of the speech model to accurately transcribe user speech into text. Furthermore, the reception unit can use image analysis technology to analyze and receive image prompts sent by users. Image analysis technology utilizes deep learning to recognize objects and text within images and process them appropriately as prompts. This allows the reception unit to flexibly accept various types of prompts, improving user convenience. In addition, the reception unit has an interface for immediately transferring data to the generation unit after receiving a prompt, increasing the overall efficiency of the system. For example, when a prompt is received, the reception unit analyzes its content, converts it into the appropriate format, and sends it to the generation unit. This process is optimized to minimize the time from user input to stamp generation.

[0076] The generation unit uses a generation AI to analyze prompts received by the reception unit and generate original stamps. For example, the generation unit uses a text generation AI (e.g., LLM) to generate stamp designs based on prompts. Specifically, it analyzes the text prompts entered by the user and determines the stamp's theme and design elements based on their content. For example, if the prompt "stamp of a dancing cat" is entered, the generation AI will generate an image of a cat and depict it dancing. The generation unit can also use an image generation AI to generate stamp images based on prompts. The image generation AI has the ability to generate various styles and artworks in response to user prompts, and can be customized to meet user requests. Furthermore, the generation unit can use a multimodal generation AI to combine text, audio, and image prompts to generate stamps. For example, if a user sends the prompt "stamp with a barking dog sound," the generation AI will generate an image of a dog and create a stamp that combines the image with a barking sound. In this way, the generation unit can generate original stamps that meet the diverse needs of users with high accuracy. Furthermore, the generation unit also has a function to evaluate the quality of the generated stamps and make corrections or optimizations as needed. This ensures that the generation unit can always provide users with high-quality stamps.

[0077] The service provider delivers stamps generated by the generation unit to the user. For example, the service provider displays the generated stamps on the user's device. Specifically, it sends the generated stamps to the user's smartphone or tablet in real time and displays them on a dedicated application or messaging platform. The service provider can also save the generated stamps to the user's account, allowing the user to access and reuse them at any time. Furthermore, the service provider can make the generated stamps downloadable to the user, allowing them to save them to their device and use them in other applications and platforms. The service provider supports multiple delivery methods to enhance user convenience. For example, it includes features for sending generated stamps via email or messaging apps, and for saving and sharing them in cloud storage. Additionally, the service provider can collect user feedback to improve the quality and delivery methods of the generated stamps. For instance, users can provide feedback on stamp design and functionality, allowing the system to optimize the stamp generation algorithm and delivery methods based on that feedback. This ensures the service provider consistently provides users with high-quality, user-friendly stamps.

[0078] The generation unit includes a reference unit that references top-selling stamp data. The reference unit, for example, prioritizes referencing the most popular stamp data based on past sales data. The reference unit can also, for example, analyze past sales data and reference stamp data related to specific seasons or events. The reference unit can also, for example, reference stamp data popular with specific user groups based on past sales data. This allows for the generation of more attractive stamps by referencing top-selling stamp data. Some or all of the above processing in the reference unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reference unit can input sales data into the generation AI, which can then select the optimal stamp data.

[0079] The service provider presents the user with multiple generated stamps. The service provider, for example, displays the multiple generated stamps on the user's device. The service provider can also, for example, save the multiple generated stamps to the user's account. The service provider can also, for example, allow the user to download the multiple generated stamps. This increases the user's choices by presenting them with multiple stamps. Some or all of the above processing in the service provider may be performed using a generation AI, or not. For example, the service provider can input the generated stamps into a generation AI, which can then select the optimal stamp.

[0080] The provisioning unit includes a selection unit for the user to select their favorite stamp. The selection unit allows the user to select their favorite stamp from among the generated stamps. The selection unit allows the user to select a stamp by tapping it. The selection unit also allows the user to select a stamp by dragging it. This allows the user to select their favorite stamp. Some or all of the above processing in the selection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the selection unit can input the user's selection data into a generation AI, which can then select the optimal stamp.

[0081] The supply unit includes a purchase unit that allows the user to purchase stamps selected by the user. The purchase unit can, for example, allow the user to purchase stamps using a credit card. The purchase unit can also, for example, allow the user to purchase stamps using electronic money. This allows the user to purchase stamps selected by the user. Some or all of the above processing in the purchase unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the purchase unit can input the user's purchase data into a generation AI, which can then select the optimal purchase method.

[0082] The reception unit can estimate the user's emotions and adjust the timing of prompt reception based on the estimated emotions. For example, if the user is stressed, the reception unit will delay prompt reception to allow for a more relaxed state. If the user is excited, for example, the reception unit will immediately accept the prompt and quickly begin stamp generation. If the user is tired, for example, the reception unit will simplify prompt reception to allow for quick input completion. This allows for adjustment of prompt reception timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using or without a generative AI. For example, the reception unit can input user emotion data into a generative AI, which can then select the optimal reception timing.

[0083] The reception unit can analyze the user's past prompt history and select the optimal reception method. For example, the reception unit may prioritize presenting prompt formats that the user has frequently used in the past. For example, the reception unit may suggest a reception method suitable for a specific time period based on the user's past prompt history. For example, the reception unit may make relevant suggestions based on the prompt content the user has previously entered. This allows the reception unit to select the optimal reception method based on the user's past prompt history. Some or all of the above processing in the reception unit may be performed using a generation AI, or not. For example, the reception unit may input the user's prompt history data into a generation AI, which can then select the optimal reception method.

[0084] The reception unit can filter prompts based on the user's current interests and preferences when receiving them. For example, the reception unit can prioritize relevant prompts based on keywords the user has recently searched for. For example, the reception unit can analyze the user's social media activity and present prompts related to topics of interest. For example, the reception unit can suggest appropriate prompts based on topics in online communities the user participates in. This allows prompts to be filtered based on the user's current interests and preferences. Some or all of the above processing in the reception unit may be performed using or without generative AI. For example, the reception unit can input user interest data into a generative AI, which can then select the most appropriate prompt.

[0085] The reception unit can estimate the user's emotions and determine the priority of prompts to receive based on the estimated emotions. For example, if the user is relaxed, the reception unit will prioritize detailed prompts. If the user is in a hurry, the reception unit will prioritize simplified prompts. If the user is excited, the reception unit will prioritize creative prompts. This allows the system to prioritize prompts according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using or without generative AI. For example, the reception unit can input user emotion data into a generative AI, which can then determine the optimal prompt priority.

[0086] The reception unit can prioritize receiving highly relevant prompts by considering the user's geographical location when receiving prompts. For example, if the user is in a specific region, the reception unit will prioritize prompts related to that region. For example, if the user is traveling, the reception unit will prioritize prompts related to the travel destination. For example, if the user is at an event venue, the reception unit will prioritize prompts related to that event. This allows the reception unit to receive highly relevant prompts based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception unit can input the user's geographical location information into a generative AI, which can then select the most appropriate prompt.

[0087] The reception unit can analyze the user's social media activity when receiving a prompt and receive relevant prompts. For example, the reception unit may prioritize receiving relevant prompts based on the user's recent posts. For example, the reception unit may suggest appropriate prompts based on the topics of accounts the user follows. For example, the reception unit may receive relevant prompts based on the activities of groups the user participates in. This allows the reception unit to receive relevant prompts based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using a generative AI, or not. For example, the reception unit can input the user's social media data into a generative AI, which can then select the most appropriate prompt.

[0088] The generation unit can estimate the user's emotions and adjust the stamp generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit will generate stamps with soft colors. For example, if the user is excited, the generation unit will generate stamps with vibrant colors. For example, if the user is sad, the generation unit will generate stamps with calm colors. This allows the stamp generation method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input user emotion data into the generation AI, which can then select the optimal stamp generation method.

[0089] The generation unit can select the optimal generation method by referring to the user's past stamp usage history when generating stamps. For example, the generation unit can generate similar stamps based on the style of stamps the user has used in the past. For example, the generation unit can generate related stamps based on themes the user has preferred to use in the past. For example, the generation unit can analyze the user's past stamp usage frequency and prioritize the generation of the most frequently used styles. This allows the generation unit to select the optimal generation method based on the user's past stamp usage history. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's stamp usage history data into a generation AI, which can then select the optimal stamp generation method.

[0090] The generation unit can customize the content generated when creating stamps based on the user's current interests and preferences. For example, the generation unit can generate relevant stamps based on keywords the user has recently searched for. For example, the generation unit can analyze the user's social media activity and generate stamps related to topics of interest. For example, the generation unit can generate appropriate stamps by referring to topics in online communities the user participates in. This allows the content of stamp generation to be customized based on the user's current interests and preferences. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input user interest data into a generation AI, which can then generate the most suitable stamps.

[0091] The generation unit can estimate the user's emotions and determine the priority of stamps to generate based on the estimated emotions. For example, if the user is relaxed, the generation unit will prioritize generating detailed stamps. If the user is in a hurry, the generation unit will prioritize generating simplified stamps. If the user is excited, the generation unit will prioritize generating creative stamps. This allows the priority of stamps to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input user emotion data into the generation AI, which can then determine the optimal stamp priority.

[0092] The generation unit can generate highly relevant stamps by considering the user's geographical location information when generating stamps. For example, if the user is in a specific region, the generation unit will generate stamps related to that region. For example, if the user is traveling, the generation unit will generate stamps related to the travel destination. For example, if the user is at an event venue, the generation unit will generate stamps related to that event. This allows for the generation of highly relevant stamps based on the user's geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI, which can then generate the most suitable stamps.

[0093] The generation unit can analyze the user's social media activity and generate relevant stamps when generating stamps. For example, the generation unit can generate relevant stamps based on the user's recent posts. For example, the generation unit can generate appropriate stamps based on the topics of accounts the user follows. For example, the generation unit can generate relevant stamps based on the activities of groups the user participates in. This allows the generation of relevant stamps to be generated based on the user's social media activity. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's social media data into a generation AI, which can then generate the optimal stamp.

[0094] The delivery unit can estimate the user's emotions and adjust the way stamps are delivered based on the estimated emotions. For example, if the user is relaxed, the delivery unit will select a delivery method that includes a detailed explanation. For example, if the user is in a hurry, the delivery unit will select a delivery method that includes a concise explanation. For example, if the user is excited, the delivery unit will select a visually appealing delivery method. This allows the delivery unit to adjust the way stamps are delivered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the delivery unit may be performed using or without a generative AI. For example, the delivery unit can input user emotion data into a generative AI, which can then select the optimal delivery method.

[0095] The distribution unit can select the optimal distribution method by referring to the user's past stamp purchase history when providing stamps. For example, the distribution unit may provide similar stamps based on the style of stamps the user has purchased in the past. For example, the distribution unit may provide related stamps based on themes the user has preferred to purchase in the past. For example, the distribution unit may analyze the user's past stamp purchase frequency and prioritize providing the most frequently purchased style. This allows the distribution unit to select the optimal distribution method based on the user's past stamp purchase history. Some or all of the above processing in the distribution unit may be performed using a generation AI, or not. For example, the distribution unit may input the user's purchase history data into a generation AI, which can then select the optimal distribution method.

[0096] The service provider can customize the content offered based on the user's current interests when providing stamps. For example, the service provider can provide relevant stamps based on keywords the user has recently searched for. For example, the service provider can analyze the user's social media activity and provide stamps related to topics of interest. For example, the service provider can provide appropriate stamps based on topics in online communities the user participates in. This allows the service provider to customize the content offered based on the user's current interests. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input user interest data into a generative AI, which can then provide the most suitable stamps.

[0097] The service provider can estimate the user's emotions and determine the priority of the stamps to provide based on the estimated emotions. For example, if the user is relaxed, the service provider may prioritize detailed stamps. If the user is in a hurry, the service provider may prioritize simplified stamps. If the user is excited, the service provider may prioritize creative stamps. This allows the service provider to determine the priority of stamps according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can input user emotion data into a generative AI, which can then determine the optimal priority of stamps.

[0098] The service provider can provide stamps that are highly relevant to the user, taking into account the user's geographical location information. For example, if the user is in a specific region, the service provider can provide stamps related to that region. For example, if the user is traveling, the service provider can provide stamps related to the travel destination. For example, if the user is at an event venue, the service provider can provide stamps related to that event. This allows the service provider to provide stamps that are highly relevant based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's geographical location information into a generation AI, which can then provide the most suitable stamp.

[0099] The service provider can analyze a user's social media activity and provide relevant stamps when providing stamps. For example, the service provider can provide relevant stamps based on the user's recent posts. For example, the service provider can provide appropriate stamps based on the topics of accounts the user follows. For example, the service provider can provide relevant stamps based on the activities of groups the user participates in. This allows the service provider to provide relevant stamps based on the user's social media activity. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input the user's social media data into a generative AI, which can then provide the most suitable stamps.

[0100] The reference unit can estimate the user's emotions and select stamp data to reference based on the estimated emotions. For example, if the user is relaxed, the reference unit will reference stamp data with soft colors. For example, if the user is excited, the reference unit will reference stamp data with bright colors. For example, if the user is sad, the reference unit will reference stamp data with calm colors. This allows the reference unit to select stamp data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reference unit may be performed using the generative AI or not. For example, the reference unit can input user emotion data into the generative AI, and the generative AI can select the most appropriate stamp data.

[0101] The reference unit can select the most suitable stamp data by referencing past sales data during the reference process. For example, the reference unit may prioritize referencing the most popular stamp data from past sales data. For example, the reference unit may analyze past sales data and reference stamp data related to a specific season or event. For example, the reference unit may reference stamp data popular with a specific user group based on past sales data. This allows for the selection of the most suitable stamp data based on past sales data. Some or all of the above processing in the reference unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reference unit can input sales data into a generation AI, which can then select the most suitable stamp data.

[0102] The reference unit can customize the content it references based on the user's current interests and preferences. For example, it can reference relevant stamp data based on keywords the user has recently searched for. For example, it can analyze the user's social media activity and reference stamp data related to topics of interest. For example, it can reference appropriate stamp data based on topics in online communities the user participates in. This allows the content to be customized based on the user's current interests and preferences. Some or all of the above processing in the reference unit may be performed using or without a generative AI. For example, the reference unit can input the user's interest data into a generative AI, which can then reference the most suitable stamp data.

[0103] The reference unit can estimate the user's emotions and determine the priority of stamp data to reference based on the estimated emotions. For example, if the user is relaxed, the reference unit will prioritize detailed stamp data. If the user is in a hurry, the reference unit will prioritize simplified stamp data. If the user is excited, the reference unit will prioritize creative stamp data. This allows the priority of stamp data to reference to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reference unit may be performed using or without a generative AI. For example, the reference unit can input user emotion data into a generative AI, which can then determine the optimal priority of stamp data.

[0104] The reference unit can retrieve highly relevant stamp data while considering the user's geographical location information. For example, if the user is in a specific region, the reference unit will prioritize retrieving stamp data related to that region. For example, if the user is traveling, the reference unit will prioritize retrieving stamp data related to the travel destination. For example, if the user is at an event venue, the reference unit will prioritize retrieving stamp data related to that event. This allows the reference unit to retrieve highly relevant stamp data based on the user's geographical location information. Some or all of the above processing in the reference unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reference unit can input the user's geographical location information into a generation AI, which can then retrieve the most suitable stamp data.

[0105] The reference unit can analyze the user's social media activity and reference relevant stamp data when referencing data. For example, the reference unit can reference relevant stamp data based on the user's recent posts. For example, the reference unit can reference appropriate stamp data based on the topics of accounts the user follows. For example, the reference unit can reference relevant stamp data based on the activities of groups the user participates in. This allows the reference unit to reference relevant stamp data based on the user's social media activity. Some or all of the above processing in the reference unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the reference unit can input the user's social media data into a generative AI, which can then reference the most suitable stamp data.

[0106] The selection unit can estimate the user's emotions and adjust the stamp selection method based on the estimated emotions. For example, if the user is relaxed, the selection unit provides a selection method that includes a detailed explanation. For example, if the user is in a hurry, the selection unit provides a selection method that includes a concise explanation. For example, if the user is excited, the selection unit provides a visually appealing selection method. This allows the stamp selection method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using the generative AI or not. For example, the selection unit can input user emotion data into the generative AI, and the generative AI can select the optimal selection method.

[0107] The selection unit can select the optimal selection method by referring to the user's past stamp usage history when selecting stamps. For example, the selection unit may prioritize selecting similar stamps based on the style of stamps the user has used in the past. For example, the selection unit may prioritize selecting related stamps based on themes the user has preferred to use in the past. For example, the selection unit may analyze the user's past stamp usage frequency and prioritize selecting the most frequently used style. This allows the selection unit to determine the optimal selection method based on the user's past stamp usage history. Some or all of the above processing in the selection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the selection unit can input the user's stamp usage history data into a generation AI, which can then select the optimal selection method.

[0108] The selection unit can estimate the user's emotions and determine the priority of stamps to select based on the estimated emotions. For example, if the user is relaxed, the selection unit will prioritize detailed stamps. If the user is in a hurry, the selection unit will prioritize simplified stamps. If the user is excited, the selection unit will prioritize creative stamps. This allows the selection unit to determine the priority of stamps to select according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using or without a generative AI. For example, the selection unit can input user emotion data into a generative AI, which can then determine the optimal priority of stamps.

[0109] The selection unit can select stamps that are highly relevant to the user's geographical location when selecting stamps. For example, if the user is in a specific region, the selection unit will prioritize selecting stamps related to that region. For example, if the user is traveling, the selection unit will prioritize selecting stamps related to the travel destination. For example, if the user is at an event venue, the selection unit will prioritize selecting stamps related to that event. This allows for the selection of highly relevant stamps based on the user's geographical location. Some or all of the above processing in the selection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the selection unit can input the user's geographical location information into a generation AI, which can then select the most suitable stamp.

[0110] The purchasing unit can estimate the user's emotions and adjust the stamp purchase method based on the estimated emotions. For example, if the user is relaxed, the purchasing unit provides a purchase method with a detailed explanation. If the user is in a hurry, the purchasing unit provides a purchase method with a concise explanation. If the user is excited, the purchasing unit provides a visually appealing purchase method. This allows the stamp purchase method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the purchasing unit may be performed using or without a generative AI. For example, the purchasing unit can input user emotion data into a generative AI, which can then select the optimal purchase method.

[0111] The purchasing unit can select the optimal purchasing method by referring to the user's past purchase history when purchasing stamps. For example, the purchasing unit may prioritize providing similar stamps based on the style of stamps the user has purchased in the past. For example, the purchasing unit may prioritize providing related stamps based on the themes the user has preferred to purchase in the past. For example, the purchasing unit may analyze the user's past stamp purchase frequency and prioritize providing the most frequently purchased style. This allows the system to select the optimal purchasing method based on the user's past purchase history. Some or all of the above processing in the purchasing unit may be performed using a generative AI, or not. For example, the purchasing unit may input the user's purchase history data into a generative AI, which can then select the optimal purchasing method.

[0112] The purchasing unit can estimate the user's emotions and determine the priority of stamps to purchase based on the estimated emotions. For example, if the user is relaxed, the purchasing unit will prioritize detailed stamps. If the user is in a hurry, the purchasing unit will prioritize simplified stamps. If the user is excited, the purchasing unit will prioritize creative stamps. This allows the system to determine the priority of stamps to purchase according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the purchasing unit may be performed using or without a generative AI. For example, the purchasing unit can input user emotion data into a generative AI, which can then determine the optimal stamp priority.

[0113] The purchasing unit can purchase stamps that are highly relevant to the user's geographical location when the user purchases stamps. For example, if the user is in a specific region, the purchasing unit will prioritize providing stamps related to that region. For example, if the user is traveling, the purchasing unit will prioritize providing stamps related to the travel destination. For example, if the user is at an event venue, the purchasing unit will prioritize providing stamps related to that event. This allows the user to purchase stamps that are highly relevant based on their geographical location. Some or all of the above processing in the purchasing unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the purchasing unit can input the user's geographical location information into a generation AI, which can then select the most suitable stamps.

[0114] The purchasing unit can analyze a user's social media activity when they purchase stamps and purchase relevant stamps. For example, the purchasing unit can provide relevant stamps based on the user's recent posts. For example, the purchasing unit can provide appropriate stamps based on the topics of accounts the user follows. For example, the purchasing unit can provide relevant stamps based on the activities of groups the user participates in. This allows users to purchase stamps relevant to their social media activity. Some or all of the above processing in the purchasing unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the purchasing unit can input the user's social media data into a generative AI, which can then select the most suitable stamps.

[0115] The purchasing unit can make suggestions based on the user's schedule by referring to the user's calendar information when purchasing stamps. For example, the purchasing unit can refer to the schedule registered in the user's calendar and provide related stamps. For example, the purchasing unit can suggest stamps related to a specific event from the user's calendar information. For example, the purchasing unit can suggest the most suitable stamps for the schedule based on the user's calendar information. This allows the purchasing unit to suggest stamps related to the schedule based on the user's calendar information. Some or all of the above processing in the purchasing unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the purchasing unit can input the user's calendar information into a generation AI, and the generation AI can select the most suitable stamps.

[0116] The purchasing unit can suggest the most suitable stamps when a user purchases stamps, taking into account their health condition. For example, if the user is tired, the purchasing unit can suggest relaxing stamps. If the user is seeking healthy exercise, the purchasing unit can suggest energizing stamps. If the user is feeling unwell, the purchasing unit can suggest soothing stamps. This allows the system to suggest the most suitable stamps based on the user's health condition. Some or all of the above processing in the purchasing unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the purchasing unit can input the user's health data into a generation AI, which can then select the most suitable stamps.

[0117] The purchasing unit can suggest the most suitable stamps when a user purchases stamps by referring to their past purchase history. For example, the purchasing unit can prioritize providing similar stamps based on the style of stamps the user has purchased in the past. For example, the purchasing unit can prioritize providing related stamps based on the themes the user has preferred to purchase in the past. For example, the purchasing unit can analyze the user's past stamp purchase frequency and prioritize providing the most frequently purchased style. This allows the purchasing unit to suggest the most suitable stamps based on the user's past purchase history. Some or all of the above processing in the purchasing unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the purchasing unit can input the user's purchase history data into a generative AI, which can then select the most suitable stamps.

[0118] The purchasing function can customize the purchase of stamps based on the user's current interests and preferences. For example, the purchasing function can provide relevant stamps based on keywords the user has recently searched for. For example, the purchasing function can analyze the user's social media activity and provide stamps related to topics of interest. For example, the purchasing function can provide appropriate stamps based on topics in online communities the user participates in. This allows the purchase to be customized based on the user's current interests and preferences. Some or all of the above processing in the purchasing function may be performed using or without a generative AI. For example, the purchasing function can input user interest data into a generative AI, which can then select the most suitable stamps.

[0119] The purchase unit can select the optimal display method when a stamp is purchased, taking into account the user's device information. For example, if the user is using a smartphone, the purchase unit provides a display method that matches the screen size. For example, if the user is using a tablet, the purchase unit provides a display method optimized for a larger screen. For example, if the user is using a smartwatch, the purchase unit provides a concise and highly visible display method. This allows the purchase unit to select the optimal display method based on the user's device information. Some or all of the above processing in the purchase unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the purchase unit can input the user's device information into a generation AI, and the generation AI can select the optimal display method.

[0120] The purchase unit can provide a multilingual purchase method when purchasing stamps, depending on the user's language settings. For example, the purchase unit can automatically set the purchase language based on the language settings of the user's device. For example, the purchase unit can provide a language switching function if the user uses multiple languages. For example, the purchase unit can provide the purchase in a specific language if the user selects one. This allows the purchase method to be provided in a multilingual manner based on the user's language settings. Some or all of the above processing in the purchase unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the purchase unit can input the user's language setting data into a generative AI, which can then select the optimal language setting.

[0121] The purchase unit can suggest the most suitable payment method when a stamp is purchased by referring to the user's payment history. For example, the purchase unit may prioritize suggesting the same payment method the user has used in the past. For example, the purchase unit may suggest the most frequently used payment method among those the user has used in the past. For example, the purchase unit may analyze the user's payment history and suggest the most convenient payment method. This allows the purchase unit to suggest the most suitable payment method based on the user's payment history. Some or all of the above processes in the purchase unit may be performed using a generative AI, or not. For example, the purchase unit can input the user's payment history data into a generative AI, which can then select the most suitable payment method.

[0122] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0123] The reception unit can analyze the user's past prompt history and select the optimal reception method. For example, the reception unit may prioritize presenting prompt formats that the user has frequently used in the past. For example, the reception unit may suggest a reception method suitable for a specific time period based on the user's past prompt history. For example, the reception unit may make relevant suggestions based on the prompt content the user has previously entered. This allows the reception unit to select the optimal reception method based on the user's past prompt history. Some or all of the above processing in the reception unit may be performed using a generation AI, or not. For example, the reception unit may input the user's prompt history data into a generation AI, which can then select the optimal reception method.

[0124] The reception unit can filter prompts based on the user's current interests and preferences when receiving them. For example, the reception unit can prioritize relevant prompts based on keywords the user has recently searched for. For example, the reception unit can analyze the user's social media activity and present prompts related to topics of interest. For example, the reception unit can suggest appropriate prompts based on topics in online communities the user participates in. This allows prompts to be filtered based on the user's current interests and preferences. Some or all of the above processing in the reception unit may be performed using or without generative AI. For example, the reception unit can input user interest data into a generative AI, which can then select the most appropriate prompt.

[0125] The reception unit can estimate the user's emotions and adjust the timing of prompt reception based on the estimated emotions. For example, if the user is stressed, the reception unit will delay prompt reception to allow for a more relaxed state. If the user is excited, for example, the reception unit will immediately accept the prompt and quickly begin stamp generation. If the user is tired, for example, the reception unit will simplify prompt reception to allow for quick input completion. This allows for adjustment of prompt reception timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using or without a generative AI. For example, the reception unit can input user emotion data into a generative AI, which can then select the optimal reception timing.

[0126] The reception unit can estimate the user's emotions and determine the priority of prompts to receive based on the estimated emotions. For example, if the user is relaxed, the reception unit will prioritize detailed prompts. If the user is in a hurry, the reception unit will prioritize simplified prompts. If the user is excited, the reception unit will prioritize creative prompts. This allows the system to prioritize prompts according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using or without generative AI. For example, the reception unit can input user emotion data into a generative AI, which can then determine the optimal prompt priority.

[0127] The generation unit can estimate the user's emotions and adjust the stamp generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit will generate stamps with soft colors. For example, if the user is excited, the generation unit will generate stamps with vibrant colors. For example, if the user is sad, the generation unit will generate stamps with calm colors. This allows the stamp generation method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input user emotion data into the generation AI, which can then select the optimal stamp generation method.

[0128] The generation unit can select the optimal generation method by referring to the user's past stamp usage history when generating stamps. For example, the generation unit can generate similar stamps based on the style of stamps the user has used in the past. For example, the generation unit can generate related stamps based on themes the user has preferred to use in the past. For example, the generation unit can analyze the user's past stamp usage frequency and prioritize the generation of the most frequently used styles. This allows the generation unit to select the optimal generation method based on the user's past stamp usage history. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's stamp usage history data into a generation AI, which can then select the optimal stamp generation method.

[0129] The generation unit can customize the content generated when creating stamps based on the user's current interests and preferences. For example, the generation unit can generate relevant stamps based on keywords the user has recently searched for. For example, the generation unit can analyze the user's social media activity and generate stamps related to topics of interest. For example, the generation unit can generate appropriate stamps by referring to topics in online communities the user participates in. This allows the content of stamp generation to be customized based on the user's current interests and preferences. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input user interest data into a generation AI, which can then generate the most suitable stamps.

[0130] The generation unit can estimate the user's emotions and determine the priority of stamps to generate based on the estimated emotions. For example, if the user is relaxed, the generation unit will prioritize generating detailed stamps. If the user is in a hurry, the generation unit will prioritize generating simplified stamps. If the user is excited, the generation unit will prioritize generating creative stamps. This allows the priority of stamps to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input user emotion data into the generation AI, which can then determine the optimal stamp priority.

[0131] The generation unit can generate highly relevant stamps by considering the user's geographical location information when generating stamps. For example, if the user is in a specific region, the generation unit will generate stamps related to that region. For example, if the user is traveling, the generation unit will generate stamps related to the travel destination. For example, if the user is at an event venue, the generation unit will generate stamps related to that event. This allows for the generation of highly relevant stamps based on the user's geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI, which can then generate the most suitable stamps.

[0132] The delivery unit can estimate the user's emotions and adjust the way stamps are delivered based on the estimated emotions. For example, if the user is relaxed, the delivery unit will select a delivery method that includes a detailed explanation. For example, if the user is in a hurry, the delivery unit will select a delivery method that includes a concise explanation. For example, if the user is excited, the delivery unit will select a visually appealing delivery method. This allows the delivery unit to adjust the way stamps are delivered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the delivery unit may be performed using or without a generative AI. For example, the delivery unit can input user emotion data into a generative AI, which can then select the optimal delivery method.

[0133] The following briefly describes the processing flow for example form 2.

[0134] Step 1: The reception unit receives prompts from the user. Prompts can include text, voice, and images. For example, it can receive text prompts sent by the user via chat. It can also use speech recognition technology to convert prompts sent by the user via voice into text and receive them. Furthermore, it can use image analysis technology to analyze and receive image prompts sent by the user. Step 2: The generation unit uses a generation AI to analyze the prompt received by the reception unit and generate an original stamp. For example, a text generation AI (e.g., LLM) can be used to generate a stamp design based on the prompt. Alternatively, an image generation AI can be used to generate a stamp image based on the prompt. Furthermore, a multimodal generation AI can be used to combine text, voice, and image prompts to generate a stamp. Step 3: The providing unit provides the stamps generated by the generating unit to the user. For example, it displays the generated stamps on the user's device. It can also save the generated stamps to the user's account. Furthermore, it can make the generated stamps available for the user to download.

[0135] 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.

[0136] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0137] 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.

[0138] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, reference unit, selection unit, purchase unit, and emotion estimation function, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives prompts from the user. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates original stamps using generation AI. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated stamps to the user. The reference unit is implemented by the specific processing unit 290 of the data processing device 12 and references the top-selling stamp data. The selection unit is implemented by the control unit 46A of the smart device 14 and allows the user to select their favorite stamp. The purchase unit is implemented by the control unit 46A of the smart device 14 and purchases the stamp selected by the user. The emotion estimation function is implemented by the specific processing unit 290 of the data processing device 12 and estimates the user's emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0139] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] 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.

[0154] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, reference unit, selection unit, purchase unit, and emotion estimation function, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives prompts from the user. The generation unit is implemented by the identification processing unit 290 of the data processing device 12 and generates original stamps using generation AI. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated stamps to the user. The reference unit is implemented by the identification processing unit 290 of the data processing device 12 and references the top-selling stamp data. The selection unit is implemented by the control unit 46A of the smart glasses 214 and allows the user to select their favorite stamps. The purchase unit is implemented by the control unit 46A of the smart glasses 214 and purchases the stamps selected by the user. The emotion estimation function is implemented by the identification processing unit 290 of the data processing device 12 and estimates the user's emotions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0155] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.).

[0167] 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.

[0168] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0169] 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.

[0170] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, reference unit, selection unit, purchase unit, and emotion estimation function, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives prompts from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates original stamps using generation AI. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated stamps to the user. The reference unit is implemented by the specific processing unit 290 of the data processing unit 12 and references the top-selling stamp data. The selection unit is implemented by the control unit 46A of the headset terminal 314 and allows the user to select their favorite stamps. The purchase unit is implemented by the control unit 46A of the headset terminal 314 and purchases the stamps selected by the user. The emotion estimation function is implemented by the specific processing unit 290 of the data processing unit 12 and estimates the user's emotions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0171] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.).

[0184] 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.

[0185] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0186] 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.

[0187] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, reference unit, selection unit, purchase unit, and emotion estimation function, is implemented by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives prompts from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates original stamps using generation AI. The provision unit is implemented by the control unit 46A of the robot 414 and provides the generated stamps to the user. The reference unit is implemented by the specific processing unit 290 of the data processing unit 12 and references the top-selling stamp data. The selection unit is implemented by the control unit 46A of the robot 414 and allows the user to select their favorite stamp. The purchase unit is implemented by the control unit 46A of the robot 414 and purchases the stamp selected by the user. The emotion estimation function is implemented by the specific processing unit 290 of the data processing unit 12 and estimates the user's emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] (Note 1) A reception desk that receives prompts from users, A generation unit analyzes the prompt received by the reception unit and generates an original stamp, The system includes a providing unit that provides the stamps generated by the generation unit to the user. A system characterized by the following features. (Note 2) The generating unit is It includes a reference section that accesses data on top-selling stamps. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, The generated stamps are presented to the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, It features a selection section where the user can choose their favorite stamp. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, It includes a purchase section where users can buy stamps they have selected. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of prompt responses based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past prompt history to select the optimal response method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When a prompt is received, filtering is performed based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of prompts to accept based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving prompts, the system prioritizes receiving the most relevant prompts by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When a prompt is received, the system analyzes the user's social media activity and accepts relevant prompts. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is The system estimates the user's emotions and adjusts the stamp generation method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When creating stamps, the system selects the optimal creation method by referring to the user's past stamp usage history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When creating stamps, the generated content is customized based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and determines the priority of stamps to generate based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating stamps, the system takes the user's geographical location into consideration to generate stamps that are highly relevant. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When creating stamps, the system analyzes the user's social media activity and generates relevant stamps. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how stamps are provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing stamps, the system will refer to the user's past stamp purchase history to select the most suitable method of provision. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing stamps, customize the content based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the stamps to provide based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing stamps, we will consider the user's geographical location to provide stamps that are highly relevant to their location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing stamps, we analyze the user's social media activity and provide relevant stamps. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reference section is, The system estimates the user's emotions and selects stamp data to reference based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned reference section is, When referencing data, the system selects the most suitable stamp data by referring to past sales data. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned reference section is, When browsing, the content is customized based on the user's current interests and preferences. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned reference section is, The system estimates the user's emotions and determines the priority of stamp data to reference based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned reference section is, When referencing, the system takes the user's geographical location into consideration to reference the most relevant stamp data. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned reference section is, When referencing, the system analyzes the user's social media activity and references relevant stamp data. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned selection unit is It estimates the user's emotions and adjusts the stamp selection method based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 31) The aforementioned selection unit is When selecting stamps, the system will refer to the user's past stamp usage history to determine the optimal selection method. The system described in Appendix 4, characterized by the features described herein. (Note 32) The aforementioned selection unit is It estimates the user's emotions and determines the priority of stamps to select based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 33) The aforementioned selection unit is When selecting stamps, the system takes the user's geographical location into consideration to select the most relevant stamps. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned purchasing department, The system estimates the user's emotions and adjusts the stamp purchase method based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 35) The aforementioned purchasing department, When purchasing stamps, the system will refer to the user's past purchase history to select the most suitable purchase method. The system described in Appendix 5, characterized by the features described herein. (Note 36) The aforementioned purchasing department, It estimates the user's emotions and determines the priority of stamps to purchase based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 37) The aforementioned purchasing department, When purchasing stamps, the system will consider the user's geographical location to select stamps that are more relevant to their needs. The system described in Appendix 5, characterized by the features described herein. (Note 38) The aforementioned purchasing department, When purchasing stamps, the system analyzes the user's social media activity and selects stamps that are relevant to that activity. The system described in Appendix 5, characterized by the features described herein. (Note 39) The aforementioned purchasing department, When purchasing stamps, the system will refer to the user's calendar information to provide suggestions based on their schedule. The system described in Appendix 5, characterized by the features described herein. (Note 40) The aforementioned purchasing department, When purchasing stamps, the system will suggest the most suitable stamps based on the user's health condition. The system described in Appendix 5, characterized by the features described herein. (Note 41) The aforementioned purchasing department, When purchasing stamps, the system suggests the most suitable stamps by referencing the user's past purchase history. The system described in Appendix 5, characterized by the features described herein. (Note 42) The aforementioned purchasing department, When purchasing stamps, the purchase contents are customized based on the user's current interests and preferences. The system described in Appendix 5, characterized by the features described herein. (Note 43) The aforementioned purchasing department, When purchasing stamps, the system selects the optimal display method based on the user's device information. The system described in Appendix 5, characterized by the features described herein. (Note 44) The aforementioned purchasing department, When purchasing stamps, we provide a multilingual purchase method that corresponds to the user's language settings. The system described in Appendix 5, characterized by the features described herein. (Note 45) The aforementioned purchasing department, When purchasing stamps, the system will refer to the user's payment history to suggest the most suitable payment method. The system described in Appendix 5, characterized by the features described herein. [Explanation of Symbols]

[0207] 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 reception desk that receives prompts from users, A generation unit analyzes the prompt received by the reception unit and generates an original stamp, The system includes a providing unit that provides the stamps generated by the generation unit to the user. A system characterized by the following features.

2. The generating unit is It includes a reference section that accesses data on top-selling stamps. The system according to feature 1.

3. The aforementioned supply unit is, The generated stamps are presented to the user. The system according to feature 1.

4. The aforementioned supply unit is, It features a selection section where the user can choose their favorite stamp. The system according to feature 1.

5. The aforementioned supply unit is, It includes a purchase section where users can buy stamps they have selected. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of prompt responses based on the estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past prompt history to select the optimal response method. The system according to feature 1.

8. The aforementioned reception unit is When a prompt is received, filtering is performed based on the user's current interests and preferences. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and determines the priority of prompts to accept based on the estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is When receiving prompts, the system prioritizes receiving the most relevant prompts by considering the user's geographical location. The system according to feature 1.

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