system

Abstract

The system according to this embodiment aims to generate and send original stamps based on images entered by the user in text. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a transmission unit, a regeneration unit, and a storage unit. The reception unit receives text input of the image of the stamp that the user wants to generate on the chat screen. The generation unit analyzes the text input by the reception unit and generates multiple stamp candidates. The transmission unit selects a matching stamp from the stamp candidates generated by the generation unit and sends it. The regeneration unit regenerates stamps based on the stamp candidates generated by the generation unit. The storage unit saves favorite stamps for reuse.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 find a stamp that conveys their emotions and feelings at that time with high resolution.

[0005] The system according to the embodiment aims to generate and transmit an original stamp based on an image input by a user in text.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a transmission unit, a regeneration unit, and a storage unit. The reception unit receives text input from the user describing the image of the stamp they wish to generate on the chat screen. The generation unit analyzes the text input by the reception unit and generates multiple stamp candidates. The transmission unit selects a matching stamp from the candidates generated by the generation unit and sends it. The regeneration unit regenerates stamps based on the stamp candidates generated by the generation unit. The storage unit saves favorite stamps for reuse. [Effects of the Invention]

[0007] The system according to this embodiment can generate and send original stamps based on images entered by the user in text. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The chat support AI system according to an embodiment of the present invention is a system that can generate and send original stamps that convey emotions simply by inputting what the user wants to express in text on the chat screen of a messaging app. In this chat support AI system, the user inputs the image of the stamp they want to generate in text on the chat screen. The generation AI analyzes the text and generates multiple stamp candidates. The user can select a matching stamp from the generated candidates and send it. This mechanism allows the user to easily generate and send original stamps that convey their emotions and feelings with high resolution. Thus, the chat support AI system can easily generate and send original stamps that convey the user's emotions and feelings with high resolution.

[0029] The chat support AI system according to this embodiment comprises a reception unit, a generation unit, a transmission unit, a regeneration unit, and a storage unit. The reception unit receives text input from the user describing the image of the stamp they want to generate on the chat screen. For example, the user inputs a specific image such as "a cat stamp to express happiness" or "an illustrated stamp to express gratitude." This information is input to the generation AI. The generation unit uses the generation AI to analyze the text input by the reception unit and generates multiple stamp candidates. The generation AI determines the design and expression method of the stamp based on the image input by the user. For example, in response to the input "a cat stamp to express happiness," it generates multiple candidates such as a smiling cat stamp or a cat holding a heart stamp. The transmission unit selects a matching stamp from the stamp candidates generated by the generation unit and sends it. For example, the user taps and sends the stamp that best expresses their feelings from the generated stamp candidates. The regeneration unit regenerates stamps based on the stamp candidates generated by the generation unit. For example, if the user is not satisfied with the generated stamp, they can input a prompt again to generate a new stamp. The storage unit allows users to save and reuse their favorite stamps. For example, users can save their favorite stamps and reuse them later. This enables the chat support AI system according to the embodiment to easily generate and send original stamps that can convey the user's emotions and feelings with high resolution.

[0030] The reception desk receives text input from the user describing the image of the stamp they want to generate in the chat screen. For example, the user might input a specific image such as "a stamp of a cat expressing happiness" or "an illustrated stamp expressing gratitude." This information is then input into the generation AI. The reception desk provides an interface to accurately receive the text input by the user and pass it appropriately to the generation AI. When the user inputs, natural language processing technology is used to analyze the meaning of the input and convert it into a format that the generation AI can easily understand. For example, if the input is "a stamp of a cat expressing happiness," the reception desk extracts keywords such as "happy," "cat," and "stamp" and passes them to the generation AI. Furthermore, even if the text input by the user is ambiguous or contains typos, the reception desk corrects them so that the generation AI can understand them accurately. In addition, the reception desk can provide feedback on the content input by the user and request additional information as needed. For example, it can display a prompt such as "Please tell me a more specific image" to allow the user to input more detailed information. In this way, the reception desk can accurately grasp the user's intent and provide appropriate information to the generation AI, thereby improving the accuracy of stamp generation.

[0031] The generation unit uses a generation AI to analyze the text entered by the reception unit and generate multiple stamp candidates. The generation AI determines the stamp design and expression method based on the image entered by the user. For example, in response to the input "a stamp of a cat that expresses happiness," it generates multiple candidates such as a smiling cat stamp or a cat holding a heart stamp. The generation AI combines natural language processing and image generation technology to analyze the user's input and generate an appropriate stamp design. Specifically, the generation AI analyzes the keywords and phrases entered by the user and determines the stamp theme and style based on them. For example, if the keyword "happy" is included, the generation AI will select a design with bright colors and a smiling expression. If the keyword "cat" is included, it will generate a design centered around a cat character. The generation AI combines these elements to generate multiple stamp candidates and provides them to the user. Furthermore, the generation AI can learn from the user's past selection history and feedback to generate stamps that better suit the user's preferences. For example, it can generate new stamp candidates by referring to the designs and styles of stamps that the user has selected in the past. This allows the generation unit to quickly produce and provide high-quality stamps that meet the user's needs.

[0032] The sending unit selects a matching stamp from the stamp candidates generated by the generating unit and sends it. For example, the user taps and sends the stamp that best expresses their feelings from the generated stamp candidates. The sending unit provides an interface for quickly and reliably sending the stamp selected by the user. Specifically, the sending unit displays a list of generated stamp candidates, allowing the user to select intuitively. Once the user selects a stamp, the sending unit inserts that stamp into the chat screen and sends it to the recipient. The sending unit also manages the stamp sending history, making it easy for users to reuse stamps they have sent in the past. For example, if a user wants to use a stamp they previously sent again, the sending unit can select that stamp from the history and resend it. Furthermore, the sending unit collects feedback on stamp sending and provides it to the generating and regenerating units. For example, if the recipient's reaction to a stamp sent by the user is positive, this information is fed back to the generating unit and reflected in future stamp generation. This allows the sending unit to enable users to send stamps easily and quickly, improving the stamp usage experience.

[0033] The regeneration unit regenerates stamps based on the stamp candidates generated by the generation unit. For example, if a user is not satisfied with the generated stamp, they can enter a prompt again to generate a new stamp. The regeneration unit receives user feedback and provides an interface for the generation AI to enter prompts again. Specifically, users can input specific requests for the generated stamp, such as "I want a cuter design" or "I want the colors changed." The regeneration unit analyzes these requests and passes them to the generation AI as appropriate prompts. Based on the new prompts from the regeneration unit, the generation AI re-determines the stamp design and expression method and generates new stamp candidates. For example, if a user inputs "I want a cuter design," the generation AI will select softer colors and a rounder design and generate a new stamp. Furthermore, as users repeatedly regenerate stamps, the regeneration unit can consider past generation history and feedback to generate more accurate stamps. This allows the regeneration unit to quickly regenerate and provide stamps that meet the user's requests.

[0034] The storage section allows users to save and reuse their favorite stamps. For example, users can save their favorite stamps and reuse them later. The storage section provides an interface to efficiently manage and easily reuse stamps created by users. Specifically, the storage section provides a function that allows users to save their favorite stamps with a single click and displays a list of saved stamps. The storage section also provides stamp categorization and tagging functions so that users can quickly find the stamps they need. For example, by adding categories such as "gratitude," "joy," and "support," or tags such as "cat," "heart," and "smile," users can easily search for specific stamps. Furthermore, the storage section provides a backup function for saved stamps to prevent data loss. For example, it uses cloud storage to regularly back up saved stamps to prepare for device failure or loss. In this way, the storage section can improve the stamp usage experience by efficiently managing users' favorite stamps and making them available for reuse at any time.

[0035] The generation unit can determine the design and expression of a stamp based on the image entered by the user. For example, if the user enters "a stamp of a cat to express happiness," the generation unit will generate multiple options, such as a stamp of a smiling cat or a stamp of a cat holding a heart. For example, if the user enters "an illustrated stamp to express gratitude," the generation unit can also generate a stamp that expresses gratitude. For example, if the user enters "a stamp to express congratulations," the generation unit can also generate a stamp that expresses congratulations. In this way, by determining the design and expression of a stamp based on the user's input, it is possible to generate stamps that match the user's intentions.

[0036] The regeneration unit can regenerate stamps based on user feedback. For example, if the user is not satisfied with the generated stamp, the regeneration unit can generate a new stamp by prompting the user again. For example, if the user provides feedback that they would like the generated stamp to be brighter, the regeneration unit can adjust the stamp's colors and regenerate it. For example, if the user provides feedback that they would like the generated stamp to be simpler, the regeneration unit can simplify the design and regenerate it. This improves user satisfaction by regenerating stamps based on user feedback. Some or all of the above processes in the regeneration unit may be performed using AI, for example, or without AI. For example, the regeneration unit can input user feedback into a generation AI and have the generation AI perform stamp regeneration.

[0037] The storage unit allows users to save and reuse their favorite stamps. For example, the storage unit allows users to save their favorite stamps from those generated and reuse them later. For example, the storage unit allows users to save specific stamps as favorites and easily recall and use them in the chat screen. For example, the storage unit allows users to use saved stamps in other chat screens. This improves user convenience by allowing users to save and reuse their favorite stamps. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input data of stamps saved by the user into a generation AI and have the generation AI perform stamp reuse.

[0038] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display expressions that the user has frequently used in the past as candidates. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest expressions that the user will use at a specific time of day based on the user's past input history. In this way, by analyzing the user's past input history, a more efficient input method can be suggested. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI suggest the optimal input method.

[0039] The reception unit can suggest input options based on the user's current conversation content when input is made. For example, the reception unit can suggest relevant input options based on keywords the user is using in the current conversation. The reception unit can also, for example, analyze the context the user is using in the current conversation and suggest appropriate input options. The reception unit can also, for example, suggest relevant input options based on emotional expressions the user is using in the current conversation. This allows for input that generates more appropriate stamps by suggesting input options based on the user's current conversation content. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's current conversation content data into a generation AI and have the generation AI perform the task of suggesting input options.

[0040] The reception unit can present highly relevant input candidates while considering the user's geographical location information. For example, if the user is in a specific location, the reception unit can present input candidates related to that location. For example, if the user is traveling, the reception unit can also present input candidates related to the travel destination. For example, if the user is at home, the reception unit can also present input candidates related to home. By presenting input candidates while considering the user's geographical location information, it becomes possible to generate more appropriate stamps. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information data into a generation AI and have the generation AI perform the task of presenting input candidates.

[0041] The reception unit can analyze the user's social media activity during input and suggest relevant input options. For example, the reception unit can suggest relevant input options based on the user's recent social media posts. The reception unit can also suggest relevant input options based on the posts of accounts the user follows on social media. The reception unit can also suggest relevant input options based on the activities of groups the user participates in on social media. By analyzing the user's social media activity and suggesting input options, it becomes possible to input in a way that generates more appropriate stamps. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generation AI and have the generation AI perform the task of suggesting input options.

[0042] The generation unit can generate the most suitable stamps by referring to the user's past stamp usage history during the generation process. For example, the generation unit can generate new stamps based on the designs of stamps the user has frequently used in the past. The generation unit can also generate new stamps based on the themes of stamps the user has used in the past. The generation unit can also generate new stamps based on the color schemes of stamps the user has used in the past. This allows for the generation of more appropriate stamps by referring to the user's past stamp usage history. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past stamp usage history data into a generation AI and have the generation AI perform the generation of the most suitable stamps.

[0043] The generation unit can customize the stamp design based on the user's current chat content during generation. For example, the generation unit can generate relevant stamps based on keywords the user is using in the current chat. The generation unit can also analyze the context the user is using in the current chat and generate appropriate stamps. The generation unit can also generate relevant stamps based on emotional expressions the user is using in the current chat. This allows for the generation of more appropriate stamps by customizing the stamp design based on the user's current chat content. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's current chat content data into a generation AI and have the generation AI perform the customization of the stamp design.

[0044] The generation unit can generate highly relevant stamps by considering the user's geographical location information during the generation process. For example, if the user is in a specific location, the generation unit can generate stamps related to that location. For example, if the user is traveling, the generation unit can also generate stamps related to the travel destination. For example, if the user is at home, the generation unit can also generate stamps related to home. By considering the user's geographical location information during stamp generation, more appropriate stamps can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location data into a generation AI and have the generation AI perform stamp generation.

[0045] The generation unit can analyze the user's social media activity during generation and generate relevant stamps. For example, the generation unit can generate relevant stamps based on content the user has recently posted on social media. The generation unit can also generate relevant stamps based on content posted by accounts the user follows on social media. The generation unit can also generate relevant stamps based on the activities of groups the user participates in on social media. By analyzing the user's social media activity and generating stamps accordingly, more appropriate stamps can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform stamp generation.

[0046] The sending unit can suggest the most suitable stamp when sending, by referring to the user's past sending history. For example, the sending unit can suggest a new stamp based on stamps the user has frequently sent in the past. The sending unit can also suggest a new stamp based on the theme of stamps the user has sent in the past. The sending unit can also suggest a new stamp based on the color scheme of stamps the user has sent in the past. This allows for the suggestion of more appropriate stamps by referring to the user's past sending history. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input the user's past sending history data into a generating AI and have the generating AI suggest the most suitable stamp.

[0047] The sending unit can customize the stamps it sends based on the user's current chat content at the time of sending. For example, the sending unit can suggest relevant stamps based on keywords the user is using in the current chat. The sending unit can also suggest appropriate stamps by analyzing the context the user is using in the current chat. The sending unit can also suggest relevant stamps based on emotional expressions the user is using in the current chat. This allows for the sending of more appropriate stamps by customizing the stamps sent based on the user's current chat content. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input the user's current chat content data into a generating AI and have the generating AI perform the stamp customization.

[0048] The sending unit can suggest highly relevant stamps when sending, taking into account the user's geographical location information. For example, if the user is in a specific location, the sending unit can suggest stamps related to that location. For example, if the user is traveling, the sending unit can also suggest stamps related to the travel destination. For example, if the user is at home, the sending unit can also suggest stamps related to home. This allows for the sending of more appropriate stamps by suggesting stamps while considering the user's geographical location information. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input the user's geographical location data into a generating AI and have the generating AI perform stamp suggestions.

[0049] The sending unit can analyze the user's social media activity and suggest relevant stamps at the time of sending. For example, the sending unit can suggest relevant stamps based on content the user has recently posted on social media. The sending unit can also suggest relevant stamps based on content posted by accounts the user follows on social media. The sending unit can also suggest relevant stamps based on the activities of groups the user participates in on social media. This allows for the sending of more appropriate stamps by analyzing the user's social media activity and suggesting stamps accordingly. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input the user's social media activity data into a generating AI and have the generating AI perform stamp suggestions.

[0050] The regeneration unit can regenerate the optimal stamp by referring to the user's past feedback during the regeneration process. For example, the regeneration unit can regenerate the stamp design based on feedback previously provided by the user. The regeneration unit can also regenerate a new stamp based on the characteristics of stamps previously rated by the user. The regeneration unit can also regenerate a stamp by reflecting changes previously requested by the user. This allows for the regeneration of more appropriate stamps by referring to the user's past feedback. Some or all of the above processes in the regeneration unit may be performed using AI, for example, or without AI. For example, the regeneration unit can input the user's past feedback data into a generation AI and have the generation AI perform the regeneration of the optimal stamp.

[0051] The regeneration unit can customize the stamp design based on the user's current chat content during regeneration. For example, the regeneration unit can regenerate relevant stamps based on keywords the user is using in the current chat. The regeneration unit can also analyze the context the user is using in the current chat and regenerate appropriate stamps. The regeneration unit can also regenerate relevant stamps based on emotional expressions the user is using in the current chat. This allows for the regeneration of more appropriate stamps by customizing the stamp design based on the user's current chat content. Some or all of the above-described processes in the regeneration unit may be performed using AI, for example, or without AI. For example, the regeneration unit can input the user's current chat content data into a generation AI and have the generation AI perform the customization of the stamp design.

[0052] The regeneration unit can regenerate stamps that are more relevant to the user, taking into account the user's geographical location information during regeneration. For example, if the user is in a specific location, the regeneration unit can regenerate stamps related to that location. For example, if the user is traveling, the regeneration unit can also regenerate stamps related to the travel destination. For example, if the user is at home, the regeneration unit can also regenerate stamps related to home. By regenerating stamps while considering the user's geographical location information, more appropriate stamps can be provided. Some or all of the above processing in the regeneration unit may be performed using AI, for example, or without AI. For example, the regeneration unit can input the user's geographical location information data into a generation AI and have the generation AI perform stamp regeneration.

[0053] The regeneration unit can analyze the user's social media activity during regeneration and regenerate relevant stamps. For example, the regeneration unit can regenerate relevant stamps based on content recently posted by the user on social media. The regeneration unit can also regenerate relevant stamps based on content posted by accounts the user follows on social media. The regeneration unit can also regenerate relevant stamps based on the activities of groups the user participates in on social media. By analyzing the user's social media activity and regenerating stamps accordingly, more appropriate stamps can be provided. Some or all of the above-described processes in the regeneration unit may be performed using AI, for example, or without AI. For example, the regeneration unit can input the user's social media activity data into a generation AI and have the generation AI perform stamp regeneration.

[0054] The storage unit can suggest the most suitable stamps by referring to the user's past saving history when saving. For example, the storage unit can suggest new stamps based on stamps the user has frequently saved in the past. The storage unit can also suggest new stamps based on the themes of stamps the user has saved in the past. The storage unit can also suggest new stamps based on the color scheme of stamps the user has saved in the past. This allows for the suggestion of more appropriate stamps by referring to the user's past saving history. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the user's past saving history data into a generating AI and have the generating AI suggest the most suitable stamps.

[0055] The storage unit can customize the stamps to be saved based on the user's current chat content at the time of saving. For example, the storage unit can suggest relevant stamps based on keywords the user is using in the current chat. The storage unit can also suggest appropriate stamps by analyzing the context the user is using in the current chat. The storage unit can also suggest relevant stamps based on emotional expressions the user is using in the current chat. This allows for the saving of more appropriate stamps by customizing the stamps to be saved based on the user's current chat content. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the user's current chat content data into a generating AI and have the generating AI perform the stamp customization.

[0056] The storage unit can suggest highly relevant stamps when saving, taking into account the user's geographical location information. For example, if the user is in a specific location, the storage unit can suggest stamps related to that location. For example, if the user is traveling, the storage unit can also suggest stamps related to the travel destination. For example, if the user is at home, the storage unit can also suggest stamps related to home. This allows for the saving of more appropriate stamps by suggesting stamps while considering the user's geographical location information. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the user's geographical location data into a generating AI and have the generating AI perform stamp suggestions.

[0057] The storage unit can analyze the user's social media activity and suggest relevant stamps when saving. For example, the storage unit can suggest relevant stamps based on content the user has recently posted on social media. The storage unit can also suggest relevant stamps based on content posted by accounts the user follows on social media. The storage unit can also suggest relevant stamps based on the activities of groups the user participates in on social media. This allows for the saving of more appropriate stamps by analyzing the user's social media activity and suggesting stamps accordingly. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the user's social media activity data into a generating AI and have the generating AI perform stamp suggestions.

[0058] The storage unit can suggest stamps based on the user's schedule by referring to the user's calendar information during saving. For example, the storage unit can refer to the schedule registered in the user's calendar and suggest related stamps. For example, the storage unit can also suggest stamps related to a specific event from the user's calendar information. For example, the storage unit can suggest stamps that match the schedule based on the user's calendar information. This allows for saving more appropriate stamps by suggesting stamps by referring to the user's calendar information. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the user's calendar information data into a generating AI and have the generating AI perform stamp suggestions.

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

[0060] The generation unit can refer to the user's past stamp usage history and generate stamps related to specific events or seasons. For example, if a user has used many Christmas-related stamps during the Christmas season in the past, it can generate stamps appropriate for the Christmas season. Similarly, if a user has used many birthday stamps on their birthday, it can generate stamps related to their birthday. Furthermore, if a user has used many travel-related stamps while traveling, it can generate stamps related to their travel destination. This allows the system to provide more appropriate stamps by referencing the user's past stamp usage history and generating stamps related to specific events or seasons.

[0061] The regeneration function can customize stamp designs based on user feedback. For example, if a user requests a more colorful stamp, the color scheme can be adjusted and the stamp regenerated. Similarly, if a user requests a simpler design, the design can be simplified and the stamp regenerated. Furthermore, if a user requests larger text, the text size can be increased and the stamp regenerated. This allows for the creation of stamps that are more satisfying to users by customizing their designs based on user feedback.

[0062] The reception desk can suggest generating relevant stamps based on the user's current chat content. For example, if the user says "Congratulations" in the chat, it can suggest generating a congratulatory stamp. Similarly, if the user says "Thank you," it can suggest generating a thank-you stamp. Furthermore, if the user says "I'm sorry," it can suggest generating an apology stamp. This allows the system to provide more appropriate stamps by suggesting relevant stamps based on the user's current chat content.

[0063] The sending function can refer to the user's past sending history and suggest stamps related to specific events or seasons. For example, if a user has sent many Christmas-related stamps during the Christmas season in the past, it can suggest stamps appropriate for the Christmas season. Similarly, if a user has sent many birthday stamps on their birthday, it can suggest stamps related to their birthday. Furthermore, if a user has sent many travel-related stamps while traveling, it can suggest stamps related to their travel destination. By referring to the user's past sending history and suggesting stamps related to specific events or seasons, the system can provide more appropriate stamps.

[0064] The storage unit can refer to the user's calendar information and suggest stamps related to specific events or seasons. For example, if the user has Christmas plans registered in their calendar, it can suggest Christmas-related stamps. Similarly, if the user has a birthday registered in their calendar, it can suggest birthday-related stamps. Furthermore, if the user has travel plans registered in their calendar, it can suggest stamps related to the travel destination. By referring to the user's calendar information and suggesting stamps related to specific events or seasons, it can provide more appropriate stamps.

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

[0066] Step 1: The reception desk receives a text description of the stamp image the user wants to generate in the chat screen. For example, the user might enter a specific image such as "a cat stamp to express happiness" or "an illustrated stamp to express gratitude." This information is then input into the generation AI. Step 2: The generation unit uses a generation AI to analyze the text entered by the reception unit and generate multiple stamp candidates. The generation AI determines the stamp design and expression method based on the image entered by the user. For example, in response to the input "a stamp of a cat that wants to convey happiness," it generates multiple candidates such as a stamp of a smiling cat or a stamp of a cat holding a heart. Step 3: The sending unit selects a matching stamp from the stamp candidates generated by the generating unit and sends it. For example, the user taps the stamp that best expresses their feelings from the generated stamp candidates and sends it. Step 4: The regeneration unit regenerates the stamps based on the stamp candidates generated by the generation unit. For example, if the user is not satisfied with the generated stamps, they can enter a prompt again to generate new stamps. Step 5: The storage section allows users to save and reuse their favorite stamps. For example, users can save their favorite stamps and reuse them later.

[0067] (Example of form 2) The chat support AI system according to an embodiment of the present invention is a system that can generate and send original stamps that convey emotions simply by inputting what the user wants to express in text on the chat screen of a messaging app. In this chat support AI system, the user inputs the image of the stamp they want to generate in text on the chat screen. The generation AI analyzes the text and generates multiple stamp candidates. The user can select a matching stamp from the generated candidates and send it. This mechanism allows the user to easily generate and send original stamps that convey their emotions and feelings with high resolution. Thus, the chat support AI system can easily generate and send original stamps that convey the user's emotions and feelings with high resolution.

[0068] The chat support AI system according to this embodiment comprises a reception unit, a generation unit, a transmission unit, a regeneration unit, and a storage unit. The reception unit receives text input from the user describing the image of the stamp they want to generate on the chat screen. For example, the user inputs a specific image such as "a cat stamp to express happiness" or "an illustrated stamp to express gratitude." This information is input to the generation AI. The generation unit uses the generation AI to analyze the text input by the reception unit and generates multiple stamp candidates. The generation AI determines the design and expression method of the stamp based on the image input by the user. For example, in response to the input "a cat stamp to express happiness," it generates multiple candidates such as a smiling cat stamp or a cat holding a heart stamp. The transmission unit selects a matching stamp from the stamp candidates generated by the generation unit and sends it. For example, the user taps and sends the stamp that best expresses their feelings from the generated stamp candidates. The regeneration unit regenerates stamps based on the stamp candidates generated by the generation unit. For example, if the user is not satisfied with the generated stamp, they can input a prompt again to generate a new stamp. The storage unit allows users to save and reuse their favorite stamps. For example, users can save their favorite stamps and reuse them later. This enables the chat support AI system according to the embodiment to easily generate and send original stamps that can convey the user's emotions and feelings with high resolution.

[0069] The reception desk receives text input from the user describing the image of the stamp they want to generate in the chat screen. For example, the user might input a specific image such as "a stamp of a cat expressing happiness" or "an illustrated stamp expressing gratitude." This information is then input into the generation AI. The reception desk provides an interface to accurately receive the text input by the user and pass it appropriately to the generation AI. When the user inputs, natural language processing technology is used to analyze the meaning of the input and convert it into a format that the generation AI can easily understand. For example, if the input is "a stamp of a cat expressing happiness," the reception desk extracts keywords such as "happy," "cat," and "stamp" and passes them to the generation AI. Furthermore, even if the text input by the user is ambiguous or contains typos, the reception desk corrects them so that the generation AI can understand them accurately. In addition, the reception desk can provide feedback on the content input by the user and request additional information as needed. For example, it can display a prompt such as "Please tell me a more specific image" to allow the user to input more detailed information. In this way, the reception desk can accurately grasp the user's intent and provide appropriate information to the generation AI, thereby improving the accuracy of stamp generation.

[0070] The generation unit uses a generation AI to analyze the text entered by the reception unit and generate multiple stamp candidates. The generation AI determines the stamp design and expression method based on the image entered by the user. For example, in response to the input "a stamp of a cat that expresses happiness," it generates multiple candidates such as a smiling cat stamp or a cat holding a heart stamp. The generation AI combines natural language processing and image generation technology to analyze the user's input and generate an appropriate stamp design. Specifically, the generation AI analyzes the keywords and phrases entered by the user and determines the stamp theme and style based on them. For example, if the keyword "happy" is included, the generation AI will select a design with bright colors and a smiling expression. If the keyword "cat" is included, it will generate a design centered around a cat character. The generation AI combines these elements to generate multiple stamp candidates and provides them to the user. Furthermore, the generation AI can learn from the user's past selection history and feedback to generate stamps that better suit the user's preferences. For example, it can generate new stamp candidates by referring to the designs and styles of stamps that the user has selected in the past. This allows the generation unit to quickly produce and provide high-quality stamps that meet the user's needs.

[0071] The sending unit selects a matching stamp from the stamp candidates generated by the generating unit and sends it. For example, the user taps and sends the stamp that best expresses their feelings from the generated stamp candidates. The sending unit provides an interface for quickly and reliably sending the stamp selected by the user. Specifically, the sending unit displays a list of generated stamp candidates, allowing the user to select intuitively. Once the user selects a stamp, the sending unit inserts that stamp into the chat screen and sends it to the recipient. The sending unit also manages the stamp sending history, making it easy for users to reuse stamps they have sent in the past. For example, if a user wants to use a stamp they previously sent again, the sending unit can select that stamp from the history and resend it. Furthermore, the sending unit collects feedback on stamp sending and provides it to the generating and regenerating units. For example, if the recipient's reaction to a stamp sent by the user is positive, this information is fed back to the generating unit and reflected in future stamp generation. This allows the sending unit to enable users to send stamps easily and quickly, improving the stamp usage experience.

[0072] The regeneration unit regenerates stamps based on the stamp candidates generated by the generation unit. For example, if a user is not satisfied with the generated stamp, they can enter a prompt again to generate a new stamp. The regeneration unit receives user feedback and provides an interface for the generation AI to enter prompts again. Specifically, users can input specific requests for the generated stamp, such as "I want a cuter design" or "I want the colors changed." The regeneration unit analyzes these requests and passes them to the generation AI as appropriate prompts. Based on the new prompts from the regeneration unit, the generation AI re-determines the stamp design and expression method and generates new stamp candidates. For example, if a user inputs "I want a cuter design," the generation AI will select softer colors and a rounder design and generate a new stamp. Furthermore, as users repeatedly regenerate stamps, the regeneration unit can consider past generation history and feedback to generate more accurate stamps. This allows the regeneration unit to quickly regenerate and provide stamps that meet the user's requests.

[0073] The storage section allows users to save and reuse their favorite stamps. For example, users can save their favorite stamps and reuse them later. The storage section provides an interface to efficiently manage and easily reuse stamps created by users. Specifically, the storage section provides a function that allows users to save their favorite stamps with a single click and displays a list of saved stamps. The storage section also provides stamp categorization and tagging functions so that users can quickly find the stamps they need. For example, by adding categories such as "gratitude," "joy," and "support," or tags such as "cat," "heart," and "smile," users can easily search for specific stamps. Furthermore, the storage section provides a backup function for saved stamps to prevent data loss. For example, it uses cloud storage to regularly back up saved stamps to prepare for device failure or loss. In this way, the storage section can improve the stamp usage experience by efficiently managing users' favorite stamps and making them available for reuse at any time.

[0074] The generation unit can determine the design and expression of a stamp based on the image entered by the user. For example, if the user enters "a stamp of a cat to express happiness," the generation unit will generate multiple options, such as a stamp of a smiling cat or a stamp of a cat holding a heart. For example, if the user enters "an illustrated stamp to express gratitude," the generation unit can also generate a stamp that expresses gratitude. For example, if the user enters "a stamp to express congratulations," the generation unit can also generate a stamp that expresses congratulations. In this way, by determining the design and expression of a stamp based on the user's input, it is possible to generate stamps that match the user's intentions.

[0075] The regeneration unit can regenerate stamps based on user feedback. For example, if the user is not satisfied with the generated stamp, the regeneration unit can generate a new stamp by prompting the user again. For example, if the user provides feedback that they would like the generated stamp to be brighter, the regeneration unit can adjust the stamp's colors and regenerate it. For example, if the user provides feedback that they would like the generated stamp to be simpler, the regeneration unit can simplify the design and regenerate it. This improves user satisfaction by regenerating stamps based on user feedback. Some or all of the above processes in the regeneration unit may be performed using AI, for example, or without AI. For example, the regeneration unit can input user feedback into a generation AI and have the generation AI perform stamp regeneration.

[0076] The storage unit allows users to save and reuse their favorite stamps. For example, the storage unit allows users to save their favorite stamps from those generated and reuse them later. For example, the storage unit allows users to save specific stamps as favorites and easily recall and use them in the chat screen. For example, the storage unit allows users to use saved stamps in other chat screens. This improves user convenience by allowing users to save and reuse their favorite stamps. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input data of stamps saved by the user into a generation AI and have the generation AI perform stamp reuse.

[0077] The reception unit can estimate the user's emotions and automatically complete the content of input prompts based on the estimated emotions. For example, if the user is happy, the reception unit can automatically complete prompts that include positive expressions. For example, if the user is sad, the reception unit can also automatically complete prompts that include words of comfort. For example, if the user is angry, the reception unit can also automatically complete prompts that include expressions encouraging calmness. This allows for input that generates more appropriate stamps by automatically completing input prompts based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform automatic completion of input prompts.

[0078] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display expressions that the user has frequently used in the past as candidates. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest expressions that the user will use at a specific time of day based on the user's past input history. In this way, by analyzing the user's past input history, a more efficient input method can be suggested. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI suggest the optimal input method.

[0079] The reception unit can suggest input options based on the user's current conversation content when input is made. For example, the reception unit can suggest relevant input options based on keywords the user is using in the current conversation. The reception unit can also, for example, analyze the context the user is using in the current conversation and suggest appropriate input options. The reception unit can also, for example, suggest relevant input options based on emotional expressions the user is using in the current conversation. This allows for input that generates more appropriate stamps by suggesting input options based on the user's current conversation content. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's current conversation content data into a generation AI and have the generation AI perform the task of suggesting input options.

[0080] The reception unit can estimate the user's emotions and determine the priority of input prompts based on the estimated emotions. For example, if the user is happy, the reception unit will prioritize displaying positive expressions. For example, if the user is sad, the reception unit may also prioritize displaying words of comfort. For example, if the user is angry, the reception unit may also prioritize displaying expressions that encourage calmness. This allows for input that generates more appropriate stamps by prioritizing input prompts based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI determine the priority of input prompts.

[0081] The reception unit can present highly relevant input candidates while considering the user's geographical location information. For example, if the user is in a specific location, the reception unit can present input candidates related to that location. For example, if the user is traveling, the reception unit can also present input candidates related to the travel destination. For example, if the user is at home, the reception unit can also present input candidates related to home. By presenting input candidates while considering the user's geographical location information, it becomes possible to generate more appropriate stamps. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information data into a generation AI and have the generation AI perform the task of presenting input candidates.

[0082] The reception unit can analyze the user's social media activity during input and suggest relevant input options. For example, the reception unit can suggest relevant input options based on the user's recent social media posts. The reception unit can also suggest relevant input options based on the posts of accounts the user follows on social media. The reception unit can also suggest relevant input options based on the activities of groups the user participates in on social media. By analyzing the user's social media activity and suggesting input options, it becomes possible to input in a way that generates more appropriate stamps. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generation AI and have the generation AI perform the task of suggesting input options.

[0083] The generation unit can estimate the user's emotions and adjust the stamp design based on the estimated emotions. For example, if the user is happy, the generation unit can generate a stamp with bright colors. For example, if the user is sad, the generation unit can also generate a stamp with calm colors. For example, if the user is angry, the generation unit can also generate a stamp with a calming design. This allows for the generation of more appropriate stamps by adjusting the stamp design based on 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 AI, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the stamp design.

[0084] The generation unit can generate the most suitable stamps by referring to the user's past stamp usage history during the generation process. For example, the generation unit can generate new stamps based on the designs of stamps the user has frequently used in the past. The generation unit can also generate new stamps based on the themes of stamps the user has used in the past. The generation unit can also generate new stamps based on the color schemes of stamps the user has used in the past. This allows for the generation of more appropriate stamps by referring to the user's past stamp usage history. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past stamp usage history data into a generation AI and have the generation AI perform the generation of the most suitable stamps.

[0085] The generation unit can customize the stamp design based on the user's current chat content during generation. For example, the generation unit can generate relevant stamps based on keywords the user is using in the current chat. The generation unit can also analyze the context the user is using in the current chat and generate appropriate stamps. The generation unit can also generate relevant stamps based on emotional expressions the user is using in the current chat. This allows for the generation of more appropriate stamps by customizing the stamp design based on the user's current chat content. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's current chat content data into a generation AI and have the generation AI perform the customization of the stamp design.

[0086] 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 happy, the generation unit may prioritize generating positive stamps. For example, if the user is sad, the generation unit may prioritize generating comforting stamps. For example, if the user is angry, the generation unit may prioritize generating calming stamps. This allows for the generation of more appropriate stamps by prioritizing stamps based on 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 AI, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI determine the priority of stamps.

[0087] The generation unit can generate highly relevant stamps by considering the user's geographical location information during the generation process. For example, if the user is in a specific location, the generation unit can generate stamps related to that location. For example, if the user is traveling, the generation unit can also generate stamps related to the travel destination. For example, if the user is at home, the generation unit can also generate stamps related to home. By considering the user's geographical location information during stamp generation, more appropriate stamps can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location data into a generation AI and have the generation AI perform stamp generation.

[0088] The generation unit can analyze the user's social media activity during generation and generate relevant stamps. For example, the generation unit can generate relevant stamps based on content the user has recently posted on social media. The generation unit can also generate relevant stamps based on content posted by accounts the user follows on social media. The generation unit can also generate relevant stamps based on the activities of groups the user participates in on social media. By analyzing the user's social media activity and generating stamps accordingly, more appropriate stamps can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform stamp generation.

[0089] The sending unit can estimate the user's emotions and assist in selecting stamps to send based on the estimated emotions. For example, if the user is happy, the sending unit may prioritize suggesting positive stamps. For example, if the user is sad, the sending unit may prioritize suggesting comforting stamps. For example, if the user is angry, the sending unit may prioritize suggesting calming stamps. This allows for the sending of more appropriate stamps by assisting in the selection of stamps based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sending unit may be performed using AI or not using AI. For example, the sending unit can input user emotion data into a generative AI and have the generative AI perform stamp selection assistance.

[0090] The sending unit can suggest the most suitable stamp when sending, by referring to the user's past sending history. For example, the sending unit can suggest a new stamp based on stamps the user has frequently sent in the past. The sending unit can also suggest a new stamp based on the theme of stamps the user has sent in the past. The sending unit can also suggest a new stamp based on the color scheme of stamps the user has sent in the past. This allows for the suggestion of more appropriate stamps by referring to the user's past sending history. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input the user's past sending history data into a generating AI and have the generating AI suggest the most suitable stamp.

[0091] The sending unit can customize the stamps it sends based on the user's current chat content at the time of sending. For example, the sending unit can suggest relevant stamps based on keywords the user is using in the current chat. The sending unit can also suggest appropriate stamps by analyzing the context the user is using in the current chat. The sending unit can also suggest relevant stamps based on emotional expressions the user is using in the current chat. This allows for the sending of more appropriate stamps by customizing the stamps sent based on the user's current chat content. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input the user's current chat content data into a generating AI and have the generating AI perform the stamp customization.

[0092] The sending unit can estimate the user's emotions and determine the priority of stamps to send based on the estimated emotions. For example, if the user is happy, the sending unit may prioritize sending positive stamps. For example, if the user is sad, the sending unit may prioritize sending comforting stamps. For example, if the user is angry, the sending unit may prioritize sending calming stamps. This allows for the sending of more appropriate stamps by determining the priority of stamps to send based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sending unit may be performed using AI or not using AI. For example, the sending unit can input user emotion data into a generative AI and have the generative AI determine the priority of stamps.

[0093] The sending unit can suggest highly relevant stamps when sending, taking into account the user's geographical location information. For example, if the user is in a specific location, the sending unit can suggest stamps related to that location. For example, if the user is traveling, the sending unit can also suggest stamps related to the travel destination. For example, if the user is at home, the sending unit can also suggest stamps related to home. This allows for the sending of more appropriate stamps by suggesting stamps while considering the user's geographical location information. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input the user's geographical location data into a generating AI and have the generating AI perform stamp suggestions.

[0094] The sending unit can analyze the user's social media activity and suggest relevant stamps at the time of sending. For example, the sending unit can suggest relevant stamps based on content the user has recently posted on social media. The sending unit can also suggest relevant stamps based on content posted by accounts the user follows on social media. The sending unit can also suggest relevant stamps based on the activities of groups the user participates in on social media. This allows for the sending of more appropriate stamps by analyzing the user's social media activity and suggesting stamps accordingly. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input the user's social media activity data into a generating AI and have the generating AI perform stamp suggestions.

[0095] The regeneration unit can estimate the user's emotions and adjust the design of the regenerated stamp based on the estimated emotions. For example, if the user is happy, the regeneration unit may regenerate a stamp with bright colors. For example, if the user is sad, the regeneration unit may regenerate a stamp with calm colors. For example, if the user is angry, the regeneration unit may regenerate a stamp with a calming design. This allows for the provision of more appropriate stamps by regenerating the stamp design based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative 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-described processes in the regeneration unit may be performed using AI, or not using AI. For example, the regeneration unit can input user emotion data into the generative AI and have the generative AI adjust the stamp design.

[0096] The regeneration unit can regenerate the optimal stamp by referring to the user's past feedback during the regeneration process. For example, the regeneration unit can regenerate the stamp design based on feedback previously provided by the user. The regeneration unit can also regenerate a new stamp based on the characteristics of stamps previously rated by the user. The regeneration unit can also regenerate a stamp by reflecting changes previously requested by the user. This allows for the regeneration of more appropriate stamps by referring to the user's past feedback. Some or all of the above processes in the regeneration unit may be performed using AI, for example, or without AI. For example, the regeneration unit can input the user's past feedback data into a generation AI and have the generation AI perform the regeneration of the optimal stamp.

[0097] The regeneration unit can customize the stamp design based on the user's current chat content during regeneration. For example, the regeneration unit can regenerate relevant stamps based on keywords the user is using in the current chat. The regeneration unit can also analyze the context the user is using in the current chat and regenerate appropriate stamps. The regeneration unit can also regenerate relevant stamps based on emotional expressions the user is using in the current chat. This allows for the regeneration of more appropriate stamps by customizing the stamp design based on the user's current chat content. Some or all of the above-described processes in the regeneration unit may be performed using AI, for example, or without AI. For example, the regeneration unit can input the user's current chat content data into a generation AI and have the generation AI perform the customization of the stamp design.

[0098] The regeneration unit can estimate the user's emotions and determine the priority of stamps to regenerate based on the estimated emotions. For example, if the user is happy, the regeneration unit may prioritize regenerating positive stamps. For example, if the user is sad, the regeneration unit may prioritize regenerating comforting stamps. For example, if the user is angry, the regeneration unit may prioritize regenerating calming stamps. This allows for the regeneration of more appropriate stamps by determining the priority of stamps based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative 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-described processes in the regeneration unit may be performed using AI, or not using AI. For example, the regeneration unit can input user emotion data into a generative AI and have the generative AI determine the priority of stamps.

[0099] The regeneration unit can regenerate stamps that are more relevant to the user, taking into account the user's geographical location information during regeneration. For example, if the user is in a specific location, the regeneration unit can regenerate stamps related to that location. For example, if the user is traveling, the regeneration unit can also regenerate stamps related to the travel destination. For example, if the user is at home, the regeneration unit can also regenerate stamps related to home. By regenerating stamps while considering the user's geographical location information, more appropriate stamps can be provided. Some or all of the above processing in the regeneration unit may be performed using AI, for example, or without AI. For example, the regeneration unit can input the user's geographical location information data into a generation AI and have the generation AI perform stamp regeneration.

[0100] The regeneration unit can analyze the user's social media activity during regeneration and regenerate relevant stamps. For example, the regeneration unit can regenerate relevant stamps based on content recently posted by the user on social media. The regeneration unit can also regenerate relevant stamps based on content posted by accounts the user follows on social media. The regeneration unit can also regenerate relevant stamps based on the activities of groups the user participates in on social media. By analyzing the user's social media activity and regenerating stamps accordingly, more appropriate stamps can be provided. Some or all of the above-described processes in the regeneration unit may be performed using AI, for example, or without AI. For example, the regeneration unit can input the user's social media activity data into a generation AI and have the generation AI perform stamp regeneration.

[0101] The storage unit can estimate the user's emotions and assist in selecting stamps to save based on the estimated emotions. For example, if the user is happy, the storage unit may prioritize saving positive stamps. For example, if the user is sad, the storage unit may prioritize saving comforting stamps. For example, if the user is angry, the storage unit may prioritize saving calming stamps. This allows for the saving of more appropriate stamps by assisting in the selection of stamps to save based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI or not using AI. For example, the storage unit can input user emotion data into a generative AI and have the generative AI perform stamp selection assistance.

[0102] The storage unit can suggest the most suitable stamps by referring to the user's past saving history when saving. For example, the storage unit can suggest new stamps based on stamps the user has frequently saved in the past. The storage unit can also suggest new stamps based on the themes of stamps the user has saved in the past. The storage unit can also suggest new stamps based on the color scheme of stamps the user has saved in the past. This allows for the suggestion of more appropriate stamps by referring to the user's past saving history. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the user's past saving history data into a generating AI and have the generating AI suggest the most suitable stamps.

[0103] The storage unit can customize the stamps to be saved based on the user's current chat content at the time of saving. For example, the storage unit can suggest relevant stamps based on keywords the user is using in the current chat. The storage unit can also suggest appropriate stamps by analyzing the context the user is using in the current chat. The storage unit can also suggest relevant stamps based on emotional expressions the user is using in the current chat. This allows for the saving of more appropriate stamps by customizing the stamps to be saved based on the user's current chat content. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the user's current chat content data into a generating AI and have the generating AI perform the stamp customization.

[0104] The storage unit can estimate the user's emotions and determine the priority of stamps to save based on the estimated emotions. For example, if the user is happy, the storage unit may prioritize saving positive stamps. For example, if the user is sad, the storage unit may prioritize saving comforting stamps. For example, if the user is angry, the storage unit may prioritize saving calming stamps. This allows for the saving of more appropriate stamps by determining the priority of stamps to save based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 storage unit may be performed using AI or not. For example, the storage unit can input user emotion data into a generative AI and have the generative AI determine the priority of stamps.

[0105] The storage unit can suggest highly relevant stamps when saving, taking into account the user's geographical location information. For example, if the user is in a specific location, the storage unit can suggest stamps related to that location. For example, if the user is traveling, the storage unit can also suggest stamps related to the travel destination. For example, if the user is at home, the storage unit can also suggest stamps related to home. This allows for the saving of more appropriate stamps by suggesting stamps while considering the user's geographical location information. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the user's geographical location data into a generating AI and have the generating AI perform stamp suggestions.

[0106] The storage unit can analyze the user's social media activity and suggest relevant stamps when saving. For example, the storage unit can suggest relevant stamps based on content the user has recently posted on social media. The storage unit can also suggest relevant stamps based on content posted by accounts the user follows on social media. The storage unit can also suggest relevant stamps based on the activities of groups the user participates in on social media. This allows for the saving of more appropriate stamps by analyzing the user's social media activity and suggesting stamps accordingly. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the user's social media activity data into a generating AI and have the generating AI perform stamp suggestions.

[0107] The storage unit can suggest stamps based on the user's schedule by referring to the user's calendar information during saving. For example, the storage unit can refer to the schedule registered in the user's calendar and suggest related stamps. For example, the storage unit can also suggest stamps related to a specific event from the user's calendar information. For example, the storage unit can suggest stamps that match the schedule based on the user's calendar information. This allows for saving more appropriate stamps by suggesting stamps by referring to the user's calendar information. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the user's calendar information data into a generating AI and have the generating AI perform stamp suggestions.

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

[0109] The reception desk can analyze the user's voice input, estimate emotions from the voice, and reflect that in the creation of stamps. For example, if the user says "thank you" in a joyful voice, it can generate a smiling stamp or a stamp holding a heart. If the user says "I'm sorry" in a sad voice, it can generate a comforting stamp or a crying stamp. Furthermore, if the user says "stop it" in an angry voice, it can generate a stamp encouraging calmness or a stamp expressing anger. In this way, by analyzing voice input, estimating emotions, and reflecting that in stamp generation, it is possible to provide more appropriate stamps.

[0110] The generation unit can refer to the user's past stamp usage history and generate stamps related to specific events or seasons. For example, if a user has used many Christmas-related stamps during the Christmas season in the past, it can generate stamps appropriate for the Christmas season. Similarly, if a user has used many birthday stamps on their birthday, it can generate stamps related to their birthday. Furthermore, if a user has used many travel-related stamps while traveling, it can generate stamps related to their travel destination. This allows the system to provide more appropriate stamps by referencing the user's past stamp usage history and generating stamps related to specific events or seasons.

[0111] The regeneration function can customize stamp designs based on user feedback. For example, if a user requests a more colorful stamp, the color scheme can be adjusted and the stamp regenerated. Similarly, if a user requests a simpler design, the design can be simplified and the stamp regenerated. Furthermore, if a user requests larger text, the text size can be increased and the stamp regenerated. This allows for the creation of stamps that are more satisfying to users by customizing their designs based on user feedback.

[0112] The storage unit can estimate the user's emotions and suggest saving stamps based on those emotions. For example, if the user is happy, it can suggest prioritizing the saving of positive stamps. If the user is sad, it can suggest prioritizing the saving of comforting stamps. Furthermore, if the user is angry, it can suggest prioritizing the saving of calming stamps. By suggesting stamps to save based on the user's emotions, it is possible to save more appropriate stamps.

[0113] The reception desk can suggest generating relevant stamps based on the user's current chat content. For example, if the user says "Congratulations" in the chat, it can suggest generating a congratulatory stamp. Similarly, if the user says "Thank you," it can suggest generating a thank-you stamp. Furthermore, if the user says "I'm sorry," it can suggest generating an apology stamp. This allows the system to provide more appropriate stamps by suggesting relevant stamps based on the user's current chat content.

[0114] The generation unit can estimate the user's emotions and adjust the stamp design based on those emotions. For example, if the user is happy, it can generate stamps with bright colors. If the user is sad, it can generate stamps with calm colors. Furthermore, if the user is angry, it can generate stamps with a design that encourages calmness. By adjusting the stamp design based on the user's emotions, it is possible to provide more appropriate stamps.

[0115] The sending function can refer to the user's past sending history and suggest stamps related to specific events or seasons. For example, if a user has sent many Christmas-related stamps during the Christmas season in the past, it can suggest stamps appropriate for the Christmas season. Similarly, if a user has sent many birthday stamps on their birthday, it can suggest stamps related to their birthday. Furthermore, if a user has sent many travel-related stamps while traveling, it can suggest stamps related to their travel destination. By referring to the user's past sending history and suggesting stamps related to specific events or seasons, the system can provide more appropriate stamps.

[0116] The regeneration unit can estimate the user's emotions and adjust the design of the regenerated stamp based on those emotions. For example, if the user is happy, it can regenerate a stamp with bright colors. If the user is sad, it can regenerate a stamp with calm colors. Furthermore, if the user is angry, it can regenerate a stamp with a design that encourages calmness. In this way, by regenerating stamp designs based on the user's emotions, it is possible to provide more appropriate stamps.

[0117] The storage unit can refer to the user's calendar information and suggest stamps related to specific events or seasons. For example, if the user has Christmas plans registered in their calendar, it can suggest Christmas-related stamps. Similarly, if the user has a birthday registered in their calendar, it can suggest birthday-related stamps. Furthermore, if the user has travel plans registered in their calendar, it can suggest stamps related to the travel destination. By referring to the user's calendar information and suggesting stamps related to specific events or seasons, it can provide more appropriate stamps.

[0118] The sending unit can estimate the user's emotions and assist in selecting stamps to send based on those emotions. For example, if the user is happy, it can prioritize suggesting positive stamps. If the user is sad, it can prioritize suggesting comforting stamps. Furthermore, if the user is angry, it can prioritize suggesting stamps that encourage calmness. In this way, by assisting in selecting stamps to send based on the user's emotions, it can provide more appropriate stamps.

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

[0120] Step 1: The reception desk receives a text description of the stamp image the user wants to generate in the chat screen. For example, the user might enter a specific image such as "a cat stamp to express happiness" or "an illustrated stamp to express gratitude." This information is then input into the generation AI. Step 2: The generation unit uses a generation AI to analyze the text entered by the reception unit and generate multiple stamp candidates. The generation AI determines the stamp design and expression method based on the image entered by the user. For example, in response to the input "a stamp of a cat that wants to convey happiness," it generates multiple candidates such as a stamp of a smiling cat or a stamp of a cat holding a heart. Step 3: The sending unit selects a matching stamp from the stamp candidates generated by the generating unit and sends it. For example, the user taps the stamp that best expresses their feelings from the generated stamp candidates and sends it. Step 4: The regeneration unit regenerates the stamps based on the stamp candidates generated by the generation unit. For example, if the user is not satisfied with the generated stamps, they can enter a prompt again to generate new stamps. Step 5: The storage section allows users to save and reuse their favorite stamps. For example, users can save their favorite stamps and reuse them later.

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

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

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

[0124] Each of the multiple elements described above, including the reception unit, generation unit, transmission unit, regeneration unit, and storage unit, 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 reception device 38 of the smart device 14, where the user inputs the image of the stamp they want to generate in text on the chat screen. The generation unit is implemented by the identification processing unit 290 of the data processing device 12, where it analyzes the input text using a generation AI and generates multiple stamp candidates. The transmission unit is implemented by the control unit 46A of the smart device 14, where it selects a matching stamp from the generated stamp candidates and transmits it. The regeneration unit is implemented by the identification processing unit 290 of the data processing device 12, where it regenerates stamps based on the stamp candidates. The storage unit is implemented by the storage 50 of the smart device 14, where users save their favorite stamps for reuse. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

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

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

[0129] The microphone 238 receives voice 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.

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

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

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

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

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

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

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

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

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

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

[0140] Each of the multiple elements described above, including the reception unit, generation unit, transmission unit, regeneration unit, and storage unit, 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 microphone 238 of the smart glasses 214, where the user inputs the image of the stamp they want to generate in text on the chat screen. The generation unit is implemented by the identification processing unit 290 of the data processing device 12, where it analyzes the input text using a generation AI and generates multiple stamp candidates. The transmission unit is implemented by the control unit 46A of the smart glasses 214, where it selects a matching stamp from the generated stamp candidates and transmits it. The regeneration unit is implemented by the identification processing unit 290 of the data processing device 12, where it regenerates the stamp based on the stamp candidates. The storage unit is implemented by the storage 50 of the smart glasses 214, where the user saves their favorite stamps for reuse. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

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

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

[0145] The microphone 238 receives voice 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.

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

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

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

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

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

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

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

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

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

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

[0156] Each of the multiple elements described above, including the reception unit, generation unit, transmission unit, regeneration unit, and storage unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, where the user inputs the image of the stamp they want to generate on the chat screen in text. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, where it analyzes the input text using a generation AI and generates multiple stamp candidates. The transmission unit is implemented by the control unit 46A of the headset terminal 314, where it selects a matching stamp from the generated stamp candidates and transmits it. The regeneration unit is implemented by the identification processing unit 290 of the data processing unit 12, where it regenerates stamps based on the stamp candidates. The storage unit is implemented by the storage 50 of the headset terminal 314, where users save their favorite stamps for reuse. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

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

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

[0161] The microphone 238 receives voice 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.

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

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

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

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

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

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

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

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

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

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

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

[0173] Each of the multiple elements described above, including the reception unit, generation unit, transmission unit, regeneration unit, and storage unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, where the user inputs the image of the stamp they want to generate on the chat screen in text. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the input text using a generation AI and generates multiple stamp candidates. The transmission unit is implemented by, for example, the control unit 46A of the robot 414, which selects a matching stamp from the generated stamp candidates and transmits it. The regeneration unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which regenerates the stamp based on the stamp candidates. The storage unit is implemented by, for example, the storage 50 of the robot 414, where the user saves their favorite stamps for reuse. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0192] (Note 1) A reception section where the user inputs the image of the stamp they want to create in the chat screen as text, A generation unit analyzes the text input by the reception unit and generates multiple stamp candidates, A transmitting unit selects a matching stamp from among the stamp candidates generated by the generation unit and transmits it. A regeneration unit that regenerates stamps based on stamp candidates generated by the generation unit, It features a storage section for saving and reusing your favorite stamps. A system characterized by the following features. (Note 2) The generating unit is The stamp design and expression method are determined based on the image entered by the user. The system described in Appendix 1, characterized by the features described herein. (Note 3) The regeneration unit, Regenerate stamps based on user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned storage unit is Save your favorite stamps and reuse them. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and automatically completes the content of input prompts based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When inputting text, the system will suggest input options based on the user's current conversation. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and prioritizes input prompts based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When inputting data, the system considers the user's geographical location to suggest highly relevant input options. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is During input, the system analyzes the user's social media activity and suggests relevant input options. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is It estimates the user's emotions and adjusts the stamp design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is During creation, the system references the user's past stamp usage history to generate the most suitable stamp. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is During creation, the stamp design is customized based on the user's current chat content. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and 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 15) The generating unit is During generation, the system considers the user's geographical location to generate highly relevant stamps. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During creation, 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 17) The aforementioned transmitting unit It estimates the user's emotions and helps select stamps to send based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned transmitting unit When sending a message, the system will refer to the user's past sending history to suggest the most suitable stamp. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned transmitting unit When sending a message, customize the stamp sent based on the user's current chat content. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned transmitting unit It estimates the user's emotions and determines the priority of which stamps to send based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned transmitting unit When sending, the system suggests highly relevant stamps based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned transmitting unit When sending, the system analyzes the user's social media activity and suggests relevant stickers. The system described in Appendix 1, characterized by the features described herein. (Note 23) The regeneration unit, It estimates the user's emotions and adjusts the design of the stamps that are regenerated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The regeneration unit, During regeneration, the system will refer to the user's past feedback to regenerate the optimal stamp. The system described in Appendix 1, characterized by the features described herein. (Note 25) The regeneration unit, When regenerating stamps, the stamp design is customized based on the user's current chat content. The system described in Appendix 1, characterized by the features described herein. (Note 26) The regeneration unit, It estimates the user's emotions and determines the priority of stamps to regenerate based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The regeneration unit, During regeneration, the system will regenerate stamps that are more relevant to the user, taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The regeneration unit, During regeneration, the system analyzes the user's social media activity and regenerates relevant stamps. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned storage unit is It estimates the user's emotions and helps select stamps to save based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned storage unit is When saving, the system will refer to the user's past save history to suggest the most suitable stamp. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned storage unit is When saving, customize the stamps saved based on the user's current chat content. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned storage unit is It estimates the user's emotions and determines the priority of stamps to save based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned storage unit is When saving, the system suggests highly relevant stamps based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned storage unit is When saving, the system analyzes the user's social media activity and suggests relevant stamps. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned storage unit is When saving, the system will refer to the user's calendar information to suggest stamps based on their schedule. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. A reception section where the user inputs the image of the stamp they want to create in the chat screen as text, A generation unit analyzes the text input by the reception unit and generates multiple stamp candidates, A transmitting unit selects a matching stamp from among the stamp candidates generated by the generation unit and transmits it. A regeneration unit that regenerates stamps based on stamp candidates generated by the generation unit, It features a storage section for saving and reusing your favorite stamps. A system characterized by the following features.

2. The generating unit is The stamp design and expression method are determined based on the image entered by the user. The system according to feature 1.

3. The regeneration unit, Regenerate stamps based on user feedback. The system according to feature 1.

4. The aforementioned storage unit is Save your favorite stamps and reuse them. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and automatically completes the content of input prompts based on the estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

7. The aforementioned reception unit is When inputting text, the system will suggest input options based on the user's current conversation. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and prioritizes input prompts based on those emotions. The system according to feature 1.

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