System

Abstract

An object of a system according to an embodiment is to automatically generate a stamp based on the content of a conversation and suggest the stamp to a user.SOLUTION: A system includes a registration unit, an analysis unit, a generation unit, and a suggestion unit. The registration unit registers a pattern or a character that the user likes in advance. The analysis unit analyzes the content of the conversation. The generation unit automatically generates a stamp based on the content of the conversation analyzed by the analysis unit. The suggestion unit suggests the stamp generated by the generation unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology requires users to manually select stamps appropriate for the content of the conversation, which is time-consuming.

[0005] The system according to the embodiment aims to automatically generate stamps based on the content of a conversation and suggest them to a user. [Means for solving the problem]

[0006] The system according to the embodiment includes a registration unit, an analysis unit, a generation unit, and a suggestion unit. The registration unit allows a user to register their favorite images or characters in advance. The analysis unit analyzes the content of the conversation. The generation unit automatically generates stamps based on the content of the conversation analyzed by the analysis unit. The suggestion unit suggests stamps generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate stamps based on the content of a conversation and suggest them to the user. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The stamp generation system according to the embodiment of the present invention is a system in which users register their favorite pictures and characters in advance, and an image generation AI automatically generates and suggests stamps that fit perfectly with the flow of conversation. This allows users to easily use stamps that fit the flow of conversation.

[0029] A stamp generation system according to an embodiment includes a registration unit, an analysis unit, a generation unit, and a suggestion unit. The registration unit allows a user to register a favorite image or character in advance. For example, the user can upload an image file or enter a character name or description of the image in text. The user can also register a "cat character" or a "flower image." The analysis unit analyzes the content of a conversation. For example, if a user says, "Today was so much fun!", the generation AI analyzes this utterance to understand the emotion and context. The generation AI analyzes the content of the conversation using natural language processing technology. The generation unit automatically generates stamps based on the content of the conversation analyzed by the analysis unit. For example, the generation AI generates a stamp featuring a smiling cat character in response to the utterance, "Today was so much fun!" The generation AI generates stamps using image generation technology. The suggestion unit suggests stamps generated by the generation unit to the user. For example, stamp candidates are displayed near an input field in a chat app. The user can select and send from the suggested stamps. As a result, the stamp generation system according to the embodiment can automatically generate and suggest stamps that correspond to the content of the conversation, based on the images and characters that the user has registered in advance.

[0030] The registration unit allows the generation AI to automatically tag pictures or characters registered by the user and suggest other related pictures or characters. For example, if a user registers a cat character, the registration unit automatically tags it with "animal," "pet," "cute," etc., and suggests related dog or rabbit characters. The generation AI uses image recognition technology to tag. This allows the generation AI to automatically tag pictures or characters registered by the user and suggest other related pictures or characters.

[0031] The registration unit can analyze the style of a picture or character registered by a user and automatically generate variations in different art styles. For example, the registration unit uses a generation AI to automatically generate a cat character registered by a user in different art styles, such as watercolor or pop art. The generation AI generates variations in different art styles using style conversion technology. This allows the style of a picture or character registered by a user to be analyzed and variations in different art styles to be automatically generated.

[0032] The registration unit allows the user to register a picture or character using voice input. For example, when the user voice-inputs "register a cat character," the generation AI automatically registers the cat character. The generation AI converts the voice input into text using voice recognition technology and registers the picture or character. This allows the user to register a picture or character using voice input.

[0033] The registration unit can share the registered image or character with other users and obtain ratings and feedback within the community. For example, the registration unit can share a cat character registered by a user within the community and obtain ratings and feedback from other users. Sharing is performed via social networking sites or dedicated community platforms. This allows the registered image or character to be shared with other users and obtain ratings and feedback within the community.

[0034] The analysis unit can perform more accurate analysis based on the user's past conversation history. For example, the generation AI refers to the user's past conversation history and analyzes the current conversation based on specific keywords and phrases. The generation AI performs analysis using the conversation history stored in the database. This allows the analysis to be more accurate by referring to the user's past conversation history.

[0035] The analysis unit can learn the user's preferences and interests based on the conversation analysis results and reflect them in subsequent analyses. For example, the analysis unit uses a generation AI to learn the user's preferences and interests based on the conversation analysis results and reflect them in subsequent conversation analyses. The generation AI learns the user's preferences and interests using machine learning technology. This allows the analysis unit to learn the user's preferences and interests based on the conversation analysis results and reflect them in subsequent analyses.

[0036] The analysis unit can be equipped with a multilingual support function that can analyze conversations in different languages. For example, the analysis unit adds a multilingual support function that allows the generation AI to analyze conversations in different languages, and analyzes conversations in languages ​​such as English and French. The generation AI achieves multilingual support using a translation engine and language model. This allows the generation AI to be equipped with a multilingual support function that can analyze conversations in different languages.

[0037] The analysis unit can link the conversation analysis results with the user's calendar or task management app and suggest related stickers. For example, the generation AI can link with the user's calendar based on the conversation analysis results and suggest stickers related to the schedule. The generation AI shares data with the calendar or task management app using API integration. This allows the conversation analysis results to be linked with the user's calendar or task management app and suggest related stickers.

[0038] The generation unit can generate stamps that better suit the user's preferences based on the user's past stamp usage history. For example, the generation unit uses a generation AI that references the user's past stamp usage history and automatically generates stamps based on specific characters or designs. The generation AI generates stamps using usage history stored in a database. This allows the generation unit to reference the user's past stamp usage history and generate stamps that better suit the user's preferences.

[0039] The generation unit can provide an option to select different art styles or themes when automatically generating stamps. For example, the generation unit can provide an option to select different art styles, such as watercolor or pop art, when automatically generating stamps. The generation AI generates different art styles using style conversion technology. This can provide an option to select different art styles or themes when automatically generating stamps.

[0040] The generation unit can be equipped with a function that can generate animated stamps or GIF format stamps in the automatic generation of stamps. For example, the generation unit adds a function that allows the generation AI to automatically generate animated stamps, providing stamps with movement. For example, an animated stamp of a smiling cat waving. The generation AI generates animated stamps and GIF format stamps using animation generation technology. This allows the automatic generation of stamps to be equipped with a function that can also generate animated stamps and GIF format stamps.

[0041] The generation unit can share the automatically generated stamps with other users and obtain ratings and feedback. The generation unit adds a function for sharing the automatically generated stamps within a community and obtaining ratings and feedback from other users, for example. Sharing is performed via social networking sites or dedicated community platforms. This allows the automatically generated stamps to be shared with other users and obtain ratings and feedback.

[0042] The suggestion unit can learn the user's selection history for suggested stamps and reflect it in subsequent suggestions. For example, the suggestion unit can have a generation AI learn the user's selection history for suggested stamps and reflect it in subsequent suggestions. The generation AI learns the selection history using machine learning technology. This allows the user's selection history for suggested stamps to be learned and reflected in subsequent suggestions.

[0043] The suggestion unit can collect user feedback on suggested stamps and improve the algorithm of the generation AI. For example, the suggestion unit collects user feedback on stamps suggested by the generation AI and improves the algorithm. The generation AI improves the accuracy of stamp suggestions based on the feedback. This allows the generation AI to collect user feedback on suggested stamps and improve the algorithm.

[0044] The suggestion unit can link the suggested stamps with the user's calendar or task management app to suggest related stamps. For example, the suggestion unit links the suggested stamps with the user's calendar using the generation AI to suggest stamps related to the schedule. The generation AI shares data with the calendar or task management app using API integration. This allows the suggested stamps to be linked with the user's calendar or task management app to suggest related stamps.

[0045] The suggestion unit can share suggested stamps with other users and obtain ratings and feedback within the community. The suggestion unit adds, for example, a function to share suggested stamps within the community and obtain ratings and feedback from other users. Sharing is performed via SNS or a dedicated community platform. This allows suggested stamps to be shared with other users and obtain ratings and feedback within the community.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The stamp generation system can also obtain the user's geographical location information and generate stamps specific to the region. For example, if a user says, "I'm looking for delicious food" while traveling, the generation unit can generate stamps depicting local specialties. Also, if a user is participating in a specific event, it can generate stamps related to that event. This allows users to use stamps that correspond to their geographical context.

[0048] The stamp generation system can also analyze a user's music playback history and generate music-related stamps. For example, if a user says, "I love this song!", the generation unit can generate stamps based on the artist and album cover of that song. Also, if a user often listens to a particular genre of music, it can suggest stamps related to that genre. This allows users to use stamps that match their musical tastes.

[0049] The stamp generation system can also analyze a user's purchasing history and generate stamps related to purchased products. For example, if a user says, "I bought new shoes!", the generation unit can generate stamps based on the brand and design of those shoes. Also, if a user often buys a particular brand, it can suggest stamps related to that brand. This allows users to use stamps that correspond to their purchasing history.

[0050] The stamp generation system can also analyze the user's health data and generate stamps according to their health status. For example, if a user says, "I went for a run today!", the generation unit can generate stamps with motifs of running shoes and healthy eating. Also, if the user has set a specific health goal, it can suggest stamps related to that goal. This allows users to use stamps according to their health status.

[0051] The stamp generation system can also analyze a user's reading history and generate stamps related to books they have read. For example, if a user says, "This book was interesting!", the generation unit can generate stamps based on the book's cover or characters. Also, if a user often reads books in a particular genre, it can suggest stamps related to that genre. This allows users to use stamps that correspond to their reading history.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The registration section allows users to register their favorite designs or characters in advance. For example, users can upload an image file or enter a character name or description of the design in text. Users can also register a "cat character" or a "flower design." Step 2: The analysis unit analyzes the content of the conversation. For example, if a user says, "I had a lot of fun today!", the generation AI analyzes this statement and understands the emotion and context. The generation AI analyzes the content of the conversation using natural language processing technology. Step 3: The generation unit automatically generates stamps based on the content of the conversation analyzed by the analysis unit. For example, in response to the statement "I had a lot of fun today!", the generation AI generates a stamp featuring a smiling cat character. The generation AI generates stamps using image generation technology. Step 4: The suggestion unit suggests stamps generated by the generation unit to the user. For example, stamp candidates are displayed near the input field of a chat app. The user can select one from the suggested stamps and send it.

[0054] (Example 2) The stamp generation system according to the embodiment of the present invention is a system in which users register their favorite pictures and characters in advance, and an image generation AI automatically generates and suggests stamps that fit perfectly with the flow of conversation. This allows users to easily use stamps that fit the flow of conversation.

[0055] A stamp generation system according to an embodiment includes a registration unit, an analysis unit, a generation unit, and a suggestion unit. The registration unit allows a user to register a favorite image or character in advance. For example, the user can upload an image file or enter a character name or description of the image in text. The user can also register a "cat character" or a "flower image." The analysis unit analyzes the content of a conversation. For example, if a user says, "Today was so much fun!", the generation AI analyzes this utterance to understand the emotion and context. The generation AI analyzes the content of the conversation using natural language processing technology. The generation unit automatically generates stamps based on the content of the conversation analyzed by the analysis unit. For example, the generation AI generates a stamp featuring a smiling cat character in response to the utterance, "Today was so much fun!" The generation AI generates stamps using image generation technology. The suggestion unit suggests stamps generated by the generation unit to the user. For example, stamp candidates are displayed near an input field in a chat app. The user can select and send from the suggested stamps. As a result, the stamp generation system according to the embodiment can automatically generate and suggest stamps that correspond to the content of the conversation, based on the images and characters that the user has registered in advance.

[0056] The registration unit allows the generation AI to automatically tag pictures or characters registered by the user and suggest other related pictures or characters. For example, if a user registers a cat character, the registration unit automatically tags it with "animal," "pet," "cute," etc., and suggests related dog or rabbit characters. The generation AI uses image recognition technology to tag. This allows the generation AI to automatically tag pictures or characters registered by the user and suggest other related pictures or characters.

[0057] The registration unit can analyze the style of a picture or character registered by a user and automatically generate variations in different art styles. For example, the registration unit uses a generation AI to automatically generate a cat character registered by a user in different art styles, such as watercolor or pop art. The generation AI generates variations in different art styles using style conversion technology. This allows the style of a picture or character registered by a user to be analyzed and variations in different art styles to be automatically generated.

[0058] The registration unit uses the emotion estimation function to analyze the emotion a user expresses when registering and can suggest an image or character that will elicit positive emotions. For example, when a user registers a cat character, the registration unit uses the emotion estimation function to analyze the user's facial expression and suggest a smiling cat character that will elicit positive emotions. The emotion estimation function analyzes the user's emotions using facial expression recognition technology. This makes it possible to analyze the emotion a user expresses when registering and suggest an image or character that will elicit positive emotions.

[0059] The registration unit allows the user to register a picture or character using voice input. For example, when the user voice-inputs "register a cat character," the generation AI automatically registers the cat character. The generation AI converts the voice input into text using voice recognition technology and registers the picture or character. This allows the user to register a picture or character using voice input.

[0060] The registration unit can share the registered image or character with other users and obtain ratings and feedback within the community. For example, the registration unit can share a cat character registered by a user within the community and obtain ratings and feedback from other users. Sharing is performed via social networking sites or dedicated community platforms. This allows the registered image or character to be shared with other users and obtain ratings and feedback within the community.

[0061] The registration unit uses the emotion estimation function to display the emotion the user is registering in real time and make customization suggestions based on the emotion. For example, when a user registers a cat character, the registration unit uses the emotion estimation function to display the user's facial expression in real time and make customization suggestions to elicit positive emotions. The emotion estimation function uses facial expression recognition technology to analyze and display the user's emotion in real time. This allows the emotion the user is registering to be displayed in real time and make customization suggestions based on the emotion.

[0062] The analysis unit can perform more accurate analysis based on the user's past conversation history. For example, the generation AI refers to the user's past conversation history and analyzes the current conversation based on specific keywords and phrases. The generation AI performs analysis using the conversation history stored in the database. This allows the analysis to be more accurate by referring to the user's past conversation history.

[0063] The analysis unit can learn the user's preferences and interests based on the conversation analysis results and reflect them in subsequent analyses. For example, the analysis unit uses a generation AI to learn the user's preferences and interests based on the conversation analysis results and reflect them in subsequent conversation analyses. The generation AI learns the user's preferences and interests using machine learning technology. This allows the analysis unit to learn the user's preferences and interests based on the conversation analysis results and reflect them in subsequent analyses.

[0064] The analysis unit uses the emotion estimation function to analyze changes in emotions during a conversation in real time and suggest stickers that correspond to the emotions. For example, the analysis unit uses the generative AI emotion estimation function to analyze changes in emotions during a conversation in real time and suggest stickers that correspond to positive emotions. The emotion estimation function analyzes emotions using facial expression recognition technology and voice analysis technology. This makes it possible to analyze changes in emotions during a conversation in real time and suggest stickers that correspond to the emotions.

[0065] The analysis unit can be equipped with a multilingual support function that can analyze conversations in different languages. For example, the analysis unit adds a multilingual support function that allows the generation AI to analyze conversations in different languages, and analyzes conversations in languages ​​such as English and French. The generation AI achieves multilingual support using a translation engine and language model. This allows the generation AI to be equipped with a multilingual support function that can analyze conversations in different languages.

[0066] The analysis unit can link the conversation analysis results with the user's calendar or task management app and suggest related stickers. For example, the generation AI can link with the user's calendar based on the conversation analysis results and suggest stickers related to the schedule. The generation AI shares data with the calendar or task management app using API integration. This allows the conversation analysis results to be linked with the user's calendar or task management app and suggest related stickers.

[0067] The analysis unit can use the emotion estimation function to visually display emotions during a conversation, allowing the user to intuitively understand changes in emotions. For example, the analysis unit uses the emotion estimation function of the generation AI to visually display emotions during a conversation, allowing the user to intuitively understand changes in emotions. The emotion estimation function visualizes changes in emotions using graphs or icons. This visually displays emotions during a conversation, allowing the user to intuitively understand changes in emotions.

[0068] The generation unit can generate stamps that better suit the user's preferences based on the user's past stamp usage history. For example, the generation unit uses a generation AI that references the user's past stamp usage history and automatically generates stamps based on specific characters or designs. The generation AI generates stamps using usage history stored in a database. This allows the generation unit to reference the user's past stamp usage history and generate stamps that better suit the user's preferences.

[0069] The generation unit can provide an option to select different art styles or themes when automatically generating stamps. For example, the generation unit can provide an option to select different art styles, such as watercolor or pop art, when automatically generating stamps. The generation AI generates different art styles using style conversion technology. This can provide an option to select different art styles or themes when automatically generating stamps.

[0070] The generation unit can be equipped with a function that can generate animated stamps or GIF format stamps in the automatic generation of stamps. For example, the generation unit adds a function that allows the generation AI to automatically generate animated stamps, providing stamps with movement. For example, an animated stamp of a smiling cat waving. The generation AI generates animated stamps and GIF format stamps using animation generation technology. This allows the automatic generation of stamps to be equipped with a function that can also generate animated stamps and GIF format stamps.

[0071] The generation unit can share the automatically generated stamps with other users and obtain ratings and feedback. The generation unit adds a function for sharing the automatically generated stamps within a community and obtaining ratings and feedback from other users, for example. Sharing is performed via social networking sites or dedicated community platforms. This allows the automatically generated stamps to be shared with other users and obtain ratings and feedback.

[0072] The generation unit uses the emotion estimation function to monitor users' emotional reactions to the generated stamps in real time and can suggest the most suitable stamps. For example, the generation AI uses the emotion estimation function to monitor users' emotional reactions to the generated stamps in real time and suggest stamps that receive a lot of positive reactions. The emotion estimation function analyzes emotions using facial expression recognition technology and voice analysis technology. This makes it possible to monitor users' emotional reactions to the generated stamps in real time and suggest the most suitable stamps.

[0073] The suggestion unit can learn the user's selection history for suggested stamps and reflect it in subsequent suggestions. For example, the suggestion unit can have a generation AI learn the user's selection history for suggested stamps and reflect it in subsequent suggestions. The generation AI learns the selection history using machine learning technology. This allows the user's selection history for suggested stamps to be learned and reflected in subsequent suggestions.

[0074] The suggestion unit can collect user feedback on suggested stamps and improve the algorithm of the generation AI. For example, the suggestion unit collects user feedback on stamps suggested by the generation AI and improves the algorithm. The generation AI improves the accuracy of stamp suggestions based on the feedback. This allows the generation AI to collect user feedback on suggested stamps and improve the algorithm.

[0075] The suggestion unit can use the emotion estimation function to preferentially suggest stamps that correspond to the user's emotions. For example, the generation AI uses the emotion estimation function to preferentially suggest stamps that correspond to the user's positive emotions. The emotion estimation function analyzes emotions using facial expression recognition technology and voice analysis technology. This allows the emotion estimation function to preferentially suggest stamps that correspond to the user's emotions.

[0076] The suggestion unit can link the suggested stamps with the user's calendar or task management app to suggest related stamps. For example, the suggestion unit links the suggested stamps with the user's calendar using the generation AI to suggest stamps related to the schedule. The generation AI shares data with the calendar or task management app using API integration. This allows the suggested stamps to be linked with the user's calendar or task management app to suggest related stamps.

[0077] The suggestion unit can share suggested stamps with other users and obtain ratings and feedback within the community. The suggestion unit adds, for example, a function to share suggested stamps within the community and obtain ratings and feedback from other users. Sharing is performed via SNS or a dedicated community platform. This allows suggested stamps to be shared with other users and obtain ratings and feedback within the community.

[0078] The suggestion unit uses an emotion estimation function to monitor the user's emotional response to suggested stickers in real time and continuously suggest the most suitable stickers. For example, the suggestion unit uses the emotion estimation function of a generation AI to monitor the user's emotional response to suggested stickers in real time and continuously suggest stickers that receive a lot of positive responses. The emotion estimation function analyzes emotions using facial expression recognition technology and voice analysis technology. This allows the suggestion unit to monitor the user's emotional response to suggested stickers in real time and continuously suggest the most suitable stickers.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The stamp generation system can also obtain the user's geographical location information and generate stamps specific to the region. For example, if a user says, "I'm looking for delicious food" while traveling, the generation unit can generate stamps depicting local specialties. Also, if a user is participating in a specific event, it can generate stamps related to that event. This allows users to use stamps that correspond to their geographical context.

[0081] The stamp generation system can also analyze a user's music playback history and generate music-related stamps. For example, if a user says, "I love this song!", the generation unit can generate stamps based on the artist and album cover of that song. Also, if a user often listens to a particular genre of music, it can suggest stamps related to that genre. This allows users to use stamps that match their musical tastes.

[0082] The stamp generation system can also analyze a user's purchasing history and generate stamps related to purchased products. For example, if a user says, "I bought new shoes!", the generation unit can generate stamps based on the brand and design of those shoes. Also, if a user often buys a particular brand, it can suggest stamps related to that brand. This allows users to use stamps that correspond to their purchasing history.

[0083] The stamp generation system can also analyze the user's health data and generate stamps according to their health status. For example, if a user says, "I went for a run today!", the generation unit can generate stamps with motifs of running shoes and healthy eating. Also, if the user has set a specific health goal, it can suggest stamps related to that goal. This allows users to use stamps according to their health status.

[0084] The stamp generation system can also analyze a user's reading history and generate stamps related to books they have read. For example, if a user says, "This book was interesting!", the generation unit can generate stamps based on the book's cover or characters. Also, if a user often reads books in a particular genre, it can suggest stamps related to that genre. This allows users to use stamps that correspond to their reading history.

[0085] The stamp generation system can also estimate the user's emotions and generate stamps with a relaxing effect based on the estimated emotions. For example, if the user is feeling stressed, the generation unit can generate stamps of landscapes or animals with a relaxing effect. Also, if the user is tired, it can suggest stamps with a refreshing effect. This allows the user to use stamps with a relaxing effect that correspond to their emotions.

[0086] The stamp generation system can further estimate the user's emotions and generate stamps containing encouraging messages based on the estimated emotions. For example, if the user is feeling depressed, the generation unit can generate stamps containing encouraging messages. Also, if the user is feeling anxious, it is possible to suggest stamps containing messages that give a sense of security. This allows the user to use stamps containing encouraging messages that correspond to their emotions.

[0087] The stamp generation system can also estimate the user's emotions and generate humorous stamps based on the estimated emotions. For example, if the user is bored, the generation unit can generate stamps containing humorous characters and messages. Also, if the user is nervous, it can suggest humorous stamps to relax the user. This allows the user to use humorous stamps that correspond to their emotions.

[0088] The stamp generation system can also estimate the user's emotions and generate stamps that express gratitude based on the estimated emotions. For example, if a user wants to express gratitude, the generation unit can generate stamps that include a message of gratitude. Also, if a user wants to thank someone, it can suggest a thank-you stamp that suits the situation. This allows users to use stamps that express gratitude according to their emotions.

[0089] The stamp generation system can also estimate the user's emotions and generate celebratory stamps based on the estimated emotions. For example, if the user is feeling happy, the generation unit can generate stamps containing a celebratory message. Also, if the user wants to celebrate a special event, it can suggest celebratory stamps that correspond to the event. This allows the user to use celebratory stamps that correspond to their emotions.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The registration section allows users to register their favorite designs or characters in advance. For example, users can upload an image file or enter a character name or description of the design in text. Users can also register a "cat character" or a "flower design." Step 2: The analysis unit analyzes the content of the conversation. For example, if a user says, "I had a lot of fun today!", the generation AI analyzes this statement and understands the emotion and context. The generation AI analyzes the content of the conversation using natural language processing technology. Step 3: The generation unit automatically generates stamps based on the content of the conversation analyzed by the analysis unit. For example, in response to the statement "I had a lot of fun today!", the generation AI generates a stamp featuring a smiling cat character. The generation AI generates stamps using image generation technology. Step 4: The suggestion unit suggests stamps generated by the generation unit to the user. For example, stamp candidates are displayed near the input field of a chat app. The user can select one from the suggested stamps and send it.

[0092] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0098] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0099] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0100] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0101] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0102] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0103] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0104] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0106] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0107] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0112] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to 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 imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0133] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0139] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0142] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0143] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0144] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0145] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0146] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0147] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0148] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0149] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0150] 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.

[0151] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0152] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0153] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0154] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0155] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0156] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0157] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0158] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a registration section for allowing a user to register a favorite picture or character in advance; an analysis unit that analyzes the content of the conversation; a generation unit that automatically generates stamps based on the content of the conversation analyzed by the analysis unit; a suggestion unit that suggests the stamp generated by the generation unit to a user. A system characterized by:

2. The registration unit Analyzing the style of the picture or character registered by the user and automatically generating variations in different art styles 2. The system of claim 1.

3. The analysis unit Perform more accurate analysis based on the user's past conversation history 2. The system of claim 1.

4. The generation unit Based on the user's past stamp usage history, the stamp is generated to better suit the user's preferences.

2. The system of claim 1.

5. The suggestion unit The user's selection history for the suggested stamps is learned and reflected in subsequent suggestions.

2. The system of claim 1.

6. The registration unit Using an emotion estimation function, the emotion of the user when registering is analyzed, and the image or character that elicits positive emotions is proposed.

2. The system of claim 1.

7. The analysis unit Using emotion estimation function, changes in emotions during the conversation are analyzed in real time and stamps are suggested according to the emotions.

2. The system of claim 1.

8. The generation unit Using an emotion estimation function, the stamps are generated according to the emotions of the user, allowing for richer expression of emotions.

2. The system of claim 1.

Images