System

Abstract

An object of a system according to an embodiment is to enable a user to quickly select and send a stamp suitable for a message.SOLUTION: A system includes a message analysis part, a stamp generation part, a stamp presentation part, and a stamp transmission part. The message analysis unit analyzes a message transmitted by a user. The stamp generation unit generates a stamp based on the message analyzed by the message analysis unit. The stamp presenting unit presents the plurality of stamp candidates generated by the stamp generating unit to a user. The stamp sending unit sends a stamp selected by the user from the stamp candidates presented by the stamp presenting unit.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 has had the problem of making it difficult for users to quickly select and send stamps that are appropriate for their messages.

[0005] The system according to the embodiment aims to enable a user to quickly select and send a stamp suitable for a message. [Means for solving the problem]

[0006] The system according to the embodiment includes a message analysis unit, a stamp generation unit, a stamp presentation unit, and a stamp transmission unit. The message analysis unit analyzes a message sent by a user. The stamp generation unit generates a stamp based on the message analyzed by the message analysis unit. The stamp presentation unit presents to the user a plurality of stamp candidates generated by the stamp generation unit. The stamp transmission unit transmits a stamp selected by the user from the stamp candidates presented by the stamp presentation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to quickly select and send a stamp suitable for a message. [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 generates optimal stamps based on messages sent by users, presents multiple stamp candidates, and sends the stamp selected by the user. This makes communication on LINE more fun and unique.

[0029] A stamp generation system according to an embodiment includes a message analysis unit, a stamp generation unit, a stamp presentation unit, and a stamp transmission unit. The message analysis unit analyzes messages sent by users. For example, the message analysis unit analyzes the content of the messages using natural language processing technology. The message analysis unit can also analyze the emotions of the messages using a sentiment analysis algorithm. The message analysis unit can also understand the context by referring to past conversation history. For example, the message analysis unit analyzes past conversation history to understand the context of the message. The stamp generation unit generates stamps based on the messages analyzed by the message analysis unit. For example, the stamp generation unit generates illustrations and phrases that match the messages using a generation AI. The stamp generation unit can also analyze messages in different languages ​​and generate stamps in multiple languages. The stamp generation unit can also generate stamps based on original illustrations uploaded by users. For example, the stamp generation unit adds backgrounds and decorations to the original illustrations uploaded by the user to generate stamps. The stamp presentation unit presents multiple stamp candidates generated by the stamp generation unit to the user. For example, the stamp presentation unit may reflect the user's past selection history and present stamp candidates that better suit the user's preferences. The stamp presentation unit may also estimate the user's current emotions and prioritize presenting stamps that match those emotions. The stamp presentation unit may also present stamps of different themes and styles to provide the user with a variety of options. For example, the stamp presentation unit may present stamp candidates with themes such as animals, food, and landscapes. The stamp transmission unit transmits the stamp selected by the user from the stamp candidates presented by the stamp presentation unit. For example, the stamp transmission unit may automatically select stamps that match the tone and context of the message. The stamp transmission unit may also estimate the user's current emotions and automatically select stamps that match those emotions. The stamp transmission unit may also consider the preferences of the user's friends and family and automatically select stamps that will please them.For example, if a friend likes animals, the stamp sending unit automatically selects animal-themed stamps. The stamp generation system according to the embodiment can thus make communication on LINE more fun and unique by generating, presenting, and sending optimal stamps based on a user's message. For example, users can easily create their own original stamps and enjoy communicating with friends and family. Furthermore, by generating appropriate stamps for words that are not commonly used, richer expression is possible.

[0030] The message analysis unit can refer to the user's past conversation history and understand the context of the message. For example, the generation AI analyzes the user's past conversation history and understands the context of the message. For example, if there have been many messages in the past saying "It was fun," it will generate a stamp with a similar context saying "It was fun!" By referring to the past conversation history, more appropriate stamps can be generated.

[0031] The message analysis unit can refer to the user's past stamp usage history and generate stamps that suit their preferences. For example, the message analysis unit uses a generation AI to analyze the user's past stamp usage history and understand their preferred style and theme. For example, if a user likes animal illustrations, it can generate stamps with animal motifs. In this way, by referring to the user's past stamp usage history, it can generate stamps that suit the user's preferences.

[0032] The message analysis unit can also analyze messages in different languages ​​and generate stamps that support multiple languages. For example, the generation AI in the message analysis unit analyzes messages in different languages ​​and generates stamps that correspond to those languages. For example, an English message generates a single word in English and an illustration. This makes it possible to generate stamps that support multiple languages ​​by analyzing messages in different languages.

[0033] The message analysis unit can take into account the user's hobbies and interests and generate stamps related to them. For example, the message analysis unit uses a generation AI to analyze the user's hobbies and interests and generate stamps related to them. For example, for a user who likes music, it generates a music-themed illustration and a message. This allows for more personalized communication by generating stamps related to the user's hobbies and interests.

[0034] The stamp presentation unit can reflect the user's past selection history and present stamp candidates that better suit their preferences. For example, the stamp presentation unit uses a generation AI to analyze the user's past stamp selection history and present stamp candidates that suit their preferences. For example, a user who often chooses animal stamps will be presented with animal-themed stamp candidates. In this way, by reflecting the user's past selection history, stamp candidates that suit the user's preferences can be presented.

[0035] The stamp presentation unit takes into consideration the preferences of the user's friends and family and can present stamps that will please them. For example, the stamp presentation unit uses a generation AI to analyze the preferences of the user's friends and family and present stamp candidates based on that. For example, if a friend likes animals, animal-themed stamp candidates will be presented. This allows for more enjoyable communication by presenting stamps that match the preferences of friends and family.

[0036] The stamp presentation unit can present stamps of different themes and styles, providing the user with a variety of options. For example, the stamp presentation unit generates stamps of different themes and styles using a generation AI and presents them to the user. For example, it presents stamp candidates with themes such as animals, food, and landscapes. In this way, by presenting stamps of different themes and styles, it is possible to provide the user with a variety of options.

[0037] The stamp presentation unit can refer to the content of the user's past messages and present highly relevant stamps. For example, the stamp presentation unit uses a generation AI to analyze the content of the user's past messages and present highly relevant stamp candidates. For example, if there have been many messages in the past that say "Thank you," a stamp candidate with the word "Thank you!" and an illustration will be presented. This makes it possible to present highly relevant stamps by referring to the content of past messages.

[0038] The stamp sending unit can automatically select stamps that match the tone and context of the message. For example, the stamp sending unit uses a generation AI to analyze the tone and context of the message and automatically select the stamp that best suits it. For example, for a message of gratitude, it would select the word "Thank you!" and an illustration of gratitude. This allows the system to automatically select stamps that match the tone and context of the message, enabling it to send more appropriate stamps.

[0039] The stamp sending unit can refer to the user's past stamp usage history and automatically select stamps that suit their preferences. For example, the stamp sending unit uses a generation AI to analyze the user's past stamp usage history and understand their preferred style and theme. For example, if a user likes animal illustrations, stamps with animal motifs will be automatically selected. This makes it possible to automatically select stamps that suit the user's preferences by referring to the user's past stamp usage history.

[0040] The stamp sending unit can automatically select stamps in different languages ​​to realize multilingual communication. For example, the generation AI of the stamp sending unit can automatically select stamps in different languages ​​to realize multilingual communication. For example, for an English message, a stamp with a single word and an illustration in English is selected. In this way, by automatically selecting stamps in different languages, multilingual communication can be realized.

[0041] The stamp sending unit can automatically select stamps that will please the recipient, taking into consideration the preferences of the user's friends and family. For example, the stamp sending unit uses a generation AI to analyze the preferences of the user's friends and family and automatically select stamps based on that. For example, if a friend likes animals, animal-themed stamps will be automatically selected. This allows for more enjoyable communication by automatically selecting stamps that match the preferences of friends and family.

[0042] The stamp generation unit can generate multiple variations based on an original illustration uploaded by a user and present them to the user. For example, the stamp generation unit uses a generation AI to generate variations with different themes and styles based on an original illustration uploaded by a user and presents them to the user. For example, it generates variations in styles such as hand-drawn, anime, and realistic. This allows the user to generate multiple variations based on the original illustration uploaded by the user, making it possible to provide a wider variety of stamps.

[0043] The stamp generation unit can automatically add backgrounds and decorations to original illustrations uploaded by users to generate more attractive stamps. For example, the generation AI automatically adds backgrounds to the user's original illustrations to generate more attractive stamps. For example, adding a background of a landscape or pattern. In this way, by adding backgrounds and decorations to the original illustrations, more attractive stamps can be generated.

[0044] The stamp generation unit can generate stamps with different themes and styles based on original illustrations uploaded by users. For example, the stamp generation unit uses a generation AI to generate stamps with different themes and styles based on the user's original illustrations. For example, stamps with themes such as animals, food, and landscapes can be generated. This allows for the generation of stamps with different themes and styles based on original illustrations, making it possible to provide a wider variety of stamps.

[0045] The stamp generation unit can generate stamps that will please users by taking into consideration the preferences of the user's friends and family for the original illustrations uploaded by the user. For example, the stamp generation unit generates stamps based on the preferences of the user's friends and family using a generation AI that analyzes the preferences of the user's friends and family. For example, if a friend likes animals, it can generate animal-themed stamps. This allows the system to provide stamps that will be more pleasing by taking into consideration the preferences of friends and family for the original illustrations.

[0046] The stamp generation unit can understand the meaning and background of words that are not commonly used and generate appropriate stamps. For example, the stamp generation unit uses a generation AI to analyze the meaning and background of words that are not commonly used and generate stamps based on that. For example, for the message "Thank you for your hard work," it generates the words "Thank you for your hard work!" and an illustration of gratitude. This allows appropriate stamps to be generated by understanding the meaning and background of words that are not commonly used.

[0047] The stamp generation unit can refer to the user's past message history and generate highly relevant stamps for words that are not commonly used often. For example, the stamp generation unit uses a generation AI to analyze the user's past message history and generate stamps related to words that are not commonly used often. For example, for the message "Thank you for your hard work," it generates the words "Thank you for your hard work!" and an illustration of gratitude. In this way, by referring to the past message history, highly relevant stamps can be generated for words that are not commonly used often.

[0048] The stamp generation unit generates stamps in different languages ​​for words that are not commonly used, enabling multilingual communication. For example, the stamp generation unit uses a generation AI to translate words that are not commonly used into different languages ​​and generate stamps that correspond to those languages. For example, for the message "Thank you for your hard work," the unit generates the words "Good job!" in English along with an illustration of gratitude. This allows multilingual communication to be achieved by generating stamps in different languages ​​for words that are not commonly used.

[0049] The stamp generation unit can generate stamps that will please the recipient by taking into account the preferences of the user's friends and family for words that are not commonly used. For example, the stamp generation unit generates stamps based on the preferences of the user's friends and family using a generation AI that analyzes the preferences of the user's friends and family. For example, if a friend likes animals, it can generate animal-themed stamps. This allows the generation of stamps that will be more appreciated by taking into account the preferences of friends and family for words that are not commonly used.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The stamp generation system can also monitor the user's health condition and generate stamps according to that condition. For example, if the user has just exercised, it can generate exercise-themed stamps. If the user is not feeling well, it can generate encouraging or sympathetic stamps. This allows for more personalized communication by providing stamps according to the user's health condition.

[0052] The stamp generation system can also obtain the user's location information and generate stamps related to that location. For example, if the user is traveling, stamps themed around the famous places and local products of the travel destination can be generated. Also, if the user is participating in a specific event, stamps related to that event can be generated. This allows for more realistic communication by providing stamps based on the user's location information.

[0053] The stamp generation system can also take into account a user's hobbies and interests and generate stamps related to those. For example, for a user who likes music, it can generate music-themed illustrations and a message. Also, for a user who likes sports, it can generate sports-themed stamps. This allows for more personalized communication by providing stamps related to the user's hobbies and interests.

[0054] The stamp generation system can also take into account the preferences of the user's friends and family to generate stamps that will please them. For example, if a friend likes animals, animal-themed stamps can be generated. Or, if a family member likes a particular character, stamps based on that character can be generated. This allows for more enjoyable communication by providing stamps that match the preferences of friends and family.

[0055] The stamp generation system can also reference the content of a user's past messages to generate highly relevant stamps. For example, if a user has frequently sent messages using the word "thank you," it can generate a stamp with the single word "thank you!" and an illustration. It can also generate stamps with a specific theme or style based on the content of past messages. This allows for more relevant communication by providing stamps based on the content of past messages.

[0056] The stamp generation system can also refer to a user's past stamp usage history to generate stamps that suit their preferences. For example, for a user who likes animal illustrations, stamps with animal motifs can be generated. Also, for a user who likes a particular character, stamps themed around that character can be generated. This allows for communication that is more tailored to the user's preferences by providing stamps based on the user's past stamp usage history.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The message analysis unit analyzes the message sent by the user. For example, it uses natural language processing technology to analyze the content of the message and a sentiment analysis algorithm to analyze the sentiment of the message. It can also understand the context by referring to past conversation history. Step 2: The stamp generator generates stamps based on the message analyzed by the message analyzer. For example, it uses AI to generate illustrations and phrases that match the message, and analyzes messages in different languages ​​to generate stamps that support multiple languages. It can also generate stamps by adding backgrounds and decorations to original illustrations uploaded by users. Step 3: The stamp presentation unit presents the multiple stamp candidates generated by the stamp generation unit to the user. For example, it reflects the user's past selection history to present stamp candidates that better suit the user's preferences, and prioritizes presenting stamps that match the user's current emotions. It also presents stamps of different themes and styles to provide the user with a variety of options. Step 4: The stamp sending unit sends the stamp selected by the user from the stamp candidates presented by the stamp presenting unit. For example, it can automatically select stamps that match the tone and context of the message, and can also automatically select stamps that will please the recipient, taking into account the user's current emotions and the preferences of friends and family.

[0059] (Example 2) The stamp generation system according to the embodiment of the present invention generates optimal stamps based on messages sent by users, presents multiple stamp candidates, and sends the stamp selected by the user. This makes communication on LINE more fun and unique.

[0060] A stamp generation system according to an embodiment includes a message analysis unit, a stamp generation unit, a stamp presentation unit, and a stamp transmission unit. The message analysis unit analyzes messages sent by users. For example, the message analysis unit analyzes the content of the messages using natural language processing technology. The message analysis unit can also analyze the emotions of the messages using a sentiment analysis algorithm. The message analysis unit can also understand the context by referring to past conversation history. For example, the message analysis unit analyzes past conversation history to understand the context of the message. The stamp generation unit generates stamps based on the messages analyzed by the message analysis unit. For example, the stamp generation unit generates illustrations and phrases that match the messages using a generation AI. The stamp generation unit can also analyze messages in different languages ​​and generate stamps in multiple languages. The stamp generation unit can also generate stamps based on original illustrations uploaded by users. For example, the stamp generation unit adds backgrounds and decorations to the original illustrations uploaded by the user to generate stamps. The stamp presentation unit presents multiple stamp candidates generated by the stamp generation unit to the user. For example, the stamp presentation unit may reflect the user's past selection history and present stamp candidates that better suit the user's preferences. The stamp presentation unit may also estimate the user's current emotions and prioritize presenting stamps that match those emotions. The stamp presentation unit may also present stamps of different themes and styles to provide the user with a variety of options. For example, the stamp presentation unit may present stamp candidates with themes such as animals, food, and landscapes. The stamp transmission unit transmits the stamp selected by the user from the stamp candidates presented by the stamp presentation unit. For example, the stamp transmission unit may automatically select stamps that match the tone and context of the message. The stamp transmission unit may also estimate the user's current emotions and automatically select stamps that match those emotions. The stamp transmission unit may also consider the preferences of the user's friends and family and automatically select stamps that will please them.For example, if a friend likes animals, the stamp sending unit automatically selects animal-themed stamps. The stamp generation system according to the embodiment can thus make communication on LINE more fun and unique by generating, presenting, and sending optimal stamps based on a user's message. For example, users can easily create their own original stamps and enjoy communicating with friends and family. Furthermore, by generating appropriate stamps for words that are not commonly used, richer expression is possible.

[0061] The message analysis unit can refer to the user's past conversation history and understand the context of the message. For example, the generation AI analyzes the user's past conversation history and understands the context of the message. For example, if there have been many messages in the past saying "It was fun," it will generate a stamp with a similar context saying "It was fun!" By referring to the past conversation history, more appropriate stamps can be generated.

[0062] The message analysis unit can analyze the emotion of the message and generate stamps that correspond to that emotion. For example, the generation AI analyzes the emotion of the message and generates a cheerful illustration and a word for a positive emotion, and a comforting word and illustration for a negative emotion. For example, a message such as "I'm so happy!" generates an illustration of a smiling face and the word "I'm so happy!" This allows for more emotional communication by generating stamps that correspond to the emotion of the message.

[0063] The message analysis unit can refer to the user's past stamp usage history and generate stamps that suit their preferences. For example, the message analysis unit uses a generation AI to analyze the user's past stamp usage history and understand their preferred style and theme. For example, if a user likes animal illustrations, it can generate stamps with animal motifs. In this way, by referring to the user's past stamp usage history, it can generate stamps that suit the user's preferences.

[0064] The message analysis unit can also analyze messages in different languages ​​and generate stamps that support multiple languages. For example, the generation AI in the message analysis unit analyzes messages in different languages ​​and generates stamps that correspond to those languages. For example, an English message generates a single word in English and an illustration. This makes it possible to generate stamps that support multiple languages ​​by analyzing messages in different languages.

[0065] The message analysis unit can estimate the user's real-time emotions and generate stamps based on those emotions. For example, the message analysis unit uses a generation AI to estimate the user's real-time emotions and generate stamps based on those emotions. For example, if the user is typing a message with a smile, it will generate an illustration of a smiling face and the phrase "It's fun!". This allows for more emotional communication by generating stamps based on the user's real-time emotions.

[0066] The message analysis unit can take into account the user's hobbies and interests and generate stamps related to them. For example, the message analysis unit uses a generation AI to analyze the user's hobbies and interests and generate stamps related to them. For example, for a user who likes music, it generates a music-themed illustration and a message. This allows for more personalized communication by generating stamps related to the user's hobbies and interests.

[0067] The stamp presentation unit can reflect the user's past selection history and present stamp candidates that better suit their preferences. For example, the stamp presentation unit uses a generation AI to analyze the user's past stamp selection history and present stamp candidates that suit their preferences. For example, a user who often chooses animal stamps will be presented with animal-themed stamp candidates. In this way, by reflecting the user's past selection history, stamp candidates that suit the user's preferences can be presented.

[0068] The stamp presentation unit can estimate the user's current emotion and prioritize presenting stamps that match that emotion. For example, the stamp presentation unit uses a generation AI to estimate the user's current emotion and present stamp candidates that match that emotion. For example, if the user is happy, it will present stamp candidates that include an illustration of joy and a message. This allows for more emotional communication by prioritizing the presentation of stamps that match the user's current emotion.

[0069] The stamp presentation unit takes into consideration the preferences of the user's friends and family and can present stamps that will please them. For example, the stamp presentation unit uses a generation AI to analyze the preferences of the user's friends and family and present stamp candidates based on that. For example, if a friend likes animals, animal-themed stamp candidates will be presented. This allows for more enjoyable communication by presenting stamps that match the preferences of friends and family.

[0070] The stamp presentation unit can present stamps of different themes and styles, providing the user with a variety of options. For example, the stamp presentation unit generates stamps of different themes and styles using a generation AI and presents them to the user. For example, it presents stamp candidates with themes such as animals, food, and landscapes. In this way, by presenting stamps of different themes and styles, it is possible to provide the user with a variety of options.

[0071] The stamp presentation unit can refer to the content of the user's past messages and present highly relevant stamps. For example, the stamp presentation unit uses a generation AI to analyze the content of the user's past messages and present highly relevant stamp candidates. For example, if there have been many messages in the past that say "Thank you," a stamp candidate with the word "Thank you!" and an illustration will be presented. This makes it possible to present highly relevant stamps by referring to the content of past messages.

[0072] The stamp sending unit can automatically select stamps that match the tone and context of the message. For example, the stamp sending unit uses a generation AI to analyze the tone and context of the message and automatically select the stamp that best suits it. For example, for a message of gratitude, it would select the word "Thank you!" and an illustration of gratitude. This allows the system to automatically select stamps that match the tone and context of the message, enabling it to send more appropriate stamps.

[0073] The stamp sending unit can estimate the user's current emotion and automatically select a stamp that matches that emotion. For example, the stamp sending unit uses a generation AI to estimate the user's current emotion and automatically select a stamp that matches that emotion. For example, if the user is happy, a stamp with an illustration of joy and a message is selected. This automatically selects a stamp that matches the user's current emotion, enabling more emotionally rich communication.

[0074] The stamp sending unit can refer to the user's past stamp usage history and automatically select stamps that suit their preferences. For example, the stamp sending unit uses a generation AI to analyze the user's past stamp usage history and understand their preferred style and theme. For example, if a user likes animal illustrations, stamps with animal motifs will be automatically selected. This makes it possible to automatically select stamps that suit the user's preferences by referring to the user's past stamp usage history.

[0075] The stamp sending unit can automatically select stamps in different languages ​​to realize multilingual communication. For example, the generation AI of the stamp sending unit can automatically select stamps in different languages ​​to realize multilingual communication. For example, for an English message, a stamp with a single word and an illustration in English is selected. In this way, by automatically selecting stamps in different languages, multilingual communication can be realized.

[0076] The stamp sending unit can estimate the user's real-time emotions and automatically select stamps that match those emotions. For example, the stamp sending unit uses a generation AI to estimate the user's real-time emotions and automatically select stamps that match those emotions. For example, if the user is typing a message with a smile, a stamp with an illustration of a smile and a single word will be selected. This automatically selects stamps that match the user's real-time emotions, enabling more emotionally rich communication.

[0077] The stamp sending unit can automatically select stamps that will please the recipient, taking into consideration the preferences of the user's friends and family. For example, the stamp sending unit uses a generation AI to analyze the preferences of the user's friends and family and automatically select stamps based on that. For example, if a friend likes animals, animal-themed stamps will be automatically selected. This allows for more enjoyable communication by automatically selecting stamps that match the preferences of friends and family.

[0078] The stamp generation unit can generate multiple variations based on an original illustration uploaded by a user and present them to the user. For example, the stamp generation unit uses a generation AI to generate variations with different themes and styles based on an original illustration uploaded by a user and presents them to the user. For example, it generates variations in styles such as hand-drawn, anime, and realistic. This allows the user to generate multiple variations based on the original illustration uploaded by the user, making it possible to provide a wider variety of stamps.

[0079] The stamp generation unit can automatically add backgrounds and decorations to original illustrations uploaded by users to generate more attractive stamps. For example, the generation AI automatically adds backgrounds to the user's original illustrations to generate more attractive stamps. For example, adding a background of a landscape or pattern. In this way, by adding backgrounds and decorations to the original illustrations, more attractive stamps can be generated.

[0080] The stamp generation unit can estimate the emotion of an original illustration uploaded by a user and automatically generate a phrase that matches that emotion. For example, the stamp generation unit uses a generation AI to estimate the emotion of an original illustration uploaded by a user and automatically generate a phrase that matches that emotion. For example, for an illustration of a smiling face, it generates the phrase "It's fun!" This makes it possible to provide stamps with a richer sense of emotion by estimating the emotion of an original illustration and generating a phrase that matches that emotion.

[0081] The stamp generation unit can generate stamps with different themes and styles based on original illustrations uploaded by users. For example, the stamp generation unit uses a generation AI to generate stamps with different themes and styles based on the user's original illustrations. For example, stamps with themes such as animals, food, and landscapes can be generated. This allows for the generation of stamps with different themes and styles based on original illustrations, making it possible to provide a wider variety of stamps.

[0082] The stamp generation unit can estimate real-time emotions from original illustrations uploaded by users and automatically generate a phrase that matches that emotion. For example, the stamp generation unit uses a generation AI to estimate real-time emotions from original illustrations uploaded by users and automatically generate a phrase that matches that emotion. For example, for an illustration of a smiling face, it generates the phrase "It's fun!" This makes it possible to provide stamps with a richer sense of emotion by estimating real-time emotions from original illustrations and generating a phrase that matches that emotion.

[0083] The stamp generation unit can generate stamps that will please users by taking into consideration the preferences of the user's friends and family for the original illustrations uploaded by the user. For example, the stamp generation unit generates stamps based on the preferences of the user's friends and family using a generation AI that analyzes the preferences of the user's friends and family. For example, if a friend likes animals, it can generate animal-themed stamps. This allows the system to provide stamps that will be more pleasing by taking into consideration the preferences of friends and family for the original illustrations.

[0084] The stamp generation unit can understand the meaning and background of words that are not commonly used and generate appropriate stamps. For example, the stamp generation unit uses a generation AI to analyze the meaning and background of words that are not commonly used and generate stamps based on that. For example, for the message "Thank you for your hard work," it generates the words "Thank you for your hard work!" and an illustration of gratitude. This allows appropriate stamps to be generated by understanding the meaning and background of words that are not commonly used.

[0085] The stamp generation unit can estimate the emotion of words that are not commonly used and generate stamps that match that emotion. For example, the stamp generation unit uses a generation AI to analyze the emotion of words that are not commonly used and generate stamps that match that emotion. For example, for the message "Thank you for your hard work," it generates an illustration of gratitude and the single word "Thank you for your hard work!" This makes it possible to generate stamps that are richer in emotion by estimating the emotion of words that are not commonly used.

[0086] The stamp generation unit can refer to the user's past message history and generate highly relevant stamps for words that are not commonly used often. For example, the stamp generation unit uses a generation AI to analyze the user's past message history and generate stamps related to words that are not commonly used often. For example, for the message "Thank you for your hard work," it generates the words "Thank you for your hard work!" and an illustration of gratitude. In this way, by referring to the past message history, highly relevant stamps can be generated for words that are not commonly used often.

[0087] The stamp generation unit generates stamps in different languages ​​for words that are not commonly used, enabling multilingual communication. For example, the stamp generation unit uses a generation AI to translate words that are not commonly used into different languages ​​and generate stamps that correspond to those languages. For example, for the message "Thank you for your hard work," the unit generates the words "Good job!" in English along with an illustration of gratitude. This allows multilingual communication to be achieved by generating stamps in different languages ​​for words that are not commonly used.

[0088] The stamp generation unit can estimate real-time emotions for words that are not commonly used often, and generate stamps that match those emotions. For example, the stamp generation unit uses a generation AI to estimate real-time emotions for words that are not commonly used often, and generate stamps that match those emotions. For example, for the message "Thank you for your hard work," it generates an illustration of gratitude and the single word "Thank you for your hard work!" This makes it possible to generate stamps that are richer in emotion by estimating real-time emotions for words that are not commonly used often.

[0089] The stamp generation unit can generate stamps that will please the recipient by taking into account the preferences of the user's friends and family for words that are not commonly used. For example, the stamp generation unit generates stamps based on the preferences of the user's friends and family using a generation AI that analyzes the preferences of the user's friends and family. For example, if a friend likes animals, it can generate animal-themed stamps. This allows the generation of stamps that will be more appreciated by taking into account the preferences of friends and family for words that are not commonly used.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The stamp generation system can also monitor the user's health condition and generate stamps according to that condition. For example, if the user has just exercised, it can generate exercise-themed stamps. If the user is not feeling well, it can generate encouraging or sympathetic stamps. This allows for more personalized communication by providing stamps according to the user's health condition.

[0092] The stamp generation system can also obtain the user's location information and generate stamps related to that location. For example, if the user is traveling, stamps themed around the famous places and local products of the travel destination can be generated. Also, if the user is participating in a specific event, stamps related to that event can be generated. This allows for more realistic communication by providing stamps based on the user's location information.

[0093] The sticker generation system can also analyze the user's voice input and generate stickers based on that voice. For example, if the user speaks "thank you," a sticker that matches the words will be generated. It can also analyze the tone and speed of the user's voice and generate stickers that match the emotion. This allows for more natural communication by providing stickers based on voice input.

[0094] The stamp generation system can also take into account a user's hobbies and interests and generate stamps related to those. For example, for a user who likes music, it can generate music-themed illustrations and a message. Also, for a user who likes sports, it can generate sports-themed stamps. This allows for more personalized communication by providing stamps related to the user's hobbies and interests.

[0095] The stamp generation system can also estimate the user's real-time emotions and generate stamps based on those emotions. For example, if the user is smiling while typing a message, it can generate an illustration of a smiling face and the words "It's fun!". If the user is sad, it can also generate comforting words and illustrations. This allows for more emotional communication by providing stamps based on the user's real-time emotions.

[0096] The stamp generation system can also take into account the preferences of the user's friends and family to generate stamps that will please them. For example, if a friend likes animals, animal-themed stamps can be generated. Or, if a family member likes a particular character, stamps based on that character can be generated. This allows for more enjoyable communication by providing stamps that match the preferences of friends and family.

[0097] The stamp generation system can also reference the content of a user's past messages to generate highly relevant stamps. For example, if a user has frequently sent messages using the word "thank you," it can generate a stamp with the single word "thank you!" and an illustration. It can also generate stamps with a specific theme or style based on the content of past messages. This allows for more relevant communication by providing stamps based on the content of past messages.

[0098] The stamp generation system can also estimate the user's real-time emotions and generate stamps based on those emotions. For example, if the user is angry, it can generate an illustration of anger and the message "What's wrong?". If the user is surprised, it can also generate an illustration of surprise and the message "Surprise!". This allows for more emotional communication by providing stamps based on the user's real-time emotions.

[0099] The stamp generation system can also refer to a user's past stamp usage history to generate stamps that suit their preferences. For example, for a user who likes animal illustrations, stamps with animal motifs can be generated. Also, for a user who likes a particular character, stamps themed around that character can be generated. This allows for communication that is more tailored to the user's preferences by providing stamps based on the user's past stamp usage history.

[0100] The stamp generation system can also estimate the user's real-time emotions and generate stamps based on those emotions. For example, if the user is tired, it can generate an illustration that soothes fatigue and the message, "Good job!". If the user is excited, it can also generate an illustration that expresses excitement and the message, "Amazing!". This allows for more emotional communication by providing stamps based on the user's real-time emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The message analysis unit analyzes the message sent by the user. For example, it uses natural language processing technology to analyze the content of the message and a sentiment analysis algorithm to analyze the sentiment of the message. It can also understand the context by referring to past conversation history. Step 2: The stamp generator generates stamps based on the message analyzed by the message analyzer. For example, it uses AI to generate illustrations and phrases that match the message, and analyzes messages in different languages ​​to generate stamps that support multiple languages. It can also generate stamps by adding backgrounds and decorations to original illustrations uploaded by users. Step 3: The stamp presentation unit presents the multiple stamp candidates generated by the stamp generation unit to the user. For example, it reflects the user's past selection history to present stamp candidates that better suit the user's preferences, and prioritizes presenting stamps that match the user's current emotions. It also presents stamps of different themes and styles to provide the user with a variety of options. Step 4: The stamp sending unit sends the stamp selected by the user from the stamp candidates presented by the stamp presenting unit. For example, it can automatically select stamps that match the tone and context of the message, and can also automatically select stamps that will please the recipient, taking into account the user's current emotions and the preferences of friends and family.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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 message analysis unit that analyzes messages sent by users; a stamp generation unit that generates a stamp based on the message analyzed by the message analysis unit; a stamp presentation unit that presents a plurality of stamp candidates generated by the stamp generation unit to a user; a stamp sending unit that sends a stamp selected by the user from the stamp candidates presented by the stamp presenting unit. A system characterized by:

2. The message analysis unit Analyze messages in different languages ​​and generate multilingual stamps 2. The system of claim 1.

3. The stamp presentation unit Reflecting the user's past selection history, it presents stamp candidates that better suit their preferences.

2. The system of claim 1.

4. The stamp sending unit Automatically select stamps that match the tone and context of your message 2. The system of claim 1.

5. The stamp generation unit Based on the original illustration uploaded by the user, multiple variations are generated and presented to the user.

2. The system of claim 1.

6. The message analysis unit Analyze the sentiment of the message and generate stamps according to the sentiment 2. The system of claim 1.

7. The stamp presentation unit It estimates the user's current emotion and prioritizes presenting stamps that match that emotion.

2. The system of claim 1.

8. The stamp sending unit Estimate the user's current emotion and automatically select stamps that match that emotion.

2. The system of claim 1.

Images