System

Abstract

An object of the system according to the embodiment is to easily create an original stamp based on an image of a family member or a pet.SOLUTION: A system includes an image acquisition unit, a facial expression recognition unit, a phrase selection unit, and a stamp generation unit. The image acquisition unit acquires an image of a family member or a pet. The facial expression recognition unit detects a face from the image acquired by the image acquisition unit. The phrase selection section selects an appropriate phrase based on the facial expression detected by the facial expression recognition section. The stamp generation unit generates an original stamp by combining the phrase selected by the phrase selection unit and the image of the face detected by the facial expression recognition 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 techniques have had the problem that creating original stamps is complicated and cannot be used casually.

[0005] The system according to the embodiment aims to easily create original stamps based on images of family members or pets. [Means for solving the problem]

[0006] The system according to the embodiment includes an image acquisition unit, a facial expression recognition unit, a phrase selection unit, and a stamp generation unit. The image acquisition unit acquires images of family members or pets. The facial expression recognition unit detects faces from the images acquired by the image acquisition unit. The phrase selection unit selects an appropriate phrase based on the facial expression detected by the facial expression recognition unit. The stamp generation unit generates an original stamp by combining the phrase selected by the phrase selection unit with the image of the face detected by the facial expression recognition unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily create original stamps based on images of family members or pets. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 AI ​​stamp generation system according to an embodiment of the present invention automatically acquires images of family members or pets, and the generation AI recognizes their facial expressions, selects appropriate phrases, and instantly creates original stamps. This allows users to instantly create and use original stamps without any effort.

[0029] The AI ​​stamp generation system according to the embodiment includes an image acquisition unit, a facial expression recognition unit, a phrase selection unit, and a stamp generation unit. The image acquisition unit acquires images of family members or pets. For example, the image is captured using a smartphone camera. The image acquisition unit can also select images from an existing image library. The image acquisition unit can also download images from the Internet. The facial expression recognition unit detects faces from the images acquired by the image acquisition unit. For example, the facial expression recognition unit detects faces using a Haar feature classifier. The facial expression recognition unit can also use a deep learning-based detection algorithm. The facial expression recognition unit can also detect facial landmarks and identify the position and orientation of the face. The phrase selection unit selects an appropriate phrase based on the facial expression detected by the facial expression recognition unit. For example, the phrase selection unit selects a phrase such as "Fun!" for an image of a smiling face and "Sad..." for an image of a crying face. The phrase selection unit can also generate phrases based on facial expressions using a generation AI. The phrase selection unit can also select phrases from a phrase list pre-set by the user. The stamp generation unit generates an original stamp by combining a phrase selected by the phrase selection unit with an image of a face detected by the facial expression recognition unit. For example, the stamp generation unit uses image editing software to overlay a phrase on an image. The stamp generation unit can also combine a phrase and an image using a generation AI. The stamp generation unit can also generate a stamp using a design template customized by the user. As a result, the AI ​​stamp generation system according to the embodiment allows users to instantly create and use original stamps without any effort.

[0030] The facial expression recognition unit detects subtle changes in facial expression, such as eyebrow movements or the degree to which the corners of the mouth are turned up, and can generate more precise phrases based on these. The facial expression recognition unit detects subtle changes in facial expression, such as eyebrow movements or the degree to which the corners of the mouth are turned up. For example, it can distinguish between a slight smile and a hearty laugh and generate appropriate phrases for each. The facial expression recognition unit also analyzes the movements of various parts of the face (eyes, eyebrows, mouth, etc.) in real time and generates phrases based on this data. For example, it selects phrases such as "I'm happy!" or "This is great!" based on the brightness of the eyes. The facial expression recognition unit can also analyze facial muscle movements to read more precise emotions and generate phrases based on this. For example, it can generate a phrase such as "I'm a little worried..." based on a subtle eyebrow movement. This allows for the generation of more precise phrases based on subtle changes in facial expression.

[0031] The facial expression recognition unit can simultaneously detect multiple faces in an image, generate different phrases based on each facial expression, and create multiple stamps at once. For example, the facial expression recognition unit uses facial expression recognition technology to simultaneously detect multiple faces in an image and generate different phrases based on each facial expression. For example, the facial expression of each member in a family photo is analyzed to generate appropriate phrases for each. The facial expression recognition unit can also build a system that simultaneously detects multiple faces in an image and generates different phrases based on each facial expression. For example, the facial expression of each member in a photo of a group of friends is analyzed to generate individual phrases. The facial expression recognition unit can also use facial expression recognition technology to simultaneously detect multiple faces in an image, generate different phrases based on each facial expression, and create multiple stamps at once. For example, the facial expressions of all family members are analyzed to generate appropriate phrases for each member. This makes it possible to simultaneously detect multiple faces and generate different phrases based on each facial expression.

[0032] The facial expression recognition unit can analyze the facial expressions of animals and generate stamps from images of pets or wild animals. The facial expression recognition unit, for example, applies facial expression recognition technology to animal facial expressions and generates stamps from images of pets. For example, it analyzes the facial expressions of dogs and cats and generates phrases based on the expressions. The facial expression recognition unit also applies facial expression recognition technology so that stamps can be generated from images of wild animals. For example, it analyzes the facial expressions of lions and elephants photographed at the zoo and generates phrases based on the expressions. The facial expression recognition unit also develops facial expression recognition technology for analyzing animal facial expressions and generates stamps from images of pets or wild animals. For example, it analyzes the facial expressions of birds and fish and generates phrases based on the expressions. In this way, it is possible to analyze animal facial expressions and generate stamps from images of pets or wild animals.

[0033] The facial expression recognition unit can automatically extract specific frames from videos and generate stamps based on those frames. The facial expression recognition unit, for example, uses facial expression recognition technology to automatically extract specific frames from videos and generate stamps based on those frames. For example, a frame capturing a smiling moment is extracted and a phrase is generated based on that. The facial expression recognition unit also develops facial expression recognition technology for automatically extracting specific frames from videos and generates stamps based on those frames. For example, a frame capturing a moving scene is extracted and a phrase is generated based on that. The facial expression recognition unit also uses facial expression recognition technology to build a system that automatically extracts specific frames from videos and generates stamps based on those frames. For example, a smiling moment is extracted from a video of a family and a phrase is generated based on that. This makes it possible to automatically extract specific frames from videos and generate stamps based on those frames.

[0034] The stamp generation unit can generate phrases from text or voice entered by the user and combine them with an image to create a stamp. The stamp generation unit, for example, uses a generation AI to generate phrases from text entered by the user and combines them with an image to create a stamp. For example, when a user enters "Thank you," the stamp generation unit combines the phrase with an image to generate a stamp. The stamp generation unit can also analyze the voice entered by the user, generate a phrase based on the content of the voice, and combine it with an image to create a stamp. For example, when a user says "Congratulations," the stamp generation unit combines the phrase with an image to generate a stamp. The stamp generation unit can also use a generation AI to build a system that generates phrases from text or voice entered by the user and combines them with an image to create a stamp. For example, when a user enters "Good luck," the stamp generation unit combines the phrase with an image to generate a stamp. This allows phrases to be generated from text or voice entered by the user and combined with an image to create a stamp.

[0035] The stamp generation unit automatically generates backgrounds or decorative elements for stamps, allowing for the provision of stamps with a wider variety of designs. The stamp generation unit, for example, uses generation AI to build a system that automatically generates backgrounds and decorative elements for stamps. For example, it automatically adds seasonal backgrounds and decorations to match an image selected by a user. The stamp generation unit also develops generation AI for automatically generating backgrounds and decorative elements for stamps, allowing for the provision of stamps with a wider variety of designs. For example, it automatically adds decorations such as flowers and stars to match an image selected by a user. The stamp generation unit also uses generation AI to automatically generate backgrounds and decorative elements for stamps, allowing for the provision of stamps with a wider variety of designs. For example, it automatically adds colorful backgrounds and decorations to match an image selected by a user. This allows for the automatic generation of backgrounds and decorative elements for stamps, allowing for the provision of stamps with a wider variety of designs.

[0036] The stamp generation unit can customize the phrases and images selected by the generation AI according to the theme selected by the user when combining them with the image selected by the generation AI. For example, when combining a phrase and an image selected by the generation AI, the stamp generation unit customizes the phrases and images according to the theme selected by the user. For example, if a Christmas theme is selected, decorations of a Christmas tree and Santa Claus are added. The stamp generation unit also builds a system in which the generation AI customizes phrases and images based on the theme selected by the user. For example, if a summer theme is selected, decorations of a beach and sunglasses are added. The stamp generation unit also customizes the phrases and images selected by the generation AI according to the event selected by the user. For example, if a birthday theme is selected, decorations of a birthday cake and balloons are added. This allows customization according to the theme selected by the user.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The AI ​​stamp generation system can further include a location information acquisition unit. The location information acquisition unit acquires the user's current location and generates phrases and designs based on that location. For example, if the user is traveling, it generates stamps with a background of tourist attractions or local products related to that location. The location information acquisition unit can also analyze the user's past location information and customize stamps based on that history. For example, it can suggest designs and phrases related to places the user frequently visits. This makes it possible to generate original stamps based on the user's location information.

[0039] The AI ​​stamp generation system can further include a weather information acquisition unit. The weather information acquisition unit acquires weather information for the user's current location and generates phrases and designs based on that weather. For example, it selects a phrase such as "It's a nice day today!" on a sunny day, and a phrase such as "It's raining, but let's do our best!" on a rainy day. The weather information acquisition unit can also analyze past weather data and customize stamps based on that data. For example, it can add designs and phrases related to the weather of that day to images taken by the user on a specific day. This makes it possible to generate original stamps based on weather information.

[0040] The AI ​​stamp generation system can further include a music recognition unit. The music recognition unit analyzes the music the user is listening to and generates phrases and designs based on that music. For example, if the user is listening to pop music, it selects bright designs and positive phrases, and if the user is listening to classical music, it selects calm designs and elegant phrases. The music recognition unit can also analyze the user's music library and customize stamps based on that library. For example, it can suggest designs and phrases related to artists the user often listens to. This allows for the generation of original stamps based on music.

[0041] The AI ​​stamp generation system can further include a calendar linkage unit. The calendar linkage unit acquires the user's calendar information and generates phrases and designs based on those events. For example, it selects a phrase such as "Happy Birthday!" for the user's birthday and "Merry Christmas!" for Christmas. The calendar linkage unit can also analyze the user's past event data and customize stamps based on that data. For example, it can suggest designs and phrases related to events the user has participated in in the past. This makes it possible to generate original stamps based on calendar information.

[0042] The processing flow of the first embodiment will be briefly explained below.

[0043] Step 1: The image acquisition unit acquires images of family members or pets. For example, images can be taken using a smartphone camera. Images can also be selected from an existing image library or downloaded from the Internet. Step 2: The facial expression recognition unit detects faces from the images captured by the image capture unit. For example, it can use a Haar feature classifier or a deep learning-based detection algorithm. It can also detect facial landmarks and identify the position and orientation of the face. Step 3: The phrase selection unit selects an appropriate phrase based on the facial expression detected by the facial expression recognition unit. For example, it selects a phrase such as "Fun!" for a smiling image and "Sad..." for a crying image. It is also possible to generate phrases based on facial expressions using a generation AI, or select phrases from a list of phrases pre-defined by the user. Step 4: The stamp generator generates an original stamp by combining the phrase selected by the phrase selector with the face image detected by the facial expression recognizer. For example, the stamp can be generated by overlaying the phrase onto the image using image editing software, combining the phrase and image using generation AI, or using a design template customized by the user.

[0044] (Example 2) The AI ​​stamp generation system according to an embodiment of the present invention automatically acquires images of family members or pets, and the generation AI recognizes their facial expressions, selects appropriate phrases, and instantly creates original stamps. This allows users to instantly create and use original stamps without any effort.

[0045] The AI ​​stamp generation system according to the embodiment includes an image acquisition unit, a facial expression recognition unit, a phrase selection unit, and a stamp generation unit. The image acquisition unit acquires images of family members or pets. For example, the image is captured using a smartphone camera. The image acquisition unit can also select images from an existing image library. The image acquisition unit can also download images from the Internet. The facial expression recognition unit detects faces from the images acquired by the image acquisition unit. For example, the facial expression recognition unit detects faces using a Haar feature classifier. The facial expression recognition unit can also use a deep learning-based detection algorithm. The facial expression recognition unit can also detect facial landmarks and identify the position and orientation of the face. The phrase selection unit selects an appropriate phrase based on the facial expression detected by the facial expression recognition unit. For example, the phrase selection unit selects a phrase such as "Fun!" for an image of a smiling face and "Sad..." for an image of a crying face. The phrase selection unit can also generate phrases based on facial expressions using a generation AI. The phrase selection unit can also select phrases from a phrase list pre-set by the user. The stamp generation unit generates an original stamp by combining a phrase selected by the phrase selection unit with an image of a face detected by the facial expression recognition unit. For example, the stamp generation unit uses image editing software to overlay a phrase on an image. The stamp generation unit can also combine a phrase and an image using a generation AI. The stamp generation unit can also generate a stamp using a design template customized by the user. As a result, the AI ​​stamp generation system according to the embodiment allows users to instantly create and use original stamps without any effort.

[0046] The facial expression recognition unit detects subtle changes in facial expression, such as eyebrow movements or the degree to which the corners of the mouth are turned up, and can generate more precise phrases based on these. The facial expression recognition unit detects subtle changes in facial expression, such as eyebrow movements or the degree to which the corners of the mouth are turned up. For example, it can distinguish between a slight smile and a hearty laugh and generate appropriate phrases for each. The facial expression recognition unit also analyzes the movements of various parts of the face (eyes, eyebrows, mouth, etc.) in real time and generates phrases based on this data. For example, it selects phrases such as "I'm happy!" or "This is great!" based on the brightness of the eyes. The facial expression recognition unit can also analyze facial muscle movements to read more precise emotions and generate phrases based on this. For example, it can generate a phrase such as "I'm a little worried..." based on a subtle eyebrow movement. This allows for the generation of more precise phrases based on subtle changes in facial expression.

[0047] The facial expression recognition unit can estimate the emotion a user is expressing when uploading an image in real time and generate phrases that correspond to that emotion. For example, the facial expression recognition unit incorporates an emotion estimation function into the generation AI, analyzes the user's facial expression in real time when uploading an image, and generates phrases that correspond to that emotion. For example, it selects a phrase such as "Fun!" for a smiling image and "Sad..." for a sad image. The facial expression recognition unit also uses the emotion estimation function to analyze the emotion a user is expressing when uploading an image and generates phrases based on that emotion. For example, it selects a phrase such as "Surprised!" for an image with a surprised expression. The facial expression recognition unit also builds a system that estimates the emotion a user is expressing when uploading an image in real time and generates phrases that correspond to that emotion. For example, it selects a phrase such as "Angry!" for an image with an angry expression. This makes it possible to generate phrases that correspond to the user's emotion in real time.

[0048] The facial expression recognition unit can simultaneously detect multiple faces in an image, generate different phrases based on each facial expression, and create multiple stamps at once. For example, the facial expression recognition unit uses facial expression recognition technology to simultaneously detect multiple faces in an image and generate different phrases based on each facial expression. For example, the facial expression of each member in a family photo is analyzed to generate appropriate phrases for each. The facial expression recognition unit can also build a system that simultaneously detects multiple faces in an image and generates different phrases based on each facial expression. For example, the facial expression of each member in a photo of a group of friends is analyzed to generate individual phrases. The facial expression recognition unit can also use facial expression recognition technology to simultaneously detect multiple faces in an image, generate different phrases based on each facial expression, and create multiple stamps at once. For example, the facial expressions of all family members are analyzed to generate appropriate phrases for each member. This makes it possible to simultaneously detect multiple faces and generate different phrases based on each facial expression.

[0049] The facial expression recognition unit can analyze the facial expressions of animals and generate stamps from images of pets or wild animals. The facial expression recognition unit, for example, applies facial expression recognition technology to animal facial expressions and generates stamps from images of pets. For example, it analyzes the facial expressions of dogs and cats and generates phrases based on the expressions. The facial expression recognition unit also applies facial expression recognition technology so that stamps can be generated from images of wild animals. For example, it analyzes the facial expressions of lions and elephants photographed at the zoo and generates phrases based on the expressions. The facial expression recognition unit also develops facial expression recognition technology for analyzing animal facial expressions and generates stamps from images of pets or wild animals. For example, it analyzes the facial expressions of birds and fish and generates phrases based on the expressions. In this way, it is possible to analyze animal facial expressions and generate stamps from images of pets or wild animals.

[0050] The facial expression recognition unit can automatically extract specific frames from videos and generate stamps based on those frames. The facial expression recognition unit, for example, uses facial expression recognition technology to automatically extract specific frames from videos and generate stamps based on those frames. For example, a frame capturing a smiling moment is extracted and a phrase is generated based on that. The facial expression recognition unit also develops facial expression recognition technology for automatically extracting specific frames from videos and generates stamps based on those frames. For example, a frame capturing a moving scene is extracted and a phrase is generated based on that. The facial expression recognition unit also uses facial expression recognition technology to build a system that automatically extracts specific frames from videos and generates stamps based on those frames. For example, a smiling moment is extracted from a video of a family and a phrase is generated based on that. This makes it possible to automatically extract specific frames from videos and generate stamps based on those frames.

[0051] The facial expression recognition unit can analyze the emotion a user has when creating a stamp and customize the stamp design and phrases based on that emotion. The facial expression recognition unit, for example, uses an emotion estimation function to analyze the emotion a user has when creating a stamp and customize the stamp design and phrases based on that emotion. For example, if the user is happy, a bright design and positive phrases are selected. The facial expression recognition unit also analyzes the user's emotion in real time and builds a system that customizes the stamp design and phrases based on that emotion. For example, if the user is sad, a calm design and comforting phrases are selected. The facial expression recognition unit also uses the emotion estimation function to analyze the emotion a user has when creating a stamp and customize the stamp design and phrases based on that emotion. For example, if the user is surprised, a design and phrases that emphasize the expression of surprise are selected. This makes it possible to customize the stamp design and phrases based on the user's emotion.

[0052] The stamp generation unit can generate phrases from text or voice entered by the user and combine them with an image to create a stamp. The stamp generation unit, for example, uses a generation AI to generate phrases from text entered by the user and combines them with an image to create a stamp. For example, when a user enters "Thank you," the stamp generation unit combines the phrase with an image to generate a stamp. The stamp generation unit can also analyze the voice entered by the user, generate a phrase based on the content of the voice, and combine it with an image to create a stamp. For example, when a user says "Congratulations," the stamp generation unit combines the phrase with an image to generate a stamp. The stamp generation unit can also use a generation AI to build a system that generates phrases from text or voice entered by the user and combines them with an image to create a stamp. For example, when a user enters "Good luck," the stamp generation unit combines the phrase with an image to generate a stamp. This allows phrases to be generated from text or voice entered by the user and combined with an image to create a stamp.

[0053] The stamp generation unit can analyze the emotions a user feels when creating a stamp and adjust phrases based on those emotions. For example, the stamp generation unit incorporates an emotion estimation function into the generation AI, analyzes the emotions a user feels when creating a stamp, and adjusts phrases based on those emotions. For example, if the user is happy, it selects positive phrases. The stamp generation unit also builds a system that analyzes the user's emotions in real time and adjusts phrases based on those emotions. For example, if the user is sad, it selects comforting phrases. The stamp generation unit also incorporates an emotion estimation function into the generation AI, analyzes the emotions a user feels when creating a stamp, and adjusts phrases based on those emotions. For example, if the user is surprised, it selects phrases that emphasize the expression of surprise. This makes it possible to adjust phrases based on the user's emotions.

[0054] The stamp generation unit automatically generates backgrounds or decorative elements for stamps, allowing for the provision of stamps with a wider variety of designs. The stamp generation unit, for example, uses generation AI to build a system that automatically generates backgrounds and decorative elements for stamps. For example, it automatically adds seasonal backgrounds and decorations to match an image selected by a user. The stamp generation unit also develops generation AI for automatically generating backgrounds and decorative elements for stamps, allowing for the provision of stamps with a wider variety of designs. For example, it automatically adds decorations such as flowers and stars to match an image selected by a user. The stamp generation unit also uses generation AI to automatically generate backgrounds and decorative elements for stamps, allowing for the provision of stamps with a wider variety of designs. For example, it automatically adds colorful backgrounds and decorations to match an image selected by a user. This allows for the automatic generation of backgrounds and decorative elements for stamps, allowing for the provision of stamps with a wider variety of designs.

[0055] The stamp generation unit can customize the phrases and images selected by the generation AI according to the theme selected by the user when combining them with the image selected by the generation AI. For example, when combining a phrase and an image selected by the generation AI, the stamp generation unit customizes the phrases and images according to the theme selected by the user. For example, if a Christmas theme is selected, decorations of a Christmas tree and Santa Claus are added. The stamp generation unit also builds a system in which the generation AI customizes phrases and images based on the theme selected by the user. For example, if a summer theme is selected, decorations of a beach and sunglasses are added. The stamp generation unit also customizes the phrases and images selected by the generation AI according to the event selected by the user. For example, if a birthday theme is selected, decorations of a birthday cake and balloons are added. This allows customization according to the theme selected by the user.

[0056] The stamp generation unit can analyze the emotion a user has when sending a stamp and suggest stamps that correspond to that emotion. The stamp generation unit, for example, uses an emotion estimation function to analyze the emotion a user has when sending a stamp and suggest stamps that correspond to that emotion. For example, if the user is happy, a positive stamp is suggested. The stamp generation unit also builds a system that analyzes the user's emotion in real time and suggests stamps that correspond to that emotion. For example, if the user is sad, a comforting stamp is suggested. The stamp generation unit also uses the emotion estimation function to analyze the emotion a user has when sending a stamp and suggest stamps that correspond to that emotion. For example, if the user is surprised, a stamp that emphasizes the expression of surprise is suggested. In this way, stamps can be suggested that correspond to the user's emotion.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The AI ​​stamp generation system can further include a voice recognition unit. The voice recognition unit analyzes the words spoken by the user in real time and generates phrases based on the content. For example, when a user says "thank you," the system combines that phrase with an image to generate a stamp. The voice recognition unit can also analyze the emotion of the words spoken by the user and adjust the phrase based on that emotion. For example, if the user is happy, a positive phrase is selected, and if the user is sad, a comforting phrase is selected. This makes it possible to generate original stamps based on the words spoken by the user.

[0059] The AI ​​stamp generation system can further include a location information acquisition unit. The location information acquisition unit acquires the user's current location and generates phrases and designs based on that location. For example, if the user is traveling, it generates stamps with a background of tourist attractions or local products related to that location. The location information acquisition unit can also analyze the user's past location information and customize stamps based on that history. For example, it can suggest designs and phrases related to places the user frequently visits. This makes it possible to generate original stamps based on the user's location information.

[0060] The AI ​​stamp generation system can further include a weather information acquisition unit. The weather information acquisition unit acquires weather information for the user's current location and generates phrases and designs based on that weather. For example, it selects a phrase such as "It's a nice day today!" on a sunny day, and a phrase such as "It's raining, but let's do our best!" on a rainy day. The weather information acquisition unit can also analyze past weather data and customize stamps based on that data. For example, it can add designs and phrases related to the weather of that day to images taken by the user on a specific day. This makes it possible to generate original stamps based on weather information.

[0061] The AI ​​stamp generation system can further include a music recognition unit. The music recognition unit analyzes the music the user is listening to and generates phrases and designs based on that music. For example, if the user is listening to pop music, it selects bright designs and positive phrases, and if the user is listening to classical music, it selects calm designs and elegant phrases. The music recognition unit can also analyze the user's music library and customize stamps based on that library. For example, it can suggest designs and phrases related to artists the user often listens to. This allows for the generation of original stamps based on music.

[0062] The AI ​​stamp generation system can further include a calendar linkage unit. The calendar linkage unit acquires the user's calendar information and generates phrases and designs based on those events. For example, it selects a phrase such as "Happy Birthday!" for the user's birthday and "Merry Christmas!" for Christmas. The calendar linkage unit can also analyze the user's past event data and customize stamps based on that data. For example, it can suggest designs and phrases related to events the user has participated in in the past. This makes it possible to generate original stamps based on calendar information.

[0063] The AI ​​stamp generation system also uses an emotion estimation function to analyze the emotions of users when they send stamps in real time and suggest stamps that correspond to those emotions. For example, if a user is happy, it will suggest positive stamps, and if they are sad, it will suggest comforting stamps. The emotion estimation function can also be used to analyze the emotions of users when they send stamps and customize the stamp design and phrases based on those emotions. For example, if a user is surprised, it will select a design and phrase that emphasizes the expression of surprise. This makes it possible to suggest and customize stamps that correspond to the user's emotions.

[0064] The AI ​​stamp generation system can further use its emotion estimation function to analyze the emotions of users when they create stamps in real time and customize the stamp design and phrases based on those emotions. For example, if the user is happy, it can select a bright design and positive phrases, and if the user is sad, it can select a calm design and comforting phrases. The emotion estimation function can also be used to build a system that analyzes the emotions of users when they create stamps and customizes the stamp design and phrases based on those emotions. This makes it possible to customize stamp designs and phrases based on the user's emotions.

[0065] The AI ​​stamp generation system can also use its emotion estimation function to analyze the emotions expressed by users when they upload images in real time and generate phrases that correspond to those emotions. For example, when a user uploads a smiling image, it selects a phrase such as "Fun!", and when a user uploads a sad image, it selects a phrase such as "Sad...". It is also possible to use the emotion estimation function to build a system that analyzes the emotions expressed by users when they upload images and generates phrases based on those emotions. This makes it possible to generate phrases that correspond to the user's emotions in real time.

[0066] The AI ​​stamp generation system can further use its emotion estimation function to analyze the emotions of users when they create stamps in real time and customize the stamp design and phrases based on those emotions. For example, if the user is happy, it can select a bright design and positive phrases, and if the user is sad, it can select a calm design and comforting phrases. The emotion estimation function can also be used to build a system that analyzes the emotions of users when they create stamps and customizes the stamp design and phrases based on those emotions. This makes it possible to customize stamp designs and phrases based on the user's emotions.

[0067] The AI ​​stamp generation system also uses an emotion estimation function to analyze the emotions of users when they send stamps in real time and suggest stamps that correspond to those emotions. For example, if a user is happy, it will suggest positive stamps, and if they are sad, it will suggest comforting stamps. The emotion estimation function can also be used to analyze the emotions of users when they send stamps and customize the stamp design and phrases based on those emotions. For example, if a user is surprised, it will select a design and phrase that emphasizes the expression of surprise. This makes it possible to suggest and customize stamps that correspond to the user's emotions.

[0068] The processing flow of the second embodiment will be briefly explained below.

[0069] Step 1: The image acquisition unit acquires images of family members or pets. For example, images can be taken using a smartphone camera. Images can also be selected from an existing image library or downloaded from the Internet. Step 2: The facial expression recognition unit detects faces from the images captured by the image capture unit. For example, it can use a Haar feature classifier or a deep learning-based detection algorithm. It can also detect facial landmarks and identify the position and orientation of the face. Step 3: The phrase selection unit selects an appropriate phrase based on the facial expression detected by the facial expression recognition unit. For example, it selects a phrase such as "Fun!" for a smiling image and "Sad..." for a crying image. It is also possible to generate phrases based on facial expressions using a generation AI, or select phrases from a list of phrases pre-defined by the user. Step 4: The stamp generator generates an original stamp by combining the phrase selected by the phrase selector with the face image detected by the facial expression recognizer. For example, the stamp can be generated by overlaying the phrase onto the image using image editing software, combining the phrase and image using generation AI, or using a design template customized by the user.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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).

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0083] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0089] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0104] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0114] In the robot 414, 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. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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."

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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]

[0137] 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. an image acquisition unit that acquires images of family members and pets; an expression recognition unit that detects a face from the image acquired by the image acquisition unit; a phrase selection unit that selects an appropriate phrase based on the facial expression detected by the facial expression recognition unit; a stamp generation unit that generates an original stamp by combining the phrase selected by the phrase selection unit and the face image detected by the facial expression recognition unit. A system characterized by:

2. The facial expression recognition unit Detects subtle changes in facial expression, such as eyebrow movements or the upturning of the corners of the mouth, and generates more refined phrases based on these.

2. The system of claim 1.

3. The facial expression recognition unit Estimates the user's emotions in real time when uploading an image and generates phrases based on those emotions.

2. The system of claim 1.

4. The facial expression recognition unit Detect multiple faces in an image at the same time, generate different phrases based on their expressions, and create multiple stamps at once 2. The system of claim 1.

5. The facial expression recognition unit Analyze animal facial expressions and generate stamps from images of pets or wildlife 2. The system of claim 1.

Images