System

Abstract

An object of a system according to an embodiment is to generate an ideal photograph in photowedding.SOLUTION: A system according to an embodiment includes an expression complement unit and a scene complement unit. The facial expression complement unit complements a facial expression of the user. The scene complement unit complements a scene of the photograph including the facial expression complemented by the facial expression complement 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] With conventional technology, it was difficult to take ideal photos at photo weddings due to factors such as weather and tension.

[0005] The system according to the embodiment aims to generate ideal photos for a photo wedding. [Means for solving the problem]

[0006] The system according to the embodiment includes an expression complementing unit and a scene complementing unit. The expression complementing unit complements a user's expression. The scene complementing unit complements a scene in a photograph including the expression complemented by the expression complementing unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate ideal photos for a photo wedding. [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 photo wedding system according to the embodiment of the present invention is a system that uses AI generation to complement the user's facial expressions and scenery to provide the best possible photos. By complementing the user's facial expressions and scenery, the photo wedding system can provide memories without regrets and the best possible photos.

[0029] The photo wedding system according to the embodiment includes an expression complementing unit and a scenery complementing unit. The expression complementing unit complements the user's facial expression. For example, the generation AI analyzes the user's facial expression and generates a natural smile. The generation AI can also reference the user's past photo data to generate the most natural facial expression. The generation AI can also analyze the user's tone of voice and speaking style to generate mouth movements that match the facial expression. The scenery complementing unit complements the scenery in photos that include the facial expression complemented by the expression complementing unit. For example, the generation AI can add blue skies or sunlight when the weather is bad. The generation AI can also consider seasonal changes in scenery and add the optimal seasonal feel. The generation AI can also generate scenery for different time periods and allow the user to select. This allows the photo wedding system to complement the user's facial expression and scenery to provide the best photos.

[0030] The facial expression completion unit can generate the most natural facial expression by referencing the user's past photo data. For example, the generation AI in the facial expression completion unit analyzes the user's past photo data and generates the most natural facial expression, such as a smile or a serious expression. For example, it references past wedding or travel photos and reproduces similar expressions. The facial expression completion unit also uses the user's past photo data to generate facial expressions under different angles and lighting conditions. For example, it references photos taken under different lighting conditions to generate the optimal facial expression. The facial expression completion unit also uses the user's past photo data to generate facial expressions that express specific emotions. For example, it learns expressions of joy and surprise from past photos and generates natural expressions. In this way, the most natural facial expressions can be generated by referencing the user's past photo data.

[0031] The facial expression completion unit can analyze the user's tone of voice and speaking style and generate mouth movements that match the facial expression. For example, the generation AI of the facial expression completion unit analyzes the user's tone of voice and speaking style and generates mouth movements that match facial expressions such as smiling and surprise. For example, it analyzes the user's laughing voice and generates a smiling facial expression. The facial expression completion unit also analyzes the user's speaking style and the generation AI generates mouth movements that are most suitable for that speaking style. For example, it adjusts the mouth movements depending on what the user is saying. The facial expression completion unit also generates mouth movements that express emotions based on the generation AI of the user's tone of voice. For example, it analyzes the user's surprised voice and generates a surprised facial expression. In this way, by analyzing the user's tone of voice and speaking style, it is possible to generate mouth movements that match facial expressions.

[0032] The scenery complementing unit can refer to past weather data and recreate the most ideal weather conditions. In the scenery complementing unit, for example, the generation AI analyzes past weather data and recreates the most ideal weather conditions. For example, it adds blue skies and sunlight on a sunny day. The scenery complementing unit also recreates ideal weather conditions based on past weather data. For example, it makes a photo taken on a cloudy day look like a sunny day. The scenery complementing unit also recreates the most ideal weather conditions by referring to past weather data. For example, it makes a photo taken on a rainy day look like a sunny day. In this way, the most ideal weather conditions can be recreated by referring to past weather data.

[0033] The scenery completion unit takes into account seasonal changes in scenery and can add the most appropriate seasonal feel. In the scenery completion unit, for example, the generation AI analyzes seasonal changes in scenery and adds the most appropriate seasonal feel. For example, it adds cherry blossoms in spring or autumn leaves to a photo. The scenery completion unit also takes into account seasonal changes in scenery and adds the most appropriate seasonal feel. For example, it adds a snowy winter scene or a blue summer sky to a photo. The scenery completion unit also analyzes seasonal changes in scenery and adds the most appropriate seasonal feel. For example, it adds the sea in summer or a snowy mountain in winter to a photo. In this way, the most appropriate seasonal feel can be added by taking into account seasonal changes in scenery.

[0034] The scenery completion unit can generate scenery at different time periods and allow the user to select from it. In the scenery completion unit, for example, a generation AI generates scenery at different time periods and allows the user to select from it. For example, it generates morning scenery, daytime scenery, and nighttime scenery. The scenery completion unit also provides a function for generating scenery at different time periods and allowing the user to select from it. For example, it generates scenery of a sunrise or sunset. In addition, the scenery completion unit generates scenery at different time periods and allows the user to select from it. For example, it generates a blue sky in the daytime or a starry sky at night. In this way, scenery at different time periods is generated and allows the user to select from it.

[0035] The scenery completion unit can provide a function that converts the results of scenery completion into a 3D model, allowing the user to experience it in virtual reality. For example, the scenery completion unit provides a function that converts the results of scenery completion into a 3D model by a generation AI, allowing the user to experience it in virtual reality. For example, the complemented scenery can be experienced with VR goggles. The scenery completion unit also provides a function that converts the results of scenery completion into a 3D model, allowing the user to experience it in virtual reality. For example, the complemented scenery can be displayed in a 360-degree view. The scenery completion unit also provides a function that converts the results of scenery completion into a 3D model, allowing the user to experience it in virtual reality. For example, the complemented scenery can be walked around in a virtual space. This allows the results of scenery completion to be converted into a 3D model, allowing the user to experience it in virtual reality.

[0036] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0037] The photo wedding system can further include a costume rental unit. The costume rental unit provides a service of renting costumes selected by the user. For example, the costume rental unit provides the dress or suit selected by the user on the day of the photo shoot. The costume rental unit can also adjust the size to fit the user's body type. Furthermore, the costume rental unit can also provide accessories and small items according to the user's preferences. This allows the user to easily select the most suitable costume and get ready for the photo shoot.

[0038] The photo wedding system can further include a makeup suggestion unit. The makeup suggestion unit suggests optimal makeup based on the user's facial features and preferences. For example, the makeup suggestion unit analyzes the user's skin color and face shape to suggest makeup that suits them. The makeup suggestion unit can also refer to the user's past photo data to suggest the most natural makeup. Furthermore, the makeup suggestion unit can also suggest makeup that matches the shooting scene. This allows the user to apply optimal makeup and take better photos.

[0039] The photo wedding system can further include a hairstyle suggestion unit. The hairstyle suggestion unit suggests the most suitable hairstyle based on the user's facial features and preferences. For example, the hairstyle suggestion unit analyzes the user's hair quality and length and suggests a hairstyle that suits them. The hairstyle suggestion unit can also refer to the user's past photo data and suggest the most natural hairstyle. Furthermore, the hairstyle suggestion unit can suggest a hairstyle that matches the shooting scene. This allows the user to choose the most suitable hairstyle and take better photos.

[0040] The processing flow of the first embodiment will be briefly explained below.

[0041] Step 1: The facial expression complementation unit complements the user's facial expression. For example, the generation AI analyzes the user's facial expression and generates a natural smile. The generation AI can also refer to the user's past photo data to generate the most natural facial expression. Furthermore, the generation AI can analyze the user's tone of voice and speaking style and generate mouth movements that match the facial expression. Step 2: The scenery complementation unit complements the scenery in the photo, including the facial expressions complemented by the facial expression complementation unit. For example, the generation AI adds blue skies and sunlight when the weather is bad. The generation AI can also take into account seasonal changes in the scenery and add the optimal seasonal feel. Furthermore, the generation AI can generate scenery for different time periods and allow the user to select.

[0042] (Example 2) The photo wedding system according to the embodiment of the present invention is a system that uses AI generation to complement the user's facial expressions and scenery to provide the best possible photos. By complementing the user's facial expressions and scenery, the photo wedding system can provide memories without regrets and the best possible photos.

[0043] The photo wedding system according to the embodiment includes an expression complementing unit and a scenery complementing unit. The expression complementing unit complements the user's facial expression. For example, the generation AI analyzes the user's facial expression and generates a natural smile. The generation AI can also reference the user's past photo data to generate the most natural facial expression. The generation AI can also analyze the user's tone of voice and speaking style to generate mouth movements that match the facial expression. The scenery complementing unit complements the scenery in photos that include the facial expression complemented by the expression complementing unit. For example, the generation AI can add blue skies or sunlight when the weather is bad. The generation AI can also consider seasonal changes in scenery and add the optimal seasonal feel. The generation AI can also generate scenery for different time periods and allow the user to select. This allows the photo wedding system to complement the user's facial expression and scenery to provide the best photos.

[0044] The facial expression completion unit can generate the most natural facial expression by referencing the user's past photo data. For example, the generation AI in the facial expression completion unit analyzes the user's past photo data and generates the most natural facial expression, such as a smile or a serious expression. For example, it references past wedding or travel photos and reproduces similar expressions. The facial expression completion unit also uses the user's past photo data to generate facial expressions under different angles and lighting conditions. For example, it references photos taken under different lighting conditions to generate the optimal facial expression. The facial expression completion unit also uses the user's past photo data to generate facial expressions that express specific emotions. For example, it learns expressions of joy and surprise from past photos and generates natural expressions. In this way, the most natural facial expressions can be generated by referencing the user's past photo data.

[0045] The facial expression completion unit can analyze the user's tone of voice and speaking style and generate mouth movements that match the facial expression. For example, the generation AI of the facial expression completion unit analyzes the user's tone of voice and speaking style and generates mouth movements that match facial expressions such as smiling and surprise. For example, it analyzes the user's laughing voice and generates a smiling facial expression. The facial expression completion unit also analyzes the user's speaking style and the generation AI generates mouth movements that are most suitable for that speaking style. For example, it adjusts the mouth movements depending on what the user is saying. The facial expression completion unit also generates mouth movements that express emotions based on the generation AI of the user's tone of voice. For example, it analyzes the user's surprised voice and generates a surprised facial expression. In this way, by analyzing the user's tone of voice and speaking style, it is possible to generate mouth movements that match facial expressions.

[0046] The facial expression completion unit can use the emotion estimation function to estimate the user's emotion in real time and generate an expression that best suits that emotion. For example, the facial expression completion unit uses the emotion estimation function to analyze the user's emotion in real time and generate an expression that best suits that emotion. For example, if the user is happy, it generates a smiling expression. The facial expression completion unit also uses a generation AI to estimate the user's emotion in real time and generate an expression that corresponds to that emotion. For example, if the user is surprised, it generates a surprised expression. The facial expression completion unit also uses the emotion estimation function to analyze the user's emotion and generate an expression that best suits that emotion. For example, if the user is sad, it generates a sad expression. This makes it possible to estimate the user's emotion in real time and generate an expression that best suits that emotion.

[0047] The scenery complementing unit can refer to past weather data and recreate the most ideal weather conditions. In the scenery complementing unit, for example, the generation AI analyzes past weather data and recreates the most ideal weather conditions. For example, it adds blue skies and sunlight on a sunny day. The scenery complementing unit also recreates ideal weather conditions based on past weather data. For example, it makes a photo taken on a cloudy day look like a sunny day. The scenery complementing unit also recreates the most ideal weather conditions by referring to past weather data. For example, it makes a photo taken on a rainy day look like a sunny day. In this way, the most ideal weather conditions can be recreated by referring to past weather data.

[0048] The scenery completion unit takes into account seasonal changes in scenery and can add the most appropriate seasonal feel. In the scenery completion unit, for example, the generation AI analyzes seasonal changes in scenery and adds the most appropriate seasonal feel. For example, it adds cherry blossoms in spring or autumn leaves to a photo. The scenery completion unit also takes into account seasonal changes in scenery and adds the most appropriate seasonal feel. For example, it adds a snowy winter scene or a blue summer sky to a photo. The scenery completion unit also analyzes seasonal changes in scenery and adds the most appropriate seasonal feel. For example, it adds the sea in summer or a snowy mountain in winter to a photo. In this way, the most appropriate seasonal feel can be added by taking into account seasonal changes in scenery.

[0049] The scenery completion unit can use the emotion estimation function to generate a scenery that moves the user the most and apply that scenery to a photo. The scenery completion unit, for example, uses the emotion estimation function to generate a scenery that moves the user the most and apply that scenery to a photo. For example, it adds a blue sky or sunset that moves the user to a photo. In addition, the scenery completion unit uses a generation AI to analyze the user's emotions and generate a scenery that moves the user the most and applies that scenery to a photo. For example, it adds a mountain or ocean view that moves the user to a photo. In addition, the scenery completion unit can use the emotion estimation function to generate a scenery that moves the user the most and apply that scenery to a photo. For example, it adds a flower field or forest view that moves the user to a photo. In this way, it is possible to generate a scenery that moves the user the most and apply that scenery to a photo.

[0050] The scenery completion unit can generate scenery at different time periods and allow the user to select from it. In the scenery completion unit, for example, a generation AI generates scenery at different time periods and allows the user to select from it. For example, it generates morning scenery, daytime scenery, and nighttime scenery. The scenery completion unit also provides a function for generating scenery at different time periods and allowing the user to select from it. For example, it generates scenery of a sunrise or sunset. In addition, the scenery completion unit generates scenery at different time periods and allows the user to select from it. For example, it generates a blue sky in the daytime or a starry sky at night. In this way, scenery at different time periods is generated and allows the user to select from it.

[0051] The scenery completion unit can provide a function that converts the results of scenery completion into a 3D model, allowing the user to experience it in virtual reality. For example, the scenery completion unit provides a function that converts the results of scenery completion into a 3D model by a generation AI, allowing the user to experience it in virtual reality. For example, the complemented scenery can be experienced with VR goggles. The scenery completion unit also provides a function that converts the results of scenery completion into a 3D model, allowing the user to experience it in virtual reality. For example, the complemented scenery can be displayed in a 360-degree view. The scenery completion unit also provides a function that converts the results of scenery completion into a 3D model, allowing the user to experience it in virtual reality. For example, the complemented scenery can be walked around in a virtual space. This allows the results of scenery completion to be converted into a 3D model, allowing the user to experience it in virtual reality.

[0052] The scenery completion unit can use the emotion estimation function to generate a scenery that the user finds most relaxing and apply that scenery to multiple photos. For example, the scenery completion unit uses the emotion estimation function to generate a scenery that the user finds most relaxing and apply that scenery to multiple photos. For example, it adds a sea or mountain scenery that the user finds relaxing to a photo. In addition, the scenery completion unit uses a generation AI to analyze the user's emotions and generate a scenery that the user finds most relaxing and apply that scenery to multiple photos. For example, it adds a forest or lake scenery that the user finds relaxing to a photo. In addition, the scenery completion unit uses the emotion estimation function to generate a scenery that the user finds most relaxing and apply that scenery to multiple photos. For example, it adds a flower field or grassland scenery that the user finds relaxing to a photo. In this way, it is possible to generate a scenery that the user finds most relaxing and apply that scenery to multiple photos.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The photo wedding system can further include an audio guide unit. The audio guide unit provides the user with audio instructions on poses and facial expressions during photography. For example, the audio guide unit can issue specific instructions to the user, such as "Smile" or "Look slightly to the right." The audio guide unit can also analyze the user's tone of voice and speaking style to issue instructions at the optimal timing. Furthermore, the audio guide unit can estimate the user's emotions and provide music or a tone of voice to relax the user. This allows the user to adopt natural facial expressions and poses, resulting in better photos.

[0055] The photo wedding system can further include a costume suggestion unit. The costume suggestion unit suggests the most suitable costume based on the user's body type and preferences. For example, the costume suggestion unit analyzes the user's past photo data and suggests colors and styles that suit them. The costume suggestion unit can also estimate the user's emotions and suggest costumes that match those emotions. For example, if the user is relaxed, it can suggest casual outfits. The costume suggestion unit can also suggest outfits that suit the season and weather. This allows the user to choose the most suitable outfit and take better photos.

[0056] The photo wedding system can further include a background music generation unit. The background music generation unit generates music that matches the user's emotions and the shooting scene, and plays it during shooting. For example, if the user is relaxed, the background music generation unit plays calm music. The background music generation unit can also estimate the user's emotions and generate music that matches those emotions. For example, if the user is happy, it plays cheerful music. The background music generation unit can also generate music that matches the shooting scene. For example, when shooting at the beach, it plays music that includes the sound of waves. This allows the user to relax and adopt natural expressions and poses.

[0057] The photo wedding system may further include a lighting adjustment unit. The lighting adjustment unit adjusts lighting according to the user's emotions and the shooting scene. For example, if the user is relaxed, the lighting adjustment unit provides soft light. The lighting adjustment unit can also estimate the user's emotions and provide lighting that matches those emotions. For example, if the user is happy, the lighting adjustment unit provides bright light. The lighting adjustment unit can also provide lighting that matches the shooting scene. For example, in a sunset scene, the lighting adjustment unit provides warm-colored light. This allows the user to adopt natural expressions and poses, resulting in better photos.

[0058] The photo wedding system can further include a pose suggestion unit. The pose suggestion unit suggests optimal poses based on the user's body type and preferences. For example, the pose suggestion unit analyzes the user's past photo data and suggests poses that suit them. The pose suggestion unit can also estimate the user's emotions and suggest poses that match those emotions. For example, if the user is relaxed, it will suggest a natural pose. The pose suggestion unit can also suggest poses that match the shooting scene. This allows the user to take optimal poses and take better photos.

[0059] The photo wedding system can further include a costume rental unit. The costume rental unit provides a service of renting costumes selected by the user. For example, the costume rental unit provides the dress or suit selected by the user on the day of the photo shoot. The costume rental unit can also adjust the size to fit the user's body type. Furthermore, the costume rental unit can also provide accessories and small items according to the user's preferences. This allows the user to easily select the most suitable costume and get ready for the photo shoot.

[0060] The photo wedding system can further include a makeup suggestion unit. The makeup suggestion unit suggests optimal makeup based on the user's facial features and preferences. For example, the makeup suggestion unit analyzes the user's skin color and face shape to suggest makeup that suits them. The makeup suggestion unit can also refer to the user's past photo data to suggest the most natural makeup. Furthermore, the makeup suggestion unit can also suggest makeup that matches the shooting scene. This allows the user to apply optimal makeup and take better photos.

[0061] The photo wedding system can further include a hairstyle suggestion unit. The hairstyle suggestion unit suggests the most suitable hairstyle based on the user's facial features and preferences. For example, the hairstyle suggestion unit analyzes the user's hair quality and length and suggests a hairstyle that suits them. The hairstyle suggestion unit can also refer to the user's past photo data and suggest the most natural hairstyle. Furthermore, the hairstyle suggestion unit can suggest a hairstyle that matches the shooting scene. This allows the user to choose the most suitable hairstyle and take better photos.

[0062] The photo wedding system can further include a photography location suggestion unit. The photography location suggestion unit suggests the most suitable photography location based on the user's preferences and theme. For example, the photography location suggestion unit analyzes the user's past photo data and suggests locations that suit the user. The photography location suggestion unit can also estimate the user's emotions and suggest locations that match those emotions. For example, if the user is feeling relaxed, it can suggest a location in nature. The photography location suggestion unit can also suggest locations depending on the season and weather. This allows the user to choose the most suitable photography location and take better photos.

[0063] The photo wedding system can further include an album creation unit. The album creation unit creates an optimal album based on the photographs taken. For example, the album creation unit suggests layouts and designs according to the user's preferences. The album creation unit can also estimate the user's emotions and create an album that matches those emotions. For example, if the user is moved, the album creation unit can suggest an album with an inspiring design. The album creation unit can also create an album that matches the shooting scene. This allows the user to beautifully preserve their memories.

[0064] The processing flow of the second embodiment will be briefly explained below.

[0065] Step 1: The facial expression complementation unit complements the user's facial expression. For example, the generation AI analyzes the user's facial expression and generates a natural smile. The generation AI can also refer to the user's past photo data to generate the most natural facial expression. Furthermore, the generation AI can analyze the user's tone of voice and speaking style and generate mouth movements that match the facial expression. Step 2: The scenery complementation unit complements the scenery in the photo, including the facial expressions complemented by the facial expression complementation unit. For example, the generation AI adds blue skies and sunlight when the weather is bad. The generation AI can also take into account seasonal changes in the scenery and add the optimal seasonal feel. Furthermore, the generation AI can generate scenery for different time periods and allow the user to select.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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).

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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."

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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]

[0133] 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 expression complementing unit that complements a user's expression; a scenery complementing unit that complements scenery in a photograph including the facial expression complemented by the facial expression complementing unit; A system characterized by:

2. The facial expression complementing unit Reference the user's past photo data to generate the most natural facial expression 2. The system of claim 1.

3. The facial expression complementing unit Analyzing the user's tone of voice and speaking style to generate mouth movements that match their facial expressions 2. The system of claim 1.

4. The facial expression complementing unit The user's emotions are estimated in real time, and a facial expression that best suits the emotions is generated.

2. The system of claim 1.

5. The scenery complementing unit Referencing past weather data to recreate the most ideal weather conditions 2. The system of claim 1.

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