System

Abstract

An object of the system according to the embodiment is to easily convert a photograph of a user into another person or animal.SOLUTION: A system includes a photograph transmission unit, an analysis unit, a conversion unit, and an output unit. The photograph transmission unit transmits the photograph of the user to the generation AI. The analysis unit analyzes the photograph transmitted by the photograph transmission unit. The conversion unit converts the photograph analyzed by the analysis unit into another person or animal. The output unit outputs the photograph converted by the conversion unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem that the process of converting a user's photo into another person or animal is time-consuming and not easy to enjoy.

[0005] The system according to the embodiment aims to easily convert a user's photo into another person or animal. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo sending unit, an analysis unit, a conversion unit, and an output unit. The photo sending unit sends a user's photo to the generation AI. The analysis unit analyzes the photo sent by the photo sending unit. The conversion unit converts the photo analyzed by the analysis unit into another person or animal. The output unit outputs the photo converted by the conversion unit. [Effects of the Invention]

[0007] The system according to the embodiment can easily convert a user's photo into another person or animal. [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 processing system according to the embodiment of the present invention is a system in which a user sends their own photo to a generation AI and converts the photo into another person or animal. This allows the user to enjoy their photo in a variety of ways.

[0029] A photo processing system according to an embodiment includes a photo sending unit, an analysis unit, a conversion unit, and an output unit. The photo sending unit sends a user's photo to a generation AI. For example, the photo sending unit can send photos in JPEG or PNG format. The photo sending unit can also provide an interface for users to upload photos. The analysis unit analyzes the photo sent by the photo sending unit. For example, the analysis unit can analyze facial features and backgrounds in the photo using a generation AI. The analysis unit can also extract features of the photo using image recognition technology. The conversion unit converts the photo analyzed by the analysis unit into another person or animal. For example, the conversion unit can convert the user's photo into a celebrity or anime character using a generation AI. The conversion unit can also convert the user's photo into a cat or dog using the generation AI. The output unit outputs the photo converted by the conversion unit. For example, the output unit displays the converted photo to the user. The output unit can also provide an interface for sharing the converted photo on a social networking site or a messaging app. This allows the photo processing system according to an embodiment to convert the user's photo into another person or animal and output it.

[0030] The photo sending unit allows the user to specify a specific situation and can perform conversions that suit the situation. For example, the photo sending unit provides an interface that allows the user to select a specific situation when sending a photo. For example, by selecting a situation such as a party or a trip, the photo sending unit performs conversions that suit that situation. The photo sending unit also uses a generation AI to analyze the photo based on the situation and suggest appropriate conversion targets. For example, if a travel situation is selected, the photo is converted into a famous character or animal from the travel destination. The photo sending unit also uses a situation specification function to select a specific situation when sending a photo and automatically applies backgrounds and effects that suit that situation. For example, if a party situation is selected, the photo is converted into a character with a party atmosphere. This allows conversions that suit the situation specified by the user.

[0031] When analyzing a photo, the analysis unit also analyzes background information in detail and can perform conversions that harmonize with the background. For example, the generation AI in the analysis unit analyzes the background information of the photo in detail and performs conversions that harmonize with the background. For example, if the background is the sea, it will convert to characters or animals related to the sea. The analysis unit also allows the generation AI to select appropriate conversion targets based on the background information. For example, if the background is an urban landscape, it will convert to characters or animals related to the city. The analysis unit also analyzes background information in detail and applies effects and filters that harmonize with the background. For example, if the background is a forest, it will convert to characters or animals that fit the atmosphere of the forest. This allows for conversions that harmonize with the background information.

[0032] The photo sending unit can enable the user to specify the conversion target using a voice command. For example, when a user sends a photo, the photo sending unit provides an interface that allows the user to specify the conversion target using a voice command. For example, by giving a voice command such as "convert to cat," the generation AI converts it to cat. The photo sending unit also uses voice recognition technology to build a system in which the user specifies the conversion target using a voice command. For example, by giving a voice command such as "convert to celebrity A," the generation AI converts it to that celebrity. When the user specifies the conversion target using a voice command, the photo sending unit also allows the generation AI to analyze the voice in real time and convert it to the specified target. For example, by giving a voice command such as "convert to anime character B," the generation AI converts it to that anime character. This allows the user to specify the conversion target using a voice command.

[0033] The photo sending unit can add a function to send multiple photos at once and convert them into a group photo. The photo sending unit provides a function, for example, that allows a user to send multiple photos at once, and the generation AI analyzes and converts them into a group photo. For example, when a family photo is sent, everyone is converted into an animal. When converting into a group photo, the generation AI analyzes the characteristics of each photo and converts them into a harmonious whole. For example, when a photo of a group of friends is sent, everyone is converted into characters with the same theme. The photo sending unit also adds a function to send multiple photos at once, and the generation AI converts them into a group photo. For example, when a workplace group photo is sent, everyone is converted into characters related to the workplace. This allows multiple photos to be converted into a group photo.

[0034] The conversion unit can refer to the user's past conversion history and suggest conversions that suit their preferences. For example, the generation AI in the conversion unit refers to the user's past conversion history and suggests conversion targets that suit their preferences. For example, for a user who has often converted to cats in the past, cats or other animals are suggested. The conversion unit also selects the optimal conversion target based on the user's past conversion history. For example, for a user who has often converted to celebrities in the past, other celebrities are suggested. The conversion unit also analyzes the user's past conversion history and automatically selects conversion targets that suit their preferences. For example, for a user who has often converted to anime characters in the past, other anime characters are suggested. This makes it possible to suggest conversions that suit the user's preferences based on the user's past conversion history.

[0035] The conversion unit can perform optimal conversions according to the user's age and gender. For example, the generation AI in the conversion unit selects the optimal conversion target taking into account the user's age and gender. For example, it may suggest anime characters for children and celebrities for adults. The conversion unit also selects the appropriate conversion target based on the user's age and gender. For example, it may suggest female characters for female users and male characters for male users. The conversion unit also analyzes the user's age and gender and performs the optimal conversion accordingly. For example, it may suggest pop culture characters for young people and classic characters for older people. This allows for optimal conversions according to the user's age and gender.

[0036] The conversion unit can perform hybrid conversion that combines multiple conversion targets specified by the user. For example, the conversion unit combines multiple conversion targets specified by the user, and the generation AI performs hybrid conversion. For example, converting into a character that combines a cat and a famous person. The conversion unit also has the generation AI analyze multiple conversion targets and perform hybrid conversion that combines them. For example, converting into a character that combines an anime character and an animal. The conversion unit also has the generation AI perform the optimal hybrid conversion based on the multiple conversion targets specified by the user. For example, converting into a character that combines a famous person and an anime character. This makes it possible to perform hybrid conversion that combines multiple conversion targets.

[0037] The conversion unit can perform conversion to match a specific style specified by the user. For example, in the conversion unit, the generation AI performs conversion to match a specific style specified by the user. For example, if a retro style is specified, the character will be converted to one with a retro feel. In addition, in the conversion unit, the generation AI analyzes the style specified by the user and selects a conversion target that matches that style. For example, if a modern style is specified, the character will be converted to one with a modern feel. In addition, in the conversion unit, the generation AI performs the optimal conversion based on the style specified by the user. For example, if a vintage style is specified, the character will be converted to one with a vintage feel. This allows conversion to be performed to match a specific style specified by the user.

[0038] The output unit can enable the converted photo to be output in a specific format specified by the user when outputting the converted photo. For example, the output unit provides an interface that allows the user to output the converted photo in a specific format specified by the user when outputting the converted photo. For example, the user can select a postcard or poster format. The output unit also enables the generation AI to automatically adjust the converted photo to fit the format specified by the user. For example, the output unit outputs a photo optimized for postcard size. The output unit also builds a system in which the generation AI outputs the converted photo based on the format specified by the user. For example, the output unit outputs a high-resolution photo that fits poster size. This allows the converted photo to be output in the specific format specified by the user.

[0039] When outputting the converted photo, the output unit can refer to the user's past sharing history and suggest the optimal sharing method. For example, when outputting the converted photo, the output unit refers to the user's past sharing history and suggest the optimal sharing method. For example, for a user who has often shared on social media in the past, it suggests sharing on social media. Furthermore, the output unit has a generation AI that analyzes the user's past sharing history and automatically selects the optimal sharing method. For example, for a user who has often shared on messaging apps in the past, it suggests sharing via messaging apps. Furthermore, the output unit builds a system in which the generation AI suggests the optimal sharing method based on the user's past sharing history. For example, for a user who has often shared via email in the past, it suggests sharing via email. In this way, it is possible to suggest the optimal sharing method based on the user's past sharing history.

[0040] The output unit can add a function to automatically post the converted photo to a specific social media platform designated by the user when outputting the converted photo. For example, the output unit provides a function to automatically post the converted photo to a specific social media platform designated by the user when outputting the converted photo. For example, automatically posting to Instagram or Facebook. The output unit also has the generation AI automatically adjust the converted photo to match the social media platform designated by the user and post it. For example, posting a photo optimized for the Instagram feed. The output unit also builds a system in which the generation AI automatically posts the converted photo based on the social media platform designated by the user. For example, automatically posting a photo optimized for Twitter. This allows automatic posting to the specific social media platform designated by the user.

[0041] The output unit can add a function to automatically send the converted photo to a specific messaging app designated by the user when outputting the converted photo. For example, the output unit provides a function to automatically send the converted photo to a specific messaging app designated by the user when outputting the converted photo. For example, automatically sending to WhatsApp or LINE. The output unit also has the generation AI automatically adjust the converted photo to match the messaging app designated by the user and send it. For example, sending a photo optimized for WhatsApp chat. The output unit also builds a system in which the generation AI automatically sends the converted photo based on the messaging app designated by the user. For example, automatically sending a photo optimized for LINE chat. This allows it to be automatically sent to a specific messaging app designated by the user.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The photo sending unit can provide an interface that allows a user to select a specific theme when sending a photo. For example, if a user selects a theme such as "fantasy" or "science fiction," the photo will be converted to match that theme. The photo sending unit can also use a generation AI to analyze the photo based on the theme and suggest appropriate conversion targets. For example, if fantasy is selected, the photo will be converted to a character such as a dragon or fairy. The photo sending unit can also use a theme specification function to allow a user to select a specific theme when sending a photo and automatically apply backgrounds and effects that match that theme. For example, if science fiction is selected, the photo will be converted to a character with a futuristic atmosphere. This allows the photo to be converted to match the theme specified by the user.

[0044] The conversion unit can perform conversions that match a specific art style specified by the user. For example, if the user specifies an art style such as "impressionism" or "pop art," the conversion unit will convert the character to match that style. The conversion unit also allows the generation AI to analyze the art style specified by the user and select a conversion target that matches that style. For example, if impressionism is specified, the conversion will be to a character with soft colors. The conversion unit also allows the generation AI to perform the optimal conversion based on the art style specified by the user. For example, if pop art is specified, the conversion will be to a character with vibrant colors. This allows conversions to be performed that match the specific art style specified by the user.

[0045] The photo sending unit can provide an interface that allows a user to specify a specific season or event when sending a photo. For example, if a user selects an event such as "Christmas" or "Halloween," the photo will be converted to suit that event. The photo sending unit can also use a generation AI to analyze the photo based on the season or event and suggest appropriate conversion targets. For example, if Christmas is selected, the photo will be converted to a character such as Santa Claus or a reindeer. The photo sending unit can also use the season or event specification function to select a specific season or event when sending a photo, and automatically apply backgrounds and effects that match that theme. For example, if Halloween is selected, the photo will be converted to a character with a Halloween atmosphere. This allows conversion to suit the season or event specified by the user.

[0046] The conversion unit can perform conversions that are tailored to specific cultures and regions specified by the user. For example, if a user specifies a culture or region such as "Japan" or "America," the conversion unit will convert to a character that suits that culture or region. The conversion unit also allows the generation AI to analyze the culture or region specified by the user and select a conversion target that suits that culture or region. For example, if Japan is specified, the conversion will be to a Japanese-style character. The conversion unit also allows the generation AI to perform the optimal conversion based on the culture or region specified by the user. For example, if America is specified, the conversion will be to an American-style character. This allows conversions to be tailored to the specific culture or region specified by the user.

[0047] The photo sending unit can provide an interface that allows a user to specify a specific time period or season when sending a photo. For example, if the user selects a time period such as "morning" or "night," the conversion will be tailored to that time period. The photo sending unit can also use a generation AI to analyze the photo based on the time period or season and suggest appropriate conversion targets. For example, if morning is selected, the character will be converted to one with a morning atmosphere. The photo sending unit can also use the time period or season specification function to select a specific time period or season when sending a photo and automatically apply backgrounds and effects that match that theme. For example, if night is selected, the character will be converted to one with a night atmosphere. This allows conversion to be tailored to the time period or season specified by the user.

[0048] The conversion unit can perform conversion to suit a specific style specified by the user. For example, if the user specifies a style such as "retro" or "modern," the conversion unit will convert the character to suit that style. The conversion unit also allows the generation AI to analyze the style specified by the user and select a conversion target that suits that style. For example, if retro is specified, the character will be converted to one with a retro feel. The conversion unit also allows the generation AI to perform the optimal conversion based on the style specified by the user. For example, if modern is specified, the character will be converted to one with a modern feel. This allows conversion to suit the specific style specified by the user.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The photo sending unit sends the user's photo to the generation AI. For example, the photo sending unit can send photos in JPEG or PNG format. The photo sending unit can also provide an interface for users to upload photos. Step 2: The analysis unit analyzes the photo sent by the photo sending unit. For example, the analysis unit uses generative AI to analyze the facial features and background of the photo. The analysis unit can also extract features of the photo using image recognition technology. Step 3: The conversion unit converts the photo analyzed by the analysis unit into another person or animal. For example, the conversion unit converts the user's photo into a celebrity or anime character using the generation AI. The conversion unit can also convert the user's photo into a cat or dog using the generation AI. Step 4: The output unit outputs the photo converted by the conversion unit. For example, the output unit displays the converted photo to the user. The output unit can also provide an interface for sharing the converted photo on a social networking site or a messaging app.

[0051] (Example 2) The photo processing system according to the embodiment of the present invention is a system in which a user sends their own photo to a generation AI and converts the photo into another person or animal. This allows the user to enjoy their photo in a variety of ways.

[0052] A photo processing system according to an embodiment includes a photo sending unit, an analysis unit, a conversion unit, and an output unit. The photo sending unit sends a user's photo to a generation AI. For example, the photo sending unit can send photos in JPEG or PNG format. The photo sending unit can also provide an interface for users to upload photos. The analysis unit analyzes the photo sent by the photo sending unit. For example, the analysis unit can analyze facial features and backgrounds in the photo using a generation AI. The analysis unit can also extract features of the photo using image recognition technology. The conversion unit converts the photo analyzed by the analysis unit into another person or animal. For example, the conversion unit can convert the user's photo into a celebrity or anime character using a generation AI. The conversion unit can also convert the user's photo into a cat or dog using the generation AI. The output unit outputs the photo converted by the conversion unit. For example, the output unit displays the converted photo to the user. The output unit can also provide an interface for sharing the converted photo on a social networking site or a messaging app. This allows the photo processing system according to an embodiment to convert the user's photo into another person or animal and output it.

[0053] When analyzing a photo, the analysis unit can estimate the user's emotions and suggest the optimal conversion target based on the emotions. For example, when the generation AI analyzes a photo, the analysis unit estimates emotions from the user's facial expressions and voice and suggests the optimal conversion target based on those emotions. For example, if the user is smiling, the analysis unit suggests a fun character or animal. Furthermore, when the user sends a photo, the analysis unit allows the generation AI to analyze the user's emotions in real time and automatically select a conversion target according to the emotion. For example, if the user has a surprised expression, the analysis unit suggests a character that is good at expressing surprise. Furthermore, the analysis unit uses the emotion estimation function to analyze the user's emotions when sending a photo and customize the conversion target based on that emotion. For example, if the user is sad, the analysis unit suggests an uplifting character or animal. This makes it possible to suggest the optimal conversion target based on the user's emotions.

[0054] The photo sending unit allows the user to specify a specific situation and can perform conversions that suit the situation. For example, the photo sending unit provides an interface that allows the user to select a specific situation when sending a photo. For example, by selecting a situation such as a party or a trip, the photo sending unit performs conversions that suit that situation. The photo sending unit also uses a generation AI to analyze the photo based on the situation and suggest appropriate conversion targets. For example, if a travel situation is selected, the photo is converted into a famous character or animal from the travel destination. The photo sending unit also uses a situation specification function to select a specific situation when sending a photo and automatically applies backgrounds and effects that suit that situation. For example, if a party situation is selected, the photo is converted into a character with a party atmosphere. This allows conversions that suit the situation specified by the user.

[0055] When analyzing a photo, the analysis unit also analyzes background information in detail and can perform conversions that harmonize with the background. For example, the generation AI in the analysis unit analyzes the background information of the photo in detail and performs conversions that harmonize with the background. For example, if the background is the sea, it will convert to characters or animals related to the sea. The analysis unit also allows the generation AI to select appropriate conversion targets based on the background information. For example, if the background is an urban landscape, it will convert to characters or animals related to the city. The analysis unit also analyzes background information in detail and applies effects and filters that harmonize with the background. For example, if the background is a forest, it will convert to characters or animals that fit the atmosphere of the forest. This allows for conversions that harmonize with the background information.

[0056] The photo sending unit can enable the user to specify the conversion target using a voice command. For example, when a user sends a photo, the photo sending unit provides an interface that allows the user to specify the conversion target using a voice command. For example, by giving a voice command such as "convert to cat," the generation AI converts it to cat. The photo sending unit also uses voice recognition technology to build a system in which the user specifies the conversion target using a voice command. For example, by giving a voice command such as "convert to celebrity A," the generation AI converts it to that celebrity. When the user specifies the conversion target using a voice command, the photo sending unit also allows the generation AI to analyze the voice in real time and convert it to the specified target. For example, by giving a voice command such as "convert to anime character B," the generation AI converts it to that anime character. This allows the user to specify the conversion target using a voice command.

[0057] The photo sending unit can add a function to send multiple photos at once and convert them into a group photo. The photo sending unit provides a function, for example, that allows a user to send multiple photos at once, and the generation AI analyzes and converts them into a group photo. For example, when a family photo is sent, everyone is converted into an animal. When converting into a group photo, the generation AI analyzes the characteristics of each photo and converts them into a harmonious whole. For example, when a photo of a group of friends is sent, everyone is converted into characters with the same theme. The photo sending unit also adds a function to send multiple photos at once, and the generation AI converts them into a group photo. For example, when a workplace group photo is sent, everyone is converted into characters related to the workplace. This allows multiple photos to be converted into a group photo.

[0058] When analyzing a photo, the analysis unit can analyze the user's emotions in real time and provide feedback to elicit positive emotions. For example, using an emotion estimation function, the analysis unit can analyze the user's emotions when sending a photo in real time and provide feedback to elicit positive emotions. For example, if the user is not smiling, the analysis unit can make suggestions to help them smile. The analysis unit can also analyze the user's emotions in real time and provide messages and effects to elicit positive emotions. For example, if the user is nervous, the analysis unit can display a message to help the user relax. The analysis unit can also use the emotion estimation function to analyze the user's emotions when sending a photo and provide an interface to elicit positive emotions. For example, if the user is sad, the analysis unit can apply an uplifting effect. In this way, the analysis unit can analyze the user's emotions in real time and provide feedback to elicit positive emotions.

[0059] The conversion unit can refer to the user's past conversion history and suggest conversions that suit their preferences. For example, the generation AI in the conversion unit refers to the user's past conversion history and suggests conversion targets that suit their preferences. For example, for a user who has often converted to cats in the past, cats or other animals are suggested. The conversion unit also selects the optimal conversion target based on the user's past conversion history. For example, for a user who has often converted to celebrities in the past, other celebrities are suggested. The conversion unit also analyzes the user's past conversion history and automatically selects conversion targets that suit their preferences. For example, for a user who has often converted to anime characters in the past, other anime characters are suggested. This makes it possible to suggest conversions that suit the user's preferences based on the user's past conversion history.

[0060] The conversion unit can perform optimal conversions according to the user's age and gender. For example, the generation AI in the conversion unit selects the optimal conversion target taking into account the user's age and gender. For example, it may suggest anime characters for children and celebrities for adults. The conversion unit also selects the appropriate conversion target based on the user's age and gender. For example, it may suggest female characters for female users and male characters for male users. The conversion unit also analyzes the user's age and gender and performs the optimal conversion accordingly. For example, it may suggest pop culture characters for young people and classic characters for older people. This allows for optimal conversions according to the user's age and gender.

[0061] The conversion unit can estimate the user's emotions and perform optimal conversion based on those emotions. For example, the conversion unit uses a generation AI to estimate the user's emotions and select the optimal conversion target based on those emotions. For example, if the user is having fun, it will suggest a fun character or animal. The conversion unit also analyzes the user's emotions in real time and automatically selects the conversion target according to the emotion. For example, if the user has a surprised expression, it will suggest a character that is good at expressing surprise. The conversion unit also uses an emotion estimation function to analyze the emotion the user is expressing when sending a photo and customizes the conversion target based on that emotion. For example, if the user is sad, it will suggest an uplifting character or animal. This allows optimal conversion to be performed based on the user's emotions.

[0062] The conversion unit can perform hybrid conversion that combines multiple conversion targets specified by the user. For example, the conversion unit combines multiple conversion targets specified by the user, and the generation AI performs hybrid conversion. For example, converting into a character that combines a cat and a famous person. The conversion unit also has the generation AI analyze multiple conversion targets and perform hybrid conversion that combines them. For example, converting into a character that combines an anime character and an animal. The conversion unit also has the generation AI perform the optimal hybrid conversion based on the multiple conversion targets specified by the user. For example, converting into a character that combines a famous person and an anime character. This makes it possible to perform hybrid conversion that combines multiple conversion targets.

[0063] The conversion unit can perform conversion to match a specific style specified by the user. For example, in the conversion unit, the generation AI performs conversion to match a specific style specified by the user. For example, if a retro style is specified, the character will be converted to one with a retro feel. In addition, in the conversion unit, the generation AI analyzes the style specified by the user and selects a conversion target that matches that style. For example, if a modern style is specified, the character will be converted to one with a modern feel. In addition, in the conversion unit, the generation AI performs the optimal conversion based on the style specified by the user. For example, if a vintage style is specified, the character will be converted to one with a vintage feel. This allows conversion to be performed to match a specific style specified by the user.

[0064] The conversion unit can analyze the user's emotions in real time and suggest the optimal conversion target based on the emotions. For example, the conversion unit uses an emotion estimation function to analyze the user's emotions when selecting a conversion target in real time and suggest the optimal conversion target. For example, if the user is having fun, a fun character or animal is suggested. The conversion unit also analyzes the user's emotions in real time and automatically selects a conversion target according to the emotion. For example, if the user has a surprised expression, a character that is good at expressing surprise is suggested. The conversion unit also uses the emotion estimation function to analyze the user's emotions when selecting a conversion target and customizes the conversion target based on the emotion. For example, if the user is sad, an uplifting character or animal is suggested. In this way, the user's emotions can be analyzed in real time and the optimal conversion target can be suggested.

[0065] The output unit can estimate the user's emotions when outputting the converted photo and apply optimal filters and effects based on the emotions. For example, when outputting the converted photo, the output unit estimates the user's emotions and applies optimal filters and effects based on the emotions. For example, if the user is having fun, a bright filter is applied. The output unit also uses the emotion estimation function to automatically select filters and effects according to the user's emotions. For example, if the user is surprised, an effect that emphasizes the expression of surprise is applied. The output unit also analyzes the user's emotions in real time and applies optimal filters and effects to the converted photo based on the emotions. For example, if the user is sad, an uplifting filter is applied. This makes it possible to apply optimal filters and effects based on the user's emotions.

[0066] The output unit can enable the converted photo to be output in a specific format specified by the user when outputting the converted photo. For example, the output unit provides an interface that allows the user to output the converted photo in a specific format specified by the user when outputting the converted photo. For example, the user can select a postcard or poster format. The output unit also enables the generation AI to automatically adjust the converted photo to fit the format specified by the user. For example, the output unit outputs a photo optimized for postcard size. The output unit also builds a system in which the generation AI outputs the converted photo based on the format specified by the user. For example, the output unit outputs a high-resolution photo that fits poster size. This allows the converted photo to be output in the specific format specified by the user.

[0067] When outputting the converted photo, the output unit can refer to the user's past sharing history and suggest the optimal sharing method. For example, when outputting the converted photo, the output unit refers to the user's past sharing history and suggest the optimal sharing method. For example, for a user who has often shared on social media in the past, it suggests sharing on social media. Furthermore, the output unit has a generation AI that analyzes the user's past sharing history and automatically selects the optimal sharing method. For example, for a user who has often shared on messaging apps in the past, it suggests sharing via messaging apps. Furthermore, the output unit builds a system in which the generation AI suggests the optimal sharing method based on the user's past sharing history. For example, for a user who has often shared via email in the past, it suggests sharing via email. In this way, it is possible to suggest the optimal sharing method based on the user's past sharing history.

[0068] The output unit can add a function to automatically post the converted photo to a specific social media platform designated by the user when outputting the converted photo. For example, the output unit provides a function to automatically post the converted photo to a specific social media platform designated by the user when outputting the converted photo. For example, automatically posting to Instagram or Facebook. The output unit also has the generation AI automatically adjust the converted photo to match the social media platform designated by the user and post it. For example, posting a photo optimized for the Instagram feed. The output unit also builds a system in which the generation AI automatically posts the converted photo based on the social media platform designated by the user. For example, automatically posting a photo optimized for Twitter. This allows automatic posting to the specific social media platform designated by the user.

[0069] The output unit can add a function to automatically send the converted photo to a specific messaging app designated by the user when outputting the converted photo. For example, the output unit provides a function to automatically send the converted photo to a specific messaging app designated by the user when outputting the converted photo. For example, automatically sending to WhatsApp or LINE. The output unit also has the generation AI automatically adjust the converted photo to match the messaging app designated by the user and send it. For example, sending a photo optimized for WhatsApp chat. The output unit also builds a system in which the generation AI automatically sends the converted photo based on the messaging app designated by the user. For example, automatically sending a photo optimized for LINE chat. This allows it to be automatically sent to a specific messaging app designated by the user.

[0070] The output unit can analyze the user's emotions in real time when sharing the converted photo and suggest an optimal sharing method based on the emotions. For example, using an emotion estimation function, the output unit can analyze the user's emotions in real time when sharing the converted photo and suggest an optimal sharing method. For example, if the user is enjoying the photo, the output unit can suggest sharing on a social networking site. The output unit can also analyze the user's emotions in real time and automatically select a sharing method based on the emotions. For example, if the user is surprised, the output unit can suggest sharing on a messaging app. The output unit can also use the emotion estimation function to analyze the user's emotions when sharing the converted photo and suggest an optimal sharing method based on the emotions. For example, if the user is sad, the output unit can suggest a private sharing method. This makes it possible to suggest an optimal sharing method based on the user's emotions.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The photo sending unit can provide an interface that allows a user to select a specific theme when sending a photo. For example, if a user selects a theme such as "fantasy" or "science fiction," the photo will be converted to match that theme. The photo sending unit can also use a generation AI to analyze the photo based on the theme and suggest appropriate conversion targets. For example, if fantasy is selected, the photo will be converted to a character such as a dragon or fairy. The photo sending unit can also use a theme specification function to allow a user to select a specific theme when sending a photo and automatically apply backgrounds and effects that match that theme. For example, if science fiction is selected, the photo will be converted to a character with a futuristic atmosphere. This allows the photo to be converted to match the theme specified by the user.

[0073] When analyzing a photo, the analysis unit can infer the user's emotions and apply optimal filters and effects based on the emotions. For example, if the user is having fun, a bright filter is applied. The analysis unit can also use the emotion estimation function to automatically select filters and effects according to the user's emotions. For example, if the user is surprised, an effect that emphasizes the expression of surprise is applied. The analysis unit can also analyze the user's emotions in real time and apply optimal filters and effects to the converted photo based on that emotion. For example, if the user is sad, an uplifting filter is applied. This allows optimal filters and effects to be applied based on the user's emotions.

[0074] The conversion unit can perform conversions that match a specific art style specified by the user. For example, if the user specifies an art style such as "impressionism" or "pop art," the conversion unit will convert the character to match that style. The conversion unit also allows the generation AI to analyze the art style specified by the user and select a conversion target that matches that style. For example, if impressionism is specified, the conversion will be to a character with soft colors. The conversion unit also allows the generation AI to perform the optimal conversion based on the art style specified by the user. For example, if pop art is specified, the conversion will be to a character with vibrant colors. This allows conversions to be performed that match the specific art style specified by the user.

[0075] When analyzing a photo, the analysis unit can estimate the user's emotions and suggest the optimal conversion target based on the emotions. For example, if the user is having fun, it will suggest a fun character or animal. The analysis unit can also analyze the user's emotions in real time and automatically select a conversion target according to the emotion. For example, if the user has a surprised expression, it will suggest a character that is good at expressing surprise. The analysis unit can also use the emotion estimation function to analyze the emotion the user is feeling when sending a photo and customize the conversion target based on that emotion. For example, if the user is sad, it will suggest an uplifting character or animal. This makes it possible to suggest the optimal conversion target based on the user's emotions.

[0076] The photo sending unit can provide an interface that allows a user to specify a specific season or event when sending a photo. For example, if a user selects an event such as "Christmas" or "Halloween," the photo will be converted to suit that event. The photo sending unit can also use a generation AI to analyze the photo based on the season or event and suggest appropriate conversion targets. For example, if Christmas is selected, the photo will be converted to a character such as Santa Claus or a reindeer. The photo sending unit can also use the season or event specification function to select a specific season or event when sending a photo, and automatically apply backgrounds and effects that match that theme. For example, if Halloween is selected, the photo will be converted to a character with a Halloween atmosphere. This allows conversion to suit the season or event specified by the user.

[0077] When analyzing a photo, the analysis unit can estimate the user's emotions and suggest the optimal conversion target based on the emotions. For example, if the user is having fun, it will suggest a fun character or animal. The analysis unit can also analyze the user's emotions in real time and automatically select a conversion target according to the emotion. For example, if the user has a surprised expression, it will suggest a character that is good at expressing surprise. The analysis unit can also use the emotion estimation function to analyze the emotion the user is feeling when sending a photo and customize the conversion target based on that emotion. For example, if the user is sad, it will suggest an uplifting character or animal. This makes it possible to suggest the optimal conversion target based on the user's emotions.

[0078] The conversion unit can perform conversions that are tailored to specific cultures and regions specified by the user. For example, if a user specifies a culture or region such as "Japan" or "America," the conversion unit will convert to a character that suits that culture or region. The conversion unit also allows the generation AI to analyze the culture or region specified by the user and select a conversion target that suits that culture or region. For example, if Japan is specified, the conversion will be to a Japanese-style character. The conversion unit also allows the generation AI to perform the optimal conversion based on the culture or region specified by the user. For example, if America is specified, the conversion will be to an American-style character. This allows conversions to be tailored to the specific culture or region specified by the user.

[0079] When analyzing a photo, the analysis unit can estimate the user's emotions and suggest the optimal conversion target based on the emotions. For example, if the user is having fun, it will suggest a fun character or animal. The analysis unit can also analyze the user's emotions in real time and automatically select a conversion target according to the emotion. For example, if the user has a surprised expression, it will suggest a character that is good at expressing surprise. The analysis unit can also use the emotion estimation function to analyze the emotion the user is feeling when sending a photo and customize the conversion target based on that emotion. For example, if the user is sad, it will suggest an uplifting character or animal. This makes it possible to suggest the optimal conversion target based on the user's emotions.

[0080] The photo sending unit can provide an interface that allows a user to specify a specific time period or season when sending a photo. For example, if the user selects a time period such as "morning" or "night," the conversion will be tailored to that time period. The photo sending unit can also use a generation AI to analyze the photo based on the time period or season and suggest appropriate conversion targets. For example, if morning is selected, the character will be converted to one with a morning atmosphere. The photo sending unit can also use the time period or season specification function to select a specific time period or season when sending a photo and automatically apply backgrounds and effects that match that theme. For example, if night is selected, the character will be converted to one with a night atmosphere. This allows conversion to be tailored to the time period or season specified by the user.

[0081] The conversion unit can perform conversion to suit a specific style specified by the user. For example, if the user specifies a style such as "retro" or "modern," the conversion unit will convert the character to suit that style. The conversion unit also allows the generation AI to analyze the style specified by the user and select a conversion target that suits that style. For example, if retro is specified, the character will be converted to one with a retro feel. The conversion unit also allows the generation AI to perform the optimal conversion based on the style specified by the user. For example, if modern is specified, the character will be converted to one with a modern feel. This allows conversion to suit the specific style specified by the user.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The photo sending unit sends the user's photo to the generation AI. For example, the photo sending unit can send photos in JPEG or PNG format. The photo sending unit can also provide an interface for users to upload photos. Step 2: The analysis unit analyzes the photo sent by the photo sending unit. For example, the analysis unit uses generative AI to analyze the facial features and background of the photo. The analysis unit can also extract features of the photo using image recognition technology. Step 3: The conversion unit converts the photo analyzed by the analysis unit into another person or animal. For example, the conversion unit converts the user's photo into a celebrity or anime character using the generation AI. The conversion unit can also convert the user's photo into a cat or dog using the generation AI. Step 4: The output unit outputs the photo converted by the conversion unit. For example, the output unit displays the converted photo to the user. The output unit can also provide an interface for sharing the converted photo on a social networking site or a messaging app.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a photo sending unit that sends the user's photo to the generation AI; an analysis unit that analyzes the photo transmitted by the photo transmission unit; a conversion unit that converts the photograph analyzed by the analysis unit into another person or animal; an output unit that outputs the photo converted by the conversion unit; A system characterized by:

2. The analysis unit When analyzing photos, it estimates the user's emotions and suggests the best conversion options based on those emotions.

2. The system of claim 1.

3. The photo sending unit Allows the user to specify a specific situation and performs conversions appropriate to that situation 2. The system of claim 1.

4. The analysis unit When analyzing a photo, the background information is also analyzed in detail, and the image is converted to match the background.

2. The system of claim 1.

5. The photo sending unit Allows you to specify what to convert using voice commands 2. The system of claim 1.

6. The photo sending unit Add the ability to send multiple photos at once and convert them into a group photo 2. The system of claim 1.

7. The analysis unit When analyzing photos, it analyzes the user's emotions in real time and provides feedback to elicit positive emotions.

2. The system of claim 1.

8. The conversion unit Refer to the user's past conversion history and suggest conversions that match the user's preferences 2. The system of claim 1.

Images