System

Abstract

An object of a system according to an embodiment is to generate an image in accordance with a user's request and to enable detailed partial correction.SOLUTION: A system includes a sentence generation AI, an image generation AI, a correction unit, and an interaction unit. The text generation AI supplements missing information based on an instruction from the user. The image generation AI generates an image based on the information complemented by the sentence generation AI. The correction unit corrects a part of the generated image. The interaction unit generates the entire image or modifies a part of the image while interacting with the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to create the desired image even when using image generation AI, and it was difficult to make detailed corrections according to the user's requests.

[0005] The system according to the embodiment aims to generate an image according to the user's request and enable fine correction of parts. [Means for solving the problem]

[0006] The system according to the embodiment includes a sentence generation AI, an image generation AI, a correction unit, and a dialogue unit. The sentence generation AI completes missing information based on user instructions. The image generation AI generates an image based on the information completed by the sentence generation AI. The correction unit corrects a portion of the generated image. The dialogue unit generates the entire image or corrects a portion of it while interacting with the user. [Effects of the Invention]

[0007] The system according to the embodiment can generate an image according to the user's request and enable fine correction of parts. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The image generation system according to the embodiment of the present invention utilizes a text generation AI to supplement missing information and modify / edit a portion of a generated image. This allows the image generation system to supplement missing information and modify / edit a portion of a generated image based on user instructions.

[0029] An image generation system according to an embodiment includes a text generation AI, an image generation AI, a correction unit, and a dialogue unit. The text generation AI completes missing information based on user instructions. For example, if a user instructs the generation AI to create an image of a family having a picnic under a blue sky, the text generation AI analyzes keywords such as "blue sky," "picnic," and "family" and provides specific instructions to the image generation AI. The image generation AI generates an image based on the information completed by the text generation AI. For example, the image is generated based on the user's instructions using deep learning technology. The correction unit corrects parts of the generated image, for example, by changing the background color, swapping characters' clothing, or adding a logo or text. The dialogue unit generates or partially corrects the entire image while interacting with the user. For example, if a user requests the generation AI to "make the background a little brighter," the generation AI corrects the image according to the request. This allows the image generation system according to an embodiment to complete missing information based on user instructions, correct parts of the generated image, and generate or partially correct the entire image while interacting with the user.

[0030] The text generation AI can learn the user's past instruction history and provide information completion that reflects the user's preferences and tendencies. For example, the text generation AI can analyze the user's past instruction history and learn the image styles and themes that the user prefers. For example, if the user has generated many "natural landscapes" or "family photos" in the past, these elements will be prioritized for completion. This allows the AI ​​to learn the user's past instruction history and provide information completion that reflects the user's preferences and tendencies.

[0031] The sentence generation AI can instantly reflect feedback provided by the user in real time on the supplementary information. For example, the sentence generation AI builds an interface that allows the user to provide feedback in real time on the generated supplementary information. For example, if the user provides feedback such as "I want the color to be brighter," it is reflected immediately. This allows the feedback provided by the user in real time to be reflected immediately.

[0032] Text generation AI can complement information in different languages, making it possible to accommodate international users. For example, text generation AI can complement information in different languages, building a system that can accommodate international users. For example, it can complement information in multiple languages, such as English, French, and Chinese. This makes it possible to complement information in different languages, making it possible to accommodate international users.

[0033] The text generation AI can collect ratings and comments from other users on the supplemental information and optimize the information based on those ratings. For example, the text generation AI builds a system that collects ratings and comments from other users on the supplemental information it generates. For example, a user rates a generated image, and the information is optimized based on that rating. This makes it possible to collect ratings and comments from other users and optimize the information based on that rating.

[0034] The correction unit takes into account the context surrounding the portion designated by the user and can perform natural correction. For example, when correcting a portion of an image, the correction unit builds a system that takes into account the context surrounding the portion designated by the user and performs natural correction. For example, when changing the color of the background, the system adjusts the color so that it harmonizes with the surrounding colors. This allows the correction to be performed naturally by taking into account the context surrounding the portion designated by the user.

[0035] The correction unit may generate multiple correction candidates when correcting a portion of an image and allow the user to select from them. For example, the correction unit may construct a system that generates multiple correction candidates when correcting a portion of an image and allows the user to select from them. For example, when changing the color of a background, multiple shades of color may be suggested. This allows the multiple correction candidates to be generated and allow the user to select from them.

[0036] The modification unit can apply different styles and art filters when modifying a portion of an image, allowing the user to select from them. For example, the modification unit can build a system that applies different styles and art filters when modifying a portion of an image, allowing the user to select from them. For example, when changing the color of a background, multiple art filters can be suggested. This allows the user to apply different styles and art filters and select from them.

[0037] The correction unit can refer to correction examples made by other users when correcting a part of an image and propose the optimal correction method. For example, the correction unit builds a system that refers to correction examples made by other users when correcting a part of an image and proposes the optimal correction method. For example, when changing the background color, it proposes the hue chosen by other users. This makes it possible to refer to correction examples made by other users and propose the optimal correction method.

[0038] The dialogue unit learns the dialogue history with the user and can make more appropriate suggestions in subsequent dialogues. The dialogue unit, for example, builds a system that learns the dialogue history with the user and makes more appropriate suggestions in subsequent dialogues. For example, if the user has previously preferred "bright colors," the system will prioritize suggestions for "bright colors" in the next dialogue. This allows the dialogue unit to learn the dialogue history with the user and make more appropriate suggestions in subsequent dialogues.

[0039] The dialogue unit can analyze feedback provided by a user during dialogue in real time and optimize image generation based on that feedback. The dialogue unit, for example, builds a system that analyzes feedback provided by a user during dialogue in real time and optimizes image generation based on that feedback. For example, if a user provides feedback such as "I want it to be brighter," this is reflected immediately. This allows the feedback provided by a user during dialogue to be analyzed in real time and image generation to be optimized based on that feedback.

[0040] The dialogue unit can generate images that are suitable for users of different cultures and regions through interactive dialogue. The dialogue unit, for example, builds a system that generates images that are suitable for users of different cultures and regions through interactive dialogue. For example, if a user is interested in Japanese culture, the dialogue unit generates images that include Japanese landscapes and traditional elements. This makes it possible to generate images that are suitable for users of different cultures and regions.

[0041] The dialogue unit can provide a function for a user to share an image generated by the user with other users and receive feedback through interactive dialogue. The dialogue unit, for example, builds a system that provides a function for a user to share an image generated by the user with other users and receive feedback through interactive dialogue. For example, the generated image is shared on a social networking site or a dedicated platform. This can provide a function for a user to share an image generated by the user with other users and receive feedback.

[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 image generation system may further include a history analysis unit that analyzes images generated by the user in the past. The history analysis unit may, for example, analyze the style and theme of images generated by the user in the past and learn the user's preferences. This allows the system to make suggestions based on the user's past preferences when generating a new image. For example, if the user has generated many "natural landscapes" or "family photos" in the past, these elements may be preferentially suggested. This allows the system to generate images that reflect the user's preferences and tendencies.

[0044] The image generation system can further include a multilingual support unit that complements information in different languages. The multilingual support unit can complement information in multiple languages, such as English, French, and Chinese. This allows for international users to be accommodated, and users who speak different languages ​​can give instructions in their own language. For example, if a user instructs in English, "A family having a picnic under a blue sky," the instruction can be analyzed and provided to the image generation AI. This allows for information complementation in different languages, making it possible to accommodate international users.

[0045] The image generation system may further include an evaluation collection unit that collects evaluations and comments from other users. The evaluation collection unit may, for example, collect evaluations and comments from other users on the generated image and optimize information based on the evaluations. For example, if a user receives many comments such as "I like this color" on an image generated by the user, that color may be preferentially suggested. This allows the evaluations and comments from other users to be collected and information to be optimized based on the evaluations.

[0046] The image generation system may further include a context analysis unit that takes into account the context surrounding the portion designated by the user. For example, when modifying a portion of an image, the context analysis unit may take into account the context surrounding the portion designated by the user, thereby enabling a natural modification. For example, when changing the color of the background, the color may be adjusted to harmonize with the surrounding color. This allows a natural modification to be performed by taking into account the context surrounding the portion designated by the user.

[0047] The image generation system may further include a candidate generator that generates multiple correction candidates. For example, when correcting a portion of an image, the candidate generator may generate multiple correction candidates and allow the user to select one. For example, when changing the color of a background, multiple shades of color may be suggested. This allows the user to select the most appropriate one from the multiple correction candidates.

[0048] The image generation system may further include a style application unit that applies different styles and art filters. For example, when modifying a portion of an image, the style application unit may apply different styles and art filters and allow the user to select one. For example, when changing the background color, the unit may suggest multiple art filters. This allows the user to select the most appropriate one from the different styles and art filters.

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

[0050] Step 1: The text generation AI fills in missing information based on the user's instructions. For example, if the user instructs the generation AI to create an image of a family having a picnic under a blue sky, the text generation AI analyzes keywords such as "blue sky," "picnic," and "family" and provides specific instructions to the image generation AI. Step 2: The image generation AI generates an image based on the information supplemented by the text generation AI. For example, it uses deep learning technology to generate an image based on the user's instructions. Step 3: The editing unit edits parts of the generated image, for example, changing the background color, swapping the characters' clothing, or adding logos or text. Step 4: The dialogue unit generates the entire image or modifies parts of it while interacting with the user. For example, if the user tells the generation AI a request such as "I want the background color to be a little brighter," the generation AI will modify the image accordingly.

[0051] (Example 2) The image generation system according to the embodiment of the present invention utilizes a text generation AI to supplement missing information and modify / edit a portion of a generated image. This allows the image generation system to supplement missing information and modify / edit a portion of a generated image based on user instructions.

[0052] An image generation system according to an embodiment includes a text generation AI, an image generation AI, a correction unit, and a dialogue unit. The text generation AI completes missing information based on user instructions. For example, if a user instructs the generation AI to create an image of a family having a picnic under a blue sky, the text generation AI analyzes keywords such as "blue sky," "picnic," and "family" and provides specific instructions to the image generation AI. The image generation AI generates an image based on the information completed by the text generation AI. For example, the image is generated based on the user's instructions using deep learning technology. The correction unit corrects parts of the generated image, for example, by changing the background color, swapping characters' clothing, or adding a logo or text. The dialogue unit generates or partially corrects the entire image while interacting with the user. For example, if a user requests the generation AI to "make the background a little brighter," the generation AI corrects the image according to the request. This allows the image generation system according to an embodiment to complete missing information based on user instructions, correct parts of the generated image, and generate or partially correct the entire image while interacting with the user.

[0053] The text generation AI can learn the user's past instruction history and provide information completion that reflects the user's preferences and tendencies. For example, the text generation AI can analyze the user's past instruction history and learn the image styles and themes that the user prefers. For example, if the user has generated many "natural landscapes" or "family photos" in the past, these elements will be prioritized for completion. This allows the AI ​​to learn the user's past instruction history and provide information completion that reflects the user's preferences and tendencies.

[0054] The sentence generation AI can instantly reflect feedback provided by the user in real time on the supplementary information. For example, the sentence generation AI builds an interface that allows the user to provide feedback in real time on the generated supplementary information. For example, if the user provides feedback such as "I want the color to be brighter," it is reflected immediately. This allows the feedback provided by the user in real time to be reflected immediately.

[0055] Sentence generation AI can use emotion estimation to estimate the user's emotional state and provide information supplementation that elicits positive emotions. For example, sentence generation AI can use emotion estimation to analyze the user's emotional state in real time and provide information supplementation that elicits positive emotions. For example, if the user is feeling stressed, it can suggest a relaxing landscape image. This allows it to estimate the user's emotional state and provide information supplementation that elicits positive emotions.

[0056] Text generation AI can complement information in different languages, making it possible to accommodate international users. For example, text generation AI can complement information in different languages, building a system that can accommodate international users. For example, it can complement information in multiple languages, such as English, French, and Chinese. This makes it possible to complement information in different languages, making it possible to accommodate international users.

[0057] The text generation AI can collect ratings and comments from other users on the supplemental information and optimize the information based on those ratings. For example, the text generation AI builds a system that collects ratings and comments from other users on the supplemental information it generates. For example, a user rates a generated image, and the information is optimized based on that rating. This makes it possible to collect ratings and comments from other users and optimize the information based on that rating.

[0058] The text generation AI can use its emotion estimation function to analyze the emotion of the text entered by the user and complete information according to that emotion. For example, the text generation AI can use its emotion estimation function to analyze the emotion of the text entered by the user and create a system that completes information according to that emotion. For example, if the user is feeling "fun," it can generate an image with a fun atmosphere. This allows the emotion of the text entered by the user to be analyzed and information to be completed according to that emotion.

[0059] The correction unit takes into account the context surrounding the portion designated by the user and can perform natural correction. For example, when correcting a portion of an image, the correction unit builds a system that takes into account the context surrounding the portion designated by the user and performs natural correction. For example, when changing the color of the background, the system adjusts the color so that it harmonizes with the surrounding colors. This allows the correction to be performed naturally by taking into account the context surrounding the portion designated by the user.

[0060] The correction unit may generate multiple correction candidates when correcting a portion of an image and allow the user to select from them. For example, the correction unit may construct a system that generates multiple correction candidates when correcting a portion of an image and allows the user to select from them. For example, when changing the color of a background, multiple shades of color may be suggested. This allows the multiple correction candidates to be generated and allow the user to select from them.

[0061] The correction unit can use the emotion estimation function to analyze the emotion the user has regarding the part they want to correct, and make corrections based on that emotion. For example, the correction unit uses the emotion estimation function to analyze the emotion the user has regarding the part they want to correct, and builds a system that makes corrections based on that emotion. For example, if the user feels that they want to make it "brighter," the correction is made to a brighter color tone. This allows the user's emotion regarding the part they want to correct to be analyzed, and corrections to be made based on that emotion.

[0062] The modification unit can apply different styles and art filters when modifying a portion of an image, allowing the user to select from them. For example, the modification unit can build a system that applies different styles and art filters when modifying a portion of an image, allowing the user to select from them. For example, when changing the color of a background, multiple art filters can be suggested. This allows the user to apply different styles and art filters and select from them.

[0063] The correction unit can refer to correction examples made by other users when correcting a part of an image and propose the optimal correction method. For example, the correction unit builds a system that refers to correction examples made by other users when correcting a part of an image and proposes the optimal correction method. For example, when changing the background color, it proposes the hue chosen by other users. This makes it possible to refer to correction examples made by other users and propose the optimal correction method.

[0064] The correction unit can use the emotion estimation function to monitor in real time the emotion the user feels about the part they want to correct, and make corrections according to that emotion. For example, the correction unit uses the emotion estimation function to monitor in real time the emotion the user feels about the part they want to correct, and builds a system that makes corrections according to that emotion. For example, if the user feels that they want to make it "brighter," the color tone is corrected to a brighter tone. This makes it possible to monitor in real time the emotion the user feels about the part they want to correct, and make corrections according to that emotion.

[0065] The dialogue unit learns the dialogue history with the user and can make more appropriate suggestions in subsequent dialogues. The dialogue unit, for example, builds a system that learns the dialogue history with the user and makes more appropriate suggestions in subsequent dialogues. For example, if the user has previously preferred "bright colors," the system will prioritize suggestions for "bright colors" in the next dialogue. This allows the dialogue unit to learn the dialogue history with the user and make more appropriate suggestions in subsequent dialogues.

[0066] The dialogue unit can analyze feedback provided by a user during dialogue in real time and optimize image generation based on that feedback. The dialogue unit, for example, builds a system that analyzes feedback provided by a user during dialogue in real time and optimizes image generation based on that feedback. For example, if a user provides feedback such as "I want it to be brighter," this is reflected immediately. This allows the feedback provided by a user during dialogue to be analyzed in real time and image generation to be optimized based on that feedback.

[0067] The dialogue unit can use the emotion estimation function to analyze the user's emotional state and engage in dialogue that elicits positive emotions. For example, the dialogue unit can use the emotion estimation function to analyze the user's emotional state in real time and build a system that engages in dialogue that elicits positive emotions. For example, if the user is feeling stressed, the dialogue unit can suggest a relaxing landscape image. This allows the dialogue unit to analyze the user's emotional state and engage in dialogue that elicits positive emotions.

[0068] The dialogue unit can generate images that are suitable for users of different cultures and regions through interactive dialogue. The dialogue unit, for example, builds a system that generates images that are suitable for users of different cultures and regions through interactive dialogue. For example, if a user is interested in Japanese culture, the dialogue unit generates images that include Japanese landscapes and traditional elements. This makes it possible to generate images that are suitable for users of different cultures and regions.

[0069] The dialogue unit can provide a function for a user to share an image generated by the user with other users and receive feedback through interactive dialogue. The dialogue unit, for example, builds a system that provides a function for a user to share an image generated by the user with other users and receive feedback through interactive dialogue. For example, the generated image is shared on a social networking site or a dedicated platform. This can provide a function for a user to share an image generated by the user with other users and receive feedback.

[0070] The dialogue unit can monitor the user's emotional state in real time using the emotion estimation function and engage in dialogue according to that emotion. For example, the dialogue unit can build a system that uses the emotion estimation function to monitor the user's emotional state in real time and engage in dialogue according to that emotion. For example, if the user is feeling stressed, it can suggest a relaxing landscape image. This makes it possible to monitor the user's emotional state in real time and engage in dialogue according to that emotion.

[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 image generation system can further include a voice recognition unit that analyzes the user's voice instructions. For example, if the user issues a voice command such as "I want an image of a family having a picnic under a blue sky," the voice recognition unit converts the voice into text and provides it to the text generation AI. This allows the user to issue voice commands without using a keyboard, making image generation more intuitive. The voice recognition unit can also infer emotions from the user's tone and speed of voice and optimize image generation instructions based on those emotions. For example, if the user is excited, it can generate more vivid and dynamic images.

[0073] The image generation system may further include a history analysis unit that analyzes images generated by the user in the past. The history analysis unit may, for example, analyze the style and theme of images generated by the user in the past and learn the user's preferences. This allows the system to make suggestions based on the user's past preferences when generating a new image. For example, if the user has generated many "natural landscapes" or "family photos" in the past, these elements may be preferentially suggested. This allows the system to generate images that reflect the user's preferences and tendencies.

[0074] The image generation system can further include a feedback analysis unit that analyzes real-time user feedback. For example, if a user provides feedback on a generated image such as "I want brighter colors," the feedback analysis unit immediately analyzes that feedback and reflects it in the image generation AI. This allows the user's real-time feedback to be immediately reflected, enabling the system to generate images that more closely match the user's intentions. The feedback analysis unit can also infer emotions from the content of the user's feedback and optimize image generation instructions based on those emotions. For example, if the user feels that "I want a more cheerful atmosphere," brighter colors and fun elements can be added.

[0075] The image generation system can further include a multilingual support unit that complements information in different languages. The multilingual support unit can complement information in multiple languages, such as English, French, and Chinese. This allows for international users to be accommodated, and users who speak different languages ​​can give instructions in their own language. For example, if a user instructs in English, "A family having a picnic under a blue sky," the instruction can be analyzed and provided to the image generation AI. This allows for information complementation in different languages, making it possible to accommodate international users.

[0076] The image generation system may further include an evaluation collection unit that collects evaluations and comments from other users. The evaluation collection unit may, for example, collect evaluations and comments from other users on the generated image and optimize information based on the evaluations. For example, if a user receives many comments such as "I like this color" on an image generated by the user, that color may be preferentially suggested. This allows the evaluations and comments from other users to be collected and information to be optimized based on the evaluations.

[0077] The image generation system can further include an emotion analysis unit that analyzes the emotion of the text entered by the user. For example, if the user feels "fun," the emotion analysis unit can supplement information according to that emotion. For example, if the user instructs, "I want an image of a fun picnic to be generated," the emotion can be analyzed and an image with a fun atmosphere can be generated. This allows the emotion of the text entered by the user to be analyzed and information to be supplemented according to that emotion.

[0078] The image generation system may further include a context analysis unit that takes into account the context surrounding the portion designated by the user. For example, when modifying a portion of an image, the context analysis unit may take into account the context surrounding the portion designated by the user, thereby enabling a natural modification. For example, when changing the color of the background, the color may be adjusted to harmonize with the surrounding color. This allows a natural modification to be performed by taking into account the context surrounding the portion designated by the user.

[0079] The image generation system may further include a candidate generator that generates multiple correction candidates. For example, when correcting a portion of an image, the candidate generator may generate multiple correction candidates and allow the user to select one. For example, when changing the color of a background, multiple shades of color may be suggested. This allows the user to select the most appropriate one from the multiple correction candidates.

[0080] The image generation system can further include an emotion analysis unit that analyzes the user's emotion regarding the part they want to modify. For example, if the user feels that they want to make it brighter, the emotion analysis unit can make modifications based on that emotion. For example, if the user feels that they want to make it brighter, the color tone can be modified to a brighter tone. This allows the user's emotion regarding the part they want to modify to be analyzed and modifications based on that emotion to be made.

[0081] The image generation system may further include a style application unit that applies different styles and art filters. For example, when modifying a portion of an image, the style application unit may apply different styles and art filters and allow the user to select one. For example, when changing the background color, the unit may suggest multiple art filters. This allows the user to select the most appropriate one from the different styles and art filters.

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

[0083] Step 1: The text generation AI fills in missing information based on the user's instructions. For example, if the user instructs the generation AI to create an image of a family having a picnic under a blue sky, the text generation AI analyzes keywords such as "blue sky," "picnic," and "family" and provides specific instructions to the image generation AI. Step 2: The image generation AI generates an image based on the information supplemented by the text generation AI. For example, it uses deep learning technology to generate an image based on the user's instructions. Step 3: The editing unit edits parts of the generated image, for example, changing the background color, swapping the characters' clothing, or adding logos or text. Step 4: The dialogue unit generates the entire image or modifies parts of it while interacting with the user. For example, if the user tells the generation AI a request such as "I want the background color to be a little brighter," the generation AI will modify the image accordingly.

[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 a 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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, in order to avoid confusion and to 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. Text generation AI and Image generation AI and a correction unit that corrects a portion of the generated image; a dialogue unit that dialogues with a user, The sentence generation AI is Fill in missing information based on user instructions, The image generation AI is Generate an image based on the information supplemented by the sentence generation AI, The correction unit Modifying a portion of the generated image; The dialogue unit Interactively generate or modify an entire image A system characterized by:

2. The sentence generation AI is Complement information in different languages ​​to accommodate international users 2. The system of claim 1.

3. The correction unit Considers the surrounding context of the user-specified part and performs natural corrections 2. The system of claim 1.

4. The dialogue unit Learns the conversation history with the user and makes more appropriate suggestions in subsequent conversations 2. The system of claim 1.

5. The sentence generation AI is The emotion estimation function is used to estimate the user's emotional state and provide information supplementation that elicits positive emotions.

2. The system of claim 1.

6. The correction unit Using an emotion estimation function, the emotion of the user regarding the part that the user wants to correct is analyzed, and corrections are made based on the emotion.

2. The system of claim 1.

7. The dialogue unit Analyzing the emotional state of the user using an emotion estimation function and conducting the dialogue in a way that elicits positive emotions.

2. The system of claim 1.

8. The dialogue unit Using an emotion estimation function, the emotional state of the user is monitored in real time, and the dialogue is carried out according to the emotional state.

2. The system of claim 1.

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