System

Abstract

An object of the system according to the embodiment is to accurately identify a work based on generated AI and a handwritten work.SOLUTION: A system includes an analysis part, an identification part, and a display part. The analysis unit analyzes a work. An identification part identifies whether the work is generated by the generation AI or handwritten based on the result analyzed by the analysis part. The display unit displays a result identified by the identification 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 the problem of making it difficult to accurately distinguish between works created by generative AI and handwritten works.

[0005] The system according to the embodiment aims to accurately distinguish between works created by generative AI and handwritten works. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a classification unit, and a display unit. The analysis unit analyzes the artwork. The classification unit classifies whether the artwork is generated by a generative AI or handwritten based on the analysis results by the analysis unit. The display unit displays the classification results by the classification unit. [Effects of the Invention]

[0007] The system according to the embodiment can accurately distinguish between works created by generative AI and handwritten works. [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 generative AI system according to an embodiment of the present invention is a system that distinguishes between works created by generative AI and hand-drawn works. In this system, the generative AI analyzes works and determines whether "this was created by AI" or "this was not AI-generated." This allows the generative AI system to protect the jobs of creators such as designers and illustrators, and preserve delicate and beautiful hand-drawn works for future generations.

[0029] A generative AI system according to an embodiment includes an analysis unit, a recognition unit, and a display unit. The analysis unit analyzes the artwork. For example, the analysis unit receives image data of the artwork as input and analyzes its characteristics, such as changes in pen pressure, smoothness of lines, and color gradations. The analysis unit can also detect subtle features of the artwork and identify handwritten features. The recognition unit recognizes whether the artwork is created by a generative AI or is handwritten based on the analysis results. For example, the recognition unit compares AI-generated features with handwritten features based on the analysis results to determine the origin of the artwork. The recognition unit can perform more accurate recognition based on data learned by the generative AI. The display unit displays the recognition results identified by the recognition unit. For example, the display unit displays the recognition results in text or graphical format and provides them to the user. The display unit can also display the recognition results via a web application or a mobile application. This enables the generative AI system to accurately distinguish between a generative AI artwork and a handwritten artwork.

[0030] When analyzing changes in pen pressure and the smoothness of lines, the analysis unit also takes into account subtle fluctuations in handwriting and the degree of ink bleeding, allowing it to identify features unique to handwriting. For example, when the generation AI analyzes changes in pen pressure in a work, the analysis unit detects subtle fluctuations in pen pressure and identifies features unique to handwriting based on the pattern of those fluctuations. For example, it analyzes in detail changes in the strength of pen pressure and changes in handwriting thickness. Furthermore, when the generation AI analyzes the smoothness of lines, the analysis unit detects subtle fluctuations and irregular fluctuations in lines and identifies features unique to handwriting based on the fluctuation pattern. For example, it analyzes in detail the smoothness of lines and irregular line fluctuations. Furthermore, when the generation AI analyzes the degree of ink bleeding, the analysis unit detects subtle ink bleeding patterns and identifies features unique to handwriting based on the degree of bleeding. For example, it analyzes in detail the spread of ink bleeding and changes in shade. This allows for more detailed identification of features unique to handwriting.

[0031] When analyzing color gradations, the analysis unit analyzes color overlap and color mixing patterns, and can identify color techniques unique to handwriting. For example, when the generation AI analyzes color gradations, the analysis unit detects the degree of color overlap and identifies color techniques unique to handwriting based on the overlapping pattern. For example, it analyzes in detail subtle color changes due to color overlap. Furthermore, when the generation AI analyzes color mixing patterns, the analysis unit detects the degree of color mixing and identifies color techniques unique to handwriting based on the color mixing pattern. For example, it analyzes in detail subtle color changes due to color mixing. Furthermore, when the generation AI analyzes color gradations, the analysis unit detects changes in color shade and identifies color techniques unique to handwriting based on the shade pattern. For example, it analyzes in detail subtle color changes due to color shade. This makes it possible to identify color techniques unique to handwriting.

[0032] The analysis unit also takes into account the material of the work and traces of the tools used, and is able to identify physical characteristics unique to handwriting. For example, when the generation AI analyzes the material of the work, the analysis unit detects the subtle characteristics of the material and identifies physical characteristics unique to handwriting based on those characteristics. For example, it analyzes the texture of the paper and the type of ink in detail. Furthermore, when the generation AI analyzes traces of tools used, the analysis unit detects the subtle traces of the tools and identifies physical characteristics unique to handwriting based on those traces. For example, it analyzes brush and pen marks in detail. Furthermore, when the generation AI analyzes the material of the work and traces of tools, the analysis unit detects combinations of materials and tools and identifies physical characteristics unique to handwriting based on those combinations. For example, it analyzes the combination of paper and ink in detail. This allows it to identify physical characteristics unique to handwriting.

[0033] The analysis unit records the process of creating a work as a video and can determine whether it is handwritten or AI-generated based on the video data. For example, the analysis unit may have a generation AI record the process of creating a work as a video and analyze the video data to identify characteristics unique to handwriting. For example, it may analyze brush movements and hand movements in detail. The analysis unit may also build a system in which a generation AI records the process of creating a work as a video and determines whether it is handwritten or AI-generated based on the video data. For example, it may analyze the movement patterns during the production process. The analysis unit may also have a generation AI record the process of creating a work as a video and identify characteristics unique to handwriting based on the video data. For example, it may analyze the speed and rhythm of the production process in detail. This makes it possible to determine whether it is handwritten or AI-generated based on the production process.

[0034] The analysis unit can identify a production process specific to handwriting by also taking into account data on the production time and the production environment. For example, the analysis unit has the generation AI analyze the production time of the work and identify a production process specific to handwriting based on that time data. For example, the analysis unit analyzes in detail the length of time it takes to produce the work and the frequency of breaks. The analysis unit also has the generation AI analyze data on the production environment of the work and identify a production process specific to handwriting based on that environmental data. For example, the analysis unit analyzes in detail the temperature and humidity of the production environment. The analysis unit also has the generation AI analyze data on the production time of the work and the production environment and identify a production process specific to handwriting based on that data. For example, the analysis unit analyzes in detail fluctuations in production time and changes in the environment. This makes it possible to identify a production process specific to handwriting based on data on the production time and the production environment.

[0035] The analysis unit analyzes composition and layout patterns and can identify design techniques unique to handwriting. For example, the analysis unit uses a generation AI to analyze the composition of a work and identify design techniques unique to handwriting based on that composition pattern. For example, it analyzes in detail the balance of the composition and patterns of eye guidance. The analysis unit also uses a generation AI to analyze the layout of a work and identify design techniques unique to handwriting based on that layout pattern. For example, it analyzes in detail the layout arrangement and use of space. The analysis unit also uses a generation AI to analyze the composition and layout patterns of a work and identify design techniques unique to handwriting based on that pattern. For example, it analyzes in detail changes in composition and layout. This makes it possible to identify design techniques unique to handwriting based on composition and layout patterns.

[0036] The analysis unit analyzes the story and concept behind the work and can identify the creative intention unique to handwriting. For example, the analysis unit has the generation AI analyze the story behind the work and identify the creative intention unique to handwriting based on that story. For example, it analyzes the development of the story and the theme in detail. The analysis unit also has the generation AI analyze the concept behind the work and identify the creative intention unique to handwriting based on that concept. For example, it analyzes the uniqueness and consistency of the concept in detail. The analysis unit also has the generation AI analyze the story and concept behind the work and identify the creative intention unique to handwriting based on the analysis results. For example, it analyzes changes in the story and concept in detail. This makes it possible to identify the creative intention unique to handwriting based on the story and concept behind the work.

[0037] The analysis unit also takes into consideration data on the exhibition method and exhibition environment of the work, and is able to identify exhibition techniques specific to handwriting. For example, the analysis unit has the generation AI analyze the exhibition method of the work, and identify exhibition techniques specific to handwriting based on that exhibition method. For example, it analyzes in detail the layout of the exhibit and the use of lighting. The analysis unit also has the generation AI analyze data on the exhibition environment of the work, and identify exhibition techniques specific to handwriting based on that environmental data. For example, it analyzes in detail the temperature and humidity of the exhibition environment. The analysis unit also has the generation AI analyze data on the exhibition method and exhibition environment of the work, and identify exhibition techniques specific to handwriting based on that data. For example, it analyzes in detail changes in the exhibition method and environment. This makes it possible to identify exhibition techniques specific to handwriting based on data on the exhibition method and exhibition environment.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The generative AI system can further include a tactile analysis unit. The tactile analysis unit analyzes tactile data of the creator when creating a work and can identify characteristics unique to handwriting based on the tactile data. For example, the tactile analysis unit analyzes the pen pressure and hand movements of the creator when creating a work and can identify characteristics unique to handwriting based on the fluctuation patterns. The tactile analysis unit can also analyze changes in the temperature and humidity of the creator's hands and can identify characteristics unique to handwriting based on those fluctuations. Furthermore, the tactile analysis unit can analyze tactile feedback when the creator creates a work and can identify characteristics unique to handwriting based on fluctuations in that feedback. In this way, characteristics unique to handwriting can be identified based on tactile data.

[0040] The generative AI system can further include a vibration analysis unit. The vibration analysis unit analyzes the vibrations of the creator's hands when creating a work and can identify handwriting-specific features based on the vibration data. For example, the vibration analysis unit analyzes the strength of hand vibrations when the creator creates a work and can identify handwriting-specific features based on the fluctuation pattern. The vibration analysis unit can also analyze the frequency of the creator's hand vibrations and can identify handwriting-specific features based on the fluctuation. Furthermore, the vibration analysis unit can analyze fluctuations in hand vibrations over time when the creator creates a work and can identify handwriting-specific features based on the fluctuation pattern. This makes it possible to identify handwriting-specific features based on vibration data.

[0041] The generative AI system can further include a light analysis unit. The light analysis unit can analyze the lighting environment around the creator when creating the work and identify characteristics unique to handwriting based on the light data. For example, the light analysis unit can analyze the light intensity and color temperature when the creator creates the work and identify characteristics unique to handwriting based on the pattern of fluctuations. The light analysis unit can also analyze changes in the light around the creator and identify characteristics unique to handwriting based on those fluctuations. Furthermore, the light analysis unit can analyze fluctuations in light over time when the creator creates the work and identify characteristics unique to handwriting based on the pattern of fluctuations. This makes it possible to identify characteristics unique to handwriting based on light data.

[0042] The generative AI system can further include an odor analysis unit. The odor analysis unit analyzes the odor environment around the creator when creating a work and can identify characteristics unique to handwriting based on the odor data. For example, the odor analysis unit analyzes the strength and type of odor when the creator creates a work and can identify characteristics unique to handwriting based on the pattern of fluctuations. The odor analysis unit can also analyze changes in odor around the creator and can identify characteristics unique to handwriting based on those fluctuations. Furthermore, the odor analysis unit can analyze odor fluctuations over time when the creator creates a work and can identify characteristics unique to handwriting based on the pattern of those fluctuations. This makes it possible to identify characteristics unique to handwriting based on odor data.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The analysis unit analyzes the artwork. For example, the analysis unit receives image data of the artwork as input and analyzes features such as changes in pen pressure, smoothness of lines, and color gradation. The analysis unit can also detect subtle features of the artwork and identify characteristics unique to handwriting. Step 2: The identification unit identifies whether the work was created by the generating AI or handwritten based on the results of the analysis by the analysis unit. For example, the identification unit compares the AI-generated features with the handwritten features based on the analysis results to determine the origin of the work. The identification unit can make more accurate identification based on the data learned by the generating AI. Step 3: The display unit displays the results identified by the identification unit. For example, the display unit displays the results in text or graphical format and provides them to the user. The display unit can also display the results through a web application or a mobile application.

[0045] (Example 2) The generative AI system according to an embodiment of the present invention is a system that distinguishes between works created by generative AI and hand-drawn works. In this system, the generative AI analyzes works and determines whether "this was created by AI" or "this was not AI-generated." This allows the generative AI system to protect the jobs of creators such as designers and illustrators, and preserve delicate and beautiful hand-drawn works for future generations.

[0046] A generative AI system according to an embodiment includes an analysis unit, a recognition unit, and a display unit. The analysis unit analyzes the artwork. For example, the analysis unit receives image data of the artwork as input and analyzes its characteristics, such as changes in pen pressure, smoothness of lines, and color gradations. The analysis unit can also detect subtle features of the artwork and identify handwritten features. The recognition unit recognizes whether the artwork is created by a generative AI or is handwritten based on the analysis results. For example, the recognition unit compares AI-generated features with handwritten features based on the analysis results to determine the origin of the artwork. The recognition unit can perform more accurate recognition based on data learned by the generative AI. The display unit displays the recognition results identified by the recognition unit. For example, the display unit displays the recognition results in text or graphical format and provides them to the user. The display unit can also display the recognition results via a web application or a mobile application. This enables the generative AI system to accurately distinguish between a generative AI artwork and a handwritten artwork.

[0047] When analyzing changes in pen pressure and the smoothness of lines, the analysis unit also takes into account subtle fluctuations in handwriting and the degree of ink bleeding, allowing it to identify features unique to handwriting. For example, when the generation AI analyzes changes in pen pressure in a work, the analysis unit detects subtle fluctuations in pen pressure and identifies features unique to handwriting based on the pattern of those fluctuations. For example, it analyzes in detail changes in the strength of pen pressure and changes in handwriting thickness. Furthermore, when the generation AI analyzes the smoothness of lines, the analysis unit detects subtle fluctuations and irregular fluctuations in lines and identifies features unique to handwriting based on the fluctuation pattern. For example, it analyzes in detail the smoothness of lines and irregular line fluctuations. Furthermore, when the generation AI analyzes the degree of ink bleeding, the analysis unit detects subtle ink bleeding patterns and identifies features unique to handwriting based on the degree of bleeding. For example, it analyzes in detail the spread of ink bleeding and changes in shade. This allows for more detailed identification of features unique to handwriting.

[0048] When analyzing color gradations, the analysis unit analyzes color overlap and color mixing patterns, and can identify color techniques unique to handwriting. For example, when the generation AI analyzes color gradations, the analysis unit detects the degree of color overlap and identifies color techniques unique to handwriting based on the overlapping pattern. For example, it analyzes in detail subtle color changes due to color overlap. Furthermore, when the generation AI analyzes color mixing patterns, the analysis unit detects the degree of color mixing and identifies color techniques unique to handwriting based on the color mixing pattern. For example, it analyzes in detail subtle color changes due to color mixing. Furthermore, when the generation AI analyzes color gradations, the analysis unit detects changes in color shade and identifies color techniques unique to handwriting based on the shade pattern. For example, it analyzes in detail subtle color changes due to color shade. This makes it possible to identify color techniques unique to handwriting.

[0049] The analysis unit can use the emotion estimation function to estimate the emotions of the creator when they created the work and analyze the traces of those emotions as characteristics of the work. For example, when the generation AI analyzes a work, the analysis unit estimates the emotions of the creator and identifies characteristics unique to handwriting based on the traces of those emotions. For example, it analyzes in detail the strength and fluctuations of emotions. Furthermore, when the generation AI analyzes a work, the analysis unit estimates changes in the creator's emotions and identifies characteristics unique to handwriting based on patterns of change in emotion. For example, it analyzes in detail the fluctuations in handwriting due to changes in emotion. Furthermore, when the generation AI analyzes a work, the analysis unit estimates traces of the creator's emotions and identifies characteristics unique to handwriting based on those traces. For example, it analyzes in detail color changes due to traces of emotions. In this way, it is possible to identify characteristics unique to handwriting based on traces of the creator's emotions.

[0050] The analysis unit also takes into account the material of the work and traces of the tools used, and is able to identify physical characteristics unique to handwriting. For example, when the generation AI analyzes the material of the work, the analysis unit detects the subtle characteristics of the material and identifies physical characteristics unique to handwriting based on those characteristics. For example, it analyzes the texture of the paper and the type of ink in detail. Furthermore, when the generation AI analyzes traces of tools used, the analysis unit detects the subtle traces of the tools and identifies physical characteristics unique to handwriting based on those traces. For example, it analyzes brush and pen marks in detail. Furthermore, when the generation AI analyzes the material of the work and traces of tools, the analysis unit detects combinations of materials and tools and identifies physical characteristics unique to handwriting based on those combinations. For example, it analyzes the combination of paper and ink in detail. This allows it to identify physical characteristics unique to handwriting.

[0051] The analysis unit records the process of creating a work as a video and can determine whether it is handwritten or AI-generated based on the video data. For example, the analysis unit may have a generation AI record the process of creating a work as a video and analyze the video data to identify characteristics unique to handwriting. For example, it may analyze brush movements and hand movements in detail. The analysis unit may also build a system in which a generation AI records the process of creating a work as a video and determines whether it is handwritten or AI-generated based on the video data. For example, it may analyze the movement patterns during the production process. The analysis unit may also have a generation AI record the process of creating a work as a video and identify characteristics unique to handwriting based on the video data. For example, it may analyze the speed and rhythm of the production process in detail. This makes it possible to determine whether it is handwritten or AI-generated based on the production process.

[0052] The analysis unit uses the emotion estimation function to analyze the emotional response of the user viewing the work and can determine whether it is handwritten or AI-generated based on that response. For example, the analysis unit uses the generation AI to analyze the emotional response of the user viewing the work and identify features unique to handwriting based on that response. For example, it analyzes the strength and fluctuations of emotions in detail. The analysis unit also builds a system in which the generation AI analyzes the emotional response of the user viewing the work and determines whether it is handwritten or AI-generated based on that response. For example, it analyzes patterns of emotional change. The analysis unit also builds a system in which the generation AI analyzes the emotional response of the user viewing the work and identify features unique to handwriting based on that response. For example, it analyzes color changes due to traces of emotion in detail. This makes it possible to determine whether it is handwritten or AI-generated based on the user's emotional response.

[0053] The analysis unit can identify a production process specific to handwriting by also taking into account data on the production time and the production environment. For example, the analysis unit has the generation AI analyze the production time of the work and identify a production process specific to handwriting based on that time data. For example, the analysis unit analyzes in detail the length of time it takes to produce the work and the frequency of breaks. The analysis unit also has the generation AI analyze data on the production environment of the work and identify a production process specific to handwriting based on that environmental data. For example, the analysis unit analyzes in detail the temperature and humidity of the production environment. The analysis unit also has the generation AI analyze data on the production time of the work and the production environment and identify a production process specific to handwriting based on that data. For example, the analysis unit analyzes in detail fluctuations in production time and changes in the environment. This makes it possible to identify a production process specific to handwriting based on data on the production time and the production environment.

[0054] The analysis unit analyzes composition and layout patterns and can identify design techniques unique to handwriting. For example, the analysis unit uses a generation AI to analyze the composition of a work and identify design techniques unique to handwriting based on that composition pattern. For example, it analyzes in detail the balance of the composition and patterns of eye guidance. The analysis unit also uses a generation AI to analyze the layout of a work and identify design techniques unique to handwriting based on that layout pattern. For example, it analyzes in detail the layout arrangement and use of space. The analysis unit also uses a generation AI to analyze the composition and layout patterns of a work and identify design techniques unique to handwriting based on that pattern. For example, it analyzes in detail changes in composition and layout. This makes it possible to identify design techniques unique to handwriting based on composition and layout patterns.

[0055] The analysis unit uses the emotion estimation function to analyze the changes in the creator's emotions over time when the work was created, and can determine whether the work is handwritten or AI-generated based on those changes in emotions. For example, the analysis unit analyzes the changes in the creator's emotions over time when the generation AI created the work, and identifies features unique to handwriting based on the patterns of those changes. For example, it analyzes the strength and fluctuations of emotions in detail. The analysis unit also analyzes the changes in the creator's emotions over time when the generation AI created the work, and builds a system that determines whether the work is handwritten or AI-generated based on the patterns of those changes. For example, it analyzes the changes in handwriting due to changes in emotions. The analysis unit also analyzes the changes in the creator's emotions over time when the generation AI created the work, and identifies features unique to handwriting based on the patterns of those changes. For example, it analyzes the changes in color due to changes in emotions in detail. This makes it possible to determine whether the work is handwritten or AI-generated based on the changes in the creator's emotions.

[0056] The analysis unit analyzes the story and concept behind the work and can identify the creative intention unique to handwriting. For example, the analysis unit has the generation AI analyze the story behind the work and identify the creative intention unique to handwriting based on that story. For example, it analyzes the development of the story and the theme in detail. The analysis unit also has the generation AI analyze the concept behind the work and identify the creative intention unique to handwriting based on that concept. For example, it analyzes the uniqueness and consistency of the concept in detail. The analysis unit also has the generation AI analyze the story and concept behind the work and identify the creative intention unique to handwriting based on the analysis results. For example, it analyzes changes in the story and concept in detail. This makes it possible to identify the creative intention unique to handwriting based on the story and concept behind the work.

[0057] The analysis unit also takes into consideration data on the exhibition method and exhibition environment of the work, and is able to identify exhibition techniques specific to handwriting. For example, the analysis unit has the generation AI analyze the exhibition method of the work, and identify exhibition techniques specific to handwriting based on that exhibition method. For example, it analyzes in detail the layout of the exhibit and the use of lighting. The analysis unit also has the generation AI analyze data on the exhibition environment of the work, and identify exhibition techniques specific to handwriting based on that environmental data. For example, it analyzes in detail the temperature and humidity of the exhibition environment. The analysis unit also has the generation AI analyze data on the exhibition method and exhibition environment of the work, and identify exhibition techniques specific to handwriting based on that data. For example, it analyzes in detail changes in the exhibition method and environment. This makes it possible to identify exhibition techniques specific to handwriting based on data on the exhibition method and exhibition environment.

[0058] The analysis unit uses the emotion estimation function to analyze changes in the emotions of users viewing a work over time, and can determine whether the work is handwritten or AI-generated based on those changes in emotion. For example, the analysis unit uses the generation AI to analyze changes in the emotions of users viewing a work over time, and identify features unique to handwriting based on the pattern of those changes. For example, it analyzes the strength and fluctuations of emotions in detail. The analysis unit also builds a system in which the generation AI analyzes changes in the emotions of users viewing a work over time, and determines whether the work is handwritten or AI-generated based on the pattern of those changes. For example, it analyzes eye movements due to changes in emotion. The analysis unit also analyzes changes in the emotions of users viewing a work over time, and identify features unique to handwriting based on the pattern of those changes. For example, it analyzes changes in facial expressions due to changes in emotion in detail. This makes it possible to determine whether the work is handwritten or AI-generated based on changes in the user's emotions.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The generative AI system can further include a voice analysis unit. The voice analysis unit can analyze the voice of the creator when creating a work and identify handwriting-specific features based on the voice data. For example, the voice analysis unit can analyze the tone and rhythm of the creator's voice when creating a work and identify handwriting-specific features based on the fluctuation patterns. The voice analysis unit can also analyze emotional changes in the creator's voice and identify handwriting-specific features based on the emotional fluctuations. Furthermore, the voice analysis unit can analyze environmental sounds when the creator is creating a work and identify handwriting-specific features based on the fluctuations in the environmental sounds. This makes it possible to identify handwriting-specific features based on voice data.

[0061] The generative AI system can further include a tactile analysis unit. The tactile analysis unit analyzes tactile data of the creator when creating a work and can identify characteristics unique to handwriting based on the tactile data. For example, the tactile analysis unit analyzes the pen pressure and hand movements of the creator when creating a work and can identify characteristics unique to handwriting based on the fluctuation patterns. The tactile analysis unit can also analyze changes in the temperature and humidity of the creator's hands and can identify characteristics unique to handwriting based on those fluctuations. Furthermore, the tactile analysis unit can analyze tactile feedback when the creator creates a work and can identify characteristics unique to handwriting based on fluctuations in that feedback. In this way, characteristics unique to handwriting can be identified based on tactile data.

[0062] The generative AI system can further include a temperature analysis unit. The temperature analysis unit can analyze the creator's body temperature when creating a work and identify handwriting-specific features based on the body temperature data. For example, the temperature analysis unit can analyze body temperature fluctuations when the creator creates a work and identify handwriting-specific features based on the fluctuation pattern. The temperature analysis unit can also analyze changes in the creator's body temperature and identify handwriting-specific features based on the fluctuation. Furthermore, the temperature analysis unit can analyze body temperature fluctuations over time when the creator creates a work and identify handwriting-specific features based on the fluctuation pattern. This makes it possible to identify handwriting-specific features based on body temperature data.

[0063] The generative AI system can further include a vibration analysis unit. The vibration analysis unit analyzes the vibrations of the creator's hands when creating a work and can identify handwriting-specific features based on the vibration data. For example, the vibration analysis unit analyzes the strength of hand vibrations when the creator creates a work and can identify handwriting-specific features based on the fluctuation pattern. The vibration analysis unit can also analyze the frequency of the creator's hand vibrations and can identify handwriting-specific features based on the fluctuation. Furthermore, the vibration analysis unit can analyze fluctuations in hand vibrations over time when the creator creates a work and can identify handwriting-specific features based on the fluctuation pattern. This makes it possible to identify handwriting-specific features based on vibration data.

[0064] The generative AI system can further include a light analysis unit. The light analysis unit can analyze the lighting environment around the creator when creating the work and identify characteristics unique to handwriting based on the light data. For example, the light analysis unit can analyze the light intensity and color temperature when the creator creates the work and identify characteristics unique to handwriting based on the pattern of fluctuations. The light analysis unit can also analyze changes in the light around the creator and identify characteristics unique to handwriting based on those fluctuations. Furthermore, the light analysis unit can analyze fluctuations in light over time when the creator creates the work and identify characteristics unique to handwriting based on the pattern of fluctuations. This makes it possible to identify characteristics unique to handwriting based on light data.

[0065] The generative AI system can further include an odor analysis unit. The odor analysis unit analyzes the odor environment around the creator when creating a work and can identify characteristics unique to handwriting based on the odor data. For example, the odor analysis unit analyzes the strength and type of odor when the creator creates a work and can identify characteristics unique to handwriting based on the pattern of fluctuations. The odor analysis unit can also analyze changes in odor around the creator and can identify characteristics unique to handwriting based on those fluctuations. Furthermore, the odor analysis unit can analyze odor fluctuations over time when the creator creates a work and can identify characteristics unique to handwriting based on the pattern of those fluctuations. This makes it possible to identify characteristics unique to handwriting based on odor data.

[0066] The generative AI system further uses the emotion estimation function to analyze the creator's heart rate when creating a work and identify handwriting-specific features based on the heart rate data. For example, the heart rate analysis unit analyzes heart rate fluctuations when the creator creates a work and identifies handwriting-specific features based on the fluctuation patterns. The heart rate analysis unit can also analyze changes in the creator's heart rate and identify handwriting-specific features based on the fluctuations. The heart rate analysis unit can also analyze heart rate fluctuations over time when the creator creates a work and identify handwriting-specific features based on the fluctuation patterns. This makes it possible to identify handwriting-specific features based on heart rate data.

[0067] The generative AI system further uses its emotion estimation function to analyze the breathing patterns of creators when creating a work and identify handwriting-specific features based on the breathing data. For example, the breathing analysis unit analyzes the rhythm and depth of breathing when a creator creates a work and identifies handwriting-specific features based on the variation patterns. The breathing analysis unit can also analyze changes in the creator's breathing and identify handwriting-specific features based on those variations. Furthermore, the breathing analysis unit can analyze variations in breathing over time when a creator creates a work and identify handwriting-specific features based on those variation patterns. This makes it possible to identify handwriting-specific features based on breathing data.

[0068] The generative AI system can further use its emotion estimation function to analyze the creator's facial expressions when creating a work and identify handwriting-specific features based on that facial expression data. For example, the facial expression analysis unit can analyze changes in the creator's facial expression when creating a work and identify handwriting-specific features based on the pattern of those changes. The facial expression analysis unit can also analyze changes in the creator's facial expression when creating a work and identify handwriting-specific features based on those changes. Furthermore, the facial expression analysis unit can analyze changes in the creator's facial expression over time when creating a work and identify handwriting-specific features based on those change patterns. This makes it possible to identify handwriting-specific features based on facial expression data.

[0069] The generative AI system can further use its emotion estimation function to analyze the creator's brain waves when creating a work and identify characteristics unique to handwriting based on the brain wave data. For example, the brain wave analysis unit can analyze the fluctuations in brain waves when the creator creates a work and identify characteristics unique to handwriting based on the fluctuation patterns. The brain wave analysis unit can also analyze changes in the creator's brain waves and identify characteristics unique to handwriting based on the fluctuations. Furthermore, the brain wave analysis unit can analyze the fluctuations in brain waves over time when the creator creates a work and identify characteristics unique to handwriting based on the fluctuation patterns. This makes it possible to identify characteristics unique to handwriting based on brain wave data.

[0070] The processing flow of the second embodiment will be briefly explained below.

[0071] Step 1: The analysis unit analyzes the artwork. For example, the analysis unit receives image data of the artwork as input and analyzes features such as changes in pen pressure, smoothness of lines, and color gradation. The analysis unit can also detect subtle features of the artwork and identify characteristics unique to handwriting. Step 2: The identification unit identifies whether the work was created by the generating AI or handwritten based on the results of the analysis by the analysis unit. For example, the identification unit compares the AI-generated features with the handwritten features based on the analysis results to determine the origin of the work. The identification unit can make more accurate identification based on the data learned by the generating AI. Step 3: The display unit displays the results identified by the identification unit. For example, the display unit displays the results in text or graphical format and provides them to the user. The display unit can also display the results through a web application or a mobile application.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0116] 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 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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."

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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]

[0139] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An analysis department that analyzes the works, an identification unit that identifies whether a work is generated by a generation AI or handwritten based on the analysis result by the analysis unit; a display unit that displays the results of the identification by the identification unit. A system characterized by:

2. The analysis unit When analyzing changes in writing pressure and the smoothness of the lines, the system takes into account subtle fluctuations in handwriting and ink bleeding to identify the unique characteristics of handwriting.

2. The system of claim 1.

3. The analysis unit When analyzing the color gradation, the overlapping and blending patterns of the colors are analyzed to identify color techniques specific to handwriting.

2. The system of claim 1.

4. The analysis unit Estimate the emotions of the creator when creating the work, and analyze the traces of those emotions as characteristics of the work.

2. The system of claim 1.

5. The analysis unit Identify the unique physical characteristics of handwriting, taking into account the materials used and traces of the tools used.

2. The system of claim 1.

6. The analysis unit The creation process of the work is recorded as a video, and the video data is used to determine whether it is hand-drawn or AI-generated.

2. The system of claim 1.

7. The analysis unit Analyze the emotional response of users viewing the artwork and determine whether it should be hand-drawn or AI-generated based on that response.

2. The system of claim 1.

8. The analysis unit Identify the handwritten production process by taking into account production time and production environment data.

2. The system of claim 1.

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