System

Abstract

An object of the system according to the embodiment is to accurately identify a creator of content and certify the creator.SOLUTION: A system according to an embodiment includes an analysis unit, an identification unit, a certificate issuance unit, and a database management unit. The analysis unit analyzes the content. The identification unit identifies a creator of the content analyzed by the analysis unit. The certificate issuing unit issues a certificate based on a result identified by the identification unit. The database management unit stores the certificate issued by the certificate issuing unit in the database.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult to accurately identify and prove the creator of content.

[0005] The system according to the embodiment aims to accurately identify and certify the creator of content. [Means for solving the problem]

[0006] A system according to an embodiment includes an analysis unit, an identification unit, a certificate issuance unit, and a database management unit. The analysis unit analyzes content. The identification unit identifies the creator of the content analyzed by the analysis unit. The certificate issuance unit issues a certificate based on the results of identification by the identification unit. The database management unit stores the certificates issued by the certificate issuance unit in a database. [Effects of the Invention]

[0007] The system according to the embodiment can accurately identify and authenticate the creator of the content. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The certification system according to an embodiment of the present invention is a system that distinguishes between content created by AI and content created by humans and certifies the distinction. As a result, the certification system can accurately distinguish between content created by AI and content created by humans and certify the distinction.

[0029] The certification system according to the embodiment includes an analysis unit, an identification unit, a certificate issuance unit, and a database management unit. The analysis unit analyzes content. For example, the analysis unit analyzes text using text analysis technology. The analysis unit can also analyze images using image analysis technology. The analysis unit can also analyze audio using audio analysis technology. The identification unit identifies the creator of the content analyzed by the analysis unit. For example, the identification unit extracts specific writing style or expression characteristics based on the analysis results to identify the creator. The identification unit can also analyze the style and color usage of images to identify the creator. The identification unit can also analyze the tone and rhythm of audio to identify the creator. The certificate issuance unit issues a certificate based on the results of identification by the identification unit. For example, the certificate issuance unit issues a digital certificate. The certificate issuance unit can also issue a paper certificate. The certificate issuance unit can also include details of the analysis results and the basis for certification in the certificate. The database management unit stores the certificates issued by the certificate issuance unit in a database. For example, the database management unit stores the certificate issuance history in a database. The database management unit can also store analysis results in a database. The database management unit can also manage the database. As a result, the certification system according to the embodiment can accurately distinguish between content created by AI and content created by humans and certify the distinction. For example, when a company creates advertising or marketing materials, it can improve reliability by proving that the materials were created by humans. This is also expected to contribute to the protection of copyrights and intellectual property rights.

[0030] The identification unit can use generative AI to perform a detailed analysis of the writing style and expression characteristics of the content creator and identify individual creators. For example, the identification unit uses generative AI to analyze the writing style and expression characteristics of input text and identify a specific creator. For example, it detects consistency in writing style and unique expression patterns and compares them with past data. The identification unit also uses image analysis technology to identify the creator of an image. For example, it analyzes features such as the style, color usage, and composition of an image to identify a specific artist or designer. The identification unit also uses audio analysis technology to identify the creator of audio data. For example, it analyzes the tone, rhythm, and pronunciation characteristics of the voice to identify a specific speaker. This improves the accuracy of identifying the creator of content.

[0031] The identification unit can track the content creation process and identify the tools or software used in its creation. For example, the identification unit uses generative AI to analyze the creation process of input text and identify the tools and software used. For example, it detects traces of the use of a specific writing style generation tool or translation software. The identification unit also uses image analysis technology to track the image creation process and identify the design software and filters used. For example, it detects the characteristic filter effects of specific image editing software. The identification unit also uses audio analysis technology to track the audio data creation process and identify the recording equipment and editing software used. For example, it detects traces of effects and noise reduction in specific audio editing software. This improves reliability by identifying the content creation process.

[0032] The analysis unit can expand the analysis target to at least one of multimedia content, including video and 3D models, in addition to text, images, and audio. The analysis unit, for example, uses generative AI to analyze videos to identify specific creators and the tools used. For example, it can identify creators from the video's editing style and effects. The analysis unit can also analyze 3D models to identify specific designers and the software used. For example, it can identify creators from the structure and texture of 3D models. The analysis unit can also analyze the entire multimedia content and comprehensively evaluate each element of text, images, audio, video, and 3D models. For example, it can identify creators of content that combines multiple media formats. This expansion of the analysis target makes it possible to identify a wide range of content.

[0033] The analysis unit can analyze content from different languages ​​or cultures to improve identification accuracy from a global perspective. For example, the analysis unit uses generative AI to analyze texts from different languages ​​and identify specific authors and tools used. For example, it develops a multilingual stylistic analysis algorithm. The analysis unit also uses image analysis technology to analyze images from different cultures and identify specific authors and tools used. For example, it identifies culture-specific design elements. The analysis unit also uses audio analysis technology to analyze audio data from different languages ​​or cultures and identify specific authors and tools used. For example, it identifies language-specific pronunciation and rhythm. As a result, by analyzing content from different languages ​​and cultures, identification accuracy from a global perspective can be improved.

[0034] The certificate issuing unit can incorporate blockchain technology into certificates and record the certificate issuance history in an unalterable form. The certificate issuing unit, for example, uses blockchain technology to build a system that records the certificate issuance history in an unalterable form. For example, it registers the issuance information of each certificate on the blockchain. The certificate issuing unit also manages the certificate issuance history on the blockchain, allowing third parties to verify that history. For example, it verifies the authenticity of certificates using public key cryptography. The certificate issuing unit also uses blockchain technology to make the entire certificate issuance process transparent and improve reliability. For example, it records information such as the issuer and issuance date and time on the blockchain. This improves reliability by recording the certificate issuance history in an unalterable form.

[0035] The certificate issuing unit can assign a QR code to a certificate, allowing the authenticity to be easily verified on a smartphone. The certificate issuing unit, for example, builds a system that assigns a QR code to a certificate and allows the authenticity to be easily verified on a smartphone. For example, detailed certificate information can be accessed by scanning the QR code. The certificate issuing unit also uses the QR code to display the certificate issuance history and analysis results on a smartphone. For example, the authenticity of a certificate can be verified by scanning the QR code. The certificate issuing unit also develops a certificate authenticity verification system that uses QR codes, allowing users to easily verify the reliability of a certificate. For example, the issuer and date and time of issuance of the certificate can be displayed by scanning the QR code. This makes it easy to verify the authenticity of a certificate by using the QR code.

[0036] The certificate issuing unit can expand the scope of certificate issuance to include not only digital content but also physical products or services. For example, the certificate issuing unit may expand the scope of certificate issuance to include physical products and build a system that certifies the authenticity of the product. For example, the certificate may be issued based on a serial number assigned to the product. The certificate issuing unit may also expand the scope of certificate issuance to include services and develop a system that certifies the quality of the service and the reliability of the provider. For example, the certificate may be issued based on the evaluation and history of the service provider. The certificate issuing unit may also build a certificate issuing system that is compatible with not only digital content but also physical products and services, thereby improving reliability in a wide range of fields. For example, the certificate may be issued based on the manufacturing history of a product or the provision history of a service. In this way, expanding the scope of certificate issuance improves reliability in a wide range of fields.

[0037] The certificate issuing unit can automate the certificate issuance process and build a system that can issue certificates in real time. The certificate issuing unit, for example, automates the certificate issuance process and builds a system that can issue certificates in real time. For example, a certificate is automatically generated the moment the analysis results are obtained. The certificate issuing unit also develops an automated certificate issuance system that allows users to instantly obtain certificates. For example, certificates are issued in real time on an online platform. The certificate issuing unit also automates the entire certificate issuance process and builds a system that saves users time and effort. For example, a certificate is issued at the same time the analysis results are registered in a database. In this way, by automating the certificate issuance process, certificates can be issued in real time.

[0038] The database management unit can add a function to automatically analyze content trends or patterns using the analysis results stored in the database. The database management unit, for example, uses the analysis results stored in the database to build a system that automatically analyzes content trends and patterns. For example, it extracts the characteristics of content that was generated frequently during a specific period. The database management unit also adds a function to visualize content trends and patterns based on the analysis results. For example, it displays trends using graphs or charts. The database management unit also develops a system that predicts future content trends using the analysis results stored in the database. For example, it introduces an algorithm that predicts future trends based on past data. This makes it possible to automatically analyze content trends and patterns using the analysis results stored in the database.

[0039] The database management unit can analyze the database access history and understand user behavior patterns. The database management unit, for example, analyzes the database access history and builds a system that understands user behavior patterns. For example, it analyzes which content is frequently accessed. The database management unit also adds a function to identify user interests and concerns based on the access history. For example, it identifies users who frequently access a particular topic. The database management unit also analyzes user behavior patterns and develops a system that optimizes the database based on that data. For example, it introduces an algorithm that prioritizes displaying popular content. In this way, it is possible to understand user behavior patterns by analyzing the database access history.

[0040] The database management department can share the database with companies or research institutions and build a platform for joint analysis. The database management department, for example, shares the database with other companies or research institutions and builds a platform for joint analysis. For example, it grants access rights to the database and promotes joint research. The database management department also uses a joint analysis platform to develop a system for multiple companies and research institutions to share data and share analysis results. For example, it performs joint trend analysis and pattern recognition. The database management department also builds a system for collaborating with other companies and research institutions through the database sharing platform to gain new knowledge. For example, it integrates data from different fields to gain new insights. In this way, by sharing the database, it is possible to conduct joint analysis and gain new knowledge.

[0041] The database management unit can visualize the contents of the database and provide an interface that allows users to intuitively understand the data. The database management unit, for example, provides an interface that visualizes the contents of the database, allowing users to intuitively understand the data. For example, it displays data using graphs and charts. The database management unit also visually displays the contents of the database through a visualization interface, building a system that allows users to easily manipulate the data. For example, it adds a function to manipulate data using drag and drop. The database management unit also visualizes the contents of the database in real time, providing an interface that allows users to access the latest data. For example, it develops a dashboard that immediately reflects data updates. In this way, by visualizing the contents of the database, users can intuitively understand the data.

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

[0043] In analyzing content, the analysis unit can also use natural language processing technology to analyze the meaning and intent of text. For example, it can automatically extract the subject or topic of a text and classify the content. The analysis unit can also use image analysis technology to analyze the content of an image in detail and identify specific objects or scenes. For example, it can automatically recognize and tag people and objects in an image. The analysis unit can also use voice analysis technology to convert the content of audio data into text and analyze the meaning and intent of the audio. For example, it can automatically transcribe the content of the audio and extract specific keywords and phrases. This allows the analysis unit to perform a more advanced identification by analyzing the meaning and intent of the content in detail.

[0044] The identification unit uses generative AI to perform a detailed analysis of the writing style and expression characteristics of content creators, enabling it to identify individual creators. For example, it detects consistency in writing style and unique expression patterns and compares them with past data. The identification unit also uses image analysis technology to identify the creator of an image. For example, it analyzes characteristics such as image style, color usage, and composition to identify specific artists or designers. The identification unit also uses audio analysis technology to identify the creator of audio data. For example, it analyzes the tone, rhythm, and pronunciation characteristics of the voice to identify specific speakers. This improves the accuracy of identifying content creators.

[0045] The identification unit can track the content creation process and identify the tools or software used to create it. For example, it can use generative AI to analyze the creation process of input text and identify the tools and software used. For example, it can detect traces of the use of a specific writing style generation tool or translation software. The identification unit can also use image analysis technology to track the image creation process and identify the design software and filters used. For example, it can detect the characteristic filter effects of specific image editing software. The identification unit can also use audio analysis technology to track the audio data creation process and identify the recording equipment and editing software used. For example, it can detect traces of effects and noise reduction in specific audio editing software. This improves reliability by identifying the content creation process.

[0046] The analysis unit can expand its analysis target to include at least one multimedia content, including video and 3D models, in addition to text, images, and audio. For example, generative AI can be used to analyze video to identify a specific creator and the tools used. For example, the creator can be identified from the video's editing style and effects. The analysis unit can also analyze 3D models to identify a specific designer and the software used. For example, the creator can be identified from the 3D model's structure and texture. The analysis unit can also analyze the entire multimedia content and comprehensively evaluate each element of text, images, audio, video, and 3D models. For example, the creator of content that combines multiple media formats can be identified. This expansion of the analysis target makes it possible to identify a wide range of content.

[0047] The analysis unit can analyze content from different languages ​​or cultures to improve identification accuracy from a global perspective. For example, generative AI can be used to analyze texts from different languages ​​and identify specific authors and tools used. For example, a multilingual stylistic analysis algorithm can be developed. The analysis unit can also use image analysis technology to analyze images from different cultures and identify specific authors and tools used. For example, it can identify culture-specific design elements. The analysis unit can also use audio analysis technology to analyze audio data from different languages ​​or cultures and identify specific authors and tools used. For example, it can identify language-specific pronunciation and rhythm. This allows analysis of content from different languages ​​and cultures to improve identification accuracy from a global perspective.

[0048] The certificate issuing unit can incorporate blockchain technology into certificates to record the certificate issuance history in an unalterable form. For example, a system can be built using blockchain technology to record the certificate issuance history in an unalterable form. For example, the issuance information for each certificate can be registered on the blockchain. The certificate issuing unit can also manage the certificate issuance history on the blockchain, allowing third parties to verify that history. For example, it can verify the authenticity of certificates using public key cryptography. The certificate issuing unit can also use blockchain technology to make the entire certificate issuance process transparent and improve reliability. For example, it can record information such as the issuer and issuance date and time on the blockchain. This improves reliability by recording the certificate issuance history in an unalterable form.

[0049] The certificate issuing unit can assign a QR code to a certificate, allowing the authenticity to be easily verified using a smartphone. For example, a system can be constructed in which a QR code is assigned to a certificate, allowing the authenticity to be easily verified using a smartphone. For example, detailed certificate information can be accessed by scanning the QR code. The certificate issuing unit can also use the QR code to display the certificate issuance history and analysis results on a smartphone. For example, the authenticity of a certificate can be verified by scanning the QR code. The certificate issuing unit can also develop a certificate authenticity verification system using QR codes, allowing users to easily verify the reliability of a certificate. For example, scanning the QR code displays the issuer of the certificate and the date and time of issuance. This makes it easy to verify the authenticity of a certificate using the QR code.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The analysis unit analyzes the content. For example, the analysis unit can analyze text using text analysis technology, images using image analysis technology, and audio using audio analysis technology. Step 2: The identification unit identifies the creator of the content analyzed by the analysis unit. For example, the identification unit may identify the creator by extracting specific writing style or expression characteristics based on the analysis results. The identification unit may also identify the creator by analyzing the style and color usage of images, or the tone and rhythm of audio. Step 3: The certificate issuing unit issues a certificate based on the results of the identification by the identification unit. For example, the certificate issuing unit may issue a digital certificate or a paper certificate, and may include details of the analysis results and the basis for the certification in the certificate. Step 4: The database management unit stores the certificates issued by the certificate issuing unit in a database. For example, the database management unit stores the certificate issuance history and analysis results in a database and manages the database.

[0052] (Example 2) The certification system according to an embodiment of the present invention is a system that distinguishes between content created by AI and content created by humans and certifies the distinction. As a result, the certification system can accurately distinguish between content created by AI and content created by humans and certify the distinction.

[0053] The certification system according to the embodiment includes an analysis unit, an identification unit, a certificate issuance unit, and a database management unit. The analysis unit analyzes content. For example, the analysis unit analyzes text using text analysis technology. The analysis unit can also analyze images using image analysis technology. The analysis unit can also analyze audio using audio analysis technology. The identification unit identifies the creator of the content analyzed by the analysis unit. For example, the identification unit extracts specific writing style or expression characteristics based on the analysis results to identify the creator. The identification unit can also analyze the style and color usage of images to identify the creator. The identification unit can also analyze the tone and rhythm of audio to identify the creator. The certificate issuance unit issues a certificate based on the results of identification by the identification unit. For example, the certificate issuance unit issues a digital certificate. The certificate issuance unit can also issue a paper certificate. The certificate issuance unit can also include details of the analysis results and the basis for certification in the certificate. The database management unit stores the certificates issued by the certificate issuance unit in a database. For example, the database management unit stores the certificate issuance history in a database. The database management unit can also store analysis results in a database. The database management unit can also manage the database. As a result, the certification system according to the embodiment can accurately distinguish between content created by AI and content created by humans and certify the distinction. For example, when a company creates advertising or marketing materials, it can improve reliability by proving that the materials were created by humans. This is also expected to contribute to the protection of copyrights and intellectual property rights.

[0054] The identification unit can use generative AI to perform a detailed analysis of the writing style and expression characteristics of the content creator and identify individual creators. For example, the identification unit uses generative AI to analyze the writing style and expression characteristics of input text and identify a specific creator. For example, it detects consistency in writing style and unique expression patterns and compares them with past data. The identification unit also uses image analysis technology to identify the creator of an image. For example, it analyzes features such as the style, color usage, and composition of an image to identify a specific artist or designer. The identification unit also uses audio analysis technology to identify the creator of audio data. For example, it analyzes the tone, rhythm, and pronunciation characteristics of the voice to identify a specific speaker. This improves the accuracy of identifying the creator of content.

[0055] The identification unit can track the content creation process and identify the tools or software used in its creation. For example, the identification unit uses generative AI to analyze the creation process of input text and identify the tools and software used. For example, it detects traces of the use of a specific writing style generation tool or translation software. The identification unit also uses image analysis technology to track the image creation process and identify the design software and filters used. For example, it detects the characteristic filter effects of specific image editing software. The identification unit also uses audio analysis technology to track the audio data creation process and identify the recording equipment and editing software used. For example, it detects traces of effects and noise reduction in specific audio editing software. This improves reliability by identifying the content creation process.

[0056] The identification unit uses an emotion estimation function to analyze the emotions of the creator when creating the content and can distinguish whether the content was created by a human or an AI based on the emotional pattern. For example, the identification unit uses the emotion estimation function to analyze the emotional pattern of input text and identify the emotional state of the creator. For example, it determines that text with a strong positive emotion is likely to have been created by a human. The identification unit also uses image analysis technology to analyze the emotional pattern of an image and identify the emotional state of the creator. For example, it estimates emotions from the use of color and composition and distinguishes whether the content was created by a human or an AI. The identification unit also uses audio analysis technology to analyze the emotional pattern of audio data and identify the emotional state of the creator. For example, it estimates emotions from the tone and rhythm of the voice and distinguishes whether the content was created by a human or an AI. This improves the accuracy of identifying the creator of content based on the creator's emotions.

[0057] The analysis unit can expand the analysis target to at least one of multimedia content, including video and 3D models, in addition to text, images, and audio. The analysis unit, for example, uses generative AI to analyze videos to identify specific creators and the tools used. For example, it can identify creators from the video's editing style and effects. The analysis unit can also analyze 3D models to identify specific designers and the software used. For example, it can identify creators from the structure and texture of 3D models. The analysis unit can also analyze the entire multimedia content and comprehensively evaluate each element of text, images, audio, video, and 3D models. For example, it can identify creators of content that combines multiple media formats. This expansion of the analysis target makes it possible to identify a wide range of content.

[0058] The analysis unit can analyze content from different languages ​​or cultures to improve identification accuracy from a global perspective. For example, the analysis unit uses generative AI to analyze texts from different languages ​​and identify specific authors and tools used. For example, it develops a multilingual stylistic analysis algorithm. The analysis unit also uses image analysis technology to analyze images from different cultures and identify specific authors and tools used. For example, it identifies culture-specific design elements. The analysis unit also uses audio analysis technology to analyze audio data from different languages ​​or cultures and identify specific authors and tools used. For example, it identifies language-specific pronunciation and rhythm. As a result, by analyzing content from different languages ​​and cultures, identification accuracy from a global perspective can be improved.

[0059] The identification unit can use the emotion estimation function to analyze the emotional response of the recipient of the content and reinforce the identification result based on the emotional response. For example, the identification unit uses the emotion estimation function to analyze the emotional response of the recipient of the content and reinforces the identification result based on the data. For example, it determines that content with a large number of positive responses is likely to have been created by a human. The identification unit also uses image analysis technology to analyze the recipient's facial expressions and gaze to identify the emotional response. For example, it estimates emotions from gaze movements and changes in facial expression and reinforces the identification result. The identification unit also uses voice analysis technology to analyze the tone and rhythm of the recipient's voice to identify the emotional response. For example, it estimates emotions from changes in vocal tone and reinforces the identification result. In this way, by reinforcing the identification result based on the recipient's emotional response, the identification accuracy is improved.

[0060] The certificate issuing unit can incorporate blockchain technology into certificates and record the certificate issuance history in an unalterable form. The certificate issuing unit, for example, uses blockchain technology to build a system that records the certificate issuance history in an unalterable form. For example, it registers the issuance information of each certificate on the blockchain. The certificate issuing unit also manages the certificate issuance history on the blockchain, allowing third parties to verify that history. For example, it verifies the authenticity of certificates using public key cryptography. The certificate issuing unit also uses blockchain technology to make the entire certificate issuance process transparent and improve reliability. For example, it records information such as the issuer and issuance date and time on the blockchain. This improves reliability by recording the certificate issuance history in an unalterable form.

[0061] The certificate issuing unit can assign a QR code to a certificate, allowing the authenticity to be easily verified on a smartphone. The certificate issuing unit, for example, builds a system that assigns a QR code to a certificate and allows the authenticity to be easily verified on a smartphone. For example, detailed certificate information can be accessed by scanning the QR code. The certificate issuing unit also uses the QR code to display the certificate issuance history and analysis results on a smartphone. For example, the authenticity of a certificate can be verified by scanning the QR code. The certificate issuing unit also develops a certificate authenticity verification system that uses QR codes, allowing users to easily verify the reliability of a certificate. For example, the issuer and date and time of issuance of the certificate can be displayed by scanning the QR code. This makes it easy to verify the authenticity of a certificate by using the QR code.

[0062] The certificate issuing unit can expand the scope of certificate issuance to include not only digital content but also physical products or services. For example, the certificate issuing unit may expand the scope of certificate issuance to include physical products and build a system that certifies the authenticity of the product. For example, the certificate may be issued based on a serial number assigned to the product. The certificate issuing unit may also expand the scope of certificate issuance to include services and develop a system that certifies the quality of the service and the reliability of the provider. For example, the certificate may be issued based on the evaluation and history of the service provider. The certificate issuing unit may also build a certificate issuing system that is compatible with not only digital content but also physical products and services, thereby improving reliability in a wide range of fields. For example, the certificate may be issued based on the manufacturing history of a product or the provision history of a service. In this way, expanding the scope of certificate issuance improves reliability in a wide range of fields.

[0063] The certificate issuing unit can automate the certificate issuance process and build a system that can issue certificates in real time. The certificate issuing unit, for example, automates the certificate issuance process and builds a system that can issue certificates in real time. For example, a certificate is automatically generated the moment the analysis results are obtained. The certificate issuing unit also develops an automated certificate issuance system that allows users to instantly obtain certificates. For example, certificates are issued in real time on an online platform. The certificate issuing unit also automates the entire certificate issuance process and builds a system that saves users time and effort. For example, a certificate is issued at the same time the analysis results are registered in a database. In this way, by automating the certificate issuance process, certificates can be issued in real time.

[0064] The certificate issuance unit can use the emotion estimation function to analyze the emotional response of the certificate recipient and optimize the design and content of the certificate. For example, the certificate issuance unit uses the emotion estimation function to analyze the emotional response of the certificate recipient and optimize the design and content of the certificate based on that data. For example, it may adopt a design that evokes strong positive emotions. The certificate issuance unit also builds a system that optimizes the content of the certificate based on the emotional data of the certificate recipient. For example, it may prioritize displaying content with a high emotion score. The certificate issuance unit also uses the emotion estimation data to develop a system that dynamically adjusts the design and content of the certificate. For example, it may change the layout or wording of the certificate depending on the emotional response of the recipient. This improves credibility by optimizing the design and content of the certificate based on the emotional response of the recipient.

[0065] The database management unit can add a function to automatically analyze content trends or patterns using the analysis results stored in the database. The database management unit, for example, uses the analysis results stored in the database to build a system that automatically analyzes content trends and patterns. For example, it extracts the characteristics of content that was generated frequently during a specific period. The database management unit also adds a function to visualize content trends and patterns based on the analysis results. For example, it displays trends using graphs or charts. The database management unit also develops a system that predicts future content trends using the analysis results stored in the database. For example, it introduces an algorithm that predicts future trends based on past data. This makes it possible to automatically analyze content trends and patterns using the analysis results stored in the database.

[0066] The database management unit can analyze the database access history and understand user behavior patterns. The database management unit, for example, analyzes the database access history and builds a system that understands user behavior patterns. For example, it analyzes which content is frequently accessed. The database management unit also adds a function to identify user interests and concerns based on the access history. For example, it identifies users who frequently access a particular topic. The database management unit also analyzes user behavior patterns and develops a system that optimizes the database based on that data. For example, it introduces an algorithm that prioritizes displaying popular content. In this way, it is possible to understand user behavior patterns by analyzing the database access history.

[0067] The database management unit uses the emotion estimation function to record users' emotional responses to content stored in the database, thereby improving the value of the database. For example, the database management unit uses the emotion estimation function to build a system that records users' emotional responses to content stored in the database. For example, the system analyzes the user's facial expressions and voice and calculates an emotion score. The database management unit also adds a function that improves the value of the database based on the user's emotional response data. For example, the system prioritizes displaying content with a high number of positive emotional responses. The database management unit also uses the emotion estimation data to develop a system that dynamically adjusts the content in the database. For example, the system changes the display order of content depending on the user's emotional response. In this way, the value of the database is improved by recording the user's emotional responses.

[0068] The database management department can share the database with companies or research institutions and build a platform for joint analysis. The database management department, for example, shares the database with other companies or research institutions and builds a platform for joint analysis. For example, it grants access rights to the database and promotes joint research. The database management department also uses a joint analysis platform to develop a system for multiple companies and research institutions to share data and share analysis results. For example, it performs joint trend analysis and pattern recognition. The database management department also builds a system for collaborating with other companies and research institutions through the database sharing platform to gain new knowledge. For example, it integrates data from different fields to gain new insights. In this way, by sharing the database, it is possible to conduct joint analysis and gain new knowledge.

[0069] The database management unit can visualize the contents of the database and provide an interface that allows users to intuitively understand the data. The database management unit, for example, provides an interface that visualizes the contents of the database, allowing users to intuitively understand the data. For example, it displays data using graphs and charts. The database management unit also visually displays the contents of the database through a visualization interface, building a system that allows users to easily manipulate the data. For example, it adds a function to manipulate data using drag and drop. The database management unit also visualizes the contents of the database in real time, providing an interface that allows users to access the latest data. For example, it develops a dashboard that immediately reflects data updates. In this way, by visualizing the contents of the database, users can intuitively understand the data.

[0070] The database management unit can use the emotion estimation function to analyze the emotional responses of database users and improve the usability of the database. For example, the database management unit uses the emotion estimation function to analyze the emotional responses of database users and build a system that improves the usability of the database based on that data. For example, the database management unit adjusts the interface based on the user's emotion score. The database management unit also adds functions that improve the usability of the database based on the user's emotional response data. For example, it adopts an interface design that elicits a high number of positive emotional responses. The database management unit also uses the emotion estimation data to develop a system that dynamically adjusts the database interface. For example, it changes the interface layout and functions according to the user's emotional response. In this way, the usability of the database is improved by analyzing the user's emotional response.

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

[0072] In analyzing content, the analysis unit can also use natural language processing technology to analyze the meaning and intent of text. For example, it can automatically extract the subject or topic of a text and classify the content. The analysis unit can also use image analysis technology to analyze the content of an image in detail and identify specific objects or scenes. For example, it can automatically recognize and tag people and objects in an image. The analysis unit can also use voice analysis technology to convert the content of audio data into text and analyze the meaning and intent of the audio. For example, it can automatically transcribe the content of the audio and extract specific keywords and phrases. This allows the analysis unit to perform a more advanced identification by analyzing the meaning and intent of the content in detail.

[0073] The identification unit uses generative AI to perform a detailed analysis of the writing style and expression characteristics of content creators, enabling it to identify individual creators. For example, it detects consistency in writing style and unique expression patterns and compares them with past data. The identification unit also uses image analysis technology to identify the creator of an image. For example, it analyzes characteristics such as image style, color usage, and composition to identify specific artists or designers. The identification unit also uses audio analysis technology to identify the creator of audio data. For example, it analyzes the tone, rhythm, and pronunciation characteristics of the voice to identify specific speakers. This improves the accuracy of identifying content creators.

[0074] The identification unit can track the content creation process and identify the tools or software used to create it. For example, it can use generative AI to analyze the creation process of input text and identify the tools and software used. For example, it can detect traces of the use of a specific writing style generation tool or translation software. The identification unit can also use image analysis technology to track the image creation process and identify the design software and filters used. For example, it can detect the characteristic filter effects of specific image editing software. The identification unit can also use audio analysis technology to track the audio data creation process and identify the recording equipment and editing software used. For example, it can detect traces of effects and noise reduction in specific audio editing software. This improves reliability by identifying the content creation process.

[0075] The identification unit uses an emotion estimation function to analyze the emotions of the creator when creating the content and can distinguish whether the content was created by a human or an AI based on the emotional pattern. For example, the emotion estimation function can be used to analyze the emotional pattern of input text to identify the emotional state of the creator. For example, it can determine that text with a strong positive emotion is likely to have been created by a human. The identification unit can also use image analysis technology to analyze the emotional pattern of an image to identify the emotional state of the creator. For example, it can estimate emotions from the use of color and composition to distinguish whether the content was created by a human or an AI. The identification unit can also use audio analysis technology to analyze the emotional pattern of audio data to identify the emotional state of the creator. For example, it can estimate emotions from the tone and rhythm of the voice to distinguish whether the content was created by a human or an AI. This improves the accuracy of identifying the creator of content based on the creator's emotions.

[0076] The analysis unit can expand its analysis target to include at least one multimedia content, including video and 3D models, in addition to text, images, and audio. For example, generative AI can be used to analyze video to identify a specific creator and the tools used. For example, the creator can be identified from the video's editing style and effects. The analysis unit can also analyze 3D models to identify a specific designer and the software used. For example, the creator can be identified from the 3D model's structure and texture. The analysis unit can also analyze the entire multimedia content and comprehensively evaluate each element of text, images, audio, video, and 3D models. For example, the creator of content that combines multiple media formats can be identified. This expansion of the analysis target makes it possible to identify a wide range of content.

[0077] The analysis unit can analyze content from different languages ​​or cultures to improve identification accuracy from a global perspective. For example, generative AI can be used to analyze texts from different languages ​​and identify specific authors and tools used. For example, a multilingual stylistic analysis algorithm can be developed. The analysis unit can also use image analysis technology to analyze images from different cultures and identify specific authors and tools used. For example, it can identify culture-specific design elements. The analysis unit can also use audio analysis technology to analyze audio data from different languages ​​or cultures and identify specific authors and tools used. For example, it can identify language-specific pronunciation and rhythm. This allows analysis of content from different languages ​​and cultures to improve identification accuracy from a global perspective.

[0078] The identification unit can use the emotion estimation function to analyze the emotional response of the recipient of the content and reinforce the identification result based on that emotional response. For example, the emotion estimation function can be used to analyze the emotional response of the recipient of the content and reinforce the identification result based on that data. For example, content with a high number of positive responses can be determined to be highly likely to have been created by a human. The identification unit can also use image analysis technology to analyze the recipient's facial expressions and gaze to identify the emotional response. For example, the identification unit can infer emotions from gaze movements and changes in facial expression to reinforce the identification result. The identification unit can also use voice analysis technology to analyze the tone and rhythm of the recipient's voice to identify the emotional response. For example, the identification unit can infer emotions from changes in vocal tone to reinforce the identification result. In this way, the identification result can be reinforced based on the recipient's emotional response, thereby improving the accuracy of identification.

[0079] The certificate issuing unit can incorporate blockchain technology into certificates to record the certificate issuance history in an unalterable form. For example, a system can be built using blockchain technology to record the certificate issuance history in an unalterable form. For example, the issuance information for each certificate can be registered on the blockchain. The certificate issuing unit can also manage the certificate issuance history on the blockchain, allowing third parties to verify that history. For example, it can verify the authenticity of certificates using public key cryptography. The certificate issuing unit can also use blockchain technology to make the entire certificate issuance process transparent and improve reliability. For example, it can record information such as the issuer and issuance date and time on the blockchain. This improves reliability by recording the certificate issuance history in an unalterable form.

[0080] The certificate issuing unit can assign a QR code to a certificate, allowing the authenticity to be easily verified using a smartphone. For example, a system can be constructed in which a QR code is assigned to a certificate, allowing the authenticity to be easily verified using a smartphone. For example, detailed certificate information can be accessed by scanning the QR code. The certificate issuing unit can also use the QR code to display the certificate issuance history and analysis results on a smartphone. For example, the authenticity of a certificate can be verified by scanning the QR code. The certificate issuing unit can also develop a certificate authenticity verification system using QR codes, allowing users to easily verify the reliability of a certificate. For example, scanning the QR code displays the issuer of the certificate and the date and time of issuance. This makes it easy to verify the authenticity of a certificate using the QR code.

[0081] The certificate issuing unit can use the emotion estimation function to analyze the emotional response of the certificate recipient and optimize the design and content of the certificate. For example, the emotion estimation function can be used to analyze the emotional response of the certificate recipient and optimize the design and content of the certificate based on that data. For example, a design that evokes strong positive emotions can be adopted. The certificate issuing unit also builds a system that optimizes the content of the certificate based on the emotional data of the certificate recipient. For example, content with a high emotional score can be displayed preferentially. The certificate issuing unit also uses the emotion estimation data to develop a system that dynamically adjusts the design and content of the certificate. For example, the layout and wording of the certificate can be changed depending on the emotional response of the recipient. In this way, the design and content of the certificate can be optimized based on the emotional response of the recipient, thereby improving trust.

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

[0083] Step 1: The analysis unit analyzes the content. For example, the analysis unit can analyze text using text analysis technology, images using image analysis technology, and audio using audio analysis technology. Step 2: The identification unit identifies the creator of the content analyzed by the analysis unit. For example, the identification unit may identify the creator by extracting specific writing style or expression characteristics based on the analysis results. The identification unit may also identify the creator by analyzing the style and color usage of images, or the tone and rhythm of audio. Step 3: The certificate issuing unit issues a certificate based on the results of the identification by the identification unit. For example, the certificate issuing unit may issue a digital certificate or a paper certificate, and may include details of the analysis results and the basis for the certification in the certificate. Step 4: The database management unit stores the certificates issued by the certificate issuing unit in a database. For example, the database management unit stores the certificate issuance history and analysis results in a database and manages the database.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0088] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0143] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

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

Claims

1. an analysis unit that analyzes the content; an identification unit that identifies the creator of the content analyzed by the analysis unit; a certificate issuing unit that issues a certificate based on the result of identification by the identification unit; a database management unit that stores the certificates issued by the certificate issuing unit in a database. A system characterized by:

2. The identification unit Using generative AI, the style and expression characteristics of the creator of the content are analyzed in detail to identify the individual creator.

2. The system of claim 1.

3. The analysis unit The analysis target is expanded to at least one of the multimedia content of the video or the 3D model in addition to the text, image, and audio.

2. The system of claim 1.

4. The certificate issuing unit Blockchain technology is introduced into the certificate, and the issuance history of the certificate is recorded in an unalterable form.

2. The system of claim 1.

5. The database management unit Adding functionality to automatically analyze trends or patterns in the content using the analysis results stored in the database.

2. The system of claim 1.

6. The identification unit Analyze the creator's emotions when creating content and distinguish whether it was created by a human or an AI based on the emotional patterns.

2. The system of claim 1.

7. The certificate issuing unit Recording the user's feelings when issuing a certificate and improving the reliability of said certificate 2. The system of claim 1.

8. The database management unit Recording users' emotional responses to the content stored in the database to improve the value of the database 2. The system of claim 1.

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