System

Abstract

An object of the system according to the embodiment is to prove a creation process of a creator and to evaluate a technique of the creator.SOLUTION: A system according to an embodiment includes a creation process submission unit, a certificate issuance unit, and a link issuance unit. The creation process submission unit submits a creation process of the creator. The certificate issuing unit analyzes the data of the creation process submitted by the creation process submitting unit and issues an author certificate. The link issuing part provides the author certificate issued by the certificate issuing part in a link form.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, generative AI risks diminishing the relative value of a creator's craft.

[0005] The system according to the embodiment aims to certify the creator's creation process and evaluate the creator's skills. [Means for solving the problem]

[0006] The system according to the embodiment includes a creation process submission unit, a certificate issuing unit, and a link issuing unit. The creation process submission unit submits the creator's creation process. The certificate issuing unit analyzes the creation process data submitted by the creation process submission unit and issues an author certificate. The link issuing unit provides the author certificate issued by the certificate issuing unit in the form of a link. [Effects of the Invention]

[0007] The system according to the embodiment can verify the creator's creation process and evaluate the creator's skills. [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 creator evaluation system according to an embodiment of the present invention is a system in which creators submit their creative process, a generation AI analyzes it, and issues a proof of authorship, which can then be used on social media, etc. This allows the creator evaluation system to fairly evaluate and protect the skills and efforts of creators.

[0029] A creator evaluation system according to an embodiment includes a creation process submission unit, a certificate issuing unit, and a link issuing unit. The creation process submission unit submits a creator's creation process. For example, it may record each step of the creator's illustration as a video or image and submit the video or image. The creation process submission unit may also record in detail the tools and techniques used by the creator. For example, information on the software and hardware used may be included. The certificate issuing unit analyzes the creation process data submitted by the creation process submission unit and issues an author certificate. For example, the generation AI may issue a certificate to prove that the creator actually created the work based on the submitted data. The certificate issuing unit may also issue a detailed author certificate based on biometric information such as the creator's pen pressure and drawing speed. For example, it may analyze changes in pen pressure and strength to prove the creator's uniqueness. The link issuing unit provides the author certificate issued by the certificate issuing unit in the form of a link. For example, the creator may use the link to prove the authenticity of their work by pasting it on their social media account or website. The link issuing unit may also automatically generate a highlight video of the creation process and make it available for viewing at the link destination. For example, by creating a video that extracts important steps and technical points, the creator evaluation system can properly evaluate and protect the skills and efforts of creators. For example, when creators post their illustrations on social media, they can include a link to prove their work's authenticity, which can be used to appeal to followers and viewers.

[0030] The proof issuing unit can issue a detailed proof of authorship based on at least one biometric information of the creator, either pen pressure or drawing speed. For example, the proof issuing unit collects pen pressure data when the creator draws, and the generation AI issues a detailed proof of authorship based on that data. For example, the change and strength of pen pressure can be analyzed to prove the creator's originality. The proof issuing unit can also collect drawing speed data, and the generation AI can issue a detailed proof of authorship based on that data. For example, the change and pattern of drawing speed can be analyzed to prove the creator's skill. This makes it possible to prove the creator's originality.

[0031] The proof issuing unit can automatically provide comments and advice to data during the creation process, supporting creators in improving their skills. For example, the proof issuing unit uses a generative AI to analyze data during the creation process and automatically provide technical comments and advice. For example, it provides advice on the balance of the drawing and the use of color. The proof issuing unit can also make design improvements and technical suggestions to data during the creation process. For example, it provides advice on adjusting the composition and choosing colors. This can support creators in improving their skills.

[0032] The proof issuing unit can compare data from the creation process between different generation AIs and issue a comprehensive proof of authorship that integrates the evaluations from multiple AIs. For example, the proof issuing unit analyzes data from the creation process using different generation AIs and integrates their respective evaluations to issue a comprehensive proof of authorship. For example, the proof issuing unit can weighted-average the evaluation results of each AI and reflect this in the certificate. The proof issuing unit can also compare the evaluation criteria of different generation AIs and select the most appropriate evaluation to reflect in the certificate. For example, it can perform a comprehensive evaluation based on quality evaluation and technical evaluation. This makes it possible to issue a comprehensive proof of authorship that integrates the evaluations from multiple AIs.

[0033] The certificate issuing unit can build a system that shares data on the creation process with other creators and issues certificates of collaborative creation. The certificate issuing unit, for example, develops a system that shares data on the creation process with other creators and issues certificates of collaborative creation. For example, it issues a certificate for a work created jointly by multiple creators. The certificate issuing unit can also record the collaborative creation process in detail and issue certificates based on that data. For example, it can record the contribution and division of roles of each creator and reflect this in the certificate. This makes it possible to issue a certificate of collaborative creation.

[0034] The link publisher can automatically generate a highlight video of the creation process and make it viewable at the link destination. For example, the link publisher will develop a system in which a generation AI automatically generates a highlight video of the creation process and embeds it in the creator link. For example, the link publisher can create a video by extracting important steps and technical points. The link publisher can also edit the highlight video and provide it in a format that is easy for viewers to watch. For example, the link publisher can adjust the length and content of the video to provide content that is appealing to viewers. This allows viewers to watch the highlight video of the creation process.

[0035] The link publisher can automatically add information about the background of the creation of a work or the source of inspiration, and make it possible to view the information at the link destination. For example, the link publisher will develop a system in which a generating AI automatically adds information about the background of the creation of a work or the source of inspiration to the creator link. For example, the link publisher may include the creator's comments and reference materials. The link publisher can also visually display information about the background of the creation or the source of inspiration. For example, the link publisher may provide information using images or videos. This will allow the information about the background of the creation of a work or the source of inspiration to be viewed.

[0036] The link publisher can improve link compatibility by enabling uniform use across different SNS platforms. For example, the link publisher can develop a system that enables uniform use of creator links across different SNS platforms. For example, the link publisher can standardize the link format and optimize display on each platform. The link publisher can also make adjustments to fit the specifications of each platform to improve link compatibility. For example, the link display method and click behavior can be optimized. This can improve link compatibility.

[0037] The link publisher can add a function to indicate the relationship with other creators' works and promote mutual evaluation. The link publisher can, for example, add a function to Creator Link that indicates the relationship with other creators' works and develop a system that promotes mutual evaluation. For example, related works can be displayed in the link. The link publisher can also automatically recommend highly related works and promote interaction between creators. For example, works can be recommended based on similarities in theme or technology. This can indicate the relationship with other creators' works and promote mutual evaluation.

[0038] The proof issuing unit can record a detailed log of each generation step for the object generated by the generation AI and issue a detailed proof of authorship based on that log. For example, the proof issuing unit can develop a system that records a detailed log of each generation step for the object generated by the generation AI and issues a detailed proof of authorship based on that log. For example, each step in the generation process can be recorded with a timestamp. The proof issuing unit can also record a detailed operation log of the generation steps and issue a proof of authorship based on that log. For example, it can record the change history of the tools used and settings. This makes it possible to issue a detailed proof of authorship based on a detailed log of each generation step.

[0039] The proof issuing unit can record environmental data during the generation process for the object generated by the generation AI and include it in the certificate. For example, the proof issuing unit will develop a system that records environmental data during the generation process (e.g., the tools and settings used) for the object generated by the generation AI and includes this in the certificate. For example, it will record information about the software and hardware used. The proof issuing unit can also record detailed environmental data during the generation process and issue a certificate based on that data. For example, it will record information about setting changes and materials used during the generation process. This allows environmental data during the generation process to be included in the certificate.

[0040] The proof issuing unit enables collaboration between different generation AIs and can issue certificates of joint creation. The proof issuing unit will develop a system that enables collaboration between different generation AIs and issues certificates of joint creation. For example, it will issue a certificate for a work jointly created by multiple generation AIs. The proof issuing unit can also record the collaboration process in detail and issue certificates based on that data. For example, it will record the contribution and division of roles of each generation AI and reflect this in the certificate. This will enable collaboration between different generation AIs and issue certificates of joint creation.

[0041] The proof issuing unit can provide a function that streams the generation process in real time for objects generated by the generation AI, allowing viewers to watch the generation process. For example, the proof issuing unit develops a system that provides a function that streams the generation process in real time for objects generated by the generation AI. For example, the generation process can be live-streamed so that viewers can watch it in real time. The proof issuing unit can also visually display the streaming of the generation process, allowing viewers to check the process in detail. For example, each step of the generation process can be displayed on a timeline. This allows the generation process to be streamed in real time and viewers to watch it.

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

[0043] The creator evaluation system can also be equipped with a function to stream creators' creative processes in real time. For example, a creator can live-stream the process of creating a work, allowing viewers to watch the process in real time. Viewers can also interact with creators through comments and reactions. Furthermore, feedback from viewers can be collected during the stream and used to help creators improve their skills. This enables two-way communication between creators and viewers, contributing to increased motivation for creators.

[0044] The creator evaluation system can further analyze a creator's creative process and provide a function to recommend similar works by other creators. For example, it can analyze the techniques and style used by a creator and automatically recommend works by other creators with similar techniques and styles. Furthermore, if the creator provides feedback on the recommended works, the system can make more accurate recommendations. This allows creators to gain new inspiration and improve their skills.

[0045] The creator evaluation system can also analyze the creator's creative process and provide advice to improve work efficiency. For example, it can suggest optimal ways to use the tools and technologies used by the creator. It can also monitor the progress of work in real time and suggest efficient work procedures. This will enable creators to improve their work efficiency and produce high-quality works in a shorter time.

[0046] The creator evaluation system could also analyze creators' creative processes and provide health management advice. For example, it could detect fatigue caused by working for long periods of time and send a notification urging them to take a break. It could also suggest exercises to improve posture and reduce eye strain while working. This would allow creators to continue working while maintaining their health, supporting their long-term creative activities.

[0047] The creator evaluation system can also analyze the creator's creative process and evaluate the market value of their work. For example, it can predict the market value of a creator's work based on past sales data and market trends. It can also provide advice on setting prices for their work. This allows creators to understand the market value of their work and sell it at an appropriate price.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The creation process submission section submits the creator's creation process. For example, the creator can record each step of the illustration as a video or image and submit it. The creation process submission section can also record in detail the tools and techniques used by the creator. For example, it can include information about the software and hardware used. Step 2: The proof issuing unit analyzes the creation process data submitted by the creation process submission unit and issues a proof of authorship. For example, the generation AI issues a certificate to prove that the creator actually created the work based on the submitted data. The proof issuing unit can also issue a detailed proof of authorship based on biometric information such as the creator's pen pressure and drawing speed. For example, it can analyze changes in pen pressure and strength to prove the creator's uniqueness. Step 3: The Link Issuer provides the proof of authorship issued by the Proof Issuer in the form of a link. For example, creators can use this to prove the authenticity of their work by pasting it on their social media accounts or websites. The Link Issuer can also automatically generate a highlight video of the creation process and make it available for viewing at the linked destination. For example, a video can be created by extracting important steps or technical points.

[0050] (Example 2) The creator evaluation system according to an embodiment of the present invention is a system in which creators submit their creative process, a generation AI analyzes it, and issues a proof of authorship, which can then be used on social media, etc. This allows the creator evaluation system to fairly evaluate and protect the skills and efforts of creators.

[0051] A creator evaluation system according to an embodiment includes a creation process submission unit, a certificate issuing unit, and a link issuing unit. The creation process submission unit submits a creator's creation process. For example, it may record each step of the creator's illustration as a video or image and submit the video or image. The creation process submission unit may also record in detail the tools and techniques used by the creator. For example, information on the software and hardware used may be included. The certificate issuing unit analyzes the creation process data submitted by the creation process submission unit and issues an author certificate. For example, the generation AI may issue a certificate to prove that the creator actually created the work based on the submitted data. The certificate issuing unit may also issue a detailed author certificate based on biometric information such as the creator's pen pressure and drawing speed. For example, it may analyze changes in pen pressure and strength to prove the creator's uniqueness. The link issuing unit provides the author certificate issued by the certificate issuing unit in the form of a link. For example, the creator may use the link to prove the authenticity of their work by pasting it on their social media account or website. The link issuing unit may also automatically generate a highlight video of the creation process and make it available for viewing at the link destination. For example, by creating a video that extracts important steps and technical points, the creator evaluation system can properly evaluate and protect the skills and efforts of creators. For example, when creators post their illustrations on social media, they can include a link to prove their work's authenticity, which can be used to appeal to followers and viewers.

[0052] The proof issuing unit can issue a detailed proof of authorship based on at least one biometric information of the creator, either pen pressure or drawing speed. For example, the proof issuing unit collects pen pressure data when the creator draws, and the generation AI issues a detailed proof of authorship based on that data. For example, the change and strength of pen pressure can be analyzed to prove the creator's originality. The proof issuing unit can also collect drawing speed data, and the generation AI can issue a detailed proof of authorship based on that data. For example, the change and pattern of drawing speed can be analyzed to prove the creator's skill. This makes it possible to prove the creator's originality.

[0053] The proof issuing unit can automatically provide comments and advice to data during the creation process, supporting creators in improving their skills. For example, the proof issuing unit uses a generative AI to analyze data during the creation process and automatically provide technical comments and advice. For example, it provides advice on the balance of the drawing and the use of color. The proof issuing unit can also make design improvements and technical suggestions to data during the creation process. For example, it provides advice on adjusting the composition and choosing colors. This can support creators in improving their skills.

[0054] The certificate issuing unit can use an emotion estimation function to analyze the creator's emotions during the creation process and reflect those emotional changes in the certificate. For example, the certificate issuing unit uses a generation AI to analyze the creator's facial expressions and voice during the creation process and estimate changes in emotions. For example, the degree of joy or concentration can be quantified and reflected in the certificate. The certificate issuing unit can also collect the creator's biometric data (heart rate and electrodermal activity) and analyze changes in emotions. For example, it can calculate an emotion score based on fluctuations in heart rate and reflect this in the certificate. This allows changes in the creator's emotions to be reflected in the certificate.

[0055] The proof issuing unit can compare data from the creation process between different generation AIs and issue a comprehensive proof of authorship that integrates the evaluations from multiple AIs. For example, the proof issuing unit analyzes data from the creation process using different generation AIs and integrates their respective evaluations to issue a comprehensive proof of authorship. For example, the proof issuing unit can weighted-average the evaluation results of each AI and reflect this in the certificate. The proof issuing unit can also compare the evaluation criteria of different generation AIs and select the most appropriate evaluation to reflect in the certificate. For example, it can perform a comprehensive evaluation based on quality evaluation and technical evaluation. This makes it possible to issue a comprehensive proof of authorship that integrates the evaluations from multiple AIs.

[0056] The certificate issuing unit can build a system that shares data on the creation process with other creators and issues certificates of collaborative creation. The certificate issuing unit, for example, develops a system that shares data on the creation process with other creators and issues certificates of collaborative creation. For example, it issues a certificate for a work created jointly by multiple creators. The certificate issuing unit can also record the collaborative creation process in detail and issue certificates based on that data. For example, it can record the contribution and division of roles of each creator and reflect this in the certificate. This makes it possible to issue a certificate of collaborative creation.

[0057] The proof issuing unit can use the emotion estimation function to display the creator's emotions during the creation process in real time and provide an interface that allows viewers to share those emotions. The proof issuing unit, for example, uses the emotion estimation function to develop an interface that displays the creator's emotions during the creation process in real time. For example, the creator's emotion score is visually displayed. The proof issuing unit can also display changes in the creator's emotions in real time and provide an interface that allows viewers to share those emotions. For example, the changes in emotions are displayed in a graph or chart. This allows viewers to share the creator's emotions in real time.

[0058] The link publisher can automatically generate a highlight video of the creation process and make it viewable at the link destination. For example, the link publisher will develop a system in which a generation AI automatically generates a highlight video of the creation process and embeds it in the creator link. For example, the link publisher can create a video by extracting important steps and technical points. The link publisher can also edit the highlight video and provide it in a format that is easy for viewers to watch. For example, the link publisher can adjust the length and content of the video to provide content that is appealing to viewers. This allows viewers to watch the highlight video of the creation process.

[0059] The link publisher can automatically add information about the background of the creation of a work or the source of inspiration, and make it possible to view the information at the link destination. For example, the link publisher will develop a system in which a generating AI automatically adds information about the background of the creation of a work or the source of inspiration to the creator link. For example, the link publisher may include the creator's comments and reference materials. The link publisher can also visually display information about the background of the creation or the source of inspiration. For example, the link publisher may provide information using images or videos. This will allow the information about the background of the creation of a work or the source of inspiration to be viewed.

[0060] The link issuing unit can use the emotion estimation function to add a timeline showing the transition of a creator's emotions, making it possible to view the transition at the linked destination. For example, the link issuing unit can develop a system that uses the emotion estimation function to add a timeline showing the transition of a creator's emotions to a creator link. For example, the transition of emotions during creation can be displayed in a graph. The link issuing unit can also visually display the transition of emotions, allowing viewers to track the changes. For example, the transition of emotions can be displayed on a timeline, allowing viewers to understand the flow of emotions. This allows viewers to view the transition of a creator's emotions.

[0061] The link publisher can improve link compatibility by enabling uniform use across different SNS platforms. For example, the link publisher can develop a system that enables uniform use of creator links across different SNS platforms. For example, the link publisher can standardize the link format and optimize display on each platform. The link publisher can also make adjustments to fit the specifications of each platform to improve link compatibility. For example, the link display method and click behavior can be optimized. This can improve link compatibility.

[0062] The link publisher can add a function to indicate the relationship with other creators' works and promote mutual evaluation. The link publisher can, for example, add a function to Creator Link that indicates the relationship with other creators' works and develop a system that promotes mutual evaluation. For example, related works can be displayed in the link. The link publisher can also automatically recommend highly related works and promote interaction between creators. For example, works can be recommended based on similarities in theme or technology. This can indicate the relationship with other creators' works and promote mutual evaluation.

[0063] The proof issuing unit can record a detailed log of each generation step for the object generated by the generation AI and issue a detailed proof of authorship based on that log. For example, the proof issuing unit can develop a system that records a detailed log of each generation step for the object generated by the generation AI and issues a detailed proof of authorship based on that log. For example, each step in the generation process can be recorded with a timestamp. The proof issuing unit can also record a detailed operation log of the generation steps and issue a proof of authorship based on that log. For example, it can record the change history of the tools used and settings. This makes it possible to issue a detailed proof of authorship based on a detailed log of each generation step.

[0064] The proof issuing unit can record environmental data during the generation process for the object generated by the generation AI and include it in the certificate. For example, the proof issuing unit will develop a system that records environmental data during the generation process (e.g., the tools and settings used) for the object generated by the generation AI and includes this in the certificate. For example, it will record information about the software and hardware used. The proof issuing unit can also record detailed environmental data during the generation process and issue a certificate based on that data. For example, it will record information about setting changes and materials used during the generation process. This allows environmental data during the generation process to be included in the certificate.

[0065] The certificate issuing unit can use the emotion estimation function to analyze the creator's emotions toward the object generated by the generative AI and issue a certificate that reflects those emotions. For example, the certificate issuing unit can develop a system that uses the emotion estimation function to analyze the creator's emotions toward the object generated by the generative AI and issue a certificate that reflects those emotions. For example, the creator's emotion score can be recorded in the certificate. The certificate issuing unit can also record changes in the creator's emotions in detail and issue a certificate based on that data. For example, changes in emotions during creation can be displayed in a graph and reflected in the certificate. This makes it possible to issue a certificate that reflects the creator's emotions.

[0066] The proof issuing unit enables collaboration between different generation AIs and can issue certificates of joint creation. The proof issuing unit will develop a system that enables collaboration between different generation AIs and issues certificates of joint creation. For example, it will issue a certificate for a work jointly created by multiple generation AIs. The proof issuing unit can also record the collaboration process in detail and issue certificates based on that data. For example, it will record the contribution and division of roles of each generation AI and reflect this in the certificate. This will enable collaboration between different generation AIs and issue certificates of joint creation.

[0067] The proof issuing unit can provide a function that streams the generation process in real time for objects generated by the generation AI, allowing viewers to watch the generation process. For example, the proof issuing unit develops a system that provides a function that streams the generation process in real time for objects generated by the generation AI. For example, the generation process can be live-streamed so that viewers can watch it in real time. The proof issuing unit can also visually display the streaming of the generation process, allowing viewers to check the process in detail. For example, each step of the generation process can be displayed on a timeline. This allows the generation process to be streamed in real time and viewers to watch it.

[0068] The proof issuing unit can use the emotion estimation function to collect users' emotional reactions to objects generated by the generation AI and improve the generation algorithm of the generation AI based on that data. For example, the proof issuing unit can use the emotion estimation function to develop a system that collects users' emotional reactions to objects generated by the generation AI. For example, it can analyze the user's facial expressions and voice and calculate an emotional score. The proof issuing unit can also improve the generation algorithm of the generation AI based on the collected emotional reactions. For example, it can analyze the user's emotional score and reflect it in the generation algorithm. This makes it possible to improve the generation algorithm based on the user's emotional reactions.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The creator evaluation system can also be equipped with a function to stream creators' creative processes in real time. For example, a creator can live-stream the process of creating a work, allowing viewers to watch the process in real time. Viewers can also interact with creators through comments and reactions. Furthermore, feedback from viewers can be collected during the stream and used to help creators improve their skills. This enables two-way communication between creators and viewers, contributing to increased motivation for creators.

[0071] The creator evaluation system can further analyze a creator's creative process and provide a function to recommend similar works by other creators. For example, it can analyze the techniques and style used by a creator and automatically recommend works by other creators with similar techniques and styles. Furthermore, if the creator provides feedback on the recommended works, the system can make more accurate recommendations. This allows creators to gain new inspiration and improve their skills.

[0072] The creator evaluation system can also analyze the creator's creative process and provide advice to improve work efficiency. For example, it can suggest optimal ways to use the tools and technologies used by the creator. It can also monitor the progress of work in real time and suggest efficient work procedures. This will enable creators to improve their work efficiency and produce high-quality works in a shorter time.

[0073] The creator evaluation system could also analyze creators' creative processes and provide health management advice. For example, it could detect fatigue caused by working for long periods of time and send a notification urging them to take a break. It could also suggest exercises to improve posture and reduce eye strain while working. This would allow creators to continue working while maintaining their health, supporting their long-term creative activities.

[0074] The creator evaluation system can also analyze the creator's creative process and evaluate the market value of their work. For example, it can predict the market value of a creator's work based on past sales data and market trends. It can also provide advice on setting prices for their work. This allows creators to understand the market value of their work and sell it at an appropriate price.

[0075] The creator evaluation system can further utilize the emotion estimation function to provide music and environmental sounds based on the creator's emotions. For example, it can automatically play relaxing music when the creator is concentrating, and refreshing music when the creator is feeling stressed. It can also improve the creator's work efficiency by providing environmental sounds tailored to the creator's work environment. This allows the creator to continue working in a comfortable environment.

[0076] The creator evaluation system can also use emotion estimation to provide feedback based on the creator's emotions. For example, if a creator is feeling anxious, it can automatically send an encouraging message, and if they are feeling happy, it can automatically send a message of praise. It can also maintain the creator's motivation by adjusting the content of the feedback according to changes in their emotions. This allows creators to receive appropriate feedback according to their emotions.

[0077] The creator evaluation system can also use emotion estimation to suggest a work schedule based on the creator's emotions. For example, it can automatically suggest a schedule with more breaks when the creator is feeling tired, or a schedule with longer work hours when the creator is concentrating. It can also adjust the schedule in real time according to changes in emotions. This allows creators to realize the optimal work schedule that matches their emotions.

[0078] The creator evaluation system can also use emotion estimation to adjust the work environment based on the creator's emotions. For example, the system can change the lighting to warm colors when the creator wants to relax, and cool colors when the creator wants to concentrate. It can also adjust the room temperature and music volume according to changes in emotions. This allows creators to create the optimal work environment that matches their emotions.

[0079] The creator evaluation system can also use an emotion estimation function to provide a function for managing work progress based on the creator's emotions. For example, it can slow down work progress when the creator is feeling stressed and speed up work progress when the creator is relaxed. It can also reduce the burden on the creator by adjusting the progress management method according to changes in emotions. This allows creators to achieve optimal work progress management that matches their own emotions.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The creation process submission section submits the creator's creation process. For example, the creator can record each step of the illustration as a video or image and submit it. The creation process submission section can also record in detail the tools and techniques used by the creator. For example, it can include information about the software and hardware used. Step 2: The proof issuing unit analyzes the creation process data submitted by the creation process submission unit and issues a proof of authorship. For example, the generation AI issues a certificate to prove that the creator actually created the work based on the submitted data. The proof issuing unit can also issue a detailed proof of authorship based on biometric information such as the creator's pen pressure and drawing speed. For example, it can analyze changes in pen pressure and strength to prove the creator's uniqueness. Step 3: The Link Issuer provides the proof of authorship issued by the Proof Issuer in the form of a link. For example, creators can use this to prove the authenticity of their work by pasting it on their social media accounts or websites. The Link Issuer can also automatically generate a highlight video of the creation process and make it available for viewing at the linked destination. For example, a video can be created by extracting important steps or technical points.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] 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.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] The data processing system 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a creation process submission section for submitting the creator's creation process; a certificate issuing unit that analyzes the creation process data submitted by the creation process submitting unit and issues an author certificate; a link issuing unit that provides the author certificate issued by the certificate issuing unit in the form of a link. A system characterized by:

2. The certificate issuing unit The data from the creation process is compared between different AI generators, and a comprehensive proof of authorship is issued that integrates the evaluations of multiple AIs.

2. The system of claim 1.

3. The link issuing unit A highlight video of the creation process will be automatically generated and made available for viewing at the link destination.

2. The system of claim 1.

4. The certificate issuing unit A detailed log of each generation step for the object generated by the AI ​​is recorded, and a detailed proof of authorship is issued based on the log.

2. The system of claim 1.

5. The certificate issuing unit Analyze the creator's emotions during the creation process and reflect changes in those emotions in the certificate.

2. The system of claim 1.

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