System

Abstract

To provide a system that determines whether a created work is similar to an existing copyrighted work, and solves a copyright problem.SOLUTION: A system comprises: a registration unit; a characteristic-amount extraction unit; a collation unit; and a warning unit. The registration unit is configured to register copyright information on a created work in a block chain. The characteristic-amount extraction unit is configured to extract a characteristic amount of the created work on the basis of the copyright information on the created work registered by the registration unit. The collation unit is configured to determine whether the created work is similar to an existing copyrighted work based on the characteristic amount thereof extracted by the characteristic-amount extraction unit. The warning unit is configured to issue a warning based on a result confirmed by the warning unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to verify whether the products created by generative AI were similar to existing works, posing the risk of copyright infringement.

[0005] The system according to the embodiment aims to check whether a product is similar to an existing copyrighted work and to resolve copyright issues. [Means for solving the problem]

[0006] The system according to the embodiment includes a registration unit, a feature extraction unit, a matching unit, and a warning unit. The registration unit registers copyright information of a product in a blockchain. The feature extraction unit extracts features of the product based on the copyright information of the product registered by the registration unit. The matching unit checks whether the product is similar to an existing copyrighted work based on the features extracted by the feature extraction unit. The warning unit issues a warning based on the results checked by the matching unit. Effect of the Invention

[0007] The system according to the embodiment can check whether the product is similar to an existing copyrighted work and resolve copyright issues. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The copyright management system according to the embodiment of the present invention is a system that uses a blockchain to manage the copyright of products such as text, images, and videos created using a generation AI. This system also has a function to check whether the product is similar to an existing work. Specifically, it is composed of the following steps. First, the generation AI creates a product. Next, the copyright information of the product is registered in the blockchain. This ensures that the copyright of the product is managed. Furthermore, the product is compared with an existing work database to check whether the product is similar to an existing work. If the product is similar to an existing work based on the result of this comparison, a warning is issued. For example, when the generation AI creates a new image, the copyright information of the image is registered in the blockchain. Next, the image is compared with an existing image database to check whether there is a similar image. If a similar image is found, a warning is issued to the user, and the risk of copyright infringement is avoided. This system ensures that copyright management of products created using the generation AI is performed, and the risk of copyright infringement can be reduced by checking the similarity with existing works. As a result, the copyright management system ensures that the copyright of the product is managed, and the risk of copyright infringement can be reduced by checking the similarity with existing works.

[0029] The copyright management system according to the embodiment includes a registration unit, a feature extraction unit, a comparison unit, and a warning unit. The registration unit registers copyright information of the product in the blockchain. The copyright information of the product includes, for example, the name of the copyright holder, the copyright registration number, the creation date and time, and creator information, but is not limited to such examples. The registration unit registers, for example, metadata of the product in the blockchain. The metadata includes, for example, the creation date and time of the product and creator information. The feature extraction unit extracts features of the product. The features include, for example, image features, text features, and video features, but are not limited to such examples. The feature extraction unit applies, for example, an image analysis algorithm to extract image features. The feature extraction unit applies a text analysis algorithm to extract text features. The feature extraction unit applies a frame-by-frame analysis algorithm to extract video features. The comparison unit checks whether the product is similar to an existing work based on the features extracted by the feature extraction unit. The comparison unit calculates similarity using, for example, a method such as cosine similarity or Jaccard coefficient. The collation unit compares the feature amount of the product with a database of existing copyrighted works to check whether they are similar. The warning unit issues a warning when the product is similar to an existing copyrighted work based on the result confirmed by the collation unit. The warning unit issues a warning in the form of, for example, a text message or an alert sound. In this way, the copyright management system according to the embodiment can reliably manage the copyright of the product and check the similarity with existing copyrighted works, thereby reducing the risk of copyright infringement.

[0030] The registration unit registers the copyright information of the product in the blockchain. The copyright information of the product includes, for example, the name of the copyright holder, the copyright registration number, the creation date and time, and the creator information, but is not limited to such examples. Specifically, the registration unit registers the metadata of the product in the blockchain. The metadata includes the creation date and time of the product and the creator information. By using blockchain technology, the registered information becomes difficult to tamper with and is more reliable. The registration unit can record the information in the blockchain in real time from the moment the product is created. This ensures that the copyright information of the product is protected and prevents subsequent tampering or unauthorized changes. Furthermore, the registration unit can register not only the metadata of the product but also the hash value of the product itself in the blockchain. The hash value is like a digital fingerprint for uniquely identifying the contents of the product, and this can ensure the authenticity of the product. For example, by calculating the hash value of an image file and registering the hash value in the blockchain, it can be confirmed that the image has not been tampered with later. In addition, the registration unit allows the copyright holder to easily register copyright information through a user interface. For example, a mechanism is provided in which the copyright holder simply uploads the product and inputs the necessary information, and the product is automatically registered in the blockchain. This allows copyright holders to register copyright information without hassle, making copyright management easier.

[0031] The feature extraction unit extracts the feature of the product. The feature includes, for example, an image feature, a text feature, a video feature, and the like, but is not limited to such examples. Specifically, the feature extraction unit applies an image analysis algorithm to extract the feature of the image. For example, the feature of the image is extracted using techniques such as edge detection, color histogram analysis, and shape analysis of the image. To extract the feature of the text, a text analysis algorithm is applied. For example, the frequency of occurrence of words, context information, grammatical structure, and the like are analyzed to extract the feature of the text. To extract the feature of the video, an analysis algorithm for each frame is applied. For example, characteristic scenes are extracted from each frame of the video, and the feature of those scenes is analyzed. The feature extraction unit can utilize high-performance hardware and parallel processing technology to efficiently execute these analysis algorithms. For example, parallel processing using a GPU can analyze large amounts of image and video data at high speed. The feature extraction unit can also extract the feature using a machine learning model. For example, an image recognition model or a natural language processing model using deep learning is trained, and the feature of the product is extracted using those models. This enables more accurate feature extraction and enables accurate evaluation of the similarity of products. Furthermore, the feature extraction unit is equipped with a database for efficiently managing the extracted feature values, allowing features to be quickly searched and referenced as needed.

[0032] The matching unit checks whether the product is similar to an existing copyrighted work based on the features extracted by the feature extraction unit. The matching unit calculates the similarity using a method such as cosine similarity or Jaccard coefficient. Specifically, the matching unit checks whether the product is similar to an existing copyrighted work database. For example, the matching unit uses the image features to match with an existing image database and calculates the similarity. The text features are used to match with an existing text database and calculate the similarity. The video features are used to match with an existing video database and calculate the similarity. The matching unit can utilize high-speed search algorithms and indexing technology to efficiently perform these similarity calculations. For example, a data structure such as a hash table or a KD tree is used to speed up the search for features. In addition, the matching unit evaluates how similar the product is to an existing copyrighted work based on the result of the similarity calculation. For example, if the similarity exceeds a certain threshold, it is determined that the product is similar to an existing copyrighted work. The matching unit analyzes the result of the similarity calculation in detail and determines whether the product is at risk of copyright infringement. Furthermore, the matching unit has a function to visualize the results of similarity calculations, allowing users to intuitively understand the similarity between the new product and existing works. For example, it can list works with high similarity and display their similarity scores. It also supports detailed analysis of similarity by displaying graphs and heat maps that compare the features of the new product and existing works.

[0033] The warning unit issues a warning when the product is similar to an existing work based on the result confirmed by the comparison unit. The warning unit issues a warning in the form of, for example, a text message or an alert sound. Specifically, the warning unit transmits a warning message to a copyright holder or a user when the product is similar to an existing work. For example, when a product is uploaded and a highly similar work is detected, a warning message is displayed to notify the risk of copyright infringement. In addition to the warning message, the warning unit can suggest specific measures and actions. For example, a list of highly similar works is provided to prompt the copyright holder to check. The risk of copyright infringement is reduced by suggesting modification or deletion of the product. The warning unit has a function of intuitively displaying a warning message through a user interface. For example, the warning message is displayed in a pop-up window or a notification bar so that the user can immediately check it. The warning unit also manages the history of warning messages and allows past warning contents to be referenced. This allows the user to check the past warning contents and take appropriate action. Furthermore, the warning unit can flexibly set the destination of the warning message. For example, a warning message can be sent not only to copyright holders but also to administrators and other relevant parties, thereby supporting rapid response. The warning unit can also issue warnings in different formats and means depending on the importance of the warning message. For example, in the case of a highly urgent warning, a voice call or SMS can be used to immediately notify the user. This allows the warning unit to quickly and reliably inform users of the risk of copyright infringement of the created work and encourage them to take appropriate measures.

[0034] The registration unit can register metadata of the product in the blockchain. The metadata includes, for example, the creation date and time, creator information, the purpose of use, and the scope of use, but is not limited to such examples. The registration unit registers metadata including, for example, the creation date and time of the product and creator information in the blockchain. The creation date and time of the product is recorded as a timestamp. The creator information includes the creator's name and contact information. The purpose of use includes whether the product is used commercially or for educational purposes, etc. The scope of use includes whether the product is used in a limited area or for a limited period of time, etc. This allows the metadata of the product to be reliably managed.

[0035] The feature extraction unit can extract the feature of the product. The feature includes, but is not limited to, for example, an image feature, a text feature, and a video feature. For example, the feature extraction unit applies an image analysis algorithm to extract the image feature. The image analysis algorithm includes SIFT (Scale-Invariant Feature Transform) and SURF (Speed-Up Robust Features). The feature extraction unit applies a text analysis algorithm to extract the text feature. The text analysis algorithm includes TF-IDF (Term Frequency-Inverse Document Frequency) and word embedding. The feature extraction unit applies a frame-by-frame analysis algorithm to extract the video feature. The frame-by-frame analysis algorithm includes CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network). This allows the feature of the product to be accurately extracted.

[0036] The matching unit can match the existing copyrighted work based on the feature of the product. The matching unit calculates the similarity using, for example, a method such as cosine similarity or Jaccard coefficient. Cosine similarity calculates the angle between two vectors in a vector space model, and determines that the smaller the angle, the higher the similarity. The Jaccard coefficient is a value obtained by dividing the size of the common part of two sets by the total size, and indicates the degree of overlap of the sets. The matching unit matches the feature of the product with an existing copyrighted work database to check whether they are similar. For example, the matching unit matches the feature of the product with an existing image database to check whether there are similar images. The matching unit matches the feature of the product with an existing text database to check whether there are similar texts. The matching unit matches the feature of the product with an existing video database to check whether there are similar videos. This makes it possible to check whether the product is similar to the existing copyrighted work.

[0037] The warning unit can issue a warning when the product is similar to an existing copyrighted work. The warning unit issues a warning in the form of, for example, a text message or an alert sound. The text message describes that the product is similar to an existing copyrighted work. The alert sound is used to inform the user of the warning. The warning unit issues a warning to the user when the product is similar to an existing copyrighted work, thereby avoiding the risk of copyright infringement. For example, the warning unit issues a warning to the user when the product is similar to an existing image. The warning unit issues a warning to the user when the product is similar to existing text. The warning unit issues a warning to the user when the product is similar to an existing video. This makes it possible to avoid the risk of copyright infringement.

[0038] In addition to the metadata of the product, the registration unit can also register log information of the creation process of the product in the blockchain. The log information includes, for example, the creation date and time of the product, information of the creator, editing history, version information, information of the tools and software used, and the like, but is not limited to such examples. The registration unit registers log information including, for example, the creation date and time of the product and information of the creator in the blockchain. The editing history of the product records details of how the product was edited. The version information records different versions of the product. The information of the tools and software used includes the specific tools and software versions used to create the product. This allows the information of the creation process of the product to be reliably managed.

[0039] The registration unit may provide an option to specify the purpose and scope of use of the product when registering metadata of the product. The purpose of use includes, for example, but is not limited to, whether the product is used commercially, for educational purposes, or for personal use. The scope of use includes whether the product is used in a limited region or for a limited period of time. For example, when the product is used commercially, the registration unit provides an option to specify the purpose of use. When the product is used for educational purposes, the registration unit provides an option to specify the scope of use. When the product is used for personal use, the registration unit provides an option to specify the purpose of use. This allows the purpose and scope of use of the product to be clearly managed.

[0040] The registration unit can register the metadata of the product while taking into consideration the past copyright infringement history of the creator of the product. For example, if the creator of the product has committed copyright infringement in the past, the registration unit displays a warning. The registration unit can also determine whether or not to register the product based on the past copyright infringement history of the creator of the product. Furthermore, if the creator of the product has not committed copyright infringement in the past, the registration unit can smoothly register the product. This allows the product to be registered while taking into consideration the past copyright infringement history. The copyright infringement history includes, for example, past litigation records and violation reports, but is not limited to such examples. When registering the metadata of the product, the registration unit refers to the past copyright infringement history of the creator of the product and takes appropriate action. This reduces the risk of copyright infringement.

[0041] When registering metadata of a product, the registration unit may also register legal documents and contract information related to the product. Examples of legal documents include, but are not limited to, copyright agreements, license agreements, and license information. For example, the registration unit registers the copyright agreement of the product together with the metadata. The registration unit registers the license agreement of the product together with the metadata. The registration unit registers the license information of the product together with the metadata. This makes it possible to reliably manage the legal documents and contract information of the product. The legal documents include details such as the contract period and contract conditions. This makes it possible to centrally manage the legal documents and contract information of the product and strengthen copyright management.

[0042] The feature extraction unit can apply different extraction algorithms depending on the category of the product. For example, in the case of a sentence, the feature extraction unit applies a text analysis algorithm to extract features. In the case of an image, the feature extraction unit applies an image analysis algorithm to extract features. In the case of a video, the feature extraction unit applies a frame-by-frame analysis algorithm to extract features. This allows features to be extracted using an appropriate algorithm depending on the category of the product. Text analysis algorithms include Term Frequency-Inverse Document Frequency (TF-IDF) and word embedding. Image analysis algorithms include Scale-Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF). Video analysis algorithms include Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). This allows features to be extracted using an appropriate algorithm depending on the category of the product.

[0043] The feature extraction unit can extract features taking into consideration the creation date and time of the product and information on the creation environment. The feature extraction unit extracts temporal features, for example, taking into consideration the creation date and time of the product. The feature extraction unit can also extract technical features taking into consideration the creation environment of the product (the tools and software used). Furthermore, the feature extraction unit can extract personal features taking into consideration information about the creator of the product. This makes it possible to extract features taking into consideration the creation date and time of the product and the creation environment. The creation date and time is recorded as a timestamp. The creation environment includes the versions of the tools and software used. The creator information includes the name and contact information of the creator. This makes it possible to extract features taking into consideration the creation date and time of the product and the creation environment.

[0044] The feature extraction unit can perform extraction by referring to the feature amounts of past works of the creator of the product. For example, the feature extraction unit extracts the feature amounts of a new product based on the feature amounts of past works of the creator of the product. The feature extraction unit can also extract feature amounts by considering the style of the past works of the creator of the product. Furthermore, the feature extraction unit can also extract feature amounts by referring to technical features of the past works of the creator of the product. This makes it possible to extract feature amounts by referring to the creator's past works. Past works include works from the past five years and works in a specific genre. The feature amounts include image features, text features, video features, and the like. This makes it possible to extract feature amounts by referring to the creator's past works.

[0045] The feature extraction unit can extract features taking into consideration market data and trend information related to the product. The feature extraction unit extracts features of the product, for example, taking into consideration a current market trend. The feature extraction unit can also extract features based on market data related to the product. Furthermore, the feature extraction unit can extract features taking into consideration a target market for the product. This makes it possible to extract features taking into consideration market data and trend information. Market data includes sales data and consumer trends. Trend information includes social media trends and search engine trends. This makes it possible to extract features taking into consideration market data and trend information related to the product.

[0046] The matching unit can perform matching while taking into consideration the update frequency and latest information of the existing work database when matching the feature quantities of the product. The matching unit performs matching, for example, based on the latest information of the existing work database. The matching unit can also perform matching while taking into consideration the update frequency of the existing work database. Furthermore, the matching unit can perform matching while taking into consideration the reliability of the existing work database. This makes it possible to perform matching based on the latest information. The update frequency includes, for example, standards such as daily and weekly. The latest information includes the latest work data and the latest legal information. The reliability includes a reliability evaluation of the database. This makes it possible to perform matching while taking into consideration the update frequency and latest information of the existing work database when matching the feature quantities of the product.

[0047] When matching the features of the product, the matching unit can apply different matching algorithms depending on the purpose and range of use of the product. For example, in the case of commercial use, the matching unit applies a strict matching algorithm. In addition, in the case of educational use, the matching unit can also apply a relaxed matching algorithm. In addition, in the case of personal use, the matching unit can also apply a simplified matching algorithm. This allows matching to be performed using an appropriate algorithm depending on the purpose and range of use. The purpose of use includes commercial use, personal use, educational use, etc. The range of use includes regional limitations, time limitations, etc. Matching algorithms include SIFT (Scale-Invariant Feature Transform) and SURF (Speed-Up Robust Features), etc. This allows matching to be performed using an appropriate algorithm depending on the purpose and range of use of the product.

[0048] The matching unit can perform matching taking into consideration the geographical distribution of existing copyrighted work databases when matching the feature quantities of the products. The matching unit performs matching based on the geographical distribution of the existing copyrighted work databases, for example. The matching unit can also perform matching taking into consideration regional characteristics of the existing copyrighted work databases. Furthermore, the matching unit can perform matching taking into consideration the international distribution of the existing copyrighted work databases. This makes it possible to perform matching taking into consideration the geographical distribution. The geographical distribution includes criteria such as by country or by region. The regional characteristics include trends and characteristics of works in a specific region. The international distribution includes the distribution of works in different countries or regions. This makes it possible to perform matching taking into consideration the geographical distribution of existing copyrighted work databases when matching the feature quantities of the products.

[0049] When comparing the features of the product, the matching unit can also perform the matching by referring to legal documents and contract information related to the product. The matching unit performs the matching by referring to, for example, a copyright agreement related to the product. The matching unit can also perform the matching by referring to a license agreement of the product. Furthermore, the matching unit can also perform the matching by referring to license information of the product. In this way, the matching can be performed by referring to legal documents and contract information. Legal documents include contracts and license agreements. Contract information includes contract periods, contract conditions, and the like. In this way, when comparing the features of the product, the matching can be performed by referring to legal documents and contract information related to the product.

[0050] The warning unit can provide an option to adjust the level of detail of the warning or the warning content when the product is similar to an existing copyrighted work. For example, the warning unit issues a detailed warning when the product has a high similarity to the existing copyrighted work. The warning unit can also issue a simplified warning when the product has a medium similarity to the existing copyrighted work. Furthermore, the warning unit can also issue a simple warning when the product has a low similarity to the existing copyrighted work. This makes it possible to issue an appropriate warning according to the similarity. The level of detail of the warning includes a simple warning, a detailed warning, and the like. The warning content includes a risk of copyright infringement, recommended measures, and the like. This makes it possible to provide an option to adjust the level of detail of the warning or the warning content when the product is similar to an existing copyrighted work.

[0051] The warning unit may add a function of presenting an alternative or solution together with a warning when the product is similar to an existing copyrighted work. For example, the warning unit may suggest a different design as an alternative when the product is similar to an existing copyrighted work. The warning unit may also provide an option to contact the copyright holder as a solution when the product is similar to an existing copyrighted work. Furthermore, the warning unit may suggest some changes as a correction when the product is similar to an existing copyrighted work. This makes it possible to provide an alternative or solution when there is similarity. Alternatives include other design proposals and other technical proposals. Solutions include legal advice, technical advice, and the like. This makes it possible to add a function of presenting an alternative or solution together with a warning when the product is similar to an existing copyrighted work.

[0052] The warning unit may provide relevant legal documents and contract information together with the warning when the product is similar to an existing work. For example, the warning unit may provide a relevant copyright agreement when the product is similar to an existing work. The warning unit may also provide a license agreement when the product is similar to an existing work. Furthermore, the warning unit may provide license information when the product is similar to an existing work. This makes it possible to provide relevant legal documents and contract information when there is similarity. The legal documents include contracts and license agreements. The contract information includes contract terms, contract conditions, and the like. This makes it possible to provide relevant legal documents and contract information together with the warning when the product is similar to an existing work.

[0053] The warning unit can present a modification or improvement proposal for the product together with a warning when the product is similar to an existing work. For example, the warning unit can propose a different design as a modification proposal when the product is similar to an existing work. The warning unit can also propose a partial change as an improvement proposal when the product is similar to an existing work. Furthermore, the warning unit can also propose a different color scheme as a modification proposal when the product is similar to an existing work. This makes it possible to provide a modification or improvement proposal when there is similarity. The modification proposal includes a design modification proposal, a technology modification proposal, and the like. The improvement proposal includes a function improvement proposal, a performance improvement proposal, and the like. This makes it possible to present a modification or improvement proposal for the product together with a warning when the product is similar to an existing work.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The copyright management system can further analyze the user's behavior history and use it to manage the copyright of works. For example, it can record what works the user has created in the past and what copyright issues they have faced, and provide this information as reference information the next time the user creates a work. In addition, if a user tends to create works in a particular genre or style, it can analyze this tendency and strengthen warnings against existing works that are highly similar. Furthermore, it can record specific measures that the user has taken in the past to avoid the risk of copyright infringement, and automatically suggest them when a similar situation occurs. In this way, more effective copyright management can be achieved by utilizing the user's behavior history.

[0056] The copyright management system can further include a function for evaluating a product. For example, it can predict how well a product is likely to be received in the market and provide the result to the user. This evaluation is based on past market data and trend information. It can also analyze how well a product will be received by a specific target audience and provide the result as feedback to the user. Furthermore, it can also suggest improvements and enhancements to the user based on the evaluation result of the product. This makes it possible to grasp the market value of a product in advance and provide a guideline for creating a higher quality product.

[0057] The copyright management system can further include a function for monitoring the usage of the product in real time. For example, it can track how and in which region the product is being used, and identify regions with a high risk of copyright infringement. If the product is being used illegally, it can immediately notify the user of this information and take appropriate measures. It can also flexibly change copyright management policies based on the usage of the product. This allows the usage of the product to be grasped in detail, and appropriate copyright management to be carried out in real time.

[0058] The copyright management system can further strengthen the license management function of the created product. For example, it can manage the license information of the created product in detail and notify the user of the license expiration date and usage conditions. In addition, if the license needs to be renewed, the procedure can be automated to reduce the user's effort. Furthermore, if a license violation occurs, it can immediately notify the user of the information and take appropriate measures. This makes it possible to make the license management of the created product more efficient and reduce the risk of license violation.

[0059] The copyright management system may further include a version management function for the product. For example, different versions of the product may be managed in a centralized manner, and each version may be tracked in what way. The system may also compare the differences between versions of the product and suggest the most suitable version to the user. Furthermore, based on the version management of the product, it may be possible to analyze copyright issues with past versions and predict future risks. This may improve the efficiency of the version management of the product and the accuracy of copyright management.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The registration unit registers the copyright information of the product in the blockchain. The copyright information of the product includes the name of the copyright holder, the copyright registration number, the creation date and time, and creator information. For example, the metadata of the product is registered in the blockchain. The metadata includes the creation date and time of the product and creator information. Step 2: The feature extraction unit extracts the features of the product. The features include image features, text features, video features, etc. For example, an image analysis algorithm is applied to extract image features, a text analysis algorithm is applied to extract text features, and a frame-by-frame analysis algorithm is applied to extract video features. Step 3: The matching unit checks whether the product is similar to existing works based on the features extracted by the feature extraction unit. For example, the matching unit calculates the similarity using a method such as cosine similarity or Jaccard coefficient, and matches the features of the product with a database of existing works. Step 4: The warning unit issues a warning if the generated work is similar to an existing copyrighted work based on the results confirmed by the matching unit, for example, in the form of a text message or an alert sound.

[0062] (Example 2) The copyright management system according to the embodiment of the present invention is a system that uses a blockchain to manage the copyright of products such as text, images, and videos created using a generation AI. This system also has a function to check whether the product is similar to an existing work. Specifically, it is composed of the following steps. First, the generation AI creates a product. Next, the copyright information of the product is registered in the blockchain. This ensures that the copyright of the product is managed. Furthermore, the product is compared with an existing work database to check whether the product is similar to an existing work. If the product is similar to an existing work based on the result of this comparison, a warning is issued. For example, when the generation AI creates a new image, the copyright information of the image is registered in the blockchain. Next, the image is compared with an existing image database to check whether there is a similar image. If a similar image is found, a warning is issued to the user, and the risk of copyright infringement is avoided. This system ensures that copyright management of products created using the generation AI is performed, and the risk of copyright infringement can be reduced by checking the similarity with existing works. As a result, the copyright management system ensures that the copyright of the product is managed, and the risk of copyright infringement can be reduced by checking the similarity with existing works.

[0063] The copyright management system according to the embodiment includes a registration unit, a feature extraction unit, a comparison unit, and a warning unit. The registration unit registers copyright information of the product in the blockchain. The copyright information of the product includes, for example, the name of the copyright holder, the copyright registration number, the creation date and time, and creator information, but is not limited to such examples. The registration unit registers, for example, metadata of the product in the blockchain. The metadata includes, for example, the creation date and time of the product and creator information. The feature extraction unit extracts features of the product. The features include, for example, image features, text features, and video features, but are not limited to such examples. The feature extraction unit applies, for example, an image analysis algorithm to extract image features. The feature extraction unit applies a text analysis algorithm to extract text features. The feature extraction unit applies a frame-by-frame analysis algorithm to extract video features. The comparison unit checks whether the product is similar to an existing work based on the features extracted by the feature extraction unit. The comparison unit calculates similarity using, for example, a method such as cosine similarity or Jaccard coefficient. The collation unit compares the feature amount of the product with a database of existing copyrighted works to check whether they are similar. The warning unit issues a warning when the product is similar to an existing copyrighted work based on the result confirmed by the collation unit. The warning unit issues a warning in the form of, for example, a text message or an alert sound. In this way, the copyright management system according to the embodiment can reliably manage the copyright of the product and check the similarity with existing copyrighted works, thereby reducing the risk of copyright infringement.

[0064] The registration unit registers the copyright information of the product in the blockchain. The copyright information of the product includes, for example, the name of the copyright holder, the copyright registration number, the creation date and time, and the creator information, but is not limited to such examples. Specifically, the registration unit registers the metadata of the product in the blockchain. The metadata includes the creation date and time of the product and the creator information. By using blockchain technology, the registered information becomes difficult to tamper with and is more reliable. The registration unit can record the information in the blockchain in real time from the moment the product is created. This ensures that the copyright information of the product is protected and prevents subsequent tampering or unauthorized changes. Furthermore, the registration unit can register not only the metadata of the product but also the hash value of the product itself in the blockchain. The hash value is like a digital fingerprint for uniquely identifying the contents of the product, and this can ensure the authenticity of the product. For example, by calculating the hash value of an image file and registering the hash value in the blockchain, it can be confirmed that the image has not been tampered with later. In addition, the registration unit allows the copyright holder to easily register copyright information through a user interface. For example, a mechanism is provided in which the copyright holder simply uploads the product and inputs the necessary information, and the product is automatically registered in the blockchain. This allows copyright holders to register copyright information without hassle, making copyright management easier.

[0065] The feature extraction unit extracts the feature of the product. The feature includes, for example, an image feature, a text feature, a video feature, and the like, but is not limited to such examples. Specifically, the feature extraction unit applies an image analysis algorithm to extract the feature of the image. For example, the feature of the image is extracted using techniques such as edge detection, color histogram analysis, and shape analysis of the image. To extract the feature of the text, a text analysis algorithm is applied. For example, the frequency of occurrence of words, context information, grammatical structure, and the like are analyzed to extract the feature of the text. To extract the feature of the video, an analysis algorithm for each frame is applied. For example, characteristic scenes are extracted from each frame of the video, and the feature of those scenes is analyzed. The feature extraction unit can utilize high-performance hardware and parallel processing technology to efficiently execute these analysis algorithms. For example, parallel processing using a GPU can analyze large amounts of image and video data at high speed. The feature extraction unit can also extract the feature using a machine learning model. For example, an image recognition model or a natural language processing model using deep learning is trained, and the feature of the product is extracted using those models. This enables more accurate feature extraction and enables accurate evaluation of the similarity of products. Furthermore, the feature extraction unit is equipped with a database for efficiently managing the extracted feature values, allowing features to be quickly searched and referenced as needed.

[0066] The matching unit checks whether the product is similar to an existing copyrighted work based on the features extracted by the feature extraction unit. The matching unit calculates the similarity using a method such as cosine similarity or Jaccard coefficient. Specifically, the matching unit checks whether the product is similar to an existing copyrighted work database. For example, the matching unit uses the image features to match with an existing image database and calculates the similarity. The text features are used to match with an existing text database and calculate the similarity. The video features are used to match with an existing video database and calculate the similarity. The matching unit can utilize high-speed search algorithms and indexing technology to efficiently perform these similarity calculations. For example, a data structure such as a hash table or a KD tree is used to speed up the search for features. In addition, the matching unit evaluates how similar the product is to an existing copyrighted work based on the result of the similarity calculation. For example, if the similarity exceeds a certain threshold, it is determined that the product is similar to an existing copyrighted work. The matching unit analyzes the result of the similarity calculation in detail and determines whether the product is at risk of copyright infringement. Furthermore, the matching unit has a function to visualize the results of similarity calculations, allowing users to intuitively understand the similarity between the new product and existing works. For example, it can list works with high similarity and display their similarity scores. It also supports detailed analysis of similarity by displaying graphs and heat maps that compare the features of the new product and existing works.

[0067] The warning unit issues a warning when the product is similar to an existing work based on the result confirmed by the comparison unit. The warning unit issues a warning in the form of, for example, a text message or an alert sound. Specifically, the warning unit transmits a warning message to a copyright holder or a user when the product is similar to an existing work. For example, when a product is uploaded and a highly similar work is detected, a warning message is displayed to notify the risk of copyright infringement. In addition to the warning message, the warning unit can suggest specific measures and actions. For example, a list of highly similar works is provided to prompt the copyright holder to check. The risk of copyright infringement is reduced by suggesting modification or deletion of the product. The warning unit has a function of intuitively displaying a warning message through a user interface. For example, the warning message is displayed in a pop-up window or a notification bar so that the user can immediately check it. The warning unit also manages the history of warning messages and allows past warning contents to be referenced. This allows the user to check the past warning contents and take appropriate action. Furthermore, the warning unit can flexibly set the destination of the warning message. For example, a warning message can be sent not only to copyright holders but also to administrators and other relevant parties, thereby supporting rapid response. The warning unit can also issue warnings in different formats and means depending on the importance of the warning message. For example, in the case of a highly urgent warning, a voice call or SMS can be used to immediately notify the user. This allows the warning unit to quickly and reliably inform users of the risk of copyright infringement of the created work and encourage them to take appropriate measures.

[0068] The registration unit can register metadata of the product in the blockchain. The metadata includes, for example, the creation date and time, creator information, the purpose of use, and the scope of use, but is not limited to such examples. The registration unit registers metadata including, for example, the creation date and time of the product and creator information in the blockchain. The creation date and time of the product is recorded as a timestamp. The creator information includes the creator's name and contact information. The purpose of use includes whether the product is used commercially or for educational purposes. The scope of use includes whether the product is used in a limited area or for a limited period of time. This allows the metadata of the product to be reliably managed.

[0069] The feature extraction unit can extract the feature of the product. The feature includes, but is not limited to, for example, an image feature, a text feature, and a video feature. For example, the feature extraction unit applies an image analysis algorithm to extract the image feature. The image analysis algorithm includes SIFT (Scale-Invariant Feature Transform) and SURF (Speed-Up Robust Features). The feature extraction unit applies a text analysis algorithm to extract the text feature. The text analysis algorithm includes TF-IDF (Term Frequency-Inverse Document Frequency) and word embedding. The feature extraction unit applies a frame-by-frame analysis algorithm to extract the video feature. The frame-by-frame analysis algorithm includes CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network). This allows the feature of the product to be accurately extracted.

[0070] The matching unit can match the existing copyrighted work based on the feature of the product. The matching unit calculates the similarity using, for example, a method such as cosine similarity or Jaccard coefficient. Cosine similarity calculates the angle between two vectors in a vector space model, and determines that the smaller the angle, the higher the similarity. The Jaccard coefficient is a value obtained by dividing the size of the common part of two sets by the total size, and indicates the degree of overlap of the sets. The matching unit matches the feature of the product with an existing copyrighted work database to check whether they are similar. For example, the matching unit matches the feature of the product with an existing image database to check whether there are similar images. The matching unit matches the feature of the product with an existing text database to check whether there are similar texts. The matching unit matches the feature of the product with an existing video database to check whether there are similar videos. This makes it possible to check whether the product is similar to the existing copyrighted work.

[0071] The warning unit can issue a warning when the product is similar to an existing copyrighted work. The warning unit issues a warning in the form of, for example, a text message or an alert sound. The text message describes that the product is similar to an existing copyrighted work. The alert sound is used to inform the user of the warning. The warning unit issues a warning to the user when the product is similar to an existing copyrighted work, thereby avoiding the risk of copyright infringement. For example, the warning unit issues a warning to the user when the product is similar to an existing image. The warning unit issues a warning to the user when the product is similar to existing text. The warning unit issues a warning to the user when the product is similar to an existing video. This makes it possible to avoid the risk of copyright infringement.

[0072] The registration unit can estimate the user's emotion and adjust the timing of registering the product based on the estimated user's emotion. For example, when the user is stressed, the registration unit automatically registers the product, saving the user's trouble. In addition, when the user is relaxed, the registration unit can provide an option to manually register the product, giving the user freedom of choice. Furthermore, when the user is in a hurry, the registration unit can provide a simplified interface for quickly registering the product. This allows the product to be registered at an appropriate timing according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the registration unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The registration unit can also record the user's voice and estimate the emotion using a voice analysis technology. The registration unit can also collect the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the user's emotions to be understood in detail and the timing of product registration to be adjusted.

[0073] In addition to the metadata of the product, the registration unit can also register log information of the creation process of the product in the blockchain. The log information includes, for example, the creation date and time of the product, information of the creator, editing history, version information, information of the tools and software used, and the like, but is not limited to such examples. The registration unit registers log information including, for example, the creation date and time of the product and information of the creator in the blockchain. The editing history of the product records details of how the product was edited. The version information records different versions of the product. The information of the tools and software used includes the specific tools and software versions used to create the product. This allows the information of the creation process of the product to be reliably managed.

[0074] The registration unit may provide an option to specify the purpose and scope of use of the product when registering metadata of the product. The purpose of use includes, for example, but is not limited to, whether the product is used commercially, for educational purposes, or for personal use. The scope of use includes whether the product is used in a limited region or for a limited period of time. For example, when the product is used commercially, the registration unit provides an option to specify the purpose of use. When the product is used for educational purposes, the registration unit provides an option to specify the scope of use. When the product is used for personal use, the registration unit provides an option to specify the purpose of use. This allows the purpose and scope of use of the product to be clearly managed.

[0075] The registration unit can estimate the user's emotion and determine the priority order of products to be registered based on the estimated user's emotion. For example, when the user is feeling stressed, the registration unit preferentially registers important products. In addition, when the user is relaxed, the registration unit can also allow the user to select the order of product registration. Furthermore, when the user is in a hurry, the registration unit can preferentially register products with high urgency. This allows products to be registered in a priority order according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the registration unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The registration unit can also record the user's voice and estimate the emotion using a voice analysis technology. The registration unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the user's emotion to be grasped in detail and the priority order of products to be determined.

[0076] The registration unit can register the metadata of the product while taking into consideration the past copyright infringement history of the creator of the product. For example, if the creator of the product has committed copyright infringement in the past, the registration unit displays a warning. The registration unit can also determine whether or not to register the product based on the past copyright infringement history of the creator of the product. Furthermore, if the creator of the product has not committed copyright infringement in the past, the registration unit can smoothly register the product. This allows the product to be registered while taking into consideration the past copyright infringement history. The copyright infringement history includes, for example, past litigation records and violation reports, but is not limited to such examples. When registering the metadata of the product, the registration unit refers to the past copyright infringement history of the creator of the product and takes appropriate action. This reduces the risk of copyright infringement.

[0077] When registering metadata of a product, the registration unit may also register legal documents and contract information related to the product. Examples of legal documents include, but are not limited to, copyright agreements, license agreements, and license information. For example, the registration unit registers the copyright agreement of the product together with the metadata. The registration unit registers the license agreement of the product together with the metadata. The registration unit registers the license information of the product together with the metadata. This makes it possible to reliably manage the legal documents and contract information of the product. The legal documents include details such as the contract period and contract conditions. This makes it possible to centrally manage the legal documents and contract information of the product and strengthen copyright management.

[0078] The feature extraction unit can estimate the user's emotion and adjust the method of feature extraction based on the estimated user's emotion. For example, when the user is relaxed, the feature extraction unit extracts detailed features. Also, when the user is in a hurry, the feature extraction unit can extract only major features. Furthermore, when the user is excited, the feature extraction unit can prioritize and extract visually prominent features. This makes it possible to extract features in a manner according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the feature extraction unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The feature extraction unit can also record the user's voice and estimate the emotion using a voice analysis technology. The feature extraction unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows a detailed understanding of the user's emotions and allows the feature extraction method to be adjusted.

[0079] The feature extraction unit can apply different extraction algorithms depending on the category of the product. For example, in the case of a sentence, the feature extraction unit applies a text analysis algorithm to extract features. In the case of an image, the feature extraction unit applies an image analysis algorithm to extract features. In the case of a video, the feature extraction unit applies a frame-by-frame analysis algorithm to extract features. This allows features to be extracted using an appropriate algorithm depending on the category of the product. Text analysis algorithms include Term Frequency-Inverse Document Frequency (TF-IDF) and word embedding. Image analysis algorithms include Scale-Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF). Video analysis algorithms include Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). This allows features to be extracted using an appropriate algorithm depending on the category of the product.

[0080] The feature extraction unit can extract features taking into consideration the creation date and time of the product and information on the creation environment. The feature extraction unit extracts temporal features, for example, taking into consideration the creation date and time of the product. The feature extraction unit can also extract technical features taking into consideration the creation environment of the product (the tools and software used). Furthermore, the feature extraction unit can extract personal features taking into consideration information about the creator of the product. This makes it possible to extract features taking into consideration the creation date and time of the product and the creation environment. The creation date and time is recorded as a timestamp. The creation environment includes the versions of the tools and software used. The creator information includes the name and contact information of the creator. This makes it possible to extract features taking into consideration the creation date and time of the product and the creation environment.

[0081] The feature extraction unit can estimate the user's emotion and determine the priority of the feature to be extracted based on the estimated user's emotion. For example, when the user is relaxed, the feature extraction unit prioritizes extraction of detailed feature. In addition, when the user is in a hurry, the feature extraction unit can also prioritize extraction of main feature. Furthermore, when the user is excited, the feature extraction unit can also prioritize extraction of visually prominent feature. In this way, the feature can be extracted in a priority order according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the feature extraction unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The feature extraction unit can also record the user's voice and estimate the emotion using a voice analysis technology. The feature extraction unit can also collect the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This makes it possible to grasp the user's emotions in detail and determine the priority order of feature amounts to be extracted.

[0082] The feature extraction unit can perform extraction by referring to the feature amounts of past works of the creator of the product. For example, the feature extraction unit extracts the feature amounts of a new product based on the feature amounts of past works of the creator of the product. The feature extraction unit can also extract feature amounts by considering the style of the past works of the creator of the product. Furthermore, the feature extraction unit can also extract feature amounts by referring to technical features of the past works of the creator of the product. This makes it possible to extract feature amounts by referring to the creator's past works. Past works include works from the past five years and works in a specific genre. The feature amounts include image features, text features, video features, and the like. This makes it possible to extract feature amounts by referring to the creator's past works.

[0083] The feature extraction unit can extract features taking into consideration market data and trend information related to the product. The feature extraction unit extracts features of the product, for example, taking into consideration a current market trend. The feature extraction unit can also extract features based on market data related to the product. Furthermore, the feature extraction unit can extract features taking into consideration a target market for the product. This makes it possible to extract features taking into consideration market data and trend information. Market data includes sales data and consumer trends. Trend information includes social media trends and search engine trends. This makes it possible to extract features taking into consideration market data and trend information related to the product.

[0084] The matching unit can estimate the user's emotion and adjust the matching criteria based on the estimated user's emotion. For example, when the user is relaxed, the matching unit applies strict matching criteria. Also, when the user is in a hurry, the matching unit can apply simplified matching criteria. Furthermore, when the user is excited, the matching unit can prioritize visually prominent features for matching. This allows matching to be performed with criteria according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the matching unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The matching unit can also record the user's voice and estimate the emotion using a voice analysis technology. The matching unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the user's emotion to be grasped in detail and the matching criteria to be adjusted.

[0085] The matching unit can perform matching while taking into consideration the update frequency and latest information of the existing work database when matching the feature quantities of the product. The matching unit performs matching, for example, based on the latest information of the existing work database. The matching unit can also perform matching while taking into consideration the update frequency of the existing work database. Furthermore, the matching unit can perform matching while taking into consideration the reliability of the existing work database. This makes it possible to perform matching based on the latest information. The update frequency includes, for example, standards such as daily and weekly. The latest information includes the latest work data and the latest legal information. The reliability includes a reliability evaluation of the database. This makes it possible to perform matching while taking into consideration the update frequency and latest information of the existing work database when matching the feature quantities of the product.

[0086] When matching the features of the product, the matching unit can apply different matching algorithms depending on the purpose and range of use of the product. For example, in the case of commercial use, the matching unit applies a strict matching algorithm. In addition, in the case of educational use, the matching unit can also apply a relaxed matching algorithm. Furthermore, in the case of personal use, the matching unit can also apply a simplified matching algorithm. This allows matching to be performed using an appropriate algorithm depending on the purpose and range of use. The purpose of use includes commercial use, personal use, educational use, etc. The range of use includes regional limitations, time limitations, etc. Matching algorithms include SIFT (Scale-Invariant Feature Transform) and SURF (Speed-Up Robust Features), etc. This allows matching to be performed using an appropriate algorithm depending on the purpose and range of use of the product.

[0087] The matching unit can estimate the user's emotion and adjust the display method of the matching result based on the estimated user's emotion. For example, when the user is nervous, the matching unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the matching unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the matching unit can also provide a display method that focuses on the main points. This makes it possible to provide the matching result in a display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the matching unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The matching unit can also record the user's voice and estimate the emotion using a voice analysis technology. The matching unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This makes it possible to grasp the user's emotion in detail and adjust the display method of the matching result.

[0088] The matching unit can perform matching taking into consideration the geographical distribution of existing copyrighted work databases when matching the feature quantities of the products. The matching unit performs matching based on the geographical distribution of the existing copyrighted work databases, for example. The matching unit can also perform matching taking into consideration regional characteristics of the existing copyrighted work databases. Furthermore, the matching unit can perform matching taking into consideration the international distribution of the existing copyrighted work databases. This makes it possible to perform matching taking into consideration the geographical distribution. The geographical distribution includes criteria such as by country or by region. The regional characteristics include trends and characteristics of works in a specific region. The international distribution includes the distribution of works in different countries or regions. This makes it possible to perform matching taking into consideration the geographical distribution of existing copyrighted work databases when matching the feature quantities of the products.

[0089] When comparing the features of the product, the matching unit can also perform the matching by referring to legal documents and contract information related to the product. The matching unit performs the matching by referring to, for example, a copyright agreement related to the product. The matching unit can also perform the matching by referring to a license agreement of the product. Furthermore, the matching unit can also perform the matching by referring to license information of the product. In this way, the matching can be performed by referring to legal documents and contract information. Legal documents include contracts and license agreements. Contract information includes contract periods, contract conditions, and the like. In this way, when comparing the features of the product, the matching can be performed by referring to legal documents and contract information related to the product.

[0090] The warning unit can estimate the user's emotion and adjust the expression method of the warning based on the estimated user's emotion. For example, when the user is nervous, the warning unit issues a warning in a gentle expression. Also, when the user is relaxed, the warning unit can issue a warning including detailed information. Furthermore, when the user is in a hurry, the warning unit can issue a concise and quick warning. This makes it possible to issue a warning in an expression method according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. For example, the warning unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The warning unit can also record the user's voice and estimate the emotion using a voice analysis technology. The warning unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This makes it possible to grasp the user's emotion in detail and adjust the expression method of the warning.

[0091] The warning unit can provide an option to adjust the level of detail of the warning or the warning content when the product is similar to an existing copyrighted work. For example, the warning unit issues a detailed warning when the product has a high similarity to the existing copyrighted work. The warning unit can also issue a simplified warning when the product has a medium similarity to the existing copyrighted work. Furthermore, the warning unit can also issue a simple warning when the product has a low similarity to the existing copyrighted work. This makes it possible to issue an appropriate warning according to the similarity. The level of detail of the warning includes a simple warning, a detailed warning, and the like. The warning content includes a risk of copyright infringement, recommended measures, and the like. This makes it possible to provide an option to adjust the level of detail of the warning or the warning content when the product is similar to an existing copyrighted work.

[0092] The warning unit may add a function of presenting an alternative or solution together with a warning when the product is similar to an existing copyrighted work. For example, the warning unit may suggest a different design as an alternative when the product is similar to an existing copyrighted work. The warning unit may also provide an option to contact the copyright holder as a solution when the product is similar to an existing copyrighted work. Furthermore, the warning unit may suggest some changes as a correction when the product is similar to an existing copyrighted work. This makes it possible to provide an alternative or solution when there is similarity. Alternatives include other design proposals and other technical proposals. Solutions include legal advice, technical advice, and the like. This makes it possible to add a function of presenting an alternative or solution together with a warning when the product is similar to an existing copyrighted work.

[0093] The warning unit can estimate the user's emotion and determine the priority of the warning based on the estimated user's emotion. For example, when the user is nervous, the warning unit can display important warnings with priority. In addition, when the user is relaxed, the warning unit can also allow the user to select the display order of warnings. Furthermore, when the user is in a hurry, the warning unit can also display warnings with high urgency with priority. This makes it possible to issue warnings with priority according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the warning unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The warning unit can also record the user's voice and estimate the emotion using a voice analysis technology. The warning unit can also collect the user's biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This makes it possible to grasp the user's emotion in detail and determine the priority of the warning.

[0094] The warning unit may provide relevant legal documents and contract information together with the warning when the product is similar to an existing work. For example, the warning unit may provide a relevant copyright agreement when the product is similar to an existing work. The warning unit may also provide a license agreement when the product is similar to an existing work. Furthermore, the warning unit may provide license information when the product is similar to an existing work. This makes it possible to provide relevant legal documents and contract information when there is similarity. The legal documents include contracts and license agreements. The contract information includes contract terms, contract conditions, and the like. This makes it possible to provide relevant legal documents and contract information together with the warning when the product is similar to an existing work.

[0095] The warning unit can present a modification or improvement proposal for the product together with a warning when the product is similar to an existing work. For example, the warning unit can propose a different design as a modification proposal when the product is similar to an existing work. The warning unit can also propose a partial change as an improvement proposal when the product is similar to an existing work. Furthermore, the warning unit can also propose a different color scheme as a modification proposal when the product is similar to an existing work. This makes it possible to provide a modification or improvement proposal when there is similarity. The modification proposal includes a design modification proposal, a technology modification proposal, and the like. The improvement proposal includes a function improvement proposal, a performance improvement proposal, and the like. This makes it possible to present a modification or improvement proposal for the product together with a warning when the product is similar to an existing work.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The copyright management system can further analyze the user's behavior history and use it to manage the copyright of works. For example, it can record what works the user has created in the past and what copyright issues they have faced, and provide this information as reference information the next time the user creates a work. In addition, if a user tends to create works in a particular genre or style, it can analyze this tendency and strengthen warnings against existing works that are highly similar. Furthermore, it can record specific measures that the user has taken in the past to avoid the risk of copyright infringement, and automatically suggest them when a similar situation occurs. In this way, more effective copyright management can be achieved by utilizing the user's behavior history.

[0098] The copyright management system can further include a function for evaluating a product. For example, it can predict how well a product is likely to be received in the market and provide the result to the user. This evaluation is based on past market data and trend information. It can also analyze how well a product will be received by a specific target audience and provide the result as feedback to the user. Furthermore, it can also suggest improvements and enhancements to the user based on the evaluation result of the product. This makes it possible to grasp the market value of a product in advance and provide a guideline for creating a higher quality product.

[0099] The copyright management system can further include a function for monitoring the usage of the product in real time. For example, it can track how and in which region the product is being used, and identify regions with a high risk of copyright infringement. If the product is being used illegally, it can immediately notify the user of this information and take appropriate measures. It can also flexibly change copyright management policies based on the usage of the product. This allows the usage of the product to be grasped in detail, and appropriate copyright management to be carried out in real time.

[0100] The copyright management system can further strengthen the license management function of the created product. For example, it can manage the license information of the created product in detail and notify the user of the license expiration date and usage conditions. In addition, if the license needs to be renewed, the procedure can be automated to reduce the user's effort. Furthermore, if a license violation occurs, it can immediately notify the user of the information and take appropriate measures. This makes it possible to make the license management of the created product more efficient and reduce the risk of license violation.

[0101] The copyright management system may further include a version management function for the product. For example, different versions of the product may be managed in a centralized manner, and each version may be tracked in what way. The system may also compare the differences between versions of the product and suggest the most suitable version to the user. Furthermore, based on the version management of the product, it may be possible to analyze copyright issues with past versions and predict future risks. This may improve the efficiency of the version management of the product and the accuracy of copyright management.

[0102] The copyright management system can estimate the user's emotions and adjust the display method of the copyright information of the product based on the estimated user's emotions. For example, if the user is stressed, a concise and highly visible display method can be provided. Also, if the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to provide copyright information in a display method according to the user's emotions. Estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. For example, the copyright information display unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The copyright information display unit can also record the user's voice and estimate the emotion using a voice analysis technology. The copyright information display unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This makes it possible to grasp the user's emotions in detail and adjust the display method of the copyright information.

[0103] The copyright management system can estimate the user's emotions and adjust the update frequency of the copyright information of the product based on the estimated user's emotions. For example, when the user is stressed, the copyright information can be automatically updated to save the user's trouble. Also, when the user is relaxed, an option to manually update the copyright information can be provided to give the user freedom of choice. Furthermore, when the user is in a hurry, a simplified interface can be provided to quickly update the copyright information. This allows the copyright information to be updated at an appropriate time according to the user's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, the copyright information update unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The copyright information update unit can also record the user's voice and estimate the emotion using a voice analysis technology. The copyright information update unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows a detailed understanding of user sentiment and allows the frequency of copyright information updates to be adjusted.

[0104] The copyright management system can estimate the user's emotions and adjust the notification method of the copyright information of the product based on the estimated user's emotions. For example, if the user is stressed, a concise and highly visible notification method can be provided. Also, if the user is relaxed, a notification method including detailed information can be provided. Furthermore, if the user is in a hurry, a notification method that focuses on the main points can be provided. This makes it possible to provide copyright information in a notification method according to the user's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the copyright information notification unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The copyright information notification unit can also record the user's voice and estimate the emotion using a voice analysis technology. The copyright information notification unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This makes it possible to grasp the user's emotions in detail and adjust the notification method of the copyright information.

[0105] The copyright management system can estimate the user's emotions and determine the priority of the copyright information of the product based on the estimated user's emotions. For example, if the user is feeling stressed, important copyright information is notified with priority. In addition, if the user is relaxed, the user can be allowed to choose the order of notification of copyright information. Furthermore, if the user is in a hurry, copyright information with high urgency can be notified with priority. This makes it possible to provide copyright information with a priority according to the user's emotions. Estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. For example, the copyright information priority determination unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The copyright information priority determination unit can also record the user's voice and estimate the emotion using a voice analysis technology. The copyright information priority determination unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This makes it possible to grasp the user's emotions in detail and determine the priority of the copyright information.

[0106] The copyright management system can estimate the user's emotion and adjust the display format of the copyright information of the product based on the estimated user's emotion. For example, if the user is stressed, a concise and highly visible display format can be provided. Also, if the user is relaxed, a display format including detailed information can be provided. Furthermore, if the user is in a hurry, a display format that focuses on the main points can be provided. This makes it possible to provide copyright information in a display format according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the copyright information display format adjustment unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The copyright information display format adjustment unit can also record the user's voice and estimate the emotion using a voice analysis technology. The copyright information display format adjustment unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This makes it possible to grasp the user's emotion in detail and adjust the display format of the copyright information.

[0107] The process flow of the second embodiment will be briefly described below.

[0108] Step 1: The registration unit registers the copyright information of the product in the blockchain. The copyright information of the product includes the name of the copyright holder, the copyright registration number, the creation date and time, and creator information. For example, the metadata of the product is registered in the blockchain. The metadata includes the creation date and time of the product and creator information. Step 2: The feature extraction unit extracts the features of the product. The features include image features, text features, video features, etc. For example, an image analysis algorithm is applied to extract image features, a text analysis algorithm is applied to extract text features, and a frame-by-frame analysis algorithm is applied to extract video features. Step 3: The matching unit checks whether the product is similar to existing works based on the features extracted by the feature extraction unit. For example, the matching unit calculates the similarity using a method such as cosine similarity or Jaccard coefficient, and matches the features of the product with a database of existing works. Step 4: The warning unit issues a warning if the generated work is similar to an existing copyrighted work based on the results confirmed by the matching unit, for example, in the form of a text message or an alert sound.

[0109] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0111] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements including the above-mentioned registration unit, feature extraction unit, comparison unit, and warning unit is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the registration unit registers copyright information of the product in the blockchain by the control unit 46A of the smart device 14. The feature extraction unit extracts the feature of the product by, for example, the specific processing unit 290 of the data processing device 12. The comparison unit compares the feature of the product with an existing copyrighted work database by, for example, the specific processing unit 290 of the data processing device 12. The warning unit issues a warning by, for example, the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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, the RAM 30, and the 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.

[0116] 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.

[0117] 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 the voice according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0119] 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 performed in a secure state.

[0120] Fig. 4 shows an example of 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.

[0121] 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.

[0122] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0125] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0126] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0127] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements including the above-mentioned registration unit, feature extraction unit, comparison unit, and warning unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the registration unit registers copyright information of the product in the blockchain by the control unit 46A of the smart glasses 214. The feature extraction unit extracts the feature of the product by, for example, the specific processing unit 290 of the data processing device 12. The comparison unit compares the feature of the product with an existing copyrighted work database by, for example, the specific processing unit 290 of the data processing device 12. The warning unit issues a warning by, for example, the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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, the RAM 30, and the 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.

[0132] 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.

[0133] 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 the voice according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0135] 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 performed in a secure state.

[0136] 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.

[0137] 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.

[0138] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0139] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0140] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0141] 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 voice indicating a user input for 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.

[0142] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0143] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements including the above-mentioned registration unit, feature extraction unit, comparison unit, and warning unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the registration unit registers copyright information of the product in the blockchain by the control unit 46A of the headset type terminal 314. The feature extraction unit extracts the feature of the product by the specific processing unit 290 of the data processing device 12, for example. The comparison unit compares the feature of the product with an existing copyrighted work database by the specific processing unit 290 of the data processing device 12, for example. The warning unit issues a warning by the control unit 46A of the headset type terminal 314, for example. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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, the RAM 30, and the 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.

[0148] 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.

[0149] 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 the voice according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0151] 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 performed in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0153] 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.

[0154] 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.

[0155] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0156] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0157] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0158] 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 a voice indicating a user input for 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.

[0159] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0160] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0161] Each of the multiple elements including the above-mentioned registration unit, feature extraction unit, comparison unit, and warning unit is realized, for example, in at least one of the robot 414 and the data processing device 12. For example, the registration unit registers copyright information of the product in the blockchain by the control unit 46A of the robot 414. The feature extraction unit extracts the feature of the product by, for example, the specific processing unit 290 of the data processing device 12. The comparison unit compares the feature of the product with an existing copyrighted work database by, for example, the specific processing unit 290 of the data processing device 12. The warning unit issues a warning by, for example, the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0163] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0165] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0167] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0169] 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.

[0170] 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 Universal Serial Bus (USB) 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.

[0171] In addition, 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 upon request from the data processing device 12.

[0172] 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.

[0173] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0174] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0175] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0176] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0178] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0180] (Appendix 1) a registration unit that registers copyright information of the product on the blockchain; a feature extraction unit that extracts a feature of a product based on the copyright information of the product registered by the registration unit; a collation unit that checks whether a product is similar to an existing work based on the feature extracted by the feature extraction unit; a warning unit that issues a warning based on the result confirmed by the comparison unit. (Appendix 2) The registration unit is Register the metadata of the product on the blockchain 2. The system of claim 1, (Appendix 3) The feature extraction unit Extracting the features of the product 2. The system of claim 1, (Appendix 4) The collation unit is Matching the generated work with existing works based on the features of the generated work 2. The system of claim 1, (Appendix 5) The warning unit is Issue a warning if the creation is similar to an existing work 2. The system of claim 1, (Appendix 6) The registration unit is To estimate a user's emotion and adjust a registration timing of a product based on the estimated user's emotion. 2. The system of claim 1, (Appendix 7) The registration unit is In addition to the metadata of the product, the log information of the product creation process is also registered on the blockchain. 2. The system of claim 1, (Appendix 8) The registration unit is When registering metadata for a product, provide the option to specify the purpose and scope of use of the product. 2. The system of claim 1, (Appendix 9) The registration unit is Inferring a user's emotion and prioritizing products to be registered based on the estimated user's emotion. 2. The system of claim 1, (Appendix 10) The registration unit is When registering metadata for a work, the creator of the work should take into account his or her past copyright infringement history. 2. The system of claim 1, (Appendix 11) The registration unit is When registering metadata for a product, include any legal or contractual information related to that product. 2. The system of claim 1, (Appendix 12) The feature extraction unit To estimate a user's emotion and adjust a feature extraction method based on the estimated user's emotion. 2. The system of claim 1, (Appendix 13) The feature extraction unit is Apply different extraction algorithms depending on the product category 2. The system of claim 1, (Appendix 14) The feature extraction unit Extract features taking into account the creation date and time of the product and information on the creation environment 2. The system of claim 1, (Appendix 15) The feature extraction unit To estimate a user's emotion and determine the priority of features to be extracted based on the estimated user's emotion. 2. The system of claim 1, (Appendix 16) The feature extraction unit is Extraction is performed by referring to the features of the creator's previous works. 2. The system of claim 1, (Appendix 17) The feature extraction unit Extract features by taking into account market data and trend information related to the product 2. The system of claim 1, (Appendix 18) The collation unit is Inferring a user's emotion and adjusting matching criteria based on the estimated user's emotion 2. The system of claim 1, (Appendix 19) The collation unit is When matching the features of the products, the matching is performed taking into account the update frequency and latest information of the existing copyright database. 2. The system of claim 1, (Appendix 20) The collation unit is When matching the features of a product, different matching algorithms are applied depending on the purpose and scope of use of the product. 2. The system of claim 1, (Appendix 21) The collation unit is To estimate a user's emotion and adjust a display method of a matching result based on the estimated user's emotion. 2. The system of claim 1, (Appendix 22) The collation unit is When matching the features of the products, the geographical distribution of existing copyright databases is taken into account. 2. The system of claim 1, (Appendix 23) The collation unit is When matching the product features, the matching is also performed by referring to the product's related legal documents and contract information. 2. The system of claim 1, (Appendix 24) The warning unit is To estimate a user's emotion and adjust a warning expression based on the estimated user's emotion. 2. The system of claim 1, (Appendix 25) The warning unit is Providing options to adjust the verbosity and content of warnings when the production is similar to existing work 2. The system of claim 1, (Appendix 26) The warning unit is Add a feature that warns users when a work is similar to an existing work and suggests alternatives or solutions. 2. The system of claim 1, (Appendix 27) The warning unit is Inferring user emotion and prioritizing alerts based on the estimated user emotion 2. The system of claim 1, (Appendix 28) The warning unit is If the creation is similar to existing copyrighted work, provide a warning along with any relevant legal or contractual information. 2. The system of claim 1, (Appendix 29) The warning unit is If the product is similar to an existing work, a warning is displayed along with suggestions for modifying or improving the product. 2. The system of claim 1, [Explanation of symbols]

[0181] 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 registration unit that registers copyright information of the product on the blockchain; a feature extraction unit that extracts a feature of a product based on the copyright information of the product registered by the registration unit; a collation unit that checks whether a product is similar to an existing work based on the feature extracted by the feature extraction unit; a warning unit that issues a warning based on the result confirmed by the comparison unit.

2. The registration unit is Register the metadata of the product on the blockchain 2. The system of claim 1 .

3. The feature extraction unit is Extracting the features of the product 2. The system of claim 1 .

4. The collation unit is Matching the generated work with existing works based on the features of the generated work 2. The system of claim 1 .

5. The warning unit is Issue a warning if the creation is similar to an existing work 2. The system of claim 1 .

6. The registration unit is To estimate a user's emotion and adjust a registration timing of a product based on the estimated user's emotion. The system of claim 1 .

7. The registration unit is In addition to the metadata of the product, the log information of the product creation process is also registered on the blockchain.

2. The system of claim 1 .

8. The registration unit is When registering metadata for a product, provide the option to specify the purpose and scope of use of the product.

2. The system of claim 1 .

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