[Retracted] Mode Optimization and Rule Management of Intellectual Property Rights Protection of Educational Resource Data Based on Machine Learning Algorithm

Cao, Jiawei

doi:https://doi.org/10.1155/2021/1909518

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Abstract Introduction Methods Analysis Conclusions Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

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Volume 2021 | Article ID 1909518 | https://doi.org/10.1155/2021/1909518

[Retracted] Mode Optimization and Rule Management of Intellectual Property Rights Protection of Educational Resource Data Based on Machine Learning Algorithm

Jiawei Cao^1,2

Academic Editor: Zhihan Lv

Received23 Apr 2021

Revised20 May 2021

Accepted05 Jun 2021

Published17 Jun 2021

Abstract

Educational resource data are a collection of final documents obtained by users, including full-text journals, books, dissertations, newspapers, conference papers, and other database materials. While searching for information in the educational resource database, these resources also have functions such as copying, downloading, reproduction, and dissemination, which raise the issue of expression and protection of intellectual property. Machine learning takes how computers simulate human learning behaviors as the main research content, which can independently determine learning objects, construct their characteristics, perform additional operations beyond the limitations of preset instructions, and discover value from the expression of relative works. On the basis of summarizing and analyzing previous research works, this paper expounded the current research status and significance of intellectual property expression and protection of educational resource data; elaborated the development background, current status, and future challenges of machine learning technology; introduced the methods and principles of data classification algorithm and protection authority identification; performed the technical framework design and expression system establishment of the intellectual property expression of educational resource data based on machine learning; analyzed the mode optimization and rule management of intellectual property protection of educational resource data based on machine learning; and finally conducted a simulation experiment and its result analysis. The results show that the machine learning technology can build a subject-oriented, highly integrated, and time-changing educational resource data storage environment; the comprehensive, analysis-oriented decision-supporting system formed by machine learning can give full play to the potential role of data integration and value discovery and is therefore of great significance for the intellectual property expression and protection of integrated and complexly-related educational resource data. The study results of this paper provide a reference for further research on the intellectual property expression and protection of educational resource data based on machine learning.

1. Introduction

Educational resource data are a collection of final documents obtained by users, including full-text journals, books, dissertations, newspapers, conference papers, and other database materials. In the data expression and protection of intellectual property educational resources, many documents are collected in commercial databases with the right to use, and the resources in the library are searched, downloaded, and integrated into characteristic databases [1]. These resources also have copying, downloading, copying, dissemination, and other functions, which caused intellectual property issues. According to different types of educational resources, machine learning educational resource data can be divided into government educational resource data, other public institution educational resource data, corporate educational resource data, and personal educational resource data [2]. The mode optimization can generate massive amounts of data in real time and process them in real time to ensure that intellectual property education resource data become a handy resource anytime and anywhere. In the machine learning environment, educational secret data may not be lost due to the protection of a data backup system, but educational resources may lose control of educational secret data due to data migration obstacles [3]. Machine learning may be suspected of infringement of technical measures or violations of laws and regulations, which can independently determine learning objects, construct their characteristics, perform additional operations beyond the limitations of preset instructions, and discover value from the expression of works [4].

The original material for machine learning is data, and how to deal with the rights above data, such as the right to privacy, personal information, and trade secrets, is a major legal issue facing the development of artificial intelligence technology [5]. The rule management of intellectual property rights protection can test not only the correlation between the characteristic variables and the data quality of intellectual property education resources but also the importance of the characteristic variables to the data quality of intellectual property education resources. The strict regulation and improvement of the liability clause, dispute settlement clause, and confidentiality clause of the license agreement can ensure that the work is copied and disseminated under reasonable use purposes, thereby restricting the dissemination and scope of the data work and preventing illegal use, so that the legitimate rights and interests of the right owner of the data work will not be harmed and machine learning is a science of artificial intelligence [6]. Algorithms mainly including decision trees, support vector machines, neural networks, genetic algorithms, and machine learning technology are a key link in data mining and data protection [7]. The intellectual property expression and protection of educational resource data must be established on the basis of technological innovation in order to implement the concept of public welfare, expression, and protection of knowledge [8].

On the basis of summarizing and analyzing previous research works, this paper expounded the current research status and significance of intellectual property expression and protection of educational resource data, elaborated the development background, current status and future challenges of machine learning technology, introduced the methods and principles of data classification algorithm and protection authority identification, performed the technical framework design and expression system establishment of the intellectual property expression of educational resource data based on machine learning, analyzed the mode optimization and rule management of intellectual property protection of educational resource data based on machine learning, and finally conducted a simulation experiment and its result analysis. The study results of this paper provide a reference for further researches on the intellectual property expression and protection of educational resource data based on machine learning. The detailed chapter arrangement is as follows: Section 2 introduces the methods and principles of data classification algorithm and protection authority identification; Section 3 performs the technical framework design and expression system establishment of the intellectual property expression of educational resource data; Section 4 analyzes the mode optimization and rule management of intellectual property protection of educational resource data; Section 5 conducts a simulation experiment and its result analysis; Section 6 is the conclusion.

2. Methods and Principles

2.1. Data Classification Algorithm

Intellectual property education resource data constitute two important data sets in data mining: training data and test data. In terms of content selection, the possibility of intellectual property protection is inversely proportional to the breadth of data and information; that is, the more comprehensive the collection of data and information is, the less selective it is and the less its originality in the selection of content. The comprehensiveness of the data information content is exactly where its educational value lies.

The consistency of educational resource data refers to the consistency of the distribution of data in different domains. Suppose n different fields X = {x₁, x₂, …, x_n} and the number of resources in each field is {y₁, y₂, …, y_n}, then the educational resource data consistency y (x_i) of the candidate data x_i is defined aswhere f (x_i) is the number of expressions of candidate data appearing in resource x_i. When the candidate data f (x_i) is more evenly distributed in each resource of the intellectual property, the consistency of the educational resource data y (x_i) is also greater, indicating that it is likely to be filtered data.

When the computer uses the training set to train the model, overfitting may occur; that is, the training sample reaches very high approximation accuracy, but the approximation error of the test sample first decreases and then rises with the number of training times. The random forest model will use the exponential gain Q (x_i) as the basis for the selection of the decision tree:where a (x_i) is the proportion of the number of samples of category x_i in all samples; b (x_i) is the decision tree judgment node of the samples of category x_i; c (x_i) is the number of split points of samples of category x_i; and d (x_i) is the category x_i of the number of samples in all samples.

For machine learning, it is easy by changing the protection structure of data information to circumvent copyright protection, which will make the protection of data information meaningless. The value and significance of data information lies in the information material itself rather than the structural order of the information material. The structure that loses the content of the information is just an empty shelf without a soul, with little protection value at all. Therefore, even for original data information, the principle of only protecting expressions but not protecting ideas makes the protection of data information still very weak.

The weighted average of the expression difference ratio of the candidate educational resource data is used in the field of intellectual property, and a comprehensive index to filter the infringement data in the resource data set is defined as follows:where e (x_i) and are respectively the total number of expressions of resource data x_i in the intellectual property field; h (x_i) and k (x_i) are the respective contributions of the intensity and expression difference ratio of data resources x_i; and l (x_i) and m (x_i) indicate the frequency of expression and protection of candidate data resources x_i in the field of intellectual property.

As a result, data information that does not have originality is a ubiquitous thing and will increase greatly with the development of the information service industry, but intellectual property cannot provide corresponding protection for data information that does not have originality. According to the principle that machine learning only protects expressions but not ideas, what intellectual property protects of data and information is its original choice or protected expression, not the content it chooses or protects.

2.2. Protection Authority Identification

The intellectual property expression and protection of educational resource data refer to the whole process of resource data collection, input, processing, analysis, regeneration, and output. At present, many intellectual property education resource data management systems reflect the expression and protection of some resource data. Through the realization of data expression and protection, the informatization, intellectualization, and even decision-making management of intellectual property education resource data can be completed.

The average expression probability of educational resource data x_i in a single intellectual property indicates the intensity of x_i in the resource field, so the intensity E (x_i) of resource data x_i can be expressed aswhere o (x_i) is the frequency of expression of educational resource data x_i in the entire intellectual property; and p (x_i) is the number of expressions of resource data x_i in the field of intellectual property.

In addition, how to use data warehouse technology to build a subject-oriented, highly integrated, stable, and time-changing data storage environment to form a comprehensive, analysis-oriented decision support environment has become an urgent issue for data integration of intellectual property education resources. Another important question is related to the integral educational resources, which reflects the complex correlation between intellectual property education resource data, similar to the intertwined network of relationships.

The time domain analysis method is to express the information distribution of the educational resource data x_i as a function of time; the frequency domain analysis method is to obtain the frequency domain and its energy frequency domain distribution through the transformation of the educational resource field, so the transformation of the educational resource data T (x_i) iswhere a_i is the expression range factor; b_i is the protection efficiency factor; and c_i is the total number of data to be protected. The intellectual property expression of educational resource data includes two steps: one is to establish a statistical model through the identification and calculation of the classification of the training set flow; the other is to apply the established statistical model to the unknown and new flow classification in the network traffic. So the probability that each educational resource data belongs to a specific application iswhere q (x_i) is the prior probability of educational resource data x_i; r (x_i) is the conditional probability of given educational resource data x_i; s (x_i) is the number of times the educational resource data x_i performs information automation; and t (x_i) is the total number of times of information processing of educational resource data x_i. This method assumes that the characteristics of educational resource data are independent of each other.

From an overall point of view, the competent information department takes charge of the overall situation and integrates all business systems. Based on the classification of intellectual property education resource data, data expression and protection will realize the standardization and distributed management of data and ensure the smooth flow of data. Partially, the administrative and teaching departments of universities will be responsible for the management of specific educational resource data, and data expression and protection will realize the specific business of each department.

3. Intellectual Property Expression of Educational Resource Data Based on Machine Learning

3.1. Technical Framework Design

Educational resource data types include not only structured data, but also a large amount of semistructured data and unstructured data, and structured data refer to data with a fixed format and limited length. Unstructured data refer to data with variable length and no fixed format. Data expression and products of educational resources based on machine learning are usually protected by copyright in the early days, but copyright protects the expression form of ideas, the data expression and the selection, arrangement, system, and structure of the work, rather than the protection of the data itself [9]. And if the data are provided in the form of public dissemination, it will inevitably be exposed to and obtained by an unspecified number of people, and the service provider cannot guarantee that its application purpose must comply with the rules, which creates the risk of copyright infringement. Therefore, the data work operator should establish a reasonable control method, use one-to-one network transmission, and protect the security of the data work in terms of identity authentication, access permission control, and control of the operating environment, so as to effectively prevent piracy, illegality, and the occurrence of copying behavior. In this case, the license agreement for the data work becomes a legally binding agreement between the user and the data operator regarding the purchase of the copyright license for the data work. Figure 1 shows the framework for intellectual property expression and protection of educational resource data based on machine learning.

As far as network access behavior is concerned, since machine learning itself is only an imitation of human access behavior, the access behavior will not constitute an infringement of human access to information that can be accessed by it. However, if the educational resource captured by the data itself has some technical measures to ensure that only specific users can access the information through these technical measures, but the machine learning operator breaks these restrictions, the machine learning operator may be suspected of infringement of technical measures or violations of laws and regulations. Since machine learning works by parsing code, in order to circumvent the antimachine learning measures set by educational resource operators, machine learning operators may adopt disguise. Therefore, through machine learning, machine learning controllers will be able to access and grab content that ordinary users cannot reach. In addition to the own risks of using data machine learning to obtain data, machine learning controllers may also face the risks of violations of laws, regulations, and even crimes by obtaining certain legally protected information. However, if the machine learning controller obtains information from its own educational resources and publicly disseminates the captured information, it may further infringe the copyright owner’s right to disseminate information on the Internet [10].

Artificial intelligence is based on the computer’s digital abstraction of the human brain and machine learning is more similar to the human learning process, so it can be said that machine learning is the reproduction of the human brain in the computer. When humans learn a new language, they usually convert training information into electrochemical traces in the brain that are specifically responsible for language areas and store them. This is the process of human learning and memory, and human reciting the work of others does not infringe the copyright of others. The reason is that, on the one hand, the economic interests of the copyright owner are not affected, and on the other hand, the regulation of reciting and memorizing is not practical. Therefore, the protection of intellectual property education resources shows that this is the case and activities are beyond the scope of copyright law. In order to ensure the accuracy of data obtained by machine learning and eliminate errors in output results, machine learning needs to incorporate as much known data as possible into the protection of intellectual property. As an institutional tool for balancing public interests and exclusive rights, rational use has played a good role in the system in some specific fields, including the field of mechanical learning in the past. But on the issue of balancing the interests of machine learning, fair use cannot balance the contradiction between the exclusive rights of copyright owners and the development of machine learning technology. The reason for the failure of the system is that the balance of interests of machine learning is contrary to the basic principles of fair use.

3.2. Expression System Establishment

The core of the new generation of artificial intelligence technology is machine learning characterized by data training algorithms. The raw material for machine learning is data, and how to deal with the rights above data, such as the right to privacy, personal information, and trade secrets, is a major legal issue facing the development of artificial intelligence technology. Among them, if there is someone else’s copyright on the data used for machine learning, this will cause the problem of the expression and protection of the intellectual property education resource data of the machine learning work. The first step uses photography or photocopying technology to convert book pages into electronic images, and the second step uses optical character recognition technology to extract text from electronic images and convert them into binary numbers [11]. Data set in the third step is to copy the data set containing the work to the computer system where the algorithm is located for further mining and analysis. Unlike human learning works, machines learn work automatically, in batches and without distinction. The intellectual property of its educational resources is huge, diverse, and broad in scope, including educational resource data and works whose intellectual property is in the public domain. There is also a difference between copyrighted works under public license agreement and copyrighted works with all rights reserved in copyrighted works. The intellectual property expression systems of educational resource data based on machine learning is shown in Figure 2.

Intellectual property education resource data are a collection of final documents obtained by users, including databases such as full-text journals, books, conference papers, dissertations, and newspapers. In the data expression and protection of intellectual property educational resources, many documents are collected in commercial databases with the right to use, and the resources in the library are searched, downloaded, and integrated into characteristic databases. These resources also have copying, downloading, dissemination, and other functions, which caused intellectual property issues. A photographic work refers to a work of art that uses equipment to record the image of an objective object on photosensitive materials or other media. A picture refers to a photographic work that uses a camera as the main tool to record or reflect the real scene in a plane. In this process, the author’s creation or labor is condensed, and the photographic work is expressed in a certain form of carrier. That being the case, in accordance with the provisions of the copyright law, pictures are of course protected by the copyright law. Therefore, in the construction of characteristic database resources, image resources will also cause infringement. Instructors invest in intellectual work and use open software to select, arrange, and refine the results of the public domain or others to form courseware that expresses their own ideas. The selection, arrangement, refinement, and presentation of materials are all manifestations of independent creativity and should be protected by copyright law.

Intellectual property education resource data constitute two important data sets in data mining: training data and test data. The training data are historical data, used to train the classifier, and the test data are real-time data, used to detect the accuracy of the classifier. In traffic recognition, the choice of training data and test data has a great impact on the classification results. If they are collected from the same node, the accuracy of the final recognition will be higher. If the collection time period is also the same, there will be a problem of transition fitting. If the two data collection locations are from different nodes, the recognition preparation rate will certainly not be higher than the former [12]. At the same time, the collection size of the training data also has an impact on the recognition accuracy. For example, when the size of the training set continues to decrease, the accuracy of recognition also continues to decrease. Feature selection, also known as attribute selection, is a very important part of the data mining process. If the feature selection is correct, then redundant and irrelevant content in the intellectual property education resource data can be removed and the selection will not lose the original data. The smallest attribute subset is on the basis of value, thereby improving the quality of data and accelerating the speed of learning. Therefore, the effective selection of attribute characteristics must select different attribute characteristics for specific applications, so that specific applications can be detected quickly and accurately from numerous network applications.

4. Intellectual Property Protection of Educational Resource Data Based on Machine Learning

4.1. Protection Mode Optimization

The intellectual property of educational resource data has expanded the pure storage function into a collection of multiple functions, including design, development, utilization, management, evaluation, and other functions. Based on machine learning technology, the application of data mining, learning analysis, and other emerging intelligent technologies can also accurately and intelligently push corresponding educational resource data to learners, making educational resource data humanized and intelligent. Decision tree is a method of approximating the value of a discrete function, which first processes the data, uses an induction algorithm to generate readable rules and decision trees, and then uses the decision tree to analyze the new data. After each data analysis, the analyzed data are retained, and the supervised learning module is used to update the training data set in order to improve the accuracy of machine learning module recognition. Educational resource data are in a stage of rapid development, and many technologies will continue to support the development of educational resource data from various aspects, making educational resource data show various characteristic applications and results (Figure 3). However, the overload of data makes it difficult to express and protect intellectual property and poses the challenge of discerning choices and choosing the best. The intellectual property expression of educational resource data based on machine learning includes critical thinking ability, communication ability, cooperation ability, and creative ability, that is, the development of learner’s wisdom.

(a)

(b)

Machine learning educational resource data are various programs, applications, and information that are deployed and stored in the machine learning system of educational resources. According to different types of educational resources, machine learning educational resource data can be divided into government educational resource data, other public institution educational resource data, corporate educational resource data, and personal educational resource data. According to the different legal attributes of the data, educational resource data can be divided into educational secrets, works, personal privacy, and other data. In a machine learning environment, educational secret data may not be lost due to the protection of a data backup system, but educational resources may lose control of educational secret data due to data migration obstacles. Machine learning educational resource data migration means that educational resources retrieve relevant data stored in one machine learning and move it to machine learning. The demand for data migration of educational resources usually arises after the contract with the machine learning provider is terminated. If the machine learning used by educational resources is not compatible with other machine learning, data migration is not technically feasible or the migration cost is very high. However, if the machine learning provider prohibits the migration of educational resource data in the service contract or restricts the migration of educational resource data by means such as high fees, then the migration of data will be difficult to achieve smoothly [13].

In machine learning, the computer can further examine the more complex nonlinear relationship between the number of cited intellectual property educational resource data and the subsequent citation of intellectual property educational resource data, as well as the number of cited intellectual property educational resource data, the number of intellectual property educational resource data of the same family, etc. The common effect of data characteristics of different intellectual property educational resources is on the quality of intellectual property educational resource data. In addition, the random forest model’s calculation of the exponential gain of characteristic variables can not only test the correlation between the characteristic variables and the data quality of intellectual property education resources, but also the importance of the characteristic variables to the data quality of intellectual property education resources [14]. The authorization of intellectual property educational resource data is the basic manifestation of the quality of the intellectual property educational resource data. Therefore, the statistics of authorized citing intellectual property educational resource data and statistics of all citing intellectual property educational resource data can better reflect the intellectual property education in the correlation between resource data and existing high-quality intellectual property education resource data. If the scope of technological protection is greater than the dividing point, the intellectual property education resource data will be classified into the left branch to enter the judgment of the next node; otherwise, the intellectual property educational resource data will be classified into the right branch.

4.2. Protection Rule Management

The intellectual property expression of educational resource data mainly includes statistical expression methods, machine learning expression methods, and linguistic expression methods. Among them, the machine learning expression method is based on a large number of databases. The keyword extraction problem is regarded as a binary classification problem. The training and collection are trained by using decision trees and other methods to learn the characteristics of keywords and construct a classification model. If it adopts paper-based review and approval activities, the approval status cannot be tracked in real time, the countersignature cannot be delivered to the counter-signer at the same time, the consistency of the approval comments cannot be guaranteed, and the approver cannot be reminded in time. This is the implicit demand for informatization management in the standard (Figure 4). It does not clearly state the use of informatization means to do it, but if informatization means are used, the effect of the standard requirements can be better achieved. Automatic collection and classification are mainly used in the acquisition of competitive intelligence, such as the automatic collection of competitors’ patents, including automatic identification of competitors and automatic classification of competitors’ patent technologies [15–17]. Machine learning will analyze all patents of educational resources and newly published or authorized patents, calculate the similarity, and determine that the applicant has achieved the purpose of automatically identifying competitors. Through the continuous training of new published and authorized patents every week, the accuracy of competitor identification will be improved.

The intellectual property expression and protection of educational resource data must be established on the basis of technological innovation in order to implement the concept of intellectual public welfare, expression, and protection. In the era of machine learning, machine learning collection, analysis, and processing technologies have made it easier and easier to obtain data close to the whole. From the perspective of data collection technology, the extensive application of mobile network can generate massive amounts of data in real time and process them in real time to ensure that intellectual property education resource data become a handy resource anytime, anywhere, and to meet the seamless and fragmented learning in the machine learning era. Demands have enhanced the convenience, timeliness, and pertinence of data acquisition of intellectual property education resources [18–20]. There is no doubt that machine learning technology will become a key factor in enhancing the effectiveness of educational resources. Therefore, the main body of intellectual property education resource data construction should be good at learning and innovating machine learning–related technologies to promote the expression and protection of resources. On the machine learning platform, resource builders can conduct a more effective and precise analysis of learning needs. The digital fragments left by the students during the learning process, such as resource selection, online learning, communication, opinions, and suggestions, will be further analyzed to explore the laws, preferences, and patterns of educational resources in the data age.

The choice of data for machine training will also affect how machine learning autonomously defines the algorithm rules between feature quantities and target effects. At this stage, machine learning does not yet have the ability to create independently. Only in the future when super machine learning implements self-correcting neural network algorithms and collects and learns external data independently will it have the ability to create independently. Therefore, the current control of the creative intentions and effects of machine learning creations is achieved by controlling the artificial neural network algorithm program, the selection of machine learning data and the provision of creative materials and the input of creative requirements. It is worth noting that the control of the final educational resources is a kind of abstract and general scope of creative content and effect control, which is equivalent to drawing a circle of the final educational resources, and machine learning is to find a point in this circle [21–24]. The essence of this process is that the designer indirectly expresses his own way of thinking and path of learning and understanding of educational resources in the form of algorithm code. Therefore, the method and path of machine learning to learn educational resources, the strength and effect of learning ability, are all set by the designer, which reflects the designer’s intellectual creative activities [25–29].

5. Simulation Experiment and Analysis

5.1. Simulation Experiment Design

There are many types and huge amounts of intellectual property education resource data. The collection, organization, and sorting process require developers to invest a lot of time, energy, and funds, and it will add value to users in the process of using it. Therefore, digital education resources research on several intellectual property issues is inevitable. Adding, deleting, moving, splicing, and reorganizing network information and editing rights, as a right of the original author or other copyright holders, the content of the work can be edited only under the consent of the original author or other copyright holders and the form of expression of the original work cannot be changed [30–32]. Therefore, this actually infringes the copyright owner’s right to edit, modify, and protect the integrity of the work, and when it comes to the issue of website links, whether the link directly to another person’s website or the information in another person’s website is infringing, if the other person’s website is infringed linking by code meaning that it gives the impression that it is the behavior of the linker’s own website, or adding a page or specific content from another person’s website to the relevant item of his website, it may be regarded as infringement by machine learning (Figure 5). At the same time, since the issue of database protection is still a new topic, further research and exploration are needed. In many countries, it is still difficult to make specific regulations.

(a)

(b)

The actual problems of intellectual property protection of educational resource data usually appear in the digital realm, so new legislative measures on database protection should probably exclude the paper world. In practice, electronic databases may be more comprehensive than paper versions and easier to update frequently and can provide targeted searches for individual user needs. In addition to the format limitation that cannot be avoided in the physical process of storing computer information randomly, any form of information arrangement will damage the practicality of the database [33–35]. After searching, users can add their own layout to the search results to achieve their own information presentation requirements. Generally speaking, the usefulness of a database is inversely proportional to the initial degree of arrangement in the database, and more structure equals less utility. The administrative and teaching departments of classification methods will be responsible for the management of specific educational resource data, and data expression and protection will realize the specific business of each department. Since machine learning works by parsing code, in order to circumvent the classification methods by educational resource operators, machine learning operators may adopt disguise. The classification methods for intellectual property of its educational resources is huge, diverse, and broad in scope, including educational resource data and works whose intellectual property is in the public domain.

5.2. Result Analysis

Statistics-based methods use large-scale data for machine learning and are divided into unsupervised and supervised machine learning methods according to whether the data are manually labeled. In terms of effect, the method of combining rules and statistics is more dominant. In theory, conventional intelligent search machine learning, the more the number of results selected, the higher the recall rate, but when the number of results exceeds a certain number, the improvement of the recall rate will be greatly slowed down. In the multimachine learning integration strategy, it is impossible to unrestrictedly select too many results. Therefore, it is necessary to find the critical point with the relatively best recall rate through evaluation and use this critical point to extract the result set when multimachine learning is integrated (Figure 6). The main idea of this ordering strategy is to make full use of the results and ordering, use the relevance ordering results to make multiple determinations, and linearly add the similarity values of the results to obtain a multidetermined patent result set. In single machine learning evaluation, the evaluation level of multiple machine learning may be quite different. At this time, the weight of linear integration needs to be determined, and the weight needs to be more biased towards the best machine learning, so as to ensure that users can get better educational resource data expression results based on the best single machine learning.

(a)

(b)

Whether the scope of reasonable use should be expanded or contracted in the digital network environment cannot be simply generalized, which should be specifically analyzed for different situations. As far as the sharing of digital education resources is concerned, it has a strong social welfare and should be included in the scope of fair use. Of course, when setting specific fair use clauses, it should be divided into different levels, so as to protect the reasonable rights and interests of copyright owners and give maximum convenience to education and sharing educational resources (Figure 7). With the development and popularization of digital network technology, in the field of school teaching, whether teachers or students use the network to read and browse copyrighted works has gradually formed a trend. It is foreseeable that, in the future school education and teaching, the use of computer network-assisted teaching and learning will become an important way of teaching activities. In order to meet the needs of teachers and students to obtain teaching resources through the Internet, the campus construction of increasing digital teaching resources has become a trend in school education and teaching. In this process, it is inevitable that there will be occasional copying in personal computers, a small amount of printed documents, and computer network transmission within a part of the campus. This reasonable use of digital teaching resources should be confirmed by exemption clauses.

The learning users of intellectual property educational resource data are distributed in various regions around the world. In addition, the Internet has changed the dissemination and creation of educational resources, making it difficult to define the location of infringement using traditional infringement theories. The infringer uses information technology to achieve stealth, and it is difficult to accurately determine the place where the infringement occurs and the place where the infringement result occurs. Since the online real-name system has not yet been formally established, most intellectual property education resource data institutions do not require real-name registration when users log in for the first time. More in-depth analyses for enhancing the persuasive power of the experiment in this study have been supplemented as follows: the value and significance of data information lie in the information material itself rather than the structural order of the information material. Another important question is related to the integral educational resources, which reflects the complex correlation between intellectual property education resource data, similar to the intertwined network of relationships. Therefore, through machine learning, machine learning controllers will be able to access and grab content that ordinary users cannot reach. In addition to the own risks of using data machine learning to obtain data, machine learning controllers may also face the risks of violations of laws, regulations, and even crimes by obtaining certain legally protected information. It is difficult to determine the location of the infringement and the infringer, which undoubtedly makes it very difficult for the right holder to defend their rights, and the difficulty of prosecuting has become a major obstacle to protecting their rights. In this regard, the regularization idea of the machine learning model can solve the problem of independent variables, that is, add penalty items involving model complexity to the target optimization equation of parameter estimation, avoid overfitting of the prediction model, and improve the accuracy of model prediction.

6. Conclusions

This paper performed the technical framework design and expression system establishment of the intellectual property expression of educational resource data based on machine learning, analyzed the mode optimization and rule management of intellectual property protection of educational resource data based on machine learning, and finally conducted a simulation experiment and its result analysis. Machine learning technology is a key link in data mining and data protection. The intellectual property expression and protection of educational resource data must be established on the basis of technological innovation in order to implement the concept of intellectual public welfare, expression, and protection. The intellectual property expression of educational resource data mainly includes statistical expression methods, machine learning expression methods, and linguistic expression methods. Among them, the machine learning expression method is based on a large number of databases and the keyword extraction problem is regarded as a binary classification problem. The training and collection are trained by using decision trees and other methods to learn the characteristics of keywords and construct a classification model and then extract the keywords according to the classification model. If the educational resource captured by the data itself has some technical measures to ensure that only specific users can access the information through these technical measures, but the machine learning operator breaks through these restrictions, the machine learning operator’s access behavior may be suspected of infringement or violation of laws and regulations that undermine technical measures. The results show that the machine learning technology can build a subject-oriented, highly integrated, and time-changing educational resource data storage environment; the comprehensive, analysis-oriented decision-supporting system formed by machine learning can give full play to the potential role of data integration and value discovery and is therefore of great significance for the intellectual property expression and protection of integrated and complexly related educational resource data. The study results of this paper provide a reference for further researches on the intellectual property expression and protection of educational resource data based on machine learning.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The author declares no conflicts of interest.

Acknowledgments

This work was supported by the Youth Fund for Humanities and Social Sciences Research of the Ministry of Education Project: Research on the Unity of Subject’s Dual Scales Based on Information Intermediary Theory (No. 19XJC72001); the Second Batch of University Level Scientific Research Projects of City College of Xi’an Jiaotong University in 2020: Research on the Improvement PATH of Classroom Discourse Communication Quality of Ideological and Political Theory Course in Colleges and Universities in the New Era (No. 202002Y06); and Major Theoretical and Practical Research Projects of Philosophy and Social Sciences in Shaanxi Province: Research on Improving the Quality of Classroom Discourse Communication of Ideological and Political Theory Course in Colleges and Universities in the New Era (No. 2021ND0231).

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Copyright © 2021 Jiawei Cao. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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