1. A system for automated metadata creation for library collections using deep learning techniques, the system comprising: a processor configured to execute deep learning algorithms to analyze text data from library collections; a memory storing a metadata database for storing the output of the deep learning algorithms; an input module for receiving text data from library collections; a metadata generation module configured to generate metadata for the text data based on the output of the deep learning algorithms; and an output module for storing the generated metadata in the metadata database.

2. The system of claim 1, wherein the deep learning algorithms comprise natural language processing algorithms.

3. The system of claim 1, wherein the metadata generation module is configured to generate metadata for multiple languages.

4. The system of claim 1, wherein the input module is configured to receive text data from various sources, including electronic books, journals, and audio recordings.

5. A method for automated metadata creation for library collections using deep learning techniques, the method comprising: providing a text data input module configured to receive text data from library collections; providing a metadata generation module configured to generate metadata for the text data using deep learning algorithms; executing the deep learning algorithms to analyze the text data and generate metadata for the text data; and storing the generated metadata in a metadata database.

6. The method of claim 5, wherein the deep learning algorithms comprise natural language processing algorithms.

7. The method of claim 5, wherein the metadata generation module is configured to generate metadata for multiple languages.

8. The method of claim 5, further comprising receiving the text data from various sources, including electronic books, journals, and audio AUTOMATED METADATA CREATION FOR LIBRARY COLLECTIONS USING DEEP LEARNING TECHNIQUES Abstract In some implementations of the current disclosure, there is the potential for there to be a system that, via the use of various forms of deep learning, is able to create metadata for library collections automatically. A processor that is able to perform deep learning algorithms in order to analyze text data obtained from library collections may be included in this system. This processor may be configured in a certain way. In certain implementations, there is also the possibility of including a memory that is able to store a metadata database, as well as a memory that is able to store the results produced by deep learning algorithms. Input modules, which are able to receive text data from library collections, may also be included in embodiments. Such modules might be included in the system. In certain implementations, there is also a metadata production module that is configured to generate metadata for the text data based on the results that are created by the deep learning algorithms. This metadata is generated in accordance with the results of the deep learning algorithms. In certain implementations, there is also the possibility of including an output module with the intention of storing the newly formed metadata into the metadata database. , Claims:Claims :

1. A system for automated metadata creation for library collections using deep learning techniques, the system comprising: a processor configured to execute deep learning algorithms to analyze text data from library collections; a memory storing a metadata database for storing the output of the deep learning algorithms; an input module for receiving text data from library collections; a metadata generation module configured to generate metadata for the text data based on the output of the deep learning algorithms; and an output module for storing the generated metadata in the metadata database.

2. The system of claim 1, wherein the deep learning algorithms comprise natural language processing algorithms.

3. The system of claim 1, wherein the metadata generation module is configured to generate metadata for multiple languages.

4. The system of claim 1, wherein the input module is configured to receive text data from various sources, including electronic books, journals, and audio recordings.

5. A method for automated metadata creation for library collections using deep learning techniques, the method comprising: providing a text data input module configured to receive text data from library collections; providing a metadata generation module configured to generate metadata for the text data using deep learning algorithms; executing the deep learning algorithms to analyze the text data and generate metadata for the text data; and storing the generated metadata in a metadata database.

6. The method of claim 5, wherein the deep learning algorithms comprise natural language processing algorithms.

7. The method of claim 5, wherein the metadata generation module is configured to generate metadata for multiple languages.

8. The method of claim 5, further comprising receiving the text data from various sources, including electronic books, journals, and audio

Description:AUTOMATED METADATA CREATION FOR LIBRARY COLLECTIONS USING DEEP LEARNING TECHNIQUES
Field of the Invention
[0001] The present invention relates generally to the field of library science and, more particularly, to the automated creation of metadata for library collections using deep learning techniques.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Library collections have traditionally relied on manual methods to create and manage metadata for their resources. Metadata is essential for the organization, searchability, and discoverability of library collections. However, manual metadata creation can be time-consuming, error-prone, and often results in inconsistent and incomplete metadata. As library collections continue to grow in size and complexity, there is a need for more efficient and accurate methods of metadata creation.
[0004] Automated metadata creation is an emerging field that uses various techniques, such as machine learning, natural language processing, and computer vision, to automatically extract and generate metadata from digital resources. These techniques can be used to analyze and extract relevant information from a variety of sources, such as text, images, and audio, and create accurate and consistent metadata for library collections.
[0005] There are several challenges to developing an automated metadata creation system for library collections. One challenge is the need to develop algorithms and models that can accurately extract and interpret information from a variety of resource types and formats. Another challenge is the need to ensure that the metadata created is accurate, consistent, and meets the specific requirements of the library collection. Few of the prior arts of the aforementioned domain are listed below.
[0006] KR10-2019-0091596A (By: OKJEONG FARMING, JEONJU UNIVERSITY OFFICE OF INDUSTRY UNIVERSITY COOPERATION) The present invention relates to an automated packaging line and a robotics e logistics process for building a stable logistics system. By using a robotics technology, high-quality products can be produced so as to make inroads into a wider markets. In addition, by using robotics technology capable of replacing manpower, production costs can be reduced at the same time such that high-quality products can be produced at low costs. Furthermore, by replacing manpower with robotics technology, the manpower for simple labor can be educated to be a high-level manpower for managing robots such that the manpower can be highly skilled and more jobs can be created, instead of taking out jobs by robotics technology. The present invention comprises: an automated sewing device; a container and automatic taping machine; a high-tech loading robot; and a fully automatic packaging line.
[0007] US20220335340A1 (By: INTEL) Methods, apparatus, systems, and articles of manufacture are disclosed for data usage monitoring to identify and mitigate ethical divergence. Disclosed example apparatus are to orchestrate resources in an edge environment based on ingested network traffic on an edge network, the ingested network traffic associated with a source node that is to source a target data stream and a target artificial intelligence (AI) application node that is to consume at least a portion of the target data stream. Disclosed example apparatus are also to execute a machine learning model based on the ingested network traffic to generate one or more outputs including at least one a first value representative of a data stream characteristic or a second value representative of an AI application node characteristic, determine the one or more outputs satisfy a threshold value, and generate an alert in response to the one or more outputs satisfying the threshold value.
[0008] US20230016946A1 (By: INTEL) Methods, apparatus, systems, and articles of manufacture are disclosed for proactive data routing. An example apparatus includes at least one memory, machine-readable instructions, and processor circuitry to execute the machine-readable instructions to at least execute a machine-learning model to output a first data routing path in a network environment based on metadata associated with an event in the network environment. The processor circuitry is further to, after a detection of a change of the first data routing path to a second data routing path, retrain the machine-learning model to output a third data routing path based on the second data routing path. The processor circuitry is additionally to cause transmission of a second message to a first node based on the third data routing path after an identification of the event.
[0009] Even though, the prior arts have contributed significantly, several drawbacks such as complexity, etc. Thus, a further advancement in this field of technology is required.
[00010] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
[00011] It also shall be noted that as used herein and in the appended claims, the singular forms “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. This invention can be achieved by means of hardware including several different elements or by means of a suitably programmed computer. In the unit claims that list several means, several ones among these means can be specifically embodied in the same hardware item. The use of such words as first, second, third does not represent any order, which can be simply explained as names.
Summary
[00012] Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
[00013] The present invention relates generally to the field of library science and, more particularly, to the automated creation of metadata for library collections using deep learning techniques.
[00014] Embodiments of the present disclosure may include a system for automated metadata creation for library collections using deep learning techniques, wherein the system including a processor configured to execute deep learning algorithms to analyze text data from library collections. Embodiments may also include a memory storing a metadata database for storing the output of the deep learning algorithms.
[00015] Embodiments may also include an input module for receiving text data from library collections. Embodiments may also include a metadata generation module configured to generate metadata for the text data based on the output of the deep learning algorithms. Embodiments may also include an output module for storing the generated metadata in the metadata database.
[00016] In some embodiments, the deep learning algorithms may include natural language processing algorithms. In some embodiments, the metadata generation module may be configured to generate metadata for multiple languages. In some embodiments, the input module may be configured to receive text data from various sources, including electronic books, journals, and audio recordings.
[00017] Embodiments of the present disclosure may also include a method for automated metadata creation for library collections using deep learning techniques, the method includes providing a text data input module configured to receive text data from library collections. Embodiments may also include providing a metadata generation module configured to generate metadata for the text data using deep learning algorithms. Embodiments may also include executing the deep learning algorithms to analyze the text data and generate metadata for the text data. Embodiments may also include storing the generated metadata in a metadata database.
[00018] In some embodiments, the deep learning algorithms may include natural language processing algorithms. In some embodiments, the metadata generation module may be configured to generate metadata for multiple languages. In some embodiments, the method may include receiving the text data from various sources, including electronic books, journals, and audio recordings.
Brief Description of the Drawings
[00019] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00020] FIG. 1 is a block diagram illustrating a system for automated metadata creation for library collections using deep learning techniques, according to some embodiments of the present disclosure.
[00021] FIG. 2 is a block diagram further illustrating the system from FIG. 1 for automated metadata creation for library collections using deep learning techniques, according to some embodiments of the present disclosure.
[00022] FIG. 3 is a flowchart illustrating a method for automated metadata creation for library collections using deep learning techniques, according to some embodiments of the present disclosure.
Detailed Description
[00023] The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
[00024] In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
[00025] Throughout the present disclosure, the term “network” relates to an arrangement of interconnected programmable and/or non-programmable components that are configured to facilitate data communication between one or more electronic devices and/or databases, whether available or known at the time of filing or as later developed. Furthermore, the network may include, but is not limited to, one or more peer-to-peer network, a hybrid peer-to-peer network, local area networks (LANs), radio access networks (RANs), metropolitan area networks (MANS), wide area networks (WANs), all or a portion of a public network such as the global computer network known as the Internet, a private network, a cellular network and any other communication system or systems at one or more locations.
[00026] Throughout the present disclosure, the term “process”* relates to any collection or set of instructions executable by a computer or other digital system so as to configure the computer or the digital system to perform a task that is the intent of the process.
[00027] Throughout the present disclosure, the term ‘Artificial intelligence (AI)’ as used herein relates to any mechanism or computationally intelligent system that combines knowledge, techniques, and methodologies for controlling a bot or other element within a computing environment. Furthermore, the artificial intelligence (AI) is configured to apply knowledge and that can adapt it-self and learn to do better in changing environments. Additionally, employing any computationally intelligent technique, the artificial intelligence (AI) is operable to adapt to unknown or changing environment for better performance. The artificial intelligence (AI) includes fuzzy logic engines, decision-making engines, preset targeting accuracy levels, and/or programmatically intelligent software.
[00028] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items..
[00029] The present invention relates generally to the field of library science and, more particularly, to the automated creation of metadata for library collections using deep learning techniques.
[00030] In line with a number of different implementations of the current disclosure, a system 100 is disassembled into its component pieces and diagrammatically shown in FIG. 1 for automated metadata creation for library collections using deep learning techniques. The following subsystems might be a part of the system 100 in different implementations: a memory 120 that stores a metadata database that is used to store the output of the deep learning algorithms; a processor 110 that is programmed to execute deep learning algorithms to analyze text data from library collections; an input module 130 that is configured to receive text data from library collections; a metadata generation module 140 that is programmed to generate metadata for the text data based on the output of the deep learning algorithms; and an output module 150 that is programmed to output the metadata that has been generated for the text data. Depending on the particular implementation, deep learning algorithms may at times integrate natural language processing techniques inside their folds. Metadata may be generated in more than one language simultaneously if the metadata generation module 140 is set to do so in certain implementations.
[00031] The system 100 from Figure 1 (for automated metadata creation for library collections using deep learning techniques) is represented in more detail in the block diagram that can be found in Figure 2, which is in line with some implementations of the current disclosure. According to some implementations, the input module 130 could be constructed to be able to take in text data from a wide range of sources, including electronic books, journals, and audio recordings.
[00032] FIG. 3 shows a flowchart (for automated metadata creation for library collections using deep learning techniques) that offers a description of the method in accordance with different implementations of the present disclosure. The flowchart illustrates the process of automating the development of metadata for library collections. The method may, in some implementations, include, at step 310, the provision of a text data input module that is built to be able to receive text data from library collections. The method may include, at step 320, the supply of a metadata production module that is configured to develop metadata for the text data by making use of deep learning algorithms. Step 330 of the procedure may include as one of its components the execution of the deep learning algorithms in order to perform an analysis on the text data as well as the development of metadata for the text data. During the 340th step of the procedure, one alternative would be to store the recently generated metadata in a metadata database. One of the many ways that the deep learning algorithms may be implemented is by using natural language processing strategies, which is only one of many possible approaches. There are a few different configuration options available for the metadata production module, which allows it to generate metadata appropriate for a variety of languages. It is up to the particular implementation of the process to decide whether or not to include the additional step of receiving the text data from a range of various sources. There are now electronic versions of books, journals, and audio recordings.
[00033] The system and method described above are designed to automate the process of creating metadata for library collections using deep learning techniques. The system includes the processor 110 that is configured to execute deep learning algorithms to analyze text data from library collections. The text data is received by an input module, which can receive text data from various sources, including electronic books, journals, and audio recordings.
[00034] The output of the deep learning algorithms is stored in a metadata database, which is stored in memory 120. The metadata generation module is configured to generate metadata for the text data based on the output of the deep learning algorithms. The generated metadata is then stored in the metadata database using an output module.
[00035] In particular, the deep learning algorithms used in the system and method can include natural language processing algorithms, which are designed to analyze and process human language. The metadata generation module can also be configured to generate metadata for multiple languages, enabling the system to process text data in different languages.
[00036] The method for automated metadata creation includes providing a text data input module that is configured to receive text data from library collections. A metadata generation module is also provided, which is configured to generate metadata for the text data using deep learning algorithms. The deep learning algorithms are executed to analyze the text data and generate metadata for the text data. The generated metadata is then stored in a metadata database.
[00037] Again, the deep learning algorithms used in the method can include natural language processing algorithms and the metadata generation module can be configured to generate metadata for multiple languages. The method can also include receiving text data from various sources, including electronic books, journals, and audio recordings.
[00038] Overall, the system and method described above provide an automated solution for generating metadata for library collections using deep learning techniques, which can significantly improve the efficiency and accuracy of metadata creation.
[00039] In some implementations of the current disclosure, there is the potential for there to be a system that, via the use of various forms of deep learning, is able to create metadata for library collections automatically. The processor 110 that is able to perform deep learning algorithms in order to analyze text data obtained from library collections may be included in this system. In certain implementations, there is also the possibility of including the memory 120 that is able to store a metadata database, as well as a memory that is able to store the results produced by deep learning algorithms. Input modules, which are able to receive text data from library collections, may also be included in embodiments.. In certain implementations, there is also a metadata production module that is configured to generate metadata for the text data based on the results that are created by the deep learning algorithms. This metadata is generated in accordance with the results of the deep learning algorithms. In certain implementations, there is also the possibility of including an output module with the intention of storing the newly formed metadata into the metadata database.
[00040] The above description is intended to be illustrative, and not restrictive. Although the present disclosure has been described with references to specific illustrative examples and implementations, it will be recognized that the present disclosure is not limited to the examples and implementations described. The scope of the disclosure should be determined with reference to the following claims, along with the full scope of equivalents to which the claims are entitled.
[00041] Modifications, additions, or omissions may be made to the systems and apparatuses described herein without departing from the scope of the disclosure. The components of the systems and apparatuses may be integrated or separated. Moreover, the operations of the systems and apparatuses may be performed by more, fewer, or other components. Additionally, operations of the systems and apparatuses may be performed using any suitable logic comprising software, hardware, and/or other logic. As used in this document, “each” refers to each member of a set or each member of a subset of a set.
[00042] The term “memory,” as used herein relates to a volatile or persistent medium, such as a magnetic disk, or optical disk, in which a computer can store data or software for any duration. Optionally, the memory is non-volatile mass storage such as physical storage media. Furthermore, a single memory may encompass and in a scenario wherein computing system is distributed, the processing, memory and/or storage capability may be distributed as well.
[00043] Throughout the present disclosure, the term ‘server’ relates to a structure and/or module that include programmable and/or non-programmable components configured to store, process and/or share information. Optionally, the server includes any arrangement of physical or virtual computational entities capable of enhancing information to perform various computational tasks.

Claims
I/We Claim:
1. A system for automated metadata creation for library collections using deep learning techniques, the system comprising:
a processor configured to execute deep learning algorithms to analyze text data from library collections;
a memory storing a metadata database for storing the output of the deep learning algorithms;
an input module for receiving text data from library collections;
a metadata generation module configured to generate metadata for the text data based on the output of the deep learning algorithms; and
an output module for storing the generated metadata in the metadata database.
2. The system of claim 1, wherein the deep learning algorithms comprise natural language processing algorithms.
3. The system of claim 1, wherein the metadata generation module is configured to generate metadata for multiple languages.
4. The system of claim 1, wherein the input module is configured to receive text data from various sources, including electronic books, journals, and audio recordings.
5. A method for automated metadata creation for library collections using deep learning techniques, the method comprising:
providing a text data input module configured to receive text data from library collections;
providing a metadata generation module configured to generate metadata for the text data using deep learning algorithms;
executing the deep learning algorithms to analyze the text data and generate metadata for the text data; and
storing the generated metadata in a metadata database.
6. The method of claim 5, wherein the deep learning algorithms comprise natural language processing algorithms.
7. The method of claim 5, wherein the metadata generation module is configured to generate metadata for multiple languages.
8. The method of claim 5, further comprising receiving the text data from various sources, including electronic books, journals, and audio

AUTOMATED METADATA CREATION FOR LIBRARY COLLECTIONS USING DEEP LEARNING TECHNIQUES
Abstract
In some implementations of the current disclosure, there is the potential for there to be a system that, via the use of various forms of deep learning, is able to create metadata for library collections automatically. A processor that is able to perform deep learning algorithms in order to analyze text data obtained from library collections may be included in this system. This processor may be configured in a certain way. In certain implementations, there is also the possibility of including a memory that is able to store a metadata database, as well as a memory that is able to store the results produced by deep learning algorithms. Input modules, which are able to receive text data from library collections, may also be included in embodiments. Such modules might be included in the system. In certain implementations, there is also a metadata production module that is configured to generate metadata for the text data based on the results that are created by the deep learning algorithms. This metadata is generated in accordance with the results of the deep learning algorithms. In certain implementations, there is also the possibility of including an output module with the intention of storing the newly formed metadata into the metadata database. , Claims:Claims
I/We Claim:
1. A system for automated metadata creation for library collections using deep learning techniques, the system comprising:
a processor configured to execute deep learning algorithms to analyze text data from library collections;
a memory storing a metadata database for storing the output of the deep learning algorithms;
an input module for receiving text data from library collections;
a metadata generation module configured to generate metadata for the text data based on the output of the deep learning algorithms; and
an output module for storing the generated metadata in the metadata database.
2. The system of claim 1, wherein the deep learning algorithms comprise natural language processing algorithms.
3. The system of claim 1, wherein the metadata generation module is configured to generate metadata for multiple languages.
4. The system of claim 1, wherein the input module is configured to receive text data from various sources, including electronic books, journals, and audio recordings.
5. A method for automated metadata creation for library collections using deep learning techniques, the method comprising:
providing a text data input module configured to receive text data from library collections;
providing a metadata generation module configured to generate metadata for the text data using deep learning algorithms;
executing the deep learning algorithms to analyze the text data and generate metadata for the text data; and
storing the generated metadata in a metadata database.
6. The method of claim 5, wherein the deep learning algorithms comprise natural language processing algorithms.
7. The method of claim 5, wherein the metadata generation module is configured to generate metadata for multiple languages.
8. The method of claim 5, further comprising receiving the text data from various sources, including electronic books, journals, and audio