阅读说明：本技术 一种文本标注方法及装置 (Text labeling method and device ) 是由 马泽祥 杨潇峰 蔡耀华 于 2019-08-22 设计创作，主要内容包括：本说明书实施例公开了一种文本标注方法及装置。其中,该方法包括：在展示待标注文本时,根据词典库,将其他标注人员标注过的、用于待标注文本中的词语的标签作为候选标签在待标注文本中进行展示,以供当前的标注人员选择,并响应于标注人员针对候选标签的点击操作,将候选标签添加为词语的当前标签。(The embodiment of the specification discloses a text labeling method and a text labeling device. Wherein, the method comprises the following steps: when the text to be labeled is displayed, labels which are labeled by other labeling personnel and used for words in the text to be labeled are displayed in the text to be labeled as candidate labels according to the dictionary library so as to be selected by the current labeling personnel, and the candidate labels are added as the current labels of the words in response to the clicking operation of the labeling personnel on the candidate labels.)

1. A text labeling method, the method comprising:

when a text to be labeled is displayed, performing word segmentation processing on the text to be labeled through a word segmentation algorithm;

matching the words in the text to be labeled with the words in a dictionary library according to the word segmentation result;

acquiring a label corresponding to the successfully matched word from the dictionary library;

determining the obtained label as a candidate label of the word to be displayed;

and in response to the click operation aiming at the candidate label, adding the candidate label as the current label of the word.

2. The method of claim 1, further comprising:

receiving new labels added aiming at words in the text to be labeled;

adding the words and the corresponding new labels to a dictionary base.

3. The method of claim 2, further comprising:

accumulating the marking times of the labels corresponding to the words in the dictionary library;

wherein the presenting candidate labels for words in the text to be annotated comprises:

sorting the labels according to the marking times; and

and displaying the labels with the maximum marking times in the preset number as candidate labels of the words in the text to be labeled.

4. A method according to claim 2 or 3, characterized in that the method further comprises:

merging the same words and the corresponding new labels in the dictionary base.

5. The method of claim 1, wherein prior to presenting the text to be annotated, the method further comprises:

acquiring a text labeling task, wherein the text labeling task at least comprises a text to be labeled; and

and splitting the text labeling task into a plurality of labeling subtasks for distribution, wherein each labeling subtask at least comprises a part of the text to be labeled.

6. The method of claim 5, wherein the splitting the annotation task into a plurality of annotation subtasks comprises:

splitting the text labeling task into different labeling subtasks according to the designated field; alternatively, the first and second electrodes may be,

and splitting the text labeling task into a specified number of labeling subtasks.

7. The method of claim 5, wherein the splitting the text annotation task into a plurality of annotation subtasks further comprises:

and performing word segmentation processing on the text to be labeled through a word segmentation algorithm.

8. The method of claim 2, further comprising:

and automatically adding the same added label to the word which is the same as the word added with the label in the text to be labeled.

9. An apparatus for text annotation, the apparatus comprising:

the display module is used for performing word segmentation processing on the text to be labeled through a word segmentation algorithm when the text to be labeled is displayed; matching the words in the text to be labeled with the words in a dictionary library according to the word segmentation result; acquiring a label corresponding to the successfully matched word from the dictionary library; determining the obtained label as a candidate label of the word to be displayed;

and the adding module is used for responding to the clicking operation aiming at the candidate label and adding the candidate label as the current label of the word.

10. An apparatus for text annotation, comprising: a memory and a processor; the memory is configured to store instructions for controlling the processor to operate so as to perform a text annotation method according to any one of claims 1 to 8.

Technical Field

The invention relates to the technical field of natural language processing, in particular to a text labeling method and a text labeling device.

Background

Named Entity Recognition (NER) is a common task in natural language processing and is used in a very wide range. The NER text label is to identify words with specific meanings in the text, mainly including names of people, places, organizations, proper nouns and the like, and label the words.

Disclosure of Invention

An object of this specification embodiment is to provide a new technical solution for text annotation.

According to a first aspect of embodiments of the present specification, there is provided a text annotation method, the method including:

when a text to be labeled is displayed, performing word segmentation processing on the text to be labeled through a word segmentation algorithm;

matching the words in the text to be labeled with the words in a dictionary library according to the word segmentation result;

acquiring a label corresponding to the successfully matched word from the dictionary library;

determining the obtained label as a candidate label of the word to be displayed;

and in response to the click operation aiming at the candidate label, adding the candidate label as the current label of the word.

Optionally, the method further comprises:

receiving new labels added aiming at words in the text to be labeled;

adding the words and the corresponding new labels to a dictionary base.

Optionally, the method further comprises:

accumulating the marking times of the labels corresponding to the words in the dictionary library;

wherein the presenting candidate labels for words in the text to be annotated comprises:

sorting the labels according to the marking times; and

and displaying the labels with the maximum marking times in the preset number as candidate labels of the words in the text to be labeled.

Optionally, the method further comprises:

the same words and corresponding new labels are merged in the dictionary base.

Optionally, before displaying the text to be annotated, the method further includes:

acquiring a text labeling task, wherein the text labeling task at least comprises a text to be labeled; and

and splitting the labeling task into a plurality of labeling subtasks for distribution, wherein each labeling subtask at least comprises a part of the text to be labeled.

Optionally, the splitting the annotation task into a plurality of annotation subtasks includes:

splitting the text labeling task into different labeling subtasks according to the designated field; alternatively, the first and second electrodes may be,

and splitting the text labeling task into a specified number of labeling subtasks.

Optionally, the splitting the annotation task into a plurality of annotation subtasks further includes:

and performing word segmentation processing on the text to be labeled through a word segmentation algorithm.

Optionally, the method further comprises:

and automatically adding the same added label to the word which is the same as the word added with the label in the text to be labeled.

According to a second aspect of embodiments herein, there is also provided an apparatus for text annotation, the apparatus comprising:

the display module is used for performing word segmentation processing on the text to be labeled through a word segmentation algorithm when the text to be labeled is displayed; matching the words in the text to be labeled with the words in a dictionary library according to the word segmentation result; acquiring a label corresponding to the successfully matched word from the dictionary library; determining the obtained label as a candidate label of the word to be displayed;

and the adding module is used for responding to the clicking operation aiming at the candidate label and adding the candidate label as the current label of the word.

According to a third aspect of embodiments herein, there is also provided an apparatus for text annotation, including: a memory and a processor; the memory is configured to store instructions configured to control the processor to operate so as to execute the text annotation method according to any one of the first aspect of the embodiments of the present specification.

One beneficial effect of the present specification is that according to the method and apparatus of the embodiments of the present invention, when displaying a text to be labeled, according to the dictionary library, tags that are labeled by other labeling personnel and used for words in the text to be labeled are displayed in the text to be labeled as candidate tags for selection by the current labeling personnel, and in response to the click operation of the labeling personnel on the candidate tags, the candidate tags are added as the current tags of the words. Therefore, the recommendation labels of the words are automatically provided according to the dictionary database in the labeling process, and the text labeling efficiency is improved.

Other features of the present invention and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.

Fig. 1 is a block diagram showing a hardware configuration of an electronic apparatus 1000 that can be used to implement an embodiment of the present specification.

FIG. 2 shows a schematic flow diagram of a text annotation method in accordance with an embodiment of the present description.

Fig. 3 is a schematic diagram illustrating candidate tags in a text labeling method according to an embodiment of the present disclosure.

Fig. 4 is a schematic diagram illustrating an automatic word segmentation result in a text annotation method according to an embodiment of the present specification.

Fig. 5 is a schematic structural diagram illustrating an apparatus for text annotation according to a first embodiment of the present disclosure.

Fig. 6 is a schematic structural diagram illustrating an apparatus for text annotation according to a second embodiment of the present specification.

Fig. 7 is a schematic structural diagram illustrating an apparatus for text annotation according to a third embodiment of the present disclosure.

Fig. 8 is a schematic structural diagram illustrating an apparatus for text annotation according to a fourth embodiment of the present disclosure.

Fig. 9 is a schematic structural diagram illustrating an apparatus for text annotation according to a fifth embodiment of the present disclosure.

Fig. 10 is a schematic structural diagram illustrating an apparatus for text annotation according to a sixth embodiment of the present specification.

Fig. 11 is a flowchart illustrating an example of a text annotation method according to an embodiment of the present specification.

Fig. 12 is a scene schematic diagram illustrating a text labeling method according to an embodiment of the present specification.

Detailed Description

Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.

In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.

Various embodiments and examples according to embodiments of the present invention are described below with reference to the accompanying drawings.

< hardware configuration >

The essential loop for model training of artificial intelligence during data labeling is a process for changing the most original data into data available for an algorithm: raw data is typically acquired through data acquisition, and subsequent labeling of the data is equivalent to processing the data, and then training the model based on the labeled data. And (5) conveying the data to an artificial intelligence algorithm and a model for calling.

The text data is labeled, and the main content is to label the text, wherein the text is labeled to describe a category label which is predefined by us. Based on the labeling platform, multi-person collaborative labeling can be realized, and each labeling person is responsible for at least one part of the text to be labeled and completes the labeling of the text in a cooperative manner.

Therefore, in order to improve the labeling efficiency of the labeling personnel in the embodiment of the present specification, as shown in fig. 12, when the electronic device 1000 displays the text to be labeled to the labeling personnel, the tags, which are labeled by other labeling personnel and used for the words in the text to be labeled, are displayed in the text to be labeled as candidate tags according to the dictionary library, so as to be selected by the current labeling personnel, as shown in the page a. The annotator can choose to add the recommended candidate tags as the current tags of the words or can choose to add new tags to the words. For example, in page B, the annotator selects candidate tag 1 to add as the current tag of the word. After clicking on candidate tag 1, the annotator adds the candidate tag 1 as the current tag of the word, as shown in page C. Because the recommendation labels of the words are automatically provided according to the dictionary database in the labeling process, the text labeling efficiency is improved.

Fig. 1 is a block diagram showing a hardware configuration of an electronic apparatus 1000 that can be used to implement an embodiment of the present specification.

As shown in fig. 1, the electronic device 1000 of the present embodiment may be a laptop, a desktop computer, a mobile phone, a tablet computer, etc.

As shown in fig. 1, the electronic device 1000 may include a processor 1010, a memory 1020, an interface device 1030, a communication device 1040, a display device 1050, an input device 1060, a speaker 1070, a microphone 1080, and the like.

The processor 1010 may be a central processing unit CPU, a microprocessor MCU, or the like. The memory 1020 includes, for example, a ROM (read only memory), a RAM (random access memory), a nonvolatile memory such as a hard disk, and the like.

The interface device 1030 includes, for example, a USB interface, a headphone interface, and the like.

The communication device 1040 can perform wired or wireless communication, for example.

The display device 1050 is, for example, a liquid crystal display panel, a touch panel, or the like.

The input device 1060 may include, for example, a touch screen, a keyboard, and the like. A user can input/output voice information through the speaker 1070 and the microphone 1080.

In this embodiment, the memory 1020 of the electronic device 1000 is configured to store instructions for controlling the processor 1010 to operate at least to perform a text annotation method according to any of the embodiments of the present description.

It should be understood by those skilled in the art that although a plurality of means of the electronic device 1000 are shown in fig. 1, the present description may refer to only some of the means therein, for example, the electronic device 1000 refers to only the memory 1020, the processor 1010 and the display 1050. The skilled person can design the instructions according to the solution disclosed in the present specification. How the instructions control the operation of the processor is well known in the art and will not be described in detail herein.

< method examples >

The text annotation method of the present embodiment can be specifically executed by the electronic device 1000 shown in fig. 1.

As shown in fig. 2, the text annotation method of this embodiment may include steps 2000 to 2200:

step 2000, when the text to be labeled is displayed, performing word segmentation processing on the text to be labeled through a word segmentation algorithm.

The text to be labeled is the text to be labeled by the labeling personnel. The text to be annotated may be, for example, text content of articles, news, papers, advertisements, and the like.

Step 2200, matching the words in the text to be labeled with the words in the dictionary database according to the word segmentation result.

And 2400, acquiring a label corresponding to the successfully matched word from the dictionary database.

Step 2600, determining the obtained label as a candidate label of the word for displaying.

Step 2800, in response to the click operation on the candidate tag, add the candidate tag as the current tag of the word.

In practical applications, the number of labels corresponding to the successfully matched words obtained from the dictionary database may be one or more. When the number of the acquired tags is multiple, when the acquired tag is determined to be the candidate tag of the word to be presented, only one of the multiple tags may be presented, or a preset number of the multiple tags may be presented. This embodiment is not particularly limited thereto.

For example, as shown in fig. 3, the text to be annotated is "what relationship is company a and company B? "where, above the word" company a ", two candidate labels" recommendation 1: company name "and" recommendation 2: product name ".

It should be noted that the dictionary database is automatically generated according to the labels added to the words by a plurality of collaboratively labeled labeling personnel, and is continuously updated with the labels added to the words by each labeling personnel. The dictionary repository may be generated based on a single annotation task, or the dictionary repository may be generated based on a single user, or the dictionary repository may be generated based on a single annotation platform. And is not particularly limited herein.

In step 2800, the annotator can select one of the presented candidate tags to add as the current tag of the word. Therefore, candidate labels of the words are automatically provided according to the dictionary library in the labeling process of the labeling personnel, and the labeling personnel can finish the labeling of the words by only selecting one label from the candidate labels, so that the efficiency of text labeling is improved.

Further, the annotating staff can also select an option for starting the automatic word segmentation function, as shown in fig. 4, in the scene, the displayed text to be annotated can automatically recommend the labels of the words already annotated by other annotating staff as candidate labels to the annotating staff, and the annotating staff can also select to use the candidate labels as the current labels of the words.

Alternatively, if the annotator deems the presented candidate label to be unsuitable, the annotator may also choose to fill in a new label for the word. That is, the electronic device 1000 receives a new tag added for a word in the text to be annotated; and adds the word and the corresponding new label to the dictionary base.

That is to say, after the annotating personnel add a new label to the word, the word and the new label are added to the dictionary library in a combined form, so that the word and the corresponding label are provided to other annotating personnel for selection as candidate labels, and therefore the purpose of generating and updating the dictionary library through multi-person collaborative annotation is achieved, and the purpose of mutual cooperation among a plurality of annotating personnel is achieved.

In practical applications, each word in the dictionary base may correspond to a plurality of tags, and in order to determine which candidate tag of the word is displayed during the display, in this embodiment, the number of times of marking the tag corresponding to the word may also be accumulated in the dictionary base, so as to determine the candidate tag to be displayed according to the number of times of marking.

Accordingly, in the above step 2600, the labels may be sorted according to the number of times of marking; and displaying the labels with the maximum marking times in the preset number as candidate labels of the words in the text to be labeled.

For example, the number of times of labeling a label a of a word is 10, the number of times of labeling B is 20, and the number of times of labeling C is 1, and the labels of the word are classified into label B, label a, and label C according to the order of the number of times of labeling. If the preset display number is 1, the candidate label of the word in the text to be annotated for display is label B. If the preset display number is 2, the candidate labels of the text words to be labeled for display are label B and label A.

Further, if a word and its label already exist in the dictionary database, but the labeling personnel still re-input the label, for this case, the same word and the corresponding new label may be merged in the dictionary database. To avoid recommending multiple identical tags for a word.

On the basis of the foregoing embodiment, before the foregoing step 2000, the method of this embodiment may further include: acquiring a text labeling task, wherein the text labeling task at least comprises a text to be labeled; and splitting the labeling task into a plurality of labeling subtasks for distribution, wherein each labeling subtask at least comprises a part of the text to be labeled.

When the labeling task is split into a plurality of labeling subtasks, the text labeling task can be split into different labeling subtasks according to the specified field; or, the text labeling task is divided into a specified number of labeling subtasks.

For example, a field type may be specified, and the text annotation task is split into different annotation subtasks, i.e., a text and a text, according to the specified field type. For another example, the text annotation task as a whole is divided into 10 annotation subtasks. For another example, assuming that the text annotation task includes 100 text data, each of the 100 text data in the text annotation task may be designated as an annotation subtask to be processed by an annotator.

It should be noted that, in the above-mentioned steps 2000 to 2600, when displaying the candidate tag for the word in the text to be annotated, the implementation manner is described that the word segmentation processing is performed on the text to be annotated, and according to the word segmentation result and the word in the text to be annotated is matched with the word in the dictionary library, so that the tag corresponding to the successfully matched word is obtained from the dictionary library and is displayed as the candidate tag.

In another possible implementation manner of this embodiment, the operation of performing word segmentation processing on the text to be labeled may also be completed before the text to be labeled is presented. Specifically, when the text labeling task is divided into a plurality of labeling subtasks, the text to be labeled is subjected to word segmentation processing through a word segmentation algorithm.

In this implementation manner, since the word segmentation processing on the text to be labeled is completed when the text labeling task is split into a plurality of labeling subtasks, when the text to be labeled is displayed, the word segmentation processing on the text to be labeled is not required, but a tag corresponding to a successfully matched word is acquired from the dictionary database and displayed as the candidate tag according to a word segmentation result and a matching result obtained by matching the word in the text to be labeled with the word in the dictionary database, so that the processing speed can be further increased.

In order to further improve the labeling efficiency of the labeling personnel, in this embodiment, for the same word appearing multiple times in the same text to be labeled, the labeling personnel only needs to actively add a label to the word once, and the word appearing at other positions in the text to be labeled automatically fills up the same label, that is, for the word in the text to be labeled which is the same as the word added with the label, the same added label is automatically added. Therefore, the text labeling efficiency can be obviously improved.

According to the text labeling method provided by the embodiment of the specification, when the text to be labeled is displayed, the labels which are labeled by other labeling personnel and used for the words in the text to be labeled are displayed in the text to be labeled as the candidate labels according to the dictionary library so as to be selected by the current labeling personnel, and the candidate labels are added as the current labels of the words in response to the clicking operation of the labeling personnel on the candidate labels. Therefore, the recommendation labels of the words are automatically provided according to the dictionary database in the labeling process, and the text labeling efficiency is improved.

< apparatus embodiment >

Fig. 5 is a schematic structural diagram illustrating an apparatus for text annotation according to a first embodiment of the present disclosure. As shown in fig. 5, the apparatus 5000 for text annotation may include: a display module 5100 and an add module 5200.

The display module 5100 is configured to perform word segmentation processing on the text to be labeled through a word segmentation algorithm when displaying the text to be labeled; matching the words in the text to be labeled with the words in the dictionary library according to the word segmentation result; acquiring a label corresponding to the successfully matched word from the dictionary library; and determining the acquired label as a candidate label of the word for display. The adding module 5200 is configured to add the candidate tag as a current tag of the word in response to a click operation on the candidate tag.

Further, as shown in fig. 6, the apparatus 5000 for text annotation may further include: the receiving module 6100 receives a new tag added to the word in the text to be annotated. The adding module 5200 can also be used to add the word and the corresponding new tag to a dictionary repository.

Further, as shown in fig. 7, the apparatus 5000 for text annotation may further include: and an accumulation module 7100, configured to accumulate the number of times of marking the tag corresponding to the word in the dictionary repository. The display module 5100 may also be configured to sort tags according to the number of times of tagging; and displaying the labels with the maximum marking times in the preset number as candidate labels of the words in the text to be labeled.

Further, as shown in fig. 8, the apparatus 5000 for text annotation may further include: a merging module 8100, configured to merge the same word and the corresponding new tag in the dictionary repository.

Further, as shown in fig. 9, the apparatus 5000 for text annotation may further include: an obtaining module 9100, configured to obtain a text labeling task, where the text labeling task at least includes a text to be labeled; and a splitting module 9200, configured to split the annotation task into multiple annotation subtasks for distribution, where each annotation subtask includes at least a part of the text to be annotated.

The splitting module 9200 is specifically configured to split the text labeling task into different labeling subtasks according to a specified field; or, the text labeling task is divided into a specified number of labeling subtasks.

Further, the splitting module 9200 can also be used for performing word segmentation processing on the text to be labeled through a word segmentation algorithm.

Further, the adding module 5200 can also be used to automatically add the same added tag to the word in the text to be labeled, which is the same as the tagged word.

The apparatus for text annotation of this embodiment may be configured to execute the technical solution of the foregoing method embodiment, and the implementation principle and technical effect thereof are similar, and are not described herein again.

Fig. 10 is a schematic structural diagram illustrating an apparatus for text annotation according to a sixth embodiment of the present specification. As shown in fig. 10, the apparatus 100 for text annotation may specifically include a memory 110 and a processor 120. The memory 110 is used for storing instructions for controlling the processor 120 to operate so as to execute the text annotation method in the above method embodiment.

< example >

Fig. 11 is a flowchart illustrating an example of a text annotation method according to an embodiment of the present specification.

As shown in fig. 11, the text annotation method of this embodiment may include:

step 1100, acquiring a text annotation task.

The text labeling task at least comprises a text to be labeled.

Step 1102, splitting the text labeling task into a plurality of labeling subtasks, performing word segmentation on the text to be labeled through a word segmentation algorithm, and distributing the plurality of labeling subtasks.

Wherein, each labeling subtask at least comprises a part of the text to be labeled.

When the text labeling task is split into a plurality of labeling subtasks, the text labeling task can be split into different labeling subtasks according to the designated field; alternatively, the text annotation task may be split into a specified number of annotation subtasks. This is not specifically described in the present embodiment.

And 1104, when the text to be labeled is displayed, displaying the candidate labels of the words in the text to be labeled.

The candidate tag can be a tag which is accumulated in the dictionary base for the most times, so that the possibility of the candidate tag being adopted is improved, the possibility of manually inputting the tag by a labeling person is reduced, and the text labeling efficiency is improved.

Step 1106, in response to the click operation on the candidate tag, adding the candidate tag as the current tag of the word.

The tag add operation for that word is completed.

Step 1108, receiving a new label added for the word in the text to be labeled.

In this example, after receiving the new tags added to the words in the text to be labeled, in order to further improve the text labeling efficiency, the same added tags may be automatically added to the words in the text to be labeled that are the same as the words to which the tags have been added.

Step 1110, add the word and the corresponding new label to the dictionary repository.

Therefore, a plurality of marking personnel can generate and update the dictionary database in a common cooperation mode, and the text marking efficiency can be improved to a great extent.

Step 1112, merge the same words and corresponding new labels in the dictionary repository.

The situation that a plurality of same labels are recommended for a certain word is avoided, and the accuracy of recommending candidate labels can be improved.

The text labeling method provided by the embodiment can improve the text labeling efficiency.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the process of seconds back in the figures does not necessarily require a particular order to be shown or a sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing may also be possible or may be powerful.

Those skilled in the art will understand that, in the field of electronic technology, the above method can be embodied in products by software, hardware and a combination of software and hardware, and those skilled in the art can easily generate an information processing apparatus including modules for performing respective operations in the information processing method according to the above embodiment based on the method of the above embodiment of the invention.

It is well known to those skilled in the art that with the development of electronic information technology such as large scale integrated circuit technology and the trend of software hardware, it has been difficult to clearly divide the software and hardware boundaries of a computer system. As any of the operations may be implemented in software or hardware. Execution of any of the instructions may be performed by hardware, as well as by software. Whether a hardware implementation or a software implementation is employed for a certain machine function depends on non-technical factors such as price, speed, reliability, storage capacity, change period, and the like. A software implementation and a hardware implementation are equivalent for the skilled person. The skilled person can choose software or hardware to implement the above described scheme as desired. Therefore, specific software or hardware is not limited herein.

The present invention may be an apparatus, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied therewith for causing a processor to implement various aspects of the present invention.

The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.

The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.

The computer program instructions for carrying out operations of the present invention may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present invention are implemented by personalizing an electronic circuit, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), with state information of computer-readable program instructions, which can execute the computer-readable program instructions.

Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.

Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.