阅读说明：本技术 Rpa结合ai的模型融合结果获取方法、装置及电子设备 (RPA (resilient packet Access) and AI (Artificial Intelligence) combined model fusion result acquisition method and device and electronic equipment ) 是由 张原� 白泽宇 汪冠春 胡一川 褚瑞 李玮 于 2021-08-25 设计创作，主要内容包括：本公开提供了RPA结合AI的模型融合结果获取方法、装置、电子设备及存储介质,涉及人工智能领域。该方案为：由RPA系统执行,RPA系统获取待处理信息,并将待处理信息分别输入n个模型中,以基于自然语言处理NLP获取待处理信息的n个处理结果,其中,n为大于1的整数；RPA系统获取任一处理结果与其余n-1个处理结果之间的距离；RPA系统根据距离,获取待处理信息的目标处理结果,并进行展示。本公开运用RPA技术与AI技术,不再依赖模型输出结果的概率,提高模型输出结果的利用率并确保模型融合结果的准确性,同时,提高了模型融合结果的鲁棒性。(The disclosure provides a method and a device for obtaining a model fusion result of RPA and AI, electronic equipment and a storage medium, and relates to the field of artificial intelligence. The scheme is as follows: the method comprises the steps that the RPA system acquires information to be processed, the information to be processed is respectively input into n models, n processing results of the information to be processed are acquired based on natural language processing NLP, and n is an integer larger than 1; the RPA system acquires the distance between any processing result and the rest n-1 processing results; and the RPA system acquires a target processing result of the information to be processed according to the distance and displays the target processing result. The method and the device for improving the model fusion result have the advantages that the RPA technology and the AI technology are used, the probability of the model output result is not depended on, the utilization rate of the model output result is improved, the accuracy of the model fusion result is ensured, and meanwhile, the robustness of the model fusion result is improved.)

1. A method for obtaining model fusion results of RPA and AI, which is executed by RPA system, the method includes:

the RPA system acquires information to be processed, and inputs the information to be processed into n models respectively to acquire n processing results of the information to be processed based on Natural Language Processing (NLP), wherein n is an integer greater than 1;

the RPA system acquires the distance between any one processing result and the rest n-1 processing results;

and the RPA system acquires a target processing result of the information to be processed according to the distance and displays the target processing result.

2. The method according to claim 1, wherein the RPA system obtains a target processing result of the information to be processed according to the distance, and comprises:

the RPA system obtains the sum of the distances between any one processing result and the rest n-1 processing results according to the distance;

and the RPA system determines the processing result with the minimum sum of the distances between the processing result and the rest n-1 processing results according to the sum of the distances, and takes the processing result with the minimum sum of the distances as the target processing result.

3. The method according to claim 1, wherein before said RPA system obtains a distance between any one of said processing results and the remaining n-1 of said processing results, further comprising:

and the RPA system encodes all the processing results to obtain n processing result vectors.

4. The method of claim 3, wherein said RPA system encodes all of said processing results to obtain n vectors of processing results, comprising:

and the RPA system inputs all the processing results into a trained coding model, and takes the output n coding results with preset lengths as the processing result vector.

5. The method according to any of claims 3-4, wherein said RPA system obtaining a distance between any of said processing results and the remaining n-1 of said processing results comprises:

the RPA system acquires the similarity information between any processing result and the rest n-1 processing results according to the processing result vector;

and the RPA system acquires the distance according to the similarity information.

6. The method according to claim 1, wherein said RPA system obtaining a distance between any one of said processing results and the remaining n-1 of said processing results comprises:

and the RPA system acquires the edit distance or word shift distance between any one processing result and the rest n-1 processing results according to the processing results.

7. An apparatus for obtaining a model fusion result by combining RPA with AI, comprising:

the first acquisition module is used for acquiring information to be processed, inputting the information to be processed into n models respectively, and acquiring n processing results of the information to be processed based on Natural Language Processing (NLP), wherein n is an integer greater than 1;

the second acquisition module is used for acquiring the distance between any one processing result and the rest n-1 processing results;

and the display module is used for acquiring the target processing result of the information to be processed according to the distance and displaying the target processing result.

8. The apparatus of claim 7, wherein the display module is further configured to:

according to the distance, obtaining the sum of the distances between any one processing result and the rest n-1 processing results;

and determining the processing result with the minimum sum of the distances from the sum of the distances to the rest n-1 processing results, and taking the processing result with the minimum sum of the distances as the target processing result.

9. The apparatus of claim 7, wherein the second obtaining module is further configured to:

and coding all the processing results to obtain n processing result vectors.

10. The apparatus of claim 9, wherein the second obtaining module is further configured to:

and inputting all the processing results into a trained coding model, and taking the output n coding results with preset lengths as the processing result vector.

11. The apparatus according to any one of claims 9-10, wherein the second obtaining module is further configured to:

according to the processing result vector, obtaining similarity information between any one processing result and the rest n-1 processing results;

and acquiring the distance according to the similarity information.

12. The apparatus of claim 7, wherein the second obtaining module is further configured to:

and acquiring the editing distance or word shifting distance between any one processing result and the rest n-1 processing results according to the processing results.

13. An electronic device comprising a memory, a processor;

wherein the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory for implementing the method according to any one of claims 1 to 6.

14. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-6.

15. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-6.

Technical Field

The present disclosure relates to the field of artificial intelligence, and in particular, to a method and an apparatus for obtaining a model fusion result by combining RPA and AI, and an electronic device.

Background

Robot Process Automation (RPA) is a process task automatically executed according to rules by simulating human operations on a computer through specific robot software.

Artificial Intelligence (AI) is a technical science that studies and develops theories, methods, techniques and application systems for simulating, extending and expanding human intelligence.

In the related art, a common method in the model fusion method is to calculate the arithmetic mean value of all answers and then use the arithmetic mean value as a fusion result, and the method depends on the probability of the model output result, so that the utilization of the model output result is limited and the accuracy is low. Therefore, how to no longer depend on the probability of the model output result, and at the same time, improve the utilization rate of the model output result and ensure the accuracy of the model fusion result is a matter which needs to be solved urgently at present.

Disclosure of Invention

The disclosure provides a method and a device for obtaining a model fusion result of RPA and AI, and electronic equipment.

According to an aspect of the present disclosure, a method for obtaining model fusion results of RPA and AI is provided, including:

the method comprises the steps that an RPA system obtains information to be processed, the information to be processed is respectively input into n models, and n processing results of the information to be processed are obtained based on natural language processing NLP, wherein n is an integer larger than 1;

the RPA system acquires the distance between any processing result and the rest n-1 processing results;

and the RPA system acquires a target processing result of the information to be processed according to the distance and displays the target processing result.

The embodiment of the disclosure utilizes the RPA technology and the AI technology, can automatically perform model fusion, and simultaneously displays the model fusion result, thereby improving the utilization rate of the model output result and improving the robustness of the model fusion result.

According to another aspect of the present disclosure, there is provided an apparatus for obtaining model fusion result of RPA in combination with AI, including:

the first acquisition module is used for acquiring information to be processed, inputting the information to be processed into n models respectively, and acquiring n processing results of the information to be processed based on Natural Language Processing (NLP), wherein n is an integer greater than 1;

the second acquisition module is used for acquiring the distance between any processing result and the rest n-1 processing results;

and the display module is used for acquiring the target processing result of the information to be processed according to the distance and displaying the target processing result.

According to another aspect of the present disclosure, there is provided an electronic device comprising a memory, a processor; the processor reads the executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the method for obtaining the model fusion result of RPA in combination with AI according to the embodiment of the first aspect of the present disclosure.

According to another aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the RPA-AI combined model fusion result acquisition method according to an embodiment of the first aspect of the present disclosure.

According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the RPA-AI combined model fusion result acquisition method of the first aspect of the present disclosure.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

FIG. 1 is a flow chart of a method for obtaining model fusion results of RPA in combination with AI according to one embodiment of the present disclosure;

FIG. 2 is a flow chart of a method for obtaining model fusion results of RPA in combination with AI according to one embodiment of the present disclosure;

FIG. 3 is a flow chart of a method for obtaining model fusion results of RPA in combination with AI according to one embodiment of the present disclosure;

FIG. 4 is a flow chart of a method for obtaining model fusion results of RPA in combination with AI according to one embodiment of the present disclosure;

fig. 5 is a structural diagram of an apparatus for acquiring a model fusion result of RPA in combination with AI according to an embodiment of the present disclosure;

fig. 6 is a block diagram of an electronic device for implementing the method for obtaining model fusion results by combining RPA and AI according to the embodiment of the present disclosure.

Detailed Description

Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.

The RPA-AI model fusion result acquisition method, apparatus, and electronic device according to the present disclosure are described below with reference to the accompanying drawings.

Fig. 1 is a flowchart of a method for obtaining a model fusion result of RPA in combination with AI according to an embodiment of the present disclosure, as shown in fig. 1, the method includes the following steps:

s101, the RPA system acquires information to be processed, the information to be processed is respectively input into n models, and n processing results of the information to be processed are acquired based on natural language processing NLP, wherein n is an integer larger than 1.

The RPA is a relatively emerging and popular software technology, which is a technology for simulating the operation behavior of a human on a PC, and is gradually applied to enterprise production offices. The core of the RPA is that the 'substitute' is carried out on the fixed flow operation such as repeatability, low value, no need of manual decision and the like through an automation and intelligent technology, thereby effectively improving the working efficiency and reducing errors.

Natural Language Processing (NLP) is an important direction in the fields of computer science and AI. It studies various theories and methods that enable efficient communication between humans and computers using natural language. Natural language processing is a science integrating linguistics, computer science and mathematics. Therefore, the research in this field will relate to natural language, i.e. the language that people use everyday, so it is closely related to the research of linguistics, but has important difference.

The information to be processed may be any information expressed in the form of a character string. For example, the information to be processed may be a user-entered problem that is sought to be solved: "what the subject of this report is", "which team this report came from".

The model refers to any model participating in the model fusion process.

The model fusion refers to a process of fusing answers of a plurality of single models and then outputting a fused better answer.

For example, information to be processed is input into n models, respectively, and then a processing result corresponding to the information to be processed is acquired. Wherein, for model 1, the processing result is a₁For model n, the result of the processing is a_n. In this case, the model fusion result is obtained by applying the processing result a₁～a_nObtained after treatment.

S102, the RPA system acquires the distance between any processing result and the rest n-1 processing results.

The distance may be obtained in various ways, and the disclosure is not limited thereto.

As a possible implementation, the edit distance or word shift distance may be obtained and used as the distance between any one processing result and the remaining n-1 processing results.

Wherein, Edit Distance (Edit Distance), also called Levenshtein Distance, refers to the minimum Edit times required for converting one character string into another character string; word move's Distance (WMD for short) refers to a measure of similarity of text.

As another possible implementation manner, the distance between any processing result and the rest n-1 processing results can be obtained in a manner of encoding and obtaining the similarity. In the embodiment of the present disclosure, after n processing results of information to be processed are obtained, all the processing results may be encoded to obtain n processing result vectors.

Optionally, the RPA system inputs all processing results into a trained coding model, for example, a transform-based Bidirectional coding model (BERT), a Bidirectional Long-Short Memory network model (Bi-LSTM), and takes the output coding results with n lengths all being preset lengths as a processing result vector.

Further, the RPA system can calculate the distance between any processing result and the rest n-1 processing results according to the n processing result vectors and the distance calculation formula.

And S103, the RPA system acquires a target processing result of the information to be processed according to the distance and displays the target processing result.

In the embodiment of the present disclosure, after the distance between any one processing result obtained by the RPA system and the remaining n-1 processing results is obtained, a target processing result of information to be processed may be selected from all the processing results according to the distance, and displayed.

In the embodiment of the disclosure, the RPA system acquires information to be processed, and inputs the information to be processed into n models respectively to acquire n processing results of the information to be processed, then the RPA system acquires a distance between any one processing result and the rest n-1 processing results, and finally the RPA system acquires a target processing result of the information to be processed according to the distance and displays the target processing result. In the embodiment of the disclosure, the RPA technology and the AI technology are applied, the probability of the model output result is not relied on, the utilization rate of the model output result is improved, the accuracy of the model fusion result is ensured, and meanwhile, the robustness of the model fusion result is improved.

The following explains a specific process of the RPA system for obtaining the distance between any processing result and the remaining n-1 processing results, with respect to obtaining the edit distance or the word shift distance as the distance between any processing result and the remaining n-1 processing results, and obtaining the distance between any processing result and the remaining n-1 processing results by encoding and obtaining the similarity, respectively.

Optionally, the RPA system may directly obtain the edit distance or the word shift distance between any one processing result and the rest n-1 processing results according to the processing results, and use the obtained edit distance or the obtained word shift distance as the distance between any one processing result and the rest n-1 processing results. For obtaining the distance between any processing result and the rest n-1 processing results through encoding and obtaining the similarity, optionally, the distance between any processing result and the rest n-1 processing results can be obtained by using the following distance calculation formula:

where E is a sentence vector encoding function and d is a distance.

It should be noted that, in the present disclosure, all the processing results may be encoded by the RPA system before obtaining the distance between any processing result and the remaining n-1 processing results, so as to obtain n processing result vectors.

In the present disclosure, the specific manner of encoding all the processing results to obtain n processing result vectors is not limited, and may be set according to actual situations. Alternatively, the RPA system may input all the processing results into a trained coding model, and output coding results with n lengths all being preset lengths as a processing result vector.

Further, after any processing result vector is obtained, the distance between any processing result and the rest n-1 processing results can be obtained according to the similarity.

As a possible implementation manner, on the basis of the foregoing embodiment, with further reference to fig. 2, a specific process of acquiring a distance between any processing result and the remaining n-1 processing results by the RPA system for acquiring a distance between any processing result and the remaining n-1 processing results through encoding and acquiring similarity is explained, which includes the following steps:

and S201, the RPA system acquires the similarity information between any processing result and the rest n-1 processing results according to the processing result vector.

The similarity information is the similarity between individuals (processing result vectors).

And S202, the RPA system acquires the distance according to the similarity information.

Wherein, the lower the similarity between the processing result vectors, the larger the distance between them; conversely, the greater the similarity between the processing result vectors, the smaller the distance therebetween.

For example, for the processing result vectors X1, X2, and X3, the obtained similarity information between X1 and X2 and between X1 and X3 is 95% and 15%, respectively, in this case, the distance between X1 and X2 is smaller than the distance between X1 and X3.

Fig. 3 is a flowchart of a method for acquiring model fusion results by combining RPA and AI according to an embodiment of the present disclosure, and on the basis of the above embodiment, with further reference to fig. 3, a process of acquiring a target processing result of information to be processed by an RPA system according to a distance is explained, including the following steps:

s301, the RPA system obtains the sum of the distances between any processing result and the rest n-1 processing results according to the distance.

For example, for three processing results a₁、a₂、a₃，a₁And a₂And a₃Are respectively d₁、d₂，a₂And a₁And a₃Are respectively d₃、d₄，a₃And a₁And a₂Are respectively d₅、d₆In this case, a₁And a₂And a₃The sum of the distances D between₁Is d₁+d₂，a₂And a₁And a₃The sum of the distances D between₂Is d₃+d₄，a₃And a₁And a₂The sum of the distances D between₃Is d₅+d₆。

And S302, the RPA system determines a processing result with the minimum sum of the distances to the rest n-1 processing results according to the sum of the distances, and takes the processing result with the minimum sum of the distances as a target processing result.

In the embodiment of the disclosure, after the RPA system obtains the sum of the distances, a processing result with the smallest sum of the distances to the remaining n-1 processing results may be determined, and the processing result with the smallest sum of the distances may be used as the target processing result.

For example, for three processing results a₁、a₂、a₃Is obtained to a₁And a₂And a₃The sum of the distances between the two is D₁，a₂And a₁And a₃The sum of the distances between the two is D₂，a₃And a₁And a₂The sum of the distances between the two is D₃And D is₃＜D₁＜D₂In this case, a₃And a₁And a₂The sum of the distances between the two is the minimum, the processing result a can be obtained₃As a target processing result.

In the embodiment of the disclosure, the processing result with the minimum sum of distances can be used as the target processing result by the RPA system, so that the purpose of obtaining a more accurate target processing result is achieved by obtaining the processing result with the highest similarity, and the reliability and accuracy of the model fusion result are further improved.

Fig. 4 is a flowchart of a method for obtaining a model fusion result of RPA and AI according to an embodiment of the present disclosure, and as shown in fig. 4, based on the method for obtaining a model fusion result of RPA and AI provided by the present disclosure, a process of obtaining a model fusion result of RPA and AI in an actual application scenario includes the following steps:

s401, the RPA system acquires information to be processed and inputs the information to be processed into n models respectively to acquire n processing results of the information to be processed, wherein n is an integer greater than 1.

In the embodiment of the present disclosure, after the processing result is obtained, optionally, step S405 may be executed to obtain the distance by obtaining the edit distance or the word shift distance; alternatively, steps S402 to S404 may be performed to acquire the distance by acquiring the code and the similarity.

S402, the RPA system inputs all the processing results into the trained coding model, and outputs the coding results with n lengths being preset lengths as the processing result vector.

And S403, the RPA system acquires the similarity information between any processing result and the rest n-1 processing results according to the processing result vector.

And S404, the RPA system acquires the distance according to the similarity information.

S405, the RPA system obtains the edit distance or word shift distance between any processing result and the rest n-1 processing results according to the processing result vector.

It should be noted that after step S405 or step S404 is completed, step S406 is executed to obtain the sum of the distances.

S406, the RPA system obtains the sum of the distances between any processing result and the rest n-1 processing results according to the distance.

And S407, the RPA system determines the processing result with the minimum sum of the distances to the rest n-1 processing results according to the sum of the distances, and takes the processing result with the minimum sum of the distances as the target processing result.

And S408, the RPA system acquires and displays the target processing result of the information to be processed.

The following explains a model fusion result obtaining method of RPA combined with AI proposed by the present disclosure, taking distance as an example of editing distance.

Optionally, the RPA system obtains information to be processed, and inputs the information to be processed into M models respectively to obtain M processing results of the information to be processed, in this case, for the model i, the corresponding processing result may be labeled as a _ i.

Further, the sum s _ i of edit distances between the processing result a _ i output by each model and the processing results output by other models may be obtained according to the following formula:

wherein, a_i，a_jAnd M is the number of models used in the process of participating in model fusion, wherein M is the processing result of the models i and j.

Further, the processing result with the smallest sum of the distances to the remaining n-1 processing results may be obtained and displayed as the target processing result.

It should be noted that the method for obtaining model fusion results by combining RPA with AI provided by the present disclosure has a prominent effect in various application scenarios where the information to be processed is information expressed in the form of a character string.

For reading and understanding an application scene, 6 models are trained in advance, and each model can extract a paragraph (span) from a document as an answer (processing result) to a question according to the content (information to be processed) of the question.

In this case, for example, if the information to be processed is: "what is the subject of this report? ", the 6 processing results are: morning session, weather security, morning session, and the processing result with the smallest sum of distances from the remaining 5 processing results is: "morning league", the model fusion result (target processing result) is: "morning season".

For another example, if the information to be processed is: "which team this report came from? ", the 6 processing results are: the wear is comfortable, the product center research team, the financial engineering research team, Tang, the financial engineering research team, and the minimum processing result of the sum of the distances between the product center research team and the other 5 processing results is: the "financial engineering research team" then the model fusion result (target processing result) is: "financial engineering research team".

For another example, if the information to be processed is: "what is the publicity of the product? ", the 6 processing results are: the LED lighting color is clear at a glance, the humanized design is comfortable and is not more than one point, the attractive and corrosion-resistant LED lighting color is clear at a glance, and the processing result with the minimum sum of the distances between the LED lighting color and the other 5 processing results is as follows: the LED lighting color is clear at a glance, and the model fusion result (target processing result) is: the LED lighting color is clear at a glance.

It should be noted that, the model fusion method in the related art usually calculates an average value of all the model output results, and then uses the average value as the model fusion result, so that the probability depending on the model output result is not only used as the basis for obtaining the model fusion result, but also the utilization of the model output result is limited. Therefore, the concept of the median is introduced, the processing result output by each model is fully utilized, the utilization rate of the output result of the model is improved, and the reliability and the accuracy of the model fusion result in the obtaining process are further improved. Furthermore, the processing result with the minimum sum of the distances is used as the target processing result by the RPA system, so that the purpose of obtaining the more accurate target processing result is achieved by obtaining the processing result with the highest similarity, and the reliability and the accuracy of the model fusion result are further improved.

Fig. 5 is a block diagram of an RPA-AI combined model fusion result acquisition apparatus according to an embodiment of the present disclosure, and as shown in fig. 5, the RPA-AI combined model fusion result acquisition apparatus 500 includes:

a first obtaining module 510, configured to obtain information to be processed, and input the information to be processed into n models respectively, so as to obtain n processing results of the information to be processed based on natural language processing NLP, where n is an integer greater than 1;

a second obtaining module 520, configured to obtain a distance between any one of the processing results and the remaining n-1 processing results;

and the display module 530 is configured to obtain the target processing result of the information to be processed according to the distance, and display the target processing result.

The embodiment of the disclosure uses the RPA technology and the AI technology to ensure that the model output result does not depend on the probability of the model output result any more, improve the utilization rate of the model output result, ensure the accuracy of the model fusion result, and improve the robustness of the model fusion result.

It should be noted that the explanation of the embodiment of the method for obtaining the model fusion result of RPA and AI also applies to the device for obtaining the model fusion result of RPA and AI in this embodiment, and details are not repeated here.

Further, in a possible implementation manner of the embodiment of the present disclosure, the displaying module 630 is further configured to: according to the distance, obtaining the sum of the distances between any processing result and the rest n-1 processing results; and determining the processing result with the minimum sum of the distances from the sum of the distances to the rest n-1 processing results, and taking the processing result with the minimum sum of the distances as a target processing result.

Further, in a possible implementation manner of the embodiment of the present disclosure, the second obtaining module 620 is further configured to: and coding all the processing results to obtain n processing result vectors.

Further, in a possible implementation manner of the embodiment of the present disclosure, the second obtaining module 620 is further configured to: and inputting all processing results into the trained coding model, and taking the output n coding results with preset lengths as the processing result vector.

Further, in a possible implementation manner of the embodiment of the present disclosure, the second obtaining module 620 is further configured to: according to the processing result vector, obtaining the similarity information between any processing result and the rest n-1 processing results; and obtaining the distance according to the similarity information.

Further, in a possible implementation manner of the embodiment of the present disclosure, the second obtaining module 620 is further configured to: and the RPA system acquires the edit distance or word shift distance between any processing result and the rest n-1 processing results according to the processing results.

The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.

FIG. 6 illustrates a schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 6, the model fusion result acquisition method includes a memory 61, a processor 62 and a computer program stored in the memory 61 and executable on the processor 62, and when the processor 62 executes the computer program, the RPA and AI combined model fusion result acquisition method is implemented.

Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.

In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.