Patent Grant
12229526
The present application claims priority under 35 U.S.C. 119(a)-(d) to the Indian Provisional Patent Application Serial No. 202211021330, having a filing date of Apr. 9, 2022, the disclosure of which is hereby incorporated by reference in its entirety.
Machine translation (MT) is the set of tools that enable users to input content in one language, and an automatic translation engine generates a complete translation for the input content in another target language. Machine translation tools provide translations without human editing. The most basic machine translation software operates strictly by word-for-word substitution. Some technologies can implement rule-based or statistically-modeled translations for greater accuracy. Modern machine learning (ML) includes neural networks which have ushered MT into an entirely new era. The translations produced by the modern neural network-based translation engines are gradually eliminating the gap between human and machine translations. This provides for machine-enabled translation tools that can improve human productivity.
Features of the present disclosure are illustrated by way of examples shown in the following figures. In the following figures, like numerals indicate like elements, in which:
For simplicity and illustrative purposes, the present disclosure is described by referring to examples thereof. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. It will be readily apparent however that the present disclosure may be practiced without limitation to these specific details. In other instances, some methods and structures have not been described in detail so as not to unnecessarily obscure the present disclosure. Throughout the present disclosure, the terms “a” and “an” are intended to denote at least one of a particular element. As used herein, the term “includes” means includes but not limited to, the term “including” means including but not limited to. The term “based on” means based at least in part on.
A smart translation system that accesses input content to be translated via an application and enables the display of the translated content so that the translated content maintains the look and feel of the input content when displayed through the application is disclosed. The smart translation system extracts translation metadata including a source language of the input content and a target language into which the input content is to be translated explicitly from a user or implicitly via automatic language detection and inferred user preferences. In addition, the type of application providing the input content is determined. For example, applications providing the input content can include but are not limited to, email applications, web-based applications, software applications e.g., desktop/mobile applications, etc.
Initially, it is determined if translated content of the input content is present in a user cache and if a translation of the input content per the translation metadata exists in the user cache, the translated content is retrieved from the user cache. Else if the translated content of the input content is not stored in the user cache, the translation of the input content is obtained from a translation engine automatically or manually selected from a plurality of automatic translation engines. In an example, the plurality of translation engines can include one or more of external translation engines and translation engines that are local to the smart translation system. The smart translation system implements certain procedures before the automatic selection of the translation engine. The procedures include but are not limited to, determining a context including the domain context and a language context (e.g., the source language) of the input content. The domain context of the input content can be determined by extracting the textual content from the input content and further processing the textual content by parsing, tokenizing, and tagging the tokens with the parts of speech (POS) data. The textual content thus processed is provided to a machine learning (ML) classification model for domain context determination. Furthermore, the language context of the input content can be based on the target language and may be further determined based on domain and language-specific glossary datasets. The processed textual content is also examined for sensitive data identification and any identified sensitive data is masked/redacted before the textual content is transmitted for translation. The translation engine is automatically selected from the plurality of translation engines based at least on the context. The translated content is generated based on the type of the application and the translation metadata. In addition to generating the translations, the smart translation system is also configured to highlight key sections of the translated content in a display and to provide a summary of the translated content.
To determine if a translation of the input content exists in the user cache, the smart translation system accesses prior input content submitted by a user for translation and corresponding translated content stored in the user cache along with related translation metadata. The similarity of the input content with the prior input content is calculated. The translated content from the user cache is provided only if the similarity between the prior content and the input content exceeds a predetermined content similarity threshold.
As mentioned above, different translations can be produced for the input content based at least on the application. In an example wherein the application includes an email program, the input content can include one or more of emails and corresponding attachments. The headers and footers separating the emails are identified from the input content to be translated. In an example, one or more of the emails in the user selection can include attachments. Accordingly, the translated content generated for the user selection would contain not only translations of the emails but also translations of the email attachments. One or more of the translated emails and the original email can be stored with the original attachment and the translated attachment.
In an example, the application can include a web application executed within a web browser. Again, the input content including the user selection of at least a portion of content displayed by the web browser is received for translation. The translated content of the input content if not existing in the user cache is obtained from a selected translation engine based on the translation metadata. The translated content thus generated is displayed within the web browser. In an example, if it is determined that the user selected portion of the content displayed by the web browser includes an image of text, then the text is extracted from the image, and a translation of the text extracted from the image is also obtained.
In an example, the application can include the software application. The user can select at least a portion of the user interface (UI) of the software application for translation. Upon selection of the portion of UI, a context menu of default options for the translation metadata can be superimposed on the user selection, wherein the context menu provides default options automatically selected for the translation metadata. However, the default options for the translation metadata can be user-editable. The values of original fields including labels displayed on the portion of the user interface of the software application are initially extracted and translations of contents corresponding to each of the values are obtained. Labels including the translated contents are generated for each of the original fields. The background color of the generated labels is set to match the background of the original fields displayed on the portion of the user interface. Similarly, the font properties of the textual content can be set to match the original fields to the extent possible. The translated labels thus generated are superimposed on the original fields.
The smart translation system as disclosed herein provides a technical solution to a technical problem of generating fast, accurate automatic translations of textual content while maintaining data privacy. Accordingly, the smart translation system is configured to initially check the user cache to determine if the received input content was previously translated for given translation metadata. If yes, the smart translation system retrieves the translation from the user cache instead of accessing the automatic translation engines thereby saving time and processing resources and preventing data from being transmitted to external translation resources. Even when the input content is to be transmitted to the automatic translation engines, the input content is filtered for sensitive data which may be masked. The smart translation system also allows a user greater control over the extent of translation so that the user can select only a portion of a display for translation instead of the entire display thereby obtaining faster translations while conserving processor resources for automatic translations. Furthermore, the smart translation system is configured to handle translations of different types of content generated by different applications in a manner customized to the applications. For example, in the case of an email application, the translations can be obtained not only for the emails but also for the attachments of the emails. Similarly, translated UIs of the software applications are presented so that the look and feel of the software applications are maintained. Other functionalities, such as spelling and grammar corrections, key section identification, sentiment identification, and summary generation of the translated content are also enabled by the disclosed smart translation system.
The translated content 156 is provided via the application 150 in a manner in which the look and feel of the application 150 are substantially maintained. Examples of the application 150 can include but are not limited to, an email application, a browser-based application, and a software application such as a desktop application, or a mobile application (an “app”). Different versions of the translated content 156 as detailed herein can be generated based on the application 150 via which the input content 152 is received. The smart translation system 100 includes an input receiver 102, a content translator 104, and a translated content provider 106. The input receiver 102 receives the input content 152 to be translated, along with translation metadata 154. The translation metadata 154 can include without limitation, a source language of the input content 152, the target language into which the input content 152 is to be translated, identification metadata of the application 150, and other metadata such as the file format pertaining to one or more files included in the input content 152. In an example, the smart translation system 100 is smart enough to determine that no translation is needed if the source language is the same as the target language.
The input content 152 along with the translation metadata 154 are provided to the content translator 104 which enables the generation of the translated content 156. The content translator 104 includes an input processor 142, an application file processor 144, and a translation generator 146. The input processor 142 accesses the input content 152 to determine if the translated content 156 of the input content 152 already exists in a user cache 148. In an example, users of the smart translation system 100 may have specific space on a cache e.g., the user cache 148 dedicated to storing user-specific data such as user preferences in translation engines, source/target languages, and transcription services (where applicable) and the frequently requested translations. If the translated content 156 is found in the user cache 148, then it is retrieved from the user cache 148 without the need to access external translation services such as a plurality of translation engines 120-1, 120-2, . . . , 120-n. If the translated content 156 cannot be retrieved from the user cache 148, then the input content 152 may be further processed by the application file processor 144 and provided to the translation generator 146.
If the translated content 156 cannot be retrieved from the user cache 148, the input processor 142 can be further configured to determine the context 172 of the input content 152 including a domain context and a language context. In an example, the input processor 142 can be further configured for sensitive data redaction prior to sending the input content 152 to the translation generator 146 for the translation. In an example, the smart translation system 100 may be communicatively coupled to a data store 170 that stores information such as the context 172 used in the translation operations. The context 172 of the input content 152 is used by the translation generator 146 for automatically selecting one of the plurality of translation engines 120-1, 120-2, . . . , 120-n (wherein n is a natural number and n=1, 2, . . . ). In an example, the plurality of translation engines 120-1, 120-2, . . . , 120-n can include external translation services such as but not limited to, Google Translate®, translation services provided by other cloud providers such as but not limited to AWS®, Azure®, or even local translation engines hosted by the smart translation system 100. The plurality of translation engines 120-1, 120-2, . . . , 120-n may operate to provide domain-specific or language-specific translations. In an example, a user can be presented with a context menu via the application 150 which enables the user to select a specific translation engine thereby overriding the automatic selection made by the smart translation system 100. The translated version 162 output by the selected translation engine is further processed by the translated content provider 106 for presentation via the application 150.
In an example, the translation generator 146 can include an engine selector 146-1 configured to automatically select one of the plurality of translation engines 120-1, 120-2, . . . , 120-n based at least on the domain/language context and the translation metadata 154. In addition, the engine selector 146-1 can receive user feedback regarding the engine selections made for prior translations. In an example, the engine selector 146-1 can implement a model-free reinforcement learning algorithm to predict the best engines for each source/target language pair and context 172. The algorithm may use the Bellman equation to compute Q-values (or Quality values). A Q-Table is first built for each language pair and context 172 combination with n rows that correspond to the number of the plurality of translation engines 120-1, 120-2, . . . , 120-n, and m columns corresponding to the number of states associated with the translation engines wherein n, m are natural numbers and n, m≥1. Example states may include excellent, good, average, poor, unsupported, etc. Initially, all values of the Q-table are set to zero and the engine selector 146-1 makes random engine choices from the plurality of translation engines 120-1, 120-2, . . . , 120-n. As the number of iterations increase, more accurate Q-values can be computed by the engine selector 146-1 based on the feedback from the user and hence improve in its predictions of the best translation engine for each context 172 and language pair. When a new engine is introduced or during the initial setup, the engine selector 146-1 is expected to be in its training stage. At this point, language experts and domain experts can evaluate the newly added engines in random, and provide accurate feedback before handing over the new engines to the users. The input content 152 with the sensitive data redacted is transmitted to the selected translation engine for the generation of the translated version 162.
The output from one of the input processor 142 or the translation generator 146 (depending on whether the translated content 156 was retrieved from the user cache 148 or whether the translated version 162 was generated by one of the plurality of translation engines 120-1, 120-2, . . . , 120-n) is provided to the translated content provider 106 which processes the translated version 162 for display via the application 150. The translated version 162 can be further processed for post-translation refinement including spelling and grammar corrections, key section identification, summarization of translated content, etc. Furthermore, the translated version 162 can be further modified based on the language context into a form more suitable for the target language prior to being provided as the translated content 156. The translated content 156 is presented through the application 150 in a manner that maintains the look and feel of the input content 152 when displayed through the application 150. For example, a translated email corresponding to an input email can be presented/displayed in a manner that is substantially identical to the input email via the same email application from which the input email was received. Similarly, translated web content can be presented in the same web browser, and translated UIs of applications also maintain a substantially similar look and feel of the original UIs of the software application.
In an example, the cache information retriever 202 can include a cache accessor 222, a similarity calculator 224, and a cache information provider 226. The cache accessor 222 receives the input content 152 and accesses the user cache 148 to enable the determination regarding the prior translation of the input content 152 based on the translation metadata 154 which includes not only the source language/the target language but also the location/link or other identifying indicia of the application 150 including the input content 152. In an example, the name of the executable of the application 150 can be received by the smart translation system 100 and an optional parameter in the translation metadata 154. The similarity calculator 224 may calculate the similarity between the cache contents and the input content 152 including the translation metadata 154. Various similarity measures such as but not limited to cosine similarity, various distance measures, Jaccard similarity, etc., can be employed by the similarity calculator 224. The cache information provider 226 can determine if any of the contents of the user cache 148 bears a similarity greater than a predetermined content similarity threshold. If yes, then that particular content item can be returned by the cache information provider 226 as the translated version 162 of the input content 152. If no content item can be determined from the user cache 148 as having a similarity greater than the predetermined content similarity threshold, then the input content 152 is provided to the context processor 204 for context identification that enables the selection of one of the plurality of translation engines 120-1, 120-2, . . . , 120-n.
The context processor 204 includes a content extractor 242, a context identifier 244, and glossary data sets 246. The content extractor 242 can be configured to identify textual and non-textual content in the input content 152 and convert the non-textual content such as image files into textual content. For example, the input content 152 may include a selection of a portion of the webpage with image files. The image files can be processed to determine if they include text, and if so, the image files can be provided to optical character recognition (OCR) engines for conversion into textual format. The text thus obtained can be parsed, tokenized, and tagged with parts of speech (POS) by the content extractor 242 and provided to the context identifier 244. The context identifier 244 can include a machine learning (ML) classification model that classifies the textual content received in the input content 152 into the context 172 which includes one of the multiple domain/language contexts. The glossary data sets 246 can include data sets with terminologies from the different domains in different languages. For example, the glossary data sets 246 can include language-specific terminologies in English, Spanish, French, Japanese, etc., for different domains e.g., manufacturing, health care, finance, retail, education, etc. The context identifier 244 can be configured to match the word tokens provided by the content extractor 242 to the terms from the various glossary data sets 246 from domain context identification. The particular language/domain glossary data set with the highest confidence score can be output as the domain/language context of the input content 152. Identification of domain contexts enables accurate interpretation of certain terms. For example, the term ‘fine’ can mean that everything is okay in the healthcare domain, whereas the same term ‘fine’ means something very different in the finance domain. In an example, the translated version 162 generated by the selected translation engine can also be provided to the context identifier 244 for confirmation of the context initially determined for the input content 152.
Upon identifying the domain/language context of the input content 152, the tokens from the content extractor 242 can be provided to the data redactor 206 which is configured to identify and mask sensitive data. The data redactor 206 includes various ML classification models 262 for named entity recognition (NER) and entity relationship detection from the list of tuples obtained from POS tagging by the content extractor 242. A classification Recurrent Neural Network (RNN) 264 is trained to classify normal words/entities from sensitive entities representative of sensitive data that users/business entities may desire to keep private. Examples of sensitive entities can include but are not limited to individual names, addresses, contact information, and identification information, such as Social Security numbers, tax ids, financial information, account numbers, amounts, etc. Other sensitive identification techniques such as but not limited to, n-grams, inference rules, and pointwise mutual information-based approaches can also be implemented by the sensitive data identifier 266. An n-gram is a sub-sequence of n items from a given sequence. n-grams are used in various areas of statistical natural language processing and genetic sequence analysis. The items in question can be characters, words or base pairs according to the application. For example, the sequence of characters “Hatem” has a 3-gram of (“Hat”, “ate”, “tem”, “em”, . . . ), and has a 2-gram of (“Ha”, “at”, “te”, “em”, . . . ) A mutual information-based approach to fair and robust training involves Trustworthy AI in ML where, in addition to training a model that is accurate, both fair and robust training are also considered in the presence of data bias and poisoning.
The sensitive data identifier 266 can be configured to find out specific locations with the input content 152 with confidence levels higher than the predetermined confidence levels which can be indicative of the presence of sensitive information at that specific location in the input content 152. The output from the sensitive data identifier 266 can include the type of sensitive data and the location of the sensitive data within the input content 152. The data masking provider 268 receives the output of the sensitive data identifier 266 and masks, redacts, or otherwise prevents the identified sensitive data from being transmitted out to the translation engine. The data masking provider 268 can implement various data masking techniques such as but not limited to, masking by blackening the fields including sensitive data, pseudonymization i.e., replacing sensitive entities with similar data, and anonymization i.e., replacing sensitive entities with random characters.
When the application 150 is an email program, the application file processor 144 includes the email processor 302 which allows the user to select one or more emails from an email chain or portion of an email for translation. As mentioned above, the user may identify the input content to be translated via a selection operation such as a mouse click, a hold and release operation, etc. A mouse click event can be fired indicating the user selected region and the smart translation system 100 can be triggered via a pop-up menu, a button, or another UI widget. The input content 152 thus received or identified by the user is provided to the email processor 302 for preprocessing prior to translation. In an example, the email processor 302 can include a user selection identifier 322, a header-footer identifier 324, a signature identifier 326, and an attachment extractor 328. The user selection identifier 322 may identify the input content 152 based on the selection operation. The input content 152 for an email application can include a current email, a complete email chain of multiple emails, a specific portion of an email/email chain, etc. The header-footer identifier 324 implements a classification support vector machine (SVM) algorithm to identify header and footer sections of emails. The header section of an email can include information related to the sender, the recipients, the time/date the email was sent or received, the subject of the email, and any attachments the email may have included. The footer section of the email can include the signature of the email sender. In case, a chain of emails is to be processed, the headers and footers enable individual identification of each of the emails in the email chain. Again, the output including the word tokens and the POS tags of the content extractor 242 can be used for grouping features. The features used by the SVM model for the header/footer identification can include but are not limited to, the position of the words, images, or data structures in the file containing the input content 152, the number of words, named entities such as persons' names, addresses, phone numbers, logos, special patterns, line breaks, etc. The signature identifier 326 can also use the above-mentioned features for identifying email signatures. When an email is submitted for translation, the email signatures may not be translated in some examples. The attachment extractor 328 can also include an ML model trained to identify attachments from email headers. In an example, the attachments thus obtained may be provided as part of the input content 152 that is submitted for translation. In an example, the translated attachments can be put back into the original emails during replies or forwards. Therefore, the original email after translation may include two attachments, the original attachment in the source language and the translated attachment in the target language. In an example, the translated email may be stored in the email server along with the original and the translated attachments.
When the application 150 includes a web application, or a web browser, the application file processor 144 may include a web content processor 304 which may further include a user selection identifier 342 and a web content analyzer 344. The user selection identifier 342 can function similarly to the user selection identifier 322 for an email application wherein, the user selection identifier 342 identifies the user-selected portion of content from a web browser. The web content analyzer 344 analyzes the input content 152 to determine the translation metadata 154 such as automatically detecting the source language of the webpage content if not readily available in the translation metadata 154.
The application UI processor 306 also includes a user selection identifier 362, a UI element identifier 364, and a UI element property extractor 366. The user selection identifier 362 receives the UI or the portion of the UI selected by the user as the input content 152. The UI element identifier 364 identifies UI elements such as labels, buttons, combo boxes, etc. within the user-selected portion of the UI. The UI element property extractor 366 extracts the position coordinates of the UI elements along with properties such as the size, background color, foreground/text color, font size, font type, etc. within the UI from which the input content 152 is received. The UI element position information enables the smart translation system 100 to superimpose the translated labels, names, etc., at the positions of the original labels so that the look and feel of the application UI are maintained.
The grammar/spelling checker 402 provides for detecting grammatical errors and spelling mistakes in the translated version 162 using natural language processing (NLP) techniques. For example, grammatical mistakes based on tenses or those mistakes based on references can be identified and corrected. Similarly, using the language/domain glossaries, spelling mistakes can also be corrected by the grammar/spelling checker 402. An example of a dictionary-based correction can address a simple spelling error wherein ‘I acept this proposal’ is corrected to ‘I accept this proposal. Context-based spelling errors may also be corrected. For example, an input sentence ‘I accept this to be done by 3 pm’ may be corrected as ‘I expect this to be done by 3 pm’.
Each language has its way of representing text while it is been spoken or written. For example, polite speech in Japanese requires that a person be addressed with a name in combination with ‘SAN’, e.g. ‘John San’. The language context modifier 404, accommodates such language peculiarities by extracting the translated version 162 and further adding/replacing translated words/sentences with language context. Using NLP techniques, the words to be modified/replaced are identified and words from the language/domain glossary datasets are used and the sentences are generated with Natural Language Generation (NLG) techniques. This can be very useful while translating email communications.
The content summarizer 406 is configured to read the translated version 162 by implementing NLP techniques to interpret and summarize the data. For example, if the input content 152 is a long contract document, the contents summarizer 406 can summarize key points, such as the parties involved, the contract start date, the milestones to be achieved, and the termination date of the contract, etc. In an example, the content summarizer 406 can generate content summary by extracting and isolating key information from a pre-existing text, and compressing the text into summarized content.
The key section identifier 408 processes the translated version 162 to highlight the key points in the translated content 156. The key section identifier 408 can implement an ML classification model trained to identify key/non-key sections. Features such as but not limited to, the position of words, whether the word is positive or negative, number of words in the position, person name, ending characters, special patterns, number of line breaks, etc., can be used by the ML classification model to differentiate the key sections from the non-key sections. A sentiment identifier 414 is also included in the smart translation system 100 for detecting the sentiment expressed in the input content 152. In an example, a Convolutional Neural Network (CNN) classification model to detect the sentiment or emotion. A separate CNN model can be built for each language. The sentiment can be differentiated at different levels. For example, the nature of the sentiment e.g., whether negative or neutral can be detected. The intensity of the sentiment may also be detected as threat/urgency/high, medium, or low. Similarly, other contexts associated with the sentiment may also be identified. For example, based on the sentiment, a sentence may be identified as a request a query, or an answer.
The UI generator 412 can be used to generate and present the translated content 156 through the application 150 so that the look and feel of the application 150 are maintained. Therefore, when viewed in an email program, the translated email maintains the look and feel of the original email. Similarly, a translated UI screen of a software utility or other software program maintains the same appearance as the original UI screen albeit that the content is displayed in the target language instead of the source language. In order to achieve a substantially similar look and feel, the UI generator 412 is configured with an output property identifier 422, an output text generator 424, and an output positioning component 426. As the smart translation system 100 operates as a plugin, the display properties of the UIs of the application 150 may be accessed via the application programming interfaces (APIs) by the output property identifier 422. Display properties may include but are not limited to, the background color of the UI screen(s), font type, font face, font color, other font properties, positioning of specific words on the UI(s), etc. The output text generator 424 generates output text using the display properties and the output positioning component 426 places the text modified per the display properties at positions determined by the position coordinates of the corresponding text in the original UI from which the input content 152 was extracted.
It is determined at 506 that the requested translation is not stored in the user cache 148, then the method proceeds to 510 to determine the context of the input content 152. Based at least on the context and the translation metadata 154, a translation engine is selected from the plurality of translation engines 120-1, 120-2, . . . , 120-n at 512 for generating the translated version 162 of the input content 152. It is further determined at 514 if the input content 152 includes any sensitive data. If yes, the sensitive data is redacted/masked at 516 and the input content 152 with the sensitive data masked, is provided to the selected translation engine at 518. The translated version 162 is obtained from the selected translation engine at 520. The translated version 162 is further processed for presentation at 522 through spelling and grammar checks, language context modifications, summarization, key section, and sentiment identification and/or suitable UI generation. At 524, the translated content 156 thus generated from further processing of the translated version 162 is provided to the user via the application 150.
The computer system 1200 includes processor(s) 1202, such as a central processing unit, ASIC or another type of processing circuit, input/output (1/O) devices 1212, such as a display, mouse keyboard, etc., a network interface 1204, such as a Local Area Network (LAN), a wireless 802.11x LAN, a 3G, 4G or 5G mobile WAN or a WiMax WAN, and a processor-readable medium 1206. Each of these components may be operatively coupled to a bus 12012. The processor-readable or computer-readable medium 1206 may be any suitable medium that participates in providing instructions to the processor(s) 1202 for execution. For example, the processor-readable medium 1206 may be a non-transitory or non-volatile medium, such as a magnetic disk or solid-state non-volatile memory, or a volatile medium such as RAM. The instructions or modules stored on the processor-readable medium 1206 may include machine-readable instructions 1264 executed by the processor(s) 1202 that cause the processor(s) 1202 to perform the methods and functions of the smart translation system 100.
The smart translation system 100 may be implemented as software or machine-readable instructions stored on a non-transitory processor-readable medium and executed by one or more processors 1202. For example, the processor-readable medium 1206 may store an operating system 1262, such as MAC OS, MS WINDOWS, UNIX, or LINUX, and code/instructions 1264 for the smart translation system 100. The operating system 1262 may be multi-user, multiprocessing, multitasking, multithreading, real-time, and the like. For example, during runtime, the operating system 1262 is running and the code for the smart translation system 100 is executed by the processor(s) 1202.
The computer system 1200 may include a data storage 1210, which may include non-volatile data storage. The data storage 1210 stores any data used by the smart translation system 100. The data storage 1210 may be used as a local data storage of the smart translation system 100 to store the input content 152, the translation metadata 154, the translation version 162, the translated content 156, user-selected options, and other data elements which are generated and/or used during the operation of the smart translation system 100.
The network interface 1204 connects the computer system 1200 to internal systems for example, via a LAN. Also, the network interface 1204 may connect the computer system 1200 to the Internet. For example, the computer system 1200 may connect to web browsers and other external applications and systems via the network interface 1204.
What has been described and illustrated herein is an example along with some of its variations. The terms, descriptions, and figures used herein are set forth by way of illustration only and are not meant as limitations. Many variations are possible within the spirit and scope of the subject matter, which is intended to be defined by the following claims and their equivalents.
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