Patent Application
20220269820
The present disclosure relates generally to data privacy systems and more specifically, to systems for utilizing machine learning to automate redaction of information from documents.
A few decades ago users primarily interacted with service providers via in-person communications, such as visiting a doctor's office or purchasing items from a retail store. However, advancements in technology have resulted in many services providers offering access to services being offered to users via online platforms accessible over the Internet, such as online shopping, virtual doctor's visits, and the like. While the ability to access such services via the Internet has made those services more accessible, it has raised new concerns with respect to protecting users' sensitive data, also referred to as personally identifiable information (PII) data. Sensitive or PII data may include a user's name, address(es), e-mail address(es), telephone number(s), financial account or card information, location data, and other types of information that identifies, directly or indirectly, an individual person. Regulation of the use, storage, access, and distribution of sensitive data of users has significantly expanded in recent years in an effort to minimize the harm that may come from the misuse or improper access of sensitive data. As an example, General Data Protection Regulation (GDPR) requirements place significant restrictions on how entities store, share, and use sensitive data.
Efforts to comply with GDPR requirements and other regulations or laws relating to data privacy and security have taken many forms. One exemplary technique that may be used to protect sensitive data is data redaction, which may be performed by covering up certain pieces of information (e.g., with a black box), obfuscating the information (e.g., replacing the sensitive information with nonsensical or predetermined sequences of characters or symbols), or other techniques for concealing the sensitive data. Once the data has been redacted, the document may be shared with a third party without violating applicable data privacy regulations and laws.
While data redaction may minimize the chances that sensitive information is misused or improperly shared, there are several challenges that remain unsolved. For example, current processes to redact sensitive data from documents are performed manually, which can be a time consuming process. Additionally, manual redaction requires that an individual access and view the data to determine which parts of the data should be redacted and which parts of the data should remain (i.e., not be redacted). Performing data redaction in this manner requires the person doing the redaction have authorization to view the data but this can be problematic in some situations, such as when the person responsible for performing the redaction is not, in fact, authorized to access and view the data. Therefore, while data redaction provides one potential way to protect sensitive data shared with third parties, challenges still remain with respect to how the redaction is performed.
Aspects of the present disclosure provide systems, methods, apparatus, and computer-readable storage media that support automated document redaction in compliance with data privacy requirements. To facilitate data redaction, a document template may be obtained. A template expansion process may be used to generate multiple instances of the template, and each instance of the template may have data fields populated with arbitrary data (e.g., data that is not subject to the data privacy requirements). In aspects, each instance of the populated template may include different data, thereby providing multiple different copies of the populated template that share the same structural features.
Once the populated templates are generated, a template conversion process may be utilized to convert the populated templates to synthetic data. The synthetic data may have a different format than the populated templates. For example, the templates may correspond to portable data format (.pdf) files or other file types that contain various data fields that may be populated with data values during the template expansion process. The synthetic data may be generated using a structured language format, such as JavaScript Object Notation (JSON), JSON lines (JSONL), or another structured language. Utilizing the structured language format may enable the synthetic data to be more easily ingested by a computer process or software, such as a machine learning process to train a model to identify portions of a document that should be redacted in order to comply with data privacy requirements or for other reasons. The synthetic data may include information about the location of the data contents within the populated templates, a copy of the data contents, and structural information associated with the locations of the data fields and/or data used to populate the templates. In an aspect, synthetic data may also be expanded. For example, an instance of the synthetic data generated from one of the populated templates may be replicated and the data values may be altered to include different data values. The synthetic data expansion process may produce a robust set of information that shares common structural features (e.g., the locations of the data fields, etc.) but different data values, which may enable large sets of training data to be generated quickly for use in training a model.
During training of the model, the synthetic data may be analyzed to determine a set of content features and structural features. The content features may include information associated with characteristics of data within the data fields that may be indicative of sensitive data that should be redacted. For instance, content features may indicate that a term or phrase within the synthetic data that includes a sequence of numbers followed by a sequence of letters may be street address information (e.g., “1234 ZBC Street”). The structural features may include information that indicates a structure of the template, such as locations of the data fields, relationships between data fields, how many pages the template includes, or other types of information. The content features and structural features may be used to train the model to identify data that should be redacted within a document. For example, the model may be trained to identify address information, telephone number information, names, or other types of PII data that is subject to data privacy regulations or requirements. Once training is complete, the model may be used to evaluate documents requested by users and to perform data redaction prior to providing the documents to the user.
The foregoing has outlined rather broadly the features and technical advantages of the present disclosure in order that the detailed description that follows may be better understood. Additional features and advantages will be described hereinafter which form the subject of the claims of the disclosure. It should be appreciated by those skilled in the art that the conception and specific aspects disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present disclosure. It should also be realized by those skilled in the art that such equivalent constructions do not depart from the scope of the disclosure as set forth in the appended claims. The novel features which are disclosed herein, both as to organization and method of operation, together with further objects and advantages will be better understood from the following description when considered in connection with the accompanying figures. It is to be expressly understood, however, that each of the figures is provided for the purpose of illustration and description only and is not intended as a definition of the limits of the present disclosure.
For a more complete understanding of the present disclosure, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:
It should be understood that the drawings are not necessarily to scale and that the disclosed aspects are sometimes illustrated diagrammatically and in partial views. In certain instances, details which are not necessary for an understanding of the disclosed methods and apparatuses or which render other details difficult to perceive may have been omitted. It should be understood, of course, that this disclosure is not limited to the particular aspects illustrated herein.
Aspects of the present disclosure provide systems, methods, apparatus, and computer-readable storage media that support automated redaction of documents. As will be described in more detail below, embodiments may utilize a model to identify portions of a document for redaction. A training process may be utilized to teach the model how to identify content of a document that should be redacted, such as portions of the document that contain sensitive or PII data. To facilitate the training process, templates corresponding to different documents may be obtained. The templates may be blank when obtained and a template expansion process may be used to generate a plurality of populated templates, which are instances of the template that have been populated with arbitrary data (i.e., data that does not include PII data). The populated templates may provide a plurality of non-identical instances of the document that do not contain data subject to data privacy regulations or requirements, thereby enabling the templates to be used to train the model without running the risk of violating the data privacy regulations or requirements.
Once the populated templates are generated, a template conversion process may be utilized to convert the populated templates into a more compact format that is configured for ingestion by the model. For example, the conversion process may analyze the populated templates to identify the data input into the templates during the template expansion process and information about the structure of the populated templates (e.g., polygons bounding different types of information recorded in the populated templates, etc.). The template conversion process may generate synthetic data based on the analysis of the populated templates and the synthetic data may be configured to arrange the populated template data in a more structured or machine readable format. The synthetic data may then be used to train a model to identify information in a document, such as information that should be redacted. Once the model is trained, the model may then be used to analyze documents and perform data redaction.
Utilizing the techniques disclosed herein, redaction of data from documents may be performed in an automated manner, which allows the data redaction to be performed much more quickly than presently available manual techniques. Moreover, the training of the model may be performed without requiring use of documents containing sensitive data, thereby eliminating the risk that sensitive data is used in violation of data privacy regulations. Additionally, the template expansion and conversion processes may create a large data set of documents that are non-identical but share the same structure (i.e., document layout). This increases the accuracy of the model, enabling the automated redaction process to be performed with accuracy levels similar to the presently used manual techniques, but without the risk of the redaction process violating any data privacy regulations or laws.
Referring to
The redaction device 110 may include or correspond to a desktop computing device, a laptop computing device, a personal computing device, a tablet computing device, a mobile device (e.g., a smart phone, a tablet, a personal digital assistant (PDA), a wearable device, and the like), a server, a virtual reality (VR) device, an augmented reality (AR) device, an extended reality (XR) device, a vehicle (or a component thereof), an entertainment system, other computing devices, or a combination thereof, as non-limiting examples. The redaction device 110 includes one or more processors 112, a memory 114, one or more communication interfaces 118, one or more input/output (I/O) devices (not shown in
The redaction device 110 may include one or more processors 112, a memory 114, one or more communication interfaces 119, a template expansion engine 120, a template conversion engine 130, a modelling engine 140. The one or more processors 112 may include one or more microcontrollers, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), central processing units (CPUs) having one or more processing cores, or other circuitry and logic configured to facilitate the operations of the redaction device 110 in accordance with aspects of the present disclosure. The memory 114 may include random access memory (RAM) devices, read only memory (ROM) devices, erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), one or more hard disk drives (HDDs), one or more solid state drives (SSDs), flash memory devices, network accessible storage (NAS) devices, or other memory devices configured to store data in a persistent or non-persistent state. Software configured to facilitate operations and functionality of the redaction device 110 may be stored in the memory 114 as instructions 116 that, when executed by the one or more processors 112, cause the one or more processors 112 to perform the operations described herein with respect to the redaction device 110. Additionally, the memory 114 may be configured to store data and information, such as one or more databases 118. Illustrative aspects of types of information that may be stored in the one or more databases 118 are described in more detail below.
The one or more communication interfaces 119 may be configured to communicatively couple the redaction device 110 to the one or more networks 180 via wired or wireless communication links established according to one or more communication protocols or standards (e.g., an Ethernet protocol, a transmission control protocol/internet protocol (TCP/IP), an Institute of Electrical and Electronics Engineers (IEEE) 802.11 protocol, an IEEE 802.16 protocol, a 3rd Generation (3G) communication standard, a 4th Generation (4G)/long term evolution (LTE) communication standard, a 5th Generation (5G) communication standard, and the like). In some implementations, the redaction device 110 includes one or more input/output (I/O) devices (not shown in
The redaction device 110 may be configured to perform data redaction using a model provided by the modelling engine 140. The model may be trained using information generated by the template expansion engine 120 and the template conversion engine 130. For example and referring to
The template expansion engine 120 may be configured to expand the one or more templates 202 to create a plurality of populated templates 204. Expansion of the template(s) 202 may include creating instances of the template 202 and then populating the instances using arbitrary data (i.e., data that does not qualify as sensitive or PII data). Exemplary aspects of populating templates using arbitrary data are described in more detail with reference to
Referring back to
In some aspects, the template(s) may include information that identifies the document (or form) associated with the template. For example, the template 340 includes document identifier (ID) data 354. The document ID data 354 may include a string of numeric characters (e.g., 1234), alphanumeric characters (e.g., Doc. ID. 123456-1), or other types of information that may be used to identify the document corresponding to the template. It is noted that the exemplary document ID data described above has been provided for purposes of illustration, rather than by way of limitation and that templates and documents analyzed in accordance with aspects of the present disclosure may include document ID data that is different from the examples described herein or may not include document ID data.
Each of the templates 310, 330, 340 may be associated with a different document. For example, the templates 310, 330 may correspond to documents associated with the electronic device 150 and the template 340 may correspond to a document associated with the electronic device 160. In another non-limiting example, the templates 310, 330, 340 may each correspond to documents associated with the electronic device 150. The electronic devices 150, 160 may allow users to populate documents corresponding to the templates 310, 330, 340. Subsequently, the populated documents may be accessed, such as by a user of the user device 170 or shared. To ensure compliance with applicable privacy regulations and laws, access to the populated documents may involve redaction of sensitive data using a redaction device (e.g., the redaction device 110 of
Referring back to
In an aspect, the redaction device may utilize a document cataloguing process to obtain the templates. For example, a data store containing multiple instances of a document of documents may be analyzed to identify different documents. The cataloguing process may determine associations between the different documents, such as to analyze the documents and find different instances of a same document (e.g., an instance of a document populated with information of a first user and an instance of the document populated with information of a second user). Based on the identified document associations, the catalogue of documents may be divided into groups, where each group is associated with a unique document (e.g., all instances of a first documents are arranged within the catalogue as a first document category or class of documents and all instances of a second document are arranged within the catalogue as a first document category or class of documents. Once the different documents are identified, templates corresponding to each category or class of documents may then be selected, generated, (e.g., by removing data values used to populate the data fields of the document) or otherwise obtained.
As briefly described above, the template expansion engine 120 may utilize the information stored in the one or more databases 118 (e.g., a template expansion database) to perform template expansion. To illustrate and referring to FIG.
In an aspect, data fields may be identified as corresponding to sensitive data or non-sensitive data based on an analysis of the template(s). For example, information suggestive of a data field configured to capture name information may be present on the template proximate (e.g., to the left of, below, above, etc.) the data field, such as the phrase “first name,” “lase name,” “full name,” or other information being present proximate one or more data fields. As another example, the phrase “street address,” “apartment number,” “city,” “state,” “zip code,” or other types of identifying information may be present proximate data fields configured to capture address information. Similar types of phrases may be present proximate data fields configured to capture account information, telephone number information, e-mail address information, and the like. The template expansion engine may be configured to analyze the template(s) to identify these types of phrases and determine the types of data that should be used to expand the template(s). During the analysis, the template expansion engine may determine an orientation of the phrases relative to the data fields, such as to determine whether the identifying phrases are located to the left of the corresponding data fields, above the corresponding data fields, below the corresponding data fields, and the like. Such analysis may allow the template expansion engine to determine the types of data that should be used to populate the template(s) during the template expansion process. In some aspects, the types of data corresponding to each data field may be manually identified by a user and then automatically populated using the techniques described above (e.g., using a database of arbitrary data) or manually populated.
It is noted that the non-limiting example of
Referring back to
To illustrate and referring to
The populated template 500 may be provided to a template conversion engine (e.g., the template conversion engine 130 of
Referring back to
An example of synthetic data including location information associated with a populated is shown below:
As shown above, location information for “Loan ID” information may be associated with a location specified using starting and ending location offsets (e.g., “startOffset”: “215” and “endOffset”: “225”). The starting offset may indicate a position of the text associated with the offsets, such as the corresponding text for the “Loan_ID” starts at the 215th character of the populated template and ends with the 225th character of the populated template. It is noted that the exemplary location information shown above is provided for purposes of illustration, rather than by way of limitation and that synthetic data generated by the template conversion engine 130 of embodiments may include more location information than is shown in the non-limiting example above.
In addition to including the location information, the synthetic data may also include the contents of the populated template, such as contents of the identified data fields, special characters (e.g., new line “\n” indicators, etc.), or other types of information. A non-limiting example of synthetic data generated based on contents of the populated template is shown below:
As shown above, template contents may include a listing of the alpha-numeric data included in the populated template and may also include additional information, such as new line indicators (e.g., “\n”). It is noted that while the template contents may include information such as names, addresses, or other types of data that could potentially qualify as “sensitive data” subject to data privacy regulations, as described above the template is populated by the template expansion engine 120 with arbitrary data. As such, the names, addresses, and other types of information included in the populated template may not qualify as sensitive data subject to data privacy regulations and rules since it is made up or arbitrary data. It is noted that the exemplary location information shown above is provided for purposes of illustration, rather than by way of limitation and that synthetic data generated by the template conversion engine 130 of embodiments may include more template contents data and different types of template contents data than is shown in the non-limiting example above.
In addition to including the location information and the template contents, the synthetic data may also include information that associates the data with the corresponding polygons identified within the populated document. A non-limiting example of synthetic data including information that associates the data with the corresponding polygons identified within the populated template is shown below:
As shown above, the information that associates the data with the corresponding polygons identified within the populated template may include information associated with contents of the template, such as offset information and content information, page number information (e.g., information that identifies which page of the template the information is associated with), and coordinates of the polygon bounding the information. It is noted that the exemplary synthetic data shown above includes a bounding polygon specified using three different (x, y) coordinate pairs for purpose of illustration, rather than by way of limitation. As such, it should be understood that synthetic data generated by the template conversion engine 130 of embodiments may include more information than is shown in the non-limiting example above. For example, the synthetic data may specify bounding polygons using more than three (x, y) coordinate pairs, may include additional data, different types of data, or other types of information that may be used to capture information related to the relationships between the populated template contents and the bounding polygons identified by the template conversion engine 130.
In a non-limiting example, the template conversion engine 130 may also generate additional instances of synthetic data based on synthetic data derived from a populated template. For example, an instance of the synthetic data generated from one of the populated templates may be further expanded or replicated to produce additional synthetic data. During replication or expansion of the synthetic data, the template conversion engine 130 may replace the arbitrary data derived from the populated template with different arbitrary data, which may include data selected from the one or more databases 118 (e.g., the template expansion database described above with reference to
The template conversion engine 130 may provide the synthetic data (e.g., the synthetic data 206) to the modelling engine 140. As briefly described above, the synthetic data may not include sensitive information and may be created and stored in a structured format (e.g., a JSON or JSONL file, etc.), which may enable the synthetic data to be more readily used to train the model. The synthetic data may be recorded in a smaller file than the populated templates, which may be portable data format (.pdf) files. In addition to a smaller size, the structured nature of the synthetic data may enable the synthetic data to be more easily utilized by the modelling engine 140 to train a model. During training of the model, parameters (e.g., hyperparameters) may be adjusted to improve the accuracy of the model with respect to identifying data values for redaction, such as data values that are subject to data privacy regulations or laws. In an aspect, the data utilized for training the model(s) may be divided into three parts: a training dataset, a testing dataset, and a validation dataset. The validation dataset may be used to tune the hyperparameters for the model and the testing dataset may be leveraged to check the model accuracy on an unseen data set. By utilizing the training, testing, and validation data sets in this manner the robustness of the model and its performance may be evaluated. By training the model using the synthetic data, the model may learn to identify sensitive information within documents and once trained, the model may be utilized to analyze and evaluate documents (e.g., the document(s) 210 of
To illustrate and referring to
The synthetic data 602 may be provided as an input to a feature construction module 610 of the modelling engine. The feature construction module 610 may be configured to extract content features 612 and structural features 614 from the synthetic data 602. The content features 612 may identify features associated with the content of the synthetic data 602 (and the populated templates used to generate the synthetic data). For example, the synthetic data 602 may include content associated with a phone number. The phone number content may be analyzed by the feature construction module 610 to identify features that may be used to detect or identify a phone number within the synthetic data (and a document). For example, the phone number features may indicate that a series of 7 numbers (e.g., a phone number without an area code) or 10 numbers (e.g., a phone number with an area code) is a phone number. The phone number features may also indicate that a sequence of three numbers in parentheses (e.g., “(###)”) followed by 7 numbers (e.g., “(###) 1234567”) or a sequence of 3 numbers followed by a dash (“—”) followed by 4 more numbers (e.g., “(###) 123-4567”) is a phone number. Similarly, content features may indicate that one or more numbers followed by a sequence of letters may be a street address (e.g., #### ABCDEFG) and that certain abbreviations (e.g., Dr., Ave., Rd., St., and the like) may be street addresses. Additional content features may indicate that a sequence of letters followed by a comma (“,”) and then two letters and a series of numbers may indicate city, state, zip code address information (e.g., ABCD, TX #####). It is noted that the content features 612 described above are provided for purposes of illustration, rather than by way of limitation and that the feature construction module 610 of embodiments may be configured to identify additional content features or utilize other types of content features to identify the various non-limiting examples described above.
The structural features 614 may include features associated with a location of information, relationships between different data fields, or other types of template structural information that may be derived from the synthetic data 602. For example, in the exemplary populated template 500 of
The content features 612 and the structural features 614 may be utilized as inputs during a process to train a model 620. The training process may utilize machine learning to teach the model 620 to identify sensitive data and content within documents containing sensitive data (i.e., data that is subject to data privacy regulations and laws), such as the document(s) 210 of
To illustrate and referring to
Referring back to
The automated techniques for redaction of documents provided by the redaction device 110 enable sensitive data to be redacted in manner that is consistent with applicable data privacy regulations and requirements and may also enable data redaction to be performed more quickly than existing approaches while maintaining a high degree of accuracy, as described above with reference to
Referring to
At step 810, the method 800 includes obtaining, by one or more processors, a template corresponding to a document. In an aspect, the templates may be blank templates. At step 820, the method 800 includes executing, by the one or more processors, a template expansion process to produce a plurality of populated templates. In an aspect, the template expansion process may be performed by a template expansion engine, such as the template expansion engine 120 of
At step 830, the method 800 includes creating, by the one or more processors, synthetic data based on the plurality of populated templates. In an aspect, the synthetic data may be generated by a template conversion engine, such as the template conversion engine 130 of
At step 840, the method 800 includes training, by the one or more processors, a model to identify sensitive data based on the synthetic data. In an aspect, the training may be performed by a modelling engine, such as the modelling engine 140 of
At step 860, the method 800 includes evaluating, by the one or more processors, the document against the model to identify sensitive data within the document and at step 870, redacting, by the one or more processors, the sensitive data identified within the document based on the evaluating to produce a redacted document. As described above, redacting the sensitive data my include covering the sensitive data (e.g., with a black box), obfuscating the sensitive data, or other techniques to prevent the sensitive data from being viewed or accessed. At step 880, the method 800 includes transmitting, by the one or more processors, the redacted document to a user.
As described above, the method 800 supports automated redaction of documents in a manner that is consistent with applicable data privacy regulations and requirements. The data redaction techniques of the method 800 may enable data redaction to be performed more quickly than existing approaches and with a high degree of accuracy, thereby enabling documents to be shared more quickly. Additionally, the ability to automate the redaction process may also allow document redaction to be integrated within different systems and services, which may minimize the chances that documents are shared or accessed without data redaction being performed. It is noted that additional advantages and benefits provided by the method 800 and the concepts disclosed herein may be readily recognized and appreciated by persons of skill in the art and that the explicit benefits described herein are intended to merely highlight some of the advantages provided by the present disclosure.
It is noted that other types of devices and functionality may be provided according to aspects of the present disclosure and discussion of specific devices and functionality herein have been provided for purposes of illustration, rather than by way of limitation. It is noted that the operations of the method 800 of
Those of skill in the art would understand that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.
Components, the functional blocks, and the modules described herein with respect to
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure. Skilled artisans will also readily recognize that the order or combination of components, methods, or interactions that are described herein are merely examples and that the components, methods, or interactions of the various aspects of the present disclosure may be combined or performed in ways other than those illustrated and described herein.
The various illustrative logics, logical blocks, modules, circuits, and algorithm processes described in connection with the implementations disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. The interchangeability of hardware and software has been described generally, in terms of functionality, and illustrated in the various illustrative components, blocks, modules, circuits and processes described above. Whether such functionality is implemented in hardware or software depends upon the particular application and design constraints imposed on the overall system.
The hardware and data processing apparatus used to implement the various illustrative logics, logical blocks, modules, and circuits described in connection with the aspects disclosed herein may be implemented or performed with a general purpose single- or multi-chip processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, or any conventional processor, controller, microcontroller, or state machine. In some implementations, a processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. In some implementations, particular processes and methods may be performed by circuitry that is specific to a given function.
In one or more aspects, the functions described may be implemented in hardware, digital electronic circuitry, computer software, firmware, including the structures disclosed in this specification and their structural equivalents thereof, or any combination thereof. Implementations of the subject matter described in this specification also may be implemented as one or more computer programs, that is one or more modules of computer program instructions, encoded on a computer storage media for execution by, or to control the operation of, data processing apparatus.
If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. The processes of a method or algorithm disclosed herein may be implemented in a processor-executable software module which may reside on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that may be enabled to transfer a computer program from one place to another. A storage media may be any available media that may be accessed by a computer. By way of example, and not limitation, such computer-readable media can include random-access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that may be used to store desired program code in the form of instructions or data structures and that may be accessed by a computer. Also, any connection may be properly termed a computer-readable medium. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, hard disk, solid state disk, and Blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media. Additionally, the operations of a method or algorithm may reside as one or any combination or set of codes and instructions on a machine readable medium and computer-readable medium, which may be incorporated into a computer program product.
Various modifications to the implementations described in this disclosure may be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to some other implementations without departing from the spirit or scope of this disclosure. Thus, the claims are not intended to be limited to the implementations shown herein, but are to be accorded the widest scope consistent with this disclosure, the principles and the novel features disclosed herein.
Additionally, a person having ordinary skill in the art will readily appreciate, the terms “upper” and “lower” are sometimes used for ease of describing the figures, and indicate relative positions corresponding to the orientation of the figure on a properly oriented page, and may not reflect the proper orientation of any device as implemented.
Certain features that are described in this specification in the context of separate implementations also may be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation also may be implemented in multiple implementations separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination may in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.
Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Further, the drawings may schematically depict one more example processes in the form of a flow diagram. However, other operations that are not depicted may be incorporated in the example processes that are schematically illustrated. For example, one or more additional operations may be performed before, after, simultaneously, or between any of the illustrated operations. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the implementations described above should not be understood as requiring such separation in all implementations, and it should be understood that the described program components and systems may generally be integrated together in a single software product or packaged into multiple software products. Additionally, some other implementations are within the scope of the following claims. In some cases, the actions recited in the claims may be performed in a different order and still achieve desirable results.
As used herein, including in the claims, various terminology is for the purpose of describing particular implementations only and is not intended to be limiting of implementations. For example, as used herein, an ordinal term (e.g., “first,” “second,” “third,” etc.) used to modify an element, such as a structure, a component, an operation, etc., does not by itself indicate any priority or order of the element with respect to another element, but rather merely distinguishes the element from another element having a same name (but for use of the ordinal term). The term “coupled” is defined as connected, although not necessarily directly, and not necessarily mechanically; two items that are “coupled” may be unitary with each other. the term “or,” when used in a list of two or more items, means that any one of the listed items may be employed by itself, or any combination of two or more of the listed items may be employed. For example, if a composition is described as containing components A, B, or C, the composition may contain A alone; B alone; C alone; A and B in combination; A and C in combination; B and C in combination; or A, B, and C in combination. Also, as used herein, including in the claims, “or” as used in a list of items prefaced by “at least one of” indicates a disjunctive list such that, for example, a list of “at least one of A, B, or C” means A or B or C or AB or AC or BC or ABC (that is A and B and C) or any of these in any combination thereof. The term “substantially” is defined as largely but not necessarily wholly what is specified—and includes what is specified; e.g., substantially 90 degrees includes 90 degrees and substantially parallel includes parallel—as understood by a person of ordinary skill in the art. In any disclosed aspect, the term “substantially” may be substituted with “within [a percentage] of” what is specified, where the percentage includes 0.1, 1, 5, and 10 percent; and the term “approximately” may be substituted with “within 10 percent of” what is specified. The phrase “and/or” means and or.
Although the aspects of the present disclosure and their advantages have been described in detail, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit of the disclosure as defined by the appended claims. Moreover, the scope of the present application is not intended to be limited to the particular implementations of the process, machine, manufacture, composition of matter, means, methods and processes described in the specification. As one of ordinary skill in the art will readily appreciate from the present disclosure, processes, machines, manufacture, compositions of matter, means, methods, or operations, presently existing or later to be developed that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein may be utilized according to the present disclosure. Accordingly, the appended claims are intended to include within their scope such processes, machines, manufacture, compositions of matter, means, methods, or operations.
