Sensitive data policy recommendation based on compliance obligations of a data source

Systems, computer-implemented methods, and computer program products that can facilitate sensitive data policy recommendation are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an extraction component that can employ an artificial intelligence model to extract compliance data from a data source. The computer executable components can further comprise a recommendation component that can recommend a sensitive data policy based on the compliance data. In some embodiments, the recommendation component can further identify one or more sensitive data entities of a sensitive data dataset that are affected by actionable obligation data of the data source.

BACKGROUND

The subject disclosure relates to information retrieval and policy recommendation systems, and more specifically, to extraction of compliance obligations from a data source and recommendation of a sensitive data policy based on such compliance obligations.

Sensitive data policy generation involves the identification of legislation relevant to sensitive data concerns and deriving rules which can constrain the processing of existing sensitive data in accordance with such legislation. Identifying and tracking the relevant legislation as well as interpreting and converting such legislation into data specific constraints however is very time consuming.

A problem with existing information retrieval (IR) systems and/or techniques employed in sensitive data policy generation is that they fail to identify portions of legal documents containing actionable legislation and they fail to map such actionable legislation to certain sensitive data repositories. These tasks must be performed manually by large cross-disciplinary teams of lawyers and information technology (IT) experts.

Another problem with such existing IR systems and/or techniques is that they do not automatically (e.g., without action by a human user) generate sensitive data policy recommendations directly from data source documents (e.g., legal documents). Another problem with such existing IR systems and/or techniques is that they cannot capture all obligations, entities, and/or definitions of a data source document (e.g., a legal document), as they use keyword search techniques and/or pattern techniques. Another problem with such existing IR systems and/or techniques is that they do not distinguish entities mentioned within a data source document (e.g., a legal document) as being the subject of, or not, an obligation or a definition. Another problem with such existing IR systems and/or techniques is that they do not provide the ability to track and/or compare relevant regulations over time which requires additional manual efforts.

SUMMARY

According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an extraction component that can employ an artificial intelligence model to extract compliance data from a data source. The computer executable components can further comprise a recommendation component that can recommend a sensitive data policy based on the compliance data. An advantage of such a system is that it can automatically (e.g., without action by a human user): extract compliance data (e.g., obligations, obligation targets, obligation content, actionable obligations, definitions, definition targets, definition content, etc.) directly from one or more legal documents (e.g., legislation documents); and recommend an existing sensitive data policy corresponding to a sensitive data dataset that is susceptible to various sensitive data policies.

In some embodiments, the recommendation component can further identify one or more sensitive data entities of a sensitive data dataset that are affected by actionable obligation data of the data source. An advantage of such a system is that it can automatically (e.g., without action by a human user) identify one or more keywords of a sensitive data dataset that are affected by an actionable obligation, thereby enabling customized recommendation of a certain existing sensitive data policy corresponding to such one or more keywords and/or such a sensitive data dataset.

According to another embodiment, a computer-implemented method can comprise employing, by a system operatively coupled to a processor, an artificial intelligence model to extract compliance data from a data source. The computer-implemented method can further comprise recommending, by the system, a sensitive data policy based on the compliance data. An advantage of such a computer-implemented method is that it can be implemented to automatically (e.g., without action by a human user): extract compliance data (e.g., obligations, obligation targets, obligation content, actionable obligations, definitions, definition targets, definition content, etc.) directly from one or more legal documents (e.g., legislation documents); and recommend an existing sensitive data policy corresponding to a sensitive data dataset that is susceptible to various sensitive data policies.

In some embodiments, the computer-implemented method can further comprise identifying, by the system, one or more sensitive data entities of a sensitive data dataset that are affected by actionable obligation data of the data source. An advantage of such a computer-implemented method is that it can be implemented to automatically (e.g., without action by a human user) identify one or more keywords of a sensitive data dataset that are affected by an actionable obligation, thereby enabling customized recommendation of a certain existing sensitive data policy corresponding to such one or more keywords and/or such a sensitive data dataset.

According to another embodiment, a computer program product facilitating a sensitive data policy recommendation process is provided. The computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to employ, by the processor, an artificial intelligence model to extract compliance data from a data source. The program instructions are further executable by the processor to cause the processor to recommend, by the processor, a sensitive data policy based on the compliance data. An advantage of such a computer program product is that it can automatically (e.g., without action by a human user): extract compliance data (e.g., obligations, obligation targets, obligation content, actionable obligations, definitions, definition targets, definition content, etc.) directly from one or more legal documents (e.g., legislation documents); and recommend an existing sensitive data policy corresponding to a sensitive data dataset that is susceptible to various sensitive data policies.

In some embodiments, the program instructions are further executable by the processor to cause the processor to identify, by the processor, one or more sensitive data entities of a sensitive data dataset that are affected by actionable obligation data of the data source. An advantage of such a computer program product is that it can automatically (e.g., without action by a human user) identify one or more keywords of a sensitive data dataset that are affected by an actionable obligation, thereby enabling customized recommendation of a certain existing sensitive data policy corresponding to such one or more keywords and/or such a sensitive data dataset.

DETAILED DESCRIPTION

One or more embodiments are now described with reference to the drawings, wherein like referenced numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a more thorough understanding of the one or more embodiments. It is evident, however, in various cases, that the one or more embodiments can be practiced without these specific details. It is noted that the drawings of the present application are provided for illustrative purposes only and, as such, the drawings are not drawn to scale.

Given the above problem with existing information retrieval (IR) systems and/or techniques employed in data policy generation that fail to identify portions of legal documents containing actionable legislation, fail to identify entities affected by such actionable legislation, and fail to map such actionable legislation to specific sensitive data repositories, the present disclosure can be implemented to produce a solution to this problem in the form of systems, computer-implemented methods, or computer program products that can employ an artificial intelligence model to extract compliance data from a data source and recommend a sensitive data policy based on the compliance data. An advantage of such systems, computer-implemented methods, or computer program products is that they can automatically (e.g., without action by a human user): extract compliance data (e.g., obligations, obligation targets, obligation content, actionable obligations, definitions, definition targets, definition content, etc.) directly from one or more legal documents (e.g., legislation documents); and recommend an existing sensitive data policy corresponding to a sensitive data dataset that is susceptible to various sensitive data policies.

In some embodiments, the present disclosure can be implemented to produce a solution to the problem described above in the form of systems, computer-implemented methods, or computer program products that can identify one or more sensitive data entities of a sensitive data dataset that are affected by actionable obligation data of the data source. An advantage of such systems, computer-implemented methods, or computer program products is that they can automatically (e.g., without action by a human user) identify one or more keywords of a sensitive data dataset that are affected by an actionable obligation, thereby enabling customized recommendation of a certain existing sensitive data policy corresponding to such one or more keywords and/or such a sensitive data dataset.

Furthermore, given the above problem with existing information retrieval (IR) systems and/or techniques employed in data policy generation that fail to capture all obligations, entities, and/or definitions of a data source document (e.g., a legal document), the present disclosure can be implemented to produce a solution to this problem in the form of systems, computer-implemented methods, or computer program products that can automatically (e.g., without action by a human user) extract all compliance data (e.g., obligations, obligation targets, obligation content, actionable obligations, definitions, definition targets, definition content, etc.) directly from a data source document (e.g., a legal document). An advantage of such systems, computer-implemented methods, or computer program products is that they can ensure that all such compliance data is extracted and analyzed in recommending a sensitive data policy.

Moreover, given the above problem with existing information retrieval (IR) systems and/or techniques employed in data policy generation that fail to automatically (e.g., without action by a human user) generate sensitive data policy recommendations directly from data source documents (e.g., legal documents), the present disclosure can be implemented to produce a solution to this problem in the form of systems, computer-implemented methods, or computer program products that can automatically (e.g., without action by a human user) generate sensitive data policy recommendations directly from data source documents (e.g., legal documents). An advantage of such systems, computer-implemented methods, or computer program products is that they can eliminate the need of an entity (e.g., a human) to interpret such data source documents and recommend a corresponding sensitive data policy.

FIG.1illustrates a block diagram of an example, non-limiting system100that can facilitate sensitive data policy recommendation components in accordance with one or more embodiments described herein. According to several embodiments, system100can comprise a policy recommendation system102. In some embodiments, policy recommendation system102can comprise a memory104, a processor106, an extraction component108, a recommendation component110, and/or a bus112.

It should be appreciated that the embodiments of the subject disclosure depicted in various figures disclosed herein are for illustration only, and as such, the architecture of such embodiments are not limited to the systems, devices, or components depicted therein. For example, in some embodiments, system100and/or policy recommendation system102can further comprise various computer or computing-based elements described herein with reference to operating environment1000andFIG.10. In several embodiments, such computer or computing-based elements can be used in connection with implementing one or more of the systems, devices, components, or computer-implemented operations shown and described in connection withFIG.1or other figures disclosed herein.

According to multiple embodiments, memory104can store one or more computer or machine readable, writable, or executable components or instructions that, when executed by processor106, can facilitate performance of operations defined by the executable component(s) or instruction(s). For example, memory104can store computer or machine readable, writable, or executable components or instructions that, when executed by processor106, can facilitate execution of the various functions described herein relating to policy recommendation system102, extraction component108, recommendation component110, and/or another component associated with policy recommendation system102, as described herein with or without reference to the various figures of the subject disclosure.

In some embodiments, memory104can comprise volatile memory (e.g., random access memory (RAM), static RAM (SRAM), dynamic RAM (DRAM), etc.) and/or non-volatile memory (e.g., read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), etc.) that can employ one or more memory architectures. Further examples of memory104are described below with reference to system memory1016andFIG.10. Such examples of memory104can be employed to implement any embodiments of the subject disclosure.

According to multiple embodiments, processor106can comprise one or more types of processors or electronic circuitry that can implement one or more computer and/or machine readable, writable, and/or executable components and/or instructions that can be stored on memory104. For example, processor106can perform various operations that can be specified by such computer and/or machine readable, writable, and/or executable components and/or instructions including, but not limited to, logic, control, input/output (I/O), arithmetic, and/or the like. In some embodiments, processor106can comprise one or more central processing unit, multi-core processor, microprocessor, dual microprocessors, microcontroller, System on a Chip (SOC), array processor, vector processor, and/or another type of processor. Further examples of processor106are described below with reference to processing unit1014andFIG.10. Such examples of processor106can be employed to implement any embodiments of the subject disclosure.

In some embodiments, policy recommendation system102, memory104, processor106, extraction component108, recommendation component110, and/or another component of policy recommendation system102as described herein can be communicatively, electrically, and/or operatively coupled to one another via a bus112to perform functions of system100, policy recommendation system102, and/or any components coupled therewith. In several embodiments, bus112can comprise one or more memory bus, memory controller, peripheral bus, external bus, local bus, and/or another type of bus that can employ various bus architectures. Further examples of bus112are described below with reference to system bus1018andFIG.10. Such examples of bus112can be employed to implement any embodiments of the subject disclosure.

In some embodiments, policy recommendation system102can comprise any type of component, machine, device, facility, apparatus, and/or instrument that comprises a processor and/or can be capable of effective and/or operative communication with a wired and/or wireless network. All such embodiments are envisioned. For example, policy recommendation system102can comprise a server device, a computing device, a general-purpose computer, a special-purpose computer, a tablet computing device, a handheld device, a server class computing machine and/or database, a laptop computer, a notebook computer, a desktop computer, a cell phone, a smart phone, a consumer appliance and/or instrumentation, an industrial and/or commercial device, a digital assistant, a multimedia Internet enabled phone, a multimedia players, and/or another type of device.

According to multiple embodiments, such a network can comprise wired and/or wireless networks, including, but not limited to, a cellular network, a wide area network (WAN) (e.g., the Internet), and/or a local area network (LAN). For example, policy recommendation system102can communicate with one or more external systems, sources, and/or devices, for instance, computing devices (and vice versa) using virtually any desired wired or wireless technology, including but not limited to: wireless fidelity (Wi-Fi), global system for mobile communications (GSM), universal mobile telecommunications system (UMTS), worldwide interoperability for microwave access (WiMAX), enhanced general packet radio service (enhanced GPRS), third generation partnership project (3GPP) long term evolution (LTE), third generation partnership project 2 (3GPP2) ultra mobile broadband (UMB), high speed packet access (HSPA), Zigbee and other 802.XX wireless technologies or legacy telecommunication technologies, BLUETOOTH®, Session Initiation Protocol (SIP), ZIGBEE®, RF4CE protocol, WirelessHART protocol, 6LoWPAN (IPv6 over Low power Wireless Area Networks), Z-Wave, an ANT, an ultra-wideband (UWB) standard protocol, and/or other proprietary and non-proprietary communication protocols. In such an example, policy recommendation system102can thus include hardware (e.g., a central processing unit (CPU), a transceiver, a decoder), software (e.g., a set of threads, a set of processes, software in execution) and/or a combination of hardware and software that facilitates communicating information between policy recommendation system102and external systems, sources, and/or devices (e.g., computing devices, communication devices, etc.).

According to multiple embodiments, policy recommendation system102can comprise one or more computer and/or machine readable, writable, and/or executable components and/or instructions that, when executed by processor106, can facilitate performance of operations defined by such component(s) and/or instruction(s). Further, in numerous embodiments, any component associated with policy recommendation system102, as described herein with or without reference to the various figures of the subject disclosure, can comprise one or more computer and/or machine readable, writable, and/or executable components and/or instructions that, when executed by processor106, can facilitate performance of operations defined by such component(s) and/or instruction(s). For example, extraction component108, recommendation component110, and/or any other components associated with policy recommendation system102as disclosed herein (e.g., communicatively, electronically, and/or operatively coupled with or employed by policy recommendation system102), can comprise such computer and/or machine readable, writable, and/or executable component(s) and/or instruction(s). Consequently, according to numerous embodiments, policy recommendation system102and/or any components associated therewith as disclosed herein, can employ processor106to execute such computer and/or machine readable, writable, and/or executable component(s) and/or instruction(s) to facilitate performance of one or more operations described herein with reference to policy recommendation system102and/or any such components associated therewith.

In some embodiments, to implement one or more sensitive data policy recommendations, policy recommendation system102can facilitate performance of operations executed by and/or associated with extraction component108, recommendation component110, and/or another component associated with policy recommendation system102as disclosed herein. For example, as described in detail below, policy recommendation system102can facilitate: employing an artificial intelligence model to extract compliance data from a data source; recommending a sensitive data policy based on the compliance data; extracting, from the data source, compliance entity data, obligation data, obligation target data, obligation content data, actionable obligation data, non-actionable obligation data, definition data, definition target data, definition content data, data properties, entity properties, entity relationship data, and/or role of data consumer; segmenting the data source into compliance sections comprising an obligation section and/or a definition section; classifying obligation data of the data source into an actionable obligation data and/or a nonactionable obligation data; mapping actionable obligation data to one or more sensitive data entities of a sensitive data dataset; and/or identifying one or more sensitive data entities of a sensitive data dataset that are affected by actionable obligation data of the data source.

As referenced herein, an obligation (also referred to herein as obligation data) can comprise a sentence describing a regulation affecting a legal entity (e.g., “The importer and manufacturer of mercury-added button cell batteries must have the documents confirming the quantity of exported products”).

As referenced herein, an obligation target (also referred to herein as obligation target data) can comprise the legal entity affected by a regulation mentioned within an obligation (e.g., “The importer and manufacturer of mercury-added button cell batteries”).

As referenced herein, obligation content (also referred to herein as obligation content data) can comprise the regulation affecting the legal entity (e.g., “must have the documents confirming the quantity of exported products”).

As referenced herein, an actionable obligation can comprise an obligation which can be mapped directly to a set of rule-based constraints for a given set of entities stored in a database (e.g., “any direct identifiers should be redacted, e.g. name, email, phone number, signature, full address”).

As referenced herein, a non-actionable obligation can comprise an obligation which cannot be mapped to a set of rule-based constraints for a given set of entities stored in a database (e.g., “Masking of pre-specified variables can be done manually or with the use of software that can help identify variables that need redaction”).

As referenced herein, a definition (also referred to herein as definition data) can comprise a sentence which defines a specific concept for the purpose and within the context of a given legal document (e.g., “Aggregation involves the replacement of a value by a range”).

As referenced herein, a definition target (also referred to herein as definition target data) can comprise a concept mentioned in a sentence for which a definition valid specifically within the context of a given legal document is provided (e.g., “Aggregation”).

As referenced herein, definition content (also referred to herein as definition content data) can comprise the part of a sentence defining the definition target (e.g., “replacement of a value by a range”).

As referenced herein, sensitive data policies can comprise the set of actions to be applied on data in order to protect sensitive data of an individual (e.g., name, telephone number, e-mail address, etc.).

As referenced herein, masking and/or generalization can comprise one or more perturbation methods that replace original values with fictional and/or random values.

As referenced herein, consent management can comprise the framework to ensure consent is provided for the specific data access and processing.

As referenced herein, access control can comprise the set of mechanisms that ensure the correct set of roles (e.g., role of data consumer) have access to a specific asset (e.g., data, device, etc.).

As referenced herein, a compliance entity (also referred to herein as compliance entity data) can comprise, for instance, an obligation target, obligation content, a definition target, definition content, and/or another compliance entity.

As referenced herein, a data property can comprise, for instance, a name, a telephone number, an e-mail address, and/or another data property.

As referenced herein, an entity property can comprise, for instance, a name of an entity, a label of an entity, a type of an entity, a role of an entity, a synonym of an entity, and/or another entity property.

As referenced herein, data consumer can comprise the entity consuming the data (e.g., the entity using the data of another entity).

As referenced herein, a role of a data consumer can comprise the role, title, and/or function of a data consumer (e.g., a scientist, a tester, production personnel, etc.).

As referenced herein, a data processing technique can comprise, for instance, masking, grouping, generalization, and/or another data processing technique.

In some embodiments, system100and/or policy recommendation system102can receive one or more data sources and/or one or more keywords relevant to one or more sensitive data datasets (e.g., keywords of such sensitive data datasets susceptible to one or more sensitive data policies such as, for instance, names, telephone numbers, e-mails, etc.). For example, system100and/or policy recommendation system102can receive such data sources (e.g., a legal document, a legislation document, a guidance document, a regulatory document, a compliance document, a contract, etc.) and/or keywords via an interface component (e.g., an application programming interface (API), a graphical user interface (GUI), etc.) of system100and/or policy recommendation system102(e.g., not illustrated in the figures).

In some embodiments, system100and/or policy recommendation system102can receive one or more first data sources (e.g., a first legal document, a first legislation document, a first compliance document, etc.). In these embodiments, system100and/or policy recommendation system102can further receive, simultaneously or subsequently (e.g., at a later time), one or more second data sources (e.g., a second legal document, a second legislation document, a second compliance document, etc.). In these embodiments, such second data sources can comprise one or more iterations of the first data sources and/or one or more data sources that are different from the first data sources. For example, such second data sources can comprise one or more previous or subsequent iterations of the first data sources (e.g., older outdated versions of the first data sources or newer updated, modified, or otherwise revised versions of the first data sources). In another example, such second data sources can comprise one or more data sources that are different from the first data sources (e.g., different in type, different in content, different in origin, different in authorship, etc.). In some embodiments, policy recommendation system102can facilitate storing all such data sources (e.g., the first and/or second data sources described above) and/or keywords in a database such as, for example, memory104.

In some embodiments, policy recommendation system102and/or extraction component108can extract text from one or more data sources described above. For example, policy recommendation system102and/or extraction component108can extract all text, or portions thereof (e.g., one or more words, sentences, paragraphs, footnotes, etc.), from one or more data sources by employing an artificial intelligence model (e.g., as described below with reference to extraction component108) or by executing read and/or write operations using processor106.

In some embodiments, policy recommendation system102can facilitate segmenting a data source. For example, policy recommendation system102can facilitate segmenting (e.g., via segmentation component202described below with reference toFIG.2) a data source into one or more compliance sections that can comprise one or more obligation sections and/or one or more definition sections. In some embodiments, such obligation sections and/or definition sections can comprise, for instance, a sentence, a paragraph, a header, a footnote, and/or another portion of a data source.

In some embodiments, to facilitate such segmentation, policy recommendation system102can employ, for instance, a random forest algorithm, a rule-based system, or a heuristic segmentation algorithm to segment a data source into such compliance sections described above. For example, policy recommendation system102can employ a random forest algorithm, a rule-based system, or a heuristic segmentation algorithm to segment a data source into one or more obligation sections and/or one or more definition sections.

According to multiple embodiments, extraction component108can employ an artificial intelligence model (AI model) to extract compliance data from a data source. For example, extraction component108can employ an AI model to extract compliance data from the sections of a data source (e.g., the compliance sections) that can be segmented by policy recommendation system102(e.g., via segmentation component202) as described above.

In some embodiments, extraction component108can employ an artificial intelligence model (AI model) to extract compliance data from a data source including, but not limited to: a legal document, a legislation document, a guidance document, a regulatory document, a compliance document, a contract, and/or another data source. In some embodiments, extraction component108can employ an AI model to extract compliance data from one or more compliance sections of a data source (e.g., the segmented sections described above). For example, extraction component108can employ an AI model to extract compliance data including, but not limited to: compliance entity data, obligation data, obligation target data, obligation content data, actionable obligation data, non-actionable obligation data, definition data, definition target data, definition content data, a data property, an entity property, entity relationship data, a role of a data consumer, a data processing technique, and/or other compliance data. In some embodiments, such compliance data can comprise data related to

In some embodiments, to facilitate such extraction described above, extraction component108can comprise and/or employ an AI model and/or a machine learning model (ML model) including, but not limited to, a classification model, a probabilistic model, statistical-based model, an inference-based model, a deep learning model, a neural network, long short-term memory (LSTM), fuzzy logic, expert system, Bayesian model, and/or another model that can extract such compliance data described above. For example, extraction component108can comprise and/or employ an AI model that can utilize, for instance, long short-term memory (LSTM), a reasoning algorithm, natural language annotation, and/or natural language processing (NLP) to perform data extraction and/or data annotation of such compliance data described above.

In some embodiments, extraction component108can employ an AI model (e.g., LSTM, NLP, etc.) to extract one or more obligation sentences from an obligation section described above and/or one or more definition sentences from a definition section described above. In some embodiments, extraction component108can further employ an AI model (e.g., LSTM, NLP, etc.) to extract the compliance data defined above from such obligation sentences and/or definition sentences. For example, extraction component108can employ an AI model (e.g., LSTM, NLP, etc.) to extract definition data, definition target data, definition content data, and/or other data from such definition sentences. In another example, extraction component108can employ an AI model (e.g., LSTM, NLP, etc.) to extract obligation data, obligation target data, obligation content data, and/or other data from such obligation sentences.

In some embodiments, extraction component108can employ an AI model (e.g., LSTM, NLP, etc.) to extract new compliance data and/or revised compliance data from a second data source, where the second data source can comprise an iteration of the data source or a different data source. For example, as described above, system100and/or policy recommendation system102can receive (e.g., via an API, a GUI, etc.) a first data source and can further receive, simultaneously or subsequently (e.g., at a later time), a second data source, where such second data source can comprise an iteration of the first data source (e.g., a previous iteration or a subsequent iteration) or a data source that is different from the first data source (e.g., different in type, different in content, different in origin, different in authorship, etc.). In some embodiments, policy recommendation system102and/or extraction component108can compare (e.g., via reading by processor106) such a first data source to such a second data source to determine whether the second data source comprises compliance data not present in the first data source, or vice versa. For example, policy recommendation system102and/or extraction component108can compare the first data source to the second data source to determine whether the second data source comprises new compliance data and/or revised compliance data not present in the first data source, or vice versa. In some embodiments, extraction component108can further extract such new compliance data and/or revised compliance data from the second data source, and/or conversely, from the first data source. For example, extraction component108can extract such new and/or revised compliance data by employing an AI model (e.g., LSTM, NLP, etc.) or by executing read and/or write operations using processor106.

In some embodiments, policy recommendation system102can facilitate classifying data of a data source. For example, policy recommendation system102can facilitate classifying (e.g., via classification component302described below with reference toFIG.3) obligation data of a data source into actionable obligation data or nonactionable obligation data.

In some embodiments, to facilitate such classification, policy recommendation system102can employ an AI model such as, for instance, a neural network or a classification model to classify obligation data of a data source into actionable obligation data or nonactionable obligation data. For example, policy recommendation system102can employ such a neural network or classification model to classify obligation data into actionable obligations (e.g., compliance data702cand/or compliance entity704c) and/or nonactionable obligations (e.g., compliance data702dand/or compliance entity704d) described below and illustrated inFIG.7.

In some embodiments, policy recommendation system102can facilitate mapping data of a data source to one or more entities of a dataset. For example, policy recommendation system102can facilitate mapping (e.g., via map component402described below with reference toFIG.4) actionable obligation data to one or more sensitive data entities (e.g., names, telephone numbers, e-mail addresses, etc.) of a sensitive data dataset.

In some embodiments, to facilitate such mapping, policy recommendation system102can employ a keyword search or a mapping algorithm to map data of a data source to one or more entities of a dataset. For example, policy recommendation system102can employ a keyword search or a mapping algorithm to map actionable obligation data to one or more sensitive data entities of a sensitive data dataset. For instance, policy recommendation system102can employ a keyword search or a mapping algorithm to map actionable obligation data (e.g., compliance data702cand/or compliance entity704c) to one or more keywords of a sensitive data dataset received by policy recommendation system102as described above, where such keywords can constitute such sensitive data entities.

In some embodiments, recommendation component110can identify one or more sensitive data entities of a sensitive data dataset that are affected by actionable obligation data of a data source. For example, based on mapping actionable obligation data to one or more sensitive data entities of a sensitive data dataset as described above, recommendation component110can identify one or more of such sensitive data entities of the sensitive data dataset that are affected by actionable obligation data of a data source. For example, based on mapping such actionable obligation data to one or more keywords of a sensitive data dataset received by policy recommendation system102, recommendation component110can facilitate identifying one or more of such keywords of the sensitive data dataset that are affected by actionable obligation data of a data source.

In some embodiments, recommendation component110can recommend one or more sensitive data policies based on compliance data. For example, recommendation component110can recommend a sensitive data policy based on compliance data by mapping actionable obligation data (e.g., entities present within actionable obligations) to certain sensitive data policies corresponding to a given set of entities (e.g., keywords of a sensitive data dataset) stored in a database (e.g., memory104). For instance, recommendation component110can recommend a sensitive data policy based on compliance data by mapping (e.g., via a keyword search or a mapping algorithm) such actionable obligations to certain sensitive data policies comprising rule-based constraints (e.g., repository-specific data constraint rules) corresponding to such a given set of entities stored in such a database. In these examples, recommendation component110can recommend a certain sensitive data policy (e.g., a certain existing sensitive data policy) corresponding to one or more entities of a sensitive data dataset that recommendation component110can identify as being affected by actionable obligation data of a data source.

FIG.2illustrates a block diagram of an example, non-limiting system200that can facilitate sensitive data policy recommendation components in accordance with one or more embodiments described herein. In some embodiments, system200can comprise policy recommendation system102. In some embodiments, policy recommendation system102can comprise a segmentation component202. Repetitive description of like elements or processes employed in respective embodiments is omitted for sake of brevity.

According to multiple embodiments, segmentation component202can segment a data source into one or more compliance sections that can comprise one or more obligation sections and/or one or more definition sections. In some embodiments, such obligation sections and/or definition sections can comprise, for instance, a sentence, a paragraph, a header, a footnote, and/or another portion of a data source.

In some embodiments, to facilitate segmentation, segmentation component202can employ, for instance, a random forest algorithm, a rule-based system, or a heuristic segmentation algorithm to segment a data source into such compliance sections described above. For example, segmentation component202can employ a random forest algorithm, a rule-based system, or a heuristic segmentation algorithm to segment a data source into one or more obligation sections and/or one or more definition sections.

FIG.3illustrates a block diagram of an example, non-limiting system300that can facilitate sensitive data policy recommendation components in accordance with one or more embodiments described herein. In some embodiments, system300can comprise policy recommendation system102. In some embodiments, policy recommendation system102can comprise a classification component302. Repetitive description of like elements or processes employed in respective embodiments is omitted for sake of brevity.

According to multiple embodiments, classification component302can classify data of a data source. For example, classification component302can classify obligation data of a data source into actionable obligation data and/or nonactionable obligation data.

In some embodiments, to facilitate such classification, classification component302can employ an AI model such as, for instance, a neural network or a classification model to classify obligation data of a data source into actionable obligation data or nonactionable obligation data. For example, classification component302can employ such a neural network or classification model to classify obligation data into actionable obligations (e.g., compliance data702cand/or compliance entity704c) and/or nonactionable obligations (e.g., compliance data702dand/or compliance entity704d) described below and illustrated inFIG.7.

FIG.4illustrates a block diagram of an example, non-limiting system400that can facilitate sensitive data policy recommendation components in accordance with one or more embodiments described herein. In some embodiments, system400can comprise policy recommendation system102. In some embodiments, policy recommendation system102can comprise a map component402. Repetitive description of like elements or processes employed in respective embodiments is omitted for sake of brevity.

According to multiple embodiments, map component402can map actionable obligation data to one or more sensitive data entities of a sensitive data dataset. For example, map component402can map actionable obligation data to one or more sensitive data entities (e.g., names, telephone numbers, e-mail addresses, etc.) of a sensitive data dataset.

In some embodiments, to facilitate such mapping, map component402can employ a keyword search or a mapping algorithm to map data of a data source to one or more entities of a dataset. For example, map component402can employ a keyword search or a mapping algorithm to map actionable obligation data to one or more sensitive data entities of a sensitive data dataset. For instance, map component402can employ a keyword search or a mapping algorithm to map actionable obligation data (e.g., compliance data702cand/or compliance entity704c) to one or more keywords of a sensitive data dataset received by policy recommendation system102as described above, where such keywords can constitute such sensitive data entities.

FIG.5illustrates a flow diagram of an example, non-limiting computer-implemented method500that can facilitate a sensitive data policy recommendation process in accordance with one or more embodiments described herein. Repetitive description of like elements or processes employed in respective embodiments is omitted for sake of brevity.

In some embodiments, at502, computer-implemented method500can comprise receiving one or more legal documents and/or one or more keywords relevant to one or more sensitive data datasets. For example, as described above with reference toFIG.1, policy recommendation system102can receive one or more data sources that can comprise legal documents including, but not limited to, legislation documents, guidance documents, regulatory documents, compliance documents, contracts, and/or another legal document via an interface component (e.g., an API, a GUI, etc.) of policy recommendation system102(e.g., not illustrated in the figures). In this example, policy recommendation system102can further receive (e.g., via an API, GUI, etc.) one or more keywords relevant to one or more sensitive data datasets that can comprise keywords relevant to one or more sensitive data datasets that must be legislation compliant (e.g., keywords of such sensitive data dataset that can be affected by actionable obligation data of such legal document such as, for instance, names, telephone numbers, e-mails, etc.).

In some embodiments, at504, computer-implemented method500can comprise comparing (e.g., via policy recommendation system102and/or extraction component108) similar documents and/or updated documents (e.g., data sources such as, for instance, legal documents). For example, as described above, policy recommendation system102can receive (e.g., via an API, a GUI, etc.) a first data source and can further receive, simultaneously or subsequently (e.g., at a later time), a second data source, where such second data source can comprise an iteration of the first data source (e.g., a previous iteration or a subsequent iteration) or a data source that is different from the first data source (e.g., different in type, different in content, different in origin, different in authorship, etc.). In some embodiments, policy recommendation system102and/or extraction component108can compare (e.g., via reading by processor106) such a first data source to such a second data source to determine whether the second data source comprises compliance data not present in the first data source, or vice versa. For example, policy recommendation system102and/or extraction component108can compare the first data source to the second data source to determine whether the second data source comprises new compliance data and/or revised compliance data not present in the first data source, or vice versa. In some embodiments, extraction component108can further extract such new compliance data and/or revised compliance data from the second data source, and/or conversely, from the first data source. For example, extraction component108can extract such new and/or revised compliance data by employing an AI model (e.g., LSTM, NLP, etc.) or by executing read and/or write operations using processor106.

In some embodiments, at506, computer-implemented method500can comprise extracting text (e.g., via policy recommendation system102and/or extraction component108). For example, as described above with reference toFIG.1, policy recommendation system102and/or extraction component108can extract text from one or more data sources (e.g., legal documents). For instance, policy recommendation system102and/or extraction component108can extract all text, or portions thereof (e.g., one or more words, sentences, paragraphs, footnotes, etc.), from one or more data sources by employing an AI model (e.g., LSTM, NLP, etc.) or by executing read and/or write operations using processor106.

In some embodiments, at508, computer-implemented method500can comprise section segmentation (e.g., via policy recommendation system102and/or segmentation component202). For example, as described above with reference toFIGS.1and2, policy recommendation system102and/or segmentation component202can segment (e.g., by employing a random forest algorithm, a rule-based system, a heuristic segmentation algorithm, etc.) a data source (e.g., a legal document) into one or more compliance sections that can comprise one or more obligation sections and/or one or more definition sections. In some embodiments, such obligation sections and/or definition sections can comprise, for instance, a sentence, a paragraph, a header, a footnote, and/or another portion of a data source.

In some embodiments, at510, computer-implemented method500can comprise extracting (e.g., via policy recommendation system102and/or extraction component108) obligation sentences and/or definition sentences. For example, as described above with reference toFIG.1, extraction component108can employ an AI model (e.g., LSTM, NLP, etc.) to extract compliance data from one or more sections of a data source (e.g., the compliance sections of a legal document) that can be segmented by segmentation component202as described above. For instance, extraction component108can employ an AI model (e.g., LSTM, NLP, etc.) to extract one or more obligation sentences from an obligation section of such a data source and/or one or more definition sentences from a definition section of such a data source.

In some embodiments, at512aand512b, computer-implemented method500can comprise extracting (e.g., policy recommendation system102and/or extraction component108) one or more definition entities and/or one or more obligation entities. For example, as described above with reference toFIG.1, extraction component108can employ an AI model (e.g., LSTM, NLP, etc.) to extract the compliance data defined above from such obligation sentences and/or definition sentences that can be extracted by extraction component108from a data source as described above. For instance, extraction component108can employ an AI model (e.g., LSTM, NLP, etc.) to extract definition data, definition target data, definition content data, and/or other definition entities from such definition sentences. In another example, extraction component108can employ an AI model (e.g., LSTM, NLP, etc.) to extract obligation data, obligation target data, obligation content data, and/or other obligation entities from such obligation sentences.

In some embodiments, at514, computer-implemented method500can comprise classifying obligations (e.g., via policy recommendation system102and/or classification component302). For example, as described above with reference toFIGS.1and3, classification component302can classify (e.g., via a neural network or a classification model) obligation data of a data source into actionable obligation data or nonactionable obligation data.

In some embodiments, at516, computer-implemented method500can comprise linking (e.g., via policy recommendation system102and/or map component402) actionable obligation entities to sensitive data entities. For example, as described above with reference toFIGS.1and4, map component402can map (e.g., by employing a keyword search or a mapping algorithm) actionable obligation data (e.g., actionable obligation entities) to one or more sensitive data entities (e.g., names, telephone numbers, e-mail addresses, etc.) of a sensitive data dataset. For instance, map component402can employ a keyword search or a mapping algorithm to map such actionable obligation data to one or more keywords of a sensitive data dataset received by policy recommendation system102as described above, where such keywords can constitute such sensitive data entities.

In some embodiments, at518, computer-implemented method500can comprise data policy recommendation (e.g., via policy recommendation system102and/or recommendation component110). For example, as described above with reference toFIG.1, recommendation component110can identify one or more sensitive data entities of a sensitive data dataset that are affected by actionable obligation data of a data source. For instance, based on mapping actionable obligation data to one or more sensitive data entities of a sensitive data dataset as described above at516, recommendation component110can identify one or more of such sensitive data entities of the sensitive data dataset that are affected by actionable obligation data of a data source (e.g., keywords of a sensitive data dataset that are affected by actionable obligation data of a data source). In this example, based on such mapping and identifying described above, recommendation component110can recommend one or more sensitive data policies by mapping actionable obligation data (e.g., entities present within actionable obligations) to certain sensitive data policies corresponding to a given set of entities (e.g., keywords of a sensitive data dataset) stored in a database (e.g., memory104). In this example, recommendation component110can recommend a sensitive data policy by mapping (e.g., via a keyword search or a mapping algorithm) such actionable obligations to a certain sensitive data policy (e.g., a certain existing sensitive data policy) comprising rule-based constraints (e.g., repository-specific data constraint rules) corresponding to such a given set of entities stored in such a database.

In some embodiments, at520, computer-implemented method500can comprise mapping (e.g., via policy recommendation system102, recommendation component110, map component402, etc.) actionable obligations to sensitive data constraints (e.g., sensitive data policies). For example, as described above at518, recommendation component110can map (e.g., via a keyword search or a mapping algorithm) actionable obligations to rule-based constraints (e.g., repository-specific data constraint rules) corresponding to a given set of entities (e.g., keywords of a sensitive data dataset) stored in a database (e.g., memory104). In some embodiments, an entity (e.g., a human user) can employ (e.g., via an API, a GUI, etc.) policy recommendation system102and/or components thereof (e.g., extraction component108, recommendation component110, segmentation component202, classification component302, map component402, etc.) to identify one or more existing sensitive data policies based on compliance obligations of a data source (e.g., a legal document). In some embodiments, an entity (e.g., a human user) can employ (e.g., via an API, a GUI, etc.) policy recommendation system102and/or components thereof (e.g., extraction component108, recommendation component110, segmentation component202, classification component302, map component402, etc.) to generate one or more sensitive data policies based on compliance obligations of a data source (e.g., a legal document) that can be processed by policy recommendation system102as described above.

In some embodiments, at522, computer-implemented method500can comprise applying (e.g., via policy recommendation system102, processor106, an API, a GUI, a human user, etc.) one or more sensitive data policies. For example, an entity (e.g., a human user) can employ (e.g., an API, a GUI, etc.) policy recommendation system102to configure one or more policy enforcement points (e.g., actions that must be implemented to comply with sensitive data obligations of a legal document) based on one or more sensitive data policies recommended by recommendation component110.

In some embodiments, at524, computer-implemented method500can comprise identifying risk (e.g., via policy recommendation system102, processor106, an API, a GUI, a human user, etc.). For example, an entity (e.g., a human user) can employ (e.g., an API, a GUI, etc.) policy recommendation system102to identify one or more risks associated with applying one or more sensitive data policies recommended by recommendation component110and/or generated by such an entity as described above at520.

FIG.6illustrates an example, non-limiting information600that can facilitate sensitive data policy recommendation components in accordance with one or more embodiments described herein. Repetitive description of like elements or processes employed in respective embodiments is omitted for sake of brevity.

In some embodiments, information600can comprise a compliance section of a data source (e.g., a legal document) that can be extracted from such a data source by policy recommendation system102and/or extraction component108as described above with reference toFIGS.1and5. In some embodiments, the bold text of information600depicted inFIG.6can comprise one or more compliance data702a,702b,702c,702d,702e,702nand/or one or more compliance entities704a,704b,704c,704d,704e,704nof table700described below and illustrated inFIG.7.

FIG.7illustrates an example, non-limiting table700that can facilitate sensitive data policy recommendation components in accordance with one or more embodiments described herein. Repetitive description of like elements or processes employed in respective embodiments is omitted for sake of brevity.

In some embodiments, table700can comprise one or more compliance data702a,702b,702c,702d,702e,702n(where n can represent a total quantity of compliance data) and/or one or more compliance entities704a,704b,704c,704d,704e,704n(where n can represent a total quantity of compliance entities). In some embodiments, compliance data702a,702b,702c,702d,702e,702ncan comprise compliance data including, but not limited to, compliance entity data (e.g., compliance entities704a,704b,704c,704d,704e,704n), obligation data, obligation target data, obligation content data, actionable obligation data, non-actionable obligation data, definition data, definition target data, definition content data, a data property, an entity property, entity relationship data, a role of a data consumer, a data processing technique, and/or other compliance data.

In some embodiments, compliance data702a,702b,702c,702d,702e,702nand/or compliance entities704a,704b,704c,704d,704e,704ncan be extracted from a data source (e.g., via policy recommendation system102and/or extraction component108as described above with reference toFIGS.1and5). In some embodiments, such compliance data702a,702b,702c,702d,702e,702nand/or compliance entities704a,704b,704c,704d,704e,704ncan be segmented into one or more compliance sections (e.g., via policy recommendation system102and/or segmentation component202as described above with reference toFIGS.1,2, and5). In some embodiments, compliance data702a,702b,702c,702d,702e,702nand/or compliance entities704a,704b,704c,704d,704e,704ncan be classified (e.g., via policy recommendation system102and/or classification component302as described above with reference toFIGS.1,3, and5) into actionable obligations (e.g., compliance data702cand/or compliance entity704c) and/or nonactionable obligations (e.g., compliance data702dand/or compliance entity704d).

FIG.8illustrates an example, non-limiting table800that can facilitate sensitive data policy recommendation components in accordance with one or more embodiments described herein. Repetitive description of like elements or processes employed in respective embodiments is omitted for sake of brevity.

In some embodiments, table800can comprise one or more sensitive data policy rules (e.g., sensitive data policies) that can comprise the rule-based constraints (e.g., repository-specific data constraint rules) described above with reference toFIGS.1and5. In some embodiments, such sensitive data policy rules of table800can comprise one or more roles802a,802b,802n(where n can represent a total quantity of roles), which can comprise roles of a data consumer as defined above (e.g., a scientist, a tester, production personnel, etc.). In some embodiments, such sensitive data policy rules of table800can comprise one or more actions804a,804b,804n(where n can represent a total quantity of actions), which can comprise actionable obligations extracted (e.g., via extraction component108) from a data source and mapped (e.g., via map component402) to entities of a sensitive data dataset as described above with reference toFIGS.1and5(e.g., mapped to entities such as, for instance, name, e-mail, addresses, etc. as illustrated inFIG.8). In some embodiments, such sensitive data policy rules of table800can comprise one or more comments806a,806b,806n(where n can represent a total quantity of comments), which can comprise additional actionable obligation instructions and/or information corresponding to roles802a,802b,802nand/or actions804a,804b,804n.

In some embodiments, policy recommendation system102can be a sensitive data policy recommendation system and/or process associated with various technologies. For example, policy recommendation system102can be associated with machine learning technologies, artificial intelligence technologies, natural language processing (NLP) technologies, information extraction technologies, sensitive data compliance technologies, sensitive data management technologies, computer technologies, server technologies, information technology (IT) technologies, internet-of-things (IoT) technologies, automation technologies, data exchange technologies, or other technologies.

In some embodiments, policy recommendation system102can provide technical improvements to systems, devices, components, operational steps, or processing steps associated with the various technologies identified above. For example, policy recommendation system102can automatically (e.g., without action by a human user) extract compliance data (e.g., obligations, obligation targets, actionable obligations, etc.) from a data source (e.g., a legal document) and/or recommend a sensitive data policy (e.g., a set of data-specific rule-based constraints corresponding to a sensitive data dataset) based on such compliance data. In this example, such automatic extraction and/or recommendation can thereby eliminate the need of an entity (e.g., a human) to identify and/or track relevant legal documents (e.g., legislation) as well as interpret and/or convert sensitive data obligations of such legal documents into data-specific constraints.

In another example, by facilitating such extraction and/or recommendation operations described above, policy recommendation system102can provide technical improvements to traditional information retrieval (IR) systems that fail to identify portions of legal documents containing actionable legislation and fail to map such actionable legislation to certain sensitive data repositories. In this example, such tasks associated with traditional IR systems must be performed manually by large cross-disciplinary teams of lawyers and information technology (IT) experts. In this example, such traditional IR systems also do not provide the ability to track (e.g., compare) relevant regulations over time which requires additional manual efforts, whereas policy recommendation system102can facilitate such tracking (e.g., as described above with reference toFIG.1).

In another example, policy recommendation system102can reduce time of translation of actions (e.g., actionable obligations) to actual sensitive data enforcement rules. In another example, policy recommendation system102can provide scalability by processing a plurality (e.g., thousands) of sensitive data datasets and attributes to apply actions upon. In another example, policy recommendation system102can limit manual authoring (e.g., by a human) of sensitive data rules. In another example, policy recommendation system102can enhance auditability of compliance for protection of sensitive data of an individual.

In some embodiments, policy recommendation system102can provide technical improvements to a processing unit (e.g., processor106, a CPU, etc.) associated with one or more of the various technologies identified above. For example, by segmenting a data source into compliance sections from which compliance data can be extracted, policy recommendation system102can facilitate improving processing performance of such a processing unit (e.g., processor106, a CPU etc.) by reducing the amount of data such a processing unit must ingest (e.g., read, write, execute, etc.) in extracting such compliance data.

In some embodiments, policy recommendation system102can employ hardware or software to solve problems that are highly technical in nature, that are not abstract and that cannot be performed as a set of mental acts by a human. In some embodiments, some of the processes described herein may be performed by one or more specialized computers (e.g., one or more specialized processing units, a specialized computer with automated component(s), etc.) for carrying out defined tasks related to the various technologies identified above. In some embodiments, policy recommendation system102and/or components thereof, can be employed to solve new problems that arise through advancements in technologies mentioned above, employment of cloud-computing systems, computer architecture, and/or another technology.

It is to be appreciated that policy recommendation system102can comprise and/or utilize various combinations of electrical components, mechanical components, and/or circuitry that cannot be replicated in the mind of a human or performed by a human, as the various operations that can be executed by policy recommendation system102or components thereof as described herein are operations that are greater than the capability of a human mind. For instance, the amount of data processed, the speed of processing such data, or the types of data processed by policy recommendation system102over a certain period of time can be greater, faster, or different than the amount, speed, or data type that can be processed by a human mind over the same period of time.

According to several embodiments, policy recommendation system102can also be fully operational towards performing one or more other functions (e.g., fully powered on, fully executed, etc.) while also performing the above-referenced sensitive data policy recommendation process. It should be appreciated that such simultaneous multi-operational execution is beyond the capability of a human mind. It should also be appreciated that policy recommendation system102can include information that is impossible to obtain manually by an entity, such as a human user. For example, the type, amount, or variety of information included in extraction component108, recommendation component110, segmentation component202, classification component302, and/or map component402can be more complex than information obtained manually by a human user.

FIG.9illustrates a flow diagram of an example, non-limiting computer-implemented method900that can facilitate a sensitive data policy recommendation process in accordance with one or more embodiments described herein. Repetitive description of like elements or processes employed in respective embodiments is omitted for sake of brevity.

In some embodiments, at902, computer-implemented method900can comprise employing, by a system (e.g., via policy recommendation system102and/or extraction component108) operatively coupled to a processor (e.g., processor106), an artificial intelligence model (e.g., LSTM, NLP, etc.) to extract compliance data from a data source. In some embodiments, such compliance data can include, but is not limited to, compliance entity data, obligation data, obligation target data, obligation content data, actionable obligation data, non-actionable obligation data, definition data, definition target data, definition content data, a data property, an entity property, entity relationship data, a role of a data consumer, a data processing technique, and/or other compliance data. In some embodiments, such a data source can include, but is not limited to, a legal document, a legislation document, a guidance document, a regulatory document, a compliance document, a contract, and/or another data source.

In some embodiments, at904, computer-implemented method900can comprise recommending, by the system (e.g., via policy recommendation system102and/or recommendation component110), a sensitive data policy (e.g., a set of rule-based constraints corresponding to a sensitive data dataset) based on the compliance data.

In order to provide a context for the various aspects of the disclosed subject matter,FIG.10as well as the following discussion are intended to provide a general description of a suitable environment in which the various aspects of the disclosed subject matter can be implemented.FIG.10illustrates a block diagram of an example, non-limiting operating environment in which one or more embodiments described herein can be facilitated. Repetitive description of like elements or processes employed in other embodiments described herein is omitted for sake of brevity.

With reference toFIG.10, a suitable operating environment1000for implementing various aspects of this disclosure can also include a computer1012. The computer1012can also include a processing unit1014, a system memory1016, and a system bus1018. The system bus1018couples system components including, but not limited to, the system memory1016to the processing unit1014. The processing unit1014can be any of various available processors. Dual microprocessors and other multiprocessor architectures also can be employed as the processing unit1014. The system bus1018can be any of several types of bus structure(s) including the memory bus or memory controller, a peripheral bus or external bus, or a local bus using any variety of available bus architectures including, but not limited to, Industrial Standard Architecture (ISA), Micro-Channel Architecture (MSA), Extended ISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB), Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus (USB), Advanced Graphics Port (AGP), Firewire (IEEE 1394), and Small Computer Systems Interface (SCSI).

The system memory1016can also include volatile memory1020and nonvolatile memory1022. The basic input/output system (BIOS), containing the basic routines to transfer information between elements within the computer1012, such as during start-up, is stored in nonvolatile memory1022. Computer1012can also include removable/non-removable, volatile/non-volatile computer storage media.FIG.10illustrates, for example, a disk storage1024. Disk storage1024can also include, but is not limited to, devices like a magnetic disk drive, floppy disk drive, tape drive, Jaz drive, Zip drive, LS-100 drive, flash memory card, or memory stick. The disk storage1024also can include storage media separately or in combination with other storage media. To facilitate connection of the disk storage1024to the system bus1018, a removable or non-removable interface is typically used, such as interface1026.FIG.10also depicts software that acts as an intermediary between users and the basic computer resources described in the suitable operating environment1000. Such software can also include, for example, an operating system1028. Operating system1028, which can be stored on disk storage1024, acts to control and allocate resources of the computer1012.

Computer1012can operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s)1044. The remote computer(s)1044can be a computer, a server, a router, a network PC, a workstation, a microprocessor based appliance, a peer device or other common network node and the like, and typically can also include many or all of the elements described relative to computer1012. For purposes of brevity, only a memory storage device1046is illustrated with remote computer(s)1044. Remote computer(s)1044is logically connected to computer1012through a network interface1048and then physically connected via communication connection1050. Network interface1048encompasses wire or wireless communication networks such as local-area networks (LAN), wide-area networks (WAN), cellular networks, etc. LAN technologies include Fiber Distributed Data Interface (FDDI), Copper Distributed Data Interface (CDDI), Ethernet, Token Ring and the like. WAN technologies include, but are not limited to, point-to-point links, circuit switching networks like Integrated Services Digital Networks (ISDN) and variations thereon, packet switching networks, and Digital Subscriber Lines (DSL). Communication connection(s)1050refers to the hardware/software employed to connect the network interface1048to the system bus1018. While communication connection1050is shown for illustrative clarity inside computer1012, it can also be external to computer1012. The hardware/software for connection to the network interface1048can also include, for exemplary purposes only, internal and external technologies such as, modems including regular telephone grade modems, cable modems and DSL modems, ISDN adapters, and Ethernet cards.

Aspects of the present invention are described herein with reference to flowchart illustrations or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations or block diagrams, and combinations of blocks in the flowchart illustrations or block diagrams, can be implemented by computer readable program instructions. These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart or block diagram block or blocks. The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational acts to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart or block diagram block or blocks.