Systems and Methods for Subject Identification (ID) Modeling
Systems and methods for subject identification (ID) modeling are disclosed. A subject identification may be associated with information contained in one or more core domains such as a patient domain, a country domain, and/or an investigator domain. The domains can be generically designed such that data sources that are unknown at the time the domains are created can be managed. In this way, using generic structures that support the domains, data sources can be added and/or updated as additional information and/or data sources become available. Using various graphical user interfaces, a user can dynamically associate patient criteria, country criteria, investigator criteria, and/or other information with subject identifications. A subject identification may be associated with a specified capture date. Information contained in the various domains may be filtered such that only information contained in the domain on or before the capture date is available for the subject identification.
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This application claims priority to U.S. Provisional Application No. 61/663,292, filed on Jun. 22, 2012, entitled “Method and System to Manipulate Multiple Selections against a Population of Elements;” U.S. Provisional Application No. 61/663,057, filed on Jun. 22, 2012, entitled “Systems and Methods For Predictive Analytics For Site Initiation and Patient Enrollment;” U.S. Provisional Application No. 61/663,299, filed on Jun. 22, 2012, entitled “Methods and Systems for Predictive Clinical Planning and Design and integrated Execution Services;” U.S. Provisional Application No. 61/663,398, filed on Jun. 22, 2012, entitled “Systems and Methods for Subject Identification (ID) Modeling;” U.S. Provisional Application No. 61/663,219, filed Jun. 22, 2012, entitled “Systems and Methods for Analytics on Viable Patient Populations;” U.S. Provisional Application No. 61/663,357, filed Jun. 22, 2012; entitled “Methods and Systems for a Clinical Trial Development Platform;” U.S. Provisional Application No. 61/663,216, filed Jun. 22, 2012; entitled “Systems and Methods for Data Visualization.” The entirety of all of which is hereby incorporated by reference herein.
FIELD OF THE INVENTIONThe present invention relates generally to systems and methods for the creation and analysis of data associated with clinical trials. The present invention relates more specifically to systems and methods for subject identification (ID) modeling.
BACKGROUNDClinical trials for molecules that may become pharmaceutical products often last for years. The core cost of the that is affected primarily by the length of the trial. And a delay of even a single day can cost hundreds or thousands and even millions of dollars.
Data associated with clinical trials is often associated with various data sources and may be highly diverse. Highly diverse data from numerous data sources is often difficult to organize, assemble, and analyze. Therefore, systems and methods for the collation of highly diverse data into usable data would be advantageous. Furthermore, systems and methods for dynamic analysis to support better understanding of the impacts of decisions against clinical trials would be advantageous.
SUMMARYEmbodiments of the present invention provide systems and methods for subject identification (ID) modeling. In one embodiment, raw data is processed to an application using a tool that enables a user to build subject identifications dynamically. In some embodiments, such subject IDs can be created by a user without technical expertise.
In one embodiment, a subject identification can be created dynamically. For example, a user can interact with a user interface to create a subject by entering or selecting a subject name and a molecule team. In one embodiment, a unique subject identification ID is automatically created or assigned once a subject name and a molecule team have been entered or selected.
A subject identification may be associated with information contained in other tables and/or databases. For example, in one embodiment, subject identifications may be associated with information contained in one or more core domains such as a patient domain, a country domain, and/or an investigator domain. In some embodiments, one or more domains are generically designed such that data sources that are unknown at the time the domain(s) are created can be managed. In this way, using a generic structure that supports a domain, data sources can be added and/or updated as additional information and/or data sources become available. Exemplary models that depict generic structures which support such domains are disclosed herein and variations are within the scope of this disclosure.
Using various graphical user interfaces, a user can dynamically associate patient criteria, country criteria, investigator criteria, and/or other information with subject identifications. In some embodiments, as the user interacts with the graphical user interfaces, associations between subject identifications and data in other tables and/or databases is dynamically updated in real-time or substantially real time. For example, when a user selects an indicator to be associated with a particular subject identification, the association may be created. As another example, when a user selects various indicators to be associated with a subject identification and then clicks an update button on the graphical user interface, the selected indicators may be dynamically associated with the subject identification.
Information associated with a particular subject identification may be frozen at a particular date and/or time. For example, a subject identification may be associated with a specified capture date. In this embodiment, information contained in the various domains may be filtered such that only information contained in the domain on or before the capture date is available for the subject identification. In this way, information for a data model may be updated as additional information for the data model becomes available but the information available to a particular subject identification can be limited to a static point in time.
These embodiments are mentioned not to limit or define the invention, but to provide an example of an embodiment of the invention to aid understanding thereof. Embodiments are discussed in the Detailed Description, and further description of the invention is provided there. Advantages offered by the various embodiments of the present invention may be further understood by examining this specification.
These and other features, aspects, and advantages of the present invention are better understood when the following Detailed Description is read with reference to the accompanying drawings, wherein:
Embodiments of the present invention provide systems and methods for subject identification (ID) modeling.
Illustrative Embodiment of the Present InventionOne illustrative embodiment of the present invention comprises an application for creating and/or updating subject identification (ID) models for clinical trials. The embodiment allows a user to access an application that presents a variety of clinical trial-related parameters for various patients, countries, and/or investigators. These parameters may include, for example, the population of a country, the regulatory environment, and/or the level of risk associated with conducting a trial in a particular country.
Using various graphical user interfaces associated with the application, a user can create subject identifications and/or select parameters related to patients, countries, and/or investigators for subject identifications. For example, in one embodiment, a user can select patients associated with one or more ICD9 codes for a particular subject identification. As another example, a user can add or remove various indicators—such as Ace Inhibitors, Acne, etc.—and/or phases and/or trials.
As the user interacts with the graphical user interface associations with data contained in various databases are added, removed, or updated. In some embodiments, such associations may be added, removed, or updated dynamically without requiring technical expertise regarding the underlying data structures. For example, a user can select one or more ICD9 codes to associate with a particular subject identification. In one embodiment. When a user selects an ICD9 code, associations between the selected ICD9 code, patients corresponding to the ICD9 code, and/or the subject identification are created. Similarly, when a user deselects an ICD9 code, associations between subject identifications and information in other tables and/or databases may be updated or removed.
In the illustrative embodiment, for various investigators, the user is able to specify investigator-specific parameters, such as indicators, phases, trials and/or other relevant parameters. The process is iterative; the user is able to change the parameters for patients, countries and/or investigators to determine the most appropriate sites to utilize for a clinical trial. As the user changes these parameters, information and/or associations corresponding to subject identifications and/or data structures may be dynamically added, removed, or updated. The results of the user's selections can then be used as part of a larger clinical trial analysis application.
This illustrative embodiment neither limits nor defines the invention. Rather, the illustrative embodiment is meant to provide an example of how the present invention may be implemented.
Illustrative EnvironmentReferring now to the drawings, in which like numerals indicate like elements throughout the several figures,
The client 100 may be, for example, a personal computer (PC), such as a laptop or desktop computer, which includes a processor and a computer-readable media. The client 100 also includes user input devices, such as a keyboard and mouse or touch screen, and one or more output devices, such as a display. In some embodiments of the invention, the user of client 100 accesses an application or applications specific to one embodiment of the invention. In other embodiments, the user accesses a standard application, such as a web browser on client 100, to access applications running on a server such as application server 200, web server 300, or database 400. For example, in one embodiment, in the memory of client 100 are stored applications including a design studio application for planning and designing clinical trials. The client 100 may also be referred to as a terminal in some embodiments of the present invention.
Such applications may be resident in any suitable computer-readable medium and executable on any suitable processor. Such processors may comprise, for example, a microprocessor, an ASIC, a state machine, or other processor, and can be any of a number of computer processors, such as processors from Intel Corporation, Advanced Micro Devices Incorporated, and Motorola Corporation. The computer-readable media stores instructions that, when executed by the processor, cause the processor to perform the steps described herein.
The client 100 provides a software layer, which is the interface through which the user interacts with the system by receiving and displaying data to and from the user. In one embodiment, the software layer is implemented in the programming language C# (also referred to as C Sharp). In other embodiments, the software layer can be implemented in other languages such as Java or C++. The software layer may be graphical in nature, using visual representations of data to communicate said data to one or more users. The visual representations of data may also be used to receive additional data from one or more users. In one embodiment, the visual representation appears as a spider-like layout of nodes and connectors extending from each node to a central node.
Embodiments of computer-readable media comprise, but are not limited to, an electronic, optical, magnetic, or other storage device, transmission device, or other device that comprises some type of storage and that is capable of providing a processor with computer-readable instructions. Other examples of suitable media comprise, but are not limited to, a floppy disk, CD-ROM, DVD, magnetic disk, memory chip, ROM, RAM, PROM, EPROM, EEPROM, an ASIC, a configured processor, all optical media, all magnetic tape or other magnetic media, or any other medium from which a computer processor can read instructions. Also, various other forms of computer-readable media may be embedded in devices that may transmit or carry instructions to a computer, including a router, private or public network, or other transmission device or channel, both wired and wireless. The instructions may comprise code from any suitable computer-programming language, including, for example, C, C#, Visual Basic, Java, Python, Perl, and JavaScript.
The application server 200 also comprises a processor and a memory. The application server may execute business logic or other shared processes. The application server may be, for example, a Microsoft Windows Server operating in a NET framework, an IBM Weblogic server, or a Java Enterprise Edition (J2E) server. While the application server 200 is shown as a single server, the application server 200, and the other servers 300, 400 shown may be combined or may include multiple servers operating together to perform various processes. In such embodiments, techniques such as clustering or high availability clustering may be used. Benefits to architectures such as these include redundancy and performance, among others.
In the embodiment shown in
In the embodiment shown in
The network 500 may be any of a number of public or private networks, including, for example, the Internet, a local area network (“LAN”), or a wide area network (“WAN”). The network connections 150, 250, 350, and 450 may be wired or wireless networks and may use any known protocol or standard, including TCP/IP, UDP, multicast, 802.11b, 802.11g, 802.11n, or any other known protocol or standard. Further, the network 100 may represent a single network or different networks. As would be clear to one of skill in the art, the client 100, servers 200, 300, and database 400 may be in communication with each other over the network or directly with one another.
The database 400 may be one or a plurality of databases that store electronically encoded information comprising the data required to plan, design, and execute a clinical trial. In one embodiment, the data comprises one or more design elements conesponding to the various elements related to one or more clinical trials. The database 400 may be implemented as any known database, including an SQL database or an object database. Further, the database software may be any known database software, such as Microsoft SQL Server, Oracle Database, MySQL, Sybase, or others.
Illustrative Process for Adding/Updating Information for Subject IdentificationsThe method 200 shown in
After creating or selecting a subject 210, the method 200 proceeds to block 220. In block 220, patient parameters are received. For example, codes corresponding to particular diseases and/or illnesses, such as ICD9 or ICD10 codes, may be received through a graphical user interface of the application. Based on the selected patient parameters, a number of patients in one or more databases that meet the selected criteria may be displayed in the application. In some embodiments, information regarding patients and/or patient parameters may be stored in multiple databases and/or tables. In such an embodiment, the application may dynamically create or update associations between the various databases and/or tables containing information corresponding to patients and selected subject identification(s). For example, patient information may be stored in one or more patient databases corresponding to one or more generic patient data models. In such an embodiment, associations between the patient database(s) and subject may be dynamically created, modified, or removed based on selected or deselected patient parameters.
After patient parameters are received 220, the method 200 proceeds to block 230. In block 230, investigator parameters are received. For example, various indicators, phases, and/or trials may be selected or removed for one or more subject identifications. In some embodiments, information regarding investigators and/or investigators parameters may be stored in multiple databases and/or tables. In such an embodiment, the application may dynamically create or update associations between the various databases and/or tables containing information corresponding to investigators and selected subject identification(s). For example, investigator information may be stored in one or more investigator databases corresponding to one or more generic investigator data models. In such an embodiment, associations between the investigator database(s) and subjects may be dynamically created, modified, or removed based on selected or deselected investigator parameters.
After investigator parameters are received 230, the method 200 proceeds to block 240. In block 240, country parameters are received. In some embodiments, information regarding countries and/or country parameters may be stored in multiple databases and/or tables. In such an embodiment, the application may dynamically create or update associations between the various databases and/or tables containing information corresponding to countries and selected subject identification(s). For example, country information may be stored in one or more investigator databases corresponding to one or more generic country data models. In such an embodiment, associations between the country database(s) and subjects may be dynamically created, modified, or removed based on selected or deselected country parameters.
After country parameters are received 240, the method 200 proceeds to block 250. In block 250, a capture date is received. Based on the capture date for a particular subject, the application may update the subject identification such that only information associated with the databases on or before the capture date can be included in any data analysis associated with the subject. For example, one or more databases associated with a subject may periodically be updated as new information becomes available. In this embodiment, even if the database is updated, the information available for a selected subject may be limited to information associated with the database on or before the capture date associated with the subject. In this way, a static snapshot of data for a particular subject can be maintained. While allowing databases to continue to receive additional data as it becomes available. In some embodiments, the additional information may be used by other subjects that either do not have a specified capture date or that have a capture date that is after the information is received. Numerous other embodiments are disclosed herein and variations are within the scope of this disclosure.
Illustrative Associations for Subject ID and Data SetsData Steward is a software tool that can be used to directly modify databases according to embodiments of the present invention. Appendix A, which is hereby incorporated by reference in its entirety, comprises a User Guide for a Data Steward according to embodiments of the present invention.
By using the SubjectID in the FactinvestigatorPerformance table to query the SubjectID in the DIM.Subject table, additional information such as the subject name, molecule team, and/or capture can be determined for the SuhiectID contained in the FactinvestigatorPerformance table. In some embodiments, information contained in various tables and/or databases may be linked in a chain. For example, a DataSourceID in the FactinvestigatorPerformance table correspond with a DataSourceID in the Dim.Dim.DataSource table. The Dim.Dim.DataSource table, in turn, may contain a DataSourceOwnerID fro a particular DataSourceID which corresponds with a DataSourceOwnerID in the Dim.DataSourceOwner table. Thus, by querying the various tables and/or databases, a DataSourceID in the FactinvestigatorPerformance table can be used to determine information such as the LastModifiedDate in the Dim. DataSourceOwner table for the DataSource associated with the DataSourceID. Numerous other embodiments are disclosed herein and variations are within the scope of this disclosure.
Below is a description of the various tables of an investigator data model according to one embodiment of the present invention:
Below is a description of the various tables of a country data model according to one embodiment of the present invention:
Below is a description of the various tables of a patient data model according to one embodiment of the present invention:
Embodiments of the present invention provide many advantages over conventional methods of predicting the enrollment for clinical trials. For example, embodiments of the present invention allow subject identifications (Os) to be created through one or more user interfaces. In one embodiment, a user can create one or more subject IDs without technical expertise. For example, using one or more user interfaces, a user can create a new subject by entering or selecting a subject name, molecule team, and/or a capture date. A unique subject identification may be dynamically created for the subject In another embodiment, a user can update an existing subject. For example, a user may be able to add or update a capture date or other information associated with a particular subject ID. Based at least in part on the capture data, data from various tables and/or databases associated with a subject ID may be limited. For example, the capture date may provide a static point in time for which information contained in the tables and/or databases is available. Thus, if the information for a particular table and/or database specifies that the information is before the capture date, then the information is available to the subject identification. Alternatively, if the information for a particular table and/or database specifies that the information is after the capture date, then this information may not be available to the subject identification.
Embodiments of the present invention provide one or more core domains of information that may be used for analysis of a clinical trial plan. For example, patient domains, country domains, and/or investigator domains of information can be used according to one embodiment. In some embodiments, one or more core domains are built generically such that the system can manage data sources that are unknown at the time the core domain is created. In this way, using a generic structure that supports a domain, additional data sources can be added and/or updated as additional inthrmation and/or data sources become available.
Subject identifications may be associated with at least a portion of the information for one or more domains. For example, subject identifications may be associated with information contained in a patient domain, a country domain, an investigator domain, and/or other domains or data sources.
Once various parameters are chosen and associations between subject identifications and information in the domains have been created, embodiments of the present invention are able to take a mathematical approach to analyzing and presenting data regarding the actual investigators and investigation sites. The embodiments can then create graphical representations, e.g., line graphs that display information, such as predictions for likely scenarios based on average performance as well as best and worst-case scenarios based on outlier data.
GeneralNumerous specific details are set forth herein to provide a thorough understanding of the claimed subject matter. However, those skilled in the art will understand that the claimed subject matter may be practiced without these specific details. In other instances, methods, apparatuses or systems that would be known by one of ordinary skill have not been described in detail so as not to Obscure claimed subject matter.
Some portions are presented in terms of algorithms or symbolic representations of operations on data bits or binary digital signals stored within a computing system memory, such as a computer memory. These algorithmic descriptions or representations are examples of techniques used by those of ordinary skill in the data processing arts to convey the substance of their work to others skilled in the art. An algorithm is a self-consistent sequence of operations or similar processing leading to a desired result. In this context, operations or processing involves physical manipulation of physical quantities. Typically, although not necessarily, such quantities may take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared or otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to such signals as bits, data, values, elements, symbols, characters, terms, numbers, numerals or the like. It should be understood, however, that all of these and similar terms are to be associated with appropriate physical quantities and are merely convenient labels. Unless specifically stated otherwise, it is appreciated that throughout this specification discussions utilizing terms such as “processing,” “computing,” “calculating,” “determining,” and “identifying” or the like refer to actions or processes of a computing device, such as one or more computers or a similar electronic computing device or devices, that manipulate or transform data represented as physical electronic or magnetic quantities within memories, registers, or other information storage devices, transmission devices, or display devices of the computing platform.
The system or systems discussed herein are not limited to any particular hardware architecture or configuration. A computing device can include any suitable arrangement of components that provide a result conditioned on one or more inputs. Suitable computing devices include multipurpose microprocessor-based computer systems accessing stored software that programs or configures the computing system from a general purpose computing apparatus to a specialized computing apparatus implementing one or more embodiments of the present subject matter. Any suitable programming, scripting, or other type of language or combinations of languages may be used to implement the teachings contained herein in software to be used in programming or configuring a computing device.
Embodiments of the methods disclosed herein may be performed in the operation of such computing devices. The order of the blocks presented in the examples above can be varied—for example, blocks can be re-ordered, combined, and/or broken into sub-blocks. Certain blocks or processes can be performed in parallel.
The use of “adapted to” or “configured to” herein is meant as open and inclusive language that does not foreclose devices adapted to or configured to perform additional tasks or steps. Additionally, the use of “based on” is meant to be open and inclusive, in that a process, step, calculation, or other action “based on” one or more recited conditions or values may, in practice, be based on additional conditions or values beyond those recited. Headings, lists, and numbering included herein are for ease of explanation only and are not meant to be limiting.
While the present subject matter has been described in detail with respect to specific embodiments thereof, it will be appreciated that those skilled in the art, upon attaining an understanding of the foregoing may readily produce alterations to, variations of, and equivalents to such embodiments. Accordingly, it should be understood that the present disclosure has been presented for purposes of example rather than limitation, and does not preclude inclusion of such modifications, variations and/or additions to the present subject matter as would be readily apparent to one of ordinary skill in the art.
Claims
1. A method comprising:
- receiving selection of a subject identification, the subject identification corresponding with at least a subject name;
- receiving selection of a patient parameter, the patient parameter corresponding with at least one of a physical characteristic or an illness;
- receiving selection of an investigator parameter, the investigator parameter corresponding with at least one of a trial, a phase, or a medical indicator;
- receiving selection of a geographic parameter, the geographic parameter corresponding with at least one of a geographic location or a geographic statistic;
- dynamically creating associations between the subject identification and the patient parameter, the investigator parameter, and the geographic parameter; and
- determining at least one potential site for a clinical trial based at least in part on the dynamically created associations.
2. The method of claim 1, further comprising:
- conducting the clinical trial based at least in part on the determined at least one potential site.
3. The method of claim 1, where receiving selection of the subject identification comprises:
- receiving selection of the subject name from a plurality of predefined subject names.
4. The method of claim 1, where receiving selection of the subject identification comprises:
- receiving a first input, the first input indicating the subject name;
- receiving a second input, the second input indicating a molecule team; and
- dynamically creating the subject identification.
5. The method of claim 1, wherein receiving selection of the patient parameter comprises receiving selection of at least one medical code, wherein the at least one medical code comprises at least one ICD9 code or ICD10 code.
6. The method of claim 1, further comprising:
- in response to receiving the selection of the patient parameter, displaying a number of patients having the selected patient parameter.
7. The method of claim 6, wherein the number of patients having the selected patient parameter is selected from a plurality of patient databases, each patient database in the plurality of patient databases corresponding to a same generic patient data model.
8. The method of claim 1, wherein dynamically creating associations between the subject identification and the patient parameter, the investigator parameter, and the country parameter comprises:
- creating at least one association between the subject identification and at least one patient database comprising the patient parameter;
- creating at least one association between the subject identification and at least one investigator database comprising the investigator parameter; and
- creating at least one association between the subject identification and at least one country database comprising the country parameter.
9. The method of claim 8,
- wherein each patient database corresponds to a same patient data model,
- wherein each investigator database corresponds to a same investigator data model, and
- wherein each country database corresponds to a same country data model.
10. The method of claim 8, further comprising:
- receiving selection of a capture date; and
- updating the subject identification such that only information contained in the at least one patient database, the at least one investigator database, and the at least one country database on or before the capture date is available for the selected subject identification.
11. The method of claim 1, further comprising:
- creating a graphical representation based at least in part on the dynamically created associations, the graphical representation providing a prediction for one or more scenarios.
12. The method of claim 11, wherein the prediction corresponds to at least one of a likely scenario based at least on part on average performance, a best-case scenario, or a worst-case scenario.
13. A computer-readable medium comprising program code for:
- receiving selection of a subject identification, the subject identification corresponding with at least a subject name;
- receiving selection of a patient parameter, the patient parameter corresponding with at least one of a physical characteristic or an illness;
- receiving selection of an investigator parameter, the investigator parameter corresponding with at least one of a trial, a phase, or a medical indicator;
- receiving selection of a geographic parameter, the geographic parameter corresponding with at least one of a geographic location or a geographic statistic;
- dynamically creating associations between the subject identification and the patient parameter, the investigator parameter, and the geographic parameter; and
- sending information corresponding to at least one of the dynamically created associations to a clinical trial analysis application for conducting a clinical trial.
14. The computer-readable medium of claim 13, further comprising program code for:
- receiving a capture date, wherein only information contained in at least one patient database, at least one investigator database, and at least one country database as of the capture date is available for the subject identification.
15. The computer-readable medium of claim 13, further comprising program code for:
- creating a graphical representation for the clinical trial based at least in part on the dynamically created associations.
16. The computer-readable medium of claim 13, further comprising program code for:
- associating a capture date with the subject identification such that data on or before the capture date can be maintained in a database while allowing the database to continue to receive additional data after the capture date.
17. A system, comprising:
- a plurality of databases;
- an electronic device comprising: an input device; a display; and a processor in communication with the input device, the display, the plurality of databases, the processor configured for: receiving selection of a subject identification, the subject identification corresponding with at least a subject name; receiving selection of a patient parameter, the patient parameter corresponding with at least one of a physical characteristic or an illness; receiving selection of an investigator parameter, the investigator parameter corresponding with at least one of a trial, a phase, or a medical indicator; receiving selection of a geographic parameter, the geographic parameter corresponding with at least one of a geographic location or a geographic statistic; dynamically creating associations between the subject identification and the plurality of databases for use in a clinical trial analysis application, the associations comprising a first association between the subject identification and the patient parameter, a second association between the subject identification and the investigator parameter, and a third association between the subject identification and the country parameter.
18. The system of claim 17, further comprising:
- a network; and
- a server comprising: a memory, wherein the memory comprises program code for the clinical trial analysis application; a second processor, the second processor in communication with the memory, the second processor configured for: executing the program code for the clinical trial analysis application; receiving information corresponding to at least one of the dynamically created associations from the electronic device through the network; and conducting a clinical trial using at least the clinical trial analysis application and the received information.
19. The system of claim 17,
- wherein the plurality of databases comprises a patient database, an investigator database, and a country database, and
- wherein the processor is further configured for: querying the patient database with the patient parameter;
- querying the investigator database with the investigator parameter; and querying the country database with the country parameter;
20. The system of claim 17, wherein the processor is further configured for:
- receiving a capture date; and
- filtering information in the plurality of databases such that only information in the plurality of databases on or before the capture date is available to the subject identification and such that new information can be added to the plurality of databases after the capture date and the new information is not available to the subject identification.
Type: Application
Filed: Jun 24, 2013
Publication Date: Dec 26, 2013
Applicant: Quintiles Transnational Corporation (Durham, NC)
Inventor: Joseph William Charles Goodgame (Zionsville, IN)
Application Number: 13/925,187
International Classification: G06F 19/00 (20060101);