Communication data processing architecture

Description

TECHNICAL FIELD

The present disclosure relates to systems and techniques for data integration and analysis.

BACKGROUND

Commercial entities, financial entities, law firms, government entities, and others, gather and process an enormous amount of information. Some of the information gathered and processed is in the form of electronic messages or other such communications. These communications are generated by employees, contractors, or clients and are sent to reviewers (for example, communications analysts, other employees, other contractors, other clients, and so forth) for review. Reviewers may review communications related to a group of employees to flag any possible malfeasance or illegal activity being conducted by an employee.

However, because of the enormous amount of information gathered and processed, it can be difficult for reviewers to readily review the communications. While some communications may include benign information (for example, standard introductory pleasantries or typical questions), other communications may include information that indicates that an employee is engaged in some illegal activity. Thus, communications that are misidentified or that are unidentified can have serious repercussions.

The following disclosure relates to systems and methods for efficiently and flexibly processing enormous amount of communications information and then presenting that information for review. The processing and presenting of information to the reviewer are also designed to minimize the chances that communications are misidentified or unidentified.

SUMMARY OF THE DISCLOSURE

The systems, methods, and devices described herein each have several aspects, no single one of which is solely responsible for its desirable attributes. Without limiting the scope of this disclosure, several non-limiting features will now be discussed briefly.

A communication data processing system is disclosed herein that allows reviewers to properly and timely review user communications. The communications may include emails, chat room communications, instant messages, text messages, and so forth. The communication data processing system may organize and process one or more communications so that certain parts of the message are highlighted or flagged allowing reviewers to readily easily flag relevant communications. For example, different types of highlighting can be applied to certain pre-selected words, email addresses external to the company, or other information that has been determined to be a possible flag for bad behavior. In one embodiment, the highlighting may be based on the particular user, or one or more persons, being reviewed. The one or more person's emails, chat room files, instant messages, and text can be complied for review, tied to an identifier that corresponds to the person(s), and assigned one or more group identifier(s) based upon the group(s) in which the person(s) is assigned. For example, if the one or more persons is a commodities trader employee in London, then the person may be assigned to the “commodities trading” group and to the “UK” group.

In one embodiment, a reviewer is presented with one or more person's electronic communications where certain portions of the communications have been highlighted or pre-flagged. In addition, the reviewer has the option of further updating the display, such as, for example, by adding additional words, removing known innocuous text from the message (such as disclaimers or signatures), updating the time zone of the message data, highlighting specific terms in the messages, and/or changing the category of pre-selected highlighted words (such as from “commodities” to “options”). The reviewer can then determine whether any of the communications should be flagged as possibly showing that the person(s) has been engaged in illegal or other prohibited activity.

In one embodiment, the communication data processing system works in conjunction with or provides data to a message reader system, such as the message reader systems disclosed in U.S. patent application Ser. No. 14/332,312 titled “Cable Reader Labeling” filed Jul. 15, 2014 and U.S. patent application Ser. No. 14/332,306 titled “Cable Reader Snippets and Postboard” filed Jul. 15, 2014, with are both hereby incorporated by reference herein in their entirety.

One aspect of the disclosure provides a computing system configured to display computer communication data. In this embodiment, the computing system comprises a network interface. The computing system further comprises a persistent memory and a computer processor. The computing system further comprises a computer readable storage medium storing program instructions configured for execution by the computer processor in order to cause the computing system to: access a plurality of communication data files; electronically decompress the plurality of communication data files and automatically convert the plurality of communication data files into a pre-determined standard format to create a plurality of formatted communication data files; process the plurality of formatted communication data files to generate a plurality of revised formatted communication data files by performing two or more of the following: removing duplicate data among the plurality of formatted communication data files; generating thread data for at least a subset of the plurality of formatted communication data files; generating and inserting lexicon tag data for at least a subset of the plurality of formatted communication data files; removing, from the plurality of formatted communication data files, data that meets pre-identified blacklisted criteria; or generating group association data for the plurality of formatted communication data files; and store the plurality of revised formatted communication data files in the persistent memory.

The computing system of the preceding paragraph can have any sub-combination of the following features: where generating and inserting lexicon tag data includes generating offset data related to offsets of text in an original data file; where the generated group association data indicates one or more groups associated with one or more persons who correspond to data files; where automatically converting the plurality of communication data files includes applying a first conversion process to email data in the plurality of communication data files and a second conversion process to a different type of communication data in the plurality of communication data files, the first conversion process different from the second conversion process; where automatically converting the plurality of communication data files includes applying a first conversion process to a first communication data in the plurality of communication data files, and a second conversion process to second communication data in the plurality of communication data files, the first communication data generated by a communications program from a first provider, the second communication data generated by a communications program from a second provider, and the first conversion process different from the second conversion process; where the one or more stored program instructions further cause the processor to access human resources data to correlate each of the plurality of communication data files to one or more persons; and where generating group association data further includes correlating each of the plurality of communication data files to one or more groups associated with one or more persons.

Another aspect of the disclosure provides a computer-implemented method of displaying a large amount communication data. In this embodiment, the computer-implemented method comprises, as implemented by one or more computer systems comprising computer hardware and memory, the one or more computer systems configured with specific executable instructions, accessing a plurality of communication data files; electronically decompressing the plurality of communication data files and automatically convert the plurality of communication data files into a pre-determined standard format to create a plurality of formatted communication data files; processing the plurality of formatted communication data files to generate a plurality of revised formatted communication data files by preforming two or more of the following: removing duplicate data among the plurality of formatted communication data files, generating thread data for at least a subset of the plurality of formatted communication data files, generating and inserting lexicon tag data for at least a subset of the plurality of formatted communication data files, removing from the plurality of formatted communication data files data that meets pre-identified blacklisted criteria, or generating group association data for the plurality of formatted communication data files; and storing the plurality of revised formatted communication data files in the persistent memory.

The computer-implemented method of the preceding paragraph can have any sub-combination of the following features: where generating and inserting lexicon tag data includes generating offset data related to offsets of text in an original data file; where the generated group association data indicates one or more groups associated with one or more persons who correspond to data files; where automatically converting the plurality of communication data files includes applying a first conversion process to email data in the plurality of communication data files and a second conversion process to a different type of communication data in the plurality of communication data files, the first conversion process different from the second conversion process; wherein automatically converting the plurality of communication data files includes applying a first conversion process to a first communication data in the plurality of communication data files, and a second conversion process to second communication data in the plurality of communication data files, the first communication data generated by a communications program from a first provider, the second communication data generated by a communications program from a second provider, and the first conversion process different from the second conversion process; where the instructions further include accessing human resources data to correlate each of the plurality of communication data files to one or more persons; where generating group association data further includes correlating each of the plurality of communication data files to one or more groups associated with one or more persons; and where the instructions further include sending the revised formatted communication data files for display in a communications reader system.

Another aspect of the disclosure provides non-transitory computer-readable medium comprising one or more program instructions recorded thereon, the instructions configured for execution by a computing system comprising one or more processors in order to cause the computing system to: access a plurality of communication data files; electronically decompress the plurality of communication data files and automatically convert the plurality of communication data files into a pre-determined standard format to create a plurality of formatted communication data files; process the plurality of formatted communication data files to generate a plurality of revised formatted communication data files by preforming two or more of the following: removing duplicate data among the plurality of formatted communication data files, generating thread data for at least a subset of the plurality of formatted communication data files, generating and inserting lexicon tag data for at least a subset of the plurality of formatted communication data files, removing, from the plurality of formatted communication data files, data that meets pre-identified blacklisted criteria, or generating group association data for the plurality of formatted communication data files; and store the plurality of revised formatted communication data files in the persistent memory.

The non-transitory computer-readable medium of the preceding paragraph can have any sub-combination of the following features: where generating and inserting lexicon tag data includes generating offset data related to offsets of text in an original data file; where the generated group association data indicates one or more groups associated with one or more persons who correspond to data files; where automatically converting the plurality of communication data files includes applying a first conversion process to email data in the plurality of communication data files and a second conversion process to a different type of communication data in the plurality of communication data files, the first conversion process different from the second conversion process; where automatically converting the plurality of communication data files includes applying a first conversion process to a first communication data in the plurality of communication data files, and a second conversion process to second communication data in the plurality of communication data files, the first communication data generated by a communications program from a first provider, the second communication data generated by a communications program from a second provider, and the first conversion process different from the second conversion process; where the instructions are further configured to access human resources data to correlate each of the plurality of communication data files to one or more persons; where generating group association data further includes correlating each of the plurality of communication data files to one or more groups associated with one or more persons; and where the instructions are further configured to send the revised formatted communication data files for display in a communications reader system.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of a communication data processing system according to one embodiment.

FIG. 2 illustrates another block diagram of a communication data processing system according to one embodiment.

FIG. 3 illustrates an additional block diagram of a communication data processing system according to one embodiment.

FIG. 4 illustrates a block diagram of a communications analyst system according to one embodiment.

FIG. 5A illustrates one embodiment of an example user interface of the communication data processing system including a sample email message.

FIG. 5B illustrates one embodiment of an example user interface of the communication data processing system including a sample email message.

FIG. 5C illustrates one embodiment of a set of sample HTML code that includes example offset data.

FIG. 6 is a flowchart depicting one embodiment of a method of operation of the communication data processing system.

FIG. 7 is a flowchart depicting one embodiment of another method of operation of the communication data processing system.

FIG. 8 illustrates one embodiment of a computer system with which certain methods discussed herein may be implemented.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

Overview

FIG. 1 includes a broad overview of one embodiment where a communication data processing system is configured to quickly and efficiently process raw communication data from multiple devices used by a set of employees, such as, for example, a set of stock traders, and then present that communication data to a team of communications analysts for review.

FIG. 1 includes a set of employees that work for a company who have decision-making capacity with the potential to commit fraud, malfeasance, or illegal activity. For example, the employees can be comprised of equities traders capable of making illegal stock trades at a financial entity, such as an investment bank, hedge fund, mutual fund, and so forth. The employees 101 utilize electronic devices such as computers, tablets, and cell phones to generate, send and receive various electronic communications, such as, for example, email, text messages, and chat room messages. One skilled in the art would recognize that the employees 101 can use any electronic device to make electronic communications, including laptops, smart watches, automobile interface systems, and so forth. The figure is not a limiting disclosure as to what kind of electronic device the employees use to communicate.

As shown in the figure, the communications analysts 107 review the processed communication data on their devices in order to spot potential malfeasance or illegal activity. Communications analysts 107 may review the communications on any electronic device with a display, such as the computers depicted in FIG. 1. The communications analysts 107 may work for any company or government agency in an oversight capacity, and they may be tasked with either preventing the employees 101 from committing illegal activity before it occurs or catching employees 101 who have committed illegal activity. For example, the communications analysts 107 may be comprised of internal compliance analysts at a financial entity tasked with the job of ensuring that the organization and its employees 101 are operating within the relevant laws and regulations. The organization may then be able to utilize the system to confirm and verify that the organization is taking the requisite steps to monitor its traders and/or other employees. Alternatively, these communications analysts 107 could work for a government entity—for example, they could be regulators for the Securities Exchange Commission (SEC)—tasked with catching the wrongdoing of an organization and its employees 101 using communication data processing system 105.

Communication data from the devices used by employees 101 is collected and stored as raw communication data in a data store 103. In various embodiments, the raw communication data 103 can be stored in a single place or multiple places using a variety of data storage devices, methods, and structures. For example, raw communication data 103 can be stored in memory, on a hard drive, or on a cloud computing service.

After the communication data has been collected and stored as raw communication data 103, it is sent to or accessed by the communication data processing system 105. Communication data processing system 105 reads in the raw communication data 103, then processes the data based on the type of electronic communications included in the data as well as the needs of the monitoring organization and its communications analysts 107. The data is processed and presented in a readable format to reduce the number of communications that are misidentified or unidentified when they are reviewed by the communications analysts 107.

Communication Data Processing System

FIG. 2 discloses an embodiment of the communication data processing system which illustrates how it interacts with and processes the raw communication data before writing out the revised communication data to memory for use by a display application. In the illustrated embodiment, communication data processing system 105 includes a Read In module 203, a Conversion module 205 configured to convert data into a common format, a Stack of Transforms 209, a Memory Write Out module 213, and data storage 215. It is recognized, however, the communication data processing system 105 as shown in FIG. 2 is not limited to the exact modules in this specific embodiment. For example, in another embodiment, communication data processing system 105 could include storage for raw communication data 201 and/or Display Application 217 and/or one of the modules could be divided up into separate modules. Communication data processing system 105 may even include the devices used by the employees and/or the communications analysts to create and read electronic communications, such as, for example, employees 101 and communications analysts 107.

In one embodiment, the architecture of communication data processing system 105 is implemented on top of a Universal Data Pipeline (UDP), such as, for example, the one described in U.S. application Ser. No. 14/533,433, which is hereby incorporated by reference herein in its entirety. In such an embodiment, several features may be relevant for this specific application. For instance, provenance tracking allows for tracing the lineage and transforms of any piece of data, such as the transforms performed by the Stack of Transforms 209. This may be used for when evidence of the lineage and all transforms produced may need to be produced, especially when significant transforms are being explicitly applied to the data, such as by Stack of Transforms 209. UDP also allows the implementation of the architecture without requiring the use of a specific backend. For example, some companies may use an online file storage web storage service, like Amazon S3 to store the data. In other embodiments, a Java-based file system like the Hadoop Distributed File System (HDFS) can be used.

In one embodiment, the raw communication data 201 includes stored communication data received from the various devices and communications applications utilized by the employees. As mentioned earlier, raw communication data 201 can be stored in a single place or multiple places, and using a variety of data storage devices, methods, and structures. For example, raw communication data 201 can be stored in memory, on a hard drive, and/or on a cloud computing service. To deal with the potentially large number of messages and communications sent by employees, the aggregated raw communication data is also typically stored in a compressed format in order to save space. In this embodiment depicted, the raw communication data 201 is compressed and stored on a set of one or more server hard drives.

The raw communication data 201 is accessed by the Read In module 203. Read in module 203 is the part of communication data processing system 209 configured to first process the raw communication data 201 and runs processes on the data to streamline the data for the rest of the system to utilize more effectively. For example, the Read In module 203 can decompress the raw communication data. If the communication data includes data from various devices and data sources, the Read In module 203 can separate out and sort the messages, emails, and chat logs that may comprise the communication data. The Read In module 203 may separate out and sort the communications based on what kind of communications they are and/or the specific application used to generate the communications. For example, the Read In module 203 may divide or categorize the data for Employee A into a group of Microsoft Outlook Email messages, a group of Bloomberg Chat messages, a group of Yahoo Instant Message messages, and a group of iPhone chat messages. The Read In module 203 may also extract the metadata from the individual content, then package the metadata with the individual content in the communication data. After the Read In module 203 performs its preliminary processing of the communication data, it can then send processed data to the Conversion module 205 to convert the data into a predetermined, common format.

The Conversion module 205 accesses the processed data, which in some embodiments, will be the metadata and the individual content of the communications packaged together. The Conversion module 205 then converts the data into a more uniform or common format. One skilled in the art will recognize that there are many benefits for doing so at this stage in the system. For example, chat logs may be in various formats and structures since they are produced by various chat programs, such as Lync, Bloomberg, and so forth. By converting these communications, along with other types of communications like emails and messages, into a more homogeneous and unified format, it will allow for the rest of the system to efficiently and indiscriminately process all of the communication data together, regardless of the original differences between each piece of raw communication data, and the various proprietary systems that generate the data. The Conversion module 205 may separate out and sort the communications based on the type of communications, and/or the specific application used to generate the communications. The Conversion module 205 may run communications-specific or application-specific processes designed to convert each piece of communications into a homogeneous format. Thus, if an employee uses a new communications application, a new process may be generated to the Conversion module 205 to convert data generated by that new communications application to the pre-selected format.

In one embodiment, the Conversion module 205 converts the data into a common Hypertext Markup Language (HTML) format 207. However, one skilled in the art will recognize that the common format need not be HTML. The converted data format can be any format, including XML, XHTML, Palantir XML (pXML), PalantirDocXML (DocXML), JavaScript Object Notation (JSON), Resource Description Framework (RDF), Comma Separated Files (CSV), text, plaint text, as well as other public and proprietary formats. In addition, in some embodiments, more than one format may be selected. For example, the Conversion module 205 may be configured to convert emails into HTML format and to convert chat room messages into XML. In the embodiment depicted by FIG. 2, the common HTML converted data is then made available to the Stack of Transforms 209.

In one embodiment, the Stack of Transforms 209 comprise various processes for detecting, processing, and/or transforming the communication data to make the communication data more readable and manipulable by the communications analysts and to reduce the risk that the communications are misidentified or unidentified. For example, the Stack of Transforms 209 can include a process for thread calculations, which will link email communications in the same email thread making it easier for the communications analyst to read. Rather than reading multiple communications including mostly the same content, the communications analyst will then be able to just read the email thread that includes all the content in the emails of the subset. Other processes in the Stack of Transforms 209 can include a de-duplication process, which can be used to remove duplicate email communications or perform the union of chat logs to remove redundant chat room data. The Stack of Transforms 209 could also include a blacklisting process designed to remove emails from a stored blacklist of email addresses. For example, any emails from 1-800-Flowers, or known spam emails, can be deleted. The Stack of Transforms 209 may also include a process for keyword searching or lexicon calculation, which then highlights in the communications any characters or words included in a pre-set collection of search terms. The lexicons could be assigned to one or more categories such as “money laundering,” “anti-trust,” “insider trading,” “price manipulation,” and so forth. The Stack of Transforms 209 may also include a process for group calculations to assign the communications to one or more review groups. For example, a communications analyst might be tasked with oversight of the communications of ten employees of a group on a certain day. The group calculations process could allow the communication data to be broken up and sorted, so that the communications of those ten employees could be presented to that communications analyst. The next day, a different communications analyst may be tasked with reviewing the communications of that same group. Human resources data may be utilized to assist in determining which group(s) an employee's communications should be assigned. In some embodiments, the communication data may also be assigned to an identifier that corresponds to a particular employee, such that the employee's emails, chat messages, and instant messages all refer to the same identifier.

One skilled in the art will recognize that the Stack of Transforms 209 could have alternatively been performed in a pipeline manner such that each process is carried out completely before the other completes. However, doing so would be inefficient and require a significant amount of input/output time. For example, for email communications the alternative pipeline way would require the system to first perform thread calculations on the emails, write those email threads back out to disk, read the data back in, de-dupe the emails, write the data back out, read the data back in, then pass only those emails that pass blacklist filtering. In comparison, this system efficiently processes communications of all types by stacking these processes together and performing at least two of them in parallel. In one embodiment, each communication is read in only once and the revised data is written to disk only once. Converting the different communications formats into a common format allows for the set of transforms to be stacked together and executed once for all different communications formats. Then the data is written back out to the communications analyst for review.

In the embodiment of FIG. 2, the stack of transforms 209 converts the data into revised HTML 211. However, one skilled in the art will recognize that the format need not be HTML. The data format for the revised data can be any markup language, including XML, XHTML, PalantirXML (pXML), PalantirDocXML (DocXML), or any other format. One benefit of using a markup language is for improved readability for when the communications analysts review the communications later on using the Display Application 217, as opposed to plain text. In the embodiment in FIG. 2, the revised HTML files are then passed to memory write out module 213.

The memory write out module 213 accesses the revised communication data and writes it to data storage 215. After the revised communication data is in data storage 215, it can be accessed by Display Application 217. Data storage 215 may include any electronic data storage device, including, for example, a persistent memory, a hard drive, a cloud computing server, and so forth. It may or may not be the same data storage device used to store raw communication data 201. The communications analysts are then able to access the revised communication data stored in data storage 215 as needed to conduct their review. The communications analysts may be able to perform client-side transformations to further improve the chances of spotting illegal activity, without overwriting or destroying the originally processed communication data, such as, for example, additional highlighting, changing of the flagged lexicons, changing of the time zone data, removal of known innocuous data, and so forth.

In one embodiment, Display Application 217 is a software application that may run on a variety of electronic devices and can be used by the communications analysts to review the employees' electronic communications. The electronic device running Display Application 217 may include a computer, tablet, smart phone, and so forth. In other embodiments, the Display Application 217 is a server side application such that only a shell application, such as a browser, is needed on the electronic device along with some input and output components to conduct the review. The Display Application 217 is configured to display the revised, processed communication data stored in data storage 215. In one embodiment, the Display Application 217 accesses the stored markup language files and displays the formatted and stylized communications in the display for the communications analyst to read. In some embodiments, Display Application 217 shows the content of the communications, as well as some or all of the corresponding metadata and properties of the communications. Display application 217 may include tools that allow the communications analyst to easily sift through all the communication data, such as by presenting a list view of all the communications that the communications analyst is in charge of reviewing. Display application 217 may also include tools for the communications analyst to search through all of the communication data using various criteria, and then Display Application 217 may display those search results to narrow down the communications that communications analyst has to review.

In various embodiments, the Display Application 217 is configured to allow the communications analyst to input or select further client-side transformations and refinements to be performed on the communication data. For example, there may be menus, fields, buttons, checkboxes, user interface elements, and so forth, which allow the communications analyst to highlight further search terms, alter the display of timestamps and disclaimers, and so forth. These interface tools may allow the communications analyst to perform tailored or idiosyncratic processes on the communication data, of a specific scope which fits the communications analyst's needs and requirements. These tools also allow for increased flexibility and efficiency when performed client-side rather than in the pre-processing stage with the Stack of Transforms 209.

Example Communication Data Processing System

FIG. 3 illustrates one embodiment of the communication data processing system showing how it processes various types of raw communication data 103 for later use by the display application 217.

In this embodiment, the example raw communication data 103 includes data generated from communications made through Microsoft® Exchange Email 303, Bloomberg® Messages 305, Bloomberg® Chat 307, Reuters® Chat 309, Microsoft® Lync 311, and any other communications platform, labeled as Other 313. A group of raw communication data 103 is made available to Read In module, sometimes referred to as the Enrichment Stage module, 321.

In the Read In Module 321, the communication data from the various communications platforms are first decompressed by Decompression module 323. Each file may include multiple communications in plain text, which makes it more difficult for the communications analyst to review later on. Emails or messages may also include various attachments or graphics, and so forth. Because communication data may be packaged as a group, the data may need to be split into individual content or communications, which is performed by Split Into Individual Content module 325. Module 325 may also parse the substantive content included within each piece of communication. The Extract Metadata 327 module is configured to extract the metadata from each piece of communication. This metadata may be used later on in various processes, such as for thread calculations and determining which emails belong to the same chain. It may also be used to help generate the markup language. However, metadata can become irrelevant if it no longer corresponds to the individual content from the originating piece of communication. Thus, the Package Metadata with Individual Content module 329 is configured to ensure that metadata is attached to or corresponds with the appropriate individual content. This packaged information can then be made available to the Conversion module 205. Data may be made available via a push or pull process.

The Conversion module 205 is configured to convert the communication data into a common format or structure. As noted above, making the communication data, which may come from various platforms and have varying structures or formats, look as similar or homogeneous as possible can be more efficient in certain embodiments. Such homogeneity allows for later processes to be performed once, and performed indiscriminately, on all of the communication data regardless of their originating platform, structure, or format. It also may reduce the number of times that the data would have to be read in from or out to disk. In some embodiments, this conversion may be performed using a series of interfaces. For example, Email Interface 333 may sort out all the emails, such as those in Exchange Email 303, and perform email-specific conversions. The Email Interface 333 may include different conversions for the different types of email data. Thread calculations may be performed at this stage, or in some embodiments, they may be performed later on for efficiency reasons. IM Interface 335 may sort out all the instant messages, such as those in Bloomberg Messages 305, and perform IM-specific conversions. Chat Interface 337 may sort out all the chat logs, such as those in Bloomberg Chat 307, Reuters Chat 309, and Lync 311, and perform chat-specific conversions. The Chat Interface 337 may include different conversions for the different types of communication data generated by communications applications from different providers. In addition, chat unions may be performed at this stage, or in some embodiments, they may be performed later on for efficiency reasons. Other Interface 339 may sort out the other types of communications, such as those in Other 313, and perform specific conversions. The goal of these interfaces is to convert the data into a common, unified format. As discussed earlier, this format may consist of any markup language, such as HTML, XML, XHTML, PalantirXML (pXML), PalantirDocXML (DocXML), and so forth. In the embodiment of FIG. 3, the data is converted into a common HTML format and passed to Transform Stack 209.

Transform Stack 209 is configured to process the communication data for detection and transformation. In this embodiment, the transform stack 209 includes de-duplication 343, thread calculations 345, blacklisting 347, lexicons 349, and/or group calculations 351 in parallel.

De-duplication 343 removes duplicate communications or portions of communications where the same content is included within another communication. For example, a message or email from user A to user B will create two identical pieces of communications: one that resided with the sender and the one obtained by the recipient. Duplicates can be deleted. De-duplication may also be configured to perform the union of chat logs. For example, on a given day, user A may be present in a chat room from 8 am to 4 pm. User B may have been present in the same chat room from 10 am to 6 pm. Chat logs of the room which include both user A and user B can be merged to create a singular, comprehensive chat log from 8 am to 6 pm and removing the duplicate log data from 10 am to 4 pm. De-duplication 343 allows for a reduction in the amount of communication data that communications analysts have to review, reducing the chances of the communications analyst overlooking any important evidence or risky behavior.

Thread calculations 345 determine which emails belong in a single email thread, so that only the email including the entire thread needs to be reviewed by the communications analyst. One method for doing this is to match significant portions of content included within emails. However, in other embodiments, the thread calculations are performed using a union-find algorithm, which utilizes the metadata, such as, for example, metadata obtained from module 327, to link emails of a given thread.

Blacklisting 347 allows for the removal of communications sent from, or received by, parties, addresses, or devices that do not require review and are known to be innocuous. For example, blacklisting 347 may be configured to delete all emails that were sent or received by emails associated with 1-800-FLOWERS. By deleting irrelevant communications, the amount of communications that the communications analysts have to review can be reduced.

Lexicons 349 allows for pre-determined keyword searching and highlighting. The system may have pre-set lexicons, or search term lists, associated with various illegal acts, regulations, and so forth, which may be relevant to the communications analyst. For example, there may be pre-set lexicons for instances of “insider trading”, “anti-trust”, or “dodd-frank”, and so forth. In some embodiments, one or more of the categories of pre-set lexicons is designated as the default for a particular communication, employee or group. For example, a communications analyst like a SEC regulator may have to review communications for possible insider trading, so the “insider trading” pre-set lexicon may be activated. The “insider trading” lexicon may include search terms related to the possible occurrence of illegal insider trading, such as terms like “hot tip”, and so forth. Multiple lexicons may be applied at once. In addition, the application of lexicons at this stage allows for statistics to be generated for the various lexicon sets, so that it can be determined which lexicons are generating the most search term hits. That information can be used to further refine the lexicon terms or make future decisions on what lexicons may be relevant in a given situation. For example, if a term in a lexicon category ends up being in 80% of all communications, it is likely a term that should not be used.

After a search term in a lexicon is found in the communication data, that instance of the term may be marked for future highlighting. The highlighting may be marked by the insertion of a tag in HTML, XML, or any other markup language, that notifies the system that the discovered terms should be highlighted. The tag may also include information about the lexicon category(ies) so that the term is only highlighted when the corresponding category(ies) is selected.

The tag may also include an offset of the term in the original data, so that the highlighting of the discovered term may be preserved if characters are added to, or deleted from, the communication data. The offset may also allow the system to preserve the originally un-highlighted communication data, which may be useful, such as in instances where the set of lexicons being applied is changed. This tagging of offsets may also occur client-side at a later stage in the display application 217. The offset inserted in the tag may be calculated, stored, or implemented through a variety of methods. In one method, the offset for a highlighted search term or string may be a character offset comprising an integer of zero or greater. For example, a character offset value of ten may signal that the highlighted term should be displayed starting from ten characters after where it was originally displayed in the original communication data.

Group calculations 351 are configured to allow the communication data to be sorted out or organized into groups. A group may be a set of employees that a single communications analyst is assigned review, or the groups may be selected based on any other criteria. For example, the group can be based on a logical or intuitive group—for instance, all of the employees in a particular role at a company or entity. For example, a communications analyst might be tasked with oversight of the communications of ten employees of a group on a certain day (for example, the ten traders in the commodities trading group). The group calculations 351 may also be configured to allow the communication data to be broken up and sorted, so that the communications of those ten employees could be presented to that communications analyst. To generate these groups, some embodiments of the system may utilize external data, such as data from the Human Resources (HR) department at a company or entity. The group data will help the system determine how the communications will be grouped. In this embodiment, HR data set 353 is accessed by the group calculations 351 module and used to sort or organize the communication data into groups.

After the processes in the transform stack 209 are conducted, the revised communication data, in the format of any markup language, such as HTML, XML, XHTML, PalantirXML (pXML), PalantirDocXML (DocXML), and so forth, is then passed to the memory write out 213 module. Memory Write Out module 213 module accesses the revised communication data and writes it to data storage 215. Again, data storage 215 may include any electronic data storage device or method and may be memory, a hard drive, a cloud computing server, and so forth. It may or may not be the same data storage device used to store raw communication data 103. Data storage 215 allows the communications analyst to access the processed communication data at his/her leisure and perform additional client-side transformations to further improve the chances of spotting illegal activity, without overwriting or destroying the originally processed communication data. Once the revised communication data is in data storage 215, it can be accessed by the communications analyst through display application 217. Display application 217 is discussed in more detail with respect to FIGS. 4, 5A, and 5B.

Display Application

FIG. 4 illustrates one embodiment of the display application 217 and its interaction with user 407, which is typically a communications analyst 107. The display application 217 is used to review the revised communication data, and may include a graphical user interface (GUI) 403, a module for receiving input from the user 409, an update module 411, and in some embodiments, data storage 413 that may be local and client-side.

Display application 217 is configured to receive the revised communication data from the data storage 413 and to present the data to the reviewer to allow him/her to view and/or manipulate the message data via the graphical user interface (GUI) 403. The display application 217 may include GUI logic which includes program instructions configured for execution by one or more computer processors of the communications analyst's or reviewer's device 107, which are operable to receive user input and to display a graphical representation of the messages using the approaches described herein. The GUI logic may be operable to receive user input from, and display a graphical representation of the messages to, a GUI that is provided on a display 405 by the computer system on which the communications analysts' device 107 executes.

In some embodiments, the GUI logic is omitted. For example, in one embodiment, the reviewer device 107 (and specifically the display application 217) may comprise an application program or process that issues one or more function calls or application programming interface (API) calls to the message data server (not shown) to obtain information resulting from, to provide input to, and to execute along with the message data server, the processes or one or more steps thereof as described herein. For example, the reviewer device 107 may request messages using a programmatic interface, and then the reviewer, using the reviewer device 107, may use, process, log, store, label, and/or otherwise interact with the messages according to local logic. The reviewer device 107 may also interact with the message data server to provide input, labeling instructions, and/or other data as described herein using a programmatic interface, and then the message data server may use, process, log, store, label, and/or otherwise interact with the messages according to the message data server logic.

The message data server may be implemented as a special-purpose computer system having logical elements. In an embodiment, the logical elements may comprise program instructions recorded on one or more machine-readable storage media. Alternatively, the logical elements may be implemented in hardware, firmware, or a combination thereof. When executed by one or more processors of the computer system, logic in the message data server is operable to analyze and/or label the messages according to the techniques described herein. In one embodiment, the message data server may be implemented in a Java Virtual Machine (JVM) that is executing in a distributed or non-distributed computer system. In other embodiments, the message data server may be implemented as a combination of programming instructions written in any programming language (for example C++ or Visual Basic) and hardware components (for example, memory, CPU time) that have been allocated for executing the program instructions.

In this embodiment, the GUI 403 displays the messages or communications through display monitor 405 to user 407, who is typically a communications analyst. To better spot the occurrence of possible wrongdoing, user 407 may then provide inputs into the display application 217, which are received by module 409. Some of these user inputs may be directed towards ways of altering the information that is displayed on the display monitor 405, such as, for example, collapsing all disclaimers in the communications; inserting timestamps as machine representations of time or changing the time zone of the times in the communications; bolding names or email addresses in the content of the communications; toggling on/off any pre-set lexicons; identifying directionality and who the communications are coming from or going to; and/or highlighting of further search terms. In one embodiment, the revision made to the timestamp can be the result of inserting a representation of time in a tag so that the system understands that it is a representation of time and not just a string of characters.

After module 409 receives these inputs, it then directs update module 411 to make updates in the markup language. Update module 411 makes those changes, typically by inserting a HTML or XML tag so that the changes are reversible. The changes are then reflected in the GUI 403 for further review, and the cycle continues to assist the reviewer in spotting possible illegal activity.

A data storage 413 may be present in some embodiments in order to store the revised communication data as it exists at any stage conveyed by FIG. 4. Data storage 413 will typically be local and client-side, so that display application 217 can quickly and efficiently provide updates to the communication data for review, without having to disrupt the original communication data stored elsewhere or having to repeatedly acquire communication data for review. However, in other embodiments, the data storage 413 is not stored in the user's device, but is store remotely, for example, in a cloud server or in data store 215.

Display application 217 and GUI 403 will be discussed in more detail in FIGS. 5A and 5B.

Communications Analysts User Interface

FIG. 5A and FIG. 5B illustrate an embodiment of the Display Application and the GUI. FIG. 5A includes an example display of one piece of communication data. Various user inputs are gathered to update the presentment of the communication data, and the updates are reflected in FIG. 5B. The various user inputs can also be seen in FIG. 5B.

In FIG. 5A, a portion of the content of each communications message (for example, a title, an excerpt of the body of the message, a document or message identification code, and so forth) is displayed in browser window 501. The communications message (or a portion thereof) may be displayed in any shape and in any format in the browser window 501. In this embodiment, the header of the email communications can be seen in section 503 of browser window 501. This section shows a graphic of the document type, the subject of the email, the senders and recipients, and so forth. The content of the document is displayed in document text section 505. In this embodiment, “Kombucha” was part of the lexicon pre-set and instances of it have been highlighted in the browser window 501. The email in the document text has also been presented in bold. Properties section 507 displays relevant properties of the communications document in browser window 501. This information was originally stored in plain text, but the processes in the system have now broken up the information and presented it in a stylized format that makes it easier for the communications analyst to read.

Input window 511 shows a list of various user inputs that can be performed. For example, the user can run searches to narrow down the field of presented communications documents, and the results will be summarily presented in input window 511. The user can then sift through the search results and those corresponding communications documents will be displayed in browser window 501. In this embodiment, input window 511 includes a field to input search terms 513. When a search term is entered into this field 513, the display application may highlight those search terms found in the communication data. This embodiment also includes a drop-box for the user to set a time zone 515, and a check-box to show disclaimers 517. When a user conveys those inputs in input window 511, those changes will be reflected in browser window 501. The input window 511 may also include an interface tool to select a different set of lexicon categories.

In FIG. 5B, these changes can be seen reflected in browser window 501. For example, “shoots” has now been entered into search terms field 513. As a result, “shoots” has been highlighted in the document text 505 portion of browser window 501. “Kombucha” remains highlighted because that was part of a pre-set lexicon search, which the user has not yet toggled off or changed. The user has changed drop box 515 so that the time zone is now “PST” instead of “EST”, so now the email timestamp is reflected to be three hours earlier as the timestamp has changed from Eastern time to Pacific time. That change can be seen in the header section 503 of browser window 501. Finally, the show disclaimer check-box 517 has also been checked. As a result, in the document text section 505 of browser window 501, it can be seen that a disclaimer has been added to the text. That disclaimer is part of the original communication data, but in FIG. 5A, it had been omitted for display by the display application.

The embodiments shown are not meant to convey an exhaustive list of all the potential inputs or changes that can be made to the communication data. Typically these changes to the communication data will be performed in a manner that allows them to be undone, such as through the insertion or modification of a HTML or XML tag. For example, the addition of a search term in the search term field 513 may insert a tag in the data that signals the discovered term to be highlighted. The tag may also include an offset that allows that highlighting to be preserved on the discovered term regardless of any other changes to the data, such as the display/insertion of disclaimers. The tag and the offset may also serve to undo the highlighting, such as if a different search term is entered into field 513. In addition, the example displays are not meant to be limited as it is recognized that a variety of user interface tools may be used and may be arranged in different ways.

FIG. 5C shows one embodiments of a sample portion of communication data in a markup language such as HTML, XML, and so forth. As can be seen, preceding “Kombucha” is the tag:

This tag informs browser window 501 that “Kombucha” should be displayed as highlighted. Since the offset value is zero, that means “Kombucha” has not been offset at all from its original position in the communication data.

Preceding “Shoots” is the tag:

This tag informs the browser window 501 that “Shoots” should also be displayed as highlighted. In some embodiments, the highlighting may be a different color than the highlighting used on “Kombucha” to signal that the terms were found using different search processes and/or they may be assigned to different lexicon categories. This tag preceding “Shoots” also signals to the browser that the word “Shoots” has an offset of 43, which means that it occurred at character location 43 in the original communication. This allows the browser window 501 to display the updated communication data without losing information about the original data.

Similarly, preceding the email, etan@company.com, is the tag:

This tag signals to browser window 501 to display the email in bold, and that the term has an offset of 107, which means it occurred at character location 107 in the original communication.

Example Process Flows

FIG. 6 illustrates a flowchart depicting one embodiment of illustrative operations of the communication data processing system. Depending on the embodiment, the method of FIG. 6 may be performed by various computing devices, such as by the message data server, a different server, a system of servers, a cloud computing service, and so forth. Depending on the embodiment, the method of FIG. 6 may include fewer and/or additional blocks and the blocks may be performed in an order different than illustrated.

In FIG. 6, the system accesses raw communication data in block 601. Then the system converts the data into a common format in block 603. In some embodiments the system stores information about which common format to use. Different clients, process, or data may use different common formats. The system then performs communication data processing to generate revised data files in module 605. After, the system writes the revised data files to memory in block 607.

FIG. 7 is a flowchart depicting one embodiment of illustrative additional operations of the communication data processing system. Depending on the embodiment, the method of FIG. 7 may be performed by various computing device, such as by the message data server, a server, a system of servers, a cloud computing service, and so forth. Depending on the embodiment, the method of FIG. 7 may include fewer and/or additional blocks and the blocks may be performed in an order different than illustrated.

In FIG. 7, the system first accesses data files in common format in block 701. In some embodiments the system stores information about which common format to use. Different clients, process, or data may use different common formats. The data files passed, in common format, are accessed by a variety of modules, which may run in parallel. These modules may include: the remove duplicate data block 703, the calculate threads block 705, the remove files from blacklisted sources block 707, the insert tags for lexicons block 709, and the add group data block 711. These modules may process the data in parallel along the lines discussed herein and update them in the update data files block 713. These updated data files may then be reviewed, further processed, and so forth.

Implementation Mechanisms

According to one embodiment, the techniques described herein are implemented by one or more special-purpose computing devices. The special-purpose computing devices may be hard-wired to perform the techniques, or may include digital electronic devices such as one or more application-specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs) that are persistently programmed to perform the techniques, or may include one or more general purpose hardware processors programmed to perform the techniques pursuant to program instructions in firmware, memory, other storage, or a combination. Such special-purpose computing devices may also combine custom hard-wired logic, ASICs, or FPGAs with custom programming to accomplish the techniques. The special-purpose computing devices may be desktop computer systems, server computer systems, portable computer systems, handheld devices, networking devices or any other device or combination of devices that incorporate hard-wired and/or program logic to implement the techniques.

Computing device(s) are generally controlled and coordinated by operating system software, such as iOS, Android, Chrome OS, Windows XP, Windows Vista, Windows 7, Windows 8, Windows Server, Windows CE, Unix, Linux, SunOS, Solaris, iOS, Blackberry OS, VxWorks, or other compatible operating systems. In other embodiments, the computing device may be controlled by a proprietary operating system. Conventional operating systems control and schedule computer processes for execution, perform memory management, provide file system, networking, I/O services, and provide a user interface functionality, such as a graphical user interface (GUI), among other things.

For example, FIG. 8 is a block diagram that illustrates a computer system 800 upon which an embodiment may be implemented. For example, any of the computing devices discussed herein, such as the reviewer device 107, the data storage 103, the communication data processing system, and/or the companies, the employees, or their devices 101 (which may be government entities in some embodiments), may include some or all of the components and/or functionality of the computer system 800.

Computer system 800 includes a bus 802 or other communication mechanism for communicating information, and a hardware processor, or multiple processors, 804 coupled with bus 802 for processing information. Hardware processor(s) 804 may be, for example, one or more general purpose microprocessors.

Computer system 800 also includes a main memory 806, such as a random access memory (RAM), cache and/or other dynamic storage devices, coupled to bus 802 for storing information and instructions to be executed by processor 804. Main memory 806 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 804. Such instructions, when stored in storage media accessible to processor 804, render computer system 800 into a special-purpose machine that is customized to perform the operations specified in the instructions.

Computer system 800 further includes a read only memory (ROM) 808 or other static storage device coupled to bus 802 for storing static information and instructions for processor 804. A storage device 810, such as a magnetic disk, optical disk, or USB thumb drive (Flash drive), and so forth, is provided and coupled to bus 802 for storing information and instructions.

Computer system 800 may be coupled via bus 802 to a display 812, such as a cathode ray tube (CRT) or LCD display (or touch screen), for displaying information to a computer user. An input device 814, including alphanumeric and other keys, is coupled to bus 802 for communicating information and command selections to processor 804. Another type of user input device is cursor control 816, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor 804 and for controlling cursor movement on display 812. This input device typically has two degrees of freedom in two axes, a first axis (for example, x) and a second axis (for example, y), that allows the device to specify positions in a plane. In some embodiments, the same direction information and command selections as cursor control may be implemented via receiving touches on a touch screen without a cursor.

Computing system 800 may include a user interface module to implement a GUI that may be stored in a mass storage device as executable software codes that are executed by the computing device(s). This and other modules may include, by way of example, components, such as software components, object-oriented software components, class components and task components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables.

In general, the word “module,” as used herein, refers to logic embodied in hardware or firmware, or to a collection of software instructions, possibly having entry and exit points, written in a programming language, such as, for example, Java, Lua, C or C++. A software module may be compiled and linked into an executable program, installed in a dynamic link library, or may be written in an interpreted programming language such as, for example, BASIC, Perl, or Python. It will be appreciated that software modules may be callable from other modules or from themselves, and/or may be invoked in response to detected events or interrupts. Software modules configured for execution on computing devices may be provided on a computer readable medium, such as a compact disc, digital video disc, flash drive, magnetic disc, or any other tangible medium, or as a digital download (and may be originally stored in a compressed or installable format that requires installation, decompression or decryption prior to execution). Such software code may be stored, partially or fully, on a memory device of the executing computing device, for execution by the computing device. Software instructions may be embedded in firmware, such as an EPROM. It will be further appreciated that hardware modules may be comprised of connected logic units, such as gates and flip-flops, and/or may be comprised of programmable units, such as programmable gate arrays or processors. The modules or computing device functionality described herein are preferably implemented as software modules, but may be represented in hardware or firmware. Generally, the modules described herein refer to logical modules that may be combined with other modules or divided into sub-modules despite their physical organization or storage

Computer system 800 may implement the techniques described herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware and/or program logic which in combination with the computer system causes or programs computer system 800 to be a special-purpose machine. According to one embodiment, the techniques herein are performed by computer system 800 in response to processor(s) 804 executing one or more sequences of one or more instructions included in main memory 806. Such instructions may be read into main memory 806 from another storage medium, such as storage device 810. Execution of the sequences of instructions included in main memory 806 causes processor(s) 804 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions.

The term “non-transitory media,” and similar terms, as used herein refers to any media that store data and/or instructions that cause a machine to operate in a specific fashion. Such non-transitory media may comprise non-volatile media and/or volatile media. Non-volatile media includes, for example, optical or magnetic disks, such as storage device 810. Volatile media includes dynamic memory, such as main memory 806. Common forms of non-transitory media include, for example, a floppy disk, a flexible disk, hard disk, solid state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge, and networked versions of the same.

Non-transitory media is distinct from but may be used in conjunction with transmission media. Transmission media participates in transferring information between non-transitory media. For example, transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus 802. Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications.

Various forms of media may be involved in carrying one or more sequences of one or more instructions to processor 804 for execution. For example, the instructions may initially be carried on a magnetic disk or solid state drive of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system 800 can receive the data on the telephone line and use an infra-red transmitter to convert the data to an infra-red signal. An infra-red detector can receive the data carried in the infra-red signal and appropriate circuitry can place the data on bus 802. Bus 802 carries the data to main memory 806, from which processor 804 retrieves and executes the instructions. The instructions received by main memory 806 may retrieve and execute the instructions. The instructions received by main memory 806 may optionally be stored on storage device 810 either before or after execution by processor 804.

Computer system 800 also includes a communication interface 818 coupled to bus 802. Communication interface 818 provides a two-way data communication coupling to a network link 820 that is connected to a local network 822. For example, communication interface 818 may be an integrated services digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, communication interface 818 may be a local area network (LAN) card to provide a data communication connection to a compatible LAN (or WAN component to communicate with a WAN). Wireless links may also be implemented. In any such implementation, communication interface 818 sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.

Network link 820 typically provides data communication through one or more networks to other data devices. For example, network link 820 may provide a connection through local network 822 to a host computer 824 or to data equipment operated by an Internet Service Provider (ISP) 826. ISP 826 in turn provides data communication services through the world wide packet data communication network now commonly referred to as the “Internet” 828. Local network 822 and Internet 828 both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on network link 820 and through communication interface 818, which carry the digital data to and from computer system 800, are example forms of transmission media.

Computer system 800 can send messages and receive data, including program code, through the network(s), network link 820 and communication interface 818. In the Internet example, a server 830 might transmit a requested code for an application program through Internet 828, ISP 826, local network 822 and communication interface 818.

The received code may be executed by processor 804 as it is received, and/or stored in storage device 810, or other non-volatile storage for later execution.

Object Centric Data Model

To provide a framework for the above discussion of the specific systems and methods described herein, an example database system using an ontology will now be described. This description is provided for the purpose of providing an example and is not intended to limit the techniques to the example data model, the example database system, or the example database system's use of an ontology to represent information.

In one embodiment, a body of data is conceptually structured according to an object-centric data model represented by an ontology. The conceptual data model is independent of any particular database used for durably storing one or more database(s) based on the ontology. For example, each object of the conceptual data model may correspond to one or more rows in a relational database or an entry in Lightweight Directory Access Protocol (LDAP) database, or any combination of one or more databases.

An ontology, as noted above, may include stored information providing a data model for storage of data in a database. The ontology may be defined by one or more object types, which may each be associated with one or more property types. At the highest level of abstraction, data object is a container for information representing things in the world. For example, data object can represent an entity such as a person, a place, an organization, or other noun. Data object can represent an event that happens at a point in time or for a duration. Data object can represent a document or other unstructured data source such as an email message, a news report, or a written paper or article. Each data object is associated with a unique identifier that uniquely identifies the data object within the database system.

Different types of data objects may have different property types. For example, a “Person” data object might have an “Eye Color” property type and an “Event” data object might have a “Date” property type. Each property as represented by data in the database system may have a property type defined by the ontology used by the database.

Objects may be instantiated in the database in accordance with the corresponding object definition for the particular object in the ontology.

The data objects defined in the ontology may support property multiplicity. In particular, a data object may be allowed to have more than one property of the same property type. For example, a “Person” data object might have multiple “Address” properties or multiple “Name” properties.

Each link represents a connection between two data objects. In one embodiment, the connection is either through a relationship, an event, or through matching properties. A relationship connection may be asymmetrical or symmetrical. For example, “Person” data object A may be connected to “Person” data object B by a “Child Of” relationship (where “Person” data object B has an asymmetric “Parent Of” relationship to “Person” data object A), a “Kin Of” symmetric relationship to “Person” data object C, and an asymmetric “Member Of” relationship to “Organization” data object X. The type of relationship between two data objects may vary depending on the types of the data objects. For example, “Person” data object A may have an “Appears In” relationship with “Document” data object Y or have a “Participate In” relationship with “Event” data object E. As an example of an event connection, two “Person” data objects may be connected by an “Airline Flight” data object representing a particular airline flight if they traveled together on that flight, or by a “Meeting” data object representing a particular meeting if they both attended that meeting. In one embodiment, when two data objects are connected by an event, they are also connected by relationships, in which each data object has a specific relationship to the event, such as, for example, an “Appears In” relationship.

As an example of a matching properties connection, two “Person” data objects representing a brother and a sister, may both have an “Address” property that indicates where they live. If the brother and the sister live in the same home, then their “Address” properties likely include similar, if not identical property values. In one embodiment, a link between two data objects may be established based on similar or matching properties (for example, property types and/or property values) of the data objects. These are just some examples of the types of connections that may be represented by a link and other types of connections may be represented; embodiments are not limited to any particular types of connections between data objects. For example, a document might include references to two different objects. For example, a document may include a reference to a payment (one object), and a person (a second object). A link between these two objects may represent a connection between these two entities through their co-occurrence within the same document.

Each data object can have multiple links with another data object to form a link set. For example, two “Person” data objects representing a husband and a wife could be linked through a “Spouse Of” relationship, a matching “Address” property, and one or more matching “Event” properties (for example, a wedding). Each link as represented by data in a database may have a link type defined by the database ontology used by the database.

Each of the processes, methods, and algorithms described in the preceding sections may be embodied in, and fully or partially automated by, code modules executed by one or more computer systems or computer processors comprising computer hardware. The processes and algorithms may be implemented partially or wholly in application-specific circuitry.

The various features and processes described above may be used independently of one another, or may be combined in various ways. All possible combinations and sub-combinations are intended to fall within the scope of this disclosure. In addition, certain method or process blocks may be omitted in some implementations. The methods and processes described herein are also not limited to any particular sequence, and the blocks or states relating thereto can be performed in other sequences that are appropriate. For example, described blocks or states may be performed in an order other than that specifically disclosed, or multiple blocks or states may be combined in a single block or state. The example blocks or states may be performed in serial, in parallel, or in some other manner. Blocks or states may be added to or removed from the disclosed example embodiments. The example systems and components described herein may be configured differently than described. For example, elements may be added to, removed from, or rearranged compared to the disclosed example embodiments.

Conditional language, such as, among others, “can,” “could,” “might,” or “may,” unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments include, while other embodiments do not include, certain features, elements and/or steps. Thus, such conditional language is not generally intended to imply that features, elements and/or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without user input or prompting, whether these features, elements and/or steps are included or are to be performed in any particular embodiment.

Any process descriptions, elements, or blocks in the flow diagrams described herein and/or depicted in the attached figures should be understood as potentially representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process. Alternate implementations are included within the scope of the embodiments described herein in which elements or functions may be deleted, executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those skilled in the art.

It should be emphasized that many variations and modifications may be made to the above-described embodiments, the elements of which are to be understood as being among other acceptable examples. All such modifications and variations are intended to be included herein within the scope of this disclosure. The foregoing description details certain embodiments of the invention. It will be appreciated, however, that no matter how detailed the foregoing appears in text, the invention can be practiced in many ways. As is also stated above, it should be noted that the use of particular terminology when describing certain features or aspects of the invention should not be taken to imply that the terminology is being re-defined herein to be restricted to including any specific characteristics of the features or aspects of the invention with which that terminology is associated. The scope of the invention should therefore be construed in accordance with the appended claims and any equivalents thereof.

Claims

1. A computing system configured to display computer communication data, the computing system comprising: one or more processors; anda computer readable storage medium storing program instructions configured for execution by the one or more processors in order to cause the computing system to: convert a plurality of communication data files comprising text into a pre-determined format to create a plurality of formatted communication data files;generate offset tag data indicating a change in text position from an original position of text in an original data file of the plurality of communication data files to a formatted position of text in a formatted data file of the plurality of formatted communication data files, the change in text position being caused at least in part by the conversion of the plurality of communication data files into the pre-determined format; andinsert the offset tag data into a first subset of the plurality of formatted communication data files.
2. The computing system of claim 1, wherein the stored program instructions further cause the one or more processors to process the plurality of formatted communication data files to generate a plurality of revised formatted communication data files by performing two or more of the following: removing duplicate data among the plurality of formatted communication data files;generating thread data for at least a second subset of the plurality of formatted communication data files;removing, from the plurality of formatted communication data files, data that meets pre-identified blacklisted criteria; orgenerating group association data for the plurality of formatted communication data files.
3. The computing system of claim 2, wherein the stored program instructions further cause the one or more processors to: store the revised formatted communication data files in persistent memory;receive a keyword for searching;access, from the persistent memory, the revised formatted communication data files for searching;determine a character position to highlight based at least in part on the keyword and the offset tag data; andtransmit data for displaying content of a communication data file with a highlight at the character position.
4. The computing system of claim 1, wherein the stored program instructions further cause the one or more processors to process the plurality of formatted communication data files to generate group association data based at least in part on people included in the plurality of formatted communication data files.
5. The computing system of claim 1, wherein converting the plurality of communication data files includes applying a first conversion process to email data in the plurality of communication data files and applying a second conversion process to a different type of communication data in the plurality of communication data files, the first conversion process being different from the second conversion process.
6. The computing system of claim 1, wherein: converting the plurality of communication data files includes applying a first conversion process to a first communication data file in the plurality of communication data files and applying a second conversion process to second communication data file in the plurality of communication data files;the first communication data file and the second communication data file are generated by different communication providers; andthe first conversion process is different from the second conversion process.
7. A computer-implemented method comprising: converting a plurality of communication data files comprising text into a pre-determined format to create a plurality of formatted communication data files;generate offset tag data indicating a change in text position from an original position of text in an original data file of the plurality of communication data files to a formatted position of text in a formatted data file of the plurality of formatted communication data files, the change in text position being caused at least in part by the conversion of the plurality of communication data files into the pre-determined format; andinsert the offset tag data into a subset of the plurality of formatted communication data files.
8. The computer-implemented method of claim 7, further comprising processing the plurality of formatted communication data files to generate a plurality of revised formatted communication data files by performing two or more of the following: removing duplicate data among the plurality of formatted communication data files;generating thread data for at least a subset of the plurality of formatted communication data files;removing, from the plurality of formatted communication data files, data that meets pre-identified blacklisted criteria; orgenerating group association data for the plurality of formatted communication data files.
9. The computer-implemented method of claim 8, further comprising: storing the revised formatted communication data files in persistent memory;receiving a keyword for searching;accessing, from the persistent memory, the revised formatted communication data files for searching;determining a character position to highlight based at least in part on the keyword and the offset tag data; andtransmitting data for displaying content of a communication data file with a highlight at the character position.
10. The computer-implemented method of claim 7, further comprising processing the plurality of formatted communication data files to generate a plurality of revised formatted communication data files by generating group association data based at least in part on people included in the plurality of formatted communication data files.
11. The computer-implemented method of claim 10, further comprising accessing human resources data to correlate each of the plurality of communication data files to one or more people.
12. The computer-implemented method of claim 7, wherein converting the plurality of communication data files includes applying a first conversion process to email data in the plurality of communication data files and applying a second conversion process to a different type of communication data in the plurality of communication data files, the first conversion process being different from the second conversion process.
13. The computer-implemented method of claim 7, wherein converting the plurality of communication data files includes applying a first conversion process to a first communication data file in the plurality of communication data files and applying a second conversion process to second communication data file in the plurality of communication data files;the first communication data file and the second communication data file are generated by different first communication providers; andthe first conversion process is different from the second conversion process.
14. A non-transitory computer-readable medium comprising one or more program instructions recorded thereon, the program instructions configured for execution by a computing system comprising one or more processors in order to cause the computing system to: convert a plurality of communication data files comprising text into a pre-determined format to create a plurality of formatted communication data files;generate offset tag data indicating a change in text position from an original position of text in an original data file of the plurality of communication data files to a formatted position of text in a formatted data file of the plurality of formatted communication data files, the change in text position being caused at least in part by the conversion of the plurality of communication data files into the pre-determined format; andinsert the offset tag data into a subset of the plurality of formatted communication data files.
15. The non-transitory computer-readable medium of claim 14, wherein the one or more program instructions further cause the one or more processors to process the plurality of formatted communication data files to generate a plurality of revised formatted communication data files by performing two or more of the following: removing duplicate data among the plurality of formatted communication data files;generating thread data for at least a subset of the plurality of formatted communication data files;removing, from the plurality of formatted communication data files, data that meets pre-identified blacklisted criteria; orgenerating group association data for the plurality of formatted communication data files.
16. The non-transitory computer-readable medium of claim 15, wherein the one or more program instructions further cause the one or more processors to: store the revised formatted communication data files in persistent memory;receive a keyword for searching;access, from the persistent memory, the revised formatted communication data files for searching;determining a character position to highlight based at least in part on the keyword and the offset tag data; andtransmitting data to display content of a communication data file with a highlight at the character position.
17. The non-transitory computer-readable medium of claim 14, wherein the one or more program instructions further cause the one or more processors to process the plurality of formatted communication data files to generate a plurality of revised formatted communication data files by generating group association data based at least in part on people included in the plurality of formatted communication data files.
18. The non-transitory computer-readable medium of claim 17, wherein the one or more program instructions further cause the one or more processors to access human resources data to correlate each of the plurality of communication data files to one or more people.
19. The non-transitory computer-readable medium of claim 14, wherein: converting the plurality of communication data files includes: applying a first conversion process to email data in the plurality of communication data files; andapplying a second conversion process to a different type of communication data in the plurality of communication data files; andthe first conversion process is different from the second conversion process.
20. The non-transitory computer-readable medium of claim 14, wherein: converting the plurality of communication data files includes applying a first conversion process to a first communication data file in the plurality of communication data files and applying a second conversion process to a second communication data file in the plurality of communication data files;the first communication data file and the second communication data file are generated by different first communication providers; andthe first conversion process is different from the second conversion process.

PRIORITY AND INCORPORATION BY REFERENCE

This application is a continuation of U.S. patent application Ser. No. 14/750,773 filed on Jun. 25, 2015 and titled “COMMUNICATION DATA PROCESSING ARCHITECTURE,” which claims the priority and benefit of U.S. Provisional No. 62/095,655, filed Dec. 22, 2014. All of the applications listed above are hereby incorporated by reference herein in their entireties for all purposes and for all that they contain.

US Referenced Citations (700)

Number	Name	Date	Kind
5109399	Thompson	Apr 1992	A
5329108	Lamoure	Jul 1994	A
5670987	Doi et al.	Sep 1997	A
5724575	Hoover et al.	Mar 1998	A
5781704	Rossmo	Jul 1998	A
5798769	Chiu et al.	Aug 1998	A
5845300	Comer	Dec 1998	A
5872973	Mitchell et al.	Feb 1999	A
5897636	Kaeser	Apr 1999	A
6057757	Arrowsmith et al.	May 2000	A
6073129	Levine et al.	Jun 2000	A
6091956	Hollenberg	Jul 2000	A
6161098	Wallman	Dec 2000	A
6219053	Tachibana et al.	Apr 2001	B1
6232971	Haynes	May 2001	B1
6243717	Gordon et al.	Jun 2001	B1
6247019	Davies	Jun 2001	B1
6278996	Richardson	Aug 2001	B1
6279018	Kudrolli et al.	Aug 2001	B1
6304873	Klein et al.	Oct 2001	B1
6341310	Leshem et al.	Jan 2002	B1
6366933	Ball et al.	Apr 2002	B1
6369835	Lin	Apr 2002	B1
6374251	Fayyad et al.	Apr 2002	B1
6418438	Campbell	Jul 2002	B1
6430305	Decker	Aug 2002	B1
6456997	Shukla	Sep 2002	B1
6505196	Drucker et al.	Jan 2003	B2
6510504	Satyanarayana	Jan 2003	B2
6523019	Borthwick	Feb 2003	B1
6549944	Weinberg et al.	Apr 2003	B1
6560620	Ching	May 2003	B1
6567936	Yang et al.	May 2003	B1
6574635	Stauber et al.	Jun 2003	B2
6581068	Bensoussan et al.	Jun 2003	B1
6594672	Lampson et al.	Jul 2003	B1
6631496	Li et al.	Oct 2003	B1
6642945	Sharpe	Nov 2003	B1
6714936	Nevin, III	Mar 2004	B1
6745382	Zothner	Jun 2004	B1
6775675	Nwabueze et al.	Aug 2004	B1
6828920	Owen et al.	Dec 2004	B2
6839745	Dingari et al.	Jan 2005	B1
6877137	Rivette et al.	Apr 2005	B1
6976210	Silva et al.	Dec 2005	B1
6978419	Kantrowitz	Dec 2005	B1
6980984	Huffman et al.	Dec 2005	B1
6985950	Hanson et al.	Jan 2006	B1
7036085	Barros	Apr 2006	B2
7043702	Chi et al.	May 2006	B2
7055110	Kupka et al.	May 2006	B2
7058648	Lightfoot et al.	Jun 2006	B1
7111231	Huck et al.	Sep 2006	B1
7139800	Bellotti et al.	Nov 2006	B2
7158878	Rasmussen et al.	Jan 2007	B2
7162475	Ackerman	Jan 2007	B2
7168039	Bertram	Jan 2007	B2
7171427	Witowski et al.	Jan 2007	B2
7269786	Malloy et al.	Sep 2007	B1
7278105	Kitts	Oct 2007	B1
7290698	Poslinski et al.	Nov 2007	B2
7333998	Heckerman et al.	Feb 2008	B2
7370047	Gorman	May 2008	B2
7373669	Eisen	May 2008	B2
7379811	Rasmussen et al.	May 2008	B2
7379903	Caballero et al.	May 2008	B2
7418431	Nies et al.	Aug 2008	B1
7426654	Adams et al.	Sep 2008	B2
7451397	Weber et al.	Nov 2008	B2
7454466	Bellotti et al.	Nov 2008	B2
7461158	Rider et al.	Dec 2008	B2
7467375	Tondreau et al.	Dec 2008	B2
7487139	Fraleigh et al.	Feb 2009	B2
7502786	Liu et al.	Mar 2009	B2
7525422	Bishop et al.	Apr 2009	B2
7529727	Arning et al.	May 2009	B2
7558677	Jones	Jul 2009	B2
7574409	Patinkin	Aug 2009	B2
7574428	Leiserowitz et al.	Aug 2009	B2
7579965	Bucholz	Aug 2009	B2
7596285	Brown et al.	Sep 2009	B2
7614006	Molander	Nov 2009	B2
7617232	Gabbert et al.	Nov 2009	B2
7620628	Kapur et al.	Nov 2009	B2
7627812	Chamberlain et al.	Dec 2009	B2
7634717	Chamberlain et al.	Dec 2009	B2
7703021	Flam	Apr 2010	B1
7712049	Williams et al.	May 2010	B2
7716077	Mikurak	May 2010	B1
7725530	Sah et al.	May 2010	B2
7725547	Albertson et al.	May 2010	B2
7725728	Ama et al.	May 2010	B2
7730082	Sah et al.	Jun 2010	B2
7730109	Rohrs et al.	Jun 2010	B2
7770100	Chamberlain et al.	Aug 2010	B2
7783658	Bayliss	Aug 2010	B1
7805457	Viola et al.	Sep 2010	B1
7809703	Balabhadrapatruni et al.	Oct 2010	B2
7813937	Pathria et al.	Oct 2010	B1
7814102	Miller et al.	Oct 2010	B2
7818658	Chen	Oct 2010	B2
7827045	Madill et al.	Nov 2010	B2
7870493	Pall et al.	Jan 2011	B2
7894984	Rasmussen et al.	Feb 2011	B2
7899611	Downs et al.	Mar 2011	B2
7917376	Bellin et al.	Mar 2011	B2
7920963	Jouline et al.	Apr 2011	B2
7933862	Chamberlain et al.	Apr 2011	B2
7941321	Greenstein et al.	May 2011	B2
7962281	Rasmussen et al.	Jun 2011	B2
7962495	Jain et al.	Jun 2011	B2
7962848	Bertram	Jun 2011	B2
7970240	Chao et al.	Jun 2011	B1
7971150	Raskutti et al.	Jun 2011	B2
7984374	Caro et al.	Jul 2011	B2
8001465	Kudrolli et al.	Aug 2011	B2
8001482	Bhattiprolu et al.	Aug 2011	B2
8010545	Stefik et al.	Aug 2011	B2
8015487	Roy et al.	Sep 2011	B2
8024778	Cash et al.	Sep 2011	B2
8036632	Cona et al.	Oct 2011	B1
8036971	Aymeloglu et al.	Oct 2011	B2
8041714	Aymeloglu et al.	Oct 2011	B2
8046283	Burns	Oct 2011	B2
8046362	Bayliss	Oct 2011	B2
8054756	Chand et al.	Nov 2011	B2
8103543	Zwicky	Jan 2012	B1
8112425	Baum et al.	Feb 2012	B2
8126848	Wagner	Feb 2012	B2
8134457	Velipasalar et al.	Mar 2012	B2
8135679	Bayliss	Mar 2012	B2
8135719	Bayliss	Mar 2012	B2
8145703	Frishert et al.	Mar 2012	B2
8185819	Sah et al.	May 2012	B2
8214232	Tyler et al.	Jul 2012	B2
8214361	Sandler et al.	Jul 2012	B1
8214764	Gemmell et al.	Jul 2012	B2
8225201	Michael	Jul 2012	B2
8229902	Vishniac et al.	Jul 2012	B2
8229947	Fujinaga	Jul 2012	B2
8230333	Decherd et al.	Jul 2012	B2
8266168	Bayliss	Sep 2012	B2
8271461	Pike et al.	Sep 2012	B2
8280880	Aymeloglu et al.	Oct 2012	B1
8290838	Thakur et al.	Oct 2012	B1
8290942	Jones et al.	Oct 2012	B2
8301464	Cave et al.	Oct 2012	B1
8301904	Gryaznov	Oct 2012	B1
8302855	Ma et al.	Nov 2012	B2
8312367	Foster	Nov 2012	B2
8312546	Alme	Nov 2012	B2
8321943	Walters et al.	Nov 2012	B1
8347398	Weber	Jan 2013	B1
8352881	Champion et al.	Jan 2013	B2
8364642	Garrod	Jan 2013	B1
8368695	Howell et al.	Feb 2013	B2
8397171	Klassen et al.	Mar 2013	B2
8412707	Mianji	Apr 2013	B1
8447674	Choudhuri et al.	May 2013	B2
8447722	Ahuja et al.	May 2013	B1
8452790	Mianji	May 2013	B1
8463036	Ramesh et al.	Jun 2013	B1
8473454	Evanitsky et al.	Jun 2013	B2
8484115	Aymeloglu et al.	Jul 2013	B2
8484168	Bayliss	Jul 2013	B2
8489331	Kopf et al.	Jul 2013	B2
8489641	Seefeld et al.	Jul 2013	B1
8495077	Bayliss	Jul 2013	B2
8498969	Bayliss	Jul 2013	B2
8498984	Hwang et al.	Jul 2013	B1
8504542	Chang et al.	Aug 2013	B2
8514082	Cova et al.	Aug 2013	B2
8515207	Chau	Aug 2013	B2
8554579	Tribble et al.	Oct 2013	B2
8554653	Falkenborg et al.	Oct 2013	B2
8554709	Goodson et al.	Oct 2013	B2
8560413	Quarterman	Oct 2013	B1
8577911	Stepinski et al.	Nov 2013	B1
8578500	Long	Nov 2013	B2
8589273	Creeden et al.	Nov 2013	B2
8595234	Siripuapu et al.	Nov 2013	B2
8600872	Yan	Dec 2013	B1
8620641	Farnsworth et al.	Dec 2013	B2
8639522	Pathria et al.	Jan 2014	B2
8639757	Zang et al.	Jan 2014	B1
8646080	Williamson et al.	Feb 2014	B2
8676857	Adams et al.	Mar 2014	B1
8688573	Ruknoic et al.	Apr 2014	B1
8689108	Duffield et al.	Apr 2014	B1
8713467	Goldenberg et al.	Apr 2014	B1
8726379	Stiansen et al.	May 2014	B1
8739278	Varghese	May 2014	B2
8742934	Sarpy et al.	Jun 2014	B1
8745516	Mason et al.	Jun 2014	B2
8781169	Jackson et al.	Jul 2014	B2
8787939	Papakipos et al.	Jul 2014	B2
8788405	Sprague et al.	Jul 2014	B1
8788407	Singh et al.	Jul 2014	B1
8799799	Cervelli et al.	Aug 2014	B1
8812960	Sun et al.	Aug 2014	B1
8818892	Sprague et al.	Aug 2014	B1
8830322	Nerayoff et al.	Sep 2014	B2
8832594	Thompson et al.	Sep 2014	B1
8868537	Colgrove et al.	Oct 2014	B1
8917274	Ma et al.	Dec 2014	B2
8924388	Elliot et al.	Dec 2014	B2
8924389	Elliot et al.	Dec 2014	B2
8924872	Bogomolov et al.	Dec 2014	B1
8930331	McGrew et al.	Jan 2015	B2
8937619	Sharma et al.	Jan 2015	B2
8938686	Erenrich et al.	Jan 2015	B1
9009171	Grossman et al.	Apr 2015	B1
9009827	Albertson et al.	Apr 2015	B1
9021260	Falk et al.	Apr 2015	B1
9021384	Beard et al.	Apr 2015	B1
9043696	Meiklejohn et al.	May 2015	B1
9043894	Dennison et al.	May 2015	B1
9116975	Shankar et al.	Aug 2015	B2
9135658	Sprague et al.	Sep 2015	B2
9165299	Stowe et al.	Oct 2015	B1
9171334	Visbal et al.	Oct 2015	B1
9177344	Singh et al.	Nov 2015	B1
9202249	Cohen et al.	Dec 2015	B1
9230280	Maag et al.	Jan 2016	B1
9256664	Chakerian et al.	Feb 2016	B2
10275778	Maag et al.	Apr 2019	B1
10362133	Maag et al.	Jul 2019	B1
20010051949	Carey et al.	Dec 2001	A1
20010056522	Satyanarayana	Dec 2001	A1
20020033848	Sciammarella et al.	Mar 2002	A1
20020065708	Senay et al.	May 2002	A1
20020091694	Hrle et al.	Jul 2002	A1
20020091707	Keller	Jul 2002	A1
20020095360	Joao	Jul 2002	A1
20020095658	Shulman	Jul 2002	A1
20020103705	Brady	Aug 2002	A1
20020116120	Ruiz et al.	Aug 2002	A1
20020147805	Leshem et al.	Oct 2002	A1
20020174201	Ramer et al.	Nov 2002	A1
20020194119	Wright et al.	Dec 2002	A1
20030028560	Kudrolli et al.	Feb 2003	A1
20030033228	Bosworth-Davies et al.	Feb 2003	A1
20030039948	Donahue	Feb 2003	A1
20030074368	Schuetze et al.	Apr 2003	A1
20030097330	Hillmer et al.	May 2003	A1
20030105759	Bess et al.	Jun 2003	A1
20030115481	Baird et al.	Jun 2003	A1
20030120675	Stauber et al.	Jun 2003	A1
20030140106	Raguseo	Jul 2003	A1
20030144868	MacIntyre et al.	Jul 2003	A1
20030163352	Surpin et al.	Aug 2003	A1
20030212718	Tester	Nov 2003	A1
20030225755	Iwayama et al.	Dec 2003	A1
20030229848	Arend et al.	Dec 2003	A1
20040032432	Baynger	Feb 2004	A1
20040034570	Davis	Feb 2004	A1
20040064256	Barinek et al.	Apr 2004	A1
20040085318	Hassler et al.	May 2004	A1
20040095349	Bito et al.	May 2004	A1
20040111410	Burgoon et al.	Jun 2004	A1
20040111480	Yue	Jun 2004	A1
20040117345	Bamford et al.	Jun 2004	A1
20040117387	Civetta et al.	Jun 2004	A1
20040126840	Cheng et al.	Jul 2004	A1
20040143602	Ruiz et al.	Jul 2004	A1
20040143796	Lerner et al.	Jul 2004	A1
20040148301	McKay et al.	Jul 2004	A1
20040153418	Hanweck	Aug 2004	A1
20040163039	Gorman	Aug 2004	A1
20040193600	Kaasten et al.	Sep 2004	A1
20040205524	Richter et al.	Oct 2004	A1
20040221223	Yu et al.	Nov 2004	A1
20040236688	Bozeman	Nov 2004	A1
20040260702	Cragun et al.	Dec 2004	A1
20050010472	Quatse et al.	Jan 2005	A1
20050021886	Adema et al.	Jan 2005	A1
20050027705	Sadri et al.	Feb 2005	A1
20050028094	Allyn	Feb 2005	A1
20050039119	Parks et al.	Feb 2005	A1
20050080769	Gemmell	Apr 2005	A1
20050086207	Heuer et al.	Apr 2005	A1
20050097441	Herbach et al.	May 2005	A1
20050108063	Madill et al.	May 2005	A1
20050108231	Findleton et al.	May 2005	A1
20050114763	Nonomura et al.	May 2005	A1
20050125715	Di Franco et al.	Jun 2005	A1
20050162523	Darrell et al.	Jul 2005	A1
20050180330	Shapiro	Aug 2005	A1
20050182793	Keenan et al.	Aug 2005	A1
20050183005	Denoue et al.	Aug 2005	A1
20050222928	Steier et al.	Oct 2005	A1
20050246327	Yeung et al.	Nov 2005	A1
20050251786	Citron et al.	Nov 2005	A1
20050289524	McGinnes	Dec 2005	A1
20060026120	Carolan et al.	Feb 2006	A1
20060026170	Kreitler et al.	Feb 2006	A1
20060036593	Dean	Feb 2006	A1
20060059139	Robinson	Mar 2006	A1
20060074881	Vembu et al.	Apr 2006	A1
20060080316	Gilmore et al.	Apr 2006	A1
20060080619	Carlson et al.	Apr 2006	A1
20060095521	Patinkin	May 2006	A1
20060116991	Calderwood	Jun 2006	A1
20060129746	Porter	Jun 2006	A1
20060139375	Rasmussen et al.	Jun 2006	A1
20060142949	Helt	Jun 2006	A1
20060143075	Carr et al.	Jun 2006	A1
20060143079	Basak et al.	Jun 2006	A1
20060149596	Surpin et al.	Jul 2006	A1
20060161558	Tamma et al.	Jul 2006	A1
20060203337	White	Sep 2006	A1
20060218206	Bourbonnais et al.	Sep 2006	A1
20060218405	Ama et al.	Sep 2006	A1
20060218491	Grossman et al.	Sep 2006	A1
20060218637	Thomas et al.	Sep 2006	A1
20060241974	Chao et al.	Oct 2006	A1
20060242040	Rader	Oct 2006	A1
20060242630	Koike et al.	Oct 2006	A1
20060253502	Raman et al.	Nov 2006	A1
20060265397	Bryan et al.	Nov 2006	A1
20060271277	Hu et al.	Nov 2006	A1
20060279630	Aggarwal et al.	Dec 2006	A1
20070000999	Kubo et al.	Jan 2007	A1
20070011150	Frank	Jan 2007	A1
20070011304	Error	Jan 2007	A1
20070016363	Huang et al.	Jan 2007	A1
20070038962	Fuchs et al.	Feb 2007	A1
20070050429	Goldring et al.	Mar 2007	A1
20070057966	Ohno et al.	Mar 2007	A1
20070061487	Moore et al.	Mar 2007	A1
20070078832	Ott et al.	Apr 2007	A1
20070083541	Fraleigh et al.	Apr 2007	A1
20070094389	Nussey et al.	Apr 2007	A1
20070106582	Baker et al.	May 2007	A1
20070143253	Kostamaa et al.	Jun 2007	A1
20070150369	Zivin	Jun 2007	A1
20070174760	Chamberlain et al.	Jul 2007	A1
20070192143	Krishnan et al.	Aug 2007	A1
20070192265	Chopin et al.	Aug 2007	A1
20070198571	Ferguson et al.	Aug 2007	A1
20070208497	Downs et al.	Sep 2007	A1
20070208498	Barker et al.	Sep 2007	A1
20070208736	Tanigawa et al.	Sep 2007	A1
20070233756	D'Souza et al.	Oct 2007	A1
20070240062	Christena et al.	Oct 2007	A1
20070266336	Nojima et al.	Nov 2007	A1
20070271317	Carmel	Nov 2007	A1
20070284433	Domenica et al.	Dec 2007	A1
20070294200	Au	Dec 2007	A1
20070294643	Kyle	Dec 2007	A1
20070299697	Friedlander et al.	Dec 2007	A1
20080015970	Brookfield et al.	Jan 2008	A1
20080040684	Crump	Feb 2008	A1
20080051989	Welsh	Feb 2008	A1
20080052142	Bailey et al.	Feb 2008	A1
20080077597	Butler	Mar 2008	A1
20080077642	Carbone et al.	Mar 2008	A1
20080104019	Nath	May 2008	A1
20080104032	Sarkar	May 2008	A1
20080104149	Vishniac et al.	May 2008	A1
20080126951	Sood et al.	May 2008	A1
20080133567	Ames et al.	Jun 2008	A1
20080148398	Mezack et al.	Jun 2008	A1
20080155440	Trevor et al.	Jun 2008	A1
20080162616	Gross et al.	Jul 2008	A1
20080195417	Surpin et al.	Aug 2008	A1
20080195608	Clover	Aug 2008	A1
20080195672	Hamel et al.	Aug 2008	A1
20080201339	McGrew	Aug 2008	A1
20080215546	Baum et al.	Sep 2008	A1
20080222295	Robinson et al.	Sep 2008	A1
20080255973	El Wade et al.	Oct 2008	A1
20080263468	Cappione et al.	Oct 2008	A1
20080267107	Rosenberg	Oct 2008	A1
20080270316	Guidotti et al.	Oct 2008	A1
20080276167	Michael	Nov 2008	A1
20080278311	Grange et al.	Nov 2008	A1
20080288306	MacIntyre et al.	Nov 2008	A1
20080288425	Posse et al.	Nov 2008	A1
20080301378	Carrie	Dec 2008	A1
20080301643	Appleton et al.	Dec 2008	A1
20080313132	Hao et al.	Dec 2008	A1
20090002492	Velipasalar et al.	Jan 2009	A1
20090018940	Wang et al.	Jan 2009	A1
20090024505	Patel et al.	Jan 2009	A1
20090027418	Maru et al.	Jan 2009	A1
20090030915	Winter et al.	Jan 2009	A1
20090031247	Walter et al.	Jan 2009	A1
20090044279	Crawford et al.	Feb 2009	A1
20090055251	Shah et al.	Feb 2009	A1
20090082997	Tokman et al.	Mar 2009	A1
20090083184	Eisen	Mar 2009	A1
20090088964	Schaaf et al.	Apr 2009	A1
20090106178	Chu	Apr 2009	A1
20090106308	Killian et al.	Apr 2009	A1
20090112745	Stefanescu	Apr 2009	A1
20090119309	Gibson et al.	May 2009	A1
20090125369	Kloosstra et al.	May 2009	A1
20090125459	Norton et al.	May 2009	A1
20090132921	Hwangbo et al.	May 2009	A1
20090132953	Reed et al.	May 2009	A1
20090144262	White et al.	Jun 2009	A1
20090144274	Fraleigh et al.	Jun 2009	A1
20090164387	Armstrong et al.	Jun 2009	A1
20090164934	Bhattiprolu et al.	Jun 2009	A1
20090171939	Athsani et al.	Jul 2009	A1
20090172511	Decherd et al.	Jul 2009	A1
20090172821	Daira et al.	Jul 2009	A1
20090177962	Gusmorino et al.	Jul 2009	A1
20090179892	Tsuda et al.	Jul 2009	A1
20090187546	Whyte et al.	Jul 2009	A1
20090187548	Ji et al.	Jul 2009	A1
20090192957	Subramanian et al.	Jul 2009	A1
20090222400	Kupershmidt et al.	Sep 2009	A1
20090222760	Halverson et al.	Sep 2009	A1
20090234720	George et al.	Sep 2009	A1
20090249244	Robinson et al.	Oct 2009	A1
20090254970	Agarwal et al.	Oct 2009	A1
20090254971	Herz	Oct 2009	A1
20090271343	Vaiciulis et al.	Oct 2009	A1
20090271435	Yako et al.	Oct 2009	A1
20090281839	Lynn et al.	Nov 2009	A1
20090287470	Farnsworth et al.	Nov 2009	A1
20090292626	Oxford	Nov 2009	A1
20090307049	Elliott et al.	Dec 2009	A1
20090313223	Rantanen	Dec 2009	A1
20090313311	Hoffmann et al.	Dec 2009	A1
20090313463	Pang et al.	Dec 2009	A1
20090318775	Michelson et al.	Dec 2009	A1
20090319418	Herz	Dec 2009	A1
20100011282	Dollard et al.	Jan 2010	A1
20100036831	Vemuri et al.	Feb 2010	A1
20100042922	Bradateanu et al.	Feb 2010	A1
20100057622	Faith et al.	Mar 2010	A1
20100057716	Stefik et al.	Mar 2010	A1
20100070523	Delgo et al.	Mar 2010	A1
20100070842	Aymeloglu et al.	Mar 2010	A1
20100070845	Facemire et al.	Mar 2010	A1
20100070897	Aymeloglu et al.	Mar 2010	A1
20100076939	Iwaki et al.	Mar 2010	A1
20100077483	Stolfo et al.	Mar 2010	A1
20100082541	Kottomtharayil	Apr 2010	A1
20100098318	Anderson	Apr 2010	A1
20100100963	Mahaffey	Apr 2010	A1
20100103124	Kruzeniski et al.	Apr 2010	A1
20100106611	Paulsen et al.	Apr 2010	A1
20100114817	Broeder et al.	May 2010	A1
20100114831	Gilbert et al.	May 2010	A1
20100114887	Conway et al.	May 2010	A1
20100115396	Byron et al.	May 2010	A1
20100122152	Chamberlain et al.	May 2010	A1
20100125546	Barrett et al.	May 2010	A1
20100131457	Heimendinger	May 2010	A1
20100138842	Balko et al.	Jun 2010	A1
20100145909	Ngo	Jun 2010	A1
20100161565	Lee et al.	Jun 2010	A1
20100161688	Kesselman et al.	Jun 2010	A1
20100161735	Sharma	Jun 2010	A1
20100162176	Dunton	Jun 2010	A1
20100169237	Howard et al.	Jul 2010	A1
20100185691	Irmak et al.	Jul 2010	A1
20100191563	Schlaifer et al.	Jul 2010	A1
20100191884	Holenstein et al.	Jul 2010	A1
20100198684	Eraker et al.	Aug 2010	A1
20100199225	Coleman et al.	Aug 2010	A1
20100211550	Daniello et al.	Aug 2010	A1
20100211618	Anderson et al.	Aug 2010	A1
20100228812	Uomini	Sep 2010	A1
20100235606	Oreland et al.	Sep 2010	A1
20100250412	Wagner	Sep 2010	A1
20100250503	Andersen et al.	Sep 2010	A1
20100250624	Mayer	Sep 2010	A1
20100262688	Hussain et al.	Oct 2010	A1
20100280857	Liu et al.	Nov 2010	A1
20100283787	Hamedi et al.	Nov 2010	A1
20100293174	Bennett et al.	Nov 2010	A1
20100306029	Jolley	Dec 2010	A1
20100306713	Geisner et al.	Dec 2010	A1
20100313119	Baldwin et al.	Dec 2010	A1
20100318924	Frankel et al.	Dec 2010	A1
20100321399	Ellren et al.	Dec 2010	A1
20100325526	Ellis et al.	Dec 2010	A1
20100325581	Finkelstein et al.	Dec 2010	A1
20100330801	Rouh	Dec 2010	A1
20110029498	Ferguson et al.	Feb 2011	A1
20110029526	Knight et al.	Feb 2011	A1
20110047159	Baid et al.	Feb 2011	A1
20110060753	Shaked et al.	Mar 2011	A1
20110061013	Bilicki et al.	Mar 2011	A1
20110074811	Hanson et al.	Mar 2011	A1
20110078055	Faribault et al.	Mar 2011	A1
20110078173	Seligmann et al.	Mar 2011	A1
20110087519	Fordyce, III et al.	Apr 2011	A1
20110093327	Fordyce, III et al.	Apr 2011	A1
20110099133	Chang et al.	Apr 2011	A1
20110119100	Ruhl et al.	May 2011	A1
20110131122	Griffin et al.	Jun 2011	A1
20110137766	Rasmussen et al.	Jun 2011	A1
20110153384	Horne et al.	Jun 2011	A1
20110161096	Buehler et al.	Jun 2011	A1
20110167105	Ramakrishnan et al.	Jul 2011	A1
20110167493	Song et al.	Jul 2011	A1
20110170799	Carrino et al.	Jul 2011	A1
20110173032	Payne et al.	Jul 2011	A1
20110173093	Psota et al.	Jul 2011	A1
20110173619	Fish	Jul 2011	A1
20110178842	Rane et al.	Jul 2011	A1
20110184813	Barne et al.	Jul 2011	A1
20110185316	Reid et al.	Jul 2011	A1
20110208724	Jones et al.	Aug 2011	A1
20110218934	Elser	Sep 2011	A1
20110219450	McDougal et al.	Sep 2011	A1
20110225198	Edwards et al.	Sep 2011	A1
20110225650	Margolies et al.	Sep 2011	A1
20110231223	Winters	Sep 2011	A1
20110238510	Rowen et al.	Sep 2011	A1
20110238553	Raj et al.	Sep 2011	A1
20110238570	Li et al.	Sep 2011	A1
20110246229	Pacha	Oct 2011	A1
20110251951	Kolkowtiz	Oct 2011	A1
20110258158	Resende et al.	Oct 2011	A1
20110258242	Eidson et al.	Oct 2011	A1
20110270604	Qi et al.	Nov 2011	A1
20110270705	Parker	Nov 2011	A1
20110270812	Ruby	Nov 2011	A1
20110289397	Eastmond et al.	Nov 2011	A1
20110289407	Naik et al.	Nov 2011	A1
20110291851	Whisenant	Dec 2011	A1
20110307382	Siegel et al.	Dec 2011	A1
20110310005	Chen et al.	Dec 2011	A1
20110314007	Dassa et al.	Dec 2011	A1
20120004904	Shin et al.	Jan 2012	A1
20120011238	Rathod	Jan 2012	A1
20120013684	Robertson et al.	Jan 2012	A1
20120019559	Siler et al.	Jan 2012	A1
20120022945	Falkenborg et al.	Jan 2012	A1
20120036013	Neuhaus et al.	Feb 2012	A1
20120036434	Oberstein	Feb 2012	A1
20120050293	Carlhian et al.	Mar 2012	A1
20120054284	Rakshit	Mar 2012	A1
20120066166	Curbera et al.	Mar 2012	A1
20120066296	Appleton et al.	Mar 2012	A1
20120072825	Sherkin et al.	Mar 2012	A1
20120079363	Folting et al.	Mar 2012	A1
20120084135	Nissan et al.	Apr 2012	A1
20120084866	Stolfo	Apr 2012	A1
20120106801	Jackson	May 2012	A1
20120117082	Koperda et al.	May 2012	A1
20120131512	Takeuchi et al.	May 2012	A1
20120144335	Abeln et al.	Jun 2012	A1
20120150791	Willson	Jun 2012	A1
20120159307	Chung et al.	Jun 2012	A1
20120159362	Brown et al.	Jun 2012	A1
20120159399	Bastide et al.	Jun 2012	A1
20120173381	Smith	Jul 2012	A1
20120173985	Peppel	Jul 2012	A1
20120196557	Reich et al.	Aug 2012	A1
20120196558	Reich et al.	Aug 2012	A1
20120208636	Feige	Aug 2012	A1
20120215898	Shah et al.	Aug 2012	A1
20120221511	Gibson et al.	Aug 2012	A1
20120221553	Wittmer et al.	Aug 2012	A1
20120221580	Barney	Aug 2012	A1
20120245976	Kumar et al.	Sep 2012	A1
20120246148	Dror	Sep 2012	A1
20120254129	Wheeler et al.	Oct 2012	A1
20120284345	Costenaro et al.	Nov 2012	A1
20120290879	Shibuya et al.	Nov 2012	A1
20120296907	Long et al.	Nov 2012	A1
20120310831	Harris et al.	Dec 2012	A1
20120310838	Harris et al.	Dec 2012	A1
20120311684	Paulsen et al.	Dec 2012	A1
20120323888	Osann, Jr.	Dec 2012	A1
20120330908	Stowe et al.	Dec 2012	A1
20120330973	Ghuneim et al.	Dec 2012	A1
20130006426	Healey et al.	Jan 2013	A1
20130006655	Van Arkel et al.	Jan 2013	A1
20130006668	Van Arkel et al.	Jan 2013	A1
20130006725	Simanek et al.	Jan 2013	A1
20130018796	Kolhatkar et al.	Jan 2013	A1
20130024307	Fuerstenberg et al.	Jan 2013	A1
20130024339	Choudhuri et al.	Jan 2013	A1
20130036346	Cicerone	Feb 2013	A1
20130046842	Muntz et al.	Feb 2013	A1
20130060786	Serrano et al.	Mar 2013	A1
20130061169	Pearcy et al.	Mar 2013	A1
20130073377	Heath	Mar 2013	A1
20130073454	Busch	Mar 2013	A1
20130078943	Biage et al.	Mar 2013	A1
20130096988	Grossman et al.	Apr 2013	A1
20130097130	Bingol et al.	Apr 2013	A1
20130097482	Marantz et al.	Apr 2013	A1
20130101159	Chao et al.	Apr 2013	A1
20130110822	Ikeda et al.	May 2013	A1
20130110877	Bonham et al.	May 2013	A1
20130111320	Campbell et al.	May 2013	A1
20130117651	Waldman et al.	May 2013	A1
20130150004	Rosen	Jun 2013	A1
20130151148	Parundekar et al.	Jun 2013	A1
20130151388	Falkenborg et al.	Jun 2013	A1
20130151453	Bhanot et al.	Jun 2013	A1
20130157234	Gulli et al.	Jun 2013	A1
20130158983	Jakubik	Jun 2013	A1
20130160120	Malaviya et al.	Jun 2013	A1
20130166480	Popescu et al.	Jun 2013	A1
20130166550	Buchmann et al.	Jun 2013	A1
20130176321	Mitchell et al.	Jul 2013	A1
20130179420	Park et al.	Jul 2013	A1
20130185307	El-Yaniv et al.	Jul 2013	A1
20130197925	Blue	Aug 2013	A1
20130211985	Clark et al.	Aug 2013	A1
20130224696	Wolfe et al.	Aug 2013	A1
20130226865	Munemann	Aug 2013	A1
20130226953	Markovich et al.	Aug 2013	A1
20130232045	Tai et al.	Sep 2013	A1
20130238616	Rose et al.	Sep 2013	A1
20130246170	Gross et al.	Sep 2013	A1
20130246537	Gaddala	Sep 2013	A1
20130251233	Yang et al.	Sep 2013	A1
20130262527	Hunter et al.	Oct 2013	A1
20130263019	Castellanos et al.	Oct 2013	A1
20130267207	Hao et al.	Oct 2013	A1
20130268520	Fisher et al.	Oct 2013	A1
20130276799	Davidson	Oct 2013	A1
20130279757	Kephart	Oct 2013	A1
20130282696	John et al.	Oct 2013	A1
20130290011	Lynn et al.	Oct 2013	A1
20130290825	Arndt et al.	Oct 2013	A1
20130297619	Chandrasekaran et al.	Nov 2013	A1
20130304770	Boero et al.	Nov 2013	A1
20130311375	Priebatsch	Nov 2013	A1
20130318060	Chang et al.	Nov 2013	A1
20130318594	Hoy et al.	Nov 2013	A1
20130339218	Subramanian et al.	Dec 2013	A1
20140006109	Callioni et al.	Jan 2014	A1
20140012796	Petersen et al.	Jan 2014	A1
20140019936	Cohanoff	Jan 2014	A1
20140032506	Hoey et al.	Jan 2014	A1
20140033010	Richardt et al.	Jan 2014	A1
20140040371	Gurevich et al.	Feb 2014	A1
20140047357	Alfaro et al.	Feb 2014	A1
20140058754	Wild	Feb 2014	A1
20140058763	Zizzamia et al.	Feb 2014	A1
20140059038	McPherson et al.	Feb 2014	A1
20140068487	Steiger et al.	Mar 2014	A1
20140081652	Klindworth	Mar 2014	A1
20140095273	Tang et al.	Apr 2014	A1
20140095509	Patton	Apr 2014	A1
20140108068	Williams	Apr 2014	A1
20140108380	Gotz et al.	Apr 2014	A1
20140108985	Scott et al.	Apr 2014	A1
20140123279	Bishop et al.	May 2014	A1
20140129261	Bothwell et al.	May 2014	A1
20140136237	Anderson et al.	May 2014	A1
20140136285	Carvalho	May 2014	A1
20140142954	Cameron	May 2014	A1
20140143009	Brice et al.	May 2014	A1
20140149130	Getchius	May 2014	A1
20140149272	Hirani et al.	May 2014	A1
20140149436	Bahrami et al.	May 2014	A1
20140156527	Grigg et al.	Jun 2014	A1
20140157172	Peery et al.	Jun 2014	A1
20140164502	Khodorenko et al.	Jun 2014	A1
20140189536	Lange et al.	Jul 2014	A1
20140195515	Baker et al.	Jul 2014	A1
20140195887	Ellis et al.	Jul 2014	A1
20140222793	Sadkin et al.	Aug 2014	A1
20140267294	Ma	Sep 2014	A1
20140267295	Sharma	Sep 2014	A1
20140279824	Tamayo	Sep 2014	A1
20140310282	Sprague et al.	Oct 2014	A1
20140316911	Gross	Oct 2014	A1
20140333651	Cervelli et al.	Nov 2014	A1
20140337772	Cervelli et al.	Nov 2014	A1
20140344231	Stowe et al.	Nov 2014	A1
20140366132	Stiansen et al.	Dec 2014	A1
20150019394	Unser et al.	Jan 2015	A1
20150046870	Goldenberg et al.	Feb 2015	A1
20150089424	Duffield et al.	Mar 2015	A1
20150100897	Sun et al.	Apr 2015	A1
20150100907	Erenrich et al.	Apr 2015	A1
20150106347	McGrew et al.	Apr 2015	A1
20150106379	Elliot et al.	Apr 2015	A1
20150112956	Chang et al.	Apr 2015	A1
20150134666	Gattiker et al.	May 2015	A1
20150169709	Kara et al.	Jun 2015	A1
20150169726	Kara et al.	Jun 2015	A1
20150170077	Kara et al.	Jun 2015	A1
20150178825	Huerta	Jun 2015	A1
20150178877	Bogomolov et al.	Jun 2015	A1
20150227295	Meiklejohn et al.	Aug 2015	A1
20150235334	Wang et al.	Aug 2015	A1
20150261817	Harris et al.	Sep 2015	A1
20150286977	Schneeman	Oct 2015	A1
20160004764	Chakerian et al.	Jan 2016	A1
20160034470	Sprague et al.	Feb 2016	A1
20160048937	Mathura et al.	Feb 2016	A1
20160179932	Ghafourifar	Jun 2016	A1
20170300556	Ghafourifar	Oct 2017	A1
20190205897	Maag et al.	Jul 2019	A1

Foreign Referenced Citations (49)

Number	Date	Country
102546446	Jul 2012	CN
103167093	Jun 2013	CN
102054015	May 2014	CN
102014103476	Sep 2014	DE
102014103482	Sep 2014	DE
102014204827	Sep 2014	DE
102014204830	Sep 2014	DE
102014204834	Sep 2014	DE
102014215621	Feb 2015	DE
1191463	Mar 2002	EP
1672527	Jun 2006	EP
2551799	Jan 2013	EP
2555126	Feb 2013	EP
2555153	Feb 2013	EP
2560134	Feb 2013	EP
2778977	Sep 2014	EP
2835745	Feb 2015	EP
2835770	Feb 2015	EP
2838039	Feb 2015	EP
2846241	Mar 2015	EP
2851852	Mar 2015	EP
2858014	Apr 2015	EP
2858018	Apr 2015	EP
2863326	Apr 2015	EP
2863346	Apr 2015	EP
2869211	May 2015	EP
2881868	Jun 2015	EP
2884439	Jun 2015	EP
2884440	Jun 2015	EP
2891992	Jul 2015	EP
2892197	Jul 2015	EP
2911078	Aug 2015	EP
2963577	Jan 2016	EP
2514239	Nov 2014	GB
2516155	Jan 2015	GB
2518745	Apr 2015	GB
2012778	Nov 2014	NL
2013306	Feb 2015	NL
624557	Dec 2014	NZ
WO 2000009529	Feb 2000	WO
WO 2005104736	Nov 2005	WO
WO 2008011728	Jan 2008	WO
WO 2008113059	Sep 2008	WO
WO 2009061501	May 2009	WO
WO 2010000014	Jan 2010	WO
WO 2010030913	Mar 2010	WO
WO 2013010157	Jan 2013	WO
WO 2013102892	Jul 2013	WO
WO 2013126281	Aug 2013	WO

Non-Patent Literature Citations (269)

Entry
US 8,712,906 B1, 04/2014, Sprague et al. (withdrawn)
“A First Look: Predicting Market Demand for Food Retail using a Huff Analysis,” TRF Policy Solutions, Jul. 2012, pp. 30.
“A Quick Guide to UniProtKB Swiss-Prot & TrEMBL,” Sep. 2011, pp. 2.
“A Word About Banks and the Laundering of Drug Money,” Aug. 18, 2012, http://www.golemxiv.co.uk/2012/08/a-word-about-banks-and-the-laundering-of-drug-money/.
Acklen, Laura, “Absolute Beginner's Guide to Microsoft Word 2003,” Dec. 24, 2003, pp. 15-18, 34-41, 308-316.
Alfred, Rayner “Summarizing Relational Data Using Semi-Supervised Genetic Algorithm-Based Clustering Techniques”, Journal of Computer Science, 2010, vol. 6, No. 7, pp. 775-784.
Amnet, “5 Great Tools for Visualizing Your Twitter Followers,” posted Aug. 4, 2010, http://www.amnetblog.com/component/content/article/115-5-grate-tools-for-visualizing-your-twitter-followers.html.
Ananiev et al., “The New Modality API,” http://web.archive.org/web/20061211011958/http://java.sun.com/developer/technicalArticles/J2SE/Desktop/javase6/modality/ Jan. 21, 2006, pp. 8.
Anonymous, “BackTult—JD Edwards One World Version Control System,” printed Jul. 23, 2007 in 1 page.
Antoshenkov, Gennady, “Dictionary-Based Order-Preserving String Compression”, The VLDB Journal, pp. 26-39, 1997.
The Apache Cassandra Project.
Apache HBase.
Appacts, “Smart Thinking for Super Apps,” <http://www.appacts.com> Printed Jul. 18, 2013 in 4 pages.
Apsalar, “Data Powered Mobile Advertising,” “Free Mobile App Analytics” and various analytics related screen shots <http://apsalar.com> Printed Jul. 18, 2013 in 8 pages.
Baker et al., “Megastore: Providing Scalable, Highly Available Storage for Interactive Services”, 5th Biennial Conference on Innovative Data Systems Research (CIDR '11), Asilomar, California, Jan. 9-12, 2011.
Bernstein et al., “Hyder—A Transactional Record Manager for Shared Flash”, 5th Biennial Conference on Innovative Data Systems Research (CIDR '11), vol. 12, Asilomar, California, Jan. 9-12, 2011.
Bluttman et al., “Excel Formulas and Functions for Dummies,” 2005, Wiley Publishing, Inc., pp. 280, 284-286.
Boyce, Jim, “Microsoft Outlook 2010 Inside Out,” Aug. 1, 2010, retrieved from the internet https://capdtron.files.wordpress.com/2013/01/outlook-2010-inside_out.pdf.
Bugzilla@Mozilla, “Bug 18726—[feature] Long-click means of invoking contextual menus not supported,” http://bugzilla.mozilla.org/show_bug.cgi?id=18726 printed Jun. 13, 2013 in 11 pages.
Butkovic et al., “Using Whois Based Geolocation and Google Maps API for Support Cybercrime Investigations,” Recent Advances in Telecommunications and Circuits: Proceedings of the 11th International Conference on Applied Electromagnetics, Wireless and Optical Communications (Electroscience '13), Proceedings of the 2nd International Conference on Circuits, Systems, Communications, Computers and Applications (CSCCA '13), Proceedings of the 1st International Conference on Solid State Circuits (SSC '13), Proceedings of the 1st International Conference on Antennas & Propagation (ANPRO '13) :Dubrovnik, Croatia, Jun. 25-27, 2013, pp. 194-200, 2013.
Canese et al., “Chapter 2: PubMed: The Bibliographic Database,” The NCBI Handbook, Oct. 2002, pp. 1-10.
Capptain—Pilot Your Apps, <http://www.capptain.com> Printed Jul. 18, 2013 in 6 pages.
Celik, Tantek, “CSS Basic User Interface Module Level 3 (CSS3 UI),” Section 8 Resizing and Overflow, Jan. 17, 2012, retrieved from internet http://www.w3.org/TR/2012/WD-css3-ui-20120117/#resizing-amp-overflow retrieved on May 18, 2015.
Chang et al., “Bigtable: A Distributed Storage System for Structured Data”, Google, Inc., OSDI'06: Seventh Symposium on Operating System Design and Implementation, Seattle, WA, Nov. 2006.
Chen et al., “Bringing Order to the Web: Automatically Categorizing Search Results,” CHI 2000, Proceedings of the SIGCHI conference on Human Factors in Computing Systems, Apr. 1-6, 2000, The Hague, The Netherlands, pp. 145-152.
Chung, Chin-Wan, “Dataplex: An Access to Heterogeneous Distributed Databases”, Communications of the ACM, Association for Computing Machinery, Inc., vol. 33, Issue No. 1, pp. 70-80, Jan. 1, 1990.
Cohn, et al., “Semi-supervised clustering with user feedback,” Constrained Clustering: Advances in Algorithms, Theory, and Applications 4.1 (2003): 17-32.
Conner, Nancy, “Google Apps: The Missing Manual,” May 1, 2008, pp. 15.
Countly Mobile Analytics, <http://count.ly/> Printed Jul. 18, 2013 in 9 pages.
Definition “Identify”, downloaded Jan. 22, 2015, 1 page.
Definition “Overlay”, downloaded Jan. 22, 2015, 1 page.
Delcher et al., “Identifying Bacterial Genes and Endosymbiont DNA with Glimmer,” BioInformatics, vol. 23, No. 6, 2007, pp. 673-679.
Devanbu et al., “Authentic Third-party Data Publication”, http://www.cs.ucdavis.edu/˜devanbu/authdbpub.pdf, p. 19, accessed on Feb. 6, 2012.
DISTIMO—App Analytics, <http://www.distimo.com/app-analytics> Printed Jul. 18, 2013 in 5 pages.
Dramowicz, Ela, “Retail Trade Area Analysis Using the Huff Model,” Directions Magazine, Jul. 2, 2005 in 10 pages, http://www.directionsmag.com/articles/retail-trade-area-analysis-using-the-huff-model/123411.
Dreyer et al., “An Object-Oriented Data Model for a Time Series Management System”, Proceedings of the 7th International Working Conference on Scientific and Statistical Database Management, p. 12, Charlottesville, Virginia, USA, Sep. 28-30, 1994.
Elmasri et al., “Fundamentals of Database Systems”, Fourth Edition, pp. 455-491, 2004.
“The FASTA Program Package,” fasta-36.3.4, Mar. 25, 2011, pp. 29.
Flurry Analytics, <http://www.flurry.com/> Printed Jul. 18, 2013 in 14 pages.
GIS-NET 3 Public _ Department of Regional Planning. Planning & Zoning Information for Unincorporated La County. Retrieved Oct. 2, 2013 from http://gis.planning.lacounty.gov/GIS-NET3_Public/Viewer.html.
Google Analytics Official Website—Web Analytics & Reporting, <http://www.google.com/analytics.index.html> Printed Jul. 18, 2013 in 22 pages.
Gorr et al., “Crime Hot Spot Forecasting: Modeling and Comparative Evaluation”, Grant 98-IJ-CX-K005, May 6, 2002, 37 pages.
Goswami, Gautam, “Quite Writly Said!,” One Brick at a Time, Aug. 21, 2005, pp. 7.
Griffith, Daniel A., “A Generalized Huff Model,” Geographical Analysis, Apr. 1982, vol. 14, No. 2, pp. 135-144.
Gu et al., “Record Linkage: Current Practice and Future Directions,” Jan. 15, 2004, pp. 32.
Hansen et al., “Analyzing Social Media Networks with NodeXL: Insights from a Connected World”, Chapter 4, pp. 53-67 and Chapter 10, pp. 143-164, published Sep. 2010.
Hardesty, “Privacy Challenges: Analysis: It's Surprisingly Easy to Identify Individuals from Credit-Card Metadata,” MIT News on Campus and Around the World, MIT News Office, Jan. 29, 2015, 3 pages.
Hibbert et al., “Prediction of Shopping Behavior Using a Huff Model Within a GIS Framework,” Healthy Eating in Context, Mar. 18, 2011, pp. 16.
Hogue et al., “Thresher: Automating the Unwrapping of Semantic Content from the World Wide Web,” 14th International Conference on World Wide Web, WWW 2005: Chiba, Japan, May 10-14, 2005, pp. 86-95.
Hua et al., “A Multi-attribute Data Structure with Parallel Bloom Filters for Network Services”, HiPC 2006, LNCS 4297, pp. 277-288, 2006.
Huff et al., “Calibrating the Huff Model Using ArcGIS Business Analyst,” ESRI, Sep. 2008, pp. 33.
Huff, David L., “Parameter Estimation in the Huff Model,” ESRI, ArcUser, Oct.-Dec. 2003, pp. 34-36.
“HunchLab: Heat Map and Kernel Density Calculation for Crime Analysis,” Azavea Journal, printed from www.azavea.com/blogs/newsletter/v4i4/kernel-density-capabilities-added-to-hunchlab/ on Sep. 9, 2014, 2 pages.
Kahan et al., “Annotea: an Open RDF Infrastructure for Shared Web Annotations”, Computer Networks, Elsevier Science Publishers B.V., vol. 39, No. 5, dated Aug. 5, 2002, pp. 589-608.
Keylines.com, “An Introduction to KeyLines and Network Visualization,” Mar. 2014, <http://keylines.com/wp-content/uploads/2014/03/KeyLines-White-Paper.pdf> downloaded May 12, 2014 in 8 pages.
Keylines.com, “KeyLines Datasheet,” Mar. 2014, <http://keylines.com/wp-content/uploads/2014/03/KeyLines-datasheet.pdf> downloaded May 12, 2014 in 2 pages.
Keylines.com, “Visualizing Threats: Improved Cyber Security Through Network Visualization,” Apr. 2014, <http://keylines.com/wp-content/uploads/2014/04/Visualizing-Threats1.pdf> downloaded May 12, 2014 in 10 pages.
Kitts, Paul, “Chapter 14: Genome Assembly and Annotation Process,” The NCBI Handbook, Oct. 2002, pp. 1-21.
Klemmer et al., “Where Do Web Sites Come From? Capturing and Interacting with Design History,” Association for Computing Machinery, CHI 2002, Apr. 20-25, 2002, Minneapolis, MN, pp. 8.
Kokossi et al., “D7—Dynamic Ontology Management System (Design)”, Information Societies Technology Programme, pp. 1-27.
Kontagent Mobile Analytics, <http://www.kontagent.com/> Printed Jul. 18, 2013 in 9 pages.
Li et al., “Interactive Multimodal Visual Search on Mobile Device,” IEEE Transactions on Multimedia, vol. 15, No. 3, Apr. 1, 2013, pp. 594-607.
Liu, Tianshun, “Combining GIS and the Huff Model to Analyze Suitable Locations for a New Asian Supermarket in the Minneapolis and St. Paul, Minnesota USA,” Papers in Resource Analysis, 2012, vol. 14, pp. 8.
Localytics—Mobile App Marketing & Analytics, <http://www.localytics.com/> Printed Jul. 18, 2013 in 12 pages.
Madden, Tom, “Chapter 16: The BLAST Sequence Analysis Tool,” The NCBI Handbook, Oct. 2002, pp. 1-15.
Manno et al., “Introducing Collaboration in Single-user Applications through the Centralized Control Architecture,” 2010, pp. 10.
Manske, “File Saving Dialogs,” <http://www.mozilla.org/editor/ui_specs/FileSaveDialogs.html>, Jan. 20, 1999, pp. 7.
Map of San Jose, CA. Retrieved Oct. 2, 2013 from http://maps.yahoo.com.
Map of San Jose, CA. Retrieved Oct. 2, 2013 from http://maps.bing.com.
Map of San Jose, CA. Retrieved Oct. 2, 2013 from http://maps.google.com.
Mentzas et al., “An Architecture for Intelligent Assistance in the Forecasting Process”, Proceedings of the Twenty-Eighth Hawaii International Conference on System Sciences, vol. 3, pp. 167-176, Jan. 3-6, 1995.
Microsoft—Developer Network, “Getting Started with VBA in Word 2010,” Apr. 2010, <http://msdn.microsoft.com/en-us/library/ff604039%28v=office.14%29.aspx> as printed Apr. 4, 2014 in 17 pages.
Microsoft Office—Visio, “About connecting shapes,” <http://office.microsoft.com/en-us/visio-help/about-connecting-shapes-HP085050369.aspx> printed Aug. 4, 2011 in 6 pages.
Microsoft Office—Visio, “Add and glue connectors with the Connector tool,” <http://office.microsoft.com/en-us/visio-help/add-and-glue-connectors-with-the-connector-tool-HA010048532.aspx?CTT=1> printed Aug. 4, 2011 in 1 page.
Miklau et al., “Securing History: Privacy and Accountability in Database Systems”, 3rd Biennial Conference on Innovative Data Systems Research (CIDR), pp. 387-396, Asilomar, California, Jan. 7-10, 2007.
Mixpanel—Mobile Analytics, <https://mixpanel.com/> Printed Jul. 18, 2013 in 13 pages.
Mizrachi, Ilene, “Chapter 1: GenBank: The Nuckeotide Sequence Database,” The NCBI Handbook, Oct. 2002, pp. 1-14.
“Money Laundering Risks and E-Gaming: A European Overview and Assessment,” 2009, http://www.cf.ac.uk/socsi/resources/Levi_Final_Money_Laundering_Risks_egaming.pdf.
Niepert et al., “A Dynamic Ontology for a Dynamic Reference Work”, Joint Conference on Digital Libraries, pp. 1-10, Vancouver, British Columbia, Jun. 17-22, 2007.
Nierman, “Evaluating Structural Similarity in XML Documents”, 6 pages, 2002.
Nolan et al., “MCARTA: A Malicious Code Automated Run-Time Analysis Framework,” Homeland Security (HST) 2012 IEEE Conference on Technologies for, Nov. 13, 2012, pp. 13-17.
Olanoff, Drew, “Deep Dive with the New Google Maps for Desktop with Google Earth Integration, It's More than Just a Utility,” May 15, 2013, pp. 1-6, retrieved from the internet: http://web.archive.org/web/20130515230641/http://techcrunch.com/2013/05/15/deep-dive-with-the-new-google-maps-for-desktop-with-google-earth-integration-its-more-than-just-a-utility/.
Open Web Analytics (OWA), <http://www.openwebanalytics.com/> Printed Jul. 19, 2013 in 5 pages.
Palmas et al., “An Edge-Bunding Layout for Interactive Parallel Coordinates” 2014 IEEE Pacific Visualization Symposium, pp. 57-64.
Peng et al., “Large-scale Incremental Processing Using Distributed Transactions and Notifications”, Proceedings of the 9th USENIX Symposium on Operating Systems Design and Implementation, USENIX, p. 14, 2010.
Perdisci et al., “Behavioral Clustering of HTTP-Based Malware and Signature Generation Using Malicious Network Traces,” USENIX, Mar. 18, 2010, pp. 1-14.
Piwik—Free Web Analytics Software. <http://piwik.org/> Printed Jul. 19, 2013 in18 pages.
“Potential Money Laundering Warning Signs,” snapshot taken 2003, https://web.archive.org/web/20030816090055/http:/finsolinc.com/ANTI-MONEY%20LAUNDERING%20TRAINING%20GUIDES.pdf.
“Refresh CSS Ellipsis When Resizing Container—Stack Overflow,” Jul. 31, 2013, retrieved from internet http://stackoverflow.com/questions/17964681/refresh-css-ellipsis-when-resizing-container, retrieved on May 18, 2015.
Rouse, Margaret, “OLAP Cube,” <http://searchdatamanagement.techtarget.com/definition/OLAP-cube>, Apr. 28, 2012, pp. 16.
Shah, Chintan, “Periodic Connections to Control Server Offer New Way to Detect Botnets,” Oct. 24, 2013 in 6 pages, <http://www.blogs.mcafee.com/mcafee-labs/periodic-links-to-control-server-offer-new-way-to-detect-botnets>.
Shi et al., “A Scalable Implementation of Malware Detection Based on Network Connection Behaviors,” 2013 International Conference on Cyber-Enabled Distributed Computing Knowledge Discovery, IEEE, Oct. 10, 2013, pp. 59-66.
Sigrist, et al., “PROSITE, a Protein Domain Database for Functional Characterization and Annotation,” Nucleic Acids Research, 2010, vol. 38, pp. D161-D166.
Sirotkin et al., “Chapter 13: The Processing of Biological Sequence Data at NCBI,” The NCBI Handbook, Oct. 2002, pp. 1-11.
StatCounter—Free Invisible Web Tracker, Hit Counter and Web Stats, <http://statcounter.com/> Printed Jul. 19, 2013 in 17 pages.
Symantec Corporation, “E-Security Begins with Sound Security Policies,” Announcement Symantec, Jun. 14, 2001.
TestFlight—Beta Testing on the Fly, <http://testflightapp.com/> Printed Jul. 18, 2013 in 3 pages.
Thomson et al., “The Case for Determinism in Database Systems”, The 36th International Conference on Very Large Data Bases, Proceedings of the VLDB Endowment, vol. 3, Issue No. 1, p. 11, Singapore, Sep. 13-17, 2010.
TR35: the young innovators with this year's best ideas, Technology Review (Cambridge, Mass.) 109.4: 43(28). Technology Review, Inc. (Sep. 2006-Oct. 2006).
trak.io, <http://trak.io/> printed Jul. 18, 2013 in 3 pages.
Umagandhi et al., “Search Query Recommendations Using Hybrid User Profile with Query Logs,” International Journal of Computer Applications, vol. 80, No. 10, Oct. 1, 2013, pp. 7-18.
UserMetrix, <http://usermetrix.com/android-analytics> printed Jul. 18, 2013 in 3 pages.
Valentini et al., “Ensembles of Learning Machines”, M. Marinaro and R. Tagliaferri (Eds.): WIRN VIETRI 2002, LNCS 2486, pp. 3-20.
Vose et al., “Help File for ModelRisk Version 5,” 2007, Vose Software, pp. 349-353. [Uploaded in 2 Parts].
Wiggerts, T.A., “Using Clustering Algorithms in Legacy Systems Remodularization,” Reverse Engineering, Proceedings of the Fourth Working Conference, Netherlands, Oct. 6-8, 1997, IEEE Computer Soc., pp. 33-43.
Wikipedia, “Federated Database System,” Sep. 7, 2013, retrieved from the internet on Jan. 27, 2015 http://en.wikipedia.org/w/index.php?title=Federated_database_system&oldid=571954221.
Wollrath et al., “A Distributed Object Model for the Java System”, Conference on Object-Oriented Technologies and Systems, pp. 219-231, Jun. 17-21, 1996.
Yang et al., “HTML Page Analysis Based on Visual Cues”, A129, pp. 859-864, 2001.
International Search Report for International Patent Application No. PCT/US2008/054511 dated Jul. 31, 2008.
Notice of Allowance for U.S. Appl. No. 13/826,228 dated Mar. 27, 2015.
Notice of Allowance for U.S. Appl. No. 14/102,394 dated Aug. 25, 2014.
Notice of Allowance for U.S. Appl. No. 14/108,187 dated Aug. 29, 2014.
Notice of Allowance for U.S. Appl. No. 14/135,289 dated Oct. 14, 2014.
Notice of Allowance for U.S. Appl. No. 14/139,628 dated Jun. 24, 2015.
Notice of Allowance for U.S. Appl. No. 14/139,640 dated Jun. 17, 2015.
Notice of Allowance for U.S. Appl. No. 14/139,713 dated Jun. 12, 2015.
Notice of Allowance for U.S. Appl. No. 14/192,767 dated Dec. 16, 2014.
Notice of Allowance for U.S. Appl. No. 14/225,084 dated May 4, 2015.
Notice of Allowance for U.S. Appl. No. 14/264,445 dated May 14, 2015.
Notice of Allowance for U.S. Appl. No. 14/268,964 dated Dec. 3, 2014.
Notice of Allowance for U.S. Appl. No. 14/278,963 dated Sep. 2, 2015.
Notice of Allowance for U.S. Appl. No. 14/294,098 dated Dec. 29, 2014.
Notice of Allowance for U.S. Appl. No. 14/323,935 dated Oct. 1, 2015.
Notice of Allowance for U.S. Appl. No. 14/451,221 dated Aug. 4, 2015.
Notice of Allowance for U.S. Appl. No. 14/473,552 dated Jul. 24, 2015.
Notice of Allowance for U.S. Appl. No. 14/473,860 dated Jan. 5, 2015.
Notice of Allowance for U.S. Appl. No. 14/479,863 dated Mar. 31, 2015.
Notice of Allowance for U.S. Appl. No. 14/486,991 dated May 1, 2015.
Notice of Allowance for U.S. Appl. No. 14/504,103 dated May 18, 2015.
Notice of Allowance for U.S. Appl. No. 14/616,080 dated Apr. 2, 2015.
Official Communication for Australian Patent Application No. 2014201511 dated Feb. 27, 2015.
Official Communication for Australian Patent Application No. 2014202442 dated Mar. 19, 2015.
Official Communication for Australian Patent Application No. 2014210604 dated Jun. 5, 2015.
Official Communication for Australian Patent Application No. 2014210614 dated Jun. 5, 2015.
Official Communication for Australian Patent Application No. 2014213553 dated May 7, 2015.
Official Communication for Australian Patent Application No. 2014250678 dated Jun. 17, 2015.
Official Communication for Canadian Patent Application No. 2,677,464 dated Jan. 16, 2013.
Official Communication for Canadian Patent Application No. 2,677,464 dated Mar. 19, 2012.
Official Communication for European Patent Application No. 08730336.8 dated Jun. 6, 2012.
Official Communication for European Patent Application No. 12182274.6 dated Nov. 5, 2012.
Official Communication for European Patent Application No. 13157474.1 dated May 28, 2013.
Official Communication for European Patent Application No. 14158861.6 dated Jun. 16, 2014.
Official Communication for European Patent Application No. 14159464.8 dated Jul. 31, 2014.
Official Communication for European Patent Application No. 14159535.5 dated May 22, 2014.
Official Communication for European Patent Application No. 14180142.3 dated Feb. 6, 2015.
Official Communication for European Patent Application No. 14180281.9 dated Jan. 26, 2015.
Official Communication for European Patent Application No. 14180321.3 dated Apr. 17, 2015.
Official Communication for European Patent Application No. 14180432.8 dated Jun. 23, 2015.
Official Communication for European Patent Application No. 14186225.0 dated Feb. 13, 2015.
Official Communication for European Patent Application No. 14187996.5 dated Feb. 12, 2015.
Official Communication for European Patent Application No. 14189344.6 dated Feb. 20, 2015.
Official Communication for European Patent Application No. 14189347.9 dated Mar. 4, 2015.
Official Communication for European Patent Application No. 14189802.3 dated May 11, 2015.
Official Communication for European Patent Application No. 14191540.5 dated May 27, 2015.
Official Communication for European Patent Application No. 14197879.1 dated Apr. 28, 2015.
Official Communication for European Patent Application No. 14197895.7 dated Apr. 28, 2015.
Official Communication for European Patent Application No. 14199182.8 dated Mar. 13, 2015.
Official Communication for European Patent Application No. 14200298.9 dated May 13, 2015.
Official Communication for European Patent Application No. 15155845.9 dated Oct. 6, 2015.
Official Communication for European Patent Application No. 15156004.2 dated Aug. 24, 2015.
Official Communication for European Patent Application No. 15175151.8 dated Nov. 25, 2015.
Official Communication for European Patent Application No. 15180515.7 dated Dec. 14, 2015.
Official Communication for Great Britain Application No. 1404457.2 dated Aug. 14, 2014.
Official Communication for Great Britain Patent Application No. 1404486.1 dated May 21, 2015.
Official Communication for Great Britain Patent Application No. 1404486.1 dated Aug. 27, 2014.
Official Communication for Great Britain Patent Application No. 1404489.5 dated May 21, 2015.
Official Communication for Great Britain Patent Application No. 1404489.5 dated Aug. 27, 2014.
Official Communication for Great Britain Patent Application No. 1404489.5 dated Oct. 6, 2014.
Official Communication for Great Britain Patent Application No. 1404499.4 dated Jun. 11, 2015.
Official Communication for Great Britain Patent Application No. 1404499.4 dated Aug. 20, 2014.
Official Communication for Great Britain Patent Application No. 1404499.4 dated Sep. 29, 2014.
Official Communication for Great Britain Patent Application No. 1404573.6 dated Sep. 10, 2014.
Official Communication for Netherlands Patent Application No. 2013306 dated Apr. 24, 2015.
Official Communication in New Zealand Application No. 622181 dated Mar. 24, 2014.
Official Communication for New Zealand Patent Application No. 622473 dated Jun. 19, 2014.
Official Communication for New Zealand Patent Application No. 622473 dated Mar. 27, 2014.
Official Communication for New Zealand Patent Application No. 622513 dated Apr. 3, 2014.
Official Communication for New Zealand Patent Application No. 622517 dated Apr. 3, 2014.
Official Communication for New Zealand Patent Application No. 624557 dated May 14, 2014.
Official Communication for New Zealand Patent Application No. 627962 dated Aug. 5, 2014.
Official Communication for New Zealand Patent Application No. 628161 dated Aug. 25, 2014.
Official Communication for New Zealand Patent Application No. 628263 dated Aug. 12, 2014.
Official Communication for New Zealand Patent Application No. 628495 dated Aug. 19, 2014.
Official Communication for New Zealand Patent Application No. 628585 dated Aug. 26, 2014.
Official Communication for New Zealand Patent Application No. 628840 dated Aug. 28, 2014.
Official Communication for U.S. Appl. No. 13/196,788 dated Oct. 23, 2015.
Official Communication for U.S. Appl. No. 13/247,987 dated Apr. 2, 2015.
Official Communication for U.S. Appl. No. 13/827,491 dated Dec. 1, 2014.
Official Communication for U.S. Appl. No. 13/827,491 dated Jun. 22, 2015.
Official Communication for U.S. Appl. No. 13/831,791 dated Mar. 4, 2015.
Official Communication for U.S. Appl. No. 13/835,688 dated Jun. 17, 2015.
Official Communication for U.S. Appl. No. 13/949,043 dated May 7, 2015.
Official Communication for U.S. Appl. No. 14/148,568 dated Oct. 22, 2014.
Official Communication for U.S. Appl. No. 14/148,568 dated Mar. 26, 2015.
Official Communication for U.S. Appl. No. 14/170,562 dated Sep. 25, 2014.
Official Communication for U.S. Appl. No. 14/196,814 dated May 5, 2015.
Official Communication for U.S. Appl. No. 14/225,006 dated Sep. 10, 2014.
Official Communication for U.S. Appl. No. 14/225,006 dated Feb. 27, 2015.
Official Communication for U.S. Appl. No. 14/225,084 dated Sep. 2, 2014.
Official Communication for U.S. Appl. No. 14/225,084 dated Feb. 20, 2015.
Official Communication for U.S. Appl. No. 14/225,160 dated Feb. 11, 2015.
Official Communication for U.S. Appl. No. 14/225,160 dated May 20, 2015.
Official Communication for U.S. Appl. No. 14/225,160 dated Oct. 22, 2014.
Official Communication for U.S. Appl. No. 14/225,160 dated Jul. 29, 2014.
Official Communication for U.S. Appl. No. 14/251,485 dated Oct. 1, 2015.
Official Communication for U.S. Appl. No. 14/264,445 dated Apr. 17, 2015.
Official Communication for U.S. Appl. No. 14/268,964 dated Sep. 3, 2014.
Official Communication for U.S. Appl. No. 14/278,963 dated Jul. 22, 2014.
Official Communication for U.S. Appl. No. 14/278,963 dated Jan. 30, 2015.
Official Communication for U.S. Appl. No. 14/289,596 dated Jul. 18, 2014.
Official Communication for U.S. Appl. No. 14/289,596 dated Jan. 26, 2015.
Official Communication for U.S. Appl. No. 14/289,596 dated Apr. 30, 2015.
Official Communication for U.S. Appl. No. 14/289,599 dated Jul. 22, 2014.
Official Communication for U.S. Appl. No. 14/289,599 dated May 29, 2015.
Official Communication for U.S. Appl. No. 14/294,098 dated Aug. 15, 2014.
Official Communication for U.S. Appl. No. 14/294,098 dated Nov. 6, 2014.
Official Communication for U.S. Appl. No. 14/306,138 dated Feb. 18, 2015.
Official Communication for U.S. Appl. No. 14/306,138 dated Sep. 23, 2014.
Official Communication for U.S. Appl. No. 14/306,138 dated May 26, 2015.
Official Communication for U.S. Appl. No. 14/306,147 dated Feb. 19, 2015.
Official Communication for U.S. Appl. No. 14/306,147 dated Sep. 9, 2014.
Official Communication for U.S. Appl. No. 14/306,154 dated Mar. 11, 2015.
Official Communication for U.S. Appl. No. 14/306,154 dated May 15, 2015.
Official Communication for U.S. Appl. No. 14/306,154 dated Sep. 9, 2014.
Official Communication for U.S. Appl. No. 14/319,161 dated Jan. 23, 2015.
Official Communication for U.S. Appl. No. 14/319,765 dated Jun. 16, 2015.
Official Communication for U.S. Appl. No. 14/319,765 dated Nov. 25, 2014.
Official Communication for U.S. Appl. No. 14/319,765 dated Feb. 4, 2015.
Official Communication for U.S. Appl. No. 14/323,935 dated Jun. 22, 2015.
Official Communication for U.S. Appl. No. 14/323,935 dated Nov. 28, 2014.
Official Communication for U.S. Appl. No. 14/323,935 dated Mar. 31, 2015.
Official Communication for U.S. Appl. No. 14/326,738 dated Dec. 2, 2014.
Official Communication for U.S. Appl. No. 14/326,738 dated Mar. 31, 2015.
Official Communication for U.S. Appl. No. 14/449,083 dated Mar. 12, 2015.
Official Communication for U.S. Appl. No. 14/449,083 dated Oct. 2, 2014.
Official Communication for U.S. Appl. No. 14/451,221 dated Apr. 6, 2015.
Official Communication for U.S. Appl. No. 14/451,221 dated Oct. 21, 2014.
Official Communication for U.S. Appl. No. 14/463,615 dated Nov. 13, 2014.
Official Communication for U.S. Appl. No. 14/463,615 dated May 21, 2015.
Official Communication for U.S. Appl. No. 14/463,615 dated Jan. 28, 2015.
Official Communication for U.S. Appl. No. 14/473,552 dated Feb. 24, 2015.
Official Communication for U.S. Appl. No. 14/479,863 dated Dec. 26, 2014.
Official Communication for U.S. Appl. No. 14/483,527 dated Jun. 22, 2015.
Official Communication for U.S. Appl. No. 14/483,527 dated Jan. 28, 2015.
Official Communication for U.S. Appl. No. 14/486,991 dated Mar. 10, 2015.
Official Communication for U.S. Appl. No. 14/504,103 dated Mar. 31, 2015.
Official Communication for U.S. Appl. No. 14/504,103 dated Feb. 5, 2015.
Official Communication for U.S. Appl. No. 14/518,757 dated Dec. 1, 2015.
Official Communication for U.S. Appl. No. 14/518,757 dated Apr. 2, 2015.
Official Communication for U.S. Appl. No. 14/518,757 dated Jul. 20, 2015.
Official Communication for U.S. Appl. No. 14/571,098 dated Mar. 11, 2015.
Official Communication for U.S. Appl. No. 14/579,752 dated Aug. 19, 2015.
Official Communication for U.S. Appl. No. 14/579,752 dated May 26, 2015.
Official Communication for U.S. Appl. No. 14/579,752 dated Dec. 9, 2015.
Official Communication for U.S. Appl. No. 14/580,218 dated Jun. 26, 2015.
Official Communication for U.S. Appl. No. 14/581,920 dated Mar. 1, 2016.
Official Communication for U.S. Appl. No. 14/639,606 dated Oct. 16, 2015.
Official Communication for U.S. Appl. No. 14/639,606 dated May 18, 2015.
Official Communication for U.S. Appl. No. 14/639,606 dated Jul. 24, 2015.
Official Communication for U.S. Appl. No. 14/726,353 dated Mar. 1, 2016.
Official Communication for U.S. Appl. No. 14/726,353 dated Sep. 10, 2015.
Official Communication for U.S. Appl. No. 14/734,772 dated Jul. 24, 2015.
Official Communication for U.S. Appl. No. 14/746,671 dated Sep. 28, 2015.
Official Communication for U.S. Appl. No. 14/857,071 dated Mar. 2, 2016.
Official Communication for U.S. Appl. No. 14/984,505 dated Oct. 1, 2018.
Restriction Requirement for U.S. Appl. No. 14/857,071 dated Dec. 11, 2015.
Official Communication for U.S. Appl. No. 14/750,773 dated Jul. 5, 2017.
Official Communication for U.S. Appl. No. 14/750,773 dated Mar. 13, 2018.
Official Communication for U.S. Appl. No. 14/750,773 dated Sep. 19, 2018.

Related Publications (1)

	Number	Date	Country
	20190297163 A1	Sep 2019	US

Provisional Applications (1)

	Number	Date	Country
	62095655	Dec 2014	US

Continuations (1)

	Number	Date	Country
Parent	14750773	Jun 2015	US
Child	16439444		US

Communication data processing architecture

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