Patent Grant
11941635
Billions of dollars of fraudulent tax refunds are paid out every tax year. This not only puts a strain on government's ability to provide services, but it also erodes public trust in our country's tax system. With the increased reliance on electronic filing of tax returns comes an increase of the efficiency of tax operations and overall convenience. However, this has also contributed to a rise in identity theft and unwarranted or fraudulent tax refunds. Stealing identities and filing for tax refunds has become one of the fastest growing non-violent criminal activities in the country, often resulting in significant returns for the fraudster.
In one embodiment, an electronic fraud detection system is disclosed. The system may comprise: an electronic data interface module configured to electronically communicate with a first electronic data store configured to at least store tax return filing data associated with a plurality of consumers and at least one tax agency, wherein access to the first electronic data store is provided by a tax agency computing system, a second electronic data store configured to at least store consumer data associated with the plurality of consumers, and a third electronic data store configured to at least store consumer device activity data associated with a plurality of consumer devices associated with the plurality of consumers; an initial screening module configured to apply filters to tax return filing data, including at least one or more consumer attributes associated with each respective consumer and received from the electronic data interface module, to generate a set of electronic tax fraud indications that represent whether consumers records within the tax return filing data are likely fraudulent due to missing or inaccurate information; a knowledge-based authentication module configured to dynamically generate authentication questions associated with a consumer associated with one of the consumer records identified as likely fraudulent, the generated questions based on consumer credit data corresponding to the consumer that is received from the electronic data interface module, for which the answers are confidential and based on credit data, to provide the authentication questions, receive authentication response information corresponding to the authentication questions, and generate an electronic authentication indication representing an accuracy level of the authentication response information; a device authentication module configured to dynamically analyze whether a computing device used to provide the authentication response information may have been used in fraudulent activities or is related to other devices that have been used in fraudulent activities using a unique device identifier associated with the computing device, the unique device identifier generated using information collected from the computing device, and further configured to generate an electronic device indication representing a risk level that the device associated with fraud; and an accuracy reporting module configured to make the electronic authentication indication and the electronic device indication available to the tax agency computing system.
Overview
One principal method of conducting income tax refund fraud is through identity theft, specifically, by filing a tax return deceptively as someone else. According to a 2011 report by the Federal Trade Commission, identity theft has been the number one consumer complaint since 2000. Unfortunately, identities can be stolen from anywhere and resourceful identity thieves use a variety of ways to obtain an individual's personal information. Identity thieves can retrieve personal information by rummaging through the trash of businesses. In some cases identity thieves work for legitimate companies, medical offices, clinics, pharmacies, or government agencies, and take advantage of their roles at these organizations to illicitly obtain or solicit personal information.
Identity thieves have two primary concerns when they are concocting an income tax refund fraud scheme. They need to devise a clever system for moving and using the fraudulently obtained funds, and they need to figure out how to obtain another individual's social security number (“SSN”) and other identifying information, which they will use to circumvent the existing tax return review process. Typically the fraudster will file a false return electronically, early in the tax filing season before the legitimate tax filer has a chance to the file their return. Fraudsters then use the stolen information and provide false information about wages earned, taxes withheld, and other data in order to appear as the legitimate tax payer who is entitled to a tax refund. The fraudster arranges for the proceeds of the refund to be deposited into a bank account or transferred to a debit card, or other similar methods which are virtually untraceable once the payment has been released. According to the IRS's Identity Protection Incident Tracking Statistics Report, incidents of identity theft tied to taxpayers has risen three fold from 2009 to 2011 growing from 456,453 incidents in 2009 to 1,125,634 in 2011.
Unfortunately, many individuals who are victims of identity theft may be unaware that their identity has been stolen to file fraudulent tax returns. It is not until the legitimate individual files a tax return resulting in a duplicate filing under the same name and SSN that many individuals realize they are a victim of identity theft. Everyone with a social security number is potentially vulnerable to having their identity stolen.
Anyone who has access to a computer can fill out an income tax form online and hit submit. Income tax returns are processed within days or weeks, and the proceeds are then deposited into accounts or provided on debit cards. Once released, these monies are virtually untraceable, and thus an improved method to detect fraudulent tax returns prior to releasing tax refund monies is needed. According to the Tax Inspector General for Tax Administration (“TIGTA”), the number of identified fraudulent tax returns has increased by 40% from 2011 to 2012 which equates to an increase in over $4B dollars. While the number of fraudulent tax returns can be identified, the full scope of the fraud remains unknown. Additionally in 2012, TIGTA reported that, using characteristics of identity theft confirmed by the IRS, it had identified approximately 1.5 million undetected tax returns with potentially fraudulent tax refunds totaling in excess of $5.2 billion. This number only takes into consideration income tax fraud on a federal level. TIGTA also found that contributing to the growth in tax fraud is an ongoing challenge in authenticating taxpayers. Even though some revenue agencies have adopted verification techniques such as use of a Personal Identification Number (“PIN”), or providing information from a previous year's return, these controls can be circumvented and have proven inadequate in stopping identity-based income tax fraud.
Income tax refund fraud schemes vary from those committed by individual perpetrators to those that are much more large scale, with multiple players spanning several years with the number of filings rising into the thousands and the losses ranging into the millions of dollars. With the average federal tax refund amounting to roughly $3,000 and state refund averaging around $500, many taxpayers anxiously await the return of their funds and are justifiably upset when their refunds are delayed. In some embodiments the systems used in detecting income tax refund fraud are effective and simultaneously efficient such that they do not delay the release of legitimate refunds. Complicating the issue is that typical “red flags” which might trigger a fraud alert, such as having a refund sent to a new address or an unfamiliar name, happen millions of times each year for honest reasons, such as when a taxpayer gets married (and changes his/her name and/or address) or moves, thus making it even more difficult to identify the fraudulent returns from the legitimate ones.
Embodiments of an electronic fraud analysis platform system are disclosed which may be used to analyze tax returns for potential fraud. Analysis of tax return data using the tax return analysis platform computing systems and methods discussed herein may provide insight into whether a tax return may be fraudulent based on, for example, an initial screening component configured to filter tax returns which appear fraudulent due to missing or inaccurate information provided with the return; a device activity analysis component configured to identify whether a device used to submit a tax return or to provide further authentication information needed to complete processing of the return may have been used in other fraudulent activities; and a knowledge-based authentication component configured to identify potential fraudsters using dynamically generated questions for which fraudsters typically do not know the answers.
The terms “individual,” “consumer,” “customer,” “people,” “persons,” “party,” “entity,” and the like, whether singular or plural, should be interpreted to include either individuals or groups of individuals, such as, for example, married couples or domestic partners, joint tax filers, organizations, groups, business entities, non-profit entities, and other entities.
Embodiments of the disclosure will now be described with reference to the accompanying figures, wherein like numerals refer to like elements throughout. The terminology used in the description presented herein is not intended to be interpreted in any limited or restrictive manner, simply because it is being utilized in conjunction with a detailed description of certain specific embodiments of the disclosure. Furthermore, embodiments of the disclosure may include several novel features, no single one of which is solely responsible for its desirable attributes or which is essential to practicing the embodiments of the disclosure herein described.
For purposes of this disclosure, certain aspects, advantages, and novel features of various embodiments are described herein. It is to be understood that not necessarily all such advantages may be achieved in accordance with any particular embodiment of the invention. Thus, for example, those skilled in the art will recognize that one embodiment may be carried out in a manner that achieves one advantage or group of advantages as taught herein without necessarily achieving other advantages as may be taught or suggested herein.
High Level Data Flow
The exemplary data flow may begin at (1) when a consumer submits a tax return filing to a tax agency. The tax return may be submitted in any manner by which the tax agency accepts tax return filings, including traditional physical paper filings as well as electronic submissions. Traditional physical paper filings typically are digitally scanned or otherwise input to the tax agency computing system 168 to facilitate faster processing of the tax return.
In some instances, if a tax return is submitted to the tax agency electronically (for example, to the tax agency computing system 168), the tax agency may have the ability to detect or associate a user computing device used by the individual to submit the tax return. For example, in some cases an IP address, a device identifier, or other identifying information associated with the user computing device or the tax return may be automatically detected and gathered by the tax agency computing system, such as by the use of a client-side script downloaded to the user computing device, a cookie, or other methodology. Such device identifying information may be collected at various stages of an electronic tax return filing process, such as when the individual registers with the tax agency computing system 168 via a website provided by the tax agency, or when the individual submits the completed tax return to the tax agency, and so on. If device identifying information is gathered by the tax agency, the device identifying information may be provided to the TRAP system 100 and used or included in the tax refund fraud analysis processes described herein. Embodiments of various device identification systems and methods are disclosed in U.S. Pat. Nos. 7,853,533, 8,862,514, and U.S. Publication No. 2011/0082768, the entire contents of which are all hereby incorporated by reference herein. However, the TRAP system 100 may perform the tax refund fraud analysis processes even if such device identifying information is not provided by the tax agency at the outset.
Although the description with reference to (1) describes submission of a tax return for a single individual, the tax agency naturally receives tax returns numbering in the thousands or even millions depending on the size of the tax base being served. Thus it should be understood that the actions described at (1) may occur for thousands, millions, or any number of tax returns submitted to the tax agency, some in parallel and some over a period of time.
At (2), the tax agency provides tax return data for one or more consumers to be analyzed for potential fraud to the TRAP system 100. The TRAP system 100 is configured to support either analysis of a single tax return or analysis of multiple tax returns from multiple consumers via high-volume batch-mode processing. The TRAP system 100 may be configured to perform the fraud analysis in various ways and at different stages of the overall process flow as described further below.
At (3A), the TRAP system 100 performs an automated, initial data screening of the tax return data to identify tax returns which may be potentially fraudulent. The initial screening may be performed, for example, by the screening/precise ID module 128 of the TRAP system 100 as illustrated in
If the tax agency provides device identifiers associated with the tax return data then at (3B) the TRAP system 100 may optionally perform a device activity analysis (which may also be referred to as device proofing) to further identify tax returns which may be potentially fraudulent. The device proofing process is described in more detail with reference to
At (4), once the TRAP system 100 has completed the initial screen process (and optionally the device proofing process), the TRAP system 100 provides a list of flagged tax returns to the tax agency computing system 100. The list may be a one-to-one correspondence of the tax return data initially provided by the tax agency with only those particular tax returns identified by the TRAP system 100 as potentially fraudulent being flagged as such. In another embodiment, the list may include only those particular tax returns flagged by the TRAP system 100. In one embodiment, the flags may comprise one or more indicators, scores, probability, risk levels, or other information that may indicate a degree to which a respective tax return may be fraudulent. For example, a first tax return may be flagged as “low probability” and further indicate that only one consumer attribute with a low risk rate was found to be non-matching during the initial screening process. In another example, a second tax return may be flagged as “medium probability” and further indicate that several consumer attributes were found to be non-matching or unverified. In another example, a third tax return may be flagged as “high probability” and further indicate that several consumer attributes were found to be non-matching or unverified, as well as indicate that the device used to submit the tax return has been previously associated with other fraudulent activities.
In one embodiment, the tax agency can review and decide which returns to process, deny, or require additional information. For those where the tax agency wants more information which the tax agency can utilize the TRAP system 100 to conduct additional analysis. At (5), the tax agency computing system 168 may direct a flagged consumer to access a website or application provided by the tax agency in order to provide further authentication information necessary to complete processing of the tax return. For example, the tax agency computing system 168 may send a letter, an electronic message, or a text message to the flagged consumer based on the list provided by the TRAP system 100. In some instances the letter or electronic message may be sent automatically once the list is received from the TRAP system 100.
At (6), the consumer accesses the website or the application provided by the tax agency to provide the requested authentication information. The website may be preconfigured to download a script (for example, a JavaScript code or similar) to a computing device used by the consumer to access the website via a web browser, an application, or other program. The script may be provided by the TRAP system 100 to facilitate device proofing with respect to the computing device being used by the consumer to access the website.
At (7), device-related information, which may include one or more device attributes and/or identifiers, associated with the computing device used by the consumer to access the website or the application may be detected, for example by execution of the script or program of the application on the consumer's computing device. In one embodiment, the script or application may be configured to provide the device-related information to the tax agency computing system 100, which may be configured to perform internal processing and/or to forward the device-related information to the TRAP system 100. In another embodiment the script may be configured to provide the device-related information directly to the TRAP system 100. The device-related information may be used to generate a unique device identifier, which may be used, for example, as described herein to access device activity data associated with the unique device identifier. In some embodiments, generation of the unique device identifier may be performed by the TRAP system 100; by an application installed on the computing device used by the consumer (in which case the unique device identifier may be detected as part of the device-related information); or by a third party service that may offer device identity services via one or more application programmatic interfaces (“APIs”).
At (8A), once the device identifiers are received by the TRAP system 100, the system may then perform the device proofing described above and in more detail with reference to
However, in another possible scenario, the device proofing at (8A) may be at least the second time such device proofing is executed. For example, an initial device proofing may be performed at (3A) with respect to a first device used by a consumer to submit the tax return electronically to the tax agency computing system 168; and a second device proofing may be performed at (8A) with respect to a second device used by the same consumer at (6) to access the tax agency website. The first device and the second device may or may not be the same device, and as such the initial device proofing and the second device proofing may produce different results. For example, the initial device proofing may provide an indication that the first device is not associated with previous fraudulent activity, whereas the second device proofing may provide an indication that the second device is associated with previous fraudulent activity. This additional round of device proofing, if available, may provide the tax agency with an additional layer of fraud detection, as a fraudster may utilize multiple devices in an attempt to avoid detection.
At (8B), the TRAP system 100 may also initiate a knowledge-based authentication (“KBA”) process in order to further authenticate the consumer and/or to provide further fraud detection back-end support to the tax agency computing system 100. For example, the consumer may be prompted to provide personal information (for example, full name, current and/or prior addresses, and other personally identifying information or “PII”) through the tax agency website. Some or all this personal information may be gathered by the tax agency computing system 168, which may perform internal processing and/or forward the provided personal information to the TRAP system 100. In another embodiment, the personal information may be collected automatically and provided directly to the TRAP system 100, such as via a client-side script downloaded to the consumer's computing device when the tax agency website is accessed.
Once at least some personal information is received at the TRAP system 100, the TRAP system 100 can use the personal information to access consumer data, including credit data, associated with the consumer (for example, from the consumer data sources 172). The TRAP system 100 may then generate further authentication questions (for example, “out of wallet” questions) based on the accessed consumer data. For example, out of wallet questions may be generated in order to solicit responses that include information highly likely to only be known by the consumer (and/or unlikely to be known by a fraudster) which would not be found in the consumer's wallet, such as a monthly payment amount on an outstanding debt obligation which may appear in the consumer's credit data.
At (9), the TRAP system 100 provides the out-of-wallet or other authentication questions and receives and processes the responses. The questions may be provided directly to the consumer computing device, such as via a client side script downloaded to the consumer computing device when accessing the tax agency's authentication website. For example, a client side script may be provided by the TRAP system 100 to the tax agency computing system 168 for inclusion in the website. The client side script may be configured to retrieve personal information as it is entered by the consumer into a form on the website; send the personal information to the TRAP system 100; receive one or more authentication questions; and present the questions to the consumer for further authentication. The client side script may be further configured to collect responses to the presented questions and send the responses directly to the TRAP system 100. After the TRAP system 100 receives the responses, it processes them to determine whether they are accurate with respect to the accessed consumer data.
At (10), the TRAP system 100 provides one or more indicators of potential fraud (for example, scores and the like) to the tax agency computing system 168 based on any combination of the various fraud detection processes described throughout
Examples of Processes Performed by TRAP systems
Initial Tax Fraud Screening
The process 200 begins at block 205, where the TRAP system 100 (for example, via the data partition and security module 126 of
At block 210, for each consumer identified in the tax return data, the TRAP system 100 (for example, via the screening/precise ID module 128) performs an initial screening (for example, data matching, data verification, identifying duplicates, and so forth) based on the attributes associated with each respective tax return. Various attributes may be screened including but not limited to name, address, date of birth, social security number (“SSN”), driver license, phone number (wireless or landline), bank account number(s), and/or IP address. Other attributes not expressly listed herein may also be used. To perform the initial screening, the TRAP system 100 may access consumer data from consumer data source(s) 172, wherein the consumer data may be accessed using at least some of the consumer attributes associated with respective tax returns. For example, one attribute of a tax return may include a social security number (or other unique consumer identifiers), which the TRAP system 100 may then use to access consumer data associated with the social security number (or other unique consumer identifiers). The screening process may generate, for example, a validation score which predicts the likelihood that the identification information supplied (for example, name, address, SSN, phone number, date-of-birth, and so forth) is a valid combination which has been seen previously within one or multiple data sources. The screening process may also generate, for example, an ID Theft Score that predicts the likelihood that the application is originating from the true consumer.
The screening process at block 210 may involve checking or addressing multiple attributes of each tax return, including for example: whether the SSN is valid; whether the name on the return matches the SSN provided; whether the tax filer exists in any data records at all; whether the address on the return matches the tax filer's current address; whether the SSN is associated with a deceased person; whether the address is valid or if it corresponds to an institution, a vacant lot, or some other invalid location; whether the return address on the tax return is in a state where the tax filer has never resided (for example, based on past address history which may be contained in the tax filer's credit data); whether there is any indication of fraud within the tax filer's credit data; whether multiple returns are identified as going to the same address; and/or whether joint filers as stated on the return are actually connected to each other (for example, spouses, domestic partners, and so forth)
Next, at block 215, the TRAP system 100 determines whether any device identifiers associated with respective tax returns with the tax return data have been provided and/or are available in order to facilitate an initial device proofing. In response to determining that device identifiers are provided or available the process 200 may proceed to block 220. In response to determining that no device identifiers are provided or available the process 200 may proceed to block 225.
At block 220, the TRAP system 100 (for example, via the device activity analysis module 132) may optionally perform an initial device activity screening (for example, device proofing) using any device identifiers associated with respective tax returns which have been provided to or accessed by the TRAP system 100. The device proofing process is described in more detail with reference to
Once the initial device activity screening at block 220 has been completed, or in response to determining that no device activity screening need be performed at this stage of the tax fraud analysis, the process 200 proceeds to block 225. At block 225, the TRAP system 100 (for example, via the fraud detection module 134) identifies or flags consumers and/or tax returns for possible fraud, based at least in part on the initial screening performed at block 210 and/or the device activity screening performed at block 220. The flags or indicators may include for example, a plurality of indicators for individually identified items from each of the items checked in the initial screening process at block 210 and/or the device activity screening performed at block 220; one or more indicators representing aggregate or overall fraud indicators for particular categories, such as an initial screening fraud score, a device fraud score; an overall fraud score; or any other variant or combination thereof.
At block 230, the TRAP system 100 provides the list of flagged consumer tax returns and possible fraud indicators. This list may be provided, for example, to the particular tax agency which provided the tax return data for fraud analysis. The tax agency may then use the list of flagged tax returns in order to initiate further authentication of consumers who filed the flagged tax returns before completing the processing of those returns, and other purposes as described herein.
In one embodiment, the TRAP system 100 may store at least some identifying information related to flagged tax returns for possible retrieval and continued fraud analysis processes as will be described further below. Otherwise, the TRAP system 100 promptly and securely destroys or removes the tax return data once the process 200 has been completed in order to ensure privacy and maintain compliance with any regulatory requirements with respect to tax return data which may limit the purpose, use or duration under which such data may be held by non-tax agency entities.
Additional Tax Fraud Analysis
At block 305, the TRAP system 100 (for example, via the device activity analysis module 132) accesses or receives the device identifiers associated with a device used by the consumer to provide identity authentication information for a flagged tax return. For example, in one embodiment, the process 300 may be performed in response to the consumer accessing a website (or using a software application or “app”) provided by the tax agency to provide requested further authentication information in order to complete processing of a tax return filed by the consumer. The website or app may be configured to download a client-side script to the computing device used by the consumer to access the website or app, wherein the client-side script is configured to execute automatically in order to gather device identifiers associated with the consumer's computing device. These device identifiers may be collected and sent either to the tax agency computing system 168, which may in turn provide them to the TRAP system 100 for further fraud analysis; or the device identifiers may be provided directly to the TRAP system 100.
Next at block 310, the TRAP system 100 accesses and analyzes device activity data to identify potentially fraudulent activity that may be associated with the device used by the consumer to provide the requested identity authentication information. The device proofing process is described in more detail with reference to
At block 315, the TRAP system 100 (for example, the authentication module 122) performs or initiates a knowledge-based authentication (“KBA”) process to further authenticate the consumer. The KBA process is described in more detail with reference to
In one embodiment, at least some identifying information usable to initially determine an identity of the consumer may be provided to the TRAP system 100. For example, some identifying information may be provided to the TRAP system 100 as follows: when the TRAP system performs the initial screening process described previously, a temporary encrypted identifier may be generated and associated with a flagged return and provided to the tax agency computing system 168. The tax agency computing system 168 may then include the temporary encrypted identifier along with the request to the consumer to access the website or app to provide further authentication information. The encrypted identifier may be provided, for example, as part of a unique access identifier the consumer may be prompted to enter at the website, or embedded in a unique URL or hyperlink the consumer may use to access the website. Once the consumer visits the website, the encrypted identifier may be detected and retrieved, for example as part of the client-side script configured to collect device identifier data, and eventually provided back to the TRAP system 100. The encrypted identifier may then be decrypted and used to determine, for example, either that the consumer is associated with a previously-flagged tax return or to obtain at least some initially identifying information such as a name or other non-sensitive data that may be used to initiate the KBA process.
In another embodiment, as the consumer provides personal information (for example, via the website or app), the personal information may be provided directly or indirectly (for example, via the tax agency computing system 168) to the TRAP system 100. When enough identifying information is received to at least initially determine an identity of the consumer, the TRAP system 100 may access verified consumer data associated with the determined identity, such as credit data, from the consumer data sources 172 in order to generate authentication questions.
At block 320, the TRAP system 100 may optionally access previously screened tax return data to determine whether the consumer and/or the tax return were previously flagged for potential fraud, and/or to what extent such potential fraud may be been previously determined. In some embodiments this data may not be available to the TRAP system 100 or available only in a limited fashion which protects the privacy and security of the underlying tax return data. One embodiment that may permit storage and retrieval of at least the fraud indicators generated by the TRAP system 100 during the initial screening process 200 may involve the use of encrypted identifiers as described above.
Finally, at block 325, the TRAP system 100 provides one or more indicators of the potential fraud for the flagged tax return, based at least in part on: the device activity analysis performed at block 310, the KBA process performed at block 315, and/or the initial screening flag accessed at block 320 (if applicable). For example, the provided indicators may include an indication of whether the computing device has been previously associated with other fraudulent activities; a degree or level of risk that may be associated with such other fraudulent activities; an indicator of whether and/or how many authentication questions were answered correctly by the consumer; an indicator of whether and/or to what extent the tax return may have previously been flagged for potential fraud during the initial screening described in reference to process 200; an overall fraud score, range, number, letter, and so forth that may be generated in the aggregate or for each individually flagged item; and so forth.
Device Activity Analysis
The process 400 begins at block 405, where the TRAP system 100 (for example, via the device activity analysis module 132) accesses device activity data associated with a device, for example using a unique device identifier. The unique device identifier may be generated or determined, for example, based on the one or more device identifiers accessed at block 305 of
At block 410, the TRAP system 100 determines whether one or more fraudulent or potentially fraudulent activities are associated with the device based on the accessed device activity. The device activity analysis process performed at block 410 may involve accessing device activity data (such as device activity data that may be stored in one of the device activity data sources 174) using one or more of the device identifiers. The device activity data may indicate, for example, whether a particular device has been previously associated with other fraudulent activities or whether a device is in a blacklist. If a particular device associated with one or more of the device identifiers has been previously associated with other fraudulent activities, the particular device may be flagged for potential fraud as well.
Next, at block 415, the TRAP system 100 determines whether any fraudulent activities are associated with the device. In response to determining that no fraudulent activities appear to be associated with the device the process 400 may proceed to block 420. In response to determining that fraudulent activities are associated with the device the process 400 may proceed to block 425.
At block 420, the TRAP system 100 provides an indicator that the device is authenticated or otherwise does not appear to be associated with prior fraudulent activities.
At block 425, the TRAP system 100 provides at least one indicator (for example, a score, a flag, or other indicator) to describe the possible involvement of the device in the fraudulent activities. In one embodiment, the TRAP system 100 may also provide or enable access to a dashboard user interface that allows users to fully research and link seemingly unrelated events. The capability provided by the dashboard user interface can have a multiplying effect on the ability to detect fraud because, for example, the residue left by fraudsters across different transactions or accounts can be linked together for more precise detection of fraud rings.
Knowledge-Based Authentication Process
The process 500 begins at block 505, where the TRAP system 100 (for example, via the authentication module 122) access consumer data, such as credit data or a credit report, associated with the consumer (for example, from the consumer data sources 172).
At block 510, the TRAP system 100 generates one or more authentication questions (for example, “out of wallet” questions) based on the accessed consumer data in order to further authenticate the user. For example, out of wallet questions may be generated in order to solicit responses that include information highly likely to only be known by the consumer (and/or unlikely to be known by a fraudster), such as a monthly payment amount on an outstanding debt obligation which may appear on the consumer's credit report, the name or address of a particular loan servicer, the date that the last payment was posted to a credit account, and so on.
Next, at block 515, the TRAP system 100 provides the out-of-wallet or other authentication questions. The questions may be provided by the TRAP system 100 directly to the consumer computing device, such as via a client side script downloaded to the consumer computing device when accessing the tax agency's authentication website. For example, a client side script may be provided by the TRAP system 100 to the tax agency computing system 168 for inclusion in the website. The client side script may be configured to retrieve personal information as it is entered by the consumer into a form on the website; send the personal information to the TRAP system 100; receive one or more authentication questions from the TRAP system 100; and present the questions to the consumer for further authentication. The client side script may be further configured to collect responses to the presented questions and send the responses directly to the TRAP system 100.
At block 520, the TRAP system 100 receives/processes responses to the authentication questions. The responses are processed to determine whether they are accurate with respect to the accessed consumer data.
At block 525, the TRAP system 100 provides an indicator of whether and/or how many responses were correct. This information may be provided to the tax agency computing system 168 which can then use the information to determine whether the tax return should be denied, approved, or flagged for further follow-up.
Example System Implementation and Architecture
TRAP System
In the embodiment of
The authentication module 122 provides capabilities related to the knowledge-based authentication processes described, for example, with reference to
The interface module 124 provides capabilities related to interfacing between the TRAP system 100, the tax agency computing systems 168, and various data sources 170 (if applicable), 172, and 174. For example, the interface module 124 may be configured to provide various client-side scripts to the tax agency which may in turn be installed as part of a web service provided by the tax agency for consumers to access in order to further authenticate for a tax return. The interface module 124 may further be configured to receive data via the client-side scripts or from the tax agency computing systems for further processing by the various other modules described herein.
The data partition and security module 126 provides capabilities related to ensuring that tax return data accessed or received from various tax agency systems 168 and/or tax return data sources 170 are strictly separated or partitioned to maintain data privacy for each respective tax agency. In some embodiments the data partition and security module 126 may also be configured to ensure that the tax return data is promptly and securely destroyed or removed from the memory 130 and/or mass storage 120 of TRAP system 100 once the tax return data fraud analysis process(es) have completed.
The screening/precise ID module 128 provides capabilities related to performing identity screening and related routines, for example on tax returns provided by tax agency computing systems to the TRAP system 100 for fraud analysis. Some of these processes are described, with reference to
The device activity analysis module 132 provides capabilities related to performing “device proofing” to determine whether a device used by a consumer during any part of the tax return process (for example, either filing/submitting the tax return or providing further information that may be required by the tax agency in order to complete processing of the tax return, and so on). Some of these processes are described, with reference to
The fraud detection module 134 provides capabilities related to those described with respect to the authentication module 122, the screening/precise ID module 128, and/or the device activity/analysis module 132. For example, the fraud detection module 134 may receive outputs from these various other modules and use the output to generate fraud indicator information (for example, a plurality of indicators for individually identified items from each of the modules involved in the fraud analysis process; one or more indicators representing aggregate or overall fraud indicators for particular categories, such as an initial screening fraud score, a device fraud score, and/or a KBA fraud score; an overall fraud score; or any other variant or combination thereof).
The TRAP system 100 includes, for example, a server, workstation, or other computing device. In one embodiment, the exemplary TRAP system 100 includes one or more central processing units (“CPU”) 150, which may each include a conventional or proprietary microprocessor. The TRAP system 100 further includes one or more memories 130, such as random access memory (“RAM”) for temporary storage of information, one or more read only memories (“ROM”) for permanent storage of information, and one or more mass storage device 120, such as a hard drive, diskette, solid state drive, or optical media storage device. Typically, the modules of the TRAP system 100 are connected to the computer using a standard based bus system. In different embodiments, the standard based bus system could be implemented in Peripheral Component Interconnect (“PCI”), Microchannel, Small Computer System Interface (“SCSI”), Industrial Standard Architecture (“ISA”) and Extended ISA (“EISA”) architectures, for example. In addition, the functionality provided for in the components and modules of TRAP system 100 may be combined into fewer components and modules or further separated into additional components and modules.
The TRAP system 100 is generally controlled and coordinated by operating system software, such as Windows XP, Windows Vista, Windows 7, Windows 8, Windows Server, Unix, Linux, SunOS, Solaris, iOS, Blackberry OS, or other compatible operating systems. In Macintosh systems, the operating system may be any available operating system, such as MAC OS X. In other embodiments, the TRAP system 100 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, such as a graphical user interface (“GUI”), among other things.
The exemplary TRAP system 100 may include one or more commonly available input/output (I/O) devices and interfaces 110, such as a keyboard, mouse, touchpad, and printer. In one embodiment, the I/O devices and interfaces 110 include one or more display devices, such as a monitor, that allows the visual presentation of data to a user. More particularly, a display device provides for the presentation of GUIs, application software data, and multimedia analytics, for example. The TRAP system 100 may also include one or more multimedia devices 140, such as speakers, video cards, graphics accelerators, and microphones, for example.
Network
In the embodiment of
According to
Tax Return Data Sources
The tax return data source(s) 170 may store, for example, tax return data including attributes, profiles, and other data descriptive of or related to tax return filings. The tax return data may include name, address, social security number, financial data related to the return, and other such information typically provided in a local or state tax return filing. In some embodiments, due to the sensitive nature of such tax return data, the TRAP system 100 may not have direct access to the tax return data source(s) 170. Rather, the tax agency computing system(s) 168 would have access to their own respective tax return data sources 170 and provide selected tax return data to the TRAP system 100. In some embodiments the TRAP system 100 may have at least a limited access permission (as indicated by the dashed line connecting network 160 to the tax return data sources 170) which may be allowed by the tax agency or under various laws and regulatory requirements which limit access to such data by non-tax agency or non-government entities.
Consumer Data Sources
The consumer data source(s) 172 may store, for example, credit bureau data (for example, credit bureau data from File Ones℠) and/or other consumer data. Consumer data source(s) 172 may also store geographic level demographics that include one or more models, such as models that identify lifestyle and/or socio-economic attributes associated with a geographic location (for example, MOSAIC® segmentation and/or codes) and/or behavioral/attitudinal/psychographic attributes associated with a geographic location (for example, TrueTouch℠ Touch Points segmentation).
Device Activity Data Sources
The device activity data source(s) 174 may store, for example, device activity data for respective computing devices. The device activity data may include among other things indications of fraudulent activities that may be associated with particular device identifiers. For example, a fraudster may use a device to engage in a fraudulent transaction online, and thus the transaction and a device identifier associated with the device may be collected and stored in a device activity data source 174. Such information may be extremely valuable to prevent future repeat fraud with the same device, such as if a potential tax fraudster attempts to use the device in relation to filing of a fraudulent tax return.
Additional Use Cases
In some embodiments, the systems and methods maybe used to provide a variety of features, such as the features described below.
Risk Based Versus Traditional Rules Based Tax Return Analysis
One aspect of the identity authentication or screening processes described herein is that the processes may be based upon data and analytics used in the financial industry to approve millions of credit transactions daily. In some instances authentication tools may be certified under Federal Identity, Credential and Access Management (“FICAM”) at a National Institute of Standards and Technology (“NIST”) Level of Assurance (“LOA”) 3 for virtual identity authentication. Such authentication tools help organizations to mitigate risk in billions of dollars in credit transactions, the majority of which are done electronically and in virtual anonymity. One strength of these authentication tools is the ability to not only use traditional identity verification checks based upon public records review, but the addition of a risk based process providing an identity fraud score which significantly lowers the number of false positives. The most predictive authentication and fraud scores are those that incorporate multiple data assets spanning traditionally used customer information categories, such as public records and demographic data, but also utilize, when possible, credit history attributes and historical application and inquiry records. Scores that incorporate a breadth of varied data categories such as credit attributes and demographic data typically outperform models built on singular categories of data such as public record assets.
Knowledge-Based Authentication
In addition, to verify a tax filer's identity, further authentication of those returns that are identified or flagged as suspect or potentially fraudulent may be implemented to provide greater assurance that the tax refund is released to the legitimate taxpayer. In conjunction with a risk-based identity proofing process, the tax refund fraud detection process can be further strengthened by use of a knowledge based authentication (“KBA”) process, which often include “Out of Wallet” questions. The tax filer is required to answer a list of questions correctly in order to receive the requested refund. In certain embodiments, challenge-response question technology can be used to dynamically formulate questions only the true tax payer would know. With an adjustable question configuration and the ability to change strategies for each inquiry, tax agencies may be well-suited to achieve their identity verification or fraud prevention and detection objectives with various levels of authentication. Configurable time limits can prevent fraudsters from researching answers during faceless interactions, and the use of both credit and non-credit related questions provide a more accurate picture of the consumer and further assure that the refund is being released to the legitimate taxpayer. The KBA processes described herein may be provided, for example, via a web site or app.
Device Identity Proofing
Many tax filings are now conducted electronically, which further preserves anonymity for the fraudster and allows for a quicker turn around in receiving the fraudulent refund. Individuals committing tax fraud will typically use the same computer to submit tax returns, submit credit applications, open accounts, and so forth. Device proofing capabilities offered by embodiments of the systems and methods described herein can authenticate the device being used to provide additional assurance that the device is not currently involved in or tied to other fraudulent activity, nor has it been involved in or tied to any past fraudulent activity.
Fraud Detection in Other Financial Transactions
A stolen identity has a short shelf life, and fraudsters frequently will try to utilize it for multiple transactions before it is abandoned. Thus, in some embodiments, an inquiry process that utilizes a complex set of algorithms may determine if the attributes of the identity used in the tax return have been involved in other fraudulent attempts to open accounts or secure lines of credit. This independent inquiry check based on the same identity credentials being used to submit the fraudulent tax return can help identify if the fraudster has attempted to use these same credentials in other credit related activities.
Returns Linked to Multiple Bank Accounts and Addresses
One of the weaknesses in the tax filing system which is exploited by income tax fraudsters is the government's need to quickly process returns and provide refunds. Tax returns are frequently processed and refunds released within a few days or weeks. This quick turnaround may not allow the government to fully authenticate all the elements submitted on returns. Current fraud detection processes does not detect addresses or bank accounts that are receiving multiple refunds. Most income tax refund fraudsters want easy access to their fraudulent refunds and thereby chose to have the refund placed on a debit card and sent to one or two of the same addresses or deposited into one or two accounts. Having large numbers of debit card sent to the same address or refunds deposited into one account is not normal behavior for legitimate tax filers, and thus evidence of such behavior can also be used as a fraudulent flag indicator in the return analysis process.
Thus, in one embodiment, the TRAP system 100 may be configured to analyze tax return filings to, for example, determine whether a same address is used multiple times across multiple tax returns. The TRAP system 100 also may be configured to analyze tax return filings to, for example, determine whether a same bank account is used multiple times across multiple tax returns. In another embodiment, the TRAP system 100 may be configured to combine both of these features to determine whether a same address is used in conjunction with a same bank account across multiple tax returns. Any one of these determinations, alone or in combination, may contribute or give rise to a fraudulent flag indicator. In another embodiment, as another fraud safeguard the TRAP system 100 may be configured to access verified bank account data (for example, under permission from a participating bank service provider), or be configured to request verification of a bank account with respect to the individual tax filer. Thus, for example, if one or more tax returns appear potentially fraudulent based on repeated use of a same address or a same bank account, the TRAP system 100 may be configured to perform an additional bank account verification process to verify whether the tax filer(s) associated with the suspect return(s) are verified account holders with respect to the bank accounts used on the suspected return(s).
Income Check Against Reported Adjusted Gross Income (“AGI”)
As described above, the income tax refund fraudster can use a variety of methods to obtain a consumer's name, address, and Social Security Number. However, it is not as easy for a fraudster to obtain information on an individual's income. According to TIGTA, access to third-party income and withholding information at the time tax returns are processed can be an important tool in identifying and preventing tax refund fraud. Unfortunately, this information is usually not available until well after tax filing season begins, since employers are not required to file W-2 information until after the consumer filing process begins. The amounts listed on fraudulent returns can thus be falsified by the fraudster in order to increase the ultimate number of deductions and extract the largest refund without arousing suspicion. In some instances, using income estimation models, the reported income can be checked against third party data not based upon previous years' returns, but, independent financial information which can take into account a consumer's credit history and recent spending habits. While not a report of the actual income, it can provide a gauge that can be used to flag returns where reported income is well outside the expected norm for that tax filer.
The use of a risk based identity authentication process, coupled with business rules based analysis and knowledge based authentication tools can facilitate identification of fraudulent tax returns. In addition, the ability to perform non-traditional checks against device fraud activity; the use by fraudsters of same identity credentials in independent financial transactions; detecting that multiple refunds are requested to be to sent to the same address or deposited into the same bank account; and the ability check the reported income against an individual consumer's estimated income, further strengthens the tax refund fraud detection processes and helps close additional loopholes exploited by the tax fraudster while at the same time decreasing the number of false positives. Embodiments of the tax return analysis platform system and methods described herein may be easy to implement, integrate seamlessly into any existing tax return evaluation process, and/or add little to no additional time to the existing process, thereby assuring a continued quick turnaround for legitimate tax refund releases, while at the same time providing increased assurance that the refunds are being provided to the legitimate tax payer.
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 code modules may be stored on any type of non-transitory computer-readable medium or computer storage device, such as hard drives, solid state memory, optical disc, and/or the like. The systems and modules may also be transmitted as generated data signals (for example, as part of a carrier wave or other analog or digital propagated signal) on a variety of computer-readable transmission mediums, including wireless-based and wired/cable-based mediums, and may take a variety of forms (for example, as part of a single or multiplexed analog signal, or as multiple discrete digital packets or frames). The processes and algorithms may be implemented partially or wholly in application-specific circuitry. The results of the disclosed processes and process steps may be stored, persistently or otherwise, in any type of non-transitory computer storage such as, for example, volatile or non-volatile storage.
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, or any other tangible medium. Such software code may be stored, partially or fully, on a memory device of the executing computing device, such as the TRAP system 100, 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 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.
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 subcombinations 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 used herein, such as, among others, “can,” “could,” “might,” “may,” “for example,” and the like, 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 author input or prompting, whether these features, elements and/or steps are included or are to be performed in any particular embodiment. The terms “comprising,” “including,” “having,” and the like are synonymous and are used inclusively, in an open-ended fashion, and do not exclude additional elements, features, acts, operations, and so forth. Also, the term “or” is used in its inclusive sense (and not in its exclusive sense) so that when used, for example, to connect a list of elements, the term “or” means one, some, or all of the elements in the list. Conjunctive language such as the phrase “at least one of X, Y and Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to convey that an item, term, etc. may be either X, Y or Z. Thus, such conjunctive language is not generally intended to imply that certain embodiments require at least one of X, at least one of Y and at least one of Z to each be present.
While certain example embodiments have been described, these embodiments have been presented by way of example only, and are not intended to limit the scope of the disclosure. Thus, nothing in the foregoing description is intended to imply that any particular element, feature, characteristic, step, module, or block is necessary or indispensable. Indeed, the novel methods and systems described herein may be embodied in a variety of other forms; furthermore, various omissions, substitutions and changes in the form of the methods and systems described herein may be made without departing from the spirit of the inventions disclosed herein. The accompanying claims and their equivalents are intended to cover such forms or modifications as would fall within the scope and spirit of certain of the inventions disclosed herein.
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.
All of the methods and processes described above may be embodied in, and partially or fully automated via, software code modules executed by one or more general purpose computers. For example, the methods described herein may be performed by the TRAP system 100 and/or any other suitable computing device. The methods may be executed on the computing devices in response to execution of software instructions or other executable code read from a tangible computer readable medium. A tangible computer readable medium is a data storage device that can store data that is readable by a computer system. Examples of computer readable mediums include read-only memory, random-access memory, other volatile or non-volatile memory devices, CD-ROMs, magnetic tape, flash drives, and optical data storage devices.
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.
This application is a continuation of U.S. patent application Ser. No. 17/208,327 filed on Mar. 22, 2021, entitled SYSTEM AND ARCHITECTURE FOR ELECTRONIC FRAUD DETECTION, which is a continuation of U.S. patent application Ser. No. 16/443,662 filed on Jun. 17, 2019, entitled SYSTEM AND ARCHITECTURE FOR ELECTRONIC FRAUD DETECTION, which is a continuation of U.S. patent application Ser. No. 14/928,770 filed Oct. 30, 2015, entitled SYSTEM AND ARCHITECTURE FOR ELECTRONIC FRAUD DETECTION, which claims the benefit of priority from U.S. Provisional Patent Application No. 62/073,714 filed on Oct. 31, 2014, entitled SYSTEM AND ARCHITECTURE FOR ELECTRONIC FRAUD DETECTION. All above-cited applications are hereby incorporated herein by reference in their entirety.
| Number | Name | Date | Kind |
|---|---|---|---|
| 2074513 | Mills | Mar 1937 | A |
| 3316395 | Lavin et al. | Apr 1967 | A |
| 3752904 | Waterbury | Aug 1973 | A |
| 4163290 | Sutherlin et al. | Jul 1979 | A |
| 5274547 | Zoffel et al. | Dec 1993 | A |
| 5323315 | Highbloom | Jun 1994 | A |
| 5386104 | Sime | Jan 1995 | A |
| 5414833 | Hershey et al. | May 1995 | A |
| 5454030 | de Oliveira et al. | Sep 1995 | A |
| 5504675 | Cragun et al. | Apr 1996 | A |
| 5563783 | Stolfo et al. | Oct 1996 | A |
| 5627886 | Bowman | May 1997 | A |
| 5679940 | Templeton et al. | Oct 1997 | A |
| 5696907 | Tom | Dec 1997 | A |
| 5696965 | Dedrick | Dec 1997 | A |
| 5739512 | Tognazzini | Apr 1998 | A |
| 5742775 | King | Apr 1998 | A |
| 5745654 | Titan | Apr 1998 | A |
| 5752242 | Havens | May 1998 | A |
| 5754632 | Smith | May 1998 | A |
| 5774868 | Cragun et al. | Jun 1998 | A |
| 5793497 | Funk | Aug 1998 | A |
| 5809478 | Greco et al. | Sep 1998 | A |
| 5819226 | Gopinathan et al. | Oct 1998 | A |
| 5819260 | Lu et al. | Oct 1998 | A |
| 5822741 | Fischthal | Oct 1998 | A |
| 5832068 | Smith | Nov 1998 | A |
| 5842178 | Giovannoli | Nov 1998 | A |
| 5870721 | Norris | Feb 1999 | A |
| 5872921 | Zahariev et al. | Feb 1999 | A |
| 5878403 | DeFrancesco | Mar 1999 | A |
| 5879297 | Haynor et al. | Mar 1999 | A |
| 5884289 | Anderson et al. | Mar 1999 | A |
| 5912839 | Ovshinsky et al. | Jun 1999 | A |
| 5913196 | Talmor et al. | Jun 1999 | A |
| 5943666 | Kleewein et al. | Aug 1999 | A |
| 5950179 | Buchanan et al. | Sep 1999 | A |
| 5987440 | O'Neil et al. | Nov 1999 | A |
| 5999907 | Donner | Dec 1999 | A |
| 5999940 | Ranger | Dec 1999 | A |
| 6023694 | Kouchi et al. | Feb 2000 | A |
| 6029139 | Cunningham et al. | Feb 2000 | A |
| 6029149 | Dykstra et al. | Feb 2000 | A |
| 6029154 | Pettitt | Feb 2000 | A |
| 6029194 | Tilt | Feb 2000 | A |
| 6044357 | Garg | Mar 2000 | A |
| 6055570 | Nielsen | Apr 2000 | A |
| 6094643 | Anderson et al. | Jul 2000 | A |
| 6119103 | Basch et al. | Sep 2000 | A |
| 6125985 | Amdahl et al. | Oct 2000 | A |
| 6142283 | Amdahl et al. | Nov 2000 | A |
| 6144988 | Kappel | Nov 2000 | A |
| 6157707 | Baulier et al. | Dec 2000 | A |
| 6182219 | Feldbau et al. | Jan 2001 | B1 |
| 6208720 | Curtis et al. | Mar 2001 | B1 |
| 6249228 | Shirk et al. | Jun 2001 | B1 |
| 6253203 | O'Flaherty et al. | Jun 2001 | B1 |
| 6254000 | Degen et al. | Jul 2001 | B1 |
| 6263447 | French et al. | Jul 2001 | B1 |
| 6269349 | Aieta et al. | Jul 2001 | B1 |
| 6282658 | French et al. | Aug 2001 | B2 |
| 6285983 | Jenkins | Sep 2001 | B1 |
| 6285987 | Roth et al. | Sep 2001 | B1 |
| 6292795 | Peters et al. | Sep 2001 | B1 |
| 6311169 | Duhon | Oct 2001 | B2 |
| 6317783 | Freishtat et al. | Nov 2001 | B1 |
| 6321339 | French et al. | Nov 2001 | B1 |
| 6330546 | Gopinathan et al. | Dec 2001 | B1 |
| 6397197 | Gindlesperger | May 2002 | B1 |
| 6418436 | Degen et al. | Jul 2002 | B1 |
| 6424956 | Werbos | Jul 2002 | B1 |
| 6448889 | Hudson | Sep 2002 | B1 |
| 6456984 | Demoff et al. | Sep 2002 | B1 |
| 6496936 | French et al. | Dec 2002 | B1 |
| 6505193 | Musgrave et al. | Jan 2003 | B1 |
| 6510415 | Talmor et al. | Jan 2003 | B1 |
| 6513018 | Culhane | Jan 2003 | B1 |
| 6532459 | Berson | Mar 2003 | B1 |
| 6542894 | Lee et al. | Apr 2003 | B1 |
| 6543683 | Hoffman | Apr 2003 | B2 |
| 6553495 | Johansson et al. | Apr 2003 | B1 |
| 6571334 | Feldbau et al. | May 2003 | B1 |
| 6597775 | Lawyer et al. | Jul 2003 | B2 |
| 6612488 | Suzuki | Sep 2003 | B2 |
| 6615193 | Kingdon et al. | Sep 2003 | B1 |
| 6658393 | Basch et al. | Dec 2003 | B1 |
| 6662023 | Helle | Dec 2003 | B1 |
| 6696941 | Baker | Feb 2004 | B2 |
| 6700220 | Bayeur et al. | Mar 2004 | B2 |
| 6714918 | Hillmer et al. | Mar 2004 | B2 |
| 6735572 | Landesmann | May 2004 | B2 |
| 6740875 | Ishikawa et al. | May 2004 | B1 |
| 6748426 | Shaffer et al. | Jun 2004 | B1 |
| 6751626 | Brown et al. | Jun 2004 | B2 |
| 6796497 | Benkert et al. | Sep 2004 | B2 |
| 6811082 | Wong | Nov 2004 | B2 |
| 6829711 | Kwok et al. | Dec 2004 | B1 |
| 6850606 | Lawyer et al. | Feb 2005 | B2 |
| 6857073 | French et al. | Feb 2005 | B2 |
| 6866586 | Oberberger et al. | Mar 2005 | B2 |
| 6871287 | Ellingson | Mar 2005 | B1 |
| 6873979 | Fishman et al. | Mar 2005 | B2 |
| 6898574 | Regan | May 2005 | B1 |
| 6907408 | Angel | Jun 2005 | B2 |
| 6908030 | Rajasekaran et al. | Jun 2005 | B2 |
| 6913194 | Suzuki | Jul 2005 | B2 |
| 6918038 | Smith et al. | Jul 2005 | B1 |
| 6920435 | Hoffman et al. | Jul 2005 | B2 |
| 6928546 | Nanavati et al. | Aug 2005 | B1 |
| 6930707 | Bates et al. | Aug 2005 | B2 |
| 6934849 | Kramer et al. | Aug 2005 | B2 |
| 6934858 | Woodhill | Aug 2005 | B2 |
| 6965881 | Brickell et al. | Nov 2005 | B1 |
| 6965997 | Dutta | Nov 2005 | B2 |
| 6973462 | Dattero et al. | Dec 2005 | B2 |
| 6973575 | Arnold | Dec 2005 | B2 |
| 6983381 | Jerdonek | Jan 2006 | B2 |
| 6983882 | Cassone | Jan 2006 | B2 |
| 6991174 | Zuili | Jan 2006 | B2 |
| 6993659 | Milgramm et al. | Jan 2006 | B2 |
| 7007174 | Wheeler et al. | Feb 2006 | B2 |
| 7028052 | Chapman et al. | Apr 2006 | B2 |
| 7035855 | Kilger et al. | Apr 2006 | B1 |
| 7069240 | Spero et al. | Jun 2006 | B2 |
| 7083090 | Zuili | Aug 2006 | B2 |
| 7089592 | Adjaoute et al. | Aug 2006 | B2 |
| 7092891 | Maus et al. | Aug 2006 | B2 |
| 7104444 | Suzuki | Sep 2006 | B2 |
| 7158622 | Lawyer et al. | Jan 2007 | B2 |
| 7162640 | Heath et al. | Jan 2007 | B2 |
| 7174335 | Kameda | Feb 2007 | B2 |
| 7188078 | Arnett et al. | Mar 2007 | B2 |
| 7203653 | McIntosh | Apr 2007 | B1 |
| 7212995 | Schulkins | May 2007 | B2 |
| 7222779 | Pineda-Sanchez et al. | May 2007 | B1 |
| 7225977 | Davis | Jun 2007 | B2 |
| 7234156 | French et al. | Jun 2007 | B2 |
| 7240059 | Bayliss et al. | Jul 2007 | B2 |
| 7240363 | Ellingson | Jul 2007 | B1 |
| 7246067 | Austin et al. | Jul 2007 | B2 |
| 7246740 | Swift et al. | Jul 2007 | B2 |
| 7254560 | Singhal | Aug 2007 | B2 |
| 7263506 | Lee et al. | Aug 2007 | B2 |
| 7272728 | Pierson et al. | Sep 2007 | B2 |
| 7272857 | Everhart | Sep 2007 | B1 |
| 7277869 | Starkman | Oct 2007 | B2 |
| 7277875 | Serrano-Morales et al. | Oct 2007 | B2 |
| 7283974 | Katz et al. | Oct 2007 | B2 |
| 7289607 | Bhargava et al. | Oct 2007 | B2 |
| 7290704 | Ball et al. | Nov 2007 | B1 |
| 7298873 | Miller, Jr. et al. | Nov 2007 | B2 |
| 7310743 | Gagne et al. | Dec 2007 | B1 |
| 7314162 | Carr et al. | Jan 2008 | B2 |
| 7314167 | Kiliccote | Jan 2008 | B1 |
| 7330871 | Barber | Feb 2008 | B2 |
| 7333635 | Tsantes et al. | Feb 2008 | B2 |
| 7340042 | Cluff et al. | Mar 2008 | B2 |
| 7343149 | Benco | Mar 2008 | B2 |
| 7356516 | Richey et al. | Apr 2008 | B2 |
| 7370044 | Mulhern et al. | May 2008 | B2 |
| 7370351 | Ramachandran et al. | May 2008 | B1 |
| 7376618 | Anderson et al. | May 2008 | B1 |
| 7383227 | Weinflash et al. | Jun 2008 | B2 |
| 7386448 | Poss et al. | Jun 2008 | B1 |
| 7386506 | Aoki et al. | Jun 2008 | B2 |
| 7392534 | Lu et al. | Jun 2008 | B2 |
| 7395273 | Khan et al. | Jul 2008 | B2 |
| 7398915 | Pineda-Sanchez et al. | Jul 2008 | B1 |
| 7406715 | Clapper | Jul 2008 | B2 |
| 7412228 | Barclay et al. | Aug 2008 | B2 |
| 7418431 | Nies et al. | Aug 2008 | B1 |
| 7428509 | Klebanoff | Sep 2008 | B2 |
| 7433855 | Gavan et al. | Oct 2008 | B2 |
| 7433864 | Malik | Oct 2008 | B2 |
| 7438226 | Helsper et al. | Oct 2008 | B2 |
| 7444518 | Dharmarajan et al. | Oct 2008 | B1 |
| 7457401 | Lawyer et al. | Nov 2008 | B2 |
| 7458508 | Shao et al. | Dec 2008 | B1 |
| 7466235 | Kolb et al. | Dec 2008 | B1 |
| 7467401 | Cicchitto | Dec 2008 | B2 |
| 7480631 | Merced et al. | Jan 2009 | B1 |
| 7481363 | Zuili | Jan 2009 | B2 |
| 7490052 | Kilger et al. | Feb 2009 | B2 |
| 7490356 | Lieblich et al. | Feb 2009 | B2 |
| 7497374 | Helsper et al. | Mar 2009 | B2 |
| 7509117 | Yum | Mar 2009 | B2 |
| 7512221 | Toms | Mar 2009 | B2 |
| 7519558 | Ballard et al. | Apr 2009 | B2 |
| 7522060 | Tumperi et al. | Apr 2009 | B1 |
| 7533808 | Song et al. | May 2009 | B2 |
| 7536346 | Aliffi et al. | May 2009 | B2 |
| 7540021 | Page | May 2009 | B2 |
| 7542993 | Satterfield et al. | Jun 2009 | B2 |
| 7543739 | Brown et al. | Jun 2009 | B2 |
| 7543740 | Greene et al. | Jun 2009 | B2 |
| 7546271 | Chmielewski et al. | Jun 2009 | B1 |
| 7548886 | Kirkland et al. | Jun 2009 | B2 |
| 7552467 | Lindsay | Jun 2009 | B2 |
| 7562184 | Henmi et al. | Jul 2009 | B2 |
| 7562814 | Shao et al. | Jul 2009 | B1 |
| 7568616 | Zuili | Aug 2009 | B2 |
| 7575157 | Barnhardt et al. | Aug 2009 | B2 |
| 7580884 | Cook | Aug 2009 | B2 |
| 7581112 | Brown et al. | Aug 2009 | B2 |
| 7584146 | Duhon | Sep 2009 | B1 |
| 7587368 | Felsher | Sep 2009 | B2 |
| 7591425 | Zuili et al. | Sep 2009 | B1 |
| 7593891 | Kornegay et al. | Sep 2009 | B2 |
| 7606401 | Hoffman et al. | Oct 2009 | B2 |
| 7606790 | Levy | Oct 2009 | B2 |
| 7610216 | May et al. | Oct 2009 | B1 |
| 7610229 | Kornegay | Oct 2009 | B1 |
| 7610243 | Haggerty et al. | Oct 2009 | B2 |
| 7620596 | Knudson et al. | Nov 2009 | B2 |
| 7623844 | Herrmann et al. | Nov 2009 | B2 |
| 7630924 | Collins et al. | Dec 2009 | B1 |
| 7630932 | Danaher et al. | Dec 2009 | B2 |
| 7636853 | Cluts et al. | Dec 2009 | B2 |
| 7644868 | Hare | Jan 2010 | B2 |
| 7647344 | Skurtovich, Jr. et al. | Jan 2010 | B2 |
| 7647645 | Edeki et al. | Jan 2010 | B2 |
| 7653593 | Zarikian et al. | Jan 2010 | B2 |
| 7657431 | Hayakawa | Feb 2010 | B2 |
| 7668769 | Baker et al. | Feb 2010 | B2 |
| 7668840 | Bayliss et al. | Feb 2010 | B2 |
| 7668921 | Proux et al. | Feb 2010 | B2 |
| 7672865 | Kumar et al. | Mar 2010 | B2 |
| 7673793 | Greene et al. | Mar 2010 | B2 |
| 7676418 | Chung et al. | Mar 2010 | B1 |
| 7676433 | Ross et al. | Mar 2010 | B1 |
| 7685096 | Margolus et al. | Mar 2010 | B2 |
| 7686214 | Shao et al. | Mar 2010 | B1 |
| 7689007 | Bous et al. | Mar 2010 | B2 |
| 7689505 | Kasower | Mar 2010 | B2 |
| 7689506 | Fei et al. | Mar 2010 | B2 |
| 7690032 | Peirce | Mar 2010 | B1 |
| 7701364 | Zilberman | Apr 2010 | B1 |
| 7702550 | Perg et al. | Apr 2010 | B2 |
| 7707163 | Anzalone et al. | Apr 2010 | B2 |
| 7708190 | Brandt et al. | May 2010 | B2 |
| 7708200 | Helsper et al. | May 2010 | B2 |
| 7711635 | Steele et al. | May 2010 | B2 |
| 7711636 | Robida et al. | May 2010 | B2 |
| 7720750 | Brody | May 2010 | B2 |
| 7725300 | Pinto et al. | May 2010 | B2 |
| 7734523 | Cui et al. | Jun 2010 | B1 |
| 7735125 | Alvarez et al. | Jun 2010 | B1 |
| 7742982 | Chaudhuri et al. | Jun 2010 | B2 |
| 7747520 | Livermore et al. | Jun 2010 | B2 |
| 7747521 | Serio | Jun 2010 | B2 |
| 7747559 | Leitner et al. | Jun 2010 | B2 |
| 7752084 | Pettitt | Jul 2010 | B2 |
| 7752236 | Williams et al. | Jul 2010 | B2 |
| 7752554 | Biggs et al. | Jul 2010 | B2 |
| 7756783 | Crooks | Jul 2010 | B2 |
| 7761379 | Zoldi et al. | Jul 2010 | B2 |
| 7761384 | Madhogarhia | Jul 2010 | B2 |
| 7774270 | MacCloskey | Aug 2010 | B1 |
| 7778885 | Semprevivo et al. | Aug 2010 | B1 |
| 7779456 | Dennis et al. | Aug 2010 | B2 |
| 7779457 | Taylor | Aug 2010 | B2 |
| 7783281 | Cook et al. | Aug 2010 | B1 |
| 7783515 | Kumar et al. | Aug 2010 | B1 |
| 7788184 | Kane | Aug 2010 | B2 |
| 7792715 | Kasower | Sep 2010 | B1 |
| 7792864 | Rice et al. | Sep 2010 | B1 |
| 7793835 | Coggeshall et al. | Sep 2010 | B1 |
| 7801811 | Merrell et al. | Sep 2010 | B1 |
| 7801828 | Candella et al. | Sep 2010 | B2 |
| 7802104 | Dickinson | Sep 2010 | B2 |
| 7805362 | Merrell et al. | Sep 2010 | B1 |
| 7805391 | Friedlander et al. | Sep 2010 | B2 |
| 7809797 | Cooley et al. | Oct 2010 | B2 |
| 7813944 | Luk et al. | Oct 2010 | B1 |
| 7827115 | Weller et al. | Nov 2010 | B2 |
| 7832006 | Chen et al. | Nov 2010 | B2 |
| 7835983 | Lefner et al. | Nov 2010 | B2 |
| 7840459 | Loftesness et al. | Nov 2010 | B1 |
| 7841004 | Balducci et al. | Nov 2010 | B1 |
| 7844520 | Franklin | Nov 2010 | B1 |
| 7848987 | Haig | Dec 2010 | B2 |
| 7849029 | Crooks et al. | Dec 2010 | B2 |
| 7853518 | Cagan | Dec 2010 | B2 |
| 7853526 | Milana | Dec 2010 | B2 |
| 7853533 | Eisen | Dec 2010 | B2 |
| 7853998 | Blaisdell et al. | Dec 2010 | B2 |
| 7856397 | Whipple et al. | Dec 2010 | B2 |
| 7856494 | Kulkarni | Dec 2010 | B2 |
| 7860769 | Benson | Dec 2010 | B2 |
| 7860783 | Yang et al. | Dec 2010 | B2 |
| 7865427 | Wright et al. | Jan 2011 | B2 |
| 7865439 | Seifert et al. | Jan 2011 | B2 |
| 7865937 | White et al. | Jan 2011 | B1 |
| 7870078 | Clark et al. | Jan 2011 | B2 |
| 7870599 | Pemmaraju | Jan 2011 | B2 |
| 7873382 | Rydgren et al. | Jan 2011 | B2 |
| 7873566 | Templeton et al. | Jan 2011 | B1 |
| 7874488 | Parkinson | Jan 2011 | B2 |
| 7877304 | Coulter | Jan 2011 | B1 |
| 7877784 | Chow et al. | Jan 2011 | B2 |
| 7882548 | Heath et al. | Feb 2011 | B2 |
| 7890433 | Singhal | Feb 2011 | B2 |
| 7904360 | Evans | Mar 2011 | B2 |
| 7904367 | Chung et al. | Mar 2011 | B2 |
| 7908242 | Achanta | Mar 2011 | B1 |
| 7909246 | Hogg et al. | Mar 2011 | B2 |
| 7912865 | Akerman et al. | Mar 2011 | B2 |
| 7917715 | Tallman, Jr. | Mar 2011 | B2 |
| 7925582 | Kornegay et al. | Apr 2011 | B1 |
| 7929951 | Stevens et al. | Apr 2011 | B2 |
| 7933835 | Keane et al. | Apr 2011 | B2 |
| 7941363 | Tanaka et al. | May 2011 | B2 |
| 7945515 | Zoldi et al. | May 2011 | B2 |
| 7950577 | Daniel | May 2011 | B1 |
| 7958046 | Doerner et al. | Jun 2011 | B2 |
| 7961857 | Zoldi et al. | Jun 2011 | B2 |
| 7962404 | Metzger, II et al. | Jun 2011 | B1 |
| 7962467 | Howard et al. | Jun 2011 | B2 |
| 7970679 | Kasower | Jun 2011 | B2 |
| 7970698 | Gupta et al. | Jun 2011 | B2 |
| 7970701 | Lewis et al. | Jun 2011 | B2 |
| 7971246 | Emigh et al. | Jun 2011 | B1 |
| 7975299 | Balducci et al. | Jul 2011 | B1 |
| 7983976 | Nafeh et al. | Jul 2011 | B2 |
| 7983979 | Holland, IV | Jul 2011 | B2 |
| 7984849 | Berghel et al. | Jul 2011 | B2 |
| 7988043 | Davis | Aug 2011 | B2 |
| 7991201 | Bous et al. | Aug 2011 | B2 |
| 7991689 | Brunzell et al. | Aug 2011 | B1 |
| 7991716 | Crooks et al. | Aug 2011 | B2 |
| 7991751 | Peled et al. | Aug 2011 | B2 |
| 7995994 | Khetawat et al. | Aug 2011 | B2 |
| 7996521 | Chamberlain et al. | Aug 2011 | B2 |
| 8001034 | Chung et al. | Aug 2011 | B2 |
| 8001042 | Brunzell et al. | Aug 2011 | B1 |
| 8001153 | Skurtovich, Jr. et al. | Aug 2011 | B2 |
| 8001597 | Crooks | Aug 2011 | B2 |
| 8005749 | Ginsberg | Aug 2011 | B2 |
| 8006291 | Headley et al. | Aug 2011 | B2 |
| 8009873 | Chapman | Aug 2011 | B2 |
| 8019678 | Wright et al. | Sep 2011 | B2 |
| 8020763 | Kowalchyk et al. | Sep 2011 | B1 |
| 8024263 | Zarikian et al. | Sep 2011 | B2 |
| 8024271 | Grant | Sep 2011 | B2 |
| 8027439 | Zoldi et al. | Sep 2011 | B2 |
| 8027518 | Baker et al. | Sep 2011 | B2 |
| 8027947 | Hinsz et al. | Sep 2011 | B2 |
| 8028168 | Smithies et al. | Sep 2011 | B2 |
| 8028326 | Palmer et al. | Sep 2011 | B2 |
| 8028329 | Whitcomb | Sep 2011 | B2 |
| 8028896 | Carter et al. | Oct 2011 | B2 |
| 8032448 | Anderson et al. | Oct 2011 | B2 |
| 8032449 | Hu et al. | Oct 2011 | B2 |
| 8032927 | Ross | Oct 2011 | B2 |
| 8037097 | Guo et al. | Oct 2011 | B2 |
| 8037512 | Wright et al. | Oct 2011 | B2 |
| 8041597 | Li et al. | Oct 2011 | B2 |
| 8042159 | Basner et al. | Oct 2011 | B2 |
| 8042193 | Piliouras | Oct 2011 | B1 |
| 8049596 | Sato | Nov 2011 | B2 |
| 8055667 | Levy | Nov 2011 | B2 |
| 8056128 | Dingle et al. | Nov 2011 | B1 |
| 8058972 | Mohanty | Nov 2011 | B2 |
| 8060424 | Kasower | Nov 2011 | B2 |
| 8060915 | Voice et al. | Nov 2011 | B2 |
| 8060916 | Bajaj et al. | Nov 2011 | B2 |
| 8065233 | Lee et al. | Nov 2011 | B2 |
| 8065525 | Zilberman | Nov 2011 | B2 |
| 8069053 | Gervais et al. | Nov 2011 | B2 |
| 8069084 | Mackouse | Nov 2011 | B2 |
| 8069256 | Rasti | Nov 2011 | B2 |
| 8069485 | Carter | Nov 2011 | B2 |
| 8073785 | Candella et al. | Dec 2011 | B1 |
| 8078569 | Kennel | Dec 2011 | B2 |
| 8090648 | Zoldi et al. | Jan 2012 | B2 |
| 8104679 | Brown | Jan 2012 | B2 |
| 8116731 | Buhrmann et al. | Feb 2012 | B2 |
| 8121962 | Vaiciulis et al. | Feb 2012 | B2 |
| 8131615 | Diev et al. | Mar 2012 | B2 |
| 8151327 | Eisen | Apr 2012 | B2 |
| 8195549 | Kasower | Jun 2012 | B2 |
| 8201257 | Andres et al. | Jun 2012 | B1 |
| 8204774 | Chwast et al. | Jun 2012 | B2 |
| 8204982 | Casado et al. | Jun 2012 | B2 |
| 8214262 | Semprevivo et al. | Jul 2012 | B1 |
| 8214285 | Hu et al. | Jul 2012 | B2 |
| 8224723 | Bosch et al. | Jul 2012 | B2 |
| 8225395 | Atwood et al. | Jul 2012 | B2 |
| 8239677 | Colson | Aug 2012 | B2 |
| 8244629 | Lewis et al. | Aug 2012 | B2 |
| 8255978 | Dick | Aug 2012 | B2 |
| 8260914 | Ranjan | Sep 2012 | B1 |
| 8280805 | Abrahams et al. | Oct 2012 | B1 |
| 8280833 | Miltonberger | Oct 2012 | B2 |
| 8285613 | Coulter | Oct 2012 | B1 |
| 8285636 | Curry et al. | Oct 2012 | B2 |
| 8296225 | Maddipati et al. | Oct 2012 | B2 |
| 8296229 | Yellin et al. | Oct 2012 | B1 |
| 8296250 | Crooks et al. | Oct 2012 | B2 |
| 8332338 | Vaiciulis et al. | Dec 2012 | B2 |
| 8346593 | Fanelli | Jan 2013 | B2 |
| 8355896 | Kumar et al. | Jan 2013 | B2 |
| 8359278 | Domenikos et al. | Jan 2013 | B2 |
| 8364588 | Celka et al. | Jan 2013 | B2 |
| 8374973 | Herbrich et al. | Feb 2013 | B2 |
| 8386377 | Xiong et al. | Feb 2013 | B1 |
| 8429070 | Hu et al. | Apr 2013 | B2 |
| 8463904 | Casado et al. | Jun 2013 | B2 |
| 8468090 | Lesandro et al. | Jun 2013 | B2 |
| 8489479 | Slater et al. | Jul 2013 | B2 |
| 8510329 | Balkir et al. | Aug 2013 | B2 |
| 8515844 | Kasower | Aug 2013 | B2 |
| 8516439 | Brass et al. | Aug 2013 | B2 |
| 8543499 | Haggerty et al. | Sep 2013 | B2 |
| 8548137 | Zoldi et al. | Oct 2013 | B2 |
| 8548903 | Becker | Oct 2013 | B2 |
| 8549590 | de Villiers Prichard et al. | Oct 2013 | B1 |
| 8559607 | Zoldi et al. | Oct 2013 | B2 |
| 8567669 | Griegel et al. | Oct 2013 | B2 |
| 8578496 | Krishnappa | Nov 2013 | B1 |
| 8626671 | Federgreen | Jan 2014 | B2 |
| 8630938 | Cheng et al. | Jan 2014 | B2 |
| 8639920 | Stack et al. | Jan 2014 | B2 |
| 8645301 | Vaiciulis et al. | Feb 2014 | B2 |
| 8671115 | Skurtovich, Jr. et al. | Mar 2014 | B2 |
| 8676684 | Newman et al. | Mar 2014 | B2 |
| 8676726 | Hore et al. | Mar 2014 | B2 |
| 8682755 | Bucholz et al. | Mar 2014 | B2 |
| 8683586 | Crooks | Mar 2014 | B2 |
| 8694427 | Maddipati et al. | Apr 2014 | B2 |
| 8707445 | Sher-Jan et al. | Apr 2014 | B2 |
| 8725613 | Celka et al. | May 2014 | B1 |
| 8763133 | Sher-Jan et al. | Jun 2014 | B2 |
| 8776225 | Pierson et al. | Jul 2014 | B2 |
| 8781953 | Kasower | Jul 2014 | B2 |
| 8781975 | Bennett et al. | Jul 2014 | B2 |
| 8793777 | Colson | Jul 2014 | B2 |
| 8805836 | Hore et al. | Aug 2014 | B2 |
| 8812387 | Samler et al. | Aug 2014 | B1 |
| 8819793 | Gottschalk, Jr. | Aug 2014 | B2 |
| 8824648 | Zoldi et al. | Sep 2014 | B2 |
| 8826393 | Eisen | Sep 2014 | B2 |
| 8862514 | Eisen | Oct 2014 | B2 |
| 8862526 | Miltonberger | Oct 2014 | B2 |
| 8909664 | Hopkins | Dec 2014 | B2 |
| 8918891 | Coggeshall et al. | Dec 2014 | B2 |
| 8949981 | Trollope et al. | Feb 2015 | B1 |
| 9118646 | Pierson et al. | Aug 2015 | B2 |
| 9147117 | Madhu et al. | Sep 2015 | B1 |
| 9191403 | Zoldi et al. | Nov 2015 | B2 |
| 9194899 | Zoldi et al. | Nov 2015 | B2 |
| 9196004 | Eisen | Nov 2015 | B2 |
| 9210156 | Little et al. | Dec 2015 | B1 |
| 9235728 | Gottschalk, Jr. et al. | Jan 2016 | B2 |
| 9251541 | Celka et al. | Feb 2016 | B2 |
| 9256624 | Skurtovich, Jr. et al. | Feb 2016 | B2 |
| 9280658 | Coggeshall et al. | Mar 2016 | B2 |
| 9361597 | Britton et al. | Jun 2016 | B2 |
| 9367520 | Zhao et al. | Jun 2016 | B2 |
| 9390384 | Eisen | Jul 2016 | B2 |
| 9412141 | Prichard et al. | Aug 2016 | B2 |
| 9483650 | Sher-Jan et al. | Nov 2016 | B2 |
| 9489497 | MaGill et al. | Nov 2016 | B2 |
| 9531738 | Zoldi et al. | Dec 2016 | B2 |
| 9558368 | Gottschalk, Jr. et al. | Jan 2017 | B2 |
| 9595066 | Samler et al. | Mar 2017 | B2 |
| 9600845 | Nordyke et al. | Mar 2017 | B2 |
| 9652802 | Kasower | May 2017 | B1 |
| 9704195 | Zoldi | Jul 2017 | B2 |
| 9710523 | Skurtovich, Jr. et al. | Jul 2017 | B2 |
| 9710868 | Gottschalk, Jr. et al. | Jul 2017 | B2 |
| 9754256 | Britton et al. | Sep 2017 | B2 |
| 9754311 | Eisen | Sep 2017 | B2 |
| 9760885 | Ramalingam et al. | Sep 2017 | B1 |
| 9773227 | Zoldi et al. | Sep 2017 | B2 |
| 9781147 | Sher-Jan et al. | Oct 2017 | B2 |
| 9805216 | Kraska et al. | Oct 2017 | B2 |
| 9953321 | Zoldi et al. | Apr 2018 | B2 |
| 10043213 | Straub et al. | Aug 2018 | B2 |
| 10089411 | Kassa | Oct 2018 | B2 |
| 10089679 | Eisen | Oct 2018 | B2 |
| 10089686 | Straub et al. | Oct 2018 | B2 |
| 10102530 | Zoldi et al. | Oct 2018 | B2 |
| 10115153 | Zoldi et al. | Oct 2018 | B2 |
| 10152736 | Yang et al. | Dec 2018 | B2 |
| 10217163 | Straub et al. | Feb 2019 | B2 |
| 10242540 | Chen et al. | Mar 2019 | B2 |
| 10339527 | Coleman et al. | Jul 2019 | B1 |
| 10373061 | Kennel et al. | Aug 2019 | B2 |
| 10404472 | Knopf | Sep 2019 | B2 |
| 10430604 | Spinelli et al. | Oct 2019 | B2 |
| 10438308 | Prichard et al. | Oct 2019 | B2 |
| 10482542 | Jain | Nov 2019 | B1 |
| 10497034 | Yang et al. | Dec 2019 | B2 |
| 10510025 | Zoldi et al. | Dec 2019 | B2 |
| 10521857 | Shao et al. | Dec 2019 | B1 |
| 10528948 | Zoldi et al. | Jan 2020 | B2 |
| 10579938 | Zoldi et al. | Mar 2020 | B2 |
| 10592982 | Samler et al. | Mar 2020 | B2 |
| 10593004 | Gottschalk, Jr. et al. | Mar 2020 | B2 |
| 10616196 | Khitrenovich et al. | Apr 2020 | B1 |
| 10692058 | Zoldi et al. | Jun 2020 | B2 |
| 10699028 | Kennedy et al. | Jun 2020 | B1 |
| 10713711 | Zoldi | Jul 2020 | B2 |
| 10769290 | Crawford et al. | Sep 2020 | B2 |
| 10791136 | Zoldi et al. | Sep 2020 | B2 |
| 10896381 | Zoldi et al. | Jan 2021 | B2 |
| 10896472 | Stack et al. | Jan 2021 | B1 |
| 10902426 | Zoldi et al. | Jan 2021 | B2 |
| 10909617 | Kasower | Feb 2021 | B2 |
| 10958725 | Knopf | Mar 2021 | B2 |
| 10977363 | Leitner et al. | Apr 2021 | B2 |
| 10990979 | Coleman et al. | Apr 2021 | B1 |
| 10999298 | Eisen | May 2021 | B2 |
| 11023963 | Zoldi et al. | Jun 2021 | B2 |
| 11025428 | Knopf | Jun 2021 | B2 |
| 11030562 | Dean et al. | Jun 2021 | B1 |
| 11037229 | Zoldi et al. | Jun 2021 | B2 |
| 11080740 | Billman et al. | Aug 2021 | B2 |
| 11087334 | McEachern et al. | Aug 2021 | B1 |
| 11093845 | Zoldi et al. | Aug 2021 | B2 |
| 11093988 | Zoldi et al. | Aug 2021 | B2 |
| 11100506 | Zoldi et al. | Aug 2021 | B2 |
| 11108562 | Knopf et al. | Aug 2021 | B2 |
| 11151468 | Chen et al. | Oct 2021 | B1 |
| 11157650 | Kennedy et al. | Oct 2021 | B1 |
| 11256825 | Spinelli et al. | Feb 2022 | B2 |
| 11354670 | Phelan et al. | Jun 2022 | B2 |
| 11367074 | Zoldi et al. | Jun 2022 | B2 |
| 11373190 | Zoldi et al. | Jun 2022 | B2 |
| 11380171 | Chen et al. | Jul 2022 | B2 |
| 11423414 | Zoldi et al. | Aug 2022 | B2 |
| 11431736 | Brown et al. | Aug 2022 | B2 |
| 11436606 | Coleman et al. | Sep 2022 | B1 |
| 11552901 | Hoover et al. | Jan 2023 | B2 |
| 11568348 | Dean et al. | Jan 2023 | B1 |
| 11580259 | Kennedy et al. | Feb 2023 | B1 |
| 11593476 | Van Dyke | Feb 2023 | B2 |
| 11625730 | Liu et al. | Apr 2023 | B2 |
| 11658994 | Johnston et al. | May 2023 | B2 |
| 11665004 | Knopf | May 2023 | B2 |
| 11669894 | Nordyke et al. | Jun 2023 | B2 |
| 20010014868 | Herz et al. | Aug 2001 | A1 |
| 20010014878 | Mitra et al. | Aug 2001 | A1 |
| 20010027413 | Bhutta | Oct 2001 | A1 |
| 20010029470 | Schultz et al. | Oct 2001 | A1 |
| 20010034631 | Kiselik | Oct 2001 | A1 |
| 20010039523 | Iwamoto | Nov 2001 | A1 |
| 20020010684 | Moskowitz | Jan 2002 | A1 |
| 20020013899 | Faul | Jan 2002 | A1 |
| 20020019804 | Sutton | Feb 2002 | A1 |
| 20020019938 | Aarons | Feb 2002 | A1 |
| 20020032635 | Harris et al. | Mar 2002 | A1 |
| 20020040344 | Preiser et al. | Apr 2002 | A1 |
| 20020042879 | Gould et al. | Apr 2002 | A1 |
| 20020052841 | Guthrie et al. | May 2002 | A1 |
| 20020059521 | Tasler | May 2002 | A1 |
| 20020062185 | Runge et al. | May 2002 | A1 |
| 20020062281 | Singhal | May 2002 | A1 |
| 20020073044 | Singhal | Jun 2002 | A1 |
| 20020077178 | Oberberger et al. | Jun 2002 | A1 |
| 20020077964 | Brody et al. | Jun 2002 | A1 |
| 20020080256 | Bates et al. | Jun 2002 | A1 |
| 20020087460 | Hornung | Jul 2002 | A1 |
| 20020099649 | Lee et al. | Jul 2002 | A1 |
| 20020119824 | Allen | Aug 2002 | A1 |
| 20020130176 | Suzuki | Sep 2002 | A1 |
| 20020138417 | Lawrence | Sep 2002 | A1 |
| 20020138751 | Dutta | Sep 2002 | A1 |
| 20020147695 | Khedkar et al. | Oct 2002 | A1 |
| 20020156676 | Ahrens et al. | Oct 2002 | A1 |
| 20020161664 | Shaya et al. | Oct 2002 | A1 |
| 20020161711 | Sartor et al. | Oct 2002 | A1 |
| 20020173994 | Ferguson, III | Nov 2002 | A1 |
| 20020178112 | Goeller et al. | Nov 2002 | A1 |
| 20020184509 | Scheidt et al. | Dec 2002 | A1 |
| 20020188544 | Wizon et al. | Dec 2002 | A1 |
| 20030004879 | Demoff et al. | Jan 2003 | A1 |
| 20030009426 | Ruiz-Sanchez | Jan 2003 | A1 |
| 20030018549 | Fei et al. | Jan 2003 | A1 |
| 20030033261 | Knegendorf | Feb 2003 | A1 |
| 20030046554 | Leydier et al. | Mar 2003 | A1 |
| 20030048904 | Wang et al. | Mar 2003 | A1 |
| 20030050882 | Degen et al. | Mar 2003 | A1 |
| 20030057278 | Wong | Mar 2003 | A1 |
| 20030061163 | Durfield | Mar 2003 | A1 |
| 20030065563 | Elliott et al. | Apr 2003 | A1 |
| 20030070101 | Buscemi | Apr 2003 | A1 |
| 20030078877 | Beirne et al. | Apr 2003 | A1 |
| 20030093366 | Halper et al. | May 2003 | A1 |
| 20030097320 | Gordon | May 2003 | A1 |
| 20030105696 | Kalotay et al. | Jun 2003 | A1 |
| 20030115133 | Bian | Jun 2003 | A1 |
| 20030143980 | Choi et al. | Jul 2003 | A1 |
| 20030149744 | Bierre et al. | Aug 2003 | A1 |
| 20030153299 | Perfit et al. | Aug 2003 | A1 |
| 20030158751 | Suresh et al. | Aug 2003 | A1 |
| 20030158960 | Engberg | Aug 2003 | A1 |
| 20030182214 | Taylor | Sep 2003 | A1 |
| 20030195859 | Lawrence | Oct 2003 | A1 |
| 20030200447 | Sjoblom | Oct 2003 | A1 |
| 20030208428 | Raynes et al. | Nov 2003 | A1 |
| 20030222500 | Bayeur et al. | Dec 2003 | A1 |
| 20030225656 | Aberman et al. | Dec 2003 | A1 |
| 20030225692 | Bosch et al. | Dec 2003 | A1 |
| 20030225742 | Tenner et al. | Dec 2003 | A1 |
| 20030233278 | Marshall | Dec 2003 | A1 |
| 20040004117 | Suzuki | Jan 2004 | A1 |
| 20040005912 | Hubbe et al. | Jan 2004 | A1 |
| 20040010698 | Rolfe | Jan 2004 | A1 |
| 20040024709 | Yu et al. | Feb 2004 | A1 |
| 20040026496 | Zuili | Feb 2004 | A1 |
| 20040030649 | Nelson et al. | Feb 2004 | A1 |
| 20040039586 | Garvey et al. | Feb 2004 | A1 |
| 20040054619 | Watson et al. | Mar 2004 | A1 |
| 20040059653 | Verkuylen et al. | Mar 2004 | A1 |
| 20040064401 | Palaghita et al. | Apr 2004 | A1 |
| 20040078324 | Lonnberg et al. | Apr 2004 | A1 |
| 20040103147 | Flesher et al. | May 2004 | A1 |
| 20040107363 | Monteverde | Jun 2004 | A1 |
| 20040110119 | Riconda et al. | Jun 2004 | A1 |
| 20040111305 | Gavan et al. | Jun 2004 | A1 |
| 20040111335 | Black et al. | Jun 2004 | A1 |
| 20040117235 | Shacham | Jun 2004 | A1 |
| 20040128227 | Whipple et al. | Jul 2004 | A1 |
| 20040128232 | Descloux | Jul 2004 | A1 |
| 20040133440 | Carolan et al. | Jul 2004 | A1 |
| 20040143526 | Monasterio et al. | Jul 2004 | A1 |
| 20040149820 | Zuili | Aug 2004 | A1 |
| 20040149827 | Zuili | Aug 2004 | A1 |
| 20040153330 | Miller et al. | Aug 2004 | A1 |
| 20040153656 | Cluts et al. | Aug 2004 | A1 |
| 20040158520 | Noh | Aug 2004 | A1 |
| 20040158523 | Dort | Aug 2004 | A1 |
| 20040158723 | Root | Aug 2004 | A1 |
| 20040167793 | Masuoka et al. | Aug 2004 | A1 |
| 20040177046 | Ogram | Sep 2004 | A1 |
| 20040193538 | Raines | Sep 2004 | A1 |
| 20040199456 | Flint et al. | Oct 2004 | A1 |
| 20040199462 | Starrs | Oct 2004 | A1 |
| 20040204948 | Singletary et al. | Oct 2004 | A1 |
| 20040205008 | Haynie et al. | Oct 2004 | A1 |
| 20040225594 | Nolan, III et al. | Nov 2004 | A1 |
| 20040230448 | Schaich | Nov 2004 | A1 |
| 20040230527 | Hansen et al. | Nov 2004 | A1 |
| 20040230538 | Clifton et al. | Nov 2004 | A1 |
| 20040234117 | Tibor | Nov 2004 | A1 |
| 20040243514 | Wankmueller | Dec 2004 | A1 |
| 20040243518 | Clifton et al. | Dec 2004 | A1 |
| 20040243567 | Levy | Dec 2004 | A1 |
| 20040250085 | Tattan et al. | Dec 2004 | A1 |
| 20040255127 | Arnouse | Dec 2004 | A1 |
| 20040260922 | Goodman et al. | Dec 2004 | A1 |
| 20050001028 | Zuili | Jan 2005 | A1 |
| 20050005168 | Dick | Jan 2005 | A1 |
| 20050010513 | Duckworth et al. | Jan 2005 | A1 |
| 20050010780 | Kane et al. | Jan 2005 | A1 |
| 20050021476 | Candella et al. | Jan 2005 | A1 |
| 20050021519 | Ghouri | Jan 2005 | A1 |
| 20050027983 | Klawon | Feb 2005 | A1 |
| 20050038726 | Salomon et al. | Feb 2005 | A1 |
| 20050038737 | Norris | Feb 2005 | A1 |
| 20050039086 | Krishnamurthy et al. | Feb 2005 | A1 |
| 20050050577 | Westbrook et al. | Mar 2005 | A1 |
| 20050058262 | Timmins et al. | Mar 2005 | A1 |
| 20050065874 | Lefner et al. | Mar 2005 | A1 |
| 20050065950 | Chaganti et al. | Mar 2005 | A1 |
| 20050071282 | Lu et al. | Mar 2005 | A1 |
| 20050075985 | Cartmell | Apr 2005 | A1 |
| 20050081052 | Washington | Apr 2005 | A1 |
| 20050086161 | Gallant | Apr 2005 | A1 |
| 20050091164 | Varble | Apr 2005 | A1 |
| 20050097039 | Kulcsar et al. | May 2005 | A1 |
| 20050097051 | Madill, Jr. et al. | May 2005 | A1 |
| 20050097364 | Edeki et al. | May 2005 | A1 |
| 20050102206 | Savasoglu et al. | May 2005 | A1 |
| 20050105719 | Huda | May 2005 | A1 |
| 20050125226 | Magee | Jun 2005 | A1 |
| 20050125686 | Brandt | Jun 2005 | A1 |
| 20050138391 | Mandalia et al. | Jun 2005 | A1 |
| 20050144143 | Freiberg | Jun 2005 | A1 |
| 20050154664 | Guy et al. | Jul 2005 | A1 |
| 20050154665 | Kerr | Jul 2005 | A1 |
| 20050154671 | Doan et al. | Jul 2005 | A1 |
| 20050165667 | Cox | Jul 2005 | A1 |
| 20050197953 | Broadbent et al. | Sep 2005 | A1 |
| 20050203885 | Chenevich et al. | Sep 2005 | A1 |
| 20050216953 | Ellingson | Sep 2005 | A1 |
| 20050229007 | Bolle et al. | Oct 2005 | A1 |
| 20050240578 | Biederman et al. | Oct 2005 | A1 |
| 20050242173 | Suzuki | Nov 2005 | A1 |
| 20050251474 | Shinn et al. | Nov 2005 | A1 |
| 20050256809 | Sadri | Nov 2005 | A1 |
| 20050262014 | Fickes | Nov 2005 | A1 |
| 20050273333 | Morin et al. | Dec 2005 | A1 |
| 20050273442 | Bennett et al. | Dec 2005 | A1 |
| 20050278542 | Pierson et al. | Dec 2005 | A1 |
| 20050279827 | Mascavage et al. | Dec 2005 | A1 |
| 20050279869 | Barklage | Dec 2005 | A1 |
| 20060004663 | Singhal | Jan 2006 | A1 |
| 20060014129 | Coleman et al. | Jan 2006 | A1 |
| 20060032909 | Seegar | Feb 2006 | A1 |
| 20060041464 | Powers et al. | Feb 2006 | A1 |
| 20060045105 | Dobosz et al. | Mar 2006 | A1 |
| 20060047605 | Ahmad | Mar 2006 | A1 |
| 20060059073 | Walzak | Mar 2006 | A1 |
| 20060059110 | Madhok et al. | Mar 2006 | A1 |
| 20060064374 | Helsper et al. | Mar 2006 | A1 |
| 20060074798 | Din et al. | Apr 2006 | A1 |
| 20060074986 | Mallalieu et al. | Apr 2006 | A1 |
| 20060080230 | Freiberg | Apr 2006 | A1 |
| 20060080263 | Willis et al. | Apr 2006 | A1 |
| 20060089905 | Song et al. | Apr 2006 | A1 |
| 20060101508 | Taylor | May 2006 | A1 |
| 20060106605 | Saunders et al. | May 2006 | A1 |
| 20060112279 | Cohen et al. | May 2006 | A1 |
| 20060112280 | Cohen et al. | May 2006 | A1 |
| 20060129428 | Wennberg | Jun 2006 | A1 |
| 20060129481 | Bhatt et al. | Jun 2006 | A1 |
| 20060129840 | Milgramm et al. | Jun 2006 | A1 |
| 20060131390 | Kim | Jun 2006 | A1 |
| 20060136332 | Ziegler | Jun 2006 | A1 |
| 20060140460 | Coutts | Jun 2006 | A1 |
| 20060143073 | Engel et al. | Jun 2006 | A1 |
| 20060144924 | Stover | Jul 2006 | A1 |
| 20060149580 | Helsper et al. | Jul 2006 | A1 |
| 20060149674 | Cook et al. | Jul 2006 | A1 |
| 20060161435 | Atef et al. | Jul 2006 | A1 |
| 20060161592 | Ertoz et al. | Jul 2006 | A1 |
| 20060173776 | Shalley et al. | Aug 2006 | A1 |
| 20060173792 | Glass | Aug 2006 | A1 |
| 20060177226 | Ellis, III | Aug 2006 | A1 |
| 20060178971 | Owen et al. | Aug 2006 | A1 |
| 20060179004 | Fuchs | Aug 2006 | A1 |
| 20060195351 | Bayburtian | Aug 2006 | A1 |
| 20060200855 | Willis | Sep 2006 | A1 |
| 20060202012 | Grano et al. | Sep 2006 | A1 |
| 20060204051 | Holland, IV | Sep 2006 | A1 |
| 20060206725 | Milgramm et al. | Sep 2006 | A1 |
| 20060212386 | Willey et al. | Sep 2006 | A1 |
| 20060218069 | Aberman et al. | Sep 2006 | A1 |
| 20060229961 | Lyftogt et al. | Oct 2006 | A1 |
| 20060239512 | Petrillo | Oct 2006 | A1 |
| 20060239513 | Song et al. | Oct 2006 | A1 |
| 20060242046 | Haggerty et al. | Oct 2006 | A1 |
| 20060242047 | Haggerty et al. | Oct 2006 | A1 |
| 20060253358 | Delgrosso et al. | Nov 2006 | A1 |
| 20060253583 | Dixon et al. | Nov 2006 | A1 |
| 20060255914 | Westman | Nov 2006 | A1 |
| 20060262929 | Vatanen et al. | Nov 2006 | A1 |
| 20060265243 | Racho et al. | Nov 2006 | A1 |
| 20060271456 | Romain et al. | Nov 2006 | A1 |
| 20060271457 | Romain et al. | Nov 2006 | A1 |
| 20060271633 | Adler | Nov 2006 | A1 |
| 20060273158 | Suzuki | Dec 2006 | A1 |
| 20060277043 | Tomes et al. | Dec 2006 | A1 |
| 20060282285 | Helsper et al. | Dec 2006 | A1 |
| 20060282372 | Endres et al. | Dec 2006 | A1 |
| 20060282395 | Leibowitz | Dec 2006 | A1 |
| 20060287765 | Kraft | Dec 2006 | A1 |
| 20060288090 | Kraft | Dec 2006 | A1 |
| 20060294023 | Lu | Dec 2006 | A1 |
| 20070005508 | Chiang | Jan 2007 | A1 |
| 20070011100 | Libin et al. | Jan 2007 | A1 |
| 20070016500 | Chatterji et al. | Jan 2007 | A1 |
| 20070016521 | Wang | Jan 2007 | A1 |
| 20070016522 | Wang | Jan 2007 | A1 |
| 20070022141 | Singleton et al. | Jan 2007 | A1 |
| 20070038483 | Wood | Feb 2007 | A1 |
| 20070038568 | Greene et al. | Feb 2007 | A1 |
| 20070040017 | Kozlay | Feb 2007 | A1 |
| 20070040019 | Berghel et al. | Feb 2007 | A1 |
| 20070043577 | Kasower | Feb 2007 | A1 |
| 20070047770 | Swope et al. | Mar 2007 | A1 |
| 20070048765 | Abramson | Mar 2007 | A1 |
| 20070050638 | Rasti | Mar 2007 | A1 |
| 20070059442 | Sabeta | Mar 2007 | A1 |
| 20070061273 | Greene et al. | Mar 2007 | A1 |
| 20070067207 | Haggerty et al. | Mar 2007 | A1 |
| 20070067297 | Kublickis | Mar 2007 | A1 |
| 20070072190 | Aggarwal | Mar 2007 | A1 |
| 20070073622 | Kane | Mar 2007 | A1 |
| 20070073630 | Greene et al. | Mar 2007 | A1 |
| 20070078786 | Bous et al. | Apr 2007 | A1 |
| 20070078908 | Rohatgi et al. | Apr 2007 | A1 |
| 20070078985 | Shao et al. | Apr 2007 | A1 |
| 20070083460 | Bachenheimer | Apr 2007 | A1 |
| 20070087795 | Aletto et al. | Apr 2007 | A1 |
| 20070093234 | Willis et al. | Apr 2007 | A1 |
| 20070094137 | Phillips et al. | Apr 2007 | A1 |
| 20070094264 | Nair | Apr 2007 | A1 |
| 20070100774 | Abdon | May 2007 | A1 |
| 20070106582 | Baker et al. | May 2007 | A1 |
| 20070106611 | Larsen | May 2007 | A1 |
| 20070107050 | Selvarajan | May 2007 | A1 |
| 20070109103 | Jedrey et al. | May 2007 | A1 |
| 20070110282 | Millsapp | May 2007 | A1 |
| 20070112667 | Rucker | May 2007 | A1 |
| 20070112668 | Celano et al. | May 2007 | A1 |
| 20070118393 | Rosen et al. | May 2007 | A1 |
| 20070155411 | Morrison | Jul 2007 | A1 |
| 20070157299 | Hare | Jul 2007 | A1 |
| 20070168246 | Haggerty et al. | Jul 2007 | A1 |
| 20070168480 | Biggs et al. | Jul 2007 | A1 |
| 20070174208 | Black et al. | Jul 2007 | A1 |
| 20070179903 | Seinfeld et al. | Aug 2007 | A1 |
| 20070180209 | Tallman | Aug 2007 | A1 |
| 20070180263 | Delgrosso et al. | Aug 2007 | A1 |
| 20070186276 | McRae et al. | Aug 2007 | A1 |
| 20070192248 | West | Aug 2007 | A1 |
| 20070192853 | Shraim et al. | Aug 2007 | A1 |
| 20070198410 | Labgold et al. | Aug 2007 | A1 |
| 20070205266 | Carr et al. | Sep 2007 | A1 |
| 20070208669 | Rivette et al. | Sep 2007 | A1 |
| 20070214037 | Shubert et al. | Sep 2007 | A1 |
| 20070214365 | Cornett et al. | Sep 2007 | A1 |
| 20070219928 | Madhogarhia | Sep 2007 | A1 |
| 20070220594 | Tulsyan | Sep 2007 | A1 |
| 20070226093 | Chan et al. | Sep 2007 | A1 |
| 20070226129 | Liao et al. | Sep 2007 | A1 |
| 20070233614 | McNelley et al. | Oct 2007 | A1 |
| 20070234427 | Gardner et al. | Oct 2007 | A1 |
| 20070244782 | Chimento | Oct 2007 | A1 |
| 20070244807 | Andringa et al. | Oct 2007 | A1 |
| 20070250704 | Hallam-Baker | Oct 2007 | A1 |
| 20070250920 | Lindsay | Oct 2007 | A1 |
| 20070266439 | Kraft | Nov 2007 | A1 |
| 20070282730 | Carpenter et al. | Dec 2007 | A1 |
| 20070288355 | Roland et al. | Dec 2007 | A1 |
| 20070288360 | Seeklus | Dec 2007 | A1 |
| 20070288559 | Parsadayan | Dec 2007 | A1 |
| 20070291995 | Rivera | Dec 2007 | A1 |
| 20070292006 | Johnson | Dec 2007 | A1 |
| 20070294104 | Boaz et al. | Dec 2007 | A1 |
| 20070299759 | Kelly | Dec 2007 | A1 |
| 20080010203 | Grant | Jan 2008 | A1 |
| 20080010683 | Baddour et al. | Jan 2008 | A1 |
| 20080010687 | Gonen et al. | Jan 2008 | A1 |
| 20080015887 | Drabek et al. | Jan 2008 | A1 |
| 20080021804 | Deckoff | Jan 2008 | A1 |
| 20080027857 | Benson | Jan 2008 | A1 |
| 20080027858 | Benson | Jan 2008 | A1 |
| 20080052182 | Marshall | Feb 2008 | A1 |
| 20080059236 | Cartier | Mar 2008 | A1 |
| 20080059352 | Chandran | Mar 2008 | A1 |
| 20080059364 | Tidwell et al. | Mar 2008 | A1 |
| 20080059366 | Fou | Mar 2008 | A1 |
| 20080063172 | Ahuja et al. | Mar 2008 | A1 |
| 20080066188 | Kwak | Mar 2008 | A1 |
| 20080071882 | Hering et al. | Mar 2008 | A1 |
| 20080076386 | Khetawat et al. | Mar 2008 | A1 |
| 20080077526 | Arumugam | Mar 2008 | A1 |
| 20080098222 | Zilberman | Apr 2008 | A1 |
| 20080103798 | Domenikos et al. | May 2008 | A1 |
| 20080103799 | Domenikos et al. | May 2008 | A1 |
| 20080103800 | Domenikos et al. | May 2008 | A1 |
| 20080103811 | Sosa | May 2008 | A1 |
| 20080103972 | Lanc | May 2008 | A1 |
| 20080104021 | Cai et al. | May 2008 | A1 |
| 20080104672 | Lunde et al. | May 2008 | A1 |
| 20080114837 | Biggs et al. | May 2008 | A1 |
| 20080120237 | Lin | May 2008 | A1 |
| 20080126116 | Singhai | May 2008 | A1 |
| 20080126233 | Hogan | May 2008 | A1 |
| 20080140576 | Lewis et al. | Jun 2008 | A1 |
| 20080147454 | Walker et al. | Jun 2008 | A1 |
| 20080154758 | Schattmaier et al. | Jun 2008 | A1 |
| 20080162202 | Khanna et al. | Jul 2008 | A1 |
| 20080162259 | Patil et al. | Jul 2008 | A1 |
| 20080162383 | Kraft | Jul 2008 | A1 |
| 20080167883 | Thavildar Khazaneh | Jul 2008 | A1 |
| 20080175360 | Schwarz et al. | Jul 2008 | A1 |
| 20080177655 | Zalik | Jul 2008 | A1 |
| 20080177841 | Sinn et al. | Jul 2008 | A1 |
| 20080189789 | Lamontagne | Aug 2008 | A1 |
| 20080208548 | Metzger et al. | Aug 2008 | A1 |
| 20080208610 | Thomas et al. | Aug 2008 | A1 |
| 20080208726 | Tsantes et al. | Aug 2008 | A1 |
| 20080217400 | Portano | Sep 2008 | A1 |
| 20080228635 | Megdal et al. | Sep 2008 | A1 |
| 20080243680 | Megdal et al. | Oct 2008 | A1 |
| 20080244717 | Jelatis et al. | Oct 2008 | A1 |
| 20080255922 | Feldman et al. | Oct 2008 | A1 |
| 20080255992 | Lin | Oct 2008 | A1 |
| 20080256613 | Grover | Oct 2008 | A1 |
| 20080281737 | Fajardo | Nov 2008 | A1 |
| 20080281743 | Pettit | Nov 2008 | A1 |
| 20080288382 | Smith et al. | Nov 2008 | A1 |
| 20080288430 | Friedlander et al. | Nov 2008 | A1 |
| 20080288790 | Wilson | Nov 2008 | A1 |
| 20080294540 | Celka et al. | Nov 2008 | A1 |
| 20080294689 | Metzger et al. | Nov 2008 | A1 |
| 20080296367 | Parkinson | Dec 2008 | A1 |
| 20080296382 | Connell, II et al. | Dec 2008 | A1 |
| 20080300877 | Gilbert et al. | Dec 2008 | A1 |
| 20080319889 | Hammad | Dec 2008 | A1 |
| 20090007220 | Ormazabal et al. | Jan 2009 | A1 |
| 20090018934 | Peng et al. | Jan 2009 | A1 |
| 20090021349 | Errico et al. | Jan 2009 | A1 |
| 20090024417 | Marks et al. | Jan 2009 | A1 |
| 20090024505 | Patel et al. | Jan 2009 | A1 |
| 20090024636 | Shiloh | Jan 2009 | A1 |
| 20090024663 | McGovern | Jan 2009 | A1 |
| 20090026270 | Connell, II et al. | Jan 2009 | A1 |
| 20090043637 | Eder | Feb 2009 | A1 |
| 20090044279 | Crawford et al. | Feb 2009 | A1 |
| 20090048957 | Celano | Feb 2009 | A1 |
| 20090079539 | Johnson | Mar 2009 | A1 |
| 20090094311 | Awadallah et al. | Apr 2009 | A1 |
| 20090099960 | Robida et al. | Apr 2009 | A1 |
| 20090106150 | Pelegero et al. | Apr 2009 | A1 |
| 20090106153 | Ezra | Apr 2009 | A1 |
| 20090106846 | Dupray et al. | Apr 2009 | A1 |
| 20090112650 | Iwane | Apr 2009 | A1 |
| 20090119106 | Rajakumar et al. | May 2009 | A1 |
| 20090119299 | Rhodes | May 2009 | A1 |
| 20090125369 | Kloostra et al. | May 2009 | A1 |
| 20090125439 | Zarikian et al. | May 2009 | A1 |
| 20090125463 | Hido | May 2009 | A1 |
| 20090126013 | Atwood et al. | May 2009 | A1 |
| 20090138391 | Dudley et al. | May 2009 | A1 |
| 20090141318 | Hughes | Jun 2009 | A1 |
| 20090151005 | Bell et al. | Jun 2009 | A1 |
| 20090158404 | Hahn et al. | Jun 2009 | A1 |
| 20090164380 | Brown | Jun 2009 | A1 |
| 20090172815 | Gu et al. | Jul 2009 | A1 |
| 20090182653 | Zimiles | Jul 2009 | A1 |
| 20090199264 | Lang | Aug 2009 | A1 |
| 20090205032 | Hinton et al. | Aug 2009 | A1 |
| 20090206993 | Di Mambro et al. | Aug 2009 | A1 |
| 20090216560 | Siegel | Aug 2009 | A1 |
| 20090216747 | Li et al. | Aug 2009 | A1 |
| 20090222308 | Zoldi et al. | Sep 2009 | A1 |
| 20090222362 | Stood et al. | Sep 2009 | A1 |
| 20090222373 | Choudhuri et al. | Sep 2009 | A1 |
| 20090222374 | Choudhuri et al. | Sep 2009 | A1 |
| 20090222375 | Choudhuri et al. | Sep 2009 | A1 |
| 20090222376 | Choudhuri et al. | Sep 2009 | A1 |
| 20090222377 | Choudhuri et al. | Sep 2009 | A1 |
| 20090222378 | Choudhuri et al. | Sep 2009 | A1 |
| 20090222379 | Choudhuri et al. | Sep 2009 | A1 |
| 20090222380 | Choudhuri et al. | Sep 2009 | A1 |
| 20090222897 | Carow et al. | Sep 2009 | A1 |
| 20090224875 | Rabinowitz et al. | Sep 2009 | A1 |
| 20090224889 | Aggarwal et al. | Sep 2009 | A1 |
| 20090226056 | Vlachos et al. | Sep 2009 | A1 |
| 20090234738 | Britton et al. | Sep 2009 | A1 |
| 20090240609 | Cho et al. | Sep 2009 | A1 |
| 20090241168 | Readshaw | Sep 2009 | A1 |
| 20090241173 | Troyansky | Sep 2009 | A1 |
| 20090248198 | Siegel et al. | Oct 2009 | A1 |
| 20090248497 | Hueter | Oct 2009 | A1 |
| 20090248567 | Haggerty et al. | Oct 2009 | A1 |
| 20090248568 | Haggerty et al. | Oct 2009 | A1 |
| 20090248569 | Haggerty et al. | Oct 2009 | A1 |
| 20090248570 | Haggerty et al. | Oct 2009 | A1 |
| 20090248571 | Haggerty et al. | Oct 2009 | A1 |
| 20090248572 | Haggerty et al. | Oct 2009 | A1 |
| 20090248573 | Haggerty et al. | Oct 2009 | A1 |
| 20090254476 | Sharma et al. | Oct 2009 | A1 |
| 20090254484 | Forero et al. | Oct 2009 | A1 |
| 20090257595 | de Cesare et al. | Oct 2009 | A1 |
| 20090259470 | Chang | Oct 2009 | A1 |
| 20090259560 | Bachenheimer | Oct 2009 | A1 |
| 20090259588 | Lindsay | Oct 2009 | A1 |
| 20090259855 | de Cesare et al. | Oct 2009 | A1 |
| 20090261189 | Ellis, Jr. | Oct 2009 | A1 |
| 20090270126 | Liu | Oct 2009 | A1 |
| 20090271265 | Lay et al. | Oct 2009 | A1 |
| 20090271617 | Song et al. | Oct 2009 | A1 |
| 20090272801 | Connell, II et al. | Nov 2009 | A1 |
| 20090276244 | Baldwin, Jr. et al. | Nov 2009 | A1 |
| 20090281945 | Shakkarwar | Nov 2009 | A1 |
| 20090281951 | Shakkarwar | Nov 2009 | A1 |
| 20090289110 | Regen et al. | Nov 2009 | A1 |
| 20090300066 | Guo et al. | Dec 2009 | A1 |
| 20090307778 | Mardikar | Dec 2009 | A1 |
| 20090326972 | Washington | Dec 2009 | A1 |
| 20090328173 | Jakobson et al. | Dec 2009 | A1 |
| 20100024037 | Grzymala-Busse et al. | Jan 2010 | A1 |
| 20100030677 | Melik-Aslanian et al. | Feb 2010 | A1 |
| 20100031030 | Kao et al. | Feb 2010 | A1 |
| 20100037147 | Champion et al. | Feb 2010 | A1 |
| 20100037308 | Lin et al. | Feb 2010 | A1 |
| 20100042526 | Martinov | Feb 2010 | A1 |
| 20100043055 | Baumgart | Feb 2010 | A1 |
| 20100070620 | Awadallah et al. | Mar 2010 | A1 |
| 20100077006 | El Emam et al. | Mar 2010 | A1 |
| 20100085146 | Johnson | Apr 2010 | A1 |
| 20100088233 | Tattan et al. | Apr 2010 | A1 |
| 20100088338 | Pavoni, Jr. et al. | Apr 2010 | A1 |
| 20100094664 | Bush et al. | Apr 2010 | A1 |
| 20100094767 | Miltonberger | Apr 2010 | A1 |
| 20100094768 | Miltonberger | Apr 2010 | A1 |
| 20100094910 | Bayliss | Apr 2010 | A1 |
| 20100095357 | Willis et al. | Apr 2010 | A1 |
| 20100100406 | Lim | Apr 2010 | A1 |
| 20100100945 | Ozzie et al. | Apr 2010 | A1 |
| 20100107225 | Spencer et al. | Apr 2010 | A1 |
| 20100114724 | Ghosh et al. | May 2010 | A1 |
| 20100114744 | Gonen | May 2010 | A1 |
| 20100121767 | Coulter et al. | May 2010 | A1 |
| 20100130172 | Vendrow et al. | May 2010 | A1 |
| 20100131273 | Aley-Raz et al. | May 2010 | A1 |
| 20100132043 | Bjorn et al. | May 2010 | A1 |
| 20100145836 | Baker et al. | Jun 2010 | A1 |
| 20100158207 | Dhawan et al. | Jun 2010 | A1 |
| 20100169210 | Bous et al. | Jul 2010 | A1 |
| 20100169947 | Sarmah et al. | Jul 2010 | A1 |
| 20100188684 | Kumara | Jul 2010 | A1 |
| 20100205662 | Ibrahim et al. | Aug 2010 | A1 |
| 20100217837 | Ansari et al. | Aug 2010 | A1 |
| 20100218255 | Ritman et al. | Aug 2010 | A1 |
| 20100228649 | Pettitt | Sep 2010 | A1 |
| 20100228657 | Kagarlis | Sep 2010 | A1 |
| 20100229225 | Sarmah et al. | Sep 2010 | A1 |
| 20100229230 | Edeki et al. | Sep 2010 | A1 |
| 20100229245 | Singhal | Sep 2010 | A1 |
| 20100241501 | Marshall | Sep 2010 | A1 |
| 20100250364 | Song et al. | Sep 2010 | A1 |
| 20100250411 | Ogrodski | Sep 2010 | A1 |
| 20100250509 | Andersen | Sep 2010 | A1 |
| 20100250955 | Trevithick et al. | Sep 2010 | A1 |
| 20100268557 | Faith et al. | Oct 2010 | A1 |
| 20100274679 | Hammad | Oct 2010 | A1 |
| 20100275265 | Fiske et al. | Oct 2010 | A1 |
| 20100280882 | Faith et al. | Nov 2010 | A1 |
| 20100293090 | Domenikos et al. | Nov 2010 | A1 |
| 20100293114 | Khan et al. | Nov 2010 | A1 |
| 20100302157 | Zilberman | Dec 2010 | A1 |
| 20100306101 | Lefner et al. | Dec 2010 | A1 |
| 20100313273 | Freas | Dec 2010 | A1 |
| 20100325035 | Hilgers et al. | Dec 2010 | A1 |
| 20100325442 | Petrone et al. | Dec 2010 | A1 |
| 20100332292 | Anderson | Dec 2010 | A1 |
| 20100332362 | Ramsey et al. | Dec 2010 | A1 |
| 20110004498 | Readshaw | Jan 2011 | A1 |
| 20110016042 | Cho et al. | Jan 2011 | A1 |
| 20110040983 | Grzymala-Busse et al. | Feb 2011 | A1 |
| 20110047071 | Choudhuri et al. | Feb 2011 | A1 |
| 20110066547 | Clark et al. | Mar 2011 | A1 |
| 20110082768 | Eisen | Apr 2011 | A1 |
| 20110093383 | Haggerty et al. | Apr 2011 | A1 |
| 20110112958 | Haggerty et al. | May 2011 | A1 |
| 20110119291 | Rice | May 2011 | A1 |
| 20110126024 | Beatson et al. | May 2011 | A1 |
| 20110126275 | Anderson et al. | May 2011 | A1 |
| 20110131123 | Griffin et al. | Jun 2011 | A1 |
| 20110145899 | Cao et al. | Jun 2011 | A1 |
| 20110166988 | Coulter | Jul 2011 | A1 |
| 20110184838 | Winters et al. | Jul 2011 | A1 |
| 20110184851 | Megdal et al. | Jul 2011 | A1 |
| 20110196791 | Dominguez | Aug 2011 | A1 |
| 20110238566 | Santos | Sep 2011 | A1 |
| 20110260832 | Ross et al. | Oct 2011 | A1 |
| 20110276496 | Neville et al. | Nov 2011 | A1 |
| 20110282778 | Wright et al. | Nov 2011 | A1 |
| 20110289032 | Crooks et al. | Nov 2011 | A1 |
| 20110289322 | Rasti | Nov 2011 | A1 |
| 20110295721 | MacDonald | Dec 2011 | A1 |
| 20110295750 | Rammal | Dec 2011 | A1 |
| 20110296529 | Bhanoo et al. | Dec 2011 | A1 |
| 20110302412 | Deng et al. | Dec 2011 | A1 |
| 20110302641 | Hald et al. | Dec 2011 | A1 |
| 20120030079 | Slater et al. | Feb 2012 | A1 |
| 20120030080 | Slater et al. | Feb 2012 | A1 |
| 20120030083 | Newman et al. | Feb 2012 | A1 |
| 20120030771 | Pierson et al. | Feb 2012 | A1 |
| 20120036352 | Tovar et al. | Feb 2012 | A1 |
| 20120066073 | Dilip et al. | Mar 2012 | A1 |
| 20120101939 | Kasower | Apr 2012 | A1 |
| 20120158574 | Brunzell et al. | Jun 2012 | A1 |
| 20120158654 | Behren et al. | Jun 2012 | A1 |
| 20120198556 | Patel et al. | Aug 2012 | A1 |
| 20120215682 | Lent et al. | Aug 2012 | A1 |
| 20120278227 | Kolo et al. | Nov 2012 | A1 |
| 20120278249 | Duggal et al. | Nov 2012 | A1 |
| 20120290660 | Rao et al. | Nov 2012 | A1 |
| 20130004033 | Trugenberger et al. | Jan 2013 | A1 |
| 20130132060 | Badhe et al. | May 2013 | A1 |
| 20130185293 | Boback | Jul 2013 | A1 |
| 20130218797 | Prichard et al. | Aug 2013 | A1 |
| 20140007238 | Magee et al. | Jan 2014 | A1 |
| 20140012716 | Bucholz | Jan 2014 | A1 |
| 20140058910 | Abeles | Feb 2014 | A1 |
| 20140149304 | Bucholz et al. | May 2014 | A1 |
| 20140214636 | Rajsky | Jul 2014 | A1 |
| 20140283097 | Allen et al. | Sep 2014 | A1 |
| 20140304822 | Sher-Jan et al. | Oct 2014 | A1 |
| 20150106260 | Andrews et al. | Apr 2015 | A1 |
| 20150142595 | Acuña-Rohter | May 2015 | A1 |
| 20150161529 | Kondaji et al. | Jun 2015 | A1 |
| 20150186901 | Miltonberger | Jul 2015 | A1 |
| 20150199784 | Straub et al. | Jul 2015 | A1 |
| 20150205692 | Seto | Jul 2015 | A1 |
| 20150295924 | Gottschalk, Jr. | Oct 2015 | A1 |
| 20150348036 | Nordyke et al. | Dec 2015 | A1 |
| 20150348208 | Nordyke et al. | Dec 2015 | A1 |
| 20160012561 | Lappenbusch et al. | Jan 2016 | A1 |
| 20160063278 | Kraska et al. | Mar 2016 | A1 |
| 20160063645 | Houseworth et al. | Mar 2016 | A1 |
| 20160071208 | Straub et al. | Mar 2016 | A1 |
| 20160086262 | Straub et al. | Mar 2016 | A1 |
| 20160142532 | Bostick | May 2016 | A1 |
| 20160210450 | Su | Jul 2016 | A1 |
| 20160328814 | Prichard et al. | Nov 2016 | A1 |
| 20160344758 | Cohen et al. | Nov 2016 | A1 |
| 20160379011 | Koike et al. | Dec 2016 | A1 |
| 20170053369 | Gottschalk, Jr. et al. | Feb 2017 | A1 |
| 20170099314 | Klatt et al. | Apr 2017 | A1 |
| 20170177683 | Koike et al. | Jun 2017 | A1 |
| 20170206376 | Sher-Jan | Jul 2017 | A1 |
| 20170270629 | Fitzgerald | Sep 2017 | A1 |
| 20170278182 | Kasower | Sep 2017 | A1 |
| 20170287065 | Samler et al. | Oct 2017 | A1 |
| 20170357971 | Pitz et al. | Dec 2017 | A1 |
| 20170374076 | Pierson et al. | Dec 2017 | A1 |
| 20180004978 | Hebert et al. | Jan 2018 | A1 |
| 20180013786 | Knopf | Jan 2018 | A1 |
| 20180033009 | Goldman et al. | Feb 2018 | A1 |
| 20180130157 | Gottschalk, Jr. et al. | May 2018 | A1 |
| 20180184288 | De Lorenzo et al. | Jun 2018 | A1 |
| 20180322572 | Straub et al. | Nov 2018 | A1 |
| 20190073676 | Wang | Mar 2019 | A1 |
| 20190164173 | Liu et al. | May 2019 | A1 |
| 20190228178 | Sharma et al. | Jul 2019 | A1 |
| 20190266609 | Phelan et al. | Aug 2019 | A1 |
| 20190294786 | Villavicencio et al. | Sep 2019 | A1 |
| 20190311366 | Zoldi et al. | Oct 2019 | A1 |
| 20190333101 | Sohum et al. | Oct 2019 | A1 |
| 20190349351 | Verma et al. | Nov 2019 | A1 |
| 20190377896 | Spinelli et al. | Dec 2019 | A1 |
| 20200134629 | Zoldi et al. | Apr 2020 | A1 |
| 20200143465 | Chilaka et al. | May 2020 | A1 |
| 20200145436 | Brown et al. | May 2020 | A1 |
| 20200151628 | Zoldi et al. | May 2020 | A1 |
| 20200193018 | Van Dyke | Jun 2020 | A1 |
| 20200242615 | Chandra et al. | Jul 2020 | A1 |
| 20200273097 | Nordyke et al. | Aug 2020 | A1 |
| 20200293684 | Harris et al. | Sep 2020 | A1 |
| 20200380112 | Allen | Dec 2020 | A1 |
| 20200396246 | Zoldi et al. | Dec 2020 | A1 |
| 20210021631 | Okutan et al. | Jan 2021 | A1 |
| 20210150532 | Zhang et al. | May 2021 | A1 |
| 20210209230 | Leitner et al. | Jul 2021 | A1 |
| 20210326785 | McBurnett et al. | Oct 2021 | A1 |
| 20210372314 | Weigl et al. | Dec 2021 | A1 |
| 20220038481 | Jones | Feb 2022 | A1 |
| 20220046088 | Knopf | Feb 2022 | A1 |
| 20220084032 | Koehler et al. | Mar 2022 | A1 |
| 20220103589 | Shen et al. | Mar 2022 | A1 |
| 20220123946 | Knopf | Apr 2022 | A1 |
| 20220147817 | Boardman et al. | May 2022 | A1 |
| 20220207324 | Hamilton et al. | Jun 2022 | A1 |
| 20220231859 | Knopf et al. | Jul 2022 | A1 |
| 20220277308 | Ardizzi et al. | Sep 2022 | A1 |
| 20220321394 | Huang et al. | Oct 2022 | A1 |
| 20220327541 | Seguritan | Oct 2022 | A1 |
| 20220358516 | Zoldi et al. | Nov 2022 | A1 |
| 20220368704 | Brown et al. | Nov 2022 | A1 |
| 20220377096 | Johnston et al. | Nov 2022 | A1 |
| 20220391793 | Latimer et al. | Dec 2022 | A1 |
| 20220400087 | Hoover et al. | Dec 2022 | A1 |
| 20220417275 | Jones | Dec 2022 | A1 |
| 20230035336 | Knopf | Feb 2023 | A1 |
| 20230046601 | Hamilton et al. | Feb 2023 | A1 |
| 20230082708 | Hoover et al. | Mar 2023 | A1 |
| 20230113118 | Guo et al. | Apr 2023 | A1 |
| 20230196147 | McBurnett et al. | Jun 2023 | A1 |
| 20230196455 | Huber et al. | Jun 2023 | A1 |
| 20230205893 | Gjorvad et al. | Jun 2023 | A1 |
| 20230216866 | Monnig et al. | Jul 2023 | A1 |
| 20230229767 | Galli et al. | Jul 2023 | A1 |
| 20230245246 | Stack et al. | Aug 2023 | A1 |
| Number | Date | Country |
|---|---|---|
| 2022291564 | Jul 2023 | AU |
| 3 058 653 | Apr 2020 | CA |
| 104877993 | Sep 2015 | CN |
| 113011973 | Jun 2021 | CN |
| 113011973 | Jun 2021 | CN |
| 91 08 341 | Oct 1991 | DE |
| 0 554 083 | Aug 1993 | EP |
| 2 939 361 | Oct 2019 | EP |
| 2 392 748 | Mar 2004 | GB |
| 2 518 099 | Mar 2015 | GB |
| 2011-134252 | Jul 2011 | JP |
| 5191376 | May 2013 | JP |
| 10-2004-0034063 | Apr 2004 | KR |
| I256569 | Jun 2006 | TW |
| WO 94006103 | Mar 1994 | WO |
| WO 96041488 | Dec 1996 | WO |
| WO 00055778 | Sep 2000 | WO |
| WO 00055789 | Sep 2000 | WO |
| WO 00055790 | Sep 2000 | WO |
| WO 01011522 | Feb 2001 | WO |
| WO 02027610 | Apr 2002 | WO |
| WO 02097563 | Dec 2002 | WO |
| WO 03071388 | Aug 2003 | WO |
| WO 02037219 | May 2004 | WO |
| WO 2004046882 | Jun 2004 | WO |
| WO 2006069199 | Jun 2006 | WO |
| WO 2007001394 | Jan 2007 | WO |
| WO 2007106393 | Sep 2007 | WO |
| WO 2008054403 | May 2008 | WO |
| WO 2008054849 | May 2008 | WO |
| WO 2008147918 | Dec 2008 | WO |
| WO 2009062111 | May 2009 | WO |
| WO 2009117518 | Sep 2009 | WO |
| WO 2011044036 | Apr 2011 | WO |
| WO 2012054646 | Apr 2012 | WO |
| WO 2012112781 | Aug 2012 | WO |
| WO 2013026343 | Feb 2013 | WO |
| WO 2013126281 | Aug 2013 | WO |
| WO 2014008079 | Jan 2014 | WO |
| WO 2014008247 | Jan 2014 | WO |
| WO 2014150987 | Sep 2014 | WO |
| WO 2015184006 | Dec 2015 | WO |
| WO 2018175440 | Sep 2018 | WO |
| WO 2018208770 | Nov 2018 | WO |
| WO 2019006272 | Jan 2019 | WO |
| WO 2019040443 | Feb 2019 | WO |
| WO 2019050864 | Mar 2019 | WO |
| WO 2019079071 | Apr 2019 | WO |
| WO 2019125445 | Jun 2019 | WO |
| WO 2019169000 | Sep 2019 | WO |
| WO 2022020162 | Jan 2022 | WO |
| WO 2022026273 | Feb 2022 | WO |
| WO 2022031412 | Feb 2022 | WO |
| WO 2022032285 | Feb 2022 | WO |
| WO 2022072989 | Apr 2022 | WO |
| WO 2022221202 | Oct 2022 | WO |
| WO 2023060150 | Apr 2023 | WO |
| WO 2023129977 | Jul 2023 | WO |
| Entry |
|---|
| Tax aggressiveness and accounting fraud C Lennox, P Lisowsky, J Pittman—Journal of Accounting . . . , 2013—Wiley Online Library (Year: 2013). |
| U.S. Appl. No. 14/928,770, U.S. Pat. No. 10,339,527, System and Architecture for Electronic Fraud Detection, filed Oct. 30, 2015. |
| U.S. Appl. No. 16/443,662, U.S. Pat. No. 10,990,979, System and Architecture for Electronic Fraud Detection, filed Jun. 17, 2019. |
| U.S. Appl. No. 17/208,327, U.S. Pat. No. 11,436,606, System and Architecture for Electronic Fraud Detection, filed Mar. 22, 2021. |
| U.S. Appl. No. 09/557,252, filed Apr. 24, 2000, Page. |
| U.S. Appl. No. 12/705,489, filed Feb. 12, 2010, Bargoli et al. |
| U.S. Appl. No. 12/705,511, filed Feb. 12, 2010, Bargoli et al. |
| “A New Approach to Fraud Solutions”, BasePoint Science Solving Fraud, pp. 8, 2006. |
| Aad et al., “NRC Data Collection and the Privacy by Design Principles”, IEEE, Nov. 2010, pp. 5. |
| Allard et al., “Safe Realization of the Generalization Privacy Mechanism”, 2011 Ninth Annual International Conference on Privacy, Security and Trust, pp. 8. |
| “Arizona Company Has Found Key in Stopping ID Theft,” PR Newswire, New York, Aug. 10, 2005 http://proquest.umi.com/pqdweb?did=880104711&SID=1&Fmt=3&clientId=19649&RQT=309&Vname=PQD. |
| ABC News Now:Money Matters, as broadcasted Nov. 15, 2005 with guest Todd Davis (CEO of Lifelock), pp. 6. |
| AlSalamah et al., “Security Risk Management in Online System”, 2017 5th International Conference on Applied Computing and Information Technology/4th International Conference on Computational Science/Intelligence and Applied Informatics/2nd International Conference on Big Data, Cloud Computing, Data Science & Engineering, 2017, pp. 119-124. |
| Anonymous, “Feedback”, Credit Management, ABI/Inform Global, Sep. 2006, pp. 6. |
| “Beverly Hills Man Convicted of Operating ‘Bust-Out’ Schemes that Caused More than $8 Million in Losses”, Department of Justice, Jul. 25, 2006, 2 Pgs. |
| Bielski, Lauren, “Will you Spend to Thwart ID Theft?” ABA Banking Journal, Apr. 2005, pp. 54, 56-57, 60. |
| BlueCava, “What We Do”, http://www.bluecava.com/what-we-do/, printed Nov. 5, 2012 in 3 pages. |
| “Bust-Out Schemes”, Visual Analytics Inc. Technical Product Support, Newsletter vol. 4, Issue 1, Jan. 2005, pp. 7. |
| Chores & Allowances, “Do Kids Have Credit Reports?” Oct. 15, 2007, http://choresandallowances.blogspot.com/2007/10/do-kids-have-credit-reports.html, pp. 5. |
| Cowie, Norman, “Warning Bells & ‘The Bust-Out’”, Business Credit, Jul. 1, 2000, pp. 5. |
| Cullen, Terri; “The Wall Street Journal Complete Identity Theft Guidebook:How to Protect Yourself from the Most Pervasive Crime in America”; Chapter 3, pp. 59-79; Jul. 10, 2007. |
| “Data Loss Prevention (DLP) Software”, http://www.symantec.com/data-loss-prevention/ printed Apr. 8, 2013 in 8 pages. |
| “Data Protection”, http://compliantprocessing.com/data-protection/ printed Apr. 8, 2013 in 4 pages. |
| Day, Jo and Kevin; “ID-ology: A Planner's Guide to Identity Theft”; Journal of Financial Planning:Tech Talk; pp. 36-38; Sep. 2004. |
| “Dealing with Measurement Noise (A Gentle Introduction to Noise Filtering)”, Chemical and Process Engineering, University of Newcastle Upon Tyne, https://web.archive.org/web/20000418021742/http://lorien.ncl.ac.uk/ming/filter/filewma.htm, Archived Apr. 18, 2000, pp. 3. |
| EFunds Corporation, “Data & Decisioning: Debit Report” printed Apr. 1, 2007, http://www.efunds.com/web/industry-solutions/financial-services/frm-debit-report/htm in 1 page. |
| El Kalam et al., “Personal Data Anonymization for Security and Privacy in Collaborative Environments”, 2005 IEEE, pp. 56-61. |
| Equifax; “Equifax Credit Watch”; https://web.archive.org/web/20070627135447/https://www.econsumer.equifax.co.uk/consumer/uk/sitepage.ehtml?forward=gb_esn_detail, dated Jun. 27, 2007 on www.archive.org in 2 pages. |
| Experian Team, “Impact on Credit Scores of Inquiries for an Auto Loan,” Ask Experian, Mar. 1, 2009, pp. 5. |
| “Fair Isaac Introduces Falcon One System to Combat Fraud at Every Customer Interaction”, Business Wire, May 5, 2005, pp. 3. |
| “Fair Isaac Offers New Fraud Tool”, National Mortgage News & Source Media, Inc., Jun. 13, 2005, pp. 2. |
| FamilySecure.com, “Frequently Asked Questions”, http://www.familysecure.com/FAQ.aspx as archived Jul. 15, 2007 in 3 pages. |
| FamilySecure.com; “Identity Theft Protection for the Whole Family | FamilySecure.com” http://www.familysecure.com/, as retrieved on Nov. 5, 2009. |
| “Fighting the New Face of Fraud”, FinanceTech, http://www.financetech.com/showArticle.jhtml?articleID=167100405, Aug. 2, 2005. |
| “FinExtra, Basepoint Analytics Introduces Predictive Technology for Mortgage Fraud”, Oct. 5, 2005, pp. 3. |
| Fisher, Joseph, “Access to Fair Credit Reports: Current Practices and Proposed Legislation,” American Business Law Journal, Fall 1981, vol. 19, No. 3, p. 319. |
| “Fraud Alert | Learn How”. Fight Identity Theft. http://www.fightidentitytheft.com/flag.html, accessed on Nov. 5, 2009. |
| Gaudio, David, “Intelligent Adaptive Authentication: How 6 Workflow Steps Improve Customer Experience”, OneSpan, https://www.onespan.com/blog/intelligent-adaptive-authentication-how-6-workflow-steps-improve-customer-experience, Jun. 22, 2020, pp. 6. |
| Gibbs, Adrienne; “Protecting Your Children from Identity Theft,” Nov. 25, 2008, http://www.creditcards.com/credit-card-news/identity-ID-theft-and-kids-children-1282.php, pp. 4. |
| “GLBA Compliance and FFIEC Compliance” http://www.trustwave.com/financial-services.php printed Apr. 8, 2013 in 1 page. |
| Gordon et al., “Identity Fraud: A Critical National and Global Threat,” LexisNexis, Oct. 28, 2003, pp. 1-48. |
| Haglund, Christoffer, “Two-Factor Authentication with a Mobile Phone”, Fox Technologies, Uppsala, Department of Information Technology, Nov. 2, 2007, pp. 62. |
| Herzberg, Amir, “Payments and Banking with Mobile Personal Devices,” Communications of the ACM, May 2003, vol. 46, No. 5, pp. 53-58. |
| “ID Analytics ID Network”, from www.idanalytics.com, as retrieved from www.archive.org, dated Nov. 20, 2005 or earlier; attached as “ID Network (IDN)”, pp. 8. |
| ID Cops, www.idcops.com; retrieved from www.archive.org any linkage Feb. 16, 2007. |
| ID Theft Assist, “Do You Know Where Your Child's Credit Is?”, Nov. 26, 2007, http://www.idtheftassist.com/pages/story14, pp. 3. |
| “ID Thieves These Days Want Your Number, Not Your Name”, The Columbus Dispatch, Columbus, Ohio, http://www.dispatch.com/content/stories/business/2014/08/03/id-thieves-these-days-want-your-numbre-not-your-name.html, Aug. 3, 2014 in 2 pages. |
| Identity Theft Resource Center; Fact Sheet 120 A—To Order a Credit Report for a Child; Fact Sheets, Victim Resources; Apr. 30, 2007. |
| “Identity Thieves Beware: Lifelock Introduces Nation's First Guaranteed Proactive Solution to Identity Theft Protection,” PR Newswire, New York, Jun. 13, 2005 http://proquest.umi.com/pqdweb?did=852869731&sid=1&Fmt=3&clientId=19649&RQT=309&Vname=PQD. |
| “Industry News, New Technology Identifies Mortgage Fraud: Basepoint Analytics Launches FraudMark”, Inman News, American Land Title Association, Oct. 5, 2005, pp. 1. |
| Information Brokers of America, “Information Brokers of America Child Identity Theft Protection” http://web.archive.org/web/20080706135451/http://iboainfo.com/child-order.html as archived Jul. 6, 2008 in 1 page. |
| Information Brokers of America, “Safeguard Your Child's Credit”, http://web.archive.org/web/20071215210406/http://www.iboainfo.com/child-id-protect.html as archived Dec. 15, 2007 in 1 page. |
| “Intersections, Inc. Identity Guard”, from www.intersections.com and www.identityguard.com, as retrieved from Internet Archive, dated Nov. 25, 2005 or earlier; attached as “Identity Guard (IDG)”, pp. 7. |
| Iovation, Device Identification & Device Fingerprinting, http://www.iovation.com/risk-management/device-identification printed Nov. 5, 2012 in 6 pages. |
| Jacob et al., A Case Study of Checking Account Inquiries and Closures in Chicago, The Center for Financial Services Innovation, Nov. 2006. |
| Jaeger, Herbert, “A Tutorial on Training Recurrent Neural Networks, Covering BPPT, RTRL, EKF and the ‘Echo State Network’ Approach”, Fraunhofer Institute for Autonomous Intelligent Systems (AIS), International University Bremen, Oct. 2002, pp. 46. |
| Jin et al., “Network Security Risks in Online Banking”, 2005 International Conference on Wireless Communications, Networking and Mobile Computing, Jan. 2005, vol. 2, pp. 1229-1234. |
| Karlan et al., “Observing Unobservables:Identifying Information Asymmetries with a Consumer Credit Field Experiment”, Jun. 17, 2006, pp. 58, http://aida.econ.yale.edu/karlan/papers/ObservingUnobservables.KarlanZinman.pdf. |
| Khan, Muhammad Khurram, PhD., “An Efficient and Secure Remote Mutual Authentication Scheme with Smart Cards” IEEE International Symposium on Biometrics & Security Technologies (ISBAST), Apr. 23-24, 2008, pp. 1-6. |
| Lamons, Bob, “Be Smart: Offer Inquiry Qualification Services,” Marketing News, ABI/Inform Global, Nov. 6, 1995, vol. 29, No. 23, pp. 13. |
| Lee, Timothy B., “How America's Broken Tax System Makes Identity Theft Easy”, http://www.vox.com/2014/4/14/5608072/how-americas-broken-tax-system-makes-identity-theft-easy, Apr. 14, 2014, pp. 10. |
| Lee, W.A.; “Experian, on Deal Hunt, Nets Identity Theft Insurer”, American Banker: The Financial Services Daily, Jun. 4, 2003, New York, NY, 1 page. |
| Lefebvre et al., “A Robust Soft Hash Algorithm for Digital Image Signature”, International Conference on Image Processing 2:11 (ICIP), vol. 3, Oct. 2003, pp. 495-498. |
| Lennox et al., “Tax Aggressiveness and Accounting Fraud”, Journal of Accounting Research, 2013, pp. 40. |
| LifeLock, “How LifeLock Works,” http://www.lifelock.com/lifelock-for-people printed Mar. 14, 2008 in 1 page. |
| LifeLock, “LifeLock Launches First ID Theft Prevention Program for the Protection of Children,” Press Release, Oct. 14, 2005, http://www.lifelock.com/about-us/press-room/2005-press-releases/lifelock-protection-for-children. |
| LifeLock; “How Can LifeLock Protect My Kids and Family?” http://www.lifelock.com/lifelock-for-people/how-we-do-it/how-can-lifelock-protect-my-kids-and-family printed Mar. 14, 2008 in 1 page. |
| LifeLock, “Personal Identity Theft Protection & Identity Theft Products,” http://www.lifelock.com/lifelock-for-people, accessed Nov. 5, 2007. |
| LifeLock, Various Pages, www.lifelock.com/, Jan. 9, 2007, pp. 49. |
| My Call Credit http://www.mycallcredit.com/products.asp?product=ALR dated Dec. 10, 2005 on www.archive.org. |
| My Call Credit http://www.mycallcredit.com/rewrite.asp?display=faq dated Dec. 10, 2005 on www.archive.org. |
| MyReceipts, http://www.myreceipts.com/, printed Oct. 16, 2012 in 1 page. |
| MyReceipts—How it Works, http://www.myreceipts.com/howItWorks.do, printed Oct. 16, 2012 in 1 page. |
| National Alert Registry Launches RegisteredOffendersList.org to Provide Information on Registered Sex Offenders, May 16, 2005, pp. 2, http://www.prweb.com/printer/240437.htm accessed on Oct. 18, 2011. |
| National Alert Registry Offers Free Child Safety “Safe From Harm” DVD and Child Identification Kit, Oct. 24, 2006. pp. 2, http://www.prleap.com/pr/53170 accessed on Oct. 18, 2011. |
| National Alert Registry website titled, “Does a sexual offender live in your neighborhood”, Oct. 22, 2006, pp. 2, http://web.archive.org/wb/20061022204835/http://www.nationallertregistry.com/ accessed on Oct. 13, 2011. |
| Ogg, Erica, “Apple Cracks Down on UDID Use”, http://gigaom.com/apple/apple-cracks-down-on-udid-use/ printed Nov. 5, 2012 in 5 Pages. |
| Organizing Maniac's Blog—Online Receipts Provided by MyQuickReceipts.com, http://organizingmaniacs.wordpress.com/2011/01/12/online-receipts-provided-by-myquickreceipts.com/ dated Jan. 12, 2011 printed Oct. 16, 2012 in 3 pages. |
| Pagano, et al., “Information Sharing in Credit Markets,” Dec. 1993, The Journal of Finance, vol. 48, No. 5, pp. 1693-1718. |
| Partnoy, Frank, Rethinking Regulation of Credit Rating Agencies: An Institutional Investor Perspective, Council of Institutional Investors, Apr. 2009, pp. 21. |
| Planet Receipt—Home, http://www.planetreceipt.com/home printed Oct. 16, 2012 in 1 page. |
| Planet Receipt—Solutions & Features, http://www.planetreceipt.com/solutions-features printed Oct. 16, 2012 in 2 pages. |
| Press Release—“Helping Families Protect Against Identity Theft—Experian Announces FamilySecure.com; Parents and guardians are alerted for signs of potential identity theft for them and their children; product features an industry-leading $2 million guarantee”; PR Newswire; Irvine, CA; Oct. 1, 2007. |
| Privacy Rights Clearinghouse, “Identity Theft: What to do if it Happens to You,” http://web.archive.org/web/19990218180542/http://privacyrights.org/fs/fs17a.htm printed Feb. 18, 1999. |
| Quinn, Tom, “Low Credit Inquiries Affect Your Credit Score”, Credit.com, May 2, 2011, pp. 2. |
| “Recurrent Neural Network”, as downloaded from wikipedia.org <https://en.wikipedia.org/wiki/Recurrent_neural_network?oldid=717224329>, Apr. 2016, pp. 8. |
| Ribeiro et al., “Privacy Protection with Pseudonumization and Anonumization In a Health IoT System”, Results from OCARIoT, 2019 IEEE, pp. 904-908. |
| Rivera, Barbara, “New Tools for Combating Income Tax Refund Fraud”, https://gcn.com/Articles/2014/05/08/Insight-tax-fraud-tools.aspx?Page=1, May 8, 2014, pp. 3. |
| Scholastic Inc.:Parent's Request for Information http://web.archive.org/web/20070210091055/http://www.scholastic.com/inforequest/index.htm as archived Feb. 10, 2007 in 1 page. |
| Scholastic Inc.:Privacy Policy http://web.archive.org/web/20070127214753/http://www.scholastic.com/privacy.htm as archived Jan. 27, 2007 in 3 pages. |
| ShoeBoxed, https://www.shoeboxed.com/sbx-home/ printed Oct. 16, 2012 in 4 pages. |
| Singletary, Michelle, “The Littlest Victims of ID Theft”, The Washington Post, The Color of Money, Oct. 4, 2007. |
| Sumner, Anthony, “Tackling the Issue of Bust-Out Fraud”, Retail Banker International, Jul. 24, 2007, pp. 4. |
| Sumner, Anthony, “Tackling the Issue of Bust-Out Fraud”, Experian: Decision Analytics, Dec. 18, 2007, pp. 24. |
| Sumner, Anthony, “Tackling the Issue of Bust-Out Fraud”, e-News, Experian: Decision Analytics, pp. 4, [Originally Published in Retail Banker International Magazine Jul. 24, 2007]. |
| “The Return Review: Program Increases Fraud Detection; However, Full Retirement of the Electronic Fraud Detection System Will be Delayed”, Treasury Inspector General for Tax Administration, Sep. 25, 2017, Reference No. 2017-20-080, pp. 27. |
| TheMorningCall.Com, “Cheap Ways to Foil Identity Theft,” www.mcall.com/business/columnists/all-karp.5920748jul01,0 . . . , published Jul. 1, 2007. |
| Torgler, Benno, “What Do We Know about Tax Fraud ?: An Overview of Recent Developments”, Social Research: An International Quarterly, vol. 74, No. 4, Winter 2008, pp. 1239-1270. |
| “TransUnion—Child Identity Theft Inquiry”, TransUnion, http://www.transunion.com/corporate/personal/fraudIdentityTheft/fraudPrevention/childIDInquiry.page as printed Nov. 5, 2009 in 4 pages. |
| Truston, “Checking if your Child is an ID Theft Victim can be Stressful,” as posted by Michelle Pastor on Jan. 22, 2007 at http://www.mytruston.com/blog/credit/checking_if_your_child_is_an_id_theft_vi.html. |
| Vamosi, Robert, “How to Handle ID Fraud's Youngest Victims,” Nov. 21, 2008, http://news.cnet.com/8301-10789_3-10105303-57.html. |
| Webpage printed out from http://www.jpmorgan.com/cm/ContentServer?c=TS_Content&pagename=jpmorgan%2Fts%2FTS_Content%2FGeneral&cid=1139403950394 on Mar. 20, 2008, Feb. 13, 2006, New York, NY. |
| Wilson, Andrea, “Escaping the Alcatraz of Collections and Charge-Offs”, http://www.transactionworld.net/articles/2003/october/riskMgmt1.asp, Oct. 2003. |
| International Search Report and Written Opinion for Application No. PCT/US2007/06070, dated Nov. 10, 2008. |
| International Search Report and Written Opinion for Application No. PCT/US2008/064594, dated Oct. 30, 2008. |
| International Search Report and Written Opinion for Application No. PCT/US09/37565, dated May 12, 2009. |
| Official Communication in Australian Patent Application No. 2012217565, dated May 12, 2017. |
| Official Communication in Australian Patent Application No. 2017203586, dated Jun. 18, 2019. |
| Official Communication in Australian Patent Application No. 2019279982, dated Dec. 19, 2019. |
| Official Communication in Canadian Patent Application No. 2,827,478, dated Jun. 29, 2017. |
| Official Communication in Canadian Patent Application No. 2,827,478, dated May 31, 2018. |
| Official Communication in Canadian Patent Application No. 2,827,478, dated Mar. 27, 2019. |
| Extended European Search Report for Application No. EP12747205, dated Sep. 25, 2014. |
| Supplementary European Search Report for Application No. EP12747205, dated Jun. 19, 2015. |
| Extended European Search Report for Application No. EP18748000, dated Dec. 13, 2018. |
| International Search Report and Written Opinion for Application No. PCT/US2012/025456, dated May 21, 2012. |
| International Preliminary Report on Patentability in Application No. PCT/US2012/025456, dated Aug. 21, 2013. |
| International Search Report and Written Opinion for Application No. PCT/US2011/033940, dated Aug. 22, 2011. |
| Aïmeur et al., “The Scourge of Internet Personal Data Collection”, 2013 International Conference on Availability, Reliability and Security, pp. 821-828. |
| Dimopoulou et al., “Mobile Anonymization and Pseudonymization of Structured Health Data for Research,” 2022 Seventh International Conference on Mobile and Secure Services (MobiSecServ), 2022, pp. 1-6. |
| El Haddad et al., “Exploring User Behavior and Cybersecurity Knowledge—An Experimental Study in Online Shopping”, 2018 16th Annual Conference on Privacy, Security and Trust (PST), pp. 10. |
| Hu et al. “Robust Support Vector Machines for Anomaly Detection in Computer Security”, 2003, Proceedings of the 2003 International Conference on Machine Learning and Applications, pp. 7. |
| International Search Report and Written Opinion for Application No. PCT/US2022/024277, dated Jul. 18, 2022. |
| Kundu et al., “BLAST-SSAHA Hybridization for Credit Card Fraud Detection”, Oct.-Dec. 2009, IEEE Transactions on Dependable and Secure Computing, vol. 6, No. 4, pp. 309-315. |
| Pervushin et al., “Determination of loss of information during data anonymization procedure,” 2016 IEEE 10th International Conference on Application of Information and Communication Technologies (AICT), 2016, pp. 1-5. |
| Sun et al., “Enhancing Security Using Mobility-Based Anomaly Detection in Cellular Mobile Networks”, Jul. 2006, IEEE Transactions on Vehicular Technology, vol. 55, No. 4, pp. 1385-1396. |
| Trivedi et al., “Parallel Data Stream Anonymization Methods: A Review,” 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS), 2022, pp. 887-891. |
| Number | Date | Country | |
|---|---|---|---|
| 62073714 | Oct 2014 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 17208327 | Mar 2021 | US |
| Child | 17814789 | US | |
| Parent | 16443662 | Jun 2019 | US |
| Child | 17208327 | US | |
| Parent | 14928770 | Oct 2015 | US |
| Child | 16443662 | US |
