Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1, 2, 11, 12, and 20 are indicted as amended. Claim10 appears to have a markup, but the status identifier still indicates “Original”. The status identifier should be changed to “Currently Amended”. Claims 1-20 are pending.
Response to Arguments
Applicant’s arguments, see pg. 11, filed 02/12/2026, with respect to 35 U.S.C. 112(b) have been fully considered and are persuasive. The 35 U.S.C. 112(b) rejection has been withdrawn.
Applicant's arguments filed 02/12/2026 regarding 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant argues that "Prior to the invention, credit reporting systems suffered from several technical problems, including: [i]nconsistency between Metro 2@ data structures and ACDV dispute data formats, requiring significant mapping and transformations to enable comparison; [i]nability to automatically validate interdependent data fields across different months of reporting; [l]ack of mechanisms to reconcile inconsistencies between data received from furnishers and data transmitted through dispute resolution channels; [h]igh error rates due to manual or incomplete validation processes." (Decl. f 25.) "These problems are not business or legal abstractions; they arise from computer-specific data representation limitations, structured file formats, and system interoperability constraints." Examiner disagrees. What applicant is describing is precisely business processes related to financial institutions and other furnishers of credit data who frequently encounter errors in the data they report and the data that returns to them from Credit Reporting Agencies (CRAs) through dispute processes. Examiner also notes that under Step 2A Prong One of the Alice/Mayo framework, the test is whether an abstract idea is set forth or described in the claims, not whether the problems the invention is attempting to solve are not business or legal abstractions. However, an evaluation is performed to determine whether the alleged improvement is merely an improvement of the abstract idea itself or an improvement in computers or technology, which is analyzed under Step 2A Prong Two of the Alice/Mayo test in assessing whether the claims integrate the judicial exception into a practical application. Under Step 2A Prong One, there is a judicial exception set forth or described in the claims, taking claim1 for example. The invention and claims are drawn towards database object analysis, non-compliance reporting, and specifically analyzing and verifying data across different types, ensuring that all values conform to the established protocols and identify any inconsistencies that may exist between datasets. The claim limitations directly correspond to certain methods of organizing human activity (commercial interaction, business relations) as evidenced by limitations detailing analyzing data for conformance incorporating additional rules based in regulatory guidance, court settlements, client feedback, etc., to verify conformance, analyzing reports from available datasets to identify inconsistent data, and generating error reports for the identified inconsistencies. Additionally, the claims as a while recite a fundamental economic concept: collecting, normalizing, and validating data according to rules. The fact that the domain in which it is implemented is credit reporting, that does not take the claims out of the abstract idea grouping. The claims also correspond to mental processes (observation, evaluation, judgment, opinion) since the claims heavily involve the observation and evaluation of various data sets, and making a decision (judgment/opinion) regarding inconsistencies, errors, etc. based on the observed and evaluated data. Further, the error detection algorithms amounts to mathematical concepts (mathematical equations). The claims recite an abstract idea. The core of what is claimed which is taking inconsistent data, reformatting it to a standard structure, running rules to check for errors, and generating a report, is precisely what human data analysis and compliance officers have done manually even before computers. Taking those steps to automate them via computer does not make the steps less abstract. Regarding certain methods of organizing human activity, MPEP §2106.04(a)(2)(II) provides that the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer may fall within the "certain methods of organizing human activity" grouping. Further, regarding mental processes, claims can recite a mental process even if they are claimed as being performed on a computer. If the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept, the claim is considered to recite a mental process. (MPEP §2106.04(a)(2)(III)).
The Federal Circuit has explained that "the 'directed to' inquiry applies a stage-one filter to claims, considered in light of the specification, based on whether 'their character as a whole is directed to excluded subject matter."' Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335 (Fed. Cir. 2016) (quoting Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1346 (Fed. Cir. 2015)). It asks whether the focus of the claims is on a specific improvement in relevant technology or on a process that itself qualifies as an "abstract idea" for which computers are invoked merely as a tool. Here, it is clear from the Specification (including the claim language) that claim 1 focuses on an abstract idea, and not on an improvement to technology and/or a technical field. Applicant’s specification recites in [0003] “Financial institutions and other furnishers of credit data (Data Furnishers) frequently encounter errors in the data they report and the data that returns to them from Credit Reporting Agencies (CRAs) through dispute processes. These errors can stem from various sources, including data entry mistakes, system integration issues, data transformations by Credit Reporting Agencies, and the complexity of the required format for credit data reporting, the Metro 2® Format, and/or errors tied to the automated online form, the Automated Universal Dataform (AUD), used by Data Furnishers and their vendors to request out-of-cycle credit history updates, i.e., a correction to a consumer's CRA file for consideration outside of the regular activity reporting cycle process. The aforementioned errors can lead to incorrect credit reports, which may adversely affect consumers' credit scores and their ability to secure loans and other financial products. Inaccurate credit reporting not only affects consumers but also exposes financial institutions to legal risks and compliance issues. Consumers may dispute incorrect data through the Credit Reporting Agencies, leading to a process called Automated Credit Dispute Verification (ACDV). If disputes are not resolved accurately and promptly, it can result in increased litigation under the FCRA. The increasing volume of FCRA litigation highlights the need for robust error detection and correction mechanisms.” Also, in [0004] “Current error-checking solutions primarily focus on rules-based error detection at the initial data input stage, such as Metro 2, and may include rudimentary checks of the ACDV Response. However, these solutions do not comprehensively connect analyses across data sources and time to fully understand and address errors. While these solutions are useful, they often fall short in addressing errors that occur at multiple stages of the credit reporting process. Moreover, they may not effectively handle complex interdependencies between different data values or adapt to evolving industry standards. Financial data sets often contain interdependent values where the accuracy of one value depends on another. For example, if a loan balance is zero, the corresponding monthly payment should also be zero. Existing systems may not adequately handle such interdependencies, especially those involving interactions of multiple data types, leading to missed errors.” Finally, in [0005] “Accordingly, there remains a need to address the aforementioned technical drawbacks in providing an advanced credit data database object analysis and non-compliance reporting system that reduces the risk of litigation and enhances the accuracy and reliability of financial data reporting.” The cited portions of the specification along with the claim limitations confirm that the claims are directed to a judicial exception, in which computers are merely invoked as a tool, not an improvement in computers or technology. Examiner does not agree that said drawbacks are “technical” in nature. The alleged improvement is a best an improvement the judicial exception itself. It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP §2106.05(a). It is also important to keep in mind that an improvement in the judicial exception itself (e.g., a recited fundamental economic concept) is not an improvement in technology (emphasis added). For example, in Trading Technologies Int’l v. IBG LLC, the court determined that the claim simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology. Similarly, the Applicant’s claim recitations are at best an improvement in the judicial exception, not an improvement in technology. Applicant claims that the claimed invention improves computer functionality by “[e]nabling automated verification of interdependent credit data fields, from differing data formats, across multiple months of data reporting; [c]onverting heterogeneous dispute data into a uniform, machine-verifiable format; [a]pplying updated validation rules derived from regulatory guidance, court settlements, and client feedback without requiring system redesign” Examiner disagrees. What applicant is describing is not an improvement to computer functionality. Applicant is describing an improvement to an abstract business process. Technical improvements to computers or computer functionality focuses on enhancing the tools, software, or machinery, while business process improvement focuses on streamlining the steps, workflows, and methodologies people use to do their work. Applicant’s claims fall in the latter. Applicant presents no evidence showing an improvement to computer operations or functionality. The computers utilized in the applicant’s invention and claims are performing standard computer processing operations: collecting, analyzing/processing, formatting, and outputting data.
Applicant further argues that the claims recite a multi-stage computer-implemented process that requires “[r]eceiving multiple datasets of distinct, inconsistent data types (e.g., Metro 2® and ACDV); [r]eformatting and mapping those datasets into a normalized data structure compatible with Metro 2@ formatting; [a]pplying pluralities of error-detection algorithms that evaluate interdependencies between multiple data values within and across reporting periods; [g]enerating dataset-specific and cross-dataset nonconformance and inconsistency reports” and that is directed to an improved data-processing architecture for regulated financial data. Examiner disagrees. The reformatting or conversion one format to another is a longstanding practice and results in no claimed improvement of the computer itself. "The mere function of converting is not a specific improvement to the way computers operate." University of Florida Research Foundation v. GE Company, 916 F. 3D 1363 (Fed. Cir. 2019) (slip op. at 10). The error-detection algorithms evaluate data values against rules which is computational rule-checking. This is a task that a human reviewer would do manually. No technical improvement to detection methodology, for example, is claimed; just applying rules. Applicant’s claims are the opposite of McRO, Inc. dba Planet Blue v. Bandai Namco Games American Inc., 120 USPQ2d 1091 (Fed. Cir. 2016) ("McRO"), where the court found an improvement to computer technology. The basis for the court’s decision was that the claims improved a computer-related technology by enabling the computer to perform functions that previously could not be performed by a computer and that required the subjective judgement of a human. The court emphasized both the specific claiming of the rules and the specification’s explanation of how the claimed rules enabled the automation of these specific animation tasks that previously could not be automated. This enabling of functionality that could not previously be performed by a computer was what amounted to the improvement in computer-related technology, not the simple recitation of a set of particular rules. Lastly, the report generation is straightforward data output and presenting the results of data analysis, which is insufficient to confer patent eligibility.
Applicant’s references Desjardins is severely misplaced. The technology in Desjardins was directed to an improvement in machine-learning technology. No where in the applicant’s specification or claims indicates applicant’s invention improves machine-learning technology, nor an improvement the functioning of computers. Applicant’s error-detection algorithms is merely an abstract algorithm applied on a computer to data sets. As stated from Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025), "Finally, the claimed methods are not rendered patent eligible by the fact that (using existing machine learning technology) they perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025), slip op. at 15. Additionally, "[P]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025) (slip op. at 18). In addressing applicant’s insinuation that their claims change the architecture itself, the argument is unpersuasive. The improvement must be to the computer’s own functionality, not to the business process or data workflow the computer executes (see Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335 (Fed. Cir. 2016). Recharacterizing a data processing workflow as an “architecture” appears to be an argument in how the applicant is attempting to label the data processing workflow. However, the analysis is whether the claims recite a concrete technical improvement to a computer operation, not what the applicant calls the alleged improvement. Applicant appears to be conflating how information flows through a business or compliance process (which is directed to the judicial exception groupings identified above), versus how data moves through computer memory, processors, buses, or network topology which may be more concrete. Applicant’s claims describe the former. Receiving data sets, reformatting them, comparing them, and outputting nonconformance reports constitutes logical workflow. It is a seance of data handling steps that could be depicted in a flowchart that a human analysis would follow. The court in Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016) found that claims directed to collecting, analyzing, and displaying data as abstract. Converting data from one format to another compatible format is a mapping function (or translation). Reformatting does not improve the computer; it improves the usability of data for a compliance purpose. The normalized data structure compatible with Metro2® formatting simply describes the output format, not a novel data structure with technical properties that improve computer operation. Further Metro2® is an externally defined credit reporting format. Claiming that data is reformatted into a preexisting industry standard is not a technical contribution by the applicant; The architecture of Metro2® belongs to the credit reporting industry, not to this invention.
Additionally, applicant’s “additional elements” are generic computing components. The judicial exception is not integrated into a practical application simply because the claims recite the additional elements of: a database object, a computer, a server coupled with the computer, and a non-transitory computer-readable medium (claims 1 and 20). The additional elements are computer components recited at a high-level of generality performing the above-mentioned limitations and therefore amounts to no more than mere instructions to apply the exception using a generic computer. The combination of the additional elements are no more than mere instructions to apply the judicial exception using a generic computer. Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The examiner also notes that the information that appears in the respective Affidavit appear to be statements of opinion, respectively, and are not evidentiary facts, per se. Additionally, Mr. Scarborough is a named inventor and are all employed by the assignee, Bridgeforce Data Solutions, LLC, making their statements appear to be self-serving, and their conclusions do not appear to be made an objective review of patent eligibility and the 35 U.S.C. 101 rejections.
The 35 U.S.C. 101 rejection is maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Amended claims 1, 11, and 20 contain the trademark/trade name Metro2®. Where a trademark or trade name is used in a claim as a limitation to identify or describe a particular material or product, the claim does not comply with the requirements of 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph. See Ex parte Simpson, 218 USPQ 1020 (Bd. App. 1982). The claim scope is uncertain since the trademark or trade name cannot be used properly to identify any particular material or product. A trademark or trade name is used to identify a source of goods, and not the goods themselves. Thus, a trademark or trade name does not identify or describe the goods associated with the trademark or trade name. In the present case, the trademark/trade name is used to identify/describe an electronic format or layout and, accordingly, the identification/description is indefinite.
Dependent claims 2-10 and 12019 are also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, due to their dependency on the rejected claims above. Also, the trademark is also present in some of the dependent claims as well.
Claim Objections
Claim 10 is objected to because of the following informalities: claim 10 appears to have a markup, but the status identifier still indicates “Original”. The status identifier should be changed to “Currently Amended”. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Claims 1-10 recite a system (i.e. machine), claims 11-19 recite a method (i.e. process), and claim 20 recite non-transitory computer-readable storage medium (i.e. machine or article of manufacture). Therefore claims 1-20 fall within one of the four statutory categories of invention.
Claims 1, 11, and 20 recite the limitations: analyzing [a database object] comprising data values for conformance of the values to parameter protocols, and generating an error report for one or more identified inconsistencies in the data values; receive a first data set, of a first data set type, associated with [a database object], wherein the first data set originates from a first source external to [the computer]; wherein [the computer] processes the first data set by applying a plurality of error detection algorithms to the first data set, incorporating additional rules based on regulatory guidance, court settlements, and client feedback to verify conformance of the data values of the first data set to a first set of predefined parameter protocols, wherein the parameter protocols include rules based on interdependencies between two or more of the data values within the data set, for a specific month of data or across multiple months; identify and flag nonconformance of one or more of the data values of the first data set relative to the first set of predefined parameter protocols and generate a first report indicating nonconformance within the first data set; receive a second data set, of a second data set type, associated with [the object], wherein the second data set originates from a second source, external to the first source; reformat and map the data of the second data set associated with [the object], wherein the reformatting and mapping conform to the data formatting and mapping of the first data set; wherein [the computer] processes the second data set by applying a plurality of error detection algorithms to the second data set to verify conformance of the data values of the second data set to a second set of predefined parameter protocols based on interdependencies between two or more of the data values within the second data set; identify and flag nonconformance of one or more of the data values of the second data set relative to the second set of predefined parameter protocols and generate a second report indicating nonconformance within the second data set; receive a third data set, of a third data set type, associated with [the object], the third data set originating from the first source and received from the second source; reformat and map the data of the third data set associated with [the object], wherein the reformatting and mapping conform to the data formatting and mapping of the first data set; wherein [the computer] processes the third data set by applying a plurality of error detection algorithms to the third data set to verify conformance of the data values of the third data set to a third set of predefined parameter protocols based on interdependencies between two or more of the data values within the third data set; identify and flag nonconformance of one or more of the data values of the third data set relative to the second set of predefined parameter protocols and generate a third report indicating nonconformance within the third data set; reformat and map each data set from each respective data set type to conform to the data formatting and mapping of the first data set; identify and flag nonconformance of one or more of the data values for each data set for each respective data set type and generate a report indicating nonconformance for each data set; analyze the reports from available data sets across all data set types to identify inconsistent data values within one or more of the data set types; and generate an error report for one or more identified inconsistencies in the data values. Additionally claim 20 recites: receiving a fourth data set of a fourth data set type associated with [the object], wherein the fourth data set type originates from the first source and received from the second source, is in data type that conforms to an Automated Universal Dataform (AUD) standardized response format, and is furnished by, or on behalf of, the data furnisher as an additional response to the request to confirm the accuracy of the first data set; processing the fourth data set of a fourth data set type by reformatting and mapping to the first data set type and applying a plurality of [error detection algorithms] to the fourth data set to verify conformance of the values of the fourth data set to the fourth set of predefined parameter protocols based on interdependencies between two or more of the values within the fourth data set; identifying and flagging nonconformance of one or more of the values of the fourth data set relative to the fourth set of predefined parameter protocols to generate a fourth report indicating nonconformance within the fourth data set; receiving additional data sets of any or all of the data set types; identifying and flagging nonconformance of one or more of the values for each data set for each respective data set type and generate a report indicating nonconformance for each data set; analyzing the reports from available data sets across all data set types to identify inconsistent data values within one or more of the first, second, third and fourth data set types; and generating an error report for one or more identified inconsistencies in the data values. The invention and claims are drawn towards database object analysis, non-compliance reporting, and specifically analyzing and verifying data across different types, ensuring that all values conform to the established protocols and identify any inconsistencies that may exist between datasets. The claim limitations directly correspond to certain methods of organizing human activity (commercial interaction, business relations) as evidenced by limitations detailing analyzing data for conformance incorporating additional rules based in regulatory guidance, court settlements, client feedback, etc., to verify conformance, analyzing reports from available datasets to identify inconsistent data, and generating error reports for the identified inconsistencies. Additionally, the claims as a while recite a fundamental economic concept: collecting, normalizing, and validating data according to rules. The fact that the domain in which it is implemented is credit reporting, that does not take the claims out of the abstract idea grouping. The claims also correspond to mental processes (observation, evaluation, judgment, opinion) since the claims heavily involve the observation and evaluation of various data sets, and making a decision (judgment/opinion) regarding inconsistencies, errors, etc., based on the observed and evaluated data. Further, the error detection algorithms amounts to mathematical concepts (mathematical equations). The claims recite an abstract idea.
Note: the features or elements in brackets in the above Step 2A Prong One section are inserted for reading clarity, but are analyzed as “additional elements” under Step 2A Prong Two and Step 2B below.
The judicial exception is not integrated into a practical application simply because the claims recite the additional elements of: a database object, a computer, a server coupled with the computer, and a non-transitory computer-readable medium (claims 1 and 20). The additional elements are computer components recited at a high-level of generality performing the above-mentioned limitations and therefore amounts to no more than mere instructions to apply the exception using a generic computer. The combination of the additional elements are no more than mere instructions to apply the judicial exception using a generic computer. Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using a generic computer. Mere instructions to apply an exception using a generic computer cannot provide an inventive concept. Thus, when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. The claims are not patent eligible.
Dependent claims 2-10 and 12-19 recite additional limitations that are further directed to the abstract idea analyzed in the rejected claims above. The claims also recite additional elements that have been analyzed in the rejected claims above. Thus, claims 2-10 and 12-19 are also rejected under 35 U.S.C. 101.
Allowable Subject Matter
Claims 1-20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action.
The closest patent or patent application prior art reference(s) found that is/are relevant to the applicant’s invention includes Wong (2016/0267082) which discloses a system for the management of data related to risk exposure. A system may be provided includes receiving data from a plurality of data sources; a rules engine for applying one or more logical rules that are triggered by one or more conditions associated with the integrity of the received data; and one or more utilities configured to apply the one or more logical rules to validate received data and automatically request updated data from a subset of the plurality of data sources where the integrity of the received data does not meet a predefined threshold. The reference does not appear to disclose the detailed limitations of the applicant’s claims. Another reference found that is relevant to the applicant’s invention is Rausch (20210158171) which discloses receiving a request for a data catalog; in response to the request specifying a structural feature, analyzing metadata of multiple data sets for an indication of including it, and to retrieve an indicated degree of certainty of detecting it for data sets including it; in response to the request specifying a contextual aspect, analyzing context data of the multiple data sets for an indication of being subject to it, and to retrieve an indicated degree of certainty concerning it for data sets subject to it; selectively include each data set in the data catalog based on the request specifying a structural feature and/or a contextual aspect, and whether each data set meets what is specified; for each data set in the data catalog, generate a score indicative of the likelihood of meeting what is specified; and transmit the data catalog to the requesting device. The reference does not appear to disclose the detailed limitations of the applicant’s claims. The claims appear to overcome the prior art.
The closest non-patent literature reference found that is relevant to the applicant’s invention includes the publication “IEEE Recommended Practice for the Quality Management of Datasets for Medical Artificial Intelligence” (IEEE Engineering in Medicine and Biology Society; 2022) which generally discloses best practices for establishing a quality management system for data sets used for artificial intelligence in the area of medical devices. The publication covers data set management, including items, such as but not limited to, data collection, transfer, utilization, storage, maintenance, and updates. The recommended practice recommends a list of critical factors that impact the quality of data sets, such as but not limited to, data sources, data quality, annotation, privacy protection, personnel qualification/training/evaluation, tools, equipment, environment, process control, and documentation. The reference does not appear to disclose the detailed limitations of the applicant’s claims. The claims appear to overcome the prior art.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DIONE N SIMPSON whose telephone number is (571)272-5513. The examiner can normally be reached M-F; 7:30 a.m.-4:30 p.m..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sarah Monfeldt can be reached at (571) 270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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DIONE N. SIMPSON
Primary Examiner
Art Unit 3628
/DIONE N. SIMPSON/Primary Examiner, Art Unit 3629
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