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 Claims
Applicant’s submission filed 12/16/25 has been entered. Claims 15-24 are canceled. Claims 1-14 are presented for examination.
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 1-14 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
STEP 1
Are the claims directed to a process, machine, manufacture or composition of matter?
Claims 1-14 are all directed to a statutory category (e.g., a process, machine, manufacture, or composition of matter). The answer is YES.
STEP 2A. Prong 1
The claims disclose the abstract idea of auditing invoices based on rate data collected from other invoices.
Exemplary claim 8 recites the following abstract concepts that are found to include “abstract idea”:
“--generating a first plurality of rate data by scraping rate data from a first invoice;
--first classifying the first plurality of rate data based on identifying rate data types in the first plurality of rate data;
--first populating a plurality of rate tables with the first plurality of rate data based on the rate data types in the first plurality of rate data, wherein each of the plurality of rate tables are populated with rate data from previously scraped invoices and each of the plurality of rate tables tracks a different type of rate data;
--first auditing the invoice based on comparing a plurality of values generated from data in each of the plurality of rate tables; and
--generating first audit result data based on the first auditing.”
The remaining limitations are no more than computer elements (i.e., a computer) to be used as a tool to perform this abstract idea.
The recited limitations cover a process that, under its broadest reasonable interpretation, covers subject matter viewed as a certain method of organizing human activity with the additional recitation of generic computer components. For example, but for the “by a computer system” language, “generating, classifying, populating, auditing, generating“ in the context of this claim encompasses the user manually obtaining rate data, populating a spreadsheet/table, reviewing the invoice and generating results based on the review..
The practice of generating, classifying, populating, auditing data, as well as generating results based on auditing/reviewing invoices is a commercial or legal interaction long prevalent in our system of commerce. The claims recite the idea of performing various conceptual steps generically resulting in the audit/review of invoices. As determined earlier, none of these steps recites specific technological implementation details, but instead get to this result by receiving, selecting and determining data. Thus, the claims are directed to a certain method of organizing human activity
STEP 2A, Prong 2
Are there additional elements or a combination of elements in the claim that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception?
The claim recites no additional element that imposes a meaningful limit on the judicial exception.
--first classifying the first plurality of rate data based on identifying rate data types in the first plurality of rate data with a neural network trained on rate data and rate data types;
The neural network in the steps is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing data). This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component.
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim is directed to an abstract idea.
STEP 2B
The next issue is whether the claims provide an inventive concept because the additional elements recited in the claims provide significantly more than the recited judicial exception. Taking the claim elements separately, the function performed by the computer at each step of the process is purely conventional. The claim does 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, using a computer to perform the cited steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Considered as an ordered combination, the computer components of Applicants' claims add nothing that is not already present when the steps are considered separately. The claimed invention does not focus on an improvement in computers as tools, but rather certain independently abstract ideas that use computers as tools. {Elec. Power, 830 F.3d at 1354). (Step 2B: NO).
There is no indication that indication that the computer is anything other than a generic, off-the-shelf computer component, and the Symantec, TLI, and OIP Techs. Court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or receipt of data over a network is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here).
Independent claim 8 recites similar limitations as claim 1 and is therefore rejected under the same rationale.
The dependent claims when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea. The claims provide minimal technical structure or components for further consideration either individually or as ordered combinations with the independent claims. As such, additional recited limitations in the dependent claims only refine the identified abstract idea further. Further refinement of an abstract idea does not convert an abstract idea into something concrete.
Accordingly, a conclusion that the collecting step is well-understood, routine, conventional activity is supported under Berkheimer Option 2.
See MPEP 2106.05(d)(II) The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350,1355,112 USPQ2d 1093,1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hoteis.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result-a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306,1334,115 USPQ2d 1681,1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363,115 USPQ2d at 1092-93.
The claims are ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 6-11, 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Axelrod et al. (US 20160063625 A1), in view of Bishop et al. (US 10915934 B2).
Re-claim 1, Axelrod et al. teach a computer-implemented method for peer-based remediation invoice auditing, the computer-implemented method comprising:
--generating a first plurality of rate data by scraping rate data from a first invoice;
(see e.g. [0048] Returning to FIG. 2, in step 202, the facility extracts and normalizes the relevant contents of the bill received in step 202, including each of the charges. Table 1A below contains the basic information extracted from the bill shown in FIG. 3. In various embodiments, the facility does the extraction from a PDF file, image, or a web page using various combinations of automated and manual processes.
--first populating a plurality of rate tables with the first plurality of rate data based on the rate data types in the first plurality of rate data, wherein each of the plurality of rate tables are populated with rate data from previously scraped invoices and each of the plurality of rate tables tracks a different type of rate data;
(see e.g. 0032] The facility uses the information extracted from the retrieved bills to construct and maintain several models: from all of the bills, irrespective of seller identity, ---The bill further includes charges 330 including the following: a customer charge 331 of $40.29; a distribution charge 332 of $373.49.
[0050] Further, the facility classifies each charge as either a supply charge that is determined by the supplier or a distribution charge that is associated with the utility and rate class.
****The Examiner notes the customer charge and the distribution charge are different types of data used to construct the model.
----first auditing the invoice based on comparing a plurality of values generated from data in each of the plurality of rate tables;
(see e.g. 0032] compares the actual amounts buyer bill for that billing period to the predicted amounts; --As the facility continues to collect and analyze bills for different energy customers, it continues to update its models using the service as a form of machine learning.
[0033] In some embodiments, the nature of the models generated by the facility is that the facility maintains a bill corpus table in which each entry represents an energy bill received by the facility,
--In some embodiments, the facility compares the results of applying its model to bills received by buyers using the service to (1) identify and address any errors in these bills on the buyers' behalf, and (2) keep the model abreast of legitimate changes in pricing by suppliers or utilities, tax levels, etc.
[0127] In some embodiments, the facility obtains consideration from one or more sellers—such as discounted rates from the seller for buyers using the service—by providing to these sellers information about how their prices compare to competitors' prices.
--generating first audit result data based on the first auditing.
[0052] In step 203, as a basis for assessing the validity of this bill, the facility uses its model to estimate the proper amount of each of the extracted charges, such as the charges shown in Table 1C above.
[0077] Such review may reveal that the correct rate recently changed, that the bill in FIG. 3 should be treated as validated, and the bill in FIG. 5 should be removed from the bill corpus as no longer accurate; or, the facility may determine that the rate shown on the bill in FIG. 3 is erroneous, and pursue correction of the error. After steps 403, the steps shown in FIG. 4 conclude.)
The Examiner further notes one of ordinary skill in the art would know the reveal of the correct rate and the suggestions above in [0077] is a result of the review.
Although Axelrod et al. teach --first classifying the first plurality of rate data based on identifying rate data types in the first plurality of rate data;
(see e.g. [0050] Table 1C below includes charges extracted by the facility from the bill shown in FIG. 3. Because bill formats vary from utility to utility, and even among customers of the same utility, in some embodiments the facility maps variant names for charges each to a standardized name. Further, the facility classifies each charge as either a supply charge that is determined by the supplier or a distribution charge that is associated with the utility and rate class. Taxes are associated with utility and rate class because each rate class is specific to a jurisdiction.
Axelrod et al. do not explicitly teach the following limitation.
However, Bishop et al. teach ---
--first classifying the first plurality of rate data based on identifying rate data types in the first plurality of rate data with a neural network trained on rate data and rate data types;
(see e.g. col. 4, lines 15-24 --Further, the invoice management machine learning module 36 may comprise artificial intelligence that is utilized by the invoice management computing apparatus 12 to identify different types of charge data within hybrid electronic invoice data. In this particular example, the invoice management machine learning module 36 may comprise a deep neural network (DNN) that may be executed and used to identify different types of charge data in the hybrid electronic invoice data, although other types of artificial intelligence may be used.
Claim 1 ---identifying, by an invoice management computing apparatus, a pharmacy charge data record and a type of charge data associated with the pharmacy charge data record from a plurality of pharmacy charge data records and non-pharmacy charge data records in a received hybrid electronic invoice, by using a machine learning algorithm trained on historic hybrid invoice data comprising a plurality of pharmacy records, each pharmacy record characterized by a type of charge data from a plurality of types; extracting, by the invoice management computing apparatus, the charge data associated with the identified pharmacy charge data record from the received hybrid electronic invoice by tagging one or more locations in the received hybrid electronic invoice associated with the identified pharmacy charge data record of the identified charge data type; )
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Axelrod et al., and include the steps cited above, as taught by XU, in order to provide significantly faster and more cost efficient identification of different types of charge data in the electronic invoice data. (see e.g. col. 4, lines 28-32).
Re-claim 2, Axelrod et al. teach the computer-implemented method of claim 1, wherein the plurality of rate tables comprises at least one of a labor rate table, an equipment rate table and a materials rate table.
(see e.g. [0026] Energy sellers are willing to provide custom price quotes below their standard published rates because of the volume of energy to be purchased by a very large buyer (typically 5,000 megawatt hours (MWh) per year or greater.
[0032] , a model of the price charged to the buyer by the seller for energy).
[0056] Among the six entries of the bill corpus table shown in Table 2 above, the facility identifies the first, fourth, and fifth entries as relevant to estimating distribution charges for the bill shown in FIG. 3. T)
Re-claim 3, Axelrod et al. teach the limitations as in claim 1 above and further anticipate processing a second invoice and a second audit. (see e.g. abstract -- The facility analyzes at least one bill issued to the energy customer )
the computer-implemented method of claim 1 further comprising:
second generating a second plurality of rate data by scraping rate data from a second invoice; second classifying the second plurality of rate data based on identifying rate data types in the second plurality of rate data; second populating the plurality of rate tables with the second plurality of rate data based on the rate data types in the second plurality of rate data; second auditing the second invoice based on second comparing a second plurality of values generated from second data in each of the plurality of rate tables, the second data including the rate data from previously scraped invoices and the first plurality of rate data from the first invoice; and generating second audit result data based on the second auditing.
Re-claim 4, Axelrod et al. do not explicitly teach the computer-implemented method of claim 1, wherein the second data includes rate data from a third invoice scraped after the first auditing and before the second auditing.
However, since Axelrod et al. collects several sets of data from several energy suppliers ( (see e.g. abstract -The facility obtains pricing information for a plurality of second energy suppliers each different from the first energy supplier), the order in which the data is processed with regards to the auditing process is considered an obvious variation of Axelrod et al. No unpredictable results is foreseen.
Re-claims 6, 7, Axelrod et al. teach the computer-implemented method of claim 1 further comprising: generating a benchmark audit of the first audit result and flagging the first audit if the benchmark audit is above a benchmark threshold.
The computer-implemented method of claim 6, wherein the benchmark threshold value is based on at least one of invoice total, total amount of issues, total amount of labor issues, total amount of equipment issues and total amount of material issues.
(see e.g. [0077] By comparing the quantities, rates, and totals between Tables 4 and 1 c, it can be seen that the state tax adjustment rate that was predicted, −0.00212 differs from the state tax adjustment rate that was extracted from the bill shown in FIG. 3, −0.0023, causing the state tax adjustment total to also diverge. Additionally, the quantity and total for sales tax [distribution] also diverge because of a dependency of that quantity on the divergent state tax adjustment rate. The facility therefore flags this aspect of the bill in FIG. 3 to review. Such review may reveal that the correct rate recently changed, that the bill in FIG. 3 should be treated as validated, and the bill in FIG. 5 should be removed from the bill corpus as no longer accurate; or, the facility may determine that the rate shown on the bill in FIG. 3 is erroneous, and pursue correction of the error.
[0111] The facility sums the cost associated with each of these factors to produce a total cost for each candidate contract. In some embodiments, the facility considers contract with the lowest total cost the best. In some embodiments, the facility applies a weighting factor relating to clean energy. For example, in some embodiments, the buyer can specify a price premium that the buyer is willing to pay for any supplier that uses at least a threshold level of clean energy—such as a willingness to pay 10% more for any supplier that uses at least 50% clean energy..)
Claim 8 recites similar limitations as claim 1 and is therefore rejected under the same arts and rationale.
Claim 9 recites similar limitations as claim 2 and is therefore rejected under the same arts and rationale.
Claim 10 recites similar limitations as claim 3 and is therefore rejected under the same arts and rationale.
Claim 11 recites similar limitations as claim 4 and is therefore rejected under the same arts and rationale.
Claim 13 recites similar limitations as claim 6 and is therefore rejected under the same arts and rationale.
Claim 14 recites similar limitations as claim 7 and is therefore rejected under the same arts and rationale.
Claims 5,12 are rejected under 35 U.S.C. 103 as being unpatentable over Axelrod et al. (US 20160063625 A1), in view of Bishop et al. (US 10915934 B2), and further in view of XU (CN 115344613 A).
Re-claim 5, Axelrod et al., in view of Bishop et al., teach the computer-implemented method of claim 1, wherein the first audit result data is further based on at least one rule variable updated in real time.
(see e.g. abstract ----The embodiment of the application through pre-creating a plurality of inspection rules for triggering bill data inspection operation, and each inspection rule generated in the inspection bill accuracy of the generated benefit for timing or real-time calculation, so as to verify the validity of the inspection rule and corresponding processing according to the verification result, specifically is deleting or modifying, avoiding invalid extraction rule occupies the memory.
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Axelrod et al., in view of Bishop et al., and include the steps cited above, as taught by XU, in order to improve the validity of the inspection rule for triggering bill data inspection operation and also improve the accuracy of the bill (see e.g. page 4).
Claim 12 recites similar limitations as claim 5 and is therefore rejected under the same arts and rationale.
Response to Arguments
Applicant's arguments filed 12/16/25 have been fully considered but they are not persuasive.
Applicant’s remark:
Applicant submits that claims 1 and 8 recite additional elements that integrate the claims as a whole into a practical application. As discussed above, Applicant has amended claims 1 and 8 to recite that the classifying of the plurality of rate data based on rate data types is performed with a neural network trained on rate data and rate data types. These additional elements describe the specific computing component that enables identification of rate data and classification of rate data type based on scraped invoice data. However, Applicant submits that even if the claims were viewed as involving methods of organizing human activity, it integrates those concepts into the practical application of remediation invoice auditing.
1.Examiner’s answer:
The above paragraphs highlight the problem Applicants seek to solve, which is invoice auditing. It does use computer automation to solve the problem, but the problem is one of business inventory realm and uses computer technology, such as a neutral network already trained, as a solution, rather than solving a technical computer problem, or improving the performance of the neural network itself.” The current invention does not disclose such an improvement in the claims or specification.
Relying on a computer component to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible. See Alice, (use of a computer to create electronic records, track multiple transactions, and issue simultaneous instructions" is not an inventive concept);
Applicant’s remark:
Applicant specifically references the August 4, 2025, memorandum from the Deputy Commissioner for Patents, which provides guidance on distinguishing claims that recite a judicial exception from claims that merely involve a judicial exception. On pages 3 and 4, the memorandum states: --- The claim limitation "training the neural network in a first stage using the first training set" of example 39 does not recite a judicial exception.
2.Examiner’s answer:
The current invention is using a “trained” neural network to classify data. The neural network is being used as a tool. The examples cited by Applicant are not applicable.
Applicant’s remark:
Claims 1 and 8 Provide Specific Technological Improvements in the Field of Insurance Technology.
3.Examiner’s answer:
Please see the response to remark 1 above.
Applicant’s remark:
Applicant submits that Axelrod does not teach or suggest classifying of the plurality of rate data based on rate data types performed with a neural network trained on rate data and rate data types..
4.Examiner’s answer:
The Examiner notes that Bishop et al. clearly teach the limitation as claimed. Please see the rejection above.
Applicant’s remark:
The Examiner points to Axelrod paras. [0026] and [0032] as teaching the limitations of "wherein the plurality of rate tables comprises at least one of a labor rate table, an equipment rate table and a materials rate table". Applicant strongly disagrees. The "energy rate data" of Axelrod does not constitute "at least one of a labor rate table, an equipment rate table and a materials rate table" as recited in claims 2 and 9.
5.Examiner’s answer:
The distribution charge from table 2 in Axelrod is considered equivalent to labor rate
(see e.g. [0056] Among the six entries of the bill corpus table shown in Table 2 above, the facility identifies the first, fourth, and fifth entries as relevant to estimating distribution charges for the bill shown in FIG. 3. T
Applicant’s remark:
No person skilled in the art, when trying to construct a system and method for insurance providers to audit remediation or restoration service providers would find the teachings of Axelrod remotely pertinent to the problems insurance companies experience in the form of fraud, inaccuracies, inefficiencies, and waste in remediation insurance, invoice auditing, and other remediation insurance-related activities. For at least this reason, Applicant submits that Axelrod is not available for the purposes of an obviousness rejection, and the rejections of claims 1-14 under U.S.C. 103 should be withdrawn.
6.Examiner’s answer:
The claims in the current application disclose scraping rate data from an invoice, classifying the rate date, populating rate tables based on the rate data type, auditing the invoice by comparing data from the rate tables, and generating an audit report.
Axelrod et al. teach extracting data from received bills including charges, construct and maintain several models: from all of the bills, maintain a bill corpus table, -- compare the results of applying its model to bills received to identify and address any errors in these bills. Furthermore Axelrod et al. teach “ [0030] The facility also has automated auditing tools to ensure that the energy sellers and distribution utility are then properly billing the energy buyer.”
Axelrod et al. clearly teach Invoice auditing and is reasonably pertinent to the problem to be solved.
Applicant’s remark:
Lack of Motivation to Combine Axelrod and XU
--there is nothing in any of the cited references that would motivate one of ordinary skill in the art to combine the teachings of these references to produce the invention claimed in claims 5 and 12.
7.Examiner’s answer:
The examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071,5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007).
It would have been obvious to combine Axelrod and XU, in order to improve the validity of the inspection rule for triggering bill data inspection operation and also improve the accuracy of the bill (see e.g. page 4).
The Examiner asserts that the above disclosure by XU supports the motivation to combine Axelrod with XU.
Applicant’s remark:
Based on the references set forth by the Examiner, Applicant asserts that the rejection of the claims 1-14 under §103 could only have been made with hindsight bias and ex post reasoning. Specifically, Applicant submits that modifying Axelrod from an energy brokering system between a customer and an energy provider to arrive at the claimed invention could only have been made with hindsight bias and ex post reasoning. Applicant respectfully submits that the Examiner has taken features from a non-analogous reference (i.e. Axelrod), repurposed the features to serve the exact purpose of the claimed invention, and done so without any teaching, suggestion or motivation shown in the art. Applicant respectfully submits that modifying the teachings of Axelrod to arrive at the claimed invention constitutes a hindsight reconstruction, which is impermissible.
8.Examiner’s answer:
The examination process includes a search of the invention. The Examiner notes a proper search was conducted, as required by the office, based on Applicant’s claims and the search result was attached to the office action for Applicant’s review.
Furthermore, Applicants contention that the rejection relies on hindsight reconstruction is unavailing. It must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But, whereas here, the rejection takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). Further, reason for the modification/combination need not appear in one or more of relied upon references. Instead, the examiner when analyzing the evidence may employ common sense not inconsistent with the ordinary level of knowledge and skill in the art at the time of the invention. See Perfect Web Techs. v. InfoUSA, Inc., 587 F.3d 1324, 1328-29 (Fed. Cir. 2009). Reliance on common sense does not imply the use of impermissible hindsight.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. A) Anderson et al. (US 20150310406 A1) -Systems And Methods for Automated Invoice Processing.
B) Katz et al. (US 20030033179 A1) - A method for generating customized alerts related to the procurement, sourcing, strategic sourcing and/or sale of one or more items by an enterprise
THIS ACTION IS MADE FINAL. 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 LUNA CHAMPAGNE whose telephone number is (571)272-7177. The examiner can normally be reached M-F 8:00-5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Florian Zeender can be reached at 571 272-6790. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/LUNA CHAMPAGNE/Primary Examiner, Art Unit 3627 March 11, 2026