DETAILED ACTION
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on December 17, 2025 has been entered.
Claims 1, 11, and 25 have been amended.
Claims 2, 6, 12, 16, and 20 have been cancelled.
Claims 1, 3-5, 7-11, 13-15, 17-19, and 21-25 are pending.
The effective filing date of the claimed invention is April 26, 2023.
Response to Amendment
Amendments to Claims 1, 11, and 25 are acknowledged.
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, 3-5, 7-11, 13-15, 17-19, and 21-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed a judicial exception (i.e., an abstract idea) without significantly more.
Step 1 – Statutory Categories
As indicated in the preamble of the claim, the examiner finds the claim is directed to a process, machine, manufacture, or composition of matter.(Claims 1, 3-5, 7-10, 21-22, and 25 are processes and Claims 11, 13-15, 17-19 and 23-24 are machines). Accordingly, step 1 is satisfied.
Step 2A – Prong 1: was there a Judicial Exception Recited
Claim 1 (and similarly Claims 11 and 25) recites the following abstract concepts that are found to include “abstract idea.” Any additional elements will be analyzed under Step 2A-Prong 2 and Step 2B:
A computer-implemented method comprising:
receiving, by data processing hardware, a plurality of product identifiers, each product identifier of the plurality of product identifiers associated with a product of a plurality of products (See MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016));
for each product identifier of the plurality of product identifiers:
predicting, by the data processing hardware using an inventory predictor model, a mixture probability distribution over possible quantities for the associated product, wherein the mixture probability distribution comprises a probability of zero inventory, a probability of a recorded inventory, and a long-tail distribution indicating a likelihood of a phantom inventory anomaly (See MPEP 2106.04(a)(2)(I) mathematical concepts, performing a resampled statistical analysis to generate a resampled distribution, SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65, 127 USPQ2d 1597, 1598-1600 (Fed. Cir. 2018), modifying SAP America, Inc. v. InvestPic, LLC, 890 F.3d 1016, 126 USPQ2d 1638 (Fed. Cir. 2018), and MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)); and
generating, by the data processing hardware and using the mixture probability distribution, an inventory confidence score, the inventory confidence score indicating an uncertainty of an actual inventory of the associated product (See MPEP 2106.04(a)(2)(I) mathematical concepts, performing a resampled statistical analysis to generate a resampled distribution, SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65, 127 USPQ2d 1597, 1598-1600 (Fed. Cir. 2018), modifying SAP America, Inc. v. InvestPic, LLC, 890 F.3d 1016, 126 USPQ2d 1638 (Fed. Cir. 2018), and MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016));
generating, by the data processing hardware, a prioritized list of candidate products from the plurality of products, the prioritized list of candidate products sorted based on the inventory confidence score of each product of the plurality of products to resolve the phantom inventory anomaly (See MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016));
generating, by the data processing hardware, an audit notification comprising the prioritized list of candidate products (See MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)); and
transmitting, by the data processing hardware, the audit notification comprising the list of candidate products to one or more computing devices (See MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)).
Claim 1 (and similarly Claims 11 and 25) is directed to a series of steps for selecting a list of items to order, which are mental processes being calculated using mathematical concepts. The mere nominal recitation of data processing hardware and memory hardware does not take the claim out of mental processes and mathematical concepts. Thus, Claim 1 (and similarly Claims 11 and 25) recites an abstract idea.
Step 2A – Prong 2: Can the Judicial Exception Recited be integrated into a practical application
Limitations that are indicative of integration into a practical application:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
Limitations that are not indicative of integration into a practical application:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)
Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
The identified abstract idea of exemplary Claim 1 (and similarly Claims 11 and 25) is not integrated into a practical application. The additional elements are: a data processing hardware and memory hardware that implements the underlying abstract idea. These additional elements are broadly recited computer elements that do not add a meaningful limitation to the abstract idea because they amount to merely using a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application. Claim 1 (and similarly Claims 11 and 25) is directed to an abstract idea.
Step 2B – Significantly More Analysis
Claim 1 (and similarly Claims 11 and 25) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and in combination, steps a) receiving product identifiers, b) predicting, using an inventory predictor model, a mixture probability distribution for products, c) generating, using the mixture probability distribution, an inventory confidence score indicating a confidence of an actual inventory, and d) selection a list of candidate products for ordering based on an uncertainty of the actual inventory, do not add significantly more to the exception because they amount to merely using a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Claim 1 (and similarly Claims 11 and 25) is ineligible.
Claim 3 (and similarly Claim 13) recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 4 (and similarly Claim 14) recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 5 (and similarly Claim 15) recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 7 (and similarly Claim 17) recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 8 (and similarly Claim 18) recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 9 (and similarly Claim 19) recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 10 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 21 (and similarly Claim 23) recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 22 (and similarly Claim 24) recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim(s) 1, 3-5, 7-11, 13-15, 17-19, and 21-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pat Pub 2023/0081051 “Brooks”, in view of US Pat Pub 2022/0114640 “Pawar”, in view of US Pat Pub 2022/0284384 “Chaubard”, in view of US Pat Pub 2020/0143313 “Ohlsson”.
As per Claims 1, 11, and 25, Brooks discloses computer-implemented methods and system comprising:
for each product identifier of the plurality of product identifiers (Brooks: [0027] In Table 1, data for actual products are shown that indicate the percentage of products (tracked using SKUs)):
predicting, by the data processing hardware, using an inventory predictor model, a mixture probability distribution over possible quantities for the associated product, wherein the mixture probability distribution comprises a probability of zero inventory, a probability of a recorded inventory, and a likelihood of a phantom inventory anomaly (Brooks: [0073] Daily sales and reported inventory (including any phantom amounts) data generated in the synthetic world can be processed by the assortment optimization framework, and new assortments, including inventory capacities for each item, can be recommended. The optimization framework may not have access to data indicative of the true phantom inventories [0085] System 600 provides functions via user interface 636, such as assortment visualization 638 which allows modeling current arrangements and proposed changes thereto. A cluster customization 640 allows visualizing clusters of related products and the effects of changes to the clusters. A predicted sales and inventory 642 provide predictions based on changes to existing arrangements and/or new arrangements. A multi-objective goals and weighting component 644 allows changing goals that may be location specific or may change with different corporate priorities, and [0088] In FIG. 9, a flowchart shows a method according to another example embodiment to predict when products are unavailable and execute an inventory audit. The method involves inputting time-varying inventory management data into a computer model that infers product unavailability probabilities based on inventory changes and sales of a plurality of products that are stocked at specific PoPs. Based on the computer model, it is determined 901 that there is a threshold level of uncertainty in the time-varying inventory management data regarding the inventory of one or more products at each PoP.)); and
generating, by the data processing hardware and using the mixture probability distribution, an inventory confidence score, the inventory confidence score indicating an uncertainty of an actual inventory of the associated product (Brooks: [0061] a machine learning technique such as neural networks can be used to perform the product availability and/or product unavailability effect (e.g., product substitution behavior). For the former, a recurrent neural network may be used to predict product unavailability as a function of time based on time-varying inventory data from a target product and a substitution product, and [0088] In FIG. 9, a flowchart shows a method according to another example embodiment to predict when products are unavailable and execute an inventory audit. The method involves inputting time-varying inventory management data into a computer model that infers product unavailability probabilities based on inventory changes and sales of a plurality of products that are stocked at specific PoPs. Based on the computer model, it is determined 901 that there is a threshold level of uncertainty in the time-varying inventory management data regarding the inventory of one or more products at each PoP.); and
generating, by the data processing hardware, a prioritized list of candidate products from the plurality of products, the prioritized list of candidate products sorted based on the inventory confidence score of each product of the plurality of products to resolve the phantom inventory anomaly (Brooks: [0083], gathering initial and near-real-time data from various sources, such as an inventory management database 629 and customer relations database 631. One function provided via the execution of programs 612 by the processing circuitry represented by CPU 602 includes inventory prediction 630, which involves predicting the actual availability of select items versus what is reflected in the inventory database 629. This prediction 630 can be used to infer substitution behavior probabilities, and may be useful on its own, e.g., to alert store managers of phantom inventory so that timely replacement stock can be placed and/or ordered.);
generating, by the data processing hardware, an audit notification comprising the candidate products (Brooks: [0025] The computing systems of this disclosure may use the inventory prediction to provide a probability of unavailability over time, and may automatically trigger an inventory audit, order of new inventory, or other change of assortment. Inventory audits, to be conducted by people or robots, are recommended to reduce uncertainty around predictions of inventory, and to better estimate the business value of making specific assortment changes. [0043] In one embodiment, the methods further comprise specifying a list of PoPs and products to check, checking those PoPs by counting the inventory of a product available for customers to purchase, and recording the specific date and time when this is done.); and
transmitting, by the data processing hardware, the audit notification comprising the list of candidate products to one or more computing devices (Brooks: [0025] The computing systems of this disclosure may use the inventory prediction to provide a probability of unavailability over time, and may automatically trigger an inventory audit, order of new inventory, or other change of assortment. Inventory audits, to be conducted by people or robots, are recommended to reduce uncertainty around predictions of inventory, and to better estimate the business value of making specific assortment changes.).
Brooks fails to disclose computer-implemented methods and system when executed by data processing hardware causes the data processing hardware to perform operation comprising:
receiving, by data processing hardware, a plurality of product identifiers, each product identifier of the plurality of product identifiers associated with a product of a plurality of products;
wherein the mixture probability distribution comprises a long-tail distribution indicating a likelihood of an inventory anomaly;
generating, by the data processing hardware, a prioritized list of candidate products from the plurality of products, the prioritized list of candidate products sorted based on the inventory confidence score of each product of the plurality of products to resolve the phantom inventory anomaly;
generating, by the data processing hardware, the prioritized list of candidate products.
Pawar teaches computer-implemented methods and system when executed by data processing hardware causes the data processing hardware to perform operation comprising:
receiving, by data processing hardware, a plurality of product identifiers, each product identifier of the plurality of product identifiers associated with a product of a plurality of products (Pawar: [0023], the inventory management engine 202 requests and receives inventory information maintained by the warehouse 110…Inventory information includes both qualitative and qualitative information about items, including size, color, weight, SKU, serial number, and so on.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Brooks to include product identifiers as taught by Pawar, inventory availability and assortment system as taught by Brooks with the motivation of providing additional inventory information that’s useful for predicting the availability of items (Pawar: [0023]).
Brooks and Pawar fail to disclose computer-implemented methods and system when executed by data processing hardware causes the data processing hardware to perform operation comprising:
wherein the mixture probability distribution comprises a long-tail distribution indicating a likelihood of an inventory anomaly;
generating, by the data processing hardware, a prioritized list of candidate products from the plurality of products, the prioritized list of candidate products sorted based on the inventory confidence score of each product of the plurality of products;
generating, by the data processing hardware, the prioritized list of candidate products.
Chaubard teaches computer-implemented methods and system when executed by data processing hardware causes the data processing hardware to perform operation comprising:
generating, by the data processing hardware, a prioritized list of candidate products from the plurality of products, the prioritized list of candidate products sorted based on the inventory confidence score of each product of the plurality of products (Chaubard: Claim 2, wherein the product is identified in a prioritized list of products, and wherein the prioritized list is ordered based on a level of need for restocking);
generating, by the data processing hardware, the prioritized list of candidate products (Chaubard: Claim 2, wherein the product is identified in a prioritized list of products, and wherein the prioritized list is ordered based on a level of need for restocking).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Brooks and Pawar to include a prioritized list of candidate products as taught by Chaubard, inventory availability and assortment system as taught by Brooks and Pawar with the motivation to keep track of up-to-date and accurate stock information (Chaubard: [0003]).
Brooks, Pawar, and Chaubard fail to disclose computer-implemented methods and system when executed by data processing hardware causes the data processing hardware to perform operation comprising:
wherein the mixture probability distribution comprises a long-tail distribution indicating a likelihood of an inventory anomaly.
Ohlsson teaches computer-implemented methods and system when executed by data processing hardware causes the data processing hardware to perform operation comprising:
wherein the mixture probability distribution comprises a long-tail distribution indicating a likelihood of an inventory anomaly (Ohlsson: [0060] the prediction may be generated by processing the inventory dataset to fit a statistical distribution to the plurality of inventory variables. The statistical distribution may be fitted by estimating one or more statistical parameters using the historical inventory data. For example, the statistical distribution may be a parametric distribution, such as a Gaussian distribution, a Gamma distribution, or a Poisson distribution. (See Applicant’s Specification [0025] where the long tail distribution may include a gamma distribution or a t-distribution).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Brooks, Pawar, and Chaubard to include a long-tail distribution as taught by Ohlsson, with the inventory availability and assortment system as taught by Brooks, Pawar, and Chaubard with the motivation to accurately manage and predict inventory variables with future uncertainty (Ohlsson: [0004]).
As per Claims 3 and 13, Brooks discloses a method and system, wherein generating the prioritized list of candidate products comprises selecting a threshold quantity of products from the plurality of products with the lowest inventory confidence score (Brooks: [0088]).
As per Claims 4 and 14, Brooks discloses a method and system, wherein the inventory confidence score of each candidate product of the prioritized list of candidate products satisfies a maximum confidence threshold (Brooks: [0043]).
As per Claims 5 and 15, Brooks discloses a method and system, further comprising:
determining, by the data processing hardware, that the inventory confidence score of a particular candidate product of the prioritized list of candidate products satisfies a minimum confidence threshold (Brooks: [0025]); and
in response to determining that the inventory confidence score of the particular candidate product of the prioritized list of candidate products satisfies the minimum confidence threshold, triggering, by the data processing hardware, a restock event of the particular candidate product (Brooks: [0025]).
As per Claims 7 and 17, Brooks discloses a method and system, wherein the mixture probability distribution over possible quantities for the product comprises a probability of one or more of: zero inventory; correct inventory; or a long-tail distribution (Brooks: [0025]).
As per Claims 8 and 18, Brooks discloses a method and system, further comprising further sorting, by the data processing hardware, the prioritized list of candidate products based on one or more of: a sales volume of each candidate product; an inventory estimate of each candidate product; an audit history of each candidate product; or a product category of each candidate product (Brooks: [0075]).
As per Claims 9 and 19, Brooks discloses a method and system, further comprising:
receiving training data comprising a plurality of product features paired with audit correction labels (Brooks: [0043], [0046], and [0061]); and
training the inventory predictor model on the plurality of product features (Brooks: [0043], [0046], and [0061]).
As per Claim 10, Brooks discloses a method and system, wherein the plurality of product features comprises one or more of: an inventory history; a sales history; a replenishment history; an audit history; a product ID; a product category; a product description; an inventory store ID; a store type; or a store zip code (Pawar: [0032]).
As per Claims 21 and 23, Brooks discloses a method and system, wherein the inventory predictor model is a machine learning model, and wherein the method further comprises:
obtaining, by the data processing hardware and from at least one of the computing devices, a correct inventory amount for a product of the list of candidate products (Brooks: [0043]); and
updating, by the data processing hardware and based at least in part on the correct inventory amount, the inventory predictor model (Brooks: [0043]).
As per Claims 22 and 24, Brooks discloses a method and system, wherein for each product identifier of the plurality of product identifiers, the method and system comprises:
obtaining, by the data processing hardware and from a computing device, a target product availability level and a demand forecast for the associated product (Brooks: [0025]);
determining, by the data processing hardware, and based at least in part on the target product availability level, the demand forecast, and the inventory confidence score, an inventory replenishment amount (Brooks: [0025]); and
triggering, by the data processing hardware, an inventory restock event, based on the inventory replenishment amount (Brooks: [0025]).
Response to Arguments
35 USC 101
Applicant's arguments filed December 17, 2025 have been fully considered but they are not persuasive.
Applicant argues that the claims improve the integrity of the data stored in the computing system by “generating…a prioritized list…sorted based on the inventory confidence score…to resolve the phantom inventory anomaly,” and that this amounts to a specific improvement in the functioning of the inventory database system. However, calculating a distribution indicating a likelihood of a phantom inventory anomaly, and generating a prioritized list are not technical problems. They are standard auditing issues that are being executed using computing devices.
MPEP 2106.05(f) states that determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words “apply it” (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do “‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’”. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Other examples where the courts have found the additional elements to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process include:
i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)
Using this rationale, the technological components of the claims are found to be nothing more than the “apply it” rationale.
35 USC 103
Applicant’s arguments, see Applicant Arguments/Remarks Made in an Amendment, filed December 17, 2025, with respect to the rejection(s) of claim(s) 1, 3-5, 7-11, 13-15, 17-19, and 21-25 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of US Pat Pub 2023/0081051 “Brooks”, in view of US Pat Pub 2022/0114640 “Pawar”, in view of US Pat Pub 2022/0284384 “Chaubard”, in view of US Pat Pub 2020/0143313 “Ohlsson”.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REVA R MOORE whose telephone number is (571)270-7942. The examiner can normally be reached M-Th: 9:00-6:00.
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/REVA R MOORE/Examiner, Art Unit 3627
/PETER LUDWIG/Primary Examiner, Art Unit 3627
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