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 .
Status of Claims
This action is in reply to the application 18/454,697 filed on 8/23/2023. Claims 1, 11, and 20 were amended and claim 21 was newly added in the reply filed 5/23/2025. Claims 1, 11, and 20 were amended and claims 22 and 23 were newly added in the reply filed 10/2/2025. No amendments were made in the reply filed 1/16/2026. Claims 1-23 are pending. This action is final.
Response to Arguments
Regarding Applicant’s argument starting on page 14 regarding claims 1-23: Applicant’s arguments filed with respect to the rejections made under 35 USC § 101 have been fully considered, but are not persuasive.
Applicant first argues that claims 1, 11, and 20 are not directed to a judicially recognized exception to patent eligible subject matter. Specifically, Applicant argues that claims 1, 11, and 20 integrate the judicial exception into a practical application. Examiner respectfully disagrees. Claims 1, 11, and 20 are not similar to Desjardins, as alleged by Applicant, in a manner that is meaningful under 35 USC § 101. Specifically, claims 1, 11, and 20 do not represent an improvement to the functioning of a computer or another technology. The alleged improvements that Applicant’s invention provides are business improvements to a business related process, and not improvements to a computer system technology itself (See MPEP § 2106.04(d)(1) and 2106.05(a) for examples and description of what is considered an improvement to a computer-functionality or an improvement to a technology). "Identifying, analyzing, and presenting certain data to a user is not an improvement specific to computing." International Business Machines Corp. v. Zillow Group, Inc., (Fed. Cir. No. 2021-2350, Oct. 17, 2022, pg. 8). The claimed computer components are generic and broadly recited, and the alleged improvements are not to the generic computer components themselves, but to the abstract process being performed by the computer components. Examiner respectfully argues that the claimed limitations not analogous to the MPEP descriptions and examples of improvements to computer-functionality or improvements to a technology, and that the claims are directed to an abstract idea.
The alleged improvements that Applicant cites are directed to the generation of accurate forecasts. This is an improvement to the abstract idea and not an improvement to a computer, technology, or technical field itself. To reiterate, generating forecasts is not a technology as described in MPEP § 2106.05(a).
Applicant further argues that the claims reflect improvements to computational performance. Specifically, Applicant argues that its retail forecasting model lowers CPU usage and shortens execution paths. This, however, also reflects an improvement to the abstract idea and not to the computer upon which the abstract idea is being “applied.” In other words, reducing the number of computational steps required to come to a solution is an improvement to the abstract idea. For example, the improvement would remain even if all computer technology was removed from the limitations, since the human performing the calculations with pen and paper would have fewer steps to perform. "The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology." MPEP 2106.04(d)(1). These arguments also apply to Applicant’s arguments regarding the benefits of more accurate forecast. An improvement in the accuracy of forecasts is an improvement to the abstract idea, not a computer, technology, or technical field.
Applicant further argues that the claims reflect improvements to data storage. Examiner respectfully disagrees for the same reasons stated above. An improvement in the accuracy of forecasts is an improvement to the abstract idea, not a computer, technology, or technical field.
Applicant further argues that the claims reflect improvements to data structures. Examiner respectfully disagrees for reasons similar to those described above. Reducing the amount of computational power required by improving the abstract idea does not reflect an improvement to a computer, technology, or technical field.
Applicant further argues that the claims reflect improvements to the performance of computers by generating more accurate forecasts. Examiner respectfully disagrees for the same reasons stated above. An improvement in the accuracy of forecasts is an improvement to the abstract idea, not a computer, technology, or technical field.
Applicant further argues that Desjardins applies to all examination, not just AI. Examiner fully agrees with this statement. For example, the decision of Desjardins is similar to the decision in Enfish, which it cites. Both describe a path to patent eligibility via an improvement to technology via the modification of software structures. However, Examiner does not agree that the decision in Desjardins applies to the instant case. Examiner sees no analogous language in the instant claims that describe a modification of the structure of the underlying software itself. Examiner notes that a modification of data processed by the software structure is insufficient to meet this standard.
Applicant further argues that claims 1, 11, and 20 recite additional elements that amount to significantly more than any judicial exception. Examiner respectfully disagrees. Applicant cites the following as additional elements: (a) obtaining product data comprising sales data for products and product descriptions, (b) generating a retail forecasting model at least by modifying cluster-level estimated price elasticity values based on demand attributes of a plurality of products to generate a plurality of product-level price elasticity values for the plurality of products, (c) inputting sales data for the plurality of products to the retail forecasting model to generate a retail forecast for demand for a first product, among the plurality of products, for a particular time period (d) modifying operations of a client system based on the forecast. However, the only additional element in these cited limitations is a client system. The remaining language falls within the abstract idea categories of “Mathematical Concepts” (e.g., mathematical relationships) and “Certain Methods of Organizing Human Activity” (e.g., commercial interaction – business relations). Merely “applying” this recited abstract idea to a generic computer environment (including a client system) does not amount to significantly more than the abstract idea itself.
Applicant further argues that claims 1, 11, and 20 improve the technical field of generating and implementing retail forecasts. Examiner respectfully disagrees that “generating and implementing retail forecasts” is a technical field. See MPEP § 2106.05(a) for description and examples of technical fields. This same argument applies to “performing initial clustering operations to generate cluster-level cross-elasticity values, then modifying the cluster-level cross-elasticity values to generate product level cross-elasticity values.” This describes an alleged improvement to an abstract idea, not an improvement to a computer, technology, or technical field.
Applicant further argues that claims 1, 11, and 20 include limitations that are not well-understood, routine, and conventional in the field. Applicant then, once again, describes the following as additional elements: (a) obtaining product data comprising sales data for products and product descriptions, (b) generating a retail forecasting model at least by modifying cluster-level estimated price elasticity values based on demand attributes of a plurality of products to generate a plurality of product-level price elasticity values for the plurality of products, (c) inputting sales data for the plurality of products to the retail forecasting model to generate a retail forecast for demand for a first product, among the plurality of products, for a particular time period (d) modifying operations of a client system based on the forecast. However, the only additional element in these cited limitations is a client system. The remaining language falls within the abstract idea categories of “Mathematical Concepts” (e.g., mathematical relationships) and “Certain Methods of Organizing Human Activity” (e.g., commercial interaction – business relations). Merely “applying” this recited abstract idea to a generic computer environment (including a client system) does not amount to significantly more than the abstract idea itself. "The 'novelty' of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter." Diamond v. Diehr, 450 U.S. 175, 188-89 (1981). "[U]nder the Mayo/Alice framework, a claim directed to a newly discovered law of nature (or natural phenomenon or abstract idea) cannot rely on the novelty of that discovery for the inventive concept necessary for patent eligibility." Genetic Techs. Ltd. v. Merial L.L.C., 818 F.3d 1369, 1376 (Fed. Cir. 2016).
Applicant further argues that claims 1, 11, and 20 add meaningful limitations beyond generally linking a judicial exception to a particular technological environment. Applicant then, once again, describes the following as additional elements: (a) obtaining product data comprising sales data for products and product descriptions, (b) generating a retail forecasting model at least by modifying cluster-level estimated price elasticity values based on demand attributes of a plurality of products to generate a plurality of product-level price elasticity values for the plurality of products, (c) inputting sales data for the plurality of products to the retail forecasting model to generate a retail forecast for demand for a first product, among the plurality of products, for a particular time period (d) modifying operations of a client system based on the forecast. However, the only additional element in these cited limitations is a client system. The remaining language falls within the abstract idea categories of “Mathematical Concepts” (e.g., mathematical relationships) and “Certain Methods of Organizing Human Activity” (e.g., commercial interaction – business relations). Merely “applying” this recited abstract idea to a generic computer environment (including a client system) does not amount to significantly more than the abstract idea itself. "The 'novelty' of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter." Diamond v. Diehr, 450 U.S. 175, 188-89 (1981). "[U]nder the Mayo/Alice framework, a claim directed to a newly discovered law of nature (or natural phenomenon or abstract idea) cannot rely on the novelty of that discovery for the inventive concept necessary for patent eligibility." Genetic Techs. Ltd. v. Merial L.L.C., 818 F.3d 1369, 1376 (Fed. Cir. 2016).
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 1-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1, 11, and 20 recite a non-transitory computer readable medium, a method, and a system, respectively, for performing a method of receiving, by a product management platform, first sales data for a plurality of products associated with one or more client systems, wherein the one or more client systems comprise one or more of a manufacturer, an inventory storage entity, and a retailer in communication with the product management platform; comparing the first sales data to second sales data to detect a change between the first sales data and the second sales data; based on determining the change exceeds a threshold, generating, by the product management platform, a retail forecasting model for forecasting effects of price and demand changes among the plurality of products associated with the one or more client systems, at least by: accessing, by the product management platform, a set of product data comprising the first sales data for the plurality of products and product descriptions for the plurality of products; clustering the plurality of products into a plurality of product clusters according to textual similarities among the product descriptions of the plurality of products; applying a cluster-level price elasticity estimation regression algorithm to the plurality of product clusters to generate a set of cluster-level estimated price elasticity values; modifying the set of cluster-level estimated price elasticity values based on demand attributes of the plurality of products to generate a plurality of product-level price elasticity values for the plurality of products, at least by: modifying a first cluster-level estimated price elasticity value for a first product cluster with a first demand value representing a demand level for a first product in the first product cluster to generate a first product-level price elasticity value corresponding to the first product; generating the retail forecasting model for the plurality of products based on the plurality of product-level price elasticity values; inputting the first sales data for the plurality of products to the retail forecasting model to generate a retail forecast for demand for a first product, among the plurality of products, for a particular time period; and modifying operations of the one or more client systems based on the retail forecast, wherein modifying the operations of the one or more client systems comprises at least one of: initiating a first operation to increase or decrease production of a second product by the manufacturer; initiating a second operation to transfer the second product between locations by the inventory storage entity; and initiating a third operation to modify pricing for the second product by the retailer. Therefore, claims 1, 11, and 20 are each directed to one of the four statutory categories of invention: an article of manufacture, a method, and a machine, respectively.
Step 2A Prong One: The limitations receiving ... first sales data for a plurality of products associated with one or more client systems, wherein the one or more client systems comprise one or more of a manufacturer, an inventory storage entity, and a retailer in communication with ... comparing the first sales data to second sales data to detect a change between the first sales data and the second sales data; based on determining the change exceeds a threshold, generating ... a retail forecasting model for forecasting effects of price and demand changes among the plurality of products associated with the one or more client systems, at least by: accessing ... a set of product data comprising the first sales data for the plurality of products and product descriptions for the plurality of products; clustering the plurality of products into a plurality of product clusters according to textual similarities among the product descriptions of the plurality of products; applying a cluster-level price elasticity estimation regression algorithm to the plurality of product clusters to generate a set of cluster-level estimated price elasticity values; modifying the set of cluster-level estimated price elasticity values based on demand attributes of the plurality of products to generate a plurality of product-level price elasticity values for the plurality of products, at least by: modifying a first cluster-level estimated price elasticity value for a first product cluster with a first demand value representing a demand level for a first product in the first product cluster to generate a first product-level price elasticity value corresponding to the first product; generating the retail forecasting model for the plurality of products based on the plurality of product-level price elasticity values; inputting the first sales data for the plurality of products to the retail forecasting model to generate a retail forecast for demand for a first product, among the plurality of products, for a particular time period; and modifying operations of the one or more client systems based on the retail forecast, wherein modifying the operations of the one or more client systems comprises at least one of: initiating a first operation to increase or decrease production of a second product by the manufacturer; initiating a second operation to transfer the second product between locations by the inventory storage entity; and initiating a third operation to modify pricing for the second product by the retailer, as drafted, is a method that, under its broadest reasonable interpretation, only covers concepts of “Mathematical Concepts” (e.g., mathematical relationships) and “Certain Methods of Organizing Human Activity” (e.g., commercial interaction – business relations). That is, nothing in the claim elements disclose anything outside the grouping of “Mathematical Concepts” (e.g., mathematical relationships) and “Certain Methods of Organizing Human Activity” (e.g., commercial interaction – business relations). Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: The judicial exception is not integrated into a practical application. The claim as a whole merely describes how to generally “apply” the concept of the aforementioned abstract idea using generic computer components. The additional elements a non-transitory computer readable medium (claim 1), one or more hardware processors (claims 1 and 20), a system (claim 20), memory (claim 20), and a product management platform (claims 1, 11, 20) are recited at a high level of generality and are merely invoked as tools to perform the aforementioned abstract idea. Simply “applying” the abstract idea on a generic computerized system is not a practical application of the abstract idea. Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims merely describe how to generally “apply” the concept of the abstract idea using a generic computer environment. The additional elements of a non-transitory computer readable medium (described in spec. para. [0129]), one or more hardware processors (described in spec. para. [0123]), a system (described in spec. para. 0124]), memory (described in spec. para. [0125]), and a product management platform (described in spec. para. [0023]) are all recited at a high level of generality in a manner that indicates that the additional elements are sufficiently known in the art such that the specification does not need to describe the particulars of such additional elements to satisfy the statutory disclosure requirements. Thus, even when viewed as a whole, nothing in the claim adds significantly more to the abstract idea. Therefore, the claim is not patent eligible.
Claims 2-10, 12-19, and 21-23 have been given the full two part analysis including analyzing the limitations both individually and in combination. Claims 2-10, 12-19, and 21-23 when analyzed individually, and in combination, are also held to be patent ineligible under 35 U.S.C. 101. The recited limitations of the dependent claims fail to establish that the claims do not recite an abstract idea because the recited limitations of the dependent claims merely further narrow the abstract idea.
Step 2A Prong Two: The limitations of the dependent claims fail to integrate an abstract idea into a practical application because the claims as a whole merely describe how to generally “apply” the aforementioned abstract idea. Claim 21 recites the additional element a marketing platform, claim 22 recites the additional elements a stream of data packets and a client computer, and claim 23 recites the additional element a host node of a computer network, and data transmission media in the computer network which are merely considered part of a generic computer environment upon which the abstract idea is applied. Claims 2-6 and 12-16 recite the claim limitation applying a natural language processing (NLP) model. This claim limitation part of the abstract idea and not an additional element. Specifically, this claim limitation falls into the categorization of “Mental Processes” (See MPEP § 2106.04(a)(2)(III); “a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind” Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011); MPEP § 2106.04(a)(2)(III)(A)) and Mathematical Concepts” (e.g., mathematical relationships) and “Certain Methods of Organizing Human Activity” (e.g., commercial interaction – business relations). This is because the natural language processing model of claims 2-6 and 12-16 is merely using mathematical concepts in order to replicate decision-making mental processes used to sort products into clusters based on textual similarities. As mentioned in the examination of independent claims above, the abstract idea as a whole remains directed to “Certain Methods of Organizing Human Activity” (e.g., commercial interaction – business relations). This is because the abstract idea as a whole involves the organization and output of product pricing data used to manage commerce between buyers and sellers. Similarly, the term-frequency, inverse document frequency (TF-IDF) algorithm and the cosine similarity algorithm of claim 3, which are included in the NLP model, are also part of the same abstract idea categories. This is for the same reasons as the NLP model above. Claims 4 and 14 recite applying a clustering-type machine learning model to data to generate a plurality of product clusters. In this case, the function of the machine learning model is part of the abstract idea, but the machine learning model itself is an additional element. Specifically, the generating a plurality of product clusters falls into the categorization of “Mental Processes” (See MPEP § 2106.04(a)(2)(III); “a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind” Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011); MPEP § 2106.04(a)(2)(III)(A)) and Mathematical Concepts” (e.g., mathematical relationships) and “Certain Methods of Organizing Human Activity” (e.g., commercial interaction – business relations). The machine learning model itself, however, is an additional element. The claim limitations applying a clustering-type machine learning model to the plurality of embeddings to generate the plurality of product clusters of claims 4 and 14 are analogous to one of the machine learning training claims of the USPTO’s July 2024 Subject Matter Eligibility Examples. Specifically, Examiner finds the claim limitation applying a clustering-type machine learning model to the plurality of embeddings to generate the plurality of product clusters of claims 4 and 14 analogous to Example 47 Claim 2. Example 47 Claim 2 describes an artificial neural network comprising a step of training the neural network. This is because the claims of the instant application and Steps (d) and (e) both merely claim “applying” the trained neural network / machine learning process to a set of data. See the USPTO’s analysis regarding Step 2A Prong Two: “The limitations in (d) and (e) reciting “using the trained ANN” provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. The judicial exception of “detecting one or more anomalies in a data set using the trained ANN” and “analyzing the one or more detected anomalies using the trained ANN to generate anomaly data” is performed “using the trained ANN.” The trained ANN is used to generally apply the abstract idea without placing any limits on how the trained ANN functions. Rather, these limitations only recite the outcome of “detecting one or more anomalies” and “analyzing the one or more detected anomalies” and do not include any details about how the “detecting” and “analyzing” are accomplished. See MPEP 2106.05(f). The recitation of “using a trained ANN” in limitations (d) and (e) also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using a trained ANN” limits the identified judicial exceptions “detecting one or more anomalies in a data set using the trained ANN” and “analyzing the one or more detected anomalies using the trained ANN to generate anomaly data,” this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).” The Office continues its analysis in Step 2B: As explained with respect to Step 2A, Prong Two, there are four additional elements. The additional element of “using the trained ANN” in limitations (d) and (e) are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).” The claim limitation applying a clustering-type machine learning model to the plurality of embeddings to generate the plurality of product clusters similarly recites the mere use of a machine learning model to indicate a technological environment in which the judicial exception is performed. Although the additional element applying a clustering-type machine learning model limits the identified judicial exceptions to generate the plurality of product clusters, this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning model) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). The additional element of applying a clustering-type machine learning model in limitations (d) and (e) are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f). Simply “applying” the abstract idea on a generic computerized system and using generic machine learning is not a practical application of the abstract idea. Thus, even when viewed as a whole, nothing in the claims add significantly more to the abstract idea.
Step 2B: Performing the further narrowed abstract ideas of the dependent claims on the additional elements of the independent claim, individually or in combination, does not impose any meaningful limits on practicing the abstract ideas and amount to merely using a computer, in its ordinary capacity, as a tool to perform the abstract idea. Examiner finds the additional elements a marketing platform (described in spec. para. [0051]) of claim 21 part of a generic computer environment upon which the abstract idea is applied. Examiner finds the additional elements applying a clustering-type machine learning model of claims 4 and 14 analogous to the additional elements of Example 47 Claim 2 steps (d) and (e). The Step 2B analysis provided by the USPTO reiterates the conclusion of Step 2A, Prong Two; the additional element of “using the trained ANN” in limitations (d) and (e) are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f). Examiner views the claim limitations applying a clustering-type machine learning model of claims 4 and 14, and thus holds that this additional element is also at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. Thus, even when viewed as a whole, nothing in the claims add significantly more to the abstract idea. Therefore, the claims are not patent eligible.
Reasons for Novelty
The claims are considered novel under 35 U.S.C. § 102 and 103. Most of the concepts recited in the independent claims are taught by Wu (U.S. Pat. No. 8,140,381). Other similar and relevant art includes Watson (U.S. Pat. No. 8,676,632), Ganti Mahapatruni (U.S. Pub. No. 2019/0172082), Schierholt (U.S. Pub. No. 2005/0149377), Ettl (U.S. Pub. No. 2015/0317653), Le (U.S. Pat. No., 11,631,102) and Ivanov (U.S. Pub. No. 2003/0177103). However, Examiner has determined that there is no reasonable and obvious combination of the prior art which would teach the following limitations: modifying the set of cluster-level estimated price elasticity values based on demand attributes of the plurality of products to generate a plurality of product-level price elasticity values for the plurality of products, at least by: modifying a first cluster-level estimated price elasticity value for a first product cluster with a first demand value representing a demand level for a first product in the first product cluster to generate a first product-level price elasticity value corresponding to the first product. Wu teaches determining a cluster-level estimated price elasticity value for a first product cluster (see [Col. 28, Lines 49-53]) and also, separately, determining a product-level price elasticity value for each product within the cluster (see [Col. 30, Lines 57-58]). However, Wu does not teach determining the product-level price elasticity value for each product by modifying a first cluster-level estimated price elasticity value for a first product cluster with a first demand value representing a demand level for a first product in the first product cluster. Watson teaches determining a product-level price elasticity value for a product based on historical sales information of the products in the associated cluster (see [Col. 24, Line 64 – Col. 25, Line 25]), but does not teach the product-level price elasticity value for the product being determined by modifying a first cluster-level estimated price elasticity value for a first product cluster with a first demand value representing a demand level for a first product in the first product cluster. Many of the other relevant references mentioned above determine either an elasticity value for a product or an elasticity value for a cluster of products. After extensive searching, however, Examiner has not found a reasonable and obvious combination of prior art which would teach the aforementioned claim limitations. Therefore, the claims are considered novel under 35 U.S.C. § 102 and 103.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRIS GOMEZ whose telephone number is (571) 272-0926. The examiner can normally be reached on 7:30 AM – 4:30 PM EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SHANNON CAMPBELL can be reached at (571) 272-5587. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHRISTOPHER GOMEZ/ Examiner, Art Unit 3628