DETAILED ACTION
Claims 1-9 are pending and have been examined.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitations, as recited in independent claim 1, are:
a data acquisition and profiling module for acquiring and analyzing at least one data from at least one data source;
a data ingestion and processing module for ingesting and processing the at least one data from the at least one data source;
an active metadata repository for maintaining at least one metadata associated with the at least one data;
a generative Al driven carbon and environmental footprint calculator for calculating an Environmental Cost Indicator (ECI) and a weighted Ecoscore;
a dependency registry and validation module for maintaining and validating the at least one dependency identified by the private and domain specific LLM; and
a human supervision and quality control module for applying at least one rule, the at least one rule pertaining to quality, security, and/or ethics.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims are directed to an abstract idea without significantly more.
Here, under step 1 of the Alice analysis, system claims 1-6 are directed to a plurality of modules, a metadata repository, an engine, and a calculator, method claim 7 is directed to a series of steps, and computer-readable medium claims 8 and 9 are directed to storing instructions and a private and domain specific vectorized generative Al large language model (LLM), respectively. Thus the claims are directed to a machine, process, and manufacture, respectively.
Under step 2A Prong One of the analysis, the claimed invention is directed to an abstract idea without significantly more. The claims recite identifying and recommending environmentally conscious alternative materials, components, energy and parts, including acquiring, analyzing, ingesting, processing, maintaining, analyzing, calculating, maintaining, validating, and applying steps.
The limitations of acquiring, analyzing, ingesting, processing, maintaining, analyzing, calculating, maintaining, validating, and applying, are a process that, under its broadest reasonable interpretation, covers organizing human activity concepts, but for the recitation of generic computer components.
Specifically, the claim elements recite acquiring and analyzing at least one data from at least one data source; ingesting and processing the at least one data from the at least one data source; maintaining at least one metadata associated with the at least one data; analyzing at least one application requirement and for identifying at least one suitable alternative based on the at least one application requirement; calculating an Environmental Cost Indicator (ECI) and a weighted Ecoscore; maintaining and validating the at least one dependency identified by the private and domain specific LLM; and applying at least one rule, the at least one rule pertaining to quality, security, and/or ethics.
That is, other than reciting plurality of modules, a metadata repository, generative Al large language model (LLM), an engine, and a calculator, the claim limitations merely cover commercial interactions, including business relations, thus falling within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Under Step 2A Prong Two, the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This judicial exception is not integrated into a practical application. The claims include plurality of modules, a metadata repository, generative Al large language model (LLM), an engine, and a calculator. The plurality of modules, a metadata repository, generative Al large language model (LLM), an engine, and a calculator in the steps is recited at a high-level of generality, such that it amounts 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. As a result, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of plurality of modules, a metadata repository, generative Al large language model (LLM), an engine, and a calculator 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.
None of the dependent claims recite additional limitations that are sufficient to amount to significantly more than the abstract idea. Claims 2 and 3 recite additional updating, refreshing, enabling, visualizing, presenting, navigating, and simulating steps. Claims 4-6 recite additional retraining, recording, identifying, matching, and selecting steps. A more detailed abstract idea remains an abstract idea.
Under step 2B of the analysis, the claims include, inter alia, plurality of modules, a metadata repository, generative Al large language model (LLM), an engine, and a calculator.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
There isn’t any improvement to another technology or technical field, or the functioning of the computer itself. Moreover, individually, there are not any meaningful limitations beyond generally linking the abstract idea to a particular technological environment, i.e., implementation via a computer system. Further, taken as a combination, the limitations add nothing more than what is present when the limitations are considered individually. There is no indication that the combination provides any effect regarding the functioning of the computer or any improvement to another technology.
In addition, as discussed in paragraphs 033 and 034 of the specification, “In some embodiments, the functionality described herein is distributed among a plurality of systems, which can include combinations of software and/or hardware-based systems, for example Application-Specific Integrated Circuits (ASICs), computer programs, or smart phone applications. The private and domain specific Generative AI system 200 includes at least one processor 202 to perform various computational and data processing tasks, as well as other functionality. The at least one processor 202 is in communication with the at least one memory 204. In some embodiments, the at least one memory 204 comprises one or more computer readable storage media with program instructions collectively stored on the one or more computer readable storage media, with the program instructions being executable by the at least one processor 202 to cause the at least one processor 202 to perform operations described herein.”
As such, this disclosure supports the finding that no more than a general purpose computer, performing generic computer functions, is required by the claims.
Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank Int’l et al., No. 13-298 (U.S. June 19, 2014).
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.
Claims 1-4, 6, 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Sung et al (US 20250245400 A1), in view of Vierra (US 20240370595 A1), in further view of Bronevetsky et al (US 20250029042 A1).
As per claim 1, Sung et al disclose a private and domain specific Generative Al system for identifying and recommending environmentally conscious alternative materials, components, energy and parts for construction projects and manufactured products (i.e., the artificial intelligence design system 116 aims to maximize productivity, efficiency, and environmental consciousness of energy infrastructure projects. At its core, the artificial intelligence design system 116 incorporates a cutting-edge three-dimensional generative design system that converges sub-symbolic artificial intelligence algorithms, machine learning, and large-language models to revolutionize the process of designing energy infrastructure, ¶ 0035), comprising:
a data acquisition and profiling module for acquiring and analyzing at least one data from at least one data source (i.e., FIG. 3 depicts a flowchart architecture 300 that highlights where data is coming from, how data from different sources and processes may be combined and passed to the artificial intelligence search engine 120's algorithms using input channels and how it may be enriched further for usage in different scenarios using machine learning modules 122 and a large language model 124, ¶ 0020);
a data ingestion and processing module for ingesting and processing the at least one data from the at least one data source (i.e., A differentiator in the artificial intelligence design system 116 is how data is ingested, processed by exploration and exploitation focused artificial intelligence algorithms of the artificial intelligence search engine 120, ¶ 0020);
an active metadata repository for maintaining at least one metadata associated with the at least one data (i.e., The machine learning model 122 may learn from a reference dataset as part of the training of the machine learning model 122, to guide the artificial intelligence search process, learn from the natural language processor adapting the modified path options, and/or produce additional relevant paths via reliability, constructability and/or feasibility checks, ¶ 0060);
a private and domain specific generative Al large language model (LLM) (i.e., The machine learning model 122 may learn from a reference dataset as part of the training of the machine learning model 122, to guide the artificial intelligence search process, learn from the natural language processor adapting the modified path options, and/or produce additional relevant paths via reliability, constructability and/or feasibility checks, ¶ 0060);
a recommendation generation engine, powered by the private and domain specific LLM and knowledge graph, for analyzing at least one application requirement and for identifying at least one suitable alternative based on the at least one application requirement (i.e., artificial intelligence design system 116 is an artificial intelligence-driven three-dimensional generative design system that aims to revolutionize this paradigm by leveraging sub-symbolic artificial intelligence search algorithms, machine learning, and large-language models. Faster and cheaper energy infrastructure engineering, procurement, and construction projects mean lower energy bills, boosting economic competitiveness and household security. Efficiency is paramount in the transition to sustainable energy sources, and the artificial intelligence design system 116 accelerates decision-making, optimizes designs, and streamlines project delivery, ¶ 0031); and
a human supervision and quality control module for applying at least one rule, the at least one rule pertaining to quality, security, and/or ethics (i.e., The second operational layer is a machine-learning module 122 that incorporates many machine-learning models that offer a predictive design solution layer wherein the machine learning models-as-a-service is deployed by human designer in background, which learn, adapt and dynamically suggest design elements, ¶ 0019).
Sung et al does not disclose wherein the LLM is trained on a comprehensive knowledge graph of material and chemical relationships, including at least one dependency, and utilizes advanced vector-based reasoning; and a dependency registry and validation module for maintaining and validating the at least one dependency identified by the private and domain specific LLM.
Vierra discloses the AI system is equipped with a vast database of interior and exterior home designs, styles, and finishes that can be used as training data or reference data to create a unique design for the user. The AI system allows the user to input preferences for the design, including a desired layout or a machine suggested layout, color palette, materials, finishes, and/or the like (¶ 0021). The AI system optimizes material usage and construction processes, reducing waste and minimizing the environmental impact of construction projects. The AI system also applies machine learning models to help contractors select more sustainable materials and construction techniques (¶ 0028).
Matrix factorization is another type of machine learning algorithm used for recommender systems and other tasks. Matrix factorization decomposes a matrix into two or more matrices to uncover hidden patterns or relationships in the data. Support Vector Machines (SVM) are a type of supervised learning algorithm used for classification, regression, and other tasks. SVM finds a hyperplane that separates the different classes in the data. Other types of machine learning algorithms include decision trees, k-nearest neighbors, clustering algorithms, and deep learning algorithms such as convolutional neural networks (CNN), recurrent neural networks (RNN), and transformer models (¶ 0154).
Once the data has been preprocessed and the features have been extracted, the machine learning model is trained on the data. During this phase, the model learns to recognize patterns and relationships between various design elements and user preferences. The model's performance is evaluated on a separate validation set to avoid overfitting and ensure that it generalizes well to new, unseen data (¶ 0103). Validation, refinement or retraining 712: This may include updating a model based on feedback generated from the prediction phase, such as new data or user feedback (¶ 0164).
Additionally, Sung et al does not disclose a generative Al driven carbon and environmental footprint calculator for calculating an Environmental Cost Indicator (ECI) and a weighted Ecoscore.
Bronevetsky et al disclose the ML model 121 can perform simulations of failure scenarios, e.g., a task 102 failing, to estimate an impact on mitigation outcomes for different failure mechanisms 106. For example, a failure scenario can correspond to a global shortage of a sustainably-made material, such as concrete. The ML model 121 can predict, using aggregate impacts of the global shortage of concrete to the various tasks 102 based on the failure mechanisms 106, a total impact of the failure scenario. The ML model 121 can use this total impact to predict the overall risk score 117 (¶ 0075). The ML model 121 can rank the replacement candidates 126 using the replacement scores 128 (233). For example, the ranking must be replacement scores 128, which are a weighted average of the failure correlation 134 and the repair potential 136 (¶ 0099).
Sung et al, Vierra and Bronevetsky et al are concerned with effective construction project management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein the LLM is trained on a comprehensive knowledge graph of material and chemical relationships, including at least one dependency, and utilizes advanced vector-based reasoning; and a dependency registry and validation module for maintaining and validating the at least one dependency identified by the private and domain specific LLM, and a generative Al driven carbon and environmental footprint calculator for calculating an Environmental Cost Indicator (ECI) and a weighted Ecoscore in Sung et al, as seen in Vierra and Bronevetsky et al, respectively, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 2, Sung et al does not disclose a continuous learning module configured to dynamically update and refresh the private and domain specific LLM and the knowledge graph based on changes in the data sources and at least one interaction from user input and selections.
Vierra discloses once the feedback data is processed and structured, the AI system incorporates the feedback data into the training dataset as additional examples or used to modify the loss function, which guides the model's learning. By retraining the model with the updated dataset or loss function, the system can iteratively improve its design generation capabilities, refining its understanding of user preferences (¶ 0135). Validation, refinement or retraining 712: This may include updating a model based on feedback generated from the prediction phase, such as new data or user feedback (¶ 0164).
Sung et al and Vierra are concerned with effective construction project management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a continuous learning module configured to dynamically update and refresh the private and domain specific LLM and the knowledge graph based on changes in the data sources and at least one interaction from user input and selections in Sung et al, as seen in Vierra, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 3, Sung et al does not disclose a user interface configured to: enable a user to explore and select at least one option within a dynamic knowledge graph; visualize the at least one option; present a ranked set of related alternatives based the at least one option; and navigate the dynamic knowledge graph through user-driven filtering and exploration.
Vierra discloses the prompt includes a user's specific preferences for various aspects of the interior design, such as a desired layout, color scheme, materials used, and surface finishes. The user could provide these preferences through a combination of text, voice, or selecting options from a menu or interface (¶ 0047). The user inputs their preferences for the design, including desired layout, color palette, materials, finishes, and/or the like by inputting their design preferences manually via one or more prompts on a user interface and/or the AI-based system automatically identifies design elements, such as applying factors described herein including the user's past preferences or other available data. Such user constraints 302 can be inputted into the machine learning model 214 (¶ 0083).
Additionally, Sung et al does not disclose simulate the impact of at least one option on the ECI and Ecoscore ratings.
Bronevetsky et al disclose the ML model 121 can perform simulations of failure scenarios, e.g., a task 102 failing, to estimate an impact on mitigation outcomes for different failure mechanisms 106. For example, a failure scenario can correspond to a global shortage of a sustainably-made material, such as concrete. The ML model 121 can predict, using aggregate impacts of the global shortage of concrete to the various tasks 102 based on the failure mechanisms 106, a total impact of the failure scenario. The ML model 121 can use this total impact to predict the overall risk score 117 (¶ 0075).
Sung et al, Vierra and Bronevetsky et al are concerned with effective construction project management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a user interface configured to: enable a user to explore and select at least one option within a dynamic knowledge graph; visualize the at least one option; present a ranked set of related alternatives based the at least one option; and navigate the dynamic knowledge graph through user-driven filtering and exploration; and simulate the impact of at least one option on the ECI and Ecoscore ratings in Sung et al, as seen in Vierra and Bronevetsky et al, respectively, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 4, Sung et al does not disclose the Generative Al system utilizes at least one interaction and at least one recommendation to retrain the LLM.
Vierra discloses to utilize this user feedback for retraining the machine learning model, the AI system collects and preprocesses the feedback data to transform it into a format suitable for the learning process. The AI system categorizes the feedback into specific design aspects, quantifying user preferences, and establishing correlations between the feedback and the initial input parameters. Once the feedback data is processed and structured, the AI system incorporates the feedback data into the training dataset as additional examples or used to modify the loss function, which guides the model's learning. By retraining the model with the updated dataset or loss function, the system can iteratively improve its design generation capabilities, refining its understanding of user preferences, and ultimately creating more satisfactory interior design plans that align with users' expectations and needs (¶ 0135).
Sung et al and Vierra are concerned with effective construction project management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the Generative Al system utilizes at least one interaction and at least one recommendation to retrain the LLM in Sung et al, as seen in Vierra, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 6, Sung et al disclose a carbon offset identification module configured to: identify at least one carbon offset assets; match at least one identified and validated carbon emissions to the at least one carbon offset within a carbon exchange; and select an optimum carbon offset strategy based on the match (i.e., the artificial intelligence design system 116 aligns with the commitment to reducing carbon emissions, meeting renewable energy targets, and advancing environmental responsibility. A sustainable practices framework may also be incorporated, which guides the optimisation system to prioritize design choices with reduced carbon footprints, ¶ 0032).
Claim 8 is rejected based upon the same rationale as the rejection of claim 7, since it is the computer-readable medium claim corresponding to the method claim.
Claim 9 is rejected based upon the same rationale as the rejection of claim 1, since it is the computer-readable medium claim corresponding to the system claim.
Claims 5 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Sung et al (US 20250245400 A1), in view of Vierra (US 20240370595 A1), in further view of Bronevetsky et al (US 20250029042 A1), in further view of Su et al (US 20250298397 A1).
As per claim 5, Sung et al does not disclose a blockchain module configured to record validated projects, products, emissions and reductions on a blockchain ledger and generation of a blockchain token.
Su et al disclose the operating architecture is designed based on best practices for a plurality of artificial intelligence (AI) energy saving projects and research and development architectures. The AIOps is abbreviation of artificial intelligence and operations and is a technology combining artificial intelligence (AI) and operations (Ops), and aims to improve operation efficiency and quality of Internet technologies (IT) in an automatic and intelligent manner (¶ 0020). Further, the terminal and the server may further upload any data stored therein to the blockchain network for storage, to prevent the data stored therein from being tampered and improve data security (¶ 0047).
Sung et al and Su et al are concerned with effective environmental project management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a blockchain module configured to record validated projects, products, emissions and reductions on a blockchain ledger and generation of a blockchain token in Sung et al, as seen in Su et al, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 7 is rejected based upon the same rationale as the rejection of claims 1, 3, 5 and 6, since it is the method claim corresponding to the system claims.
Conclusion
The prior art made of record and not relied upon, listed in the PTO-892, considered pertinent to applicant's disclosure, discloses construction and environmental project management.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRE D BOYCE whose telephone number is (571)272-6726. The examiner can normally be reached M-F 10a-6:30p.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao (Rob) Wu can be reached at (571) 272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDRE D BOYCE/Primary Examiner, Art Unit 3623 June 26, 2026
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