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 the Application
This is a Non-Final Action in response to the claims and remarks submitted on 12/09/2025.
Claims 1-13 are withdrawn from consideration.
Claims 14-18 are examined herein.
The Effective Filling Date for the embodiment elected is 04/04/2024. The Examiner notes that the provisional filled on 04/05/2023 does not provide support for the embodiment elected. The elected embodiment is directed to negotiation strategies using LLM, as disclosed on Figure 4 (and related paragraphs) of the specification as filled on 04/04/2024.
Election/Restrictions
Applicant’s election without traverse of Group III, including claims 14-18 in the reply filed on 12/09/2025 is acknowledged.
Claim Objections
Claim 12 is objected to because of the following informalities: Claim 12 is missing claim identifier (Withdrawn). Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 14-18 are rejected under 35 U.S.C. 101 because the claims are directed to an abstract idea without significantly more.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the claims are directed to at least one potentially eligible category of subject matter (i.e., process and machine, respectively). Thus, Step 1 of the Subject Matter Eligibility test for claims 14-18 is satisfied.
With respect to Step 2A Prong One, it is next noted that the claims recite an abstract idea that falls under the “Certain Methods of Organizing Human Activity” groups within the enumerated groupings of abstract ideas set forth in the MPEP 2106 since the claims set forth steps that recite concepts directed to managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) and commercial or legal interactions (including agreements in the form of contracts; business relations).
Claim 14 recites the abstract idea of timekeeper rate negotiations[045]. In claim 14, this idea is described by the following claim steps:
training, by an organization a model with legal and financial data for the organization;
receiving, by an organization, an inquiry for a timekeeper rate structure from a service provider for a service provider;
providing, the inquiry to the model;
receiving, by the organization and from the model, an initial timekeeper rate structure;
providing, by the organization the initial timekeeper rate structure to the service provider;
receiving, by the organization and from the service provider, a response to the initial timekeeper rate structure;
providing, by the organization, the response to the initial timekeeper rate structure to the model;
receiving, by the organization and from the model, a revised timekeeper rate structure; and
providing, by the organization, the revised timekeeper rate structure to the service provider.
This idea falls within the Certain Methods of Organizing Human Activity grouping of abstract ideas because it is directed towards concepts directed to commercial and legal interactions (including business relations). The noted abstract idea is also directed to managing interactions between people such as that required during communications when negotiating a rate conforms to the requirements of more than one party.
Because the above-noted limitations recite steps falling within the Certain Methods Of Organizing Human Activity abstract idea groupings of the MPEP 2106, they have been determined to recite at least one abstract idea when evaluated under Step 2A Prong One of the eligibility inquiry.
Therefore, because the limitations above set forth activities falling within the Certain Methods Of Organizing Human Activity abstract idea groupings described in the MPEP 2106, the additional elements recited in the claims are further evaluated, individually and in combination, under Step 2A Prong Two and Step 2B below.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements that fail to integrate the abstract idea into a practical application are:
training, by an organization computer program for an organization executed by an organization computer processor, a large language model;
a service provider interface computer program;
However, using a computer environment such as a computer processor, and the generic use of large language model amounts to no more than generally linking the use of the abstract idea to a particular technological environment. Negotiating a rate structure can reasonably be performed by pen and paper until limited to a computerized environment by requiring the use of a computer and large language model to perform the steps.
In regards to the limitation “training, by an organization computer program for an organization executed by an organization computer processor, a large language model” the examiner views these additional elements as results-oriented steps given that there is no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result are currently present such that this is viewed as equivalent to “apply it” for merely implementing the abstract idea using generic computing components (See Id.).
These additional element, alone and in combination, have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or computer-executable instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), and alternatively serve to link the use of the judicial exception to a particular technological environment. See MPEP 2106.05(f) and 2106.05(h).
In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As noted above, the claims as a whole merely describes a method, computer system, and computer program product that generally “apply” the concepts discussed in prong 1 above. (See MPEP 2106.05 f (II)) In particular applicant has recited the computing components at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. As the court stated in TLI Communications v. LLC v. AV Automotive LLC, 823 F.3d 607, 613 (Fed. Cir. 2016) merely invoking generic computing components or machinery that perform their functions in their ordinary capacity to facilitate the abstract idea are mere instructions to implement the abstract idea within a computing environment and does not add significantly more to the abstract idea. Accordingly, these additional computer components do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, even when viewed as a whole, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea and as a result the claim is not patent eligible.
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
For the reasons identified with respect to Step 2A, prong 2, claim 14 fails to recite additional elements that amount to an inventive concept. For example, use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a commercial or legal interaction or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more (see MPEP 2106.05(g)). In addition, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (see MPEP 2106.05(h)).
Dependent claims 15-18 recite the same abstract idea as recited in the independent claims, and when evaluated under Step 2A Prong One are found to merely recite details that serve to narrow the same abstract idea recited in the independent claims accompanied by the same generic computing elements or software as those addressed above in the discussion of the independent claims, which is not sufficient to amount to a practical application or add significantly more, or other additional elements that fail to amount to a practical application or add significantly more, as noted above.
Dependent claim 15 further limits the abstract idea by introducing wherein the legal and financial data comprises organization requirements, historical billing data, desired rate structures, budgets, market rates, and/or historical performance of the service provider. Further embellishing the invention by describing the type of data used does not integrate the abstract idea into a practical application or adds significantly more to the abstract idea. Therefore the claims are also non-statutory subject matter.
Dependent claim 16 further limits the abstract idea by linking the judicial exception to a particular field of use by introducing the limitation wherein the large language model further provides negotiation strategies and potential counteroffers with the initial timekeeper rate structure. The examiner views these additional elements as results-oriented steps given that there is no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result are currently present such that this is viewed as equivalent to “apply it” for merely implementing the abstract idea using generic computing components (See Id.). Therefore the claims are also non-statutory subject matter.
Dependent claim 17 further limits the abstract idea by linking the judicial exception to a particular field of use by introducing the limitation wherein the inquiry comprises a prompt for the initial timekeeper rate structure for the service provider. Further embellishing that the invention is capable of communicate data in a generic computing environment does not integrate the abstract idea into a practical application or adds significantly more to the abstract idea.
Dependent claim 18 further limits the abstract idea by introducing the limitations re-training the large language model with an agreed-up timekeeper rate structure. The examiner views these additional elements as results-oriented steps given that there is no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result are currently present such that this is viewed as equivalent to “apply it” for merely implementing the abstract idea using generic computing components (See Id.). Therefore the claims are also non-statutory subject matter.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and the collective functions merely provide high level of generality computer implementation. Therefore, whether taken individually or as an order combination, the claims are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
For more information see MPEP 2106.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 14-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fields (US Patent Publication 2024/0291778) VanPuymbrouck (US Patent Publication 2020/0274834).
Regarding claim 14, Fields discloses a method, comprising:
training, by an organization computer program for an organization executed by an organization computer processor, a large language model with legal and financial data for the organization ([015] “a hosting server or user computing device, is enhanced with a trained machine learning chatbot/voice bot to accurately determine one or more products desired by a party, determine parties corresponding to the product, determine/generate terms (e.g., acceptable terms, prospective terms, counter terms) acceptable to the party, and finalize a contractual agreement between parties for agreed upon terms.” [0016] As mentioned, the model(s) may be trained using machine learning and may utilize machine learning during operation. Therefore, in these instances, the techniques of the present disclosure may further include improvements in computer functionality or in improvements to other technologies at least because the disclosure describes such models being trained with a plurality of training data (e.g., example inputs and associated example outputs, response signals, parameters, acceptable terms, prospective terms, counter terms, products, etc.) to output the system-specific conditions configured to negotiate with party/parties for product(s) on another party's behalf. Further see [0048] wherein it is disclosed the use of ChatGPT which is based on large language model. [0055] … various embodiments, examples, and/or aspects disclosed herein may include training and generating one or more ML models and/or ML chatbot 152 for the server 105 to load at runtime…);
receiving, by the organization computer program, an inquiry for a timekeeper rate from a service provider interface computer program for a service provider (See Figs.2-3 and [080-081] disclosing the system receiving from a first party a plurality of terms to be negotiated, including cost for the service. Paragraph [063] further discloses wherein the parameters to be negotiated include the hurly billing rate of a professional (i.e. timekeeper rate). See also [060, 062]);
providing, by the organization computer program, the inquiry to the large language model (See Figs.2-3 and [081] disclosing the inquiry being provided to the by the AI chatbot. “The first contracting party 240 may thereafter propose new terms (prospective terms) for the specific product and the server 105 may receive input 242 from the first contracting party 240 indicating the prospective terms. The server 105 may determine the prospective terms of input 242 to be unacceptable terms and send output 244 to the first contracting party 240 indicating a plurality of counter terms generated by the server 105.” Further see [0048] wherein it is disclosed the use of ChatGPT which is based on large language model. See also [062]);
receiving, by the organization computer program and from the large language model, initial timekeeper rate (See Fig. 2B [052] discloses the model being fed with acceptable terms, parameters and others. “wherein it is disclosed the parameters and other data received for negotiating “Data associated with the negotiation (e.g., negotiating period), such as response signals, product information, prospective terms, counter terms, acceptable terms, parameters indicated by a user,… and/or other suitable data may be captured by the server 105 as negotiating data. In some aspects, the server 105 may store the negotiating data in the database 126. The data may be cleaned, labeled, vectorized, weighted and/or otherwise processed, especially processing suitable for data used in any aspect of ML.” See also [062-063] disclosing an example with the timekeeper rate.);
providing, by the organization computer program, the initial timekeeper rate to the service provider interface computer program (See Fig. 2B and [081] wherein it is disclosed the parameters provided to the first party “The server 105 may determine the prospective terms of input 242 to be unacceptable terms and send output 244 to the first contracting party 240 indicating a plurality of counter terms generated by the server 105.” wherein the inquiry is fed to the server (AI chatbot, [079]). Further see [0048] wherein it is disclosed the AI chatbot as ChatGPT, an artificial intelligence based on large language model. See also [062-063] disclosing an example with the timekeeper rate.);
receiving, by the organization computer program and from the service provider interface computer program, a response to the initial timekeeper rate (See Fig, 2B and [0082] disclosing the first party providing a response to the parameters received by the AI chatbot “The exchange of terms via output(s) 244 and input(s) 242 may be characterized as a negotiation. The negotiation between the first contracting party 240 and the server 105 on behalf of the second contracting party 220 may continue for any number of cycles, wherein the server 105 may receive input(s) 242 indicating prospective terms and may send output(s) 244 indicating counter terms until a set of terms (e.g., prospective terms, counter terms) acceptable to both parties is finalized by a contractual agreement or the negotiation is abandoned by one or both parties. For example, the negotiation may be abandoned when the first contracting party 240 may abandon the negotiation and the server 105 may not receive the input 242, and/or the server 105 may determine subsequently generated counter terms that may be unacceptable terms to the first contracting party 240, such that the server 105 may not send the output 244 and/or may prohibit receipt of the input 242. In any event, the negotiation may include the negotiating session and/or the negotiating period, as described elsewhere herein.” See also [062-063] disclosing an example with the timekeeper rate.);
providing, by the organization computer program, the response to the initial timekeeper rate to the large language model (See Fig. 2B and [0083] wherein it is disclosed that the input/output responses provided by the first and second party are processed first by the AI chatbot “In some embodiments, the server 105 may determine prospective terms of the input 242 to be acceptable terms to the second contracting party 220 and may not send the output 244 indicating counter terms. In various embodiments, the server 105 may determine prospective terms of the input 242 to be acceptable terms and send the input/output 228 to the second contracting party 220 indicating the prospective terms. See also [062]);
receiving, by the organization computer program and from the large language model, a revised timekeeper rate (See Figure 2B and [083] disclosing receiving revised parameters by the AI chatbot. “The server 105 may subsequently receive the input/output 228 in the form of a response signal from the second contracting party 220 indicating whether the second contracting party 220 accepts the plurality of prospective terms. In certain embodiments where the response signal may indicate the second contracting party 220 accepts the plurality of prospective terms, the server 105 may finalize a contractual agreement between the first contracting party 240 and the second contracting party 220 with the plurality of prospective terms. In some embodiments where the response signal may indicate the second contracting party 220 does not accept (declines) the plurality of prospective terms, the server 105 may generate a plurality of counter terms based on the plurality of prospective terms and send the output 244 to the first contracting party 240 indicating the generated counter terms.” See also [062-063] disclosing an example with the timekeeper rate.); and
providing, by the organization computer program, the revised timekeeper rate to the service provider interface computer program (See Figure 2B and [083] disclosing providing the revised parameters to the first party. “The server 105 may subsequently receive the input/output 228 in the form of a response signal from the second contracting party 220 indicating whether the second contracting party 220 accepts the plurality of prospective terms. In certain embodiments where the response signal may indicate the second contracting party 220 accepts the plurality of prospective terms, the server 105 may finalize a contractual agreement between the first contracting party 240 and the second contracting party 220 with the plurality of prospective terms. In some embodiments where the response signal may indicate the second contracting party 220 does not accept (declines) the plurality of prospective terms, the server 105 may generate a plurality of counter terms based on the plurality of prospective terms and send the output 244 to the first contracting party 240 indicating the generated counter terms. See also [062-063] disclosing an example with the timekeeper rate.).
Fields discloses negotiating a variety of terms and parameters between a first and second party, including the hourly rate for a service to be provided by a professional. (See [063]) However does not explicitly disclose such timekeeper rate to be a timekeeper rate structure.
However VanPuymbrouck which is directed to a system and method for facilitating and negotiating the hire or discover of professionals such as legal counsels (see [005]) further teach:
Negotiating timekeeper rate structure ([0024] The price for legal services charged by outside counsel to companies is one of the most oblique aspects of the legal industry. Although law firms maintain and annually update billing rates for all of their time keepers, those “rate sheets” are not provided to clients much less prospective clients or made public and generally consist of multiple rates for each time keeper so that lawyers and law firms are free to charge clients the highest rate possible while remaining within the confines of their rate schedules. Embodiments of the instant disclosure allow companies to simultaneously compare and negotiate the rates charged by multiple outside counsel with similar experience and expertise thereby allowing companies to accurately gauge the true cost for such services and to negotiate the best price allowing them to reduce their legal spend by 50-100%.).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled to include in the negotiation of terms and parameters of Fields, a timekeeper rate structure since such modification in the system of Fields provides the well-known benefit of taking into consideration the costs of the services provided for all the timekeepers since such modification will allow the clients of the system to accurately gauge the true cost for such services and to negotiate the best price as disclosed by VanPuymbrouck.
Regarding claim 15, Fields discloses
wherein the legal and financial data comprises organization requirements, historical billing data, desired rate structures, budgets, market rates, and/or historical performance of the service provider ([015] “(e.g., acceptable terms, prospective terms, counter terms) [0016] (e.g., example inputs and associated example outputs, response signals, parameters, acceptable terms, prospective terms, counter terms, products, etc.)).
Regarding claim 16, Fields discloses
wherein the large language model further provides negotiation strategies and potential counteroffers with the initial timekeeper rate structure ([0052] In some aspects, the application may use the chatbot 150 to negotiate with a user (e.g., first contracting party) on behalf of a second user (e.g., second contracting party) until a contractual agreement between the user and the second user is finalized [053] In certain aspects, the server 105 may use the stored data and/or negotiation data to generate, train and/or retrain one or more ML models and/or chatbots 150 [054] In certain aspects, once an appropriate ML model is trained and validated to provide accurate predictions and/or responses, e.g., the ML chatbot 152 generated by MLTM 142, the trained model and/or ML chatbot 152 may be loaded into MLOM 144 at runtime, may process the user inputs (e.g., prospective terms, parameters indicated by a user, response signals), and may generate outputs (e.g., counter terms, finalizing a contractual agreement, acceptable terms). [081] The server 105 may determine the prospective terms of input 242 to be unacceptable terms and send output 244 to the first contracting party 240 indicating a plurality of counter terms generated by the server 105. ).
Regarding claim 17, Fields discloses
wherein the inquiry comprises a prompt for the initial timekeeper rate structure for the service provider (See Figs.2-3 and [080-081] disclosing the system receiving from a first party a plurality of terms to be negotiated, including cost for the service. Paragraph [063] further discloses wherein the parameters to be negotiated include the hurly billing rate of a professional (i.e. timekeeper rate). See also [060, 062]).
Regarding claim 18, Fields discloses
further comprising: re-training the large language model with an agreed-up timekeeper rate structure ([053] . In certain aspects, the server 105 may use the stored data and/or negotiation data to generate, train and/or retrain one or more ML models and/or chatbots 150, and/or for any other suitable purpose.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Beg, US 20250061452, MEDIATED ANONYMOUS NEGOTIATION SYSTEM. An electronically networked mediated anonymous negotiation system and method are provided. A seller-side communication console has a transaction panel adapted to bidirectionally communicate product offer data with a moderator-side transaction management panel. A seller-side acceptance feature is configured to transmit to the moderator-side transaction management panel a manifestation of assent to the product offer. A buyer-side communication console is similarly equipped. A moderator-side mediation console has a transaction management panel adapted to bidirectionally communicate product offer data with the seller-side transaction panel, and to bidirectionally communicate product offer data with the buyer-side transaction panel. A multilateral chat feature is in bidirectional data communication with a seller-side chat panel, and in bidirectional communication with a buyer-side chat panel.
Raghupathy, US 20100235294, System And Methods For Multi-Dimension Top Down Negotiation. The present invention relates to systems and methods for multi-dimensional top down negotiations. A user supplies negotiated deal dimensions to an allocator. The allocator may also be able to set some of the dimension values to a default value. A line item generator may then perform transformational algorithms on the negotiated deal dimensions to generate line items. A line item override module may then receive user overrides for specific line items, and update the line items using the overrides. This update may include the recalculating of other line items affected by the override. A summarizer may generate a deal summary using the updated line items. A normalization engine may also normalize the deal summary to current market value to facilitate comparisons of the deal summary. The deal summary may include any of a blended rate per hour, an income, a cost, a product margin, and a net present value. Further, the deal summary may be broken down by geography, period and resource type.
Justis, US 20240346432, SYSTEMS, METHODS, AND APPARATUS TO AUTOMATE CONSUMER ADVOCACY WITH A LARGE LANGUAGE MODEL. Systems, apparatus, articles of manufacture, and methods to automate consumer advocacy with a large language model are disclosed. Machine readable instructions may be executed to cause at least one programmable circuit to at least obtain a first message from a large language model based on a return request provided by a consumer, the return request associated with a previously purchased product to be returned to an entity, cause transmission of the first message to the entity to request authorization of the return of the previously purchased product, obtain a second message from the large language model, the second message based on the first message and a first response, the first response from the entity in response to the first message, cause transmission of the second message to the entity to continue the request to return the previously purchased product, and cause communication of a resolution message to the consumer.
F. Lopes, H. Algarvio and H. Coelho, "Bilateral contracting in multi-agent electricity markets: Negotiation strategies and a case study," 2013 10th International Conference on the European Energy Market (EEM), Stockholm, Sweden, 2013, pp. 1-8, doi: 10.1109/EEM.2013.6607343.
I. Roussaki, M. Louta and L. Pechlivanos, "An efficient negotiation model for the next generation electronic marketplace," Proceedings of the 12th IEEE Mediterranean Electrotechnical Conference (IEEE Cat. No.04CH37521), Dubrovnik, Croatia, 2004, pp. 615-618 Vol.2, doi: 10.1109/MELCON.2004.1347005.
K. Hashmi, E. Najmi, Z. Malik, B. Medjahed, A. Alhosban and A. Rezgui, "Automated Negotiation Using Semantic Rules," 2014 IEEE International Conference on Services Computing, Anchorage, AK, USA, 2014, pp. 536-543, doi: 10.1109/SCC.2014.77.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARIA C SANTOS-DIAZ whose telephone number is (571)272-6532. The examiner can normally be reached Monday-Friday 8:00AM-5:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sarah Monfeldt can be reached at 571-270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARIA C SANTOS-DIAZ/Primary Examiner, Art Unit 3629