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 .
THIS ACTION IS MADE FINAL.
Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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 mailing date of this final action.
Status of the Application
The following is a Final Office Action in response to Examiner's communication of 06/02/2025, Applicant, on 10/02/2025.
Status of Claims
Claims 1, 7, 11, and 19 are currently amended.
Claims 1-20 are currently pending following this response.
New matter
No new matter has been added to the amended claims.
Response to Arguments - 35 USC § 101
The arguments have been fully considered, but they are not persuasive.
Regarding applicant’s arguments on pages 11-24
The Examiner respectfully disagrees.
Claims can recite an abstract idea even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer’).
Collecting data, recognizing certain data within the collected data set, and storing that recognized data in a memory in Content Extraction is according to the court an abstract idea that is similar to other concepts that have been identified as abstract by the courts. Present claim 1 is collecting and analyzing data using a generic computer processor. Therefore, it is reasonable to conclude based on the similarity of the idea described in this claim to several abstract ideas found by the courts that claim 1 is directed to an abstract idea (Applicant’s arguments pages 16-20).
With respect to the use of machine learning techniques (arguments 11-12), it is common practice that such computational models/techniques and algorithms are per se of an abstract mathematical nature, irrespective of whether they can be “trained” based on training data. Hence, a mathematical method may contribute to the technical character of an invention, if it serves as technical purpose or if it regards as specific technical implementation motivated by the internal function of a computer. Elements in the present claims do not solve a technical problem, but an administrative/business method, i.e. associating a contractor with a job opportunity. Since the mathematical algorithms or models used in the present application do not serve a technical purpose, but a business purpose, and their implementation does not go beyond generic technical implementation, the use of the artificial intelligence techniques, do not contribute to a technical character and they are to be part of the abstract idea.
Further, the additional elements in the claims (“a device associated with a contractor”, “by an Artificial Intelligence (AI) engine”, “by the Al engine”, “via at least one external API”, “by at least one hardware processor”) do not improve any existing technology because the claimed steps are performed using a generic processor executing different software modules (arguments pages 12-13). Any technical improvement over the prior arts (arguments starting page 11) and arguments relating to “rooted in technology” (arguments starting page 15) are nothing but business improvements and automating a manual process. As a result, the additional elements do not integrate the abstract idea into a practical application, Step 2A Prong Two.
Because the Examiner has determined that the judicial exception is not integrated into a practical application, the Examiner proceeds to Step 2B of the Eligibility Guidelines, which asks whether there is an inventive concept. In making this Step 2B determination, the Examiner must consider whether there are specific limitations or elements recited in the claim “that are not well - understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present” or whether the claim “simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, indicative that an inventive concept may not be present.” Eligibility Guidance, 84 Fed. Reg. 56 (footnote omitted). The Examiner must also consider whether the combination of steps perform “in an unconventional way and therefore include an ‘inventive step, ’ rendering the claim eligible at Step 2B ” Id. In this part of the analysis, the Examiner considers “the elements of each claim both individually and ‘as an ordered combination’” to determine “whether the additional elements ‘transform the nature of the claim’ into a patent-eligible application.” Alice, 134 S. Ct. at 2354. As discussed above, there is no evidence in the record that the steps of associating a contractor with a job opportunity and recommending work opportunity to a user are accomplished in a non-conventional way. The Examiner therefore concludes that the claims used generic, conventional, technology to implement the abstract idea of associating a contractor with a job opportunity and that there is no improvement to an “existing technology.”
In conclusion, the Examiner maintains the rejections of the pending claims under 35 USC § 101 in the present office action.
Response to Arguments - 35 USC § 102
The arguments have been fully considered, but they are not persuasive.
Regarding applicant’s arguments on pages 24-25
The Examiner respectfully disagrees.
The Examiner introduced the newly cited reference Mathiesen which is in combination Sinan teach all the pending claims as amended. Mainly, Mathiesen teaches periodically updating, by the Al engine, the learned contractor profile based on additional contractor behavioral data and external, industry-specific job market datasets received via at least one external API; establishing one or more parameters for assessing the plurality of candidate work opportunities based at least in part on one or more of the contractor bid data or the contractor legacy data; analyzing each of the plurality of candidate work opportunities in accordance with the one or more parameters; and providing, to the device associated with the contractor, the recommendation of one or more of the plurality of candidate work opportunities. Please see 35 USC § 103 rejection (below).
In conclusion, the Examiner maintains the rejections of the pending claims under 35 USC § 103 in the present office action.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1-20 are directed to an abstract idea without additional elements to integrate the claims into a practical application or to amount to significantly more than the abstract idea.
Claims 1-20 are directed to a process, machine, or manufacture (Step 1), however the claims are directed to the abstract idea of recommending work opportunity to a user.
With respect to Step 2A Prong One of the frameworks, claim 1 recites an abstract idea. Claim 1 includes limitations for “A method for providing curated work opportunities comprising: receiving contractor bid data, the contractor bid data comprising information related to work the contractor is interested in performing; receiving contractor legacy data relating to one or more past work opportunities, projects, and contracts associated with the contractor; receiving a request for a recommendation of one or more work opportunities for which the contractor should apply, from a plurality of candidate work opportunities; associating the contractor bid data and the contractor legacy data with a learned contractor profile trained; periodically updating the learned contractor profile based on additional contractor behavioral data and external, industry-specific job market datasets received; establishing one or more parameters for assessing the plurality of candidate work opportunities based at least in part on one or more of the contractor bid data or the contractor legacy data; analyzing each of the plurality of candidate work opportunities in accordance with the one or more parameters to determine a degree of correlation between the learned contractor profile and each of the one or more candidate work opportunities using a dynamic set of weighted factors including contractor behavioral trends and real-time market conditions; and providing a recommendation of one or more of the plurality of candidate work opportunities based at least in part on the dynamically updated learned contractor profile and weighted correlation to current market data”
The limitations above recite an abstract idea under Step 2A Prong One. More particularly, the limitations above recite certain methods of organizing human activity associated with managing personal behavior or relationships or interactions between people because the claimed elements describe a process for recommending work opportunity to a user. As a result, claim 1 recites an abstract idea under Step 2A Prong One.
Claims 11 and 19 recite substantially similar limitations to those presented with respect to claim 1. As a result, claims 11 and 19 recite an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claim 1. Similarly, claims 2-10, 12-18, and 20 recite certain methods of organizing human activity associated with managing personal behavior or relationships or interactions between people because the claimed elements describe a process for recommending work opportunity to a user. As a result, claims 2-10, 12-18, and 20 recite an abstract idea under Step 2A Prong One.
With respect to Step 2A Prong Two of the framework, claim 1 does not include additional elements that integrate the abstract idea into a practical application. Claim 1 includes additional elements that do not recite an abstract idea. The additional elements of claim 1 include “a device associated with a contractor”, “by an Artificial Intelligence (AI) engine”, “by the Al engine”, “via at least one external API”, “by at least one hardware processor”. When considered in view of the claim as a whole, the step of “receiving” does not integrate the abstract idea into a practical application because “receiving” is an insignificant extra solution activity to the judicial exception. When considered in view of the claim as a whole, the recited computer elements do not integrate the abstract idea into a practical application because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As a result, claim 1 does not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
As noted above, claims 11 and 19 recite substantially similar limitations to those recited with respect to claim 1. Although claim 11 further recites “One or more non-transitory computer readable media” and claim 19 further recites “A system comprising: at least one device including a hardware processor”, when considered in view of the claim as a whole, the recited computer elements do not integrate the abstract idea into a practical application because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As a result, claims 11 and 19 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 2-10, 12-18, and 20 do not include any additional elements beyond those recited by independent claims 1, 11, and 19. As a result, claims 2-10, 12-18, and 20 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
With respect to Step 2B of the framework, claim 1 does not include additional elements amounting to significantly more than the abstract idea. As noted above, claim 1 includes additional elements that do not recite an abstract idea. The additional elements of claim 1 include “a device associated with a contractor”, “by an Artificial Intelligence (AI) engine”, “by the Al engine”, “via at least one external API”, “by at least one hardware processor”. The step of “receiving” does not amount to significantly more than the abstract idea because “receiving” is well-understood, routine, and conventional computer function in view of MPEP 2106.05(d)(ll). The recited computer elements do not amount to significantly more than the abstract idea because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As a result, claim 1 does not include additional elements that amount to significantly more than the abstract idea under Step 2B.
As noted above, claims 11 and 19 recite substantially similar limitations to those recited with respect to claim 1. Although claim 11 further recites “One or more non-transitory computer readable media” and claim 19 further recites “A system comprising: at least one device including a hardware processor”, the recited computer elements do not amount to significantly more than the abstract idea because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 11 and 19 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Claims 2-10, 12-18, and 20 do not include any additional elements beyond those recited by independent claims 1, 11, and 19. As a result, claims 2-10, 12-18, and 20 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 non-obviousness.
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 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being un-patentable over Al-Sinan et al. (US 20210304297 A1) hereinafter Sinan in view of Mathiesen et al. (US 11205144 B2).
Regarding claim 1. Sinan teaches A method for providing curated work opportunities comprising: [Sinan, claim 1, Sinan teaches “A computer-implemented method, comprising:” and “selecting, by the autonomous bidder solicitation and selection system, a winning bidder from the bidding parties”] receiving contractor bid data from a device associated with a contractor, the contractor bid data comprising information related to work the contractor is interested in performing; [Sinan, claim 1, Sinan teaches “creating, by an autonomous bidder solicitation and selection system, a pro forma contract from a procurement request received from a user” wherein the procurement request is equivalent to work the contractor is interested in performing] receiving contractor legacy data relating to one or more past work opportunities, projects, and contracts associated with the contractor; [Sinan, para. 0094, Sinan teaches “Machine learning tools such as BERT can be used to set up the Internet scraping process. Reinforcement machine learning can improve the module performance over time by also considering historical data” wherein historical contractor data] receiving a request, from the device associated with the contractor, for a recommendation of one or more work opportunities for which the contractor should apply, from a plurality of candidate work opportunities; [Sinan, claim 1, Sinan teaches “creating, by an autonomous bidder solicitation and selection system, a pro forma contract from a procurement request received from a user” wherein receiving request for work opportunity]
associating the contractor bid data and the contractor legacy data with a learned contractor profile trained by an Artificial Intelligence (AI) engine; [Sinan, Para. 0093, Sinan teaches “Selective bidding 1304 can be used when the bidders have been identified by the system. The registered suppliers list can be used to identify bidders whose registrations reflect services (performable/offered by the bidders) that are relevant to the scope of work of a procurement on which bidding is to occur. To automate this task, the system can parse the scope of work and use machine learning algorithms to identify applicable fields of service” wherein associating bidder to work using ML]
Sinan does not specifically teach, however, Mathiesen teaches periodically updating, by the Al engine, the learned contractor profile based on additional contractor behavioral data and external, industry-specific job market datasets received via at least one external API; [Mathiesen, Para. 0093, Mathiesen teaches “Selective bidding 1304 can be used when the bidders have been identified by the system. The registered suppliers list can be used to identify bidders whose registrations reflect services (performable/offered by the bidders) that are relevant to the scope of work of a procurement on which bidding is to occur. To automate this task, the system can parse the scope of work and use machine learning algorithms to identify applicable fields of service” wherein associating bidder to work using ML] establishing one or more parameters for assessing the plurality of candidate work opportunities based at least in part on one or more of the contractor bid data or the contractor legacy data; [Mathiesen, claim 1, Mathiesen teaches “subsequent to identifying the set of employment opportunities, obtaining a first assessment result related to a first qualification of the candidate; filtering, by one or more computer systems, the set of employment opportunities based on a first comparison of the first assessment result with qualifications for the set of employment opportunities to produce a first subset of employment opportunities; selecting, by the one or more computer systems, a second qualification associated with one or more employment opportunities from the first subset of employment opportunities to assess for the candidate; obtaining a second assessment result related to the second qualification of the candidate; filtering, by the one or more computer systems, the first subset of employment opportunities based on a second comparison of the second assessment result related to the second qualification of the candidate with additional qualifications for the first subset of employment opportunities to produce a second subset of employment opportunities;” wherein parameters for assessing the plurality of candidate work opportunities based at least in part on one or more of the contractor bid data or the contractor legacy data] analyzing each of the plurality of candidate work opportunities in accordance with the one or more parameters; [Mathiesen, claim 1, Mathiesen teaches “applying as input features to a machine learned model the first and second assessment results to produce match scores between the candidate and the second subset of employment opportunities” wherein analyzing each of the plurality of candidate work opportunities in accordance with the one or more parameters] and providing, to the device associated with the contractor, the recommendation of one or more of the plurality of candidate work opportunities [Mathiesen, claim 1, Mathiesen teaches “applying as input features to a machine learned model the first and second assessment results to produce match scores between the candidate and the second subset of employment opportunities” wherein outputting recommendations related to applying to one or more of the second subset of employment opportunities by the candidate]
Sinan teaches an autonomous procurement system can run an entire procurement process without human intervention, from pro forma contract creation through the selection of a winning bidder and Mathiesen teaches techniques for performing assessment-based exploration of opportunities. The two references are in the same field of endeavor as the claimed invention of managing correlation between a job seeker and a job opportunity. It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify/combine utilizing associating the contractor bid data and the contractor legacy data with a learned contractor profile trained by an Artificial Intelligence (AI) engine of Sinan with periodically updating, by the Al engine, the learned contractor profile based on additional contractor behavioral data and external, industry-specific job market datasets received via at least one external API and determine a degree of correlation between the learned contractor profile and each of the one or more candidate work opportunities using a dynamic set of weighted factors including contractor behavioral trends and real-time market conditions of Mathiesen 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, with the predictable results of optimizing recommendation of one or more of the plurality of candidate workopportunities.
Regarding claim 2. Sinan in view of Mathiesen teaches all of the limitations of claim 1 (as above). Further, Sinan teaches wherein the contractor bid data comprises one or more of the following: a CSI division, a scope of work, a service performed, one or more qualifications, supporting documentation, a geolocation, one or more resources to be used, or an availability time frame [Sinan, Para. 0038, Sinan teaches “the purpose of the prequalification evaluation is to assess the technical capabilities of potential bidders to ensure they are qualified to deliver the requirements of the scope of work” wherein “ensure they are qualified to deliver the requirements of the scope of work” is equivalent to “contractor bid data comprises a scope of work”].
Regarding claim 3. Sinan in view of Mathiesen teaches all of the limitations of claim 2 (as above). Further, Sinan teaches wherein each of the plurality of candidate work opportunities comprises one or more of the following: one or more work types, a CSI division, a bid start date, a start date, one or more qualification requirements, a geolocation, an opportunity scope overview, deliverables, supporting documentation, an internal cost estimate, or a parent project [Sinan, Para. 0037, Sinan teaches “In order to identify potential bidders who are interested in participating in the bidding process, the SOI communication can be sent by the APS to the potential bidders. The SOI communication can provide a brief description of the scope of work and the expected timeframe” wherein an opportunity scope overview].
Regarding claim 4. Sinan in view of Mathiesen teaches all of the limitations of claim 3 (as above). Further, Sinan teaches wherein the one or more parameters for assessing the plurality of candidate work opportunities comprises a minimum correlation between the one or more learned contractor profiles associated with the contractor and each of the plurality of work opportunities [Sinan, Para. 0100, Sinan teaches “FIG. 16 is a screenshot showing an example of a supplier qualification profile web page 1600, according to some implementations of the present disclosure. The supplier qualification profile web page 1600 can be used for defining prequalification data, including general information about the supplier which can be captured during the registration process” wherein contractor profile as in figure 16 used for qualification].
Regarding claim 5. Sinan in view of Mathiesen teaches all of the limitations of claim 3 (as above). Further, Sinan teaches wherein the one or more parameters for assessing the plurality of candidate work opportunities comprises a predetermined range of association of one or more weighted factors between the one or more learned contractor profiles and each of the plurality of work opportunities [Sinan, Para. 0051, Sinan teaches “Another common methodology is to assign weights for each of the technical and commercial proposals. The bidder with the overall highest score can be recommended for award” wherein weight for each of the technical and commercial proposal is equivalent to opportunity weight used for recommendation].
Regarding claim 6. Sinan in view of Mathiesen teaches all of the limitations of claim 5 (as above). Further, Sinan teaches wherein the one or more weighted factors are determined based on input from the device associated with the contractor [Sinan, Para. 0063, Sinan teaches “NLP and ML can be included in the first activity executed by the system when a user initiates a procurement request, as NLP and ML can provide key inputs to many other modules of the system” wherein user input in the request and ML analysis is done based on key inputs].
Regarding claim 7. Sinan in view of Mathiesen teaches all of the limitations of claim 3 (as above). Further, Sinan teaches wherein the recommendation comprises a single work opportunity, of the plurality of candidate work opportunities, that most closely matches the learned contractor profile associated with the contractor [Sinan, Para. 0051, Sinan teaches “The bidder with the overall highest score can be recommended for award” wherein overall highest score is equivalent to closely matches the learned contractor profile associated with the contractor].
Regarding claim 8. Sinan in view of Mathiesen teaches all of the limitations of claim 3 (as above). Further, Sinan teaches wherein the recommendation comprises the one or more work opportunities, of the plurality of candidate work opportunities, that meet the one or more parameters for assessing the plurality of candidate work opportunities [Sinan, Para. 0051, Sinan teaches “The bidder with the overall highest score can be recommended for award” wherein overall highest score the overall highest score is equivalent to closely matches the input from the device associated with the contractor is equivalent to meet the one or more parameters for assessing the plurality of candidate work opportunities].
Regarding claim 9. Sinan in view of Mathiesen teaches all of the limitations of claim 3 (as above). Further, Sinan teaches wherein the one or more candidate work opportunities are stored at a marketplace of an online platform [Sinan, Para. 0036, Sinan teaches “Although many large organizations (or buyers) may provide online supplier registration platforms, many of the organizations typically conduct manual authentication before issuing a supplier registration number (such as a supplier number). The buyer sometimes sends registration invitations to suppliers, either to build a database of potential suppliers for future business opportunities or to consider the suppliers for a particular imminent procurement” wherein opportunities are stored at a marketplace].
Regarding claim 10. Sinan in view of Mathiesen teaches all of the limitations of claim 9 (as above). Further, Sinan teaches wherein at least one of the one or more resources to be used is accessible via the marketplace [Sinan, Para. 0087, Sinan teaches “With the concept of the e-marketplace, verification of the data can be spontaneous, and alteration of the data can require the consensus of the other party. Blockchain network principles, using hyperledger algorithms for example, can be deployed in this solution. The integration of all parties such as suppliers, accounting firms, registration agencies, and government entities, can streamline the accessibility and verification of the supplier data” wherein supplier marketplace is equivalent to resources to be used is accessible via the marketplace].
Regarding claim 11, the claim recites analogous limitations to claim 1 above, and is therefore rejected on the same premise. Claim 1 is a method claim while claim 11 is directed to a non-transitory computer readable media which is anticipated by Sinan claim 11.
Regarding claims 12-16 and 17-18, claims 12-16 and 17-18 recite substantially similar limitations as claim 2-6 and 9-10, respectively; therefore, claims 12-16 and 17-18 are rejected with the same rationale, reasoning, and motivation provided above for claims 2-6 and 9-10, respectively. Claims 2-6 and 9-10 are method claims while claims 12-16 and 17-18 are directed to a non-transitory computer readable media which is anticipated by Sinan claim 11.
Regarding claim 19, the claim recites analogous limitations to claim 1 above, and is therefore rejected on the same premise. Claim 1 is a method claim while claim 19 is directed to a system comprising: at least one device including a hardware processor which is anticipated by Sinan claim 16.
Regarding claim 20, the claim recites analogous limitations to claim 4 above, and is therefore rejected on the same premise. Claim 4 is a method claim while claim 20 is directed to a system comprising: at least one device including a hardware processor which is anticipated by Sinan claim 16.
Conclusion
Applicant's amendments and arguments dated 10/02/2025 necessitated the updating of the 35 USC § 101 and the 35 USC § 103 rejections of the pending claims presented in the present 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).
Any inquiry concerning this communication from the Examiner should be directed to Abdallah El-Hagehassan whose contact information is (571) 272-0819 and Abdallah.el-hagehassan@uspto.gov The Examiner can normally be reached on Monday- Friday 8 am to 5 pm.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, Rutao Wu can be reached on (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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/ABDALLAH A EL-HAGE HASSAN/
Primary Examiner, Art Unit 3623