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 .
DETAILED ACTION
Response to Arguments
Applicant's arguments with respect to claims 1-6, 8-13, and 15-22 have been considered but are moot in view of the new ground(s) of rejection. Applicant’s arguments are directed to the amended subject matter; Regarding independent claims, as consistent with the interview from 07/13/2026, the prior art of Hu is maintained, and clarifying citations and analysis from existing prior art are provided below, otherwise present in the cancelled dependent claims, containing similar subject matter.
Regarding dependent claim 22, new prior art has been cited.
Note: The claims are not directed towards patent ineligible subject matter under 35 U.S.C. 101
Step 1: IS THE CLAIM DIRECTED TO A PROCESS, MACHINE, MANUFACTURE OR COMPOSITION OF MATTER?
Yes
Step 2A.1: IS THE CLAIM DIRECTED TO A LAW OF NATURE, A NATURAL PHENOMENON (PRODUCT OF NATURE) OR AN ABSTRACT IDEA?
Yes
Step 2A.2: DOES THE CLAIM RECITE ADDITIONAL ELEMENTS THAT INTEGRATE THE JUDICIAL EXCEPTION INTO A PRACTICAL APPLICATION?
Yes. The claims seek to improve traditional models which cannot judge whether strings are matched, improved by using model evaluation efficiency by checking for correctness or not, in addition to parsing or disassembling of text supported by the specification, and reflected by the claims e.g. in spec: in at least page 6 paragraph 2. In other words, the claims enable the invention to allow for full-automatic running can be realized, manual processing is avoided, a labor cost can be greatly reduced, an evaluation speed is increased, and an evaluation efficiency of the generative large model is effectively improved
Supported by the following:
In Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018), the claimed invention was a method of virus scanning that scans an application program, generates a security profile identifying any potentially suspicious code in the program, and links the security profile to the application program. 879 F.3d at 1303-04, 125 USPQ2d at 1285-86. The Federal Circuit noted that the recited virus screening was an abstract idea, and that merely performing virus screening on a computer does not render the claim eligible. 879 F.3d at 1304, 125 USPQ2d at 1286. The court then continued with its analysis under part one of the Alice/Mayo test by reviewing the patent’s specification, which described the claimed security profile as identifying both hostile and potentially hostile operations. The court noted that the security profile thus enables the invention to protect the user against both previously unknown viruses and “obfuscated code,” as compared to traditional virus scanning, which only recognized the presence of previously-identified viruses. The security profile also enables more flexible virus filtering and greater user customization. 879 F.3d at 1304, 125 USPQ2d at 1286. The court identified these benefits as improving computer functionality, and verified that the claims recite additional elements (e.g., specific steps of using the security profile in a particular way) that reflect this improvement. Accordingly, the court held the claims eligible as not being directed to the recited abstract idea. 879 F.3d at 1304-05, 125 USPQ2d at 1286-87. This analysis is equivalent to the Office’s analysis of determining that the additional elements integrate the judicial exception into a practical application at Step 2A Prong Two, and thus that the claims were not directed to the judicial exception (Step 2A: NO).
Examples of claims that improve technology and are not directed to a judicial exception include: Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339, 118 USPQ2d 1684, 1691-92 (Fed. Cir. 2016) (claims to a self-referential table for a computer database were directed to an improvement in computer capabilities and not directed to an abstract idea); McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1315, 120 USPQ2d 1091, 1102-03 (Fed. Cir. 2016) (claims to automatic lip synchronization and facial expression animation were directed to an improvement in computer-related technology and not directed to an abstract idea); Visual Memory LLC v. NVIDIA Corp., 867 F.3d 1253,1259-60, 123 USPQ2d 1712, 1717 (Fed. Cir. 2017) (claims to an enhanced computer memory system were directed to an improvement in computer capabilities and not an abstract idea); Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018) (claims to virus scanning were found to be an improvement in computer technology and not directed to an abstract idea); SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1303 (Fed. Cir. 2019) (claims to detecting suspicious activity by using network monitors and analyzing network packets were found to be an improvement in computer network technology and not directed to an abstract idea). Additional examples are provided in MPEP § 2106.05(a).
Regarding the December 5th 2025 Memo in light of September 26, 2025 Appeals Review Panel Decision in Ex parte Desjardins, Appeal 2024-000567 for Application 16/319,040, in deciding if a recited abstract idea does or does not direct the entire claim to an abstract idea, when a claim is considered as a whole:
Paragraph 21 of the Specification, which the Appellant cites, identifies improvements in training the machine learning model itself. Of course, such an assertion in the Specification alone is insufficient to support a patent eligibility determination, absent a subsequent determination that the claim itself reflects the disclosed improvement. See MPEP § 2106.05(a) (citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316 (Fed. Cir. 2016)). Here, however, we are persuaded that the claims reflect such an improvement. For example, one improvement identified in the 8 Appeal2024-000567 Application 16/319,040 Specification is to "effectively learn new tasks in succession whilst protecting knowledge about previous tasks." Spec. ,r 21. The Specification also recites that the claimed improvement allows artificial intelligence (AI) systems to "us[e] less of their storage capacity" and enables "reduced system complexity." Id. When evaluating the claim as a whole, we discern at least the following limitation of independent claim 1 that reflects the improvement: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task." We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation. Under a charitable view, the overbroad reasoning of the original panel below is perhaps understandable given the confusing nature of existing § 101 jurisprudence, but troubling, because this case highlights what is at stake. Categorically excluding AI innovations from patent protection in the United States jeopardizes America's leadership in this critical emerging technology. Yet, under the panel's reasoning, many AI innovations are potentially unpatentable-even if they are adequately described and nonobvious-because the panel essentially equated any machine learning with an unpatentable "algorithm" and the remaining additional elements as "generic computer components," without adequate explanation. Dec. 24. Examiners and panels should not evaluate claims at such a high level of generality.
Specifically, Ex Parte Desjardins explained the following:
Enfish ranks among the Federal Circuit's leading cases on the eligibility of technological improvements. In particular, Enfish recognized that “[m]uch of the advancement made in computer technology consists of improvements to software that, by their very nature, may not be defined by particular physical features but rather by logical structures and processes.” 822 F.3d at 1339. Moreover, because “[s]oftware can make non-abstract improvements to computer technology, just as hardware improvements can,” the Federal Circuit held that the eligibility determinations should turn on whether “the claims are directed to an improvement to computer functionality versus being directed to an abstract idea.” Id. at 1336. (Desjardins, page 8).
Further in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), the claimed invention was a method of training a machine learning model on a series of tasks. The Appeals Review Panel (ARP) overall credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements that were disclosed in the patent application specification. Specifically, the ARP upheld the Step 2A Prong One finding that the claims recited an abstract idea (i.e., mathematical concept). In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were
The claim itself does not need to explicitly recite the improvement described in the specification (e.g., “thereby increasing the bandwidth of the channel”). See, e.g., Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), in which the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation.
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.
Claims 1-6, 8-13, and 15-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 11847575 B2 Ferrucci; David et al. (hereinafter Ferrucci) in view of US 20250209383 A1 Hu; Luhui et al. (hereinafter Hu).
Re claim 1, Ferrucci teaches
1. A method for processing a model generation result applied to a text processing field, comprising: (generative model fig. 3 and fig. 4 with col 11 line 25 to col 12 line 5)
disassembling a text generation result of a generative large model to obtain a plurality of result logic units; wherein each result logic unit is a continuous segment in the text generation result and independently verifies either one premise or one conclusion in a logical inference relationship of the text generation result; and the text generation result is generated and output by the generative large model based on text input information (in fig. 4 there are at least two layers, eight in total in the example, present comprising subgraph elements e.g. at least 412 and 418 stemming from an initial continuous portion 402 comprising two sentences input at once… dissembling text such as parsing initially with a model generative model and initially with a model/parser, in which one or more groups of words are broken down and rebuilt into contextual hypothesis or premises per se col 4 lines 8-25, such as in fig. 3 and fig. 4 with col 11 line 25 to col 12 line 5)
generating a logical inference graph capable of characterizing a logical inference relationship among the plurality of result logic units based on the plurality of result logic units, each result logic unit serving as a node of the logical inference graph (using proofs or correctness based on confidence or ranking metrics col 14 line 64 to col 15 line 27 to produce a valid model iteratively through example sets and output sets to update the generative model, following producing an inference graph e.g. element 308 in fig. 3 based on an explicit inference graph is shown in fig. 4 where there are at least two layers, eight in total in the example, present comprising subgraph elements e.g. at least 412 and 418 stemming from an initial continuous portion 402 comprising two sentences input at once… producing an inference graph e.g. element 308 in fig. 3 based on example data graph element 404 in fig. 4 and example data such as reading, questions, and answers thereof utilizing a rule generating model to produce an inference graph col 14 lines Page 12 Application/Control Number: 18/667,504 Art Unit: 2655 47-58, following dissembling text such as parsing initially with a model generative model and initially with a model/parser, in which one or more groups of words are broken down and rebuilt into contextual hypothesis or premises per se col 4 lines 8-25, such as in fig. 3 and fig. 4 with col 11 line 25 to col 12 line 5)
However, while the concept of disassembly, inference graphs, and correctness via proofs and rules is established in Ferrucci, the concept of using sub-layers and sub-graphs is not:
disassembling the logical inference graph into a plurality of two-layer subgraphs; wherein each two-layer subgraph identifies one logical inference step; (Hu expressly as in fig. 3 with 314 to 320 and deeming incorrect e.g. missing, noisy, inconsistent, for instance applied to all nodes 0043 and resolution 0049…using inference graphs with sub-graphs with multiple layers 0019 0031 0037 0043 with sub-nodes as well representing context as in 0004 and 0029 with fig. 3 expressly and fig. 7)
judging, sequentially from bottom to top of the logical inference graph, whether logical inference of each two-layer subgraph is correct or not; and (Hu expressly as in fig. 3 with 314 to 320 and deeming incorrect e.g. missing, noisy, inconsistent, for instance applied to all nodes 0043 and resolution 0049…using inference graphs with sub-graphs with multiple layers 0019 0031 0037 0043 with sub-nodes as well representing context as in 0004 and 0029 with fig. 3 expressly and fig. 7)
determining that logical inference of generation of the text generation result by the generative large model is correct in response to determining that the logical inference of each of the plurality of two-layer subgraphs is correct. (Hu expressly as in fig. 3 with 314 to 320 and deeming incorrect e.g. missing, noisy, inconsistent, for instance applied to all nodes 0043 and resolution 0049…using inference graphs with sub-graphs with multiple layers 0019 0031 0037 0043 with sub-nodes as well representing context as in 0004 and 0029 with fig. 3 expressly and fig. 7)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Ferrucci to incorporate the above claim limitations as taught by Hu to allow for combining in prior art elements according to known methods to yield predictable results such as using multiple layers, sub-nodes, and sub-graphs thereof in an analogous knowledge inference graph to extract context candidates, wherein using sub-graph concepts improves performance, scalability, and interpretability by focusing computation on relevant data subsets rather than the entire graph, wherein the combination is adapted to manage complex data structures, allowing models to operate on unseen portions of a graph as shown in Hu’s complex sub-graph component e.g. in at least fig. 3 such as elements 314 to 320 and fig. 7.
Re claim 8, this claim has been rejected for teaching a broader, or narrower claim based on general inclusion of hardware alone (e.g. processor, memory, instructions), representation of claim 1 omitting/including hardware for instance, otherwise amounting to a virtually identical scope.
For instance, see fig. 2 of Ferrucci showing at least a memory and a processor with various components.
Re claim 15, this claim has been rejected for teaching a broader, or narrower claim based on general inclusion of hardware alone (e.g. processor, memory, instructions), representation of claim 1 omitting/including hardware for instance, otherwise amounting to a virtually identical scope
For instance, see fig. 2 of Ferrucci showing at least a memory and a processor with various components.
Re claims 2, 9, and 16, Ferrucci teaches
2. The method according to claim 1, wherein disassembling the text generation result of the generative large model to obtain the plurality of result logic units comprises: disassembling the text generation result of the generative large model using a pre-trained logic disassembly model to obtain the plurality of result logic units. (dissembling text such as parsing initially initially with a model/parser, in which one or more groups of words are broken down and rebuilt into contextual hypothesis or premises per se col 4 lines 8-25, such as in fig. 3 and fig. 4 with col 11 line 25 to col 12 line 5)
Re claims 3, 10, and 17, Ferrucci teaches
3. The method according to claim 1, wherein generating the logical inference graph capable of characterizing the logical inference relationship among the plurality of result logic units based on the plurality of result logic units comprises: generating the logical inference graph capable of characterizing the logical inference relationship among the plurality of result logic units based on the plurality of result logic units using a pre-trained logical inference graph generation model. (producing an inference graph e.g. element 308 in fig. 3 based on example data graph element 404 in fig. 4 and example data such as reading, questions, and answers thereof utilizing a rule generating model to produce an inference graph col 14 lines 47-58, following dissembling text such as parsing initially with a model generative model and initially with a model/parser, in which one or more groups of words are broken down and rebuilt into contextual hypothesis or premises per se col 4 lines 8-25, such as in fig. 3 and fig. 4 with col 11 line 25 to col 12 line 5)
Re claims 4, 11, and 18, Ferrucci teaches
4. The method according to claim 1, further comprising: before generating the logical inference graph capable of characterizing the logical inference relationship among the plurality of result logic units based on the plurality of result logic units, disassembling the text input information of the generative large model to obtain a plurality of input logic units; wherein each input logic unit comprises a segment in the text input information, and each segment is capable of independently identifying one premise or conclusion in the logical inference relationship. (prior to creating proofs or any graphs with valid data, dissembling text such as parsing initially with a model/parser, in which one or more groups of words are broken down and rebuilt into contextual hypothesis or premises per se col 4 lines 8-25, such as in fig. 3 and fig. 4 with col 11 line 25 to col 12 line 5)
Re claims 5, 12, and 19, Ferrucci teaches
5. The method according to claim 4, wherein generating the logical inference graph capable of characterizing the logical inference relationship among the plurality of result logic units based on the plurality of result logic units comprises: generating the logical inference graph capable of characterizing the logical inference relationship among the plurality of result logic units based on the plurality of result logic units by referring to the plurality of input logic units. (producing an inference graph e.g. element 308 in fig. 3 based on example data graph element 404 in fig. 4 and example data such as reading, questions, and answers thereof utilizing a rule generating model to produce an inference graph col 14 lines 47-58, following dissembling text such as parsing initially with a model generative model and initially with a model/parser, in which one or more groups of words are broken down and rebuilt into contextual hypothesis or premises per se col 4 lines 8-25, such as in fig. 3 and fig. 4 with col 11 line 25 to col 12 line 5)
Re claims 6, 13, and 20, Ferrucci teaches
6. The method according to claim 5, wherein generating the logical inference graph capable of characterizing the logical inference relationship among the plurality of result logic units based on the plurality of result logic units by referring to the plurality of input logic units comprises: retrieving a most relevant example in a preset example database based on the plurality of input logic units and the plurality of result logic units; the example database comprising a plurality of groups of examples, and each example comprising a plurality of input example logic units corresponding to input example information, a plurality of result example logic units corresponding to result example information, and an example logical inference graph corresponding to the plurality of result example logic units; and (using express example sets and graphs to generate a new model by iteratively updating all models as the system learns and thereby using previous data to create and use new models col 11 line 55 to col 12 line 7 with col 14 line 14 to col 15 line 41, then producing the inference graph e.g. element 308 in fig. 3 based on example data graph element 404 in fig. 4 and example data such as reading, questions, and answers thereof utilizing a rule generating model to produce an inference graph col 14 lines 47-58, following dissembling text such as parsing initially with a model generative model and initially with a model/parser, in which one or more groups of words are broken down and rebuilt into contextual hypothesis or premises per se col 4 lines 8-25, such as in fig. 3 and fig. 4 with col 11 line 25 to col 12 line 5)
generating the logical inference graph corresponding to the plurality of result logic units by another pre-trained generative large model based on the plurality of input logic units, the plurality of result logic units, the plurality of input example logic units, the plurality of result example logic units and the corresponding example logical inference graph. (another new model is used to handle new inference rules for improvement for context shifts, using express example sets and graphs to generate a new model by iteratively updating all models as the system learns and thereby using previous data to create and use new models col 11 line 55 to col 12 line 7 with col 14 line 14 to col 15 line 41, then producing the inference graph e.g. element 308 in fig. 3 based on example data graph element 404 in fig. 4 and example data such as reading, questions, and answers thereof utilizing a rule generating model to produce an inference graph col 14 lines 47-58, following dissembling text such as parsing initially with a model generative model and initially with a model/parser, in which one or more groups of words are broken down and rebuilt into contextual hypothesis or premises per se col 4 lines 8-25, such as in fig. 3 and fig. 4 with col 11 line 25 to col 12 line 5)
Re claim 21, while the concept of disassembly, inference graphs, and correctness via proofs and rules is established in Ferrucci, the concept of using sub-layers and sub-graphs is not:
21. (New) The method according to claim 1, further comprising locating, when the logical inference of at least one of the plurality of two-layer subgraphs is determined to be incorrect, the at least one incorrect two-layer subgraph. (Hu expressly as in fig. 3 with 314 to 320 and deeming incorrect e.g. missing, noisy, inconsistent, for instance applied to all nodes 0043 and resolution 0049…using inference graphs with sub-graphs with multiple layers 0019 0031 0037 0043 with sub-nodes as well representing context as in 0004 and 0029 with fig. 3 expressly and fig. 7)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Ferrucci to incorporate the above claim limitations as taught by Hu to allow for combining in prior art elements according to known methods to yield predictable results such as using multiple layers, sub-nodes, and sub-graphs thereof in an analogous knowledge inference graph to extract context candidates, wherein using sub-graph concepts improves performance, scalability, and interpretability by focusing computation on relevant data subsets rather than the entire graph, wherein the combination is adapted to manage complex data structures, allowing models to operate on unseen portions of a graph as shown in Hu’s complex sub-graph component e.g. in at least fig. 3 such as elements 314 to 320 and fig. 7.
Claims 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 11847575 B2 Ferrucci; David et al. (hereinafter Ferrucci) in view of US 20250209383 A1 Hu; Luhui et al. (hereinafter Hu) and further in view of US 20220115100 A1 BARVE; Rakesh et al. (hereinafter Barve).
Re claim 22, while the combination teaches inference graphs and subgraphs, it fails to teach:
22. (New) The method according to claim 1, further comprising:
determining, over a predetermined period of time, whether logical inference of each of a plurality of text generation results generated by the generative large model is correct; (Barve using an AI model to classify text 0035 (analogous to Ferrucci’s proofs in generative models) and creating a knowledge graph for inference 0132 (analogous to Hu’s complex concepts), as well as iteratively training or adjusting an AI model until a desired level of accuracy is achieved or matched per se)
calculating an accuracy rate based on a number of text generation results determined to be correct relative to a total number of text generation results over the predetermined period of time; and (Barve, under the ell known premise that iteration of a model until it reaches a satisfactory level e.g. accuracy must necessarily consider a time period of iteration and a total number of iterations thereof, in other words if the accuracy level is 95% such a number can be viewed under BRI as a proportion or rate per se given the cycle for iteration, also warranted by the lack of support in the present invention specification for anything outside of BRI interpretation per se, for instance iteratively training or adjusting an AI model until a desired level of accuracy is achieved or matched per se … using an AI model to classify text 0035 (analogous to Ferrucci’s proofs in generative models) and creating a knowledge graph for inference 0132 (analogous to Hu’s complex concepts))
determining that the generative large model meets a predetermined accuracy threshold when the accuracy rate reaches a predetermined proportion. (Barve, under the ell known premise that iteration of a model until it reaches a satisfactory level e.g. accuracy must necessarily consider a time period of iteration and a total number of iterations thereof, in other words if the accuracy level is 95% such a number can be viewed under BRI as a proportion or rate per se given the cycle for iteration, also warranted by the lack of support in the present invention specification for anything outside of BRI interpretation per se, for instance iteratively training or adjusting an AI model until a desired level of accuracy is achieved or matched per se … using an AI model to classify text 0035 (analogous to Ferrucci’s proofs in generative models) and creating a knowledge graph for inference 0132 (analogous to Hu’s complex concepts))
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Ferrucci in view of Hu to incorporate the above claim limitations as taught by Hu to allow for combining prior art elements according to known methods to yield predictable results such as the corrective generative models and subgraph concepts with knowledge graph and iterative accuracy level achievements, thereby expressly demonstrating model adaptability for subgraphs using known iterative accuracy thresholds, now expressly taught in Barve for reduction in hallucination or erroneous outputs when classifying related data in a system.
Conclusion
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/17/2026 has been entered.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20250148769 A1 BEYKUN; Alexander et al.
Inference sub-graphs
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL COLUCCI whose telephone number is (571)270-1847. The examiner can normally be reached on M-F 9 AM - 7 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Flanders can be reached at (571)272-7516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL COLUCCI/Primary Examiner, Art Unit 2655 (571)-270-1847
Examiner FAX: (571)-270-2847
Michael.Colucci@uspto.gov
