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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/28/2026 has been entered.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6, 13, and 20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 6, it recites “wherein the first data structure prescribes a boundary condition of the particular buried pipeline for the solver to compute responses on the receivers.” However, according to claim 1 and the specification, the first data structure is for defining the configuration of a buried pipeline and a tool for the purpose of generating simulated training data. On the other hand, a particular buried pipeline is the subject for the prediction using measured data. There is no disclosure of the particular buried pipeline being prescribed in the first data structure. One skilled in the relevant art would not be conveyed that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 13 and 20 are similarly rejected by analogy to claim 6.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, it recites “applying the solver using the configuration of the buried pipeline and the tool when the transmitter sends a known electromagnetic (EM) waveform inside the metal wall of the buried pipeline” in lines 11-13. According to the specification, the solver is to generating simulated response data, without actually transmitting a known EM waveform. It is unclear whether the limitation requires the application of the solver being performed at the time when the transmitter actually sends a known electromagnetic (EM) waveform inside the metal wall of the buried pipeline, when read in light of the specification.
For examination purpose, --applying the solver using the configuration of the buried pipeline and the tool as if the transmitter sends a known electromagnetic (EM) waveform inside the metal wall of the buried pipeline-- is assumed.
Claims 8 and 15 are similarly rejected by analogy to claim 1.
The other claim(s) not discussed above, or depending on the above claim(s), are rejected for inheriting the issue(s) from their linking claim(s).
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.
MPEP 2106 outlines a two-part analysis for Subject Matter Eligibility as shown in the chart below.
PNG
media_image1.png
930
645
media_image1.png
Greyscale
Step 1, the claimed invention must be to one of the four statutory categories. 35 U.S.C. 101 defines the four categories of invention that Congress deemed to be the appropriate subject matter of a patent: processes, machines, manufactures and compositions of matter.
Step 2, the claimed invention also must qualify as patent-eligible subject matter, i.e., the claim must not be directed to a judicial exception unless the claim as a whole includes additional limitations amounting to significantly more than the exception.
Step 2A is a two-prong inquiry, as shown in the chart below.
PNG
media_image2.png
681
881
media_image2.png
Greyscale
Prong One asks does the claim recite an abstract idea, law of nature, or natural phenomenon? In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. If the claim recites a judicial exception (i.e., an abstract idea enumerated in MPEP § 2106.04(a), a law of nature, or a natural phenomenon), the claim requires further analysis in Prong Two. If the claim does not recite a judicial exception (a law of nature, natural phenomenon, or abstract idea), then the claim cannot be directed to a judicial exception (Step 2A: NO), and thus the claim is eligible at Pathway B without further analysis. Abstract ideas can be grouped as, e.g., mathematical concepts, certain methods of organizing human activity, and mental processes.
Prong Two asks does the claim recite additional elements that integrate the judicial exception into a practical application? If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception (Step 2A: NO) and thus is eligible at Pathway B. This concludes the eligibility analysis. If, however, the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception (Step 2A: YES), and requires further analysis under Step 2B.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding claim 1, Step 1: Is the claim to a process, machine, manufacture or composition of matter? Yes.
Step 2A: Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea (judicially recognized exceptions)? Yes (see analysis below).
Prong one: Whether the claim recites a judicial exception? (Yes). The claims recites:
1. A computer-implemented method for maintaining buried pipelines subject to a wall-loss condition, the method comprising:
generating a first data structure encoding a configuration of a buried pipeline and a tool, wherein the buried pipeline comprises a metal wall enclosing an interior space, wherein the tool is part of a smart pipeline intervention gauge (PIG) device configured to navigate the buried pipeline from inside the interior space, wherein the tool comprises a transmitter and multiple receivers, and wherein the multiple receivers are circumferentially positioned in the interior space and separated from the metal wall;
obtaining a second data structure encoding a solver configured to simulate a response on one of the multiple receivers from inside the metal wall of the buried pipeline;
applying the solver using the configuration of the buried pipeline and the tool when the transmitter sends a known electromagnetic (EM) waveform inside the metal wall of the buried pipeline;
generating simulated responses on the multiple receivers from inside the metal wall of the buried pipeline;
based on, at least in part, the simulated responses, training an inference model configured to predict the wall-loss condition of a particular buried pipeline;
storing a third data structure encoding the inference model on the smart PIG device;
releasing the smart PIG device into the particular buried pipeline;
predicting, using the inference model and obtained measurement data, the wall-loss condition of the particular buried pipeline; and
flagging the predicted wall-loss condition to an attention of an operator to facilitate maintenance of the particular buried pipeline.
The claim is directed to an abstract idea because it recites the limitations as bold-faced in the claim above. These limitations are directed to mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations; and/or mental processes – concepts performed in the human mind (or with a pen and paper). Note that the step of generating a first data structure is to provide data information defining a configuration of a buried pipeline and a tool. The wherein clause in the step is a description of the configuration. The flagging step can be simply changing a data value that is used to represent the predicted condition.
Prong two: Whether the claim recites additional elements that integrate the exception into a practical application of that exception? (No). The claim recites additional element as underlined in the claim above. The computer is recited in the preamble to facilitate the application of the abstract idea. See MPEP 2106.05(f). The storing step is an insignificant extra-solution activity to facilitate the application of the abstract idea (i.e., the inference model) by a computing device, (i.e., the smart PIG device). See MPEP 2106.05(g), and its location on the smart PIG device is a field of use limitation. See MPEP 2106.05(h). The releasing step is recited at a high level of generality to release the smart PIG deice into the particular buried pipeline, without reciting any other non-abstract operations. It could be leaving the smart PIG device in the pipeline and then do nothing. As a result, it is also an insignificant extra-solution activity and its location inside the pipeline is a field of use limitation. Accordingly, the additional elements are insufficient to integrate the abstract idea into a practical application of the abstract idea.
Step 2B: Does the claim recite additional elements (other than the judicial exception) that amount to significantly more than the judicial exception? No (see analysis below).
The claim does not include additional elements that are sufficient to make the claim significantly more than the judicial exception. As discussed with respect to Step 2A Prong Two above, the additional element(s) in the claim are insignificant extra-solution activities, a field of use, and to invoke a generic computer for its computing power to facilitate the application of the abstract idea. Also, it is routine and conventional to invoke a computer for data processing. See MPEP 2106.05(d). Considered as a whole, the claim does not amount to significantly more than the abstract idea.
Claims 8 and 15 are similarly rejected by analogy to claim 1.
Dependent claims 2-7, 9-14, and 16-20 when analyzed as a whole respectively are held to be patent ineligible under 35 U.S.C. 101 because they either extend (or add more details to) the abstract idea or the additional recited limitation(s) (if any) fail(s) to establish that the claim(s) is/are not directed to an abstract idea, as discussed below: there is no additional element(s) in the dependent claims that sufficiently integrates the abstract idea into a practical application of, or makes the claims significantly more than, the judicial exception (abstract idea). The additional element(s) (if any) are mere instructions to apply an except, field of use, and/or insignificant extra-solution activities (applied to Step 2A_Prong Two and Step 2B; see MPEP 2016.05(f)-(h)) and/or well-understood, routine, or conventional (applied to Step 2B; see MPEP 2106.05(d)) to facilitate the application of the abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ooi et al. (“EM-based 2D Corrosion Azimuthal Imaging using Physics Informed Machine Learning (PIML)” SPE Offshore Europe Conference and Exhibition 2021; cited in IDS; hereinafter “Ooi”) in view of Ma et al. ("Pipeline In-Line Inspection Method, Instrumentation and Data Management" Sensors 2021, 21, 3862; hereinafter “Ma”).
Regarding claim 1, Ooi teaches a computer-implemented method for maintaining buried pipelines subject to a wall-loss condition (i.e., “This paper introduces a promising breakthrough in electromagnetism-based corrosion imaging using physics informed machine learning (PIML), tested and validated on the cross-sections of real metal casings/tubing with defects of various sizes, locations, and spacing”; see Abstract), the method comprising:
generating a first data structure encoding a configuration of a buried pipeline and a tool (see FIG. 3), wherein the buried pipeline comprises a metal wall enclosing an interior space (see FIG. 3(b)), wherein the tool (see FIG. 3(a); “The transmitter is located at the center of the tool and all receivers are located equidistant from the transmitter, as illustrated in Figure 3 (a)”; see p. 5, ¶ 1);
obtaining a second data structure encoding a solver configured to simulate a response on one of the multiple receivers from inside the metal wall of the buried pipeline (i.e., “the forward model is developed to simulate scenarios of metal loss and the corresponding voltages on the receivers”; see p. 4, Methodology);
applying the solver using the configuration of the buried pipeline and the tool when the transmitter sends a known electromagnetic (EM) waveform inside the metal wall of the buried pipeline (i.e., “Scenarios of metal losses of various numbers, sizes, shapes, and azimuthal locations are simulated using the forward model in order to create a complete and diverse dataset for the training of the neural networks”; see p. 4, Methodology; “the finite-difference time-domain (FDTD) method is used since it is one of the most popular and extensively studied methods in EM problems”; see p. 4, Forward Model; “operating frequency of the transmitter is chosen”; see p. 5, ¶ 2);
generating simulated responses on the multiple receivers from inside the metal wall of the buried pipeline (i.e., “Scenarios of metal losses of various numbers, sizes, shapes, and azimuthal locations are simulated using the forward model in order to create a complete and diverse dataset for the training of the neural networks”; see Methodology);
based on, at least in part, the simulated responses, training an inference model configured to predict the wall-loss condition of a particular buried pipeline (i.e., “Scenarios of metal losses of various numbers, sizes, shapes, and azimuthal locations are simulated using the forward model in order to create a complete and diverse dataset for the training of the neural networks….The neural networks, trained on only simulated data, are able to perform accurate predictions on casing metal loss properties using receiver data from both simulated and experimental scenarios”; see Methodology);
predicting, using the inference model and obtained measurement data, the wall-loss condition of the particular buried pipeline (i.e., “The neural networks, trained on only simulated data, are able to perform accurate predictions on casing metal loss properties using receiver data from both simulated and experimental scenarios”; see Methodology); and
Ooi does not explicitly disclose (see only the underlined):
wherein the tool is part of a smart pipeline intervention gauge (PIG) device configured to navigate the buried pipeline from inside the interior space;
releasing the smart PIG device into the particular buried pipeline.
But Ma teaches:
using a PIG to navigate a pipeline to inspect the pipeline (i.e., Abstract).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ooi in view of Ma to adapt the technique for pipeline in-line inspection using a PIG, such that the tool is part of a smart pipeline intervention gauge (PIG) device configured to navigate the buried pipeline from inside the interior space; and releasing the smart PIG device into the particular buried pipeline, as claimed. The rationale would be to facilitate pipeline in-line inspection.
Ooi does not explicitly disclose:
storing a third data structure encoding the inference model on the smart PIG device;
However, the location for storing the inference model depends on where the processor or memory is. The closer to the PIG, the quicker the inspection result can be determined. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to store a third data structure encoding the inference model on the smart PIG device, as claimed. The rationale would be to facilitate quicker determination of metal wall loss condition.
Ooi does not explicitly disclose:
flagging the predicted wall-loss condition to an attention of an operator to facilitate maintenance of the particular buried pipeline.
However, since Ooi teaching recognizing different defects (see p. 5, ¶ 3), it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to flag the predicted wall-loss condition to an attention of an operator to facilitate maintenance of the particular buried pipeline, as claimed. The rationale would be to show the result of a detected defect.
Regarding claim 2, Ooi further teaches: wherein the training further comprises:
calibrating the inference model by comparing the predicted wall-loss condition with a physically observed wall-loss condition of the particular buried pipeline (i.e., “For every pixel in the prediction task, the LSTM-RNN corrects its weight matrices and biases such that the best fit for the training dataset is found. Such values are updated in the backpropagation step of the training process and optimized using the adaptive moment (ADAM) algorithm”; see p. 8, ¶ 2; “An automated physical experiment setup is also required to produce real data to augment the training of the neural networks”; see p. 14, ¶ 2).
Regarding claim 3, Ooi further teaches: wherein the training further comprises:
adjusting the inference model to reduce a difference between the predicted wall-loss condition and the physically observed wall-loss condition (i.e., “For every pixel in the prediction task, the LSTM-RNN corrects its weight matrices and biases such that the best fit for the training dataset is found. Such values are updated in the backpropagation step of the training process and optimized using the adaptive moment (ADAM) algorithm”; see p. 8, ¶ 2; “An automated physical experiment setup is also required to produce real data to augment the training of the neural networks”; see p. 14, ¶ 2).
Regarding claim 4, Ooi further teaches:
wherein inference model comprises multiple layers of artificial neural network (ANN) (i.e., “The developed framework incorporates a finite-difference time-domain (FDTD)-based EM forward solver and an artificial neural network (ANN), namely the long short-term memory recurrent neural network (LSTM-RNN). The ANN is trained using the results generated from the FDTD solver, which simulates sensor readings for different scenarios of defects”; see Abstract).
Regarding claim 5, Ooi further teaches:
wherein the solver comprises a physics-based solver (i.e., “This paper introduces a promising breakthrough in electromagnetism-based corrosion imaging using physics informed machine learning (PIML)… The developed framework incorporates a finite-difference time-domain (FDTD)-based EM forward solver”; see Abstract).
Regarding claim 6, Ooi further teaches:
wherein the first data structure prescribes a boundary condition of the particular buried pipeline for the solver to compute responses on the receivers (i.e., “complete losses (holes), inner partial losses, or combinations of both”; see p. 5, ¶ 3 and FIGs. 13-15).
Regarding claim 7, Ooi further teaches:
wherein the wall-loss condition comprises a partially corroded circumference of the metal wall (i.e., “complete losses (holes), inner partial losses, or combinations of both”; see p. 5, ¶ 3 and FIGs. 13-15), and
wherein the partially corroded circumference corresponds to at least one of: a corrosion from inside the metal wall, a corrosion from outside the metal wall, or a total loss of the metal wall (i.e., “complete losses (holes), inner partial losses, or combinations of both”; see p. 5, ¶ 3 and FIGs. 13-15).
Regarding claim 8, the claim recites the same substantive limitations as claim 1 and is rejected by applying the same teachings. The notes that computer processors are necessary or obvious for the solver and neural network.
Regarding claim 9, the claim recites the same substantive further limitations as claim 2 and is rejected by applying the same teachings.
Regarding claim 10, the claim recites the same substantive further limitations as claim 3 and is rejected by applying the same teachings.
Regarding claim 11, the claim recites the same substantive further limitations as claim 4 and is rejected by applying the same teachings.
Regarding claim 12, the claim recites the same substantive further limitations as claim 5 and is rejected by applying the same teachings.
Regarding claim 13, the claim recites the same substantive further limitations as claim 6 and is rejected by applying the same teachings.
Regarding claim 14, the claim recites the same substantive further limitations as claim 7 and is rejected by applying the same teachings.
Regarding claim 15, the claim recites the same substantive limitations as claim 1 and is rejected by applying the same teachings. Note that the computer-readable medium and the computer are necessary or obvious for the solver and neural network.
Regarding claim 16, the claim recites the same substantive further limitations as claim 2 and is rejected by applying the same teachings.
Regarding claim 17, the claim recites the same substantive further limitations as claim 3 and is rejected by applying the same teachings.
Regarding claim 18, the claim recites the same substantive further limitations as claim 4 and is rejected by applying the same teachings.
Regarding claim 19, the claim recites the same substantive further limitations as claim 5 and is rejected by applying the same teachings.
Regarding claim 20, the claim recites the same substantive further limitations as claims 6 and 7 combined and is rejected by applying the same teachings.
Response to Arguments
Regarding 35 USC 101, Applicant argued: Step 2A, Prong One… The claims do not merely recite a mathematical formula or a process that can practically be performed in the human mind… Applicant asserts that the human mind cannot at least practically simulate physical interactions of an electromagnetic waveform penetrating a carbon steel pipeline wall and decoupling defected segments to generate simulated sensor responses across multiple circumferentially positioned receivers. Furthermore, using these simulated physical responses to train an Artificial Neural Network (ANN) to predict spatial and depth patterns of wall defects is a complex computational task tied to physical sensor data and not a mere mental process.
The Examiner respectfully submits that even if a human mind cannot technically simulate the responses, the process is nevertheless a mathematical calculation, falling in the category of abstract idea. Additionally, the claims merely recite the "training" of the inference model at a high level of generality without any details regarding the model architecture or how it is trained. It is unclear how "based on, at least in part, the simulated responses", i.e. at most specifying the type of data which is input, would achieve an improvement to how the inference model is trained. As a result, the training step is merely a mathematical process.
Applicant argued: Step 2A, Prong Two… even if the claims were deemed to recite an abstract idea, the claims integrate that concept into a practical application that provides a specific technological improvement… The claimed invention provides a specific, tangible improvement over conventional inline inspection (ILI) tools, specifically Magnetic Flux Leakage (MFL) devices. The Specification details how the specific arrangement of "a transmitter and multiple receivers... circumferentially positioned in the interior space and separated from the metal wall" solves known physical limitations in the art by providing a "more compact design than conventional inline inspection (ILI) tools" and overcoming the "very heavy and bulky packages" of MFL devices, which are prone to high false positive and false negative rates… As claimed, "releasing the smart PIG device into the particular buried pipeline" must occur *before* the step of "predicting, using the inference model and obtained measurement data." It is a necessary, physical data-gathering step integral to the operation of the autonomous robot, not a post-solution afterthought. Storing the trained model directly on the PIG device transforms it into an edge-computing industrial robot capable of "near real-time response on the health status of inspected pipelines".
The Examiner respectfully submits that the claims do not recite any use of the transmitter or receivers but instead these elements in the claims merely characterize the data being analyzed in order to simulate what these devices would do which is part of the abstract idea. Also, the smart PIG does not perform any function within the scope of the claim, it is merely released and otherwise not used. The recitations of the "tool" in the "generating" limitation is merely descriptive of the "configuration" which is encoded in a data structure, i.e., the claimed step is directed to generating a data structure with information describing the device and is not claiming the tool/device itself or its configuration, nor is it using the tool to perform any steps of the method.
Applicant further argues that storing the model on the smart PIG and using it while navigating the pipeline to predict defects. This argument is unpersuasive. The location of the model being stored on the device does not improve the functioning of the smart PIG itself nor amount to a particular transformation under MPEP 2106.05(c). The claims specify the model is encoded in a data structure (i.e. stored in general purpose computer memory) and its location on the smart PIG device is a field of use limitation. Furthermore, the claims do not recite any navigation of the smart PIG or any use of the inference model relating to how the smart PIG is operated. Instead the claims merely recite that the model is stored on the device, the device is released into the pipeline (presumably for collecting the measurements used in the prediction), and a prediction is made based on the model and obtained measurements.
Applicant argued: Step 2B… Applicant has amended independent claims 1, 8, and 15 to recite "flagging the predicted wall-loss condition to an attention of an operator to facilitate maintenance of the particular buried pipeline." Applicant asserts that this amendment at least ties the prediction to a concrete, practical application and a subsequent physical action, thereby integrating AI training methodology into a specific industrial process. Under Step 2B, Applicant asserts that the ordered combination of (1) using a physics-based solver to simulate EM waveform responses inside a metal wall to overcome real-world data scarcity, (2) training an inference model on those specific simulations, (3) storing that model directly onto a smart PIG device equipped with non-contact, circumferentially positioned receivers, (4) releasing that specific device into a pipeline to autonomously predict wall-loss conditions, and (5) flagging those conditions to facilitate maintenance is entirely novel. This highly specialized integration of AI training and physical robotic deployment is significantly more than a routine application of an abstract idea.
The Examiner respectfully submits that the flagging is recited at a high level of generality that it can be changing data value only (i.e., abstract idea). The argued ordered combination is a combination of substantial abstract idea, plus insignificant extra-solution activities and invoking a generic computer or computer components to facilitate the application of the abstract idea, as discussed in the rejection and the responses above. The claims are still focused on the abstract idea without significant more.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN C KUAN whose telephone number is (571)270-7066. The examiner can normally be reached M-F: 9:00AM-5:30PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Schechter can be reached at (571) 272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JOHN C KUAN/Primary Examiner, Art Unit 2857
Prosecution Insights