DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-14 are presented for examination.
Claim Interpretation
Office personnel are to give claims their "broadest reasonable interpretation" in light of the supporting disclosure. In re Morris, 127 F.3d 1048, 1054-55, 44 USPQ2d 1023, 1027-28 (Fed. Cir. 1997). Limitations appearing in the specification but not recited in the claim are not read into the claim. In re Prater, 415 F.2d 1393, 1404-05, 162 USPQ 541,550-551(CCPA 1969). See *also In re Zletz, 893 F.2d 319,321-22, 13 USPQ2d 1320, 1322(Fed. Cir. 1989) ("During patent examination the pending claims must be interpreted as broadly as their terms reasonably allow").... The reason is simply that during patent prosecution when claims can be amended, ambiguities should be recognized, scope and breadth of language explored, and clarification imposed.... An essential purpose of patent examination is to fashion claims that are precise, clear, correct, and unambiguous. Only in this way can uncertainties of claim scope be removed, as much as possible, during the administrative process.
Claims recite "and/or". The claims reciting "and/or" were interpreted as “or”.
Claim Objections
Claim 9 line 5 uses the acronym or variable “cGAN”, the first use of an acronym or variable in a claim should be defined to avoid any possible indefiniteness issues.
Appropriate correction or clarification is required.
Claim Rejections - 35 USC § 112
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 1-14 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 applicant regards as the invention.
Claim 1 recites the limitation "the scan of the image" in line(s) 3. There is insufficient antecedent basis for this limitation in the claim. There is no "scan of the image" anteceding this limitation in the claim.
Claim 1 recites the limitation "the input parameter" in line(s) 7. There is insufficient antecedent basis for this limitation in the claim. There is no "input parameter" anteceding this limitation in the claim.
Claim 1 recites the limitation "the physical parameter" in line(s) 8. There is insufficient antecedent basis for this limitation in the claim. Antecedent calls for “the at least one physical parameter” and not “the physical parameter".
As to claim 1, the cited "converting the input parameter into a histogram" in line(s) 7 is unclear as to what it represents, because a histogram is a chart that displays the frequency distribution of data not a "parameter". (See prior art made of record below).
Claim 1 recites the limitation "the scan or design of the engineering component" in line(s) 9-10. There is insufficient antecedent basis for this limitation in the claim. Anteceding this limitation in the claim, there is no "scan or design of the engineering component" but "a scan or design of a component".
Claim 2 recites the limitation "the physical parameters" in line(s) 3. There is insufficient antecedent basis for this limitation in the claim. Anteceding this limitation in the claim, there are no "physical parameters" but "at least one physical parameter".
As to claim 12, the same deficiency applies.
Claim 2 recites the limitation "the histograms" in line(s) 2. There is insufficient antecedent basis for this limitation in the claim. There is only one "histogram" anteceding this limitation in the claim.
Claim 9 recites the limitation "the discriminator" in line(s) 4. There is insufficient antecedent basis for this limitation in the claim. There is no "discriminator" anteceding this limitation in the claim.
Claim 9 recites the limitation "the generator" in line(s) 5. There is insufficient antecedent basis for this limitation in the claim. There is no "generator" anteceding this limitation in the claim.
Claim 9 recites the limitation "the generated images" in line(s) 6. There is insufficient antecedent basis for this limitation in the claim. There are no "generated images" anteceding this limitation in the claim.
Claim 9 recites the limitation "the program" in line(s) 7. There is insufficient antecedent basis for this limitation in the claim. There is no "program" anteceding this limitation in the claim.
Claim 11 recites the limitation "the embedded data" in line(s) 1-2. There is insufficient antecedent basis for this limitation in the claim. There is no "embedded data" anteceding this limitation in the claim.
As to claim 13, the same deficiency applies.
Claim 13 recites the limitation "the blade" in line(s) 3. There is insufficient antecedent basis for this limitation in the claim. There is no "blade" anteceding this limitation in the claim.
Dependent claims inherit the defect of the claim from which they depend.
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 1-14 are rejected because the claimed invention is directed to a judicial exception without significantly more.
Independent claim 1, Step 1: a method (process = 2019 PEG Step 1 = yes)
Independent claim 1, Step 2A, Prong One: claim recites:
converting the input parameter into a histogram and/or a glyph that represents the value of the physical parameter
The limitations are substantially drawn to mental concepts. The limitations, as drafted and under their broadest reasonable interpretation, cover performance of the limitations in the mind but for the recitation of generic computer components. Information and/or data also fall within the realm of abstract ideas because information and data are intangible. See Electric Power Group1 (Electric Power hereinafter): “Information… is an intangible”.
As to the limitations "converting the input parameter into a histogram and/or a glyph that represents the value of the physical parameter", as drafted and under a broadest reasonable interpretation, these activities can be characterized as entailing a user analyzing (observations, evaluations) and deciding/determining (judgments), i.e., processing information and/or data, that can be performed in the human mind or by a human using a pen and paper. The specification reads (underline emphasis added):
"converting numerical values that are associated with measured or determined physical characteristics and embedding these values into the images in the form of small geometrical shapes" (see page 8, 2nd paragraph)
If a claim limitation, under its broadest reasonable interpretation, covers mental processes, then it falls within the "(c) Mental processes" grouping of abstract ideas (2019 PEG Step 2A, Prong One: Abstract Idea Grouping? = Yes, (c) Mental processes—concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
Independent claim 1, Step 2A, Prong two: The claim recites the additional element computer as performing generic computer functions routinely used in computer applications.
As to the limitations “of enhancing a scan or design of an engineering component", they are no more than intended use.
As to the limitations “obtaining a scan or a design of a component and inputting the scan or design of the component into a computer; providing an input into the computer of at least one physical parameter relating to the design or the scan of the image”, these limitations describe the concept of “mere data gathering”, which corresponds to the concepts identified as abstract ideas by the courts. Data gathering, including when limited to particular content does not change its character as information, is also within the realm of abstract ideas. As to the limitations “embedding the histogram or glyph into the scan or design of the engineering component”, they are considered generic displaying. The specification reads (underline emphasis added):
"The glyph in the lower right corner of the image represents this calculation in the form of the MOLOSS performance factor for the component. In this representation the size of the portion of the semi-circle in the glyph is indicative of performance: the smaller the section of semi-circle the better the performance factor is. The additional information is coloured prior to being embedded so as to present the information within the glyph and/or histogram in a distinct differentiating colour. The extra data can be embedded pixel wise into the images" (see page 10, 2nd paragraph)
Data gathering and displaying have not been held by the courts to be enough to qualify as “significantly more”. See Electric Power.
This judicial exception is not integrated into a practical application (2019 PEG Step 2A, Prong Two: Additional elements that integrate the Judicial exception/Abstract idea into a practical application? = NO).
Independent claim 1, Step 2B: As discussed with respect to Step 2A, claim 1 recites the additional element computer. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. The use of a computer to implement the abstract idea of a mathematical or mental algorithm has not been held by the courts to be enough to qualify as “significantly more”. The implementation on a computing system is not elaborated but merely repeated in the Specification.
As discussed with respect to Step 2A, Prong two, the intended use limitations remain intended use even upon reconsideration, because no actual "enhancing" is performed in the body of the claim.
As discussed with respect to Step 2A, claim 1 recites data gathering and displaying, these limitations are recited at a high level of generality; and therefore, remain insignificant extra-solution activity even upon reconsideration.
Taken alone the individual additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the additional elements taken individually. There is no indication that their combination improves the functioning of a computer itself or improves any other technology (underline emphasis added). Therefore, the claim does not amount to significantly more than the abstract idea itself (2019 PEG Step 2B: NO).
Dependent claims, Prong One: The claim limitations further the mental concepts of their independent claim. (See Independent claim 1, Step 2A, Prong One above).
As to the limitations “8… wherein computational fluid dynamic modelling is performed on the scan or design of an engineering component and a resulting performance factor is calculated and embedded into the image as a glyph" and "13… wherein the engineering component being designed is an aerofoil, and the embedded data in the histogram and/or glyph represents damage parameters on the blade and a performance factor calculated through computational fluid dynamic modelling", they are substantially drawn to mathematical concepts: mathematical relationships, formulas or equations, and calculations. The specification reads (underline emphasis added): "The data can then undergo a series of computational fluid dynamic calculations on the resulting geometries to model their behaviour" (see page 14, last paragraph)
As to the limitations “12… wherein the physical parameters in the design created by the generator are compared with the inputted scan or design of the engineering component" and "14… wherein the glyph of the performance factor of the design generated by the generator is directly compared to that of the inputted scan or design of the engineering component", comparisons are mental in nature. These limitations, as drafted and under a broadest reasonable interpretation, can be characterized as entailing a user evaluating information (evaluations, judgments, opinions), that can be performed in the human mind or by a human using a pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers abstract ideas, then it falls within groupings of abstract ideas (2019 PEG Step 2A, Prong One: Abstract Idea Grouping? = Yes).
Dependent claims, Step 2A, Prong two:
As to the limitations “7… wherein the scan or design of an engineering component is a blade for use in a gas turbine engine and the data input into the histogram relates to damage parameters on the blade”, these limitations describe the concept of “mere data gathering”. As to the limitations “3… wherein the histogram or glyph is embedded using a separate colour channel of the scan or design of an engineering component”, "5… wherein a plurality of histograms and glyphs are embedded into the scan or design of an engineering component, and where each glyph and/or histogram represents a different physical parameter", and "6… wherein the histograms are embedded into the scan or design of an engineering component prior to a further computational calculation regarding a property, and the output of the calculation is also embedded into the scan or design of the engineering component as a glyph", they are considered generic displaying. Data gathering and displaying have not been held by the courts to be enough to qualify as “significantly more”. (See Independent claim 1, Step 2A, Prong Two above).
As to the limitations “9. A computer implemented method of designing an engineering component using a Conditional Generative Adversarial Network; wherein scans or designs of an engineering component according to claim 1 are fed into the network and are input into the discriminator, the discriminator compares the scan or design of engineering component data with data generated by the generator of the cGAN system, and wherein the generated images are created based on latent variables and labels to assist the program to create the designs", "10… wherein the Conditional Generative Adversarial Network is trained in a zero-sum adversarial manner", and "11… wherein the embedded data in the form of glyphs and/or histograms within the scan or design of an engineering component is used in the training process of the Conditional Generative Adversarial Network to assist it in learning desirable variables for the design of the component", they represent no more than just “apply it” limitations, because they invoke computers or other machinery merely as a tool to perform an existing process.
This judicial exception is not integrated into a practical application of the exception (2019 PEG Step 2A, Prong Two: Additional elements that integrate the Judicial exception/Abstract idea into a practical application? = NO).
Dependent claims, Step 2B:
As discussed with respect to Step 2A, claim 1 recites data gathering and displaying, these limitations are recited at a high level of generality; and therefore, remain insignificant extra-solution activity even upon reconsideration.
As discussed with respect to Step 2A, Prong two, limitations invoking computers or other machinery merely as a tool to perform an existing process are just “apply it” limitations. See MPEP 2106.05(f)(2).
The claims do not amount to significantly more than the abstract idea itself (2019 PEG Step 2B: NO).
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(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Examiner would like to point out that any reference to specific figures, columns and lines should not be considered limiting in any way, the entire reference is considered to provide disclosure relating to the claimed invention.
Claims 1-8 are rejected under 35 U.S.C. 103(a) as being unpatentable over Kadir Ali et al., (Kadir hereinafter), "Computational fluid dynamic and thermal stress analysis of coatings for high-temperature corrosion protection of aerospace gas turbine blades" (see IDS dated 04/14/2023), taken in view of Matthew David Fisher, (Fisher hereinafter), U.S. Pre–Grant publication 20190385346.
As to claim 1, Kadir discloses a method of enhancing a scan or design of an engineering component, the method comprising the steps of: obtaining (see "3-D geometrical model imported from ANSYS (x,y,z) workbench" in page 14, Figure 8) and inputting the (see "The meshed model described in Section 2.1 is imported into ANSYS FLUENT" in page 13, 1st paragraph); providing an input into the computer of at least one physical parameter relating to the design or the scan of the image (see "(I) Analysis type selected (linear, elastic, thermal stress analysis)" in page 8, Figure 1); (see Figs. 9-30).
About Examiner's interpretation of "
While Kadir discloses histograms representing physical parameter values, Kadir fails to expressly disclose converting the input parameter into (see "[0028]… the deep neural network architecture is utilized to automatically synthesize missing glyphs from a few image examples").
Kadir and Fisher are analogous art because they are related to design.
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Fisher with Kadir, because Fisher points out that "[0044] Generative Adversarial Networks [0045] Deep learning attempts to discover rich, hierarchical models that represent probability distributions over the kinds of data encountered in artificial intelligence applications… GANs solved some of these problems by introducing a generative model estimation procedure that sidestepped many of these difficulties", and as a result, Fisher reports that "[0046] In the GAN framework, a generative model is pitted against an adversary. In particular, a discriminative model learns to determine whether a sample is from the model distribution or the data distribution. The generative model can be thought of as analogous to a team of counterfeiters, trying to produce fake currency and use it without detection, while the discriminative model is analogous to the police, trying to detect the counterfeit currency. Competition in this game drives both teams to improve their methods until the counterfeits are indistinguishable from the genuine articles"
As to claim 2, Kadir discloses wherein the physical parameters represented by the histograms and/or are derived from computational modelling of the scan or design of an engineering component (see "simulation involves a conjugate heat transfer analysis with fully coupled thermal-stress calculation" in page 29, 1st paragraph).
As to claim 3, Kadir discloses wherein the histogram (see Figs. 9-30).
As to claim 4, Kadir discloses wherein the data in the histogram and/or glyph is scaled (see Figs. 9-30).
As to claim 5, Fisher discloses wherein a plurality of (see "[0050]… MC-GAN model may comprise two subnetworks: a GlyphNet to synthesize glyph shapes and an OrnaNet to perform synthetic ornamentation of glyph shapes generated by a GlyphNet… a GlyphNet and OrnaNet comprising an MC-GAN model may each respectively utilize a cGAN model during training. With respect to generation of synthetic images, starting from a random noise vector z, GANs train a model to generate an image y, following a specific distribution by adversarially training a generator versus a discriminator (z--–>y). While the discriminator attempts to discriminate between real and fake images, the generator opposes the discriminator by trying to generate realistic looking images. In the conditional (cGAN) scenario, this mapping is modified by feeding an observed image x alongside the random noise vector to the generator ({x,z}–>y)").
As to claim 6, Kadir discloses wherein the histograms are embedded into the(see "simulation involves a conjugate heat transfer analysis with fully coupled thermal-stress calculation" in page 29, 1st paragraph), and Fisher discloses the output of the calculation is also embedded into the scan or design of the engineering component as a glyph (see "[0050]… MC-GAN model may comprise two subnetworks: a GlyphNet to synthesize glyph shapes and an OrnaNet to perform synthetic ornamentation of glyph shapes generated by a GlyphNet… a GlyphNet and OrnaNet comprising an MC-GAN model may each respectively utilize a cGAN model during training. With respect to generation of synthetic images, starting from a random noise vector z, GANs train a model to generate an image y, following a specific distribution by adversarially training a generator versus a discriminator (z--–>y). While the discriminator attempts to discriminate between real and fake images, the generator opposes the discriminator by trying to generate realistic looking images. In the conditional (cGAN) scenario, this mapping is modified by feeding an observed image x alongside the random noise vector to the generator ({x,z}–>y)").
As to claim 7, Kadir discloses wherein the (see "Figures 9-30 present extensive results for three types of analysis. These are respectively static stress analysis (Figures 9-20), thermal stress analysis (Figures (21-29)) and finally CFD erosion simulation (Figure. 30)" in page 23, 1st paragraph).
As to claim 8, Kadir discloses wherein computational fluid dynamic modelling is performed on the(see "The CFD analysis is used to generate a representative pressure and thermal field" in page 13, 1st paragraph) and a resulting performance factor is calculated (see "The standard K-epsilon turbulence model was used in this simulation to provide enough refinement in the velocity field" in page 13, 1st paragraph) and Fisher discloses embedded into the image as a glyph (see "[0050]… MC-GAN model may comprise two subnetworks: a GlyphNet to synthesize glyph shapes and an OrnaNet to perform synthetic ornamentation of glyph shapes generated by a GlyphNet… a GlyphNet and OrnaNet comprising an MC-GAN model may each respectively utilize a cGAN model during training. With respect to generation of synthetic images, starting from a random noise vector z, GANs train a model to generate an image y, following a specific distribution by adversarially training a generator versus a discriminator (z--–>y). While the discriminator attempts to discriminate between real and fake images, the generator opposes the discriminator by trying to generate realistic looking images. In the conditional (cGAN) scenario, this mapping is modified by feeding an observed image x alongside the random noise vector to the generator ({x,z}–>y)").
Claims 9-14 are rejected under 35 U.S.C. 103(a) as being unpatentable over Kadir taken in view of Fisher as applied to claim 1 above, and further in view of Wang Yueqing et al., (Wang hereinafter), "An Intelligent Method for Predicting the Pressure Coefficient Curve of Airfoil-Based Conditional Generative Adversarial Networks" (see IDS dated 04/14/2023).
As to claim 9, Kadir and Fisher do not disclose, but Wang discloses a computer implemented method (see "we use three different platforms for different purposes. The first platform is a supercomputer called Tianhe (TH)-2 used for CFD simulation. The second platform is an artificially intelligent (AI) server for training models, which includes two Intel CPUs with a main frequency of 2.2 GHz and eight NVidia Tesla V100 GPUs. The third is a desktop used to evaluate our method" in page 3544, col. 2, 2nd paragraph) of designing an engineering component using a Conditional Generative Adversarial Network (see "generate a large number of airfoils using a physical method and our Airfoil-Gen-GAN, respectively, so as to meet the training data requirements of our proposed Airfoil-Cp-GAN" in page 3550, col. 1, last paragraph); wherein (see "Fig. 2. Network structure of our Airfoil-Gen-GAN. The Airfoil-Gen-GAN includes a generator and a discriminator" in page 3542), and wherein the generated images are created based on latent variables and labels to assist the program to create the designs (see "Pix2pix can translate an input image into a corresponding output image" in page 3539, col. 1, last paragraph).
Kadir, Fisher, and Wang are analogous art because they are related to design.
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Wang with Kadir and Fisher, because Wang generates "a large number of airfoils using a physical method and our Airfoil-Gen-GAN… Moreover, we design a new loss function to train our network" (see page 3550, col. 1, last paragraph to col. 2, 1st paragraph), and as a result, Wang reports the following improvements over his prior art: "[e]xtensive experimental results demonstrate that the Cp curve predicted by our method is very close to that of obtained via CFD simulation… our method achieves a speedup of nearly 1000x compared with CFD simulation results… our method can be extended to the calculation of other aerodynamic coefficients" (see page 3550, col. 2, 1st paragraph).
As to claim 10, Wang discloses wherein the Conditional Generative Adversarial Network is trained in a zero-sum adversarial manner (see "To train the Airfoil-Cp-GAN, we design a new loss function. The loss function of the discriminator is made up of two terms: the discriminator loss of Wasserstein GAN (WGAN) [40] and the K-Lipschitz constraint of WGANGP [20]" in page 3543, col. 2, 2nd paragraph; "The discriminator loss of both Airfoil-Gen-GAN and Airfoil-Cp-GAN have a similar trend. At first, the parameters of both discriminators are generated randomly, the generated airfoils and Cp curves are quite different from real ones, so it is easy to distinguish. However, the discriminators have not been trained well in this stage, so their losses are large. After several iterations, their discriminators can easily distinguish real and fake data. At last, the qualities of generated airfoils and Cp curves are better and better, so their discriminators cannot distinguish real and fake data, resulting in the fluctuations of discriminator loss" in page 3544, col. 1, last paragraph to col. 2, 1st paragraph).
As to claim 11, Fisher discloses wherein the embedded data in the form of glyphs (see "[0028]… the deep neural network architecture is utilized to automatically synthesize missing glyphs from a few image examples… a conditional generative adversarial network (“cGAN”) architecture is utilized to retrain a customized network for each observed character set using only a handful of observed glyphs").
As to claim 12, Wang discloses wherein the physical parameters in the design created by the generator are compared with the (see "We select the UIUC dataset as the basic dataset and employ the two-step data augmentation strategy… to generate about 10 000 airfoils, each of which corresponds to 200 inflow conditions. We further use 6000 airfoils with different inflow conditions and the corresponding Cp curves to train the Airfoil-Cp-GAN, while the remaining 4000 airfoils and their corresponding inflow conditions are used to evaluate our method" in page 3544, next to last paragraph).
As to claim 13, Wang discloses wherein the engineering component being designed is an aerofoil (see "generate a large number of airfoils using a physical method and our Airfoil-Gen-GAN, respectively, so as to meet the training data requirements of our proposed Airfoil-Cp-GAN" in page 3550, col. 1, last paragraph)… and a performance factor calculated through computational fluid dynamic modelling (see "we also run some CFD simulations on this desktop" in page 3544, col. 2, 2nd paragraph); and Kadir discloses the embedded data in the histogram (see "Figures 9-30 present extensive results for three types of analysis. These are respectively static stress analysis (Figures 9-20), thermal stress analysis (Figures (21-29)) and finally CFD erosion simulation (Figure. 30)" in page 23, 1st paragraph).
As to claim 14, Fisher discloses wherein the glyph of the performance factor of the design generated by the generator is directly compared to that of the inputted scan or design of the engineering component (see "[0028]… the deep neural network architecture is utilized to automatically synthesize missing glyphs from a few image examples… a conditional generative adversarial network (“cGAN”) architecture is utilized to retrain a customized network for each observed character set using only a handful of observed glyphs").
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tharun Mohandoss, U.S. Pre–Grant publication 20210158570, discloses "[0019]… A histogram is a graphical representation of the distribution of values of a color parameter of an image frame (e.g., the distribution of hue values of an image)… Each remaining image pair in the training data set (e.g., each image pair with similar histograms, but different content) represents a color-matched image pair that is used to train the generative adversarial network to shot-match a source image with a reference image".
Examiner would like to point out that any reference to specific figures, columns and lines should not be considered limiting in any way, the entire reference is considered to provide disclosure relating to the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUAN CARLOS OCHOA whose telephone number is (571)272-2625. The examiner can normally be reached Mondays, Tuesdays, Thursdays, and Fridays 9:30AM - 7:00 PM.
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, Renee Chavez can be reached at 571-270-1104. 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.
/JUAN C OCHOA/Primary Examiner, Art Unit 2186
1 Electric Power Group, LLC v. Alstom S.A., 119 USPQ2d 1739 Fed. Cir. 2016
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