DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is in response to submission of application on 10/20/2023.
Claims 1-10 are presented for examination.
Priority
Applicant’s claim for the benefit of a prior-filed application KR10-2023-0095430 filed on 07/21/2023 is acknowledged and admitted. Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/20/2023 and 04/08/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The Drawings filed on 10/20/2023 and 03/27/2024 are acceptable for examination purposes.
Specification
The Specification filed on 10/20/2023 is acceptable for examination purposes.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because:
Reference character “300” has been used to designate two different parts in Figure 1, additionally “300” has been used to designate a data generation system (Fig. 1, ¶43) and a data processing device (¶43, ¶57-72, & ¶177 and Figures 1-3).
Reference character “100” has been used to designate a data collection device (¶43, ¶47, …) and a data generation system (¶45, ¶46)
reference characters "300" in [¶43, ¶52, …] and "200" [Fig 2. as referenced by ¶70] references have both been used to designate the data processing device.
reference characters "300" [¶43] and "100" [¶45 & ¶46] have both been used to designate a data generation system.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to because there is a discrepancy between Figure 2, Figure 3 and the specification. Examiner respectfully suggests items were mislabeled in specification:
Figure 2 and ¶70 list reference numbers: 305, 310, 315, 320, 325, and 330 for items Communication unit, Input/output unit, Facility update unit, Facility management unit, Noise removal unit, Model lightweight unit, respectively. However, the Description of Symbols on Page 53, Lines 16-18 list reference numbers 205, 210, 215, 220, 225, and 230 for these same items.
Figure 3 lists reference numbers 250, 255, 260, 265, 270, 275, 280a, 280b, and 285. These reference numbers do not appear in the specification. However, ¶177-187 of the specification lists reference numbers 350, 355, 360, 365, 370, 375, 380a, 380b, and 385. These numbers do not appear in the submitted drawings. It appears this is a typo as the items in the Figure and Specification are used to label corresponding items, (e.g. reference 250 in Fig. 3 and reference 350 in ¶187 both refer to the processor).
. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: 10(a). Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
In addition to Replacement Sheets containing the corrected drawing figure(s), applicant is required to submit a marked-up copy of each Replacement Sheet including annotations indicating the changes made to the previous version. The marked-up copy must be clearly labeled as “Annotated Sheets” and must be presented in the amendment or remarks section that explains the change(s) to the drawings. See 37 CFR 1.121(d)(1). Failure to timely submit the proposed drawing and marked-up copy will result in the abandonment of the application.
Specification
The disclosure is objected to because of the following informalities:
Typographical error ¶158 and ¶255, "denso" should be "dense".
Appropriate correction 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 5-7 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.
The term “activation” in claim 5 is a relative term which renders the claim indefinite. The term “activation” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Activation used in the language of this claim may refer to a single activation, all activations, a specific activation output from one or more layers, or other interpretations. For the purpose of examination, this will be interpreted as a typo and read as 'quantizing activations'.
The term “previously” in claim 6 is a relative term which renders the claim indefinite. The term “previously” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear what "previously" is in relation to, does the quantizing step include activations, is the quantizing step happening before the pruning step, is quantizing the weights and activations happening before the quantizing step occurs, or a different interpretation. For the purposes of this examination, this claim is interpreted as the quantizing step includes quantizing activations.
The term “progressed” in claim 7 is a relative term which renders the claim indefinite. The term “progressed” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear what "progressed" means in this claim. While progressed implies some training has .
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.
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Official Gazette Notice 1351 OG 212, dated February 23, 2010, states “the broadest reasonable interpretation of a claim drawn to a computer readable medium…typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media.” Given that claim 10 is drawn to a computer readable medium, those claims are construed to cover both transitory and non-transitory media.
“A transitory, propagating signal … is not a ‘process, machine, manufacture, or composition of matter.’ Those four categories define the explicit scope and reach of subject matter patentable under 35 U.S.C. § 101; thus, such a signal cannot be patentable subject matter.” In re Nuijten, 84 USPQ2d 1495, 1503 (Fed. Cir. 2007).
Because the full scope of the claim encompasses non-statutory subject matter (i.e., transitory propagating signals), the claim as a whole is non-statutory. The Examiner suggests adding the limitation “non-transitory” to claim 10 to limit the claim scope to encompass only statutory subject matter. Appropriate correction is required.
Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidelines (“2019 PEG”).
Independent Claims
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, independent Claim 1 recites an abstract idea in the form of mental processes. A mental process is a process that “can be performed in the human mind, or by a human using a pen and paper” (MPEP § 2106.04(a)(2)(III), paragraph 1). Examples of mental processes include “observations, evaluations, judgments, and opinions” (MPEP § 2106.04(a)(2)(III), paragraph 2).
The following limitations of claim 1 are mental processes:
Pruning an artificial intelligence model machine learned using a first data set, [This is a mental process that can be performed by observations, evaluations, judgements, and opinions. Although pruning is further defined as “converting a corresponding weight to ‘0’ when a weight value […] is smaller than or equal to a preset value” and “analyzing sensitivity […] determining a threshold […] by multiplying […] by a standard deviation”, this does not impart a degree of complexity beyond what can be determined in a mental process.]
Quantizing the pruned artificial intelligence model, [This is a mental process that can be performed by observations, evaluations, judgements, and opinions. No specific methodology for quantizing is recited in the claim; therefore, it broadly encompasses quantizing that can be performed as a mental process.]
Learning the artificial intelligence model by imitating another artificial intelligence model previously trained using a second data set including a larger amount of data than the first data set, [This is a mental process that can be performed by observations, evaluations, judgements, and opinions. No specific methodology for learning or imitating is recited in the claim; therefore, it broadly encompasses methods that can be performed as a mental process.]
Therefore, the independent claims recite a judicial exception
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No. The judicial exception recited in the above discussed claims is not integrated into a practical application.
by a data processing device [This is mere recitation that a judicial exception is to be performed using generic computer equipment running general class of computer algorithms in their ordinary capacity. (MPEP 2106.05(f))]
Therefore, under MPEP § 2106.04(d), the additional elements of the claims do not integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No. The claims do not recite additional elements that are sufficient for the claims to amount to significantly more than the judicial exception.
Additional elements that are mere instructions to apply an exception or merely generally linking or generally linking the use of a judicial exception to a particular technological environment or field of use do not constitute significantly more than a judicial exception under MPEP § 2106.05(I)(A). Since the additional elements in the independent claims are all are mere instructions to apply an exception or are merely generally linking or generally linking the use of a judicial exception to a particular technological environment or field of use, they do not constitute significantly more than a judicial exception.
Therefore, the additional elements identified in the Step 2A Prong Two analysis do not constitute significantly more than a judicial exception.
Claim 10 is substantially similar in scope and spirit to claim 1. Therefore, it would be rejected under similar analysis.
Dependent Claims
The remaining dependent claims being rejected do not recite additional elements, whether considered individually or in combination, that are sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 2:
wherein the pruning step includes converting a corresponding weight to ‘0’ when a weight value of each layer included in the artificial intelligence model is smaller than or equal to a preset value [These further limitations merely further define the mental process recited in the parent claim and are therefore considered to be part of the mental process of the parent claim. This claim does not recite any non-abstract additional elements for the purposes of Step 2A Prong Two and Step 2B analysis]
Claim 3:
wherein the pruning step includes analyzing sensitivity of the artificial intelligence model, and determining a threshold for the weight value by multiplying a sensitivity parameter according to the analyzed sensitivity by a standard deviation of a weight value distribution of the artificial intelligence model [These further limitations merely further define the mental process recited in the parent claim and are therefore considered to be part of the mental process of the parent claim. This claim does not recite any non-abstract additional elements for the purposes of Step 2A Prong Two and Step 2B analysis]
Claim 4:
wherein the artificial intelligence model is configured as a “floating point 32-bit type”, and the quantizing step includes converting the artificial intelligence model into a “signed 8-bit integer type”. [These further limitations merely further define the mental process recited in the parent claim and are therefore considered to be part of the mental process of the parent claim. This claim does not recite any non-abstract additional elements for the purposes of Step 2A Prong Two and Step 2B analysis]
Claim 5:
wherein the quantizing step includes quantizing a plurality of weights of the artificial intelligence model, and quantizing activation at a time point of inference. [These further limitations merely further define the mental process recited in the parent claim and are therefore considered to be part of the mental process of the parent claim. This claim does not recite any non-abstract additional elements for the purposes of Step 2A Prong Two and Step 2B analysis]
Claim 6:
wherein the quantizing step includes quantizing a plurality of weights of the artificial intelligence model, and previously quantizing the plurality of weights and activations of the artificial intelligence model. [These further limitations merely further define the mental process recited in the parent claim and are therefore considered to be part of the mental process of the parent claim. This claim does not recite any non-abstract additional elements for the purposes of Step 2A Prong Two and Step 2B analysis]
Claim 7:
wherein the quantizing step includes determining a weight and performing quantization at the same time by simulating in advance an effect of applying quantization during inference at a time point when learning of the artificial intelligence model is progressed. [These further limitations merely further define the mental process recited in the parent claim and are therefore considered to be part of the mental process of the parent claim. This claim does not recite any non-abstract additional elements for the purposes of Step 2A Prong Two and Step 2B analysis]
Claim 8:
wherein the learning step includes calculating a loss by comparing outputs of the artificial intelligence model and another artificial intelligence model, and learning the artificial intelligence model so that the calculated loss is minimized. [These further limitations merely further define the mental process recited in the parent claim and are therefore considered to be part of the mental process of the parent claim. This claim does not recite any non-abstract additional elements for the purposes of Step 2A Prong Two and Step 2B analysis]
Claim 9:
wherein the artificial intelligence model is an artificial intelligence model for detecting objects on an image captured by the camera. [These further limitations merely further define the mental process recited in the parent claim and are therefore considered to be part of the mental process of the parent claim. This claim does not recite any non-abstract additional elements for the purposes of Step 2A Prong Two and Step 2B analysis]
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 for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 4, and 7-10 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al (US Patent Publication US 2022/0318633 A1) hereinafter Kim, in view of Krishnan et al (Us Patent Publication US 2023/0401831 A1) hereinafter Krishnan.
Regarding Claim 1, Kim discloses a method comprising the steps of: pruning an artificial intelligence model machine-learned using a first data set, by a data processing device; [Kim: ¶0005] “The processor implemented method includes receiving an initial neural network model. […] also includes pruning the initial neural network model” wherein an initial neural network reasonably corresponds to a model machine-learned by a dataset.
quantizing the pruned artificial intelligence model, by the data processing device; [Kim: ¶0005] “the processor-implemented method includes applying a quantization process to the pruned network to produce a pruned and quantized network”
Kim does not specifically teach learning the artificial intelligence model by imitating another artificial intelligence model previously trained using a second data set including a larger amount of data than the first data set, by the data processing device.
However, Krishnan in the same field of endeavor discloses the above limitation. [Krishnan: Abstract] “The process includes […] evaluating the performance of the student model compared with the performance of the teacher model, and providing feedback to student model to adjust the behavior of the student model based on the performance of the student model” is reasonably comparable to training a model (student) by imitating another model (teacher). [Krishnan: ¶0051] “The teacher models are pretrained using the full corpus of the data, but the student models are trained using only a subset of the training data samples.” This reasonably corresponds to the teacher model being previously trained on a larger data set than the student model. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Krishnan’s knowledge distillation techniques within Kim’s methods of model compression using pruning, quantization, and knowledge distillation. The motivation to combine is that there is [Krishnan: ¶0056] "A technical benefit of deploying the student models rather than the teacher models for use by the visual content retrieval system is that the student models are much smaller than the teacher models but provide similar performance regarding predictions as the teacher models. Thus, the computational resources and/or memory requirements for supporting the models may be significantly reduced by utilizing the student models distilled from the teacher models using the scalable knowledge distillation process"
Regarding Claim 2, the combination of Kim and Krishnan discloses the method according to claim 1, wherein the pruning step includes converting a corresponding weight to '0' when a weight value of each layer included in the artificial intelligence model is smaller than or equal to a preset value. [Kim: ¶0053] “...weights of the network 402 that are below a predefined threshold (e.g., nearly 00, less than 0.5) may be set to zero”, as the neural network is a type of artificial intelligence model, the weights of the network reasonably corresponds to the weight value of each layer.
Regarding Claim 4, the combination of Kim and Krishnan discloses the method according to claim 1, wherein the artificial intelligence model is configured as a “floating point 32-bit type”, and the quantizing step includes converting the artificial intelligence model into a “signed 8-bit integer type”. [Kim: ¶0061] “…the network 402 includes full precision weights (e.g., 32-bit), the quantized network 408 and the updated network 410 includes lower-precision weights (e.g., 4-bit or 8-bit).”
Regarding Claim 7, the combination of Kim and Krishnan discloses the method according to claim 4, wherein the quantizing step includes determining a weight and performing quantization at the same time by simulating in advance an effect of applying quantization during inference at a time point when learning of the artificial intelligence model is progressed. [Kim: ¶0025] “…a model may be trained using a joint iterative pruning and quantization-aware training (QAT). In some aspects, the model is pruned and then quantized with a learnable step size”. Quantization-aware training is a known technique where quantization effects are introduced during model training to simulate the effects of applying quantization during inference, a person having ordinary skill in the art at the time of this disclosure would recognize and understand this to be comparable to the claim as recited.
Regarding Claim 8, the combination of Kim and Krishnan the method according to claim 1, wherein calculating a loss by comparing outputs of the artificial intelligence model and another artificial intelligence model, [Kim: ¶0073] “The Kullback-Leibler (KL) divergence may be computed between the student network and the teacher network.” KL divergence is a known technique for calculating a loss based on the outputs of multiple models during training. and learning the artificial intelligence model so that the calculated loss is minimized. [Kim: ¶0067] “Example pseudocode […] 8: Update Sw, W by minimizing cross-entropy loss ℒceS […] 18: Update W by minimizing ℒKDS and ℒKDT (loss of student and teacher)” [Kim: ¶0073] “The student model may, in turn, be trained based on the cross-entropy loss. […] The training may be repeated for additional epochs. […] The network may be updated with the cross-entropy loss and the KL loss.” This demonstrates an iterative training and updating of the model based on a loss function to reduce or minimize the calculated loss.L{f}
Regarding Claim 9, the combination of Kim and Krishnan discloses the method according to claim 1, wherein the artificial intelligence model is an artificial intelligence model for detecting objects on an image captured by the camera. [Kim: ¶0035 Fig. 2D Item 200] “One type of convolutional neural network is a deep convolutional network […] designed to recognize visual features from an image 226 input from an image capturing device 230, such as a car-mounted camera”. As demonstrated through figure 2D, the methods disclosed by Kim include artificial intelligence models for detecting objects in images captured by a camera.
Claim 10 is substantially similar in scope and spirit to claim 1. Therefore, it would be rejected under similar analysis.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Krishnan, further in view of Neural Network Distiller: Weights and Pruning Algorithms to Intel AI Lab hereinafter Intel. (Cited by Applicant on IDS dated 4/8/24.)
Regarding Claim 3, the combination of Kim and Krishnan discloses the method of claim 2 as above.
The combination of Kim and Krishnan does not specifically teach analyzing sensitivity of the artificial intelligence model, and determining a threshold for the weight value by multiplying a sensitivity parameter according to the analyzed sensitivity by a standard deviation of a weight value distribution of the artificial intelligence model.
However, Intel in the same field of endeavor discloses the above limitation. [Intel: Sensitivity Pruner: Method of Operation] “1. Start by running a pruning sensitivity analysis on the model. Then use the results to set and tune the threshold of each layer, but instead of using a direct threshold use a sensitivity parameter which is multiplied by the standard-deviation of the initial weight-tensor’s distribution.” The instructions for implementing a sensitivity pruner provided by Intel are the same as those found in the claim. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Intel Distiller’s sensitivity pruner in Kim’s methods of pruning and quantization for model compression. The motivation for this is that [Intel: Sensitivity Pruner] “Finding a threshold magnitude per layer is daunting, especially since each layer’s elements have different average absolute values. We can take advantage of the fact that the weights of convolutional and fully connected layers exhibit a Gaussian distribution with a mean value roughly zero, to avoid using a direct threshold based on the values of each specific tensor.”
Claims 5 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Krishnan, further in view of Lee et al. (US Patent Publication US 2018/0341857 A1) hereinafter Lee.
Regarding Claim 5 and 6, the combination of Kim and Krishnan discloses the method of claim 4 as above, further Kim teaches quantizing a plurality of weights of the artificial intelligence model [Kim: ¶0058] “quantization may be performed on a layer-by-layer basis. Given a range of model weights [minw, maxw], the weights w may be quantized…”.
Regarding Claim 5, the combination of Kim and Krishnan does not teach the additional limitation of quantizing activation at a time point of inference.
However, Lee in the same field of endeavor discloses the above limitation. [Lee: ¶0148] “the activations may also be quantized […] such weighted entropy quantizations of full precision weights and activations may be determined and applied during the inference process”. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Lee’s quantization methods and strategies within the quantization process laid out in Kim’s framework for model compression. The motivation is that Lee provides techniques that [Lee: ¶0060] “… may be capable of minimizing accuracy loss while reducing the computational amount require for processing complex input data, e.g., compared to examples where quantization of weights and/or activations is not performed, or compared to […] linear or log quantization operations that may alternatively be performed.”
Regarding Claim 6, the combination of Kim and Krishnan does not teach the additional limitation previously quantizing the plurality of weights and activations of the artificial intelligence model.
However, Lee in the same field of endeavor discloses the above limitation, [Lee: ¶0157] “The CPU or the neural network device may implement such entropy-based quantizations during training, after training and before inference processes using a neural network, or during such inference processes.” As stated above, incorporating Lee’s quantization methods within Kim’s framework for model compression would have been obvious to one of ordinary skill in the art before the effective filing date.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ardywibowo et al. US 12591771 B2 Dynamic Quantization for Energy Efficient Deep Learning
Dynamic quantization techniques including minimizing loss during training.
Kim US 20230168921 A1 Neural Processing Unit
Neural network processing unit designed to run models efficiently on edge devices such as vehicles. Includes the compression of weights/activations and sensitivity pruning.
A et al. US 20200311552 A1 Device and Method for Compressing Machine Learning Model
Method for compressing a machine learning model, including pruning, quantizing, and knowledge distillation methods.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GRANT F FITCH whose telephone number is (571)270-0621. The examiner can normally be reached Bi-Week M-F 7-4 Friday Flex.
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, Miranda Huang can be reached at (571) 270-7092. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/VINCENT GONZALES/Primary Examiner, Art Unit 2124
GRANT F. FITCH
Examiner
Art Unit 2124
Prosecution Insights