DETAILED ACTION
Claims 1-16 are currently pending.
The previous rejections to claims 1-14 under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, are withdrawn due to Applicant’s amendment.
Response to Arguments
Applicant's arguments filed 8/24/26 have been fully considered but they are not persuasive.
The Applicant argues on page 8 of the response in essence that: The Office Action asserts that paragraph 57 of Goodwin discloses "providing a specification for the product," as required by independent Claims 1 and 15. However, paragraph 57 of Goodwin merely describes a VIS control module 122 that automates operation of a physical visual inspection system 102 so that "sample images (e.g., container images) can be generated with little or no human interaction." That is, Goodwin discloses capturing sample images of physical containers; however, capturing sample images of physical containers via an automated camera system does not constitute providing a "specification" of a product, as required by independent Claims 1 and 15.
Claim terms are given their plain meaning unless such meaning is inconsistent with the specification. See § MPEP 2111.01. The Applicant’s specification does not provide a specific definition of the term “a specification. Therefore, “a specification” is given the ordinary and customary meaning given to the term by those of ordinary skill in the art at the time of the invention. Goodwin discloses generating sample images of a product (paragraph 57), which is a specification for the product because the images exhibit the design of the product.
The Applicant argues on page 9 of the response in essence that: However, Applicant respectfully submits that Hirano is not directed to training or further training an AI model. Instead, Hirano discloses a visual inspection device that calculates a defect threshold and a determination threshold using standard statistical distributions. See, e.g., Hirano, 11 [0084], and [0133]-[0136]. In this regard, Hirano discloses calculating the optimum defect threshold by utilizing a parametric technique, such as the Smirnov-Grubbs test, or a non-parametric technique, such as a box-and-whisker plot. See, e.g., id. The "additional learning" of Hirano simply recalculates these numerical statistical thresholds based on an operator manually reclassifying false positive "NG" (defective) images as non-defective items. See, e.g., id., 11 [0151]-[0153]. However, adjusting a purely statistical threshold (e.g., standard deviation parameters) based on human reclassification does not teach or suggest automatically feeding back ground truth data to "further train the Al model," as recited in amended claim 1, and similarly required by independent Claim 15.
Goodwin discloses training an AI model (paragraph 49, Once trained and qualified, the AVI neural network(s) may be used for quality control at the time of manufacture (and/or in other contexts) to detect defects). Hirano is merely relied on to teach feeding back images of real products together with ground truth data identifying them as acceptable or defective to further train the model (paragraph 149).
The Applicant argues on page 10 of the response in essence that: Accordingly, because Goodwin does not disclose or suggest the generation of synthetic data directly from a specification and Hirano merely discloses adjusting a statistical calculation threshold rather than further training an Al model, a person having ordinary skill in the art would not arrive at Applicant's claimed invention by combining these references.
Before the effective filing date of the claimed invention, it would have obvious to a person of ordinary skill in the art to further train the AI model with real products that can be verified. The motivation for doing so would have been to improve the results of the AI model at detecting defects.
The Applicant has not traversed the Examiner’s assertion of official notice for the assertions provided in claim 4. Therefore, the common knowledge or well-known in the art statement is taken to be admitted prior art
Priority
Acknowledgment is made of Applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. GB 2118453.6, filed on 12/17/21.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function.
Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitations use a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
an input interface of claim 15, and
a network communication interface of claim 16.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If Applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 15 and 16 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 pre-AIA the applicant regards as the invention.
The claim limitations “an input interface” and “a network communication interface” listed above invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The Applicant's specification does not provide an association between the structure and the function. Therefore, claims 1-6 are indefinite and are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3 and 5-16 are rejected under 35 U.S.C. 103 as being unpatentable over Goodwin et al. US Publication 2024/0095983 (hereafter “Goodwin”) and Hirano US Publication 2013/0177232 (hereafter “Hirano”).
Referring to claim 1, Goodwin discloses a method of performing quality assessment in a process of manufacture of or processing of a product, comprising:
providing a specification for the product (paragraph 57, VIS control module 122 controls/automates operation of visual inspection system 102 such that sample images (e.g., container images) can be generated with little or no human interaction),
generating, from the specification, synthetic data representative of an appearance of the product when conforming to the specification (paragraph 75, Initially, at block 402, module 124 loads a defect image, and a container image without the defect shown in the defect image, into memory (e.g., memory unit 114). The container image (e.g., a syringe, cartridge, or vial similar to one of the containers shown in FIGS. 3A through 3C) may be a real image captured by visual inspection system 102 of FIG. 1 or visual inspection system 200 of FIG. 2, for example. Depending on the implementation, the real image may have been processed in other ways (e.g., cropped, filtered, etc.) prior to block 402) and, separately, the appearance of the product when defective (paragraph 87, at block 424 module 124 converts the modified container image matrix to a bitmap image, and saves the resulting “defect” container image (e.g., in training image library 140)),
training an Al model using the synthetic data to distinguish between acceptable products and defective products, for use of the trained Al model on images of real products in a manufacturing or processing facility, further comprising using the model at an inspection system of the manufacturing or processing facility, by capturing images of real products and classifying them as acceptable or defective using the model (paragraph 49, Once trained and qualified, the AVI neural network(s) may be used for quality control at the time of manufacture (and/or in other contexts) to detect defects).
Goodwin does not disclose expressly feeding back images of real products together with ground truth data identifying them as acceptable or defective to further train the AI model.
Hirano discloses feeding back from the inspection system images of real products together with ground truth data identifying them as acceptable or defective, and using such data to further train the Al model (paragraph 149, Specifically, when visual inspection is performed on the inspection object 6 in the operation mode, an NG determination is made on some images of the inspection objects 6. The user visually checks the plurality of images of items determined as NG on the display device 3 and classifies these images between an image of an item that may be determined as NG without any problem and an image desired to be added to the image group (learning data) regarding non-defective items, namely, an image desired to be additionally learned, for the reason that the NG determination has been made due to noise, or for some other reason).
Before the effective filing date of the claimed invention, it would have obvious to a person of ordinary skill in the art to further train the AI model with real products that can be verified. The motivation for doing so would have been to improve the results of the AI model at detecting defects. Therefore, it would have been obvious to combine Hirano with Goodwin to obtain the invention as specified in claim 1.
Referring to claim 2, Goodwin discloses measuring an accuracy of the model when trained on the synthetic data by testing the model against further synthetic data and further training the model with further synthetic data and, when an accuracy test is not passed, further training the model with further synthetic data (paragraph 105, After the model was trained, and after the above testing showed that the model was trained properly, a “final test” phase was performed. For this phase, four datasets of the same general types discussed above (“Real No Defect,” “Real Defect,” “Synthetic No Defect,” and “Synthetic Defect”) were again used, but with all images being from another source (i.e., with all images being of products different than those used in the training/validation/test phase) [i.e when testing shows that the model was not trained properly]).
Referring to claim 3, Goodwin discloses measuring an accuracy of the model when trained on the synthetic data by testing the model against further synthetic data (paragraph 105, After the model was trained, and after the above testing showed that the model was trained properly, a “final test” phase was performed. For this phase, four datasets of the same general types discussed above (“Real No Defect,” “Real Defect,” “Synthetic No Defect,” and “Synthetic Defect”) were again used, but with all images being from another source (i.e., with all images being of products different than those used in the training/validation/test phase)), and when the accuracy is passed, distributing the model to one or more manufacturing or processing facilities (paragraph 49, Once trained and qualified, the AVI neural network(s) may be used for quality control at the time of manufacture (and/or in other contexts) to detect defects).
Referring to claim 5, Goodwin discloses wherein the specification for the product comprises 2D and/or 3D technical drawings, technical specifications or images in any format (paragraph 57, VIS control module 122 controls/automates operation of visual inspection system 102 such that sample images (e.g., container images) can be generated with little or no human interaction).
Referring to claim 6, Goodwin discloses augmenting the synthetic data to provide synthetic images of products in different simulated environments (paragraph 141, Module 124 may also, or instead, use this technique to move/alter other features, such as the plunger (by digitally moving the plunger along the barrel), lyophilized vial contents (e.g., by digitally altering the fill level of the vial), and so on. In implementations where the partial convolution model is trained using target images that depict the desired feature position/appearance (i.e., the latter of the two techniques discussed above), module 124 may train and use a different model for each feature type).
Referring to claim 7, Goodwin discloses wherein the simulated environments have different lighting, noise, dust and/or vibration conditions (paragraph 119, In some deep learning implementations, module 124 inpaints images using a partial convolution model. The partial convolution model performs convolutions across the entire image, which adds an aspect of pixel noise and variation to the synthetic (inpainted) image and therefore slightly distinguishes the synthetic image from the original, even beyond the inpainted region).
Referring to claim 8, Goodwin discloses wherein the step of training an Al model comprises performing feature extraction on the synthetic data representative of acceptable and performing feature extraction on the synthetic data representative of defective products and generating and training the model in the feature domain (paragraph 76, FIG. 5 shows an example operation in which module 124 converts a feature (crack) image 500 with grayscale pixels 502 to a feature matrix 504).
Referring to claim 9, Goodwin discloses training the model to measure an aspect of a product including one or more of a dimension, a location, a colour, a pattern, a hole, and a bump (paragraph 86, At blocks 416 through 420, module 124 maps the normalized defect matrix onto the surrogate area of the container image matrix by iteratively performing a comparison for each element of the defect matrix (e.g., by scanning through the defect matrix starting at element D 11)).
Referring to claim 10, Goodwin measuring an aspect of a product to detect defects, but does not disclose expressly providing a quality score.
Hirano discloses wherein the model provides, as an output, a quality score based at least in part on the measurement (paragraph 84, The main control part 21 automatically calculates an optimum defect threshold for example by use of at least one of a parametric technique (e.g. Smirnov-Grubbs test, or the like) which is premised that a concentration to serve as an object for the statistical processing follows a regular distribution, and a non-parametric technique (e.g. a test using a box-and-whisker plot, or the like) which is premised that the concentration does not follow the regular distribution).
Before the effective filing date of the claimed invention, it would have obvious to a person of ordinary skill in the art to provide a quality score. The motivation for doing so would have been to allow a user to view results of the AI model at detecting defects. Therefore, it would have been obvious to combine Hirano with Goodwin to obtain the invention as specified in claim 10.
Referring to claim 11, Goodwin discloses wherein the inspection system captures images of products and performs feature extraction to extract features of interest from the images captured (paragraph 76, At block 404, module 124 converts the defect image and the container image into respective two-dimensional, numeric matrices, referred to herein as a “defect matrix” and a “container image matrix,” respectively).
Referring to claim 12, Goodwin discloses wherein the real products include at least one product for which no specification has been provided (paragraph 105, After the model was trained, and after the above testing showed that the model was trained properly, a “final test” phase was performed. For this phase, four datasets of the same general types discussed above (“Real No Defect,” “Real Defect,” “Synthetic No Defect,” and “Synthetic Defect”) were again used, but with all images being from another source (i.e., with all images being of products different than those used in the training/validation/test phase)).
Referring to claim 13, Goodwin discloses wherein the model is first trained on a first product using synthetic data representative of the appearance of the first product and later trained on a second product by identifying features of the second product using models of features extracted during training on the first product (paragraph 49, As another example, in an automotive context, the AVI neural network(s) may be used to detect defects in the bodywork of automobiles or other vehicles (e.g., cracks, scratches, dents, stains, etc.), during production and/or at other times (e.g., to help determine a fair resale value, to check the condition of a returned rental vehicle, etc.))
Referring to claim 14, Goodwin discloses wherein, in training the Al model to distinguish between acceptable products and defective products, respective features have respective tolerances (paragraph 4, For both deep learning and more traditional (e.g., machine vision) AVI systems, development and qualification processes that use sample image libraries should ensure that false negatives or “false accepts” (i.e., a defect is missed), as well as false positives or “false rejects” (i.e., a defect is incorrectly identified), are within tolerable thresholds).
Referring to claim 15, Goodwin discloses a system for assessing quality in a process of manufacture of or processing of a product, comprising:
means for receiving a specification for the product (paragraph 57, VIS control module 122 controls/automates operation of visual inspection system 102 such that sample images (e.g., container images) can be generated with little or no human interaction),
means for generating, from the specification, synthetic data representative of the appearance of the product when conforming to the specification (paragraph 75, Initially, at block 402, module 124 loads a defect image, and a container image without the defect shown in the defect image, into memory (e.g., memory unit 114). The container image (e.g., a syringe, cartridge, or vial similar to one of the containers shown in FIGS. 3A through 3C) may be a real image captured by visual inspection system 102 of FIG. 1 or visual inspection system 200 of FIG. 2, for example. Depending on the implementation, the real image may have been processed in other ways (e.g., cropped, filtered, etc.) prior to block 402) and, separately, the appearance of the product when defective, a processor implementing an Al model (paragraph 87, at block 424 module 124 converts the modified container image matrix to a bitmap image, and saves the resulting “defect” container image (e.g., in training image library 140)),
means for training the Al model using the synthetic data to distinguish between acceptable products and defective products, whereby the trained Al model can be used on images of real products in a manufacturing or processing facility, (paragraph 49, Once trained and qualified, the AVI neural network(s) may be used for quality control at the time of manufacture (and/or in other contexts) to detect defects), and
means for further training the model using images of real products that have been subjected to inspection using the trained Al model at the manufacturing or processing facility (paragraph 105, After the model was trained, and after the above testing showed that the model was trained properly, a “final test” phase was performed. For this phase, four datasets of the same general types discussed above (“Real No Defect,” “Real Defect,” “Synthetic No Defect,” and “Synthetic Defect”) were again used, but with all images being from another source (i.e., with all images being of products different than those used in the training/validation/test phase).
Goodwin does not disclose expressly feeding back images of real products together with ground truth data identifying them as acceptable or defective to further train the AI model.
Hirano discloses where the images are fed back to the Al model together with ground truth data identifying the images as acceptable or defective (paragraph 149, Specifically, when visual inspection is performed on the inspection object 6 in the operation mode, an NG determination is made on some images of the inspection objects 6. The user visually checks the plurality of images of items determined as NG on the display device 3 and classifies these images between an image of an item that may be determined as NG without any problem and an image desired to be added to the image group (learning data) regarding non-defective items, namely, an image desired to be additionally learned, for the reason that the NG determination has been made due to noise, or for some other reason).
Before the effective filing date of the claimed invention, it would have obvious to a person of ordinary skill in the art to further train the AI model with real products that can be verified. The motivation for doing so would have been to improve the results of the AI model at detecting defects. Therefore, it would have been obvious to combine Hirano with Goodwin to obtain the invention as specified in claim 15.
Referring to claim 16, Goodwin discloses means for communicating with one or more manufacturing or processing facilities to send the model to the manufacturing or processing facility (paragraph 55, As noted above, computer system 104 may be a distributed system, in which case one, some, or all of modules 120, 122, 124 and 126 may be implemented in whole or in part by a different computing device or system (e.g., by a remote server coupled to computer system 104 via one or more wired and/or wireless communication networks)).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Goodwin et al. US Publication 2024/0095983 and Hirano US Publication 2013/0177232 as applied to claim 1 above, and further in view of Applicant admitted prior art.
Referring to claim 4, Goodwin discloses wherein the synthetic data represents a renderable appearance of the product (paragraph 67, As used herein, the term “camera” may refer to any suitable type of imaging device (e.g., a camera that captures the portion of the frequency spectrum visible to the human eye, or an infrared camera, etc.)), but does not disclose expressly wherein the synthetic data represents a renderable ultrasound, radar or x-ray appearance of the product
Official Notice is taken that it is well known and obvious in the art to receive an image by ultrasound, radar or x-ray (See MPEP 2144.03). The motivation for doing so would have been to utilize widely available imaging devices in order to obtain images that have clear advantages over other forms of imagery. Therefore, it would have been obvious to combine well known prior art with Goodwin to obtain the invention as specified in claim 4.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PETER K HUNTSINGER whose telephone number is (571)272-7435. The examiner can normally be reached Monday - Friday 8:30 - 5:00.
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, Benny Q Tieu can be reached at 571-272-7490. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PETER K HUNTSINGER/Primary Examiner, Art Unit 2682
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