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 .
Status of Claims
This action is in reply to the claims filed on 04/09/2026.
Claims 1, 6, 11, 18, 19 and 20 are amended.
Claims 2, 4, 5, and 10 are cancelled.
Claims 21-24 are newly added.
Claims 1, 3, 6-9, and 11-24 are currently pending and have been examined.
Claim Rejections- 35 U.S.C. § 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, 3, 6-9, and 11-24 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Step 1 of the subject matter eligibility (SME) analysis described in MPEP 2106.03, the instant claims fall within the four statutory categories of invention identified by 35 U.S.C. 101. In the instant case, claims 1-17 are directed to a method, claims 18-19 and 21-24 are directed to a system, and claim 20 is directed to a manufacture. Claims 1, 18, and 20 are parallel in nature, therefore, the analysis will use claim 1 as the representative claim.
In Step 2A Prong One, it must be considered whether the claims recite a judicial exception. Claim 1, as exemplary, recites abstract concepts including: receiving a product image, analyzing the product image ... to determine particular parameters of the background of the product image; determining, based on the determined particular parameters of the background of the product image using ... parameters of existing product images, whether the product image is suitable; and providing output based on whether the product image is suitable.
These identified limitations recite the abstract idea of “analyzing a product image and providing output based on whether the product image is suitable”, which falls within the “Certain Methods of Organizing Human Activities” grouping of abstract ideas as assessing a product image for suitability is a fundamental economic practice long prevalent in our system of commerce. Accordingly, claims 1, 18, and 20 recite an abstract idea. See MPEP 2106.04.
In Step 2A Prong Two, examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application.
Instant claims 1, 18, and 20 recite additional elements including: a computer; a digital raster image defined by an array of pixels; using a segmentation process to locate a pixel boundary between a product and a background in the product image; a trained product image model; a computer system comprising: at least one processor and a memory storing instructions; and a non-transitory computer-readable storage medium storing instruction thereon. As explained in MPEP 2106.05(f), use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. The computer, computer system, and non-transitory computer-readable storage medium are recited at a high-level of generality such that they amounts to no more than “apply it” or mere instruction to implement the abstract idea on a computer. The limitations specifying that the product image is a digital raster image defined by an array of pixels, and the analyzing uses a segmentation process to locate a pixel boundary between a product and a background in the product image also do not add meaningful limits on practicing the abstract idea. These limitations link the abstract image analysis to a computerized environment using existing technology, raster images and segmentation, as tools used in their ordinary capacity to implement the abstract idea. The combination of these additional elements amounts to no more than implementing the identified abstract idea in a generic computer environment. Claims 1, 18, and 20 are thus directed to an abstract idea.
Under Step 2B of the SME analysis, if it is determined that the claims recite a judicial exception that is not integrated into a practical application of that exception, it is then necessary to evaluate the additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) individually and in combination are merely being used to apply the abstract idea to a general computer components. For the same reason, the elements are not sufficient to provide an inventive concept. As explained in MPEP 2106.05(f), implementing an abstract idea with a generic computer does not add significantly more in Step 2B. Therefore, the additional elements, alone or in ordered combination, there is no inventive concept in the claim, and thus claims 1, 18, and 20 are not patent eligible.
Dependent claim(s) 3, 7-9, 11, 15-17, 19, and 22 do not aid in the eligibility of the independent claims. These claims merely further define the abstract idea without reciting any further additional elements. Thus dependent claims 3, 7-9, 11, 15-17, 19, and 22 are also ineligible.
Dependent claim 12-14 recite additional elements including: wherein the product image model is or uses a machine learning model; wherein the machine learning model is or uses a neural network; and training the product image model ... wherein training the product image model includes providing parameters of existing product images as inputs to the neural network. These additional elements do not integrate the abstract idea into a practical application because they merely amount to no more than a general link of the use of the abstract idea to a particular technological environment or field of use (i.e., implementation via existing machine learning technology). Even in combination, these additional elements do not act to integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. Thus claims 12-14 are also ineligible.
Dependent claims 21 and 23-24 recite additional elements including: wherein the product image model is trained for at least one of a particular product type, a particular merchant, or a particular region; wherein the product image model is or uses a machine learning model, and wherein the machine learning model is or uses a neural network; and train the product image model based on the parameters of the existing product images, wherein training the product image model includes providing parameters of the existing product image as inputs to the neural network. The recited training steps fail to recite details of how a solution to a problem is accomplished and recite only the idea of a solution or outcome. Training a ML model on particular datasets, and training a neural network by providing it with existing (training) data are incident to machine learning. Furthermore, the limitations reciting that the product image model uses a ML model or neural network do no more than generally link the use of the abstract idea to a particular technological environment. Limitations that merely indicate a field of use or provide only a results-oriented solution lacking details as to how the computer achieves the outcome do not integrate an abstract idea into a judicial exception or recite significantly more in Step 2B. See MPEP 2106.05(f) and (h).
Claim Rejections - 35 U.S.C. § 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.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The 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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3, 6, 9, and 11-25 are rejected under 35 U.S.C. 103 as being unpatentable over Pyati (US 2019/0311301 A1) in view of Jain et al. (US 2020/0286151 A1).
Claim 1, Pyati discloses a computer-implemented method comprising:
receiving a product image, the product image being a digital raster image defined by an array of pixels (¶ [0104] “Graphical user interface 300D includes a user interface element, photos and videos section 320, for adding, uploading, editing, deleting, and otherwise managing a set of photos, videos, or other image data for the item listing”; ¶ [0105] Examiner notes that features such as image resolution are exclusive to raster images and therefore necessarily include a digital raster image defined by pixels.);
analyzing the product image including using a segmentation process to locate a pixel boundary between a product and a background in the product image ... (¶ [0034] “its background may comprise image data”; ¶ [0106] “the online marketplace may also be capable of evaluating the content of the photos and videos themselves using computer-vision (CV) techniques. For example, the online marketplace may process the photos and videos to extract lower-level image features, such as points, edges, or regions of interest.”; ¶ [0108] “Techniques for identifying region-based features include segmentation”);
determining, based on ... the product image using a product image model trained based on parameters of existing product images, whether the product image is suitable (¶ [0104] “a machine learning system capable of evaluating an item listing based on current inputs and evaluating the item listing by substitute one or more of the current inputs”; ¶ [0132] “For example, the computing system can receive attribute values for attributes of previous item listings, extract feature values and features from the attributes and attribute values, and build a feature vector for each previous item listing using the extracted features and feature values. The system can apply the feature vectors and target objective to a machine learning algorithm to generate the machine learning model.”); and
providing output based on whether the product image is suitable (¶ [0104] “Photo and video section 320 also includes photo and videos recommendation 322, a user interface element that offers suggestions to the user on how the user can change the set of photos or videos for the item listing ... to achieve a target objective (e.g., maximizing a selling price for the item)”).
Pyati does not explicitly disclose determine particular parameters of the background of the product image. However, Jain – which like Pyati is directed to optimizing product listings by analyzing associated product images – further teaches:
analyzing the product image ... to determine particular parameters of the background (Jain ¶ [0038] “Using the functionality of the tagging module 120, the content sharing system 104 processes the background content to identify various characteristics, such as those noted above. Based on this identification, the tagging module 120 generates the descriptive tags 132 for the background content 130. For each content item of the background content 130, for instance, the tagging module 120 generates a respective list of the descriptive tags 132 that describe the characteristics identified by the tagging module 120.”);
determining, based on the determined particular parameters of the background ... whether the product image is suitable (Jain ¶ [0055] “In one or more implementations, the scene compatibility module 206 also incorporates performance measures of the background content 130 into the scene compatibility score 212, such that the scene compatibility score 212 can also reflect influence of this content to cause conversion of listed items, e.g., how well background content combined with the listed items causes conversion.”; ¶ [0060] “The scene compatibility module 206 can be trained not only to weight tags because they describe characteristics common to well-performing content items”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the determination of particular parameters of the background, as taught by Jain, in the image analysis of Pyati in order to reduce the reliance on human users to select and upload digital visual content for inclusion with listings that optimizes conversions of those listings (Jain ¶ [0022]).
Claim 3 – Pyati in view of Jain teaches the method of 1. Pyati further discloses wherein the product image model is trained for at least one of a particular product type (¶ [0040]; FIG. 2), a particular merchant, or a particular region.
Claim 6 – The combination of Pyati in view of Jain teaches the method of claim 1. Pyati does not teach the following limitations, however Jain further teaches: wherein the output is based on a consistency of the background with backgrounds of other product images (Jain ¶ [0046] “the scene compatibility score 212 allows each of these candidate background content 130 items to be compared, e.g., to identify images that have better scene compatibility with a product being listed than other images.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the background consistency as taught by Jain in the method of Pyati in order to reduce the reliance on human users to select and upload digital visual content for inclusion with listings that optimizes conversion of those listings (Jain ¶ [0022]).
Claim 9 – Pyati in view of Jain teaches the method of 1. Pyati further discloses, wherein the output is based on an image resolution of the product image (¶ [0105]).
Claim 11 – Pyati in view of Jain teaches the method of 1. Pyati further discloses, wherein the ... parameters of the background of the product image are input into the product image model to produce an estimate of the quality of the product image (¶ [0034] “For example, item specific feature extractor 108 can include objection recognition functionality for identifying an item represented in an image or video, the color of the item, the age or condition of the item, the make and model of the item, or other item specific. Alternatively or in addition, text may include icons, its background may comprise image data”; ¶ [0094] “Evaluation stage 270 can commence upon the system transmitting feature vectors 262 as input to machine learning model 228”).
Pyati does not explicitly disclose the determined particular parameters of the background, however Jain further teaches the determined particular parameters of the background (¶ [0038] and inputting them into a product image model to estimate a quality of the product image (¶ [0055]; ¶ [0060]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the determination of particular parameters of the background, as taught by Jain, in the image analysis of Pyati in order to reduce the reliance on human users to select and upload digital visual content for inclusion with listings that optimizes conversions of those listings (Jain ¶ [0022]).
Claim 12 – Pyati in view of Jain teaches the method of 11. Pyati further discloses, wherein the product image model is or uses a machine learning model (¶ [0104]).
Claim 13 – Pyati in view of Jain teaches the method of 12. Pyati further discloses wherein the machine learning model is or uses a neural network (¶ [0069]; ¶ [0133]).
Claim 14 – Pyati in view of Jain teaches the method of 13. Pyati further discloses, comprising: training the product image model based on the parameters of the existing product images, wherein training the product image model includes providing parameters of the existing product images as inputs to the neural network (¶¶ [0068]-[0069] description of supervised learning methods, including neural networks, being trained using pre-labeled data such as previous listings).
Claim 15 – Pyati in view of Jain teaches the method of 1. Pyati further discloses,, wherein the determination as to whether the product image is suitable includes a determination as to the consistency of the product image with other product images (¶ [0095])
Claim 16 – Pyati in view of Jain teaches the method of claim 1. Pyati further discloses wherein the output includes an indication as to how the product image could be modified to improve the product image (¶ [0104] “suggestions to the user on how the user can change the set of photos or videos for the item listing”)
Claim 17 – Pyati in view of Jain teaches the method of claim 16. Pyati further discloses wherein the output is a recommendation for improving the quality of the product image (¶ [0104] see suggestions to achieve a target objection such as maximizing a selling price for the item).
Claim 18, which is directed to a system, recites limitations that are parallel in nature as those addressed above for method claim 1. Claim 18 is therefore rejected for the same reasons as set forth above for claim 1.
Claim 19 – Pyati in view of Jain teaches the system of claim 18. Pyati further discloses: wherein the output is based on the background of the product image (¶ [0034]; ¶ [0094]) and at least one of: whether the product image is blurry, whether the entire product is in focus in the product image, an image resolution of the product image (¶ [0105]), or a particular parameter of the product image corresponding to a portion of the product that is in view in the product image (¶ [0106]).
Claim 20, which is directed to a non-transitory computer-readable medium, recites limitations that are parallel in nature as those addressed above for method claim 1. Claim(s) 20 is therefore rejected for the same reasons as set forth above for claim 1.
Claim 21– Pyati in view of Jain teaches the system of claim 18 The computer system of claim 18, wherein the product image model is trained for at least one of a particular product type, a particular merchant, or a particular region (Jain ¶ [0035] “ Regardless, the tagging module 120 represents functionality to process digital visual content, such as images and videos, identify characteristics of the digital visual content, and generate a list of text tags that describe the digital visual content. In accordance with the described techniques, these digital content characteristics include recognized items, such as items recognized using object recognition techniques. These digital content characteristics also include recognized environments, such as ‘kitchen,’ ‘living room,’ ‘office,’ ‘forest,’ ‘desert,’ ‘mountains,’ ‘desk space,’ ‘counter space,’ and so on”; ¶ [0061] “The scene compatibility module 206 can use any type of machine learning techniques capable of learning how the presence of different tags describing digital visual content correlates to suitability as a background for an item to be listed, e.g., to learn how the presence of a tag correlates to conversion of listings that use the background”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the training for a particular product type and region, as taught by Jain, in the image analysis of Pyati in order to reduce the reliance on human users to select and upload digital visual content for inclusion with listings that optimizes conversions of those listings (Jain ¶ [0022]).
Claim 22 – Pyati in view of Jain teaches the system of claim 18. Pyati does not disclose limitations associated with a consistency of backgrounds, however Jain further teaches: wherein the output is based on a consistency of the background with backgrounds of other product images (Jain ¶¶ [0059]-[0060] “Using this table, the scene compatibility module 206 can further apply performance weights to the above discussed associations of background content tags, such as to apply more weight to associations computed for tags that are used frequently in content similar to the well-performing content”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the background consistency, as taught by Jain, in the output of Pyati in order to reduce the reliance on human users to select and upload digital visual content for inclusion with listings that optimizes conversions of those listings (Jain ¶ [0022]).
Claim 23 – Pyati in view of Jain teaches the system of claim 18. Pyati further discloses wherein the product image model is or uses a machine learning model, and wherein the machine learning model is or uses a neural network (¶ [0069] “Some examples of supervised learning algorithms include ... neural networks, decision trees/random forests, support vector machines (SVMs), among others”).
Claim 24 – Pyati in view of Jain teaches the system of claim 23. Pyati further disclose wherein the instructions, when executed by the at least one processor, will further cause the computer system to:
train the product image model based on the parameters of the existing product images, wherein training the product image model includes providing parameters of the existing product images as inputs to the neural network (¶ [0069] “supervised learning algorithms include ... neural networks”; ¶ [0073] “Machine learning modeler 120 can also segment the input data set (e.g., previous item listings) into a training set and a testing set. The training set can include a subset of the previous item listings applied to a machine learning algorithm to identify the parameters and functions for processing new item listings to achieve a target objective (e.g., generating a machine learning model)”).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Pyati in view of Jain, and further in view of Glasgow (US 2014/0104379 A1).
Claim 7 – The combination of Pyati in view of Jain teaches the method of claim 1. Pyati does not disclose limitations associated with whether an image is blurry, however Glasgow – which like Pyati is directed to generating recommendations for product images – further teaches, wherein the output is based on whether the product image is blurry (Glasgow ¶ [0059] “the system could examine a photograph to determine ... whether appropriate sharpness (i.e., not blurry) has been achieved”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the image blurriness as taught by Glasgow in the method of Pyati so that photographs are taken to effectively list an item for sale (Glasgow ¶ [0018]).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Pyati in view of Jain, and further in view of Ertle et al. (US 2019/0122384 A1).
Claim 8 – Pyati discloses the method of claim 1. Pyati does not explicitly disclose wherein the output is based on whether the entire product is in focus, however Ertle –which is also directed to determining how product images could be improved – further teaches: wherein the output is based on whether the entire product is in focus in the product image (Ertle ¶ [0074] “For example, the user may be prompted to move, rotate, or adjust the focus of the image capturing device to better capture the product. As another example, the user may be prompted to more closely align the item captured by the image capturing device with a field (or outline) displayed on the user interface, so as to assist the user in properly capturing the item within the image data. As yet another example, the user may be prompted via the user interface to capture the image of the product within the a target area, such as overlay 605”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the output based on whether the entire product is in focus, as taught by Ertle, in the method of Pyati because there is a need for an improved image recognition system that is capable of efficiently obtaining image data to be used as training and verification data for machine learning techniques such that the system may detect objects within image data with a certain level of confidence (Ertle ¶ [0004]).
Response to Arguments
Applicant's arguments filed 04/09/2026 with respect to the 35 U.S.C. § 101 rejections have been fully considered but they are not persuasive.
On page 6 of the Remarks, Applicant argues “claim 1 does not recite ‘analyzing a product image and providing output based on whether the product is suitable” and the amended limitations “are not reciting an abstract idea but are setting forth concrete technical features that pertain to a technical image processing operation and have nothing to do with economic practice or ‘Certain Methods of Organizing Human Activities”.
The Examiner respectfully disagrees. In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. While the terms "set forth" and "described" are thus both equated with "recite", their different language is intended to indicate that there are two ways in which an exception can be recited in a claim. For instance, the claims in Diehr, 450 U.S. at 178 n. 2, 179 n.5, 191-92, 209 USPQ at 4-5 (1981), clearly stated a mathematical equation in the repetitively calculating step, and the claims in Mayo, 566 U.S. 66, 75-77, 101 USPQ2d 1961, 1967-68 (2012), clearly stated laws of nature in the wherein clause, such that the claims "set forth" an identifiable judicial exception. Alternatively, the claims in Alice Corp., 573 U.S. at 218, 110 USPQ2d at 1982, described the concept of intermediated settlement without ever explicitly using the words "intermediated" or "settlement." See MPEP 2106.04(II)(A)(1).
Claim 1 clearly states “determining ... whether the product image is suitable” and “providing output based on whether the product image is suitable” which is a fundamental economic activity and therefore falls under the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The claims set forth or describe assessing or the act of evaluating, estimating, or judging whether the product image is suitable because the claims recite analyzing the product image to determine particular parameters of the background and then using those parameters to determine whether the product is suitable.
Limitations beyond the recited abstract idea, such as “using a segmentation process to locate a pixel boundary between a product and a background in the product image” do not change the conclusion that the claim recites an abstract in prong I. As explained in the rejection, when the additional elements do not provide integration into a practical application in prong II, nor do they recite significantly more than the abstract idea itself in Step 2B.
For at least these reasons, the Examiner is maintaining the § 101 rejections of claims 1, 3, 6-9, and 11-24.
Applicant's arguments filed 04/09/2026 with respect to the 35 U.S.C. § 102/103 have been fully considered but they are not persuasive. On page 8 of the Remarks, Applicant argues “There is no locating a pixel boundary between a product and a background in a product image to determine particular parameters of the background of the product image in Pyati and Jain”.
The Examiner respectfully disagrees. Pyati in paragraph [0108] states “[t]echniques for identifying region-based features include segmentation”. One of ordinary skill in the art would recognize image segmentation as including processing pixels to identify object boundaries and background regions (see “Conventional image segmentation algorithms process high-level visual features of each pixel, like color or brightness, to identify object boundaries and background regions” in Bergmann, Dave). The invention of Pyati is directed to item listings which include “views of items in photos and video” or product images (Pyati ¶ [0105]). Accordingly, Pyati discloses locating a pixel boundary between a product and a background in a product image.
Pyati does not explicitly disclose performing the pixel locating to determine particular parameters of the background of the product image, however Jain teaches such a result in at least paragraph [0038] (“Using the functionality of the tagging module 120, the content sharing system 104 processes the background content to identify various characteristics, such as those noted above. Based on this identification, the tagging module 120 generates the descriptive tags 132 for the background content 130”). One of ordinary skill in the art would have modified the segmentation of Pyati to include identification of background parameters, as taught by Jain, because both references are directed to optimizing item listings, both rely on image segmentation, both identify background image data (Pyati ¶ [0034]), and Jain explains in paragraph [0002] that conventional backgrounds may not be effective to cause optimal conversion of the depicted product.
For at least these reasons, the Examiner is maintaining a 103 rejections of claim 1 and the other independent claims over Pyati in view of Jain.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Wróblewska et al. (NPL Reference U) discusses business quality indicators for photos of products in search listings, as well as using frame detection techniques during photo analysis.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 KENNEDY A GIBSON-WYNN whose telephone number is (571)272-8305. The examiner can normally be reached M-F 8:30-5:30 PM.
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/K.G.W./Examiner, Art Unit 3688
/KELLY S. CAMPEN/Primary Examiner, Art Unit 3691
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