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
2. Claims 1207 are pending in this application.
Claims 1 and 11 are currently amended.
Response to Arguments
Applicant's arguments filed 03/18/2026 have been fully considered but they are not persuasive.
Applicant argues on pages 7-9 that neither Price nor Zadeh teaches or fairly suggests “a plurality of intermediate feature maps from respective different convolutions and having respectively different resolutions” as amended claim 1 recites.
However, Price teaches in Sect. [0067]-[0068], a pooling process reduces the resolution of the feature maps, thus the decoder 624 layers include up-sampling 646 layers to resize the feature maps back up to the size of the original input. The up-sampling 646 can perform different types of interpolation, such as nearest neighbor, bilinear, bicubic spline, or generalized bicubic interpolation, among other examples and a deconvolution 648 of layers of the decoder 624 perform an inverse convolution to “restore” the input feature maps that were convolved by the convolution 640 layers. The deconvolution operation may not be an attempt to truly restore an input feature map, but rather may restore an altered version of the input feature map by using a different filter than was used in convolving the input feature map. A deconvolution 648 layer can also include batch normalization of the result of the deconvolution, and/or application of non-linearity.
Thus, a taught above the prior art of Price teaches a deconvolution process that alters versions of features maps from different convolutions and the plurality of feature maps includes reduced resolutions based on a pooling process to resize the feature maps.
Thus, the prior arts of Price in view Zadeh continues to teach the amended limitations of the respective invention in the amended claims 1 and similar claim 11.
Claim Rejections - 35 USC § 103
8. 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.
9. 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.
10. The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
11. 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.
12. Claims 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Price (US PG. Pub. 2020/0364910 A1) in view of Zadeh (US PG. Pub. 2022/0121884 A1).
Referring to Claim 1, Price teaches a method (See Price, Fig. 11, Sect. [0117], Automated Image Generation Method 1100) comprising:
performing, by one or more processors (See Price, Fig. 12, Processor 1202, Sect. [0127], processor 1202 to perform the operations) of an input preprocessing computing system (See Price, Fig. 13, Sect. [0134], Computing Device 1310 of Image Editing System 1300), a plurality of convolutions upon input picture data (See Price, Sect. [0063], The convolution 640 layers perform a convolution on one or more input feature maps. The convolution layer may apply more than one filter to the set of input figure maps.);
outputting (See Price, Sect. [0052] lines 1-5, the intermediate image 416 is sufficiently similar (e.g., has fewer than 20%, 10%, 5%, or another percentage of differences or errors) to the expected output 414, and can be used as the final output of the system 400.), by the one or more processors (See Price, Fig. 12, Processor 1202, Sect. [0127], processor 1202 to perform the operations), a plurality of intermediate feature maps from respective different convolutions (See Price, Sect. [0068], The deconvolution 648 layers of the decoder 624 perform an inverse convolution to “restore” the input feature maps that were convolved by the convolution 640 layers. The deconvolution operation may not be an attempt to truly restore an input feature map, but rather may restore an altered version of the input feature map by using a different filter than was used in convolving the input feature map. A deconvolution 648 layer can also include batch normalization of the result of the deconvolution, and/or application of non-linearity) and having respectively different resolutions (See Price, Sect. [0067], Pooling reduces the resolution of the feature maps, thus the decoder 624 layers include up-sampling 646 layers to resize the feature maps back up to the size of the original input. The up-sampling 646 can perform different types of interpolation, such as nearest neighbor, bilinear, bicubic spline, or generalized bicubic interpolation, among other examples);
resizing the fused feature map to a size of the input picture data (See Price, Sect. [0067], resizing by the decoder 624 layers include up-sampling 646 layers to resize the feature maps back up to the size of the original input.).
Price fails to explicitly teach
averaging, by the one or more processors, absolute values of the plurality of intermediate feature maps to yield a fused feature map.
However, Zadeh teaches
averaging (See Zadeh, Sect. [3294] lines 6-14, feature sets (e.g., averaging) may be used in subsequent layers to effectively form a thumbnail at a higher layer. The similar feature sets (e.g., detecting geometrical shapes) would be used regardless of the scale reduction toward higher layers, to detect, for example, a small circle in a lower layer, and a larger circle at a higher layer. In one embodiment, use of various pre-learned or pre-defined feature sets helps with the regularization and reduces duplicity in learning features.), by the one or more processors, absolute values of the plurality of intermediate feature maps to yield a fused feature map (See Zadeh, Sect. [2049] lines 2-6, using the related objects list with associated probability and associated expected distance (relative or absolute values), at the main feature indicated by histogram or contrast map).
Before the effective filing date of the claimed invention, it would have obvious to a person of ordinary skill in the art to incorporate averaging, by the one or more processors, absolute values of the plurality of intermediate feature maps to yield a fused feature map. The motivation for doing so would have been to provide very efficient and fast algorithms for image processing, learning machines, NLT, pattern recognition, classification, SVM, deep/detailed analysis/discovery, applications and usages with all tools, systems, and methods provided here (See Sect. [0233] of the Zadeh reference.). Therefore, it would have been obvious to combine Price and Zadeh obtain the invention as specified in claim 1.
Referring to Claim 2, the combination of Price in view of Zadeh teaches the method of claim 1 (See Price, Fig. 11, Sect. [0117], Automated Image Generation Method 1100), further comprising performing, by the one or more processors (See Price, Fig. 12, Processor 1202, Sect. [0127], processor 1202 to perform the operations).
Price fails to explicitly teach
a Gaussian blurring transformation upon a pixel of the input picture data, the Gaussian blurring transformation taking as input a feature map value corresponding to the pixel from the fused feature map.
However, Zadeh teaches
a Gaussian blurring transformation upon a pixel of the input picture data( See Zadeh, Sect. [2950] lines 1-12, the image is blurred via convolution with a Gaussian for various transformations and/or aggregate functions used from the pixel map of the input image.), the Gaussian blurring transformation taking as input a feature map value corresponding to the pixel from the fused feature map (See Zadeh, Sect. [2633] lines 10-23, using feature points with Gaussian transformation, the system then finds local descriptors, e.g. using multi-dimensional Gabor Wavelets, for texture features on local regions, e.g. using 50-200 dimensions. In one embodiment, the system then reduces the dimensionality of the features, e.g. by 50 percent, e.g. using Principle Component Analysis (PCA) technique, to simplify the problem by reducing dimensionality and calculations. In one embodiment, the system then compares the shapes geometrically, from the extracted interesting points or features above, to find the match against the library. In one embodiment, in this stage, the system uses e.g. affine transformations for geometrical matching for shapes.).
Before the effective filing date of the claimed invention, it would have obvious to a person of ordinary skill in the art to incorporate a Gaussian blurring transformation upon a pixel of the input picture data, the Gaussian blurring transformation taking as input a feature map value corresponding to the pixel from the fused feature map. The motivation for doing so would have been to provide very efficient and fast algorithms for image processing, learning machines, NLT, pattern recognition, classification, SVM, deep/detailed analysis/discovery, applications and usages with all tools, systems, and methods provided here (See Sect. [0233] of the Zadeh reference.). Therefore, it would have been obvious to combine Price and Zadeh obtain the invention as specified in claim 2.
Referring to Claim 3, the combination of Price in view of Zadeh teaches the method of claim 2 (See Price, Fig. 11, Sect. [0117], Automated Image Generation Method 1100), wherein the feature map value corresponding to the pixel is computed (See Price, Table 2, Sect. [0072], In Table 2, the first phase (e.g., the encoder) of the restorer network produces 256 feature maps that are each ⅛ the size of the input image. The second phase (e.g., the decoder) up-samples the feature maps in stages, and applies filters that consider neighboring pixels), by the one or more processors (See Price, Fig. 12, Processor 1202, Sect. [0127], processor 1202 to perform operations), as a standard deviation value in the Gaussian blur transformation (See Price, Sect. [0088] lines 6-8, parts of the line drawing can be blurred by applying a two-dimensional Gaussian filter with the standard deviation σ ∈[0.0, 1.0].).
Referring to Claim 4, the combination of Price in view of Zadeh teaches the method of claim 2 (See Price, Fig. 11, Sect. [0117], Automated Image Generation Method 1100).
Price fails to explicitly teach
wherein the one or more processors perform a Gaussian blurring transformation upon each pixel of the input picture data.
However, Zadeh teaches
wherein the one or more processors perform a Gaussian blurring transformation upon each pixel of the input picture data (See Zadeh, Sect. [2950] lines 3-12, the image is blurred via convolution with a Gaussian for various transformations and/or aggregate functions used from the pixel map of the input image.).
Before the effective filing date of the claimed invention, it would have obvious to a person of ordinary skill in the art to incorporate wherein the one or more processors perform a Gaussian blurring transformation upon each pixel of the input picture data. The motivation for doing so would have been to provide very efficient and fast algorithms for image processing, learning machines, NLT, pattern recognition, classification, SVM, deep/detailed analysis/discovery, applications and usages with all tools, systems, and methods provided here (See Sect. [0233] of the Zadeh reference.). Therefore, it would have been obvious to combine Price and Zadeh obtain the invention as specified in claim 4.
Referring to Claim 5, the combination of Price in view of Zadeh teaches the method of claim 2 (See Price, Fig. 11, Sect. [0117], Automated Image Generation Method 1100).
Price fails to explicitly teach
wherein the plurality of convolutions are performed by the one or more processors during segmentation computations performed upon the input picture data to output an object mask.
However, Zadeh teaches
wherein the plurality of convolutions are performed by the one or more processors during segmentation computations performed upon the input picture data to output an object mask (See Zadeh, Sect. [02433], we get multimedia data, as input, which is then segmented, compressed, and stored. In addition, after segmentation step or after storage step, we summarize the data. Furthermore, after segmentation step, we extract the features and then index it, based on retrieval models in our library. In addition, after storage step and after indexing step, we display the result(s) to the user, e.g. on monitor of computer or smart phone or tablet, e.g. using user interface or GUI or browser or query engine or module or software. In one embodiment, usually, as we go from simple to more complex form of data (e.g. from text to image to video to music), we have more semantic gap between our knowledge and the meaning of the multimedia data. In one embodiment, we analyze the machine generated data, e.g. tables or lists or computer logs, for behavioral analysis for consumers for marketing purposes. In one embodiment, the retrieval is based on color, texture, or distinct points in the image (regardless of the image scale, e.g. corner of objects in the image).
Before the effective filing date of the claimed invention, it would have obvious to a person of ordinary skill in the art to incorporate wherein the plurality of convolutions are performed by the one or more processors during segmentation computations performed upon the input picture data to output an object mask. The motivation for doing so would have been to provide very efficient and fast algorithms for image processing, learning machines, NLT, pattern recognition, classification, SVM, deep/detailed analysis/discovery, applications and usages with all tools, systems, and methods provided here (See Sect. [0233] of the Zadeh reference.). Therefore, it would have been obvious to combine Price and Zadeh obtain the invention as specified in claim 5.
Referring to Claim 6, the combination of Price in view of Zadeh teaches the method of claim 5 (See Price, Fig. 11, Sect. [0117], Automated Image Generation Method 1100), further comprising modifying, by the one or more processors (See Price, Fig. 12, Processor 1202, Sect. [0127], processor 1202 to perform the operations).
Price fails to explicitly teach
the object mask to exclude each pixel of a sliding window.
However, Zadeh teaches
the object mask to exclude each pixel of a sliding window (See Zadeh, Sect. [2896] lines 13-16, for viewing different pixel regions, for field of view or window, that can be moved around, as time passes, to focus on the neighboring region(s), a sliding focused window.).
Before the effective filing date of the claimed invention, it would have obvious to a person of ordinary skill in the art to incorporate the object mask to exclude each pixel of a sliding window. The motivation for doing so would have been to provide very efficient and fast algorithms for image processing, learning machines, NLT, pattern recognition, classification, SVM, deep/detailed analysis/discovery, applications and usages with all tools, systems, and methods provided here (See Sect. [0233] of the Zadeh reference.). Therefore, it would have been obvious to combine Price and Zadeh obtain the invention as specified in claim 6.
Referring to Claim 7, the combination of Price in view of Zadeh teaches the method of claim 6 (See Price, Fig. 11, Sect. [0117], Automated Image Generation Method 1100), further comprising multiplying, by the one or more processors (See Price, Fig. 12, Processor 1202, Sect. [0127], processor 1202 to perform the operations).
Price fails to explicitly teach
the modified object mask and the input picture data to output block-based masked input picture data.
However, Zadeh teaches
the modified object mask and the input picture data to output block-based masked input picture data (See Zadeh, Sect. [1876], partial images, e.g., masked or blocked, are used for training a detection/classifier module, the image samples are prepared by masking out the portions omitted e.g., by hiding the portion of image using straight edges through the image and a randomizer generated masking parameters (e.g., the location of the mask edge). The rendering module applies the mask to the image before inputting the masked image to the recognition module. With the masked regions of the image are filled with random fill color or random texture/pattern.).
Before the effective filing date of the claimed invention, it would have obvious to a person of ordinary skill in the art to incorporate the modified object mask and the input picture data to output block-based masked input picture data. The motivation for doing so would have been to provide very efficient and fast algorithms for image processing, learning machines, NLT, pattern recognition, classification, SVM, deep/detailed analysis/discovery, applications and usages with all tools, systems, and methods provided here (See Sect. [0233] of the Zadeh reference.). Therefore, it would have been obvious to combine Price and Zadeh obtain the invention as specified in claim 7.
Referring to Claim 8, the combination of Price in view of Zadeh teaches the method of claim 7 (See Price, Fig. 11, Sect. [0117], Automated Image Generation Method 1100), wherein the one or more processors perform (See Price, Fig. 12, Processor 1202, Sect. [0127], processor 1202 to perform the operations).
Price fails to explicitly teach
a Gaussian blurring transformation upon the block-based masked input picture data.
However, Zadeh teaches
a Gaussian blurring transformation upon the block-based masked input picture data (See Zadeh, Sect. [2629], with respect to the Normal or Gaussian distribution, for each block, for the image or photo or video frame or painting or cartoon or movie or the like, to get the values as parameters, for the comparison and matching, e.g. photo matching, the system determines object shapes, histograms (e.g. for color, intensity, grey scale, or the like), range of parameters (e.g. for color, intensity, grey scale, or the like), or ratios of parameters, average of pixel values, total of pixel values, median of pixel values, rate of change of pixel values (e.g. intensity change of 20 points per pixel length in x-direction or horizontal direction), rate of change of rate-of-change of pixel values (2nd order difference or delta, or “acceleration” value), maximum value, minimum value for pixels, contrasts, patterns, standard deviation, variance, shape of distribution of the pixel values, location of distribution of the pixel values in the block, shape of the distribution for pixel values in the block .).
Before the effective filing date of the claimed invention, it would have obvious to a person of ordinary skill in the art to incorporate a Gaussian blurring transformation upon the block-based masked input picture data. The motivation for doing so would have been to provide very efficient and fast algorithms for image processing, learning machines, NLT, pattern recognition, classification, SVM, deep/detailed analysis/discovery, applications and usages with all tools, systems, and methods provided here (See Sect. [0233] of the Zadeh reference.). Therefore, it would have been obvious to combine Price and Zadeh obtain the invention as specified in claim 8.
Referring to Claim 9, the combination of Price in view of Zadeh teaches the method of claim 8 (See Price, Fig. 11, Sect. [0117], Automated Image Generation Method 1100), further comprising deciding, by the one or more processors (See Price, Sect. [0117] lines 3-8, cause the one or more processors to perform operations including the steps of the process 1100.).
Price fails to explicitly teach
based on average object mask ratio of a video sequence exceeding a threshold, to perform a Gaussian blurring transformation upon the block-based masked input picture data.
However, Zadeh teaches
based on average object mask ratio of a video sequence exceeding a threshold (See Zadeh, Sect. [2952] lines 6-16, through a user interface viewing what points/matches are up for trading. In one embodiment, an interface is provided for users to hedge against or for future point value of a match. In one embodiment, a new point value is assigned to a match based on the point trading. In one embodiment, a user interface is provided for the users to assign the matches. In one embodiment, a user interface is provided for specifying a point threshold for a match, such that when the point value reaches (or exceeds) the threshold, a process allocates the excess point to another eligible match..), to perform a Gaussian blurring transformation upon the block-based masked input picture data (See Zadeh, Sect. [2629], with respect to the Normal or Gaussian distribution, for each block, for the image or photo or video frame or painting or cartoon or movie or the like, to get the values as parameters, for the comparison and matching, e.g. photo matching, the system determines object shapes, histograms (e.g. for color, intensity, grey scale, or the like), range of parameters (e.g. for color, intensity, grey scale, or the like), or ratios of parameters, average of pixel values, total of pixel values, median of pixel values, rate of change of pixel values (e.g. intensity change of 20 points per pixel length in x-direction or horizontal direction), rate of change of rate-of-change of pixel values (2nd order difference or delta, or “acceleration” value), maximum value, minimum value for pixels, contrasts, patterns, standard deviation, variance, shape of distribution of the pixel values, location of distribution of the pixel values in the block, shape of the distribution for pixel values in the block .).
Before the effective filing date of the claimed invention, it would have obvious to a person of ordinary skill in the art to incorporate based on average object mask ratio of a video sequence exceeding a threshold, to perform a Gaussian blurring transformation upon the block-based masked input picture data. The motivation for doing so would have been to provide very efficient and fast algorithms for image processing, learning machines, NLT, pattern recognition, classification, SVM, deep/detailed analysis/discovery, applications and usages with all tools, systems, and methods provided here (See Sect. [0233] of the Zadeh reference.). Therefore, it would have been obvious to combine Price and Zadeh obtain the invention as specified in claim 9.
Referring to Claim 10, the combination of Price in view of Zadeh teaches the method of claim 8 (See Price, Fig. 11, Sect. [0117], Automated Image Generation Method 1100), further comprising deciding, by the one or more processors (See Price, Fig. 12, Processor 1202, Sect. [0127], processor 1202 to perform the operations).
Price fails to explicitly teaches
based on temporal complexity of a video sequence of a video sequence exceeding a threshold, to perform a Gaussian blurring transformation upon the block- based masked input picture data.
However, Zadeh teaches
based on temporal complexity of a video sequence of a video sequence exceeding a threshold, to perform a Gaussian blurring transformation upon the block- based masked input picture data (See Zadeh, Sect. [0674] lines 13-34, Using a Gauccian distribution, when placed at the blurry edge of A, it results in probability measure, v.sub.p5, in (0, 1) range depending on μ.sub.A(x). Such a distribution, for example, is useful for testing purposes. p.sub.6(x) demonstrates a category that encompasses portions of support or core of A, resulting in a probability measure (V.sub.p4) in (0, 1). For a category such as p.sub.1(x), the confinement is at the core of A, and therefore, the corresponding probability measure of A, v.sub.p1, is 1, (see FIG. 10(c)). Conversely, a category of distribution with little or no overlap with A, e.g., p.sub.2(x) and p.sub.3(x), have a corresponding probability measure of 0 (i.e., v.sub.p2 and v.sub.p3). The other categories resulting in probability measure (0, 1), include those such as p.sub.4(x), p.sub.5(x), and p.sub.6(x). As mentioned above, p.sub.4(x) is concentric with A, but it has large enough variance to exceed core of A, resulting probability measure (V.sub.p4) of less than 1. p.sub.5(x) resembles a delta probability distribution (i.e., with sharply defined location), which essentially picks covered values of μ.sub.A(x) as the probability measure.).
Before the effective filing date of the claimed invention, it would have obvious to a person of ordinary skill in the art to incorporate based on temporal complexity of a video sequence of a video sequence exceeding a threshold, to perform a Gaussian blurring transformation upon the block- based masked input picture data. The motivation for doing so would have been to provide very efficient and fast algorithms for image processing, learning machines, NLT, pattern recognition, classification, SVM, deep/detailed analysis/discovery, applications and usages with all tools, systems, and methods provided here (See Sect. [0233] of the Zadeh reference.). Therefore, it would have been obvious to combine Price and Zadeh obtain the invention as specified in claim 10.
Referring to Claim 11, arguments analogous to claim 1 are applicable herein. The functions of “A method” in claim 1 perform all of the operations of “A computing system” in claim 11. Thus, “A computing system” in claim11 is rejected for the same reasons as discussed in the rejection of claim 1.
Referring to Claim 12, arguments analogous to claim 2 are applicable herein. The functions of “The method” in claim 2 perform all of the operations of “The computing system” in claim 12. Thus, “The computing system” in claim12 is rejected for the same reasons as discussed in the rejection of claim 2.
Referring to Claim 13, arguments analogous to claim 3 are applicable herein. The functions of “The method” in claim 3 perform all of the operations of “The computing system” in claim 13. Thus, “The computing system” in claim13 is rejected for the same reasons as discussed in the rejection of claim 3.
Referring to Claim 14, arguments analogous to claim 4 are applicable herein. The functions of “The method” in claim 4 perform all of the operations of “The computing system” in claim 14. Thus, “The computing system” in claim14 is rejected for the same reasons as discussed in the rejection of claim 4.
Referring to Claim 15, arguments analogous to claim 5 are applicable herein. The functions of “The method” in claim 5 perform all of the operations of “The computing system” in claim 15. Thus, “The computing system” in claim15 is rejected for the same reasons as discussed in the rejection of claim 5.
Referring to Claim 16, arguments analogous to claim 6 are applicable herein. The functions of “The method” in claim 6 perform all of the operations of “The computing system” in claim 16. Thus, “The computing system” in claim16 is rejected for the same reasons as discussed in the rejection of claim 6.
Referring to Claim 17, arguments analogous to claim 7 are applicable herein. The functions of “The method” in claim 7 perform all of the operations of “The computing system” in claim 17. Thus, “The computing system” in claim17 is rejected for the same reasons as discussed in the rejection of claim 7.
Referring to Claim 18, arguments analogous to claim 8 are applicable herein. The functions of “The method” in claim 8 perform all of the operations of “The computing system” in claim 18. Thus, “The computing system” in claim18 is rejected for the same reasons as discussed in the rejection of claim 8.
Referring to Claim 19, arguments analogous to claim 9 are applicable herein. The functions of “The method” in claim 9 perform all of the operations of “The computing system” in claim 19. Thus, “The computing system” in claim19 is rejected for the same reasons as discussed in the rejection of claim 9.
Referring to Claim 20, arguments analogous to claim 10 are applicable herein. The functions of “The method” in claim 10 perform all of the operations of “The computing system” in claim 20. Thus, “The computing system” in claim 20 is rejected for the same reasons as discussed in the rejection of claim 10.
Cited Art
13. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Castro Mejia et al. (US PAT. No. 11,734,361 B1) discloses systems and methods for recognizing and categorizing documents. In some embodiments, a computing system can access an archetype template and a corresponding label for each targeted category. The computing system can analyze a set of target binary documents based on a set of sequenced and contextually triggered hashing operations. The target binary documents can be categorized based on comparing the analysis results to the archetype templates or results derived from the archetype templates.
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 DARRYL V DOTTIN whose telephone number is (571)270-5471. The examiner can normally be reached M-F 9am-5pm.
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/DARRYL V DOTTIN/Primary Examiner, Art Unit 2683
/DARRYL V DOTTIN/Primary Examiner, Art Unit 2683
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