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 .
Response to Arguments
In view of the amendment to the title, the objection to the specification is withdrawn.
In view of the amendment dated 9/26/2025, the 35 USC 112(b) rejection is withdrawn.
In view of the amendment dated 9/26/2025, the 35 USC 112(f) interpretation is withdrawn.
Applicant's arguments directed at the 35 USC 101 abstract idea rejection have been fully considered but they are not persuasive. The applicant argues, beginning on page 11 of remarks that the limitations to “… to perform image extraction that involves image reduction, region extraction of target structure and corresponding images, and to subsequently generate G2 is not the type tasks that are practically performed in a human mind.” The Examiner respectfully disagrees with the applicant’s arguments in view of the presented limitations in claims 1-9. Although examples 47-49 provide examples of patent eligible claim construction that incorporates AI models and training thereof, the presently presented limitations are more similar to claim 2 of example 47 or claim 1 of example 49 in which both are found to be ineligible. The Examiner notes that the claimed limitations of claims 1-4, 6 and 8 are not explicitly directed at training an AI model.
The applicant argues on page 11 that the invention is aimed to achieve “higher speed and accuracy when extracting objects from an input image.” While this may be the intended result, the limitations are presented provide no inherent or explicit realization of this aim sufficient to overcome the abstract idea rejection.
The inclusion of “an extraction model” in at least the independent claims does not change the assessment of the claimed limitations under the mental process evaluation because the human mind itself is an extraction model that has been trained by experience to accomplish the limitations claimed though observation, evaluation, judgment and opinion. The reduction of the target image has no claimed bounds on what that reduction is representative of. The human mind can focus on a portion of a given image thereby mentally producing a reduced image. The human mind can, in examination of the portion of the target image, the reduced image, extract regions or features from that portion thereby recognizing, i.e., extracting, a particular structure. The human mind can then, with this particular structure in mind, search over the target image to see if it otherwise contains a similar structure. The human mind has captured the image as a whole as well as mentally extracted a particular structure found in a reduced portion to potentially recognize, e.g., extract, to see if it otherwise contains a similar structure. The claimed limitations are not presented with sufficient details, i.e., at a high-level of generality, to overcome their performance in the human mind.
Further, it is noted that in the input step in each independent, other than inputting the corresponding image and the reduced structure image (both of which the human mind can remember), there is no explicit language of the argued generation of a second region. The language “to extract … to generate” is merely an intended use because neither the extract or generate is actually recited as an active action.
The 35 USC 101 rejection is maintained.
Applicant's arguments directed at the 35 USC 102 rejection have been fully considered but they are not persuasive. The applicant argues on pages 12-13 that the application of De Nigris et al., is invalid as prior art. The Examiner respectfully disagrees. Although the effective filing date of De Nigris is 9/28/2021, which is after the foreign priority date of the instant application, De Nigris filed provisional application dated 10/7/2020 (as shown on page 1 of the cited publication via the USPTO patent search tool) that is prior to the foreign priority date of the instant invention and therefore, is valid prior art. The applicant has not argued any inaccuracy of the cited portions of De Nigris as applied by the Examiner nor how the amended limitations and non-amended limitations are not taught by De Nigris. Therefore, the Examiner interprets the silence as indicative of De Nigris being properly and accurately disclosing the claimed limitations.
The application of De Nigris as prior art is maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-4, 6 and 8 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “… extract a corresponding image corresponding to the reduced structure image from the target image via the extraction model.” This limitation is indefinite because it can be interpretated in more than one way. Is the corresponding image extracted from the reduced structure image or is the corresponding image extracted from the target image? For purpose of examination, the Examiner interprets the limitation to mean the corresponding image is extracted from the target image.
Dependent claims 2-4 are rejected based on their dependency on claim 1. Independent claims 6 and 8 are rejected likewise as claim 1.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a mental without significantly more. The claim(s) recite(s) evaluating an existing image, identifying (extracting) a particular object (structure) and inputting the information into some model. This judicial exception is not integrated into a practical application because the claimed limitations can be performed in the human mind through observation, evaluation, judgment and opinion. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claimed limitations do not integrate the mental process into a practical application.
Under its broadest reasonable interpretation, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the calculations only involve the referenced gathered data.
To distinguish ineligible claims that merely recite a judicial exception from eligible claims that require an implementation of judicial exception, the Supreme Court uses a two-step framework: Step One (Step 2A), determine whether the claims at issue are directed to one of those patent-ineligible concepts; and Step Two (Step 2B), if so, ask “what else is there in the claims?” to determine whether the additional elements transform the nature of the claim into a patent eligible application.
Under the 2019 Revised Patent Subject Matter Eligibility Guidance, the first step / Prong One of Step One (Step 2A) to determine patent eligibility requires the determination of whether the claims at issue are directed to an enumerated patent ineligible concept.
Prong (1) requires the determination of (a) the specific limitations in the claim under examination (individually or in combination) that the examiner believes recites an abstract idea and (b) determining whether the identified limitations falls within the subject matter groupings of abstract ideas enumerated.
The enumerated patent ineligible concepts comprising:
(a) Mathematical Concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
(b) Certain methods of organizing human activity – fundamental economic principles / practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules / instructions) and
(c) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
Prong (2) asks does the claim recite additional elements that integrate the judicial exception into a practical application? For a claim reciting a judicial exception to be eligible, the additional elements (if any) in the claim must "transform the nature of the claim" into a patent-eligible application of the judicial exception, Alice Corp., 573 U.S. at 217, 110 USPQ2d at 1981.
The second step (Step 2B) is to determine whether the claim recites additional elements that amount to an inventive concept (aka “significantly more”) than the recited judicial exception.
Claim 1 is directed to an image processing apparatus comprising at least one processor:
A - wherein the processor is configured to: acquire a target image;
B - reduce the target image to derive a reduced image of the target image;
C - extract a first region of a target structure from the reduced image to derive a reduced structure image including the region of the target structure via an extraction model;
D - extract a corresponding image corresponding to the reduced structure image from the target image via the extraction model; and
E - input the corresponding image and the reduced structure image into an extraction model constructed by machine-learning a neural network to extract a second region of the target structure included in the corresponding image from the extraction model to generate an extracted image which includes the second region.
Step A is data gathering and is insignificant. Step B uses step A by changing its size appearance which can be done in the human mind by moving an image from view close to viewing at a distance or using a simple tool such as a magnifying glass. Step C identifies an object in the result of step B that can be accomplished using observation in the human mind. Step D corresponds the object of step C with an existing object of data used in step B. Step E inputs the result of step D into a model (technological environment) which could be the human mind’s ability to render a judgment based on observation. The claimed extraction model of steps C-E is recited at a high-level of generality and is not sufficient to qualify as significantly more.
Step 1 – yes, the claim is directed to a statutory category of a machine.
Step 2A Prong 1 – yes, the claim is directed to an abstract idea. The mere nominal recitation of a generic processor does not take the claim limitation out of the mental processes grouping. Further, the claimed extraction model is also recited at a high-level of generality and is not sufficient to qualify as significantly more.
Step 2A Prong 2 – no, there are no additional elements that integrate the abstract idea into a practical application. It’s noted that no work or use occurs at step D.
Step 2B – no, the claim does not recite additional elements that amount to an inventive concept more than the recited judicial exceptions.
Claim 2 is directed to the image processing apparatus of claim 1:
F - wherein the extraction model consists of a plurality of processing layers that perform convolution processing and an input layer has two channels, and the processor is configured to:
G - enlarge the reduced structure image to the same size as the corresponding image to derive an enlarged structure image; and
H - input the enlarged structure image and the corresponding image respectively to the two channels of the input layer of the extraction model.
Step F describes an abstract model. Step G manipulates the image of step C which can be done in the human mind with a minimal aid such as a magnifying glass. Step H inputs the result of step G into a model (technological environment) which could be the human mind’s ability to render a judgment based on observation.
Step 1 – yes, the claim is directed to a statutory category of a machine.
Step 2A Prong 1 – yes, the claim is directed to an abstract idea. The mere nominal recitation of a generic processor does not take the claim limitation out of the mental processes grouping.
Step 2A Prong 2 – no, there are no additional elements that integrate the abstract idea into a practical application. It’s noted that no work or use occurs at step E.
Step 2B – no, the claim does not recite additional elements that amount to an inventive concept more than the recited judicial exceptions.
Claim 3 is directed to the image processing apparatus of claim 1:
I - wherein the neural network consists of a plurality of processing layers that perform convolution processing, and the processing layer that processes an image having the same resolution as the reduced structure image has an additional channel for inputting the reduced structure image, and
J - the processor is configured to input the reduced structure image to the additional channel.
Step I describes the network and parameters of step E. Step J inputs the result of step C into the model (technological environment) of step E which can be the human mind’s ability to render a judgment based on observation
Step 1 – yes, the claim is directed to a statutory category of a machine.
Step 2A Prong 1 – yes, the claim is directed to an abstract idea. The mere nominal recitation of a generic processor does not take the claim limitation out of the mental processes grouping.
Step 2A Prong 2 – no, there are no additional elements that integrate the abstract idea into a practical application. It’s noted that no work or use occurs at step H.
Step 2B – no, the claim does not recite additional elements that amount to an inventive concept more than the recited judicial exceptions.
Claim 4 is directed to the image processing apparatus of claim 1:
K - wherein the processor is configured to: divide the region of the target structure extracted from the reduced image and derive a divided and reduced structure image including each of the divided regions of the target structure;
L - derive a plurality of divided corresponding images corresponding to the respective divided and reduced structure images from the corresponding image; and
M - extract the region of the target structure included in the corresponding image in units of the divided corresponding image and the divided and reduced structure image.
Steps K-M are mental processes that can be accomplished through observation and judgment.
Step 1 – yes, the claim is directed to a statutory category of a machine.
Step 2A Prong 1 – yes, the claim is directed to an abstract idea. The mere nominal recitation of a generic processor does not take the claim limitation out of the mental processes grouping.
Step 2A Prong 2 – no, there are no additional elements that integrate the abstract idea into a practical application.
Step 2B – no, the claim does not recite additional elements that amount to an inventive concept more than the recited judicial exceptions.
Claim 5 is directed to a learning apparatus:
M - at least one processor, wherein the processor is configured to: acquire a reduced structure image including a first region of a target structure, wherein the reduced structure image is derived based on a reduced image of a target image by extracting the first region of the target structure from the reduced image;
N - acquiring a corresponding image which is extracted from the target image and corresponds to the reduced structure image; and
O - construct an extraction model that is configured to extract an extracted image which includes, a second region of the target structure from the corresponding image, by machine-learning a neural network using, as supervised training data, a first image including the first region of the target structure extracted from the reduced structure image the target image including the target structure, a second image corresponding to the corresponding image, and correct answer data representing an extraction result of the target structure from the second image.
Steps M and N are a data gathering steps and are not enough to qualify as significantly more. Step O is a description of creating an abstract model that can be accomplished by the human mind by observation of related images and judgment regarding their content.
Step 1 – yes, the claim is directed to a statutory category of a machine.
Step 2A Prong 1 – yes, the claim is directed to an abstract idea. The mere nominal recitation of a generic processor does not take the claim limitation out of the mental processes grouping.
Step 2A Prong 2 – no, there are no additional elements that integrate the abstract idea into a practical application. It’s noted that no work or use occurs at step M.
Step 2B – no, the claim does not recite additional elements that amount to an inventive concept more than the recited judicial exceptions.
Claims 6 and 8 are executed or performed by the abstract idea of claim 1 and are therefore likewise rejected.
Claims 7 and 9 are executed or performed by the abstract idea of claim 5 and are therefore likewise rejected.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-9 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by De Nigris et al., (US Pub No. 20230386067).
Claim 1: De Nigris discloses an image processing apparatus [Abstract] comprising
at least one processor, wherein the processor is configured to: acquire a target image [receive a high-resolution image 107 as an input, p0020];
reduce the target image to derive a reduced image of the target image [any suitable downsampling algorithm can be implemented in downsampler 211, provided that it produces low-resolution images and segmentations that are scaled to a smaller size than the corresponding high-resolution images and segmentations, such as ½ the size, ¼ the size, etc., p0022 & p0030];
extract a first region of a target structure from the reduced image to derive a reduced structure image including the region of the target structure via an extraction model [the plurality of images can all correspond to images of the same object and/or anatomical structure to which the neural network is to be trained/fitted … Each segmentation can thus be taken as an accurate representation of the boundaries of the various anatomical regions in the 3D image to which it corresponds. Preferably, the segmentations have not been simplified, and include a high number of identified parts or regions (for example 100+ anatomical regions in the case of a brain MRI scan) … downsampled low-resolution image 213 can then be provided to the low-resolution module 103 which can generate a low-resolution inference 219 therefrom in the form of a segmentation of the low-resolution image 213 … feature maps 217 correspond to an intermediate output of the low-resolution module 103, p0023-0024, p0027-0028, p0031 & p0036];
extract a corresponding image corresponding to the reduced structure image from the target image via the extraction model [low-resolution feature maps 217 are provided as input to high-resolution module 105 … Once an anatomy is chosen, a voxel corresponding to that anatomy can then be randomly selected as the center of the patch … a patch sampler 225 is provided to extract a high-resolution image patch 207a and high-resolution segmentation patch 209a from the high-resolution image 207 and segmentation 209 … the low-resolution feature maps output 217 is cropped and aligned with the corresponding high-resolution image patch 207a prior to being provided to high-resolution module 105 as an input, p0030-0033 & p0036]; and
input the corresponding image and the reduced structure image into the extraction model constructed by machine-learning a neural network to extract a second region of the target structure included in the corresponding image from the extraction model to generate an extracted image which includes the second region [neural network module 101 comprises one or more neural networks trained on high-resolution training images such that the module 101 can recognize and produce corresponding high-resolution segmentations [e.g., second region(s)] … CNNs are suitable for analyzing imagery given that they are space or shift invariant … Following receipt of the high-resolution image patch 207a and the upsampled low-resolution feature maps 217 patch as input, the high-resolution module 105 can generate a high-resolution inference 227 therefrom in the form of a segmentation [e.g., second region] of the high-resolution image patch 207a … the received high-resolution 3D image 107 can be downsampled (such as via downsampler 211) and the downsampled image can be provided as an input to low-resolution module 103. Using the provided input, the low-resolution module 103 can generate corresponding low-resolution feature maps 217. Next, the low-resolution feature maps 217 can be provided as an input to high-resolution module 105, along with the complete (i.e. full-resolution and not downsampled) high-resolution 3D image 107. Finally, high-resolution module 105 can generate a high-resolution inference 109 in the form of a complete high-resolution segmentation, based on the low-resolution feature maps 217 and the high-resolution image 107, p0024 & p0036-0038].
Claim 2: De Nigris discloses the image processing apparatus according to claim 1, wherein the extraction model consists of a plurality of processing layers that perform convolution processing and an input layer has two channels [inputs 103 and 105 in to the CNN, p0024], and the processor is configured to: enlarge the reduced structure image to the same size as the corresponding image to derive an enlarged structure image [the extracted patch can subsequently be upsampled to match the resolution of the high-resolution patch, p0036]; and input the enlarged structure image and the corresponding image respectively to the two channels of the input layer of the extraction model [Following receipt of the high-resolution image patch 207a and the upsampled low-resolution feature maps 217 patch as input, the high-resolution module 105 can generate a high-resolution inference 227 therefrom, p0037-0038].
Claim 3: De Nigris discloses the image processing apparatus according to claim 1, wherein the neural network consists of a plurality of processing layers that perform convolution processing, and the processing layer that processes an image having the same resolution as the reduced structure image has an additional channel for inputting the reduced structure image, and the processor is configured to input the reduced structure image to the additional channel [Each of the low-resolution 103 and high-resolution 105 modules can implement neural networks that are based on a 3D convolution neural network (CNN) … the low-resolution module 103 can be trained to produce 3D low-resolution segmentations from 3D low-resolution images. Second, the high-resolution module 105 can be trained to produce high-resolution segmentation 3D patches from high-resolution 3D image patches and low-resolution 3D feature map patches obtained from the low-resolution module 103. At inference time, both modules 103, 105 can be connected together to produce a complete high-resolution 3D segmentation in a single pass … Following receipt of the high-resolution image patch 207a and the upsampled low-resolution feature maps 217 patch as input, the high-resolution module 105 can generate a high-resolution inference 227 therefrom, p0024-0025 & p0037-0038].
Claim 4: De Nigris discloses the image processing apparatus according to claim 1, wherein the processor is configured to: divide the region of the target structure extracted from the reduced image and derive a divided and reduced structure image including each of the divided regions of the target structure [Each segmentation can thus be taken as an accurate representation of the boundaries of the various anatomical regions in the 3D image to which it corresponds. Preferably, the segmentations have not been simplified, and include a high number of identified parts or regions (for example 100+ anatomical regions in the case of a brain MRI scan) … downsampled low-resolution image 213 can then be provided to the low-resolution module 103 which can generate a low-resolution inference 219 therefrom in the form of a segmentation of the low-resolution image 213, p0024, p0031]; derive a plurality of divided corresponding images corresponding to the respective divided and reduced structure images from the corresponding image [the low-resolution feature maps output 217 is cropped and aligned with the corresponding high-resolution image patch 207a prior to being provided to high-resolution module 105 as an input]; and extract the region of the target structure included in the corresponding image in units of the divided corresponding image and the divided and reduced structure image [the patch sampler 225 can extract a patch from the upsampled feature maps that is aligned with the high-resolution patch 207a. Alternatively, the feature maps patch can be extracted at the desired location, and the extracted patch can subsequently be upsampled to match the resolution of the high-resolution patch 207a, p0036].
Claim 5: De Nigris discloses a learning apparatus comprising at least one processor, wherein the processor is configured to:
acquire a reduced structure image including a first region of a target structure, wherein the reduced structure image is derived based on a reduced image of a target image by extracting the first region of the target structure from the reduced image [the plurality of images can all correspond to images of the same object and/or anatomical structure to which the neural network is to be trained/fitted … Each segmentation can thus be taken as an accurate representation of the boundaries of the various anatomical regions in the 3D image to which it corresponds. Preferably, the segmentations have not been simplified, and include a high number of identified parts or regions (for example 100+ anatomical regions in the case of a brain MRI scan) … downsampled low-resolution image 213 can then be provided to the low-resolution module 103 which can generate a low-resolution inference 219 therefrom in the form of a segmentation of the low-resolution image 213 … feature maps 217 correspond to an intermediate output of the low-resolution module 103, p0023-0024, p0027-0028, p0031 & p0036];
acquiring a corresponding image which is extracted from the target image and corresponds to the reduced structure image [low-resolution feature maps 217 are provided as input to high-resolution module 105 … Once an anatomy is chosen, a voxel corresponding to that anatomy can then be randomly selected as the center of the patch … a patch sampler 225 is provided to extract a high-resolution image patch 207a and high-resolution segmentation patch 209a from the high-resolution image 207 and segmentation 209 … the low-resolution feature maps output 217 is cropped and aligned with the corresponding high-resolution image patch 207a prior to being provided to high-resolution module 105 as an input, p0030-0033 & p0036]; and
construct an extraction model that is configured to extract an extracted image which includes, a second region of the target structure from the corresponding image, by machine-learning a neural network using, as supervised training data [training a deep 3D CNN … the neural network 101 can be trained via supervised learning techniques … the plurality of images can all correspond to images of the same object and/or anatomical structure to which the neural network is to be trained/fitted … the low-resolution module 103 of the neural network 101 is trained on low-resolution images. This can involve providing a pair of a high-resolution image 207 and corresponding high-resolution segmentation from the training dataset, and converting the pair into a corresponding low-resolution image 213 and a low-resolution segmentation 215 via a downsampler 211 … the high-resolution module 105 of the neural network 101 is trained on high-resolution images and at least some output from the low-resolution module 103. As can be appreciated, to optimize memory usage while training the CNN of the high-resolution module 105, the model can be trained on patches (i.e. subsets or portions) of high-resolution images and segmentations, as opposed to high-resolution images and segmentations in their entirety, p0027-0028 & p0030-0032], a first image including the first region of the target structure extracted from the reduced structure image the target image including the target structure [the low-resolution module 103 of the neural network 101 is trained on low-resolution images. This can involve providing a pair of a high-resolution image 207 and corresponding high-resolution segmentation from the training dataset, and converting the pair into a corresponding low-resolution image 213 and a low-resolution segmentation 215 via a downsampler 211, p0030-0032], a second image corresponding to the corresponding image [the high-resolution module 105 of the neural network 101 is trained on high-resolution images and at least some output from the low-resolution module 103, p0030-0032], and correct answer data representing an extraction result of the target structure from the second image [Following receipt of the high-resolution image patch 207a and the upsampled low-resolution feature maps 217 patch as input, the high-resolution module 105 can generate a high-resolution inference 227 [i.e., extraction result] therefrom in the form of a segmentation of the high-resolution image patch 207a. A loss function 229 can then be provided to compare the inferred high-resolution patch segmentation 227 with the truth high-resolution segmentation patch 209a [i.e., correct answer] and optimize the CNN model of the high-resolution module 105 as necessary to reduce inference error, p0037].
Claims 6 and 8 are executed or performed by the apparatus of claim 1 and are therefore likewise rejected.
Claims 7 and 9 are executed or performed by the apparatus of claim 5 and are therefore likewise rejected.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bai et al., US Patent 11328392, acquiring an input image including an object region; generating a mask image based on the input image; and to extract a fusion feature map corresponding to the input image using an encoding network according to the input image and the mask image, and to inpaint the object region in the input image using a decoding network based on the fusion feature map to obtain an inpainting result.
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 BARBARA D REINIER whose telephone number is (571)270-5082. The examiner can normally be reached M-T 10am - 6pm.
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/BARBARA D REINIER/Primary Examiner, Art Unit 2682