DETAILED ACTION
Response to Amendment
The amendment filed September 9, 2026 has been entered.
No claims are amended.
Claims 1-20 are pending this application.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Ammar (US 2009/0271146 A1) in view of Gao et al (ArXiv, 2017).
Regarding Claim 1, Ammar teaches a method for scanning a target subject and detecting concealed objects, the method comprising [0053 for using images for detecting concealed objects]:
transmitting, by an array of transmitters, a beam of electromagnetic radiations towards the target subject [0008, 0053 for using a transmitted and a receiver with a human subject];
receiving, by an array of receivers, a beam of electromagnetic radiations reflected from the target subject [0042 for complex-valued data collected at transmitting frequency f is a sample of the Fourier transform of the target reflectivity (receiving) by using frequency f, and 0053],
wherein the received electromagnetic radiations comprise a raw complex image [0060 for using feedhorn for raw image and then processing with A/D and external display];
receiving, by a pre-processing unit, the raw complex image from the receivers and generating a plurality of complex convoluted slices each comprising an array of complex slice values [0049 for a preprocessor where in-phase and quadrature (IQ) data can be coherently integrated and then transformed into the frequency domain to create high range resolution].
Ammar fails to explicitly teach and using a complex convolution neural network to process the complex convoluted slices for detecting the concealed object within the target subject.
Gao has two modifications to the CV-CNN to adapt it to radar imaging tasks (page 1, abstract) and teaches and using a complex convolution neural network to process the complex convoluted slices for detecting the concealed object within the target subject [page 2, left column, first paragraph for premise of CV-CNN-enhanced radar imaging is to confirm the imaging model, and figure 1].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the concealed object techniques, as disclosed by Ammar, further including the convolution calculations as taught by Gao for the purpose to have more efficient and suitable for radar imaging (Gao, page 2, left column, last paragraph).
Regarding Claim 14, Ammar fails to explicitly teach storing, in a database, one or more of the raw complex images received by the receiver, the convoluted slices generated by the pre-processing unit and an identification of the detected concealed object.
Gao has two modifications to the CV-CNN to adapt it to radar imaging tasks (page 1, abstract) and teaches storing, in a database, one or more of the raw complex images received by the receiver, the convoluted slices generated by the pre-processing unit and an identification of the detected concealed object [page 3, right column, last tow paragraph for training data for 50000 samples using CV-CNN].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the concealed object techniques, as disclosed by Ammar, further including the convolution calculations as taught by Gao for the purpose fast processing of large-scale problems (Gao, page 3, right column, last 2 paragraphs).
Regarding Claim 15, Ammar fails to explicitly teach training the processing unit for detecting the concealed objects using the information stored in the database.
Gao has two modifications to the CV-CNN to adapt it to radar imaging tasks (page 1, abstract) and teaches training the processing unit for detecting the concealed objects using the information stored in the database [page 3, right column, last tow paragraph for training data for 50000 samples using CV-CNN for 5 epochs].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the concealed object techniques, as disclosed by Ammar, further including the convolution calculations as taught by Gao for the purpose fast processing of large-scale problems (Gao, page 3, right column, last 2 paragraphs).
Regarding Claim 16, Ammar fails to explicitly teach the training of the processing unit for detecting the concealed objects is done using a Machine Learning (ML) algorithm.
Gao has two modifications to the CV-CNN to adapt it to radar imaging tasks (page 1, abstract) and teaches the training of the processing unit for detecting the concealed objects is done using a Machine Learning (ML) algorithm [page 3, right column, last tow paragraph for training data for 50000 samples using CV-CNN with stochastic gradient decent (ML)].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the concealed object techniques, as disclosed by Ammar, further including the convolution calculations as taught by Gao for the purpose fast processing of large-scale problems (Gao, page 3, right column, last 2 paragraphs).
Claims 2 and 7-13 are rejected under 35 U.S.C. 103 as being unpatentable over Ammar (US 2009/0271146 A1) in view of Gao et al (ArXiv, 2017) as applied to claim 1 above, and further in view of Trabelsi (ICLR, 2018).
Regarding Claim 2, Ammar fails to explicitly teach the step of using a complex convolution neural network to process the complex convoluted slices comprises: providing a complex kernel comprising an array of complex mask values; applying complex functions to the complex mask values and the complex slice values; and generating a complex output feature map.
Trabelsi has key atomic components for complex-valued deep neural networks (page 1, abstract) and teaches the step of using a complex convolution neural network to process the complex convoluted slices comprises: providing a complex kernel comprising an array of complex mask values [page 4, Section 3.2, first paragraph for convolve a complex filter matrix W = A + iB by a complex vector];
applying complex functions to the complex mask values and the complex slice values [page 4, Section 3.2, first paragraph];
and generating a complex output feature map [page 3, right column, last tow paragraph for training data for 50000 samples using CV-CNN with stochastic gradient decent (ML)].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the concealed object techniques, as disclosed by Ammar, further including the convolution calculations as taught by Trabelsi for the purpose fast processing of large-scale problems (Trabelsi, page 3, right column, last 2 paragraphs).
Regarding Claim 7, Ammar fails to explicitly teach using complex activation functions in an activation layer of the complex convolution neural network.
Trabelsi has key atomic components for complex-valued deep neural networks (page 1, abstract) and teaches using complex activation functions in an activation layer of the complex convolution neural network [page 5, Section 3.4.2 for using complex activations].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the concealed object techniques, as disclosed by Ammar, further including the convolution calculations as taught by Trabelsi for the purpose to represent real and complex numbers in the feature maps (Trabelsi, page 4, section 3.1).
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Regarding Claim 8, Ammar fails to explicitly teach the complex activation function comprises setting the value to zero if both the real and imaginary parts are negative: Relu (a+bi) = a+bi if a>0 or b>0 else 0.
Trabelsi has key atomic components for complex-valued deep neural networks (page 1, abstract) and teaches the complex activation function comprises setting the value to zero if both the real and imaginary parts are negative: Relu (a+bi) = a+bi if a>0 or b>0 else 0 [page 5, Section 3.4.2 for using complex activations and equation (4)].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the concealed object techniques, as disclosed by Ammar, further including the convolution calculations as taught by Trabelsi for the purpose to represent real and complex numbers in the feature maps (Trabelsi, page 4, section 3.1).
Regarding Claim 9, Ammar fails to explicitly teach the complex activation function comprises setting the value to zero if the magnitude is below than a minimum threshold value (such as 0.1) Relu (a+bi) = a+bi if a2 + b2> 0.1 else 0.
Trabelsi has key atomic components for complex-valued deep neural networks (page 1, abstract) and teaches the complex activation function comprises setting the value to zero if the magnitude is below than a minimum threshold value (such as 0.1) Relu (a+bi) = a+bi if a2 + b2> 0.1 else 0 [page 5, Section 3.4.2 for using complex activations and equation (3)].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the concealed object techniques, as disclosed by Ammar, further including the convolution calculations as taught by Trabelsi for the purpose to represent real and complex numbers in the feature maps (Trabelsi, page 4, section 3.1).
Regarding Claim 10, Ammar fails to explicitly teach the complex activation function comprises setting the value to zero if both the real and imaginary parts are negative or if the magnitude is below than a minimum threshold value: Relu (a+bi) = a+bi if a> 0 or b> 0 and a2 + b2 = 0.1 else 0.
Trabelsi has key atomic components for complex-valued deep neural networks (page 1, abstract) and teaches the complex activation function comprises setting the value to zero if both the real and imaginary parts are negative or if the magnitude is below than a minimum threshold value: Relu (a+bi) = a+bi if a> 0 or b> 0 and a2 + b2 = 0.1 else 0 [page 5, Section 3.4. and 3.4.2 for using complex activations and equation (3-4)].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the concealed object techniques, as disclosed by Ammar, further including the convolution calculations as taught by Trabelsi for the purpose to represent real and complex numbers in the feature maps (Trabelsi, page 4, section 3.1).
Regarding Claim 11, Ammar fails to explicitly teach the complex activation function comprises applying the function: Relu (a+bi) = (1/2) (1+cosf) a + (1/2) (1+cosf) bi, where f=arctan (b/a).
Trabelsi has key atomic components for complex-valued deep neural networks (page 1, abstract) and teaches the complex activation function comprises applying the function: Relu (a+bi) = (1/2) (1+cosf) a + (1/2) (1+cosf) bi, where f=arctan (b/a) [page 5, Section 3.4.2 for using complex activations and equation (4)].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the concealed object techniques, as disclosed by Ammar, further including the convolution calculations as taught by Trabelsi for the purpose to represent real and complex numbers in the feature maps (Trabelsi, page 4, section 3.1).
Regarding Claim 12, Ammar fails to explicitly teach the step of using a complex convolution neural network to process the complex convoluted slices comprises generating an output feature map matrix.
Trabelsi has key atomic components for complex-valued deep neural networks (page 1, abstract) and teaches the step of using a complex convolution neural network to process the complex convoluted slices comprises generating an output feature map matrix [page 4, Section 3.1 for 2D convolution layer that has N feature maps with page 15, first paragraph and figure 1a].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the concealed object techniques, as disclosed by Ammar, further including the convolution calculations as taught by Trabelsi for the purpose to represent real and complex numbers in the feature maps (Trabelsi, page 4, section 3.1).
Regarding Claim 13, Ammar fails to explicitly teach the complex image data comprises a phase space complex image data matrix M including a real component MR and an imaginary complex component MI and the output feature map matrix M` is generated by applying a complex kernel matrix K including a real component KR and an imaginary complex component KI such that M' = M*K = (MR+iMI) * KR+iKI = (MR*KR - MI*KI) + i(MR*KI + MI * KR).
Trabelsi has key atomic components for complex-valued deep neural networks (page 1, abstract) and teaches the complex image data comprises a phase space complex image data matrix M including a real component MR and an imaginary complex component MI and the output feature map matrix M` is generated by applying a complex kernel matrix K including a real component KR and an imaginary complex component KI such that M' = M*K = (MR+iMI) * KR+iKI = (MR*KR - MI*KI) + i(MR*KI + MI * KR) [page 5, Section 3.2 for using complex activations and equation (1) with page 15, first paragraph and figure 1a].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the concealed object techniques, as disclosed by Ammar, further including the convolution calculations as taught by Trabelsi for the purpose to represent real and complex numbers in the feature maps (Trabelsi, page 4, section 3.1).
Claims 3-6 are rejected under 35 U.S.C. 103 as being unpatentable over Ammar (US 2009/0271146 A1) in view of Gao et al (ArXiv, 2017) and Trabelsi (ICLR, 2018) as applied to claim 1 above, and further in view of Guberman (ArXiv, 2016).
Regarding Claim 3, Ammar fails to explicitly teach using complex pooling to generate a reduced array by selecting a representative complex element for each sub region array.
Guberman has complex network detects meaningful phase structure in the data (Abstract, last paragraph) and teaches using complex pooling to generate a reduced array by selecting a representative complex element for each sub region array [page 16, Section 4.2.2 for input is split into patches, and each patch (subregion) is replaced by one value].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the concealed object techniques, as disclosed by Ammar, further including the pooling calculations as taught by Guberman for the purpose to train a neural network and to minimize its loss function (Guberman, page 18, section 4.3, first paragraph).
Regarding Claim 4, Ammar fails to explicitly teach the complex pooling comprises selecting an element (ak + ibk) from a sub region array having the highest absolute value
√
(ak2 + bk2).
Guberman has complex network detects meaningful phase structure in the data (Abstract, last paragraph) and teaches the complex pooling comprises selecting an element (ak+ibk) from a sub region array having the highest absolute value
√
(ak2+bk2) [page 16, Section 4.2.2 third paragraph for suggested projections, f(z) = |z| is the only reasonable choice].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the concealed object techniques, as disclosed by Ammar, further including the pooling calculations as taught by Guberman for the purpose to train a neural network and to minimize its loss function (Guberman, page 18, section 4.3, first paragraph).
Regarding Claim 5, Ammar fails to explicitly teach the complex pooling comprises calculating an arithmetic mean value (Sak/N + iSbkN) for a sub region array of N elements (ak + ibk) by summing the elements and dividing by the number of elements in the sub region array.
Guberman has complex network detects meaningful phase structure in the data (Abstract, last paragraph) and teaches the complex pooling comprises calculating an arithmetic mean value (Sak/N + iSbkN) for a sub region array of N elements (ak + ibk) by summing the elements and dividing by the number of elements in the sub region array [page 16, Section 4.2.2 for input is split into patches, and each patch (subregion) is replaced by one value].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the concealed object techniques, as disclosed by Ammar, further including the pooling calculations as taught by Guberman for the purpose to train a neural network and to minimize its loss function (Guberman, page 18, section 4.3, first paragraph).
Regarding Claim 6, Ammar fails to explicitly teach the complex pooling comprises striding, selecting a median, selecting a geometric mean, selecting a weighted average or combinations thereof.
Guberman has complex network detects meaningful phase structure in the data (Abstract, last paragraph) and teaches the complex pooling comprises striding, selecting a median, selecting a geometric mean, selecting a weighted average or combinations thereof [page 17, Section 4.2.2 first paragraph for smooth transition between max, average and min.].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the concealed object techniques, as disclosed by Ammar, further including the pooling calculations as taught by Guberman for the purpose to train a neural network and to minimize its loss function (Guberman, page 18, section 4.3, first paragraph).
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Ammar (US 2009/0271146 A1) in view of Gao et al (ArXiv, 2017) as applied to claim 1 above, and further in view of Sarraiocco (US 2016/0252646 A1).
Regarding Claim 17, Ammar fails to explicitly teach transmitting, by a communicator, a notification of the detected concealed object to one or more concerned authorities through a communication network.
Sarraiocco has image screening apparatus includes an image scanner that scans an object and generates an image of the object (abstract) and teaches transmitting, by a communicator, a notification of the detected concealed object to one or more concerned authorities through a communication network [0028-0029 for bomb squads and security officers (authorities)].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the concealed object techniques, as disclosed by Ammar, further including the reporting calculations as taught by Sarraiocco for the purpose to help identify and mitigate the threat (Sarraiocco, 0029).
Claims 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Ammar (US 2009/0271146 A1) in view of Gao et al (ArXiv, 2017) as applied to claim 1 above, and further in view of Saisan et al (US 8116575 B1).
Regarding Claim 18, Ammar fails to explicitly teach detecting, by an anomaly detector, deviation of the detected concealed object from a standard identification stored in the database.
Saisan has a system for anomaly detection to detect an anomalous object in an image, such as a concealed object beneath a person's clothing (abstract) and teaches detecting, by an anomaly detector, deviation of the detected concealed object from a standard identification stored in the database [col 3, lines 20-40 for computing distance between warped image and subspace model with col 7, lines 1-10 for updating a database].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the concealed object techniques, as disclosed by Ammar, further including the image distance calculations as taught by Saisan for the purpose to classify the object as anomalous or normal (Saisan, col 3, lines 30-40).
Regarding Claim 19, Ammar fails to explicitly teach the detected deviation is indicative of detection of non-specific concealed objects.
Saisan has a system for anomaly detection to detect an anomalous object in an image, such as a concealed object beneath a person's clothing (abstract) and teaches the detected deviation is indicative of detection of non-specific concealed objects [col 2, lines 40-55 for an output can be an update to a database that the novel image deviates from the common class to Such an extent that a weapon or contraband].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the concealed object techniques, as disclosed by Ammar, further including the image distance calculations as taught by Saisan for the purpose to classify the object as anomalous or normal (Saisan, col 3, lines 30-40).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Ammar (US 2009/0271146 A1) in view of Gao et al (ArXiv, 2017) as applied to claim 1 above, and further in view of Pang et al (Sensors, 2020).
Regarding Claim 20, Ammar fails to explicitly teach detecting, by the processing unit, at least one of a position of the concealed object within the target subject, a size of the concealed object, and a shape of the concealed object.
Pang has a system for detection of objects concealed under people’s clothing (abstract) and teaches the detecting, by the processing unit, at least one of a position of the concealed object within the target subject, a size of the concealed object, and a shape of the concealed object [page 1, last paragraph for having shapes and sizes of the concealed object].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the concealed object techniques, as disclosed by Ammar, further including the object shape calculations as taught by Pang for the purpose to realize the detection of hidden objects of sequence images (Pang, page 2, first paragraph).
Response to Arguments
Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
In applicant’s arguments page 2, four paragraph of applicant’s arguments, the applicant states that Ammar fails to teach a raw complex image. The examiner respectfully disagrees: Ammar teaches its “receiver is configured to receive reflected signals” [Ammar, 0009] and that data is complex form the start and “complex-valued data collected at transmitting frequency is a sample of the Fourier transform” [Ammar, 0042] and it arrives unprocessed as IQ data, furthermore “preprocessor 95 where in-phase and quadrature (IQ) data can be coherently integrated and then transformed into the frequency domain to create high range resolution” [Ammar, 0049] therefore having raw complex data before processing.
In applicant’s arguments page 2, last paragraph of applicant’s arguments, the applicant states that Ammar fails to teach a complex convoluted slices. The examiner respectfully disagrees: Ammar’s “preprocessor 95 where in-phase and quadrature (IQ) data can be coherently integrated and then transformed into the frequency domain to create high range resolution” [Ammar, 0049] and the system can “ability to slice, in range 11, the screening area 12 from different directions by multiple sensors” [Ammar, 0025], performing both high range resolution slicing and convolution [Ammar, 0056, 0064] each projection equals “a slice of the 2D Fourier transform” [Ammar, 0062] built from complex value data.
In applicant’s arguments page 3, first two paragraphs of applicant’s arguments, the applicant states that Gao does not cure Ammar and the combination is hindsight. The examiner respectfully disagrees: both references process complex radar echoes into images, Ammar has an ANN because “Higher resolution can equate to better point scatterer separation” [Ammar, 0071] while Goa has “show the superiority of the proposed method on imaging quality
and computational efficiency” [Gao, abstract] that can “CV-CNN costs much less time than SPGL1 and can meet the requirements for most real time imaging scenarios” [Gao, page 4, right column, 2nd paragraph] matching Ammar’s goal to rapidly scan people [Ammar, 0073].
In applicant’s arguments page 3, third paragraph of applicant’s arguments, the applicant states that Gao fails to teach claim 14. The examiner respectfully disagrees: Gao saves raw complex radar echos as a training set [Gao, page 3, right column, second paragraph for examples are generated for training. Training lasts for 5 epochs (means to saving data for each pass)].
In applicant’s arguments page 3, last paragraph of applicant’s arguments, the applicant states that Trabelsi fails to teach claim 2. The examiner respectfully disagrees: Trabelsi teaches real-valued 2D convolution in the complex domain, we convolve a complex filter matrix and produces a complex out put feature maps “first N=2 feature maps to represent the real components and the remaining N=2 to represent the imaginary ones [page 4, 3rd and 4th paragraph and eq (1)] using “to imaginary and real feature maps and KI and KR refer to imaginary and real kernels [page 15, first paragraph].
In applicant’s arguments page 4, last paragraph of applicant’s arguments, the applicant states that Guberman fails to teach claim 3. The examiner respectfully disagrees: Guberman teaches “a pooling layer does so by splitting each input channel into patches and replacing each patch with a single representative value in the output layer [page 11, 7th paragraph] and its complex network explicitly includes pooling, and max pooling, are ill-defined over complex inputs due to the lack of order in the complex field. We described the problems encountered along with possible solutions [page 38, first paragraph] which means shrining the complex array by selecting stand in values from the region.
In applicant’s arguments page 5, fourth paragraph of applicant’s arguments, the applicant states that Sarraiocco fails to teach claim 17. The examiner respectfully disagrees: Sarraiocco using bomb appraisal officers having “images are transmitted wirelessly through an encrypted wireless network” [0005, 0044] providing notification about a detected object to the authorities.
In applicant’s arguments page 5, fifth paragraph of applicant’s arguments, the applicant states that Saisan fails to teach claim 18. The examiner respectfully disagrees: Saisan teache anomaly detection to detect an anomalous object in an image, such as a concealed object beneath a person's clothing” and also a normal class [Saisan, abstract] means to detect a deviation from normal to anomalous or novel.
In applicant’s arguments page 6, first paragraph of applicant’s arguments, the applicant states that Pang fails to teach claim 20. The examiner respectfully disagrees: Pang teaches predicting target bounding box that indicates the center point of the target relative to its grid [Pang, page 3, last paragraph] thereby finding “unknown positions, shapes, and sizes of hidden objects” [Pang, page 1, last paragraph].
The examiner acknowledges that this is a broader interpretation than Applicant’s. However, examiners are not only allowed to apply broad interpretations, but are required to do so, as it reduces the possibility that the claims, once issued, will be interpreted more broadly than is justified. MPEP §2111. Patentability is determined by the “broadest reasonable interpretation
consistent with the specification” (MPEP §2111), not the narrowest reasonable interpretation. And Applicant does not have an explicit lexicographical statement in line with MPEP §2111.01
subsection IV requiring a specific interpretation of the relevant phrases which forces the examiner to interpret them only one way.
The express, implicit, and inherent disclosures of a prior art reference may be relied upon in the rejection of claims under 35 U.S.C. 102 or 103. "The inherent teaching of a prior art reference, a question of fact, arises both in the context of anticipation and obviousness." In re Napier, 55 F.3d 610, 613, 34 USPQ2d 1782, 1784 (Fed. Cir. 1995).
For applicant’s benefit, portions of the cited reference(s) have been cited to aid in the review of the rejection(s). While every attempt has been made to be thorough and consistent within the rejection it is noted that the PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, including disclosures that teach away from the claims. See MPEP 2141.02 VI.
“The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments. Merck & Co. v.Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005) See MPEP 2123.
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 SAMARINA MAKHDOOM whose telephone number is (703)756-1044. The examiner can normally be reached Monday – Thursdays from 8:30 to 5:30 pm eastern time.
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/SAMARINA MAKHDOOM/
Examiner, Art Unit 3648
