DETAILED ACTION
Contents
Notice of Pre-AIA or AIA Status 2
Response to Amendment 2
Response to Arguments 2
Claim Rejections - 35 USC § 103 3
Conclusion 19
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 Amendment
This action is responsive to applicant’s amendment and remarks received on 1/14/26. Claims 1-3, 5-12, 14-23 are currently pending.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 19, 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The 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 claimedinvention is not identically disclosed as set forth in section 102 of this title, 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, 17, 19, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Choi et al (US 2017/0169313 A1) in view of Hariharan et al (CV: “Hypercolumns for Object Segmentation and Fine-grained Localization”).
Regarding claim 1, Choi teaches an image processing apparatus comprising: one or more processors which execute instructions stored in one or more memories, wherein by execution of the instructions the one or more processors function as (see 0148; instructions or software to control computing hardware, for example, one or more processors or computers, to implement the hardware components and perform the methods as described above, and any associated data, data files, and data structures, may be recorded, stored, or fixed in or on one or more non-transitory computer-readable storage media):
a feature generation unit configured to process an input image by using a hierarchical neural network (0046-0048);
an output unit configured to output a detection result of an area of the object in the input image (see 0136, 0017, 0028; In 650, the neural network learning apparatus 500 classifies the classes by objects of the image using the reinforced feature map. For example, the neural network learning apparatus 500 predicts the positions of the objects on the basis of the information of the classes by areas, which are classified using the reinforced feature map reflecting the pixel probability distribution information in the step of area classification and can generate bounding boxes including the predicted positions of the objects. In an example, the bounding box may have a rectangular shape surrounding the periphery of the predicted object, but the present disclosure is not limited thereto….. The object detector may be configured to perform box regression analysis using box coordinates in the result of the bounding box prediction and to determine whether to use the bounding box based on a confidence score of the result of the bounding box prediction…. The detecting of an object in the image may include performing box regression analysis using box coordinates in result of the bounding box prediction and determining whether to use the bounding box based on a confidence score of the result of in the bounding box prediction). Choi does not teach expressly generate a connected layer feature by connecting outputs of a plurality of layers of the hierarchical neural network; a map generation unit configured to generate a score map using a discriminator based on the connected layer feature, wherein each value in the score map indicates a likelihood that a particular point of an area of an object to be detected is present at a coordinate position in the score map.
Hariharan, in the same field of endeavor, teaches generate a connected layer feature by connecting outputs of a plurality of layers of the hierarchical neural network (see section 1); a map generation unit configured to generate a score map using a discriminator based on the connected layer feature (see abstract, section 1, 3), wherein each value in the score map indicates a likelihood that a particular point of an area of an object to be detected is present at a coordinate position in the score map (see section 3).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Choi to utilize the cited limitations as suggested by Hariharan. The suggestion/motivation for doing so would have been enable improvement in both precise location and semantics (see abstract). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Choi, while the teaching of Hariharan continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Regarding claim 17, Choi with Hariharan teaches all elements as mentioned above in claim 1. Choi with Hariharan does not teach up-sampling processing to resize operation results of the plurality of layers of the hierarchical neural network to a same size when connecting the operation results of the plurality of layers of the hierarchical neural network.
Hariharan, in the same field of endeavor, teaches up-sampling processing to resize operation results of the plurality of layers of the hierarchical neural network to a same size when connecting the operation results of the plurality of layers of the hierarchical neural network (see section 3).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Choi with Hariharan to utilize the cited limitations as suggested by Hariharan. The suggestion/motivation for doing so would have been enable improvement in both precise location and semantics (see abstract). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Choi with Hariharan, while the teaching of Hariharan continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Regarding claim 19, Choi teaches an image processing method comprising: processing an input image by using a hierarchical neural network (0046-0048); and outputting a detection result of an area of the object in the input image (see 0136, 0017, 0028; In 650, the neural network learning apparatus 500 classifies the classes by objects of the image using the reinforced feature map. For example, the neural network learning apparatus 500 predicts the positions of the objects on the basis of the information of the classes by areas, which are classified using the reinforced feature map reflecting the pixel probability distribution information in the step of area classification and can generate bounding boxes including the predicted positions of the objects. In an example, the bounding box may have a rectangular shape surrounding the periphery of the predicted object, but the present disclosure is not limited thereto….. The object detector may be configured to perform box regression analysis using box coordinates in the result of the bounding box prediction and to determine whether to use the bounding box based on a confidence score of the result of the bounding box prediction…. The detecting of an object in the image may include performing box regression analysis using box coordinates in result of the bounding box prediction and determining whether to use the bounding box based on a confidence score of the result of in the bounding box prediction). Choi does not teach expressly generating a connected layer feature by connecting outputs of a plurality of layers of the hierarchical neural network; generating a score map using a discriminator based on the connected layer feature, wherein each value in the score map indicates a likelihood that a particular point of an area of an object to be detected is present at a coordinate position in the score map.
Hariharan, in the same field of endeavor, teaches generating a connected layer feature by connecting outputs of a plurality of layers of the hierarchical neural network (see section 1); generating a score map using a discriminator based on the connected layer feature (see abstract, section 1, 3), wherein each value in the score map indicates a likelihood that a particular point of an area of an object to be detected is present at a coordinate position in the score map (see section 3).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Choi to utilize the cited limitations as suggested by Hariharan. The suggestion/motivation for doing so would have been enable improvement in both precise location and semantics (see abstract). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Choi, while the teaching of Hariharan continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Regarding claim 20, the claim is analyzed as a non-transitory computer-readable storage medium storing a computer program (see 0148; instructions or software to control computing hardware, for example, one or more processors or computers, to implement the hardware components and perform the methods as described above, and any associated data, data files, and data structures, may be recorded, stored, or fixed in or on one or more non-transitory computer-readable storage media) for causing a computer to function as the limitations of claim 1 (see rejection of claim 1).
Claims 2-3, 15, 21 are rejected under 35 U.S.C. 103 as being unpatentable over Choi et al (US 2017/0169313 A1) with Hariharan et al (CV: “Hypercolumns for Object Segmentation and Fine-grained Localization”), and further in view of Liu et al (ECCV: “SSD: Single Shot MultiBox Detector”).
Regarding claims 2-3, 15, 21, Choi with Hariharan teaches all elements as mentioned above in claim 1. Choi further teaches generates the score map for an object detection and a region determination score map for determining regions (see0052-0059). Choi does not teach expressly outputs a result concerning presence/absence of the object in the input image, based on the score map; select, as a final output, one of a plurality of categories included in the detection result; outputs detection results of areas of a plurality of type of objects.
Liu, in the same field of endeavor, teaches outputs a result concerning presence/absence of the object in the input image, based on the score map (see abstract, section 2.1, 3, 4); select, as a final output, one of a plurality of categories included in the detection result (see section 2.1); outputs detection results of areas of a plurality of type of objects (see 2.1).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Choi with Hariharan to utilize the cited limitations as suggested by Liu. The suggestion/motivation for doing so would have been enable a practical and accurate solution to multi-scale object detection (see abstract). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Choi with Hariharan, while the teaching of Liu continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Claims 5 is rejected under 35 U.S.C. 103 as being unpatentable over Choi et al (US 2017/0169313 A1) with Hariharan et al (CV: “Hypercolumns for Object Segmentation and Fine-grained Localization”), and further in view of Tompson et al (CV: “Efficient Object Localization Using Convolutional Networks”).
Regarding claim 5, Choi with Hariharan teaches all elements as mentioned above in claim 1. Choi does not teach expressly generates the score map indicating the likelihood that the particular point of the area of the object exists in a region block of the input image.
Tompson, in the same field of endeavor, teaches generates the score map indicating the likelihood that the particular point of the area of the object exists in a region block of the input image (see section 3, 4).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Choi with Hariharan to utilize the cited limitations as suggested by Tompson. The suggestion/motivation for doing so would have been enable improved accuracy (see abstract). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Choi with Hariharan, while the teaching of Tompson continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Choi et al (US 2017/0169313 A1) with Hariharan et al (CV: “Hypercolumns for Object Segmentation and Fine-grained Localization”), and further in view of Xiang et al (WACV: “Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection”).
Regarding claims 6-7, Choi with Hariharan teaches all elements as mentioned above in claim 1. Choi with Hariharan does not teach expressly generates the score map for each subcategory; subcategory classified by at least one of a depth rotation of the object, an in-plane rotation of the object, an orientation of the object, a shape of the object, a material of the object, a shape of a region of interest of the object, a size of the region of interest of the object, and an aspect ratio of the region of interest of the object.
Xiang, in the same field of endeavor, teaches generates the score map for each subcategory (see section 3, 4); subcategory classified by at least one of a depth rotation of the object, an in-plane rotation of the object, an orientation of the object, a shape of the object, a material of the object, a shape of a region of interest of the object, a size of the region of interest of the object, and an aspect ratio of the region of interest of the object (see section 3, 4).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Choi with Hariharan to utilize the cited limitations as suggested by Xiang. The suggestion/motivation for doing so would have been enable state of the art performance on both detection and pose estimation (see abstract). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Choi with Hariharan, while the teaching of Xiang continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Choi et al (US 2017/0169313 A1) with Hariharan et al (CV: “Hypercolumns for Object Segmentation and Fine-grained Localization”), and further in view of Mousavian et al (CV: “3D Bounding Box Estimation Using Deep Learning and Geometry”).
Regarding claim 8, Choi with Hariharan teaches all elements as mentioned above in claim 1. Choi with Hariharan does not teach expressly outputs information relating to at least one of a depth rotation of the object, an in-plane rotation of the object, an orientation of the object, a shape of the object, a material of the object, a shape of a region of interest of the object, a size of the region of interest of the object, and an aspect ratio of the region of interest of the object.
Mousavian, in the same field of endeavor, teaches outputs information relating to at least one of a depth rotation of the object, an in-plane rotation of the object, an orientation of the object, a shape of the object, a material of the object, a shape of a region of interest of the object, a size of the region of interest of the object, and an aspect ratio of the region of interest of the object (see abstract, section 3).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Choi with Hariharan to utilize the cited limitations as suggested by Mousavian. The suggestion/motivation for doing so would have been enable outperformance more complex and computationally expensive approaches (see abstract). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Choi with Hariharan, while the teaching of Mousavian continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Choi et al (US 2017/0169313 A1) with Hariharan et al (CV: “Hypercolumns for Object Segmentation and Fine-grained Localization”), and further in view of Fu et al (CV: “DSSD : Deconvolutional Single Shot Detector”).
Regarding claim 9, Choi with Hariharan teaches all elements as mentioned above in claim 1. Choi does not teach expressly generates the detection result of a resolution higher than a resolution of the score map.
Fu, in the same field of endeavor, teaches generates the detection result of a resolution higher than a resolution of the score map (see section 3).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Choi with Hariharan to utilize the cited limitations as suggested by Fu. The suggestion/motivation for doing so would have been enable enhanced accuracy (see abstract). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Choi with Hariharan, while the teaching of Fu continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Claims 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Choi et al (US 2017/0169313 A1) with Hariharan et al (CV: “Hypercolumns for Object Segmentation and Fine-grained Localization”), and further in view of Ren et al (CV: “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks”).
Regarding claims 10-12, Choi with Hariharan teaches all elements as mentioned above in claim 1. Choi with Hariharan does not teach expressly an estimation unit configured to estimate a size of the object, wherein the output unit outputs the estimated size of the object; outputs a result of classification of the input image; selects, based on the result of the classification, a category to be determined.
Ren, in the same field of endeavor, teaches an estimation unit configured to estimate a size of the object, wherein the output unit outputs the estimated size of the object (see abstract, sec. 3, 4);
outputs a result of classification of the input image (see abstract, sec. 3, 4);
selects, based on the result of the classification, a category to be determined (see abstract, sec. 3, 4).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Choi with Hariharan to utilize the cited limitations as suggested by Ren. The suggestion/motivation for doing so would have been enable enhanced accuracy (see abstract). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Choi with Hariharan, while the teaching of Ren continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Choi et al (US 2017/0169313 A1) with Hariharan et al (CV: “Hypercolumns for Object Segmentation and Fine-grained Localization”), and further in view of Simonyan et al (CV: “Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps”).
Regarding claim 14, , Choi with Hariharan teaches all elements as mentioned above in claim 1. Choi with Hariharan does not teach expressly a unit configured to input camera information, wherein the map generation unit uses the camera information in addition to the feature map.
Simonyan, in the same field of endeavor, teaches a unit configured to input camera information,
wherein the map generation unit uses the camera information in addition to the feature map (see sec. 3, abstract).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Choi with Hariharan to utilize the cited limitations as suggested by Simonyan. The suggestion/motivation for doing so would have been enable state of art results (see abstract). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Choi with Hariharan, while the teaching of Simonyan continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Choi et al (US 2017/0169313 A1) with Hariharan et al (CV: “Hypercolumns for Object Segmentation and Fine-grained Localization”), and further in view of Redmon et al (CV: “You Only Look Once: Unified, Real-Time Object Detection”).
Regarding claim 16 , Choi with Hariharan teaches all elements as mentioned above in claim 1. Choi with Hariharan does not teach discriminator is learned using learning data with supervisory values that indicates whether a centroid of the area of the object is contained.
Redmon, in the same field of endeavor, teaches discriminator is learned using learning data with supervisory values that indicates whether a centroid of the area of the object is contained (see abstract).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Choi with Hariharan to utilize the cited limitations as suggested by Redmon. The suggestion/motivation for doing so would have been outperform other detection methods (see abstract). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Choi with Hariharan, while the teaching of Redmon continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Choi et al (US 2017/0169313 A1) with Hariharan et al (CV: “Hypercolumns for Object Segmentation and Fine-grained Localization”), and further in view of Kim et al (CV: “Deconvolutional Feature Stacking for Weakly-Supervised Semantic Segmentation”).
Regarding claim 18, Choi with Hariharan teaches all elements as mentioned above in claim 1. Choi with Hariharan does not teach expressly deconvolution processing to resize the operation results of the plurality of layers of the hierarchical neural network to a same size when connecting the operation results of the plurality of layers of the hierarchical neural network.
Kim, in the same field of endeavor, teaches deconvolution processing to resize the operation results of the plurality of layers of the hierarchical neural network to a same size when connecting the operation results of the plurality of layers of the hierarchical neural network (see abstract, sec. 1).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Choi with Hariharan to utilize the cited limitations as suggested by Kim. The suggestion/motivation for doing so would have been enable state of the art performance (see abstract). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Choi with Hariharan, while the teaching of Kim continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Choi et al (US 2017/0169313 A1) with Hariharan et al (CV: “Hypercolumns for Object Segmentation and Fine-grained Localization”), and further in view of Balestri et al (WO 2015/011185 A1).
Regarding claim 22, Choi with Hariharan teaches all elements as mentioned above in claim 1. Choi with Hariharan does not teach determines, as the particular point of the area of the object to be detected, a point on the score map whose likelihood is the maximum as compared with those of eight adjacent points on the score map, and outputs the detection result of the area of the object corresponding to the determined particular point.
Balestri, in the same field of endeavor, teaches determines, as the particular point of the area of the object to be detected, a point on the score map whose likelihood is the maximum as compared with those of eight adjacent points on the score map, and outputs the detection result of the area of the object corresponding to the determined particular point (see pg. 18).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Choi with Hariharan to utilize the cited limitations as suggested by Balestri. The suggestion/motivation for doing so would have been identify key points within an image and reduce required requirements (see pg. 3). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Choi with Hariharan, while the teaching of Balestri continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Choi et al (US 2017/0169313 A1) with Hariharan et al (CV: “Hypercolumns for Object Segmentation and Fine-grained Localization”), and further in view of Gall et al (IEEE: “Class-Specific Hough Forests for Object Detection”).
Regarding claim 23, Choi with Hariharan teaches all elements as mentioned above in claim 1. Choi with Hariharan does not teach a centroid of the area of the object.
Gall, in the same field of endeavor, teaches a centroid of the area of the object (see abstract).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Choi with Hariharan to utilize the cited limitations as suggested by Gall. The suggestion/motivation for doing so would have been enable state of the art performance (see abstract). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Choi with Hariharan, while the teaching of Gall continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Conclusion
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 EDWARD PARK. The examiner’s contact information is as follows:
Telephone: (571)270-1576 | Fax: 571.270.2576 | Edward.Park@uspto.gov
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/EDWARD PARK/ Primary Examiner, Art Unit 2675
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