DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on February 10, 2026has been entered.
Response to Arguments
This office action is in response to the amendment filed January 15, 2026 and February 10, 2026. Claims 17-22 and 27-29 are pending in this application and have been considered below. Claims 1-16 and 23-26 have been canceled by the applicant.
Applicant’s arguments with respect to claims 17-22 and 27-29 have been considered but are moot in view of new ground(s) of rejection because of the amendments.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claim 17-22 and 27-29 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 24-31, 33-34, and 38-42 of application 18/033,226 (U.S. Patent No. 12,579,781)(application 18/033,226). Although the claims at issue are not identical, they are not patentably distinct from each other because the ‘781 patent specifically recites determining risk based on a reference point within a marginal distance of a dip. These are considered minor variations that a person of ordinary kill in the art would find obvious.
Claim 17-22 and 27-29 rejected on the ground of nonstatutory double patenting as being unpatentable over the patented claims 15-21 and 23-25 of application 18/033,385 (U.S. Patent No. 12,573,190). Although the claims at issue are not identical, they are not patentably distinct from each other because both characterize a multi-scale CNN that exhibits periodic confidence dips defined by a scaling factor and recite the specific technical solution of adapting object detection parameters, such as lowering a class threshold or increasing a confidence metric, based on the distance between an object’ reference point and those dips.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 17-22 and 27-29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Koivisto et al. (US 2024/0192320 A1 – hereinafter “Koivisto”) in view of Zhang (Making Convolutional Networks Shift-Invariant Again – hereinafter “Zhang”)
Claims 17 and 29. (Currently amended)
An object detection arrangement comprising (Koivisto ¶¶0035-0039, Fig. 1A: Object detection system 100)
a memory storing computer program instructions (Koivisto Fig. 16: Computing device with memory; ,¶0099: tensors loaded in memory); and
a controller configured to execute the computer program instructions, whereby the controller is configured to (Koivisto Figs. 15C, 16: Processor(s)/SoC connected to memory, executing object detection pipeline):
receive image data representing an image containing a candidate object (Koivisto ,¶¶0085, 0094, Fig. 3: Object detector 306 receives image data; CNN processes image to produce coverage values and bounding boxes for spatial element regions containing detected objects)
determine that the candidate object is at
convolutional neural network (CNN) (Koivisto ¶0081: "the object detection system 100
may employ multi-scale inferencing using the object detector 106. In multi-scale
inferencing, the object detector 106 may infer the same images multiple times at
different scales." Koivisto ,¶0093: Multi-headed architecture, "A first head may be
larger (e.g., have a larger stride such as 16) than a second head (e.g., having a stride of
8).")
(Koivisto ¶0094: "16x16 pixel blocks" and "8x8 pixel blocks" are the pixel areas correspond to the stride. Koivisto ¶0106: "each output cell (e.g., grid cell) may correspond to a pixel area (e.g., 16x16) in the input image. This pixel area may correspond to a spatial element region described herein, and may be based on the stride of the DNN."); and
compensate classification of the candidate object by the multi-scale CNN, to reduce the
risk of the candidate object being misclassified (Koivisto ¶0080: "an aggregated detection may be retained based at least in part on the confidence score exceeding a threshold (e.g., an adjustable value). This filtering may be performed to reduce false positives." Koivisto adjusts confidence-based filtering to reduce detection errors. Koivisto ¶¶0108-0110: Confidence score generator (MLP) determines scores that "may accurately indicate a probability that an aggregated detection corresponds to an actual object" for the purpose of reducing false/missed detections. Zhang also demonstrates that shifting the image changes the confidence (Fig. 1),teaching per-image compensation through positional adjustment.).
Koivisto discloses all of the subject matter as described above except for specifically teaching “risk of being misclassified … that exhibits periodic dips in classification confidence along an axis of the image, the periodic dips spaced according to a confidence distance defined by a scaling factor.” However, Zhang in the same field of endeavor teaches risk of being misclassified … that exhibits periodic dips in classification confidence along an axis of the image (Zhang §1, Fig. 1: "small input shifts or translations can cause drastic changes in the output." Figure 1 explicitly plots classification confidence (Prob of correct class) vs. diagonal shift in pixels. The baseline network shows periodic oscillations/dips in confidence as the image is shifted along the axis i.e. risk of being misclassified when combined with Koivisto’s adjustable threshold (¶0080). Zhang §4.3, Fig. 5: Feature distance heatmaps throughout VGG architecture show periodic stippling patterns at each downsampling layer. "On the baseline network, shift-equivariance is reduced each time down sampling takes place. Periodic-N shift-equivariance holds, with N doubling with each downsampling."), the periodic dips spaced according to a confidence distance defined by a scaling factor (Zhang §3.1: "In some cases, the definitions in Eqns. 1, 2 may hold only when shifts (Δh, Δw) are integer multiples of N. We refer to such scenarios as periodic shift-equivariance/invariance of N. For example, periodic-2 shift-invariance means that even-pixel shifts produce an identical output, but odd-pixel shifts may not." Zhang §4.3: "Periodic-N shift-equivariance still holds, as indicated by the stippling pattern in 'pool 1' , and each subsequent subsampling doubles the factor N." This means at stride 2 pooling the periodicity will be 2 pixels; likewise, after the second pooling the periodicity will be 4 pixels; and after third pooling the periodicity will be 8 pixels. The periodic spacing is defined by the accumulated scaling/stride factor. Koivisto ¶0093: Stride 16 (first head) and stride 8 (second head).
It would have been obvious to one of ordinary skill in the art to combine the teachings of Koivisto and Zhang because Koivisto's multi-scale, stride-based CNN detection system
relies on confidence scores to filter detections (¶0080), and Zhang expressly identifies that
strided CNNs of this type produce periodic confidence oscillations at intervals defined by
the network's scaling factor (§4.3, Fig. 1). This a documented deficiency that a skilled
artisan would have been motivated to detect and compensate for to improve the reliability
of Koivisto's confidence-based filtering. This motivation is supported by KSR rationale (A),
combining known elements by known methods to yield predictable results, and rationale
(G), as Zhang's analysis constitutes a suggestion in the prior art to address a known artifact
of the precise CNN architecture Koivisto employs. MPEP § 2141 (Ill).
Claim 18. (Currently amended)
The object detection arrangement of claim 17, wherein the multiscale CNN classifies the candidate object by comparing a classification confidence determined for the candidate object for each of one or more object classes with a corresponding class threshold (Koivisto ¶¶0080: Filtering based on "confidence score exceeding a threshold (e.g., an adjustable value)." ¶¶0092: "different thresholds may be used for different classes." ¶¶0093: "the detected object filter 116A may retain at least some of the detected objects ... but may use a higher threshold for the associated coverage values.").
Claim 19. (Currently amended)
The object detection arrangement of claim 18, wherein the controller is configured to compensate the classification of the candidate object by increasing the classification confidence and/or lowering the classification threshold, for at least one of the one or more object classes (Koivisto ¶¶0080: "adjustable value" for threshold -thresholds are configurable. ¶¶0093: Different heads use different thresholds; filter may "use a higher threshold for the associated coverage values" for certain detections, this implies adjustability in both directions.).
Koivisto teaches adjustable confidence thresholds. One of ordinary skill, knowing from Zhang that objects near periodic dips have artificially suppressed confidence, would have found it obvious to either increase the confidence score or lower the classification threshold for such at-risk objects. This is the natural corrective action when the cause of suppressed confidence is a known CNN architecture artifact rather than genuine low detection quality.
Claim 20. (Currently amended)
The object detection arrangement of claim 17, wherein the controller is configured to determine that the candidate object is at risk of being misclassified by a multi-scale convolutional neural network (CNN) by determining that a reference point of the candidate object lies within a defined marginal distance of one of the periodic dips, the defined
marginal distance measured in pixels (Zhang §3.1: “Periodic-N shift-equivariance/invariance
In some cases, the definitions in Eqns. 1, 2 may hold only when shifts … are integer multiples of N.” Objects at positions that are NOT multiples of N are at risk. Fig. 1: Shows confidence as continuous function of pixel shift-dips occur at specific pixel locations. §4.3: "each subsequent subsampling doubles the factor N" - the periodic dip locations are at defined pixel intervals.).
The rationale provided for the rejection of claim(s) 17 is applicable to claim 20, mutatis mutandis. Accordingly, claim 20 is rendered obvious by the combination of Koivisto and Zhang.
Claim 21. (Currently amended)
The object detection arrangement of claim 17, wherein the controller is configured to determine an amount of compensation applied based on a distance in pixels between a location of a reference point of the candidate object and a nearest one of the confidence dips (Zhang Fig. 1: Classification confidence varies as a continuous function of pixel shift – the magnitude of the confidence drop depends on the pixel distance from a periodic peak. Objects exactly at a dip suffer the largest drop; objects slightly off a dip suffer less.).
Zhang's Figure 1 shows confidence as a smooth function of pixel position. The
farther the object is from a dip, the less confidence is suppressed and the less compensation is needed. Scaling the compensation amount based on pixel distance to the nearest dip is the natural engineering approach when the confidence-vs-position relationship is known.
Claim 22. (Currently amended)
The object detection arrangement of claim 19, wherein the controller is configured to compensate the classification of the candidate object by the multiscale CNN by forming a shifted image and classifying the candidate object by processing the shifted image via the multi-scale CNN, and wherein the controller is configured to form the shifted image by shifting the image data by a defined number of pixels along one or both axes of the image to increase a distance in pixels between a reference point of the candidate object and a nearest one of the periodic dips (Zhang §3.1: "circular shifting and convolution” When shifting, pixels are rolled off one edge to the other side. §4.1 (lmageNet): "An alternative is to take a shifted crop from a larger image." Fig. 1: Explicitly demonstrates that shifting the image changes classification confidence - shifting by even 1 pixel can dramatically change output. Koivisto ¶¶0081: Multi-scale inferencing- "infer the same images multiple times at different scales." Already processes the same image multiple times.).
Zhang explicitly demonstrates that shifting the image changes classification confidence (Fig. 1). Zhang's entire experimental methodology involves classifying shifted versions of images. One of ordinary skill, understanding from Zhang that an object at a confidence dip would have higher confidence if the image were shifted by a few pixels, would have found it obvious to shift the image by a defined number of pixels to move the object's reference point away from the nearest dip. Koivisto already supports processing images at different scales; adding a shifted version is a trivial extension of the existing multi-inference pipeline.
Claim 27. (Previously presented)
The object detection arrangement of claim 17, wherein the object detection arrangement is a smartphone or a tablet computer (Koivisto ¶¶0035: "other types of devices may be used to implement the various approaches described herein, such as robots, camera systems ... etc." Fig. 16: General computing device. General computing device for object detection. CNN-based object detection on smartphones and tablets was well-known before the EFD (e.g., MobileNet, published 2017).).
Claim 28. (Previously presented)
The object detection arrangement of claim 17, wherein the object detection arrangement is an optical see-through device (Official Notice: Optical see-through AR devices (e.g., Hololens, Magic Leap) employing CNN-based object detection were well-known before the EFD. It would have been obvious to implement Koivisto's object detection on an optical see-through device for augmented reality applications.).
Conclusion
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/Ross Varndell/Primary Examiner, Art Unit 2674