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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/15/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 (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 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.
Claims 1-3, and 6 are rejected under 35 U.S.C. 103 as being unpatentable over US 2019/0391578 to Tariq et al, hereinafter “Tariq” in view of JP 2013-61802, hereinafter “JP’802.” Note that all reference/citations to JP’802 in the below are in reference to supplied English translation of JP’802.
With regard to claim 1, Tariq discloses an image recognition device comprising: an image acquisition unit that acquires a captured image, see [0039]; a first detection unit that detects a first region including a detection target in the captured image using a first detection model trained by machine learning with an image having an image size of a predetermined value or more as input, see [0040] and the “first ML model”; a second detection unit that detects a second region including the detection target in the captured image using a second detection model trained by machine learning with an image having an image size of less than the predetermined value as input, see [0040 and the “second ML model” receiving a second scaled image, see also Figure 2a and 2b. However, Tariq fails to disclose a determination unit that invalidates detection of either one of the first region and the second region when the first region and the second region overlap in the captured image.
In the same field of image recognition, JP’802 shows in Figures 4 and 5 instances where the boxes surrounding the detected pedestrians are overlapped in some instances, and wherein box 403 is a false positive. JP’802 explains on page 1, two-thirds down the page that human detection is easy to erroneously detect due to false detection of a shoulder or foot. JP’802 also explains at the bottom of page 2 and all of page 3 that a first discriminating unit 104 detects whether a human is detected in the image and any overlapping bounding boxes are then further considered by the area extraction unit 106 that detects overlapping bounding boxes, see also Figure 5 and the two overlapping areas 501 and 502. Overlap area determination unit 108 then computes that bounding box 403 should be eliminated as an invalid region as shown in Figure 10.
Therefore, it would have been obvious before the effective filing date of the claimed invention to have provided the detection method of Tariq with the ability to detect invalid bounding box areas due to overlapping detected areas in an image as taught by JP’802. The rationale being that an advantageous reduction in erroneous detection would occur without lowering the detection rate as taught by JP’802 at the bottom of page 1.
With regard to claim 2, Tariq further discloses image recognition device according to claim 1, wherein the first detection model is a recognition dictionary for a nearby area (i.e. large ROIs, see [0039] and the second detection model is a recognition dictionary for a distant area (i.e. small ROIs, see [0039], and wherein the size of an image used for machine learning for the second detection model is smaller than the size of an image used for machine learning for the first detection model, see again [0039] and [0040].
With regard to claim 3, JP’802 further teaches that the determination unit invalidates the detection of the second region when the first region and the second region overlap in the captured image (compare Figure 4 with region 403 and the finalized image in Figure 10 having eliminated/removed the erroneous region 403).
Claim 6 is rejected for reasoning applied, mutatis mutandis, as that of claim 1 above.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Tariq in view of JP’802 as applied to claims 1-3, and 6 above, and further in view of JP 2018-063675, hereinafter “JP’675.” Note that all reference/citations to JP’675 in the below are in reference to supplied English translation of JP’675.
Tariq discloses a detection method for detecting objects in images using two trained models where each model was trained on different scale images, respectively. JP’802 also teaches object detection where erroneous detection areas can be eliminated by analyzing the overlap between area bounding boxes. However, neither Tariq nor JP’802 disclose a display control unit that displays an image for display in which an additional image is superimposed on the captured image such that the display mode of the first or second region not invalidated by the determination unit is different from the display mode of the first or second region invalidated by the determination unit.
On the other hand, JP’675 discloses a detection method for detecting objects in images where regions (bounding boxes) can be invalidated, see JP’675 at paragraphs [0019]-[0022]. A display device 140 displays the image along with overlaying bounding boxes over the detected objects, see [0035] and Figure 6 with invalidated areas denoted by a broken-line rectangle and valid detection areas denoted by a solid line rectangle.
Therefore, it would have been obvious before the effective filing date of the claimed invention to have provided the object detection combination of Tariq and JP’802 with a display for displaying both the invalid detection areas as well as the valid detection areas to a user as taught by JP’675 as doing this would have permitted a user to grasp which areas of the image contained an invalid detection, see JP’675 at [0033], thus permitting the user to confirm the accuracy of the detection models.
Allowable Subject Matter
Claim 4 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art cited discloses the general state of the art surrounding the detection of objects within images by the use of machine learning models.
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DAVID OMETZ
Primary Examiner
Art Unit 2672
/DAVID OMETZ/Primary Examiner, Art Unit 2672
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