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 .
Amendments of record are entered.
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.
Claim(s) 1-6, 36 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tegzes et al. (US 2018/0315188) in view of Wang (US 2022/0366259).
As to claim 1:
Tegzes discloses:
One or more processors comprising:
circuitry one or more circuits to: use one or more neural networks to segment one or more objects of one or more images (¶0125, 0055, 0124, 0134, neural networks to perform the method and driven by processors/controller circuitry), wherein at least one of the one or more segmentations of the one or more neural networks are generated for the one or more objects associated with one or more annotated bounding boxes of the one or more images. (See Abstract, ¶0114, determining/generating bounding boxes, each capturing a suspected object in a respective region of interest. ¶0115, once bounding box is determined, the image data in the bounding box is segmented. See also Fig. 19, step 1904 through 1910. See ¶0059, 0065, 0108, 0121 one or more neural networks used for the object processing of the system, i.e. segmentation and classifications. In particular, ¶0108, forming bound box step is performed using a trained deep CNN. ¶0145, ground truth box, ¶0181, reference region is/are labelled for comparison/reference),
Tegzes is, however, silent on “the one or more neural networks comprising a student neural network that comprises parameters that are updated based, at least in part, on a comparison of one or more segmentations of a teacher neural network with one or more segmentations of the student neural network to evaluate segmentation accuracy of the student neural network as compared to the teacher neural network”
The limitation above is directed to a known methodology of training/retraining of a neural network using knowledge distillation (i.e. teacher supervising student’s performance to further refine the student network), which is disclosed in at least reference Wang as below:
Wang, in a related of field of image segmentation (¶0059), discloses a machine learning model having a teacher network and a student network performing segmentation processing on the input image(s) to produce respective output (for example per ¶0100, 0059), wherein the student network is modified/updated to ensure its output to be consistent with the teacher network via a consistency check (i.e. a comparison process), and also further based on the loss function value from the teacher network (¶0106, 0065, also 0091-0093 discussing the supervising by the teacher network’s output to improve the student network’s parameter).
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that the operational neural network performing segmentation in Tegzes is implemented to be trained/updated by a teacher network in similar manner disclosed by Wang. This implementation advantageously benefits and improve performance of the student networks per ¶0015 of Wang, (“ the student neural network understands the training process of the teacher neural network more fully, thereby effectively improving the performance (e.g. accuracy) of the student network”)
As to claim 2:
Tegzes in view of Wang discloses all limitations of claim 1, wherein the one or more segmentations of the student network comprises generating one or more segmentation maps indicating pixels of the one or more objects. (See Tegzes, ¶01030, 0133, ¶0177, output comprising generation of output with each pixel/voxel identified/labelled)
As to claim 3:
Tegzes in view of Wang discloses all limitations of claim 1, wherein the circuitry is to use at least one of the teacher neural network and the student neural network to determine one or more classifications of the one or more objects. (See Tegzes, ¶0100, 0108, 0116, 0188, using the CNNs to classify the object(s) as a particular organ or an organ of interest)
As to claim 4:
Tegzes in view of Wang discloses all limitations of claim 1, wherein the circuitry is to use at least one of the teacher neural network and the student neural network to determine instances of the one or more objects in the one or more images. (Tegzes, ¶0104, the system configured to detect presence of objects (instances) to determine whether they are relevant. ¶0137, determine more than one object of interests for identification)
As to claim 5:
Tegzes in view of Wang discloses all limitations of claim 1, wherein the circuitry is to use at least one of the teacher neural network and the student neural network to determine correspondence between the one or more objects in the one or more images. (Tegzes, ¶0140, 0155, for example, for paired organs (eyeballs) two similar size boxes, as the system determines they are separate instances but related as the same type of organ. ¶0159, determining whether objects in images are related, for example objects of the body are determined as the set of object to be considered, whereas object outside the body are determined as irrelevant)
As to claim 6:
Tegzes in view of Wang discloses all limitations of claim 1, wherein the circuitry is cause the teacher neural network to generate one or more labels associated with the one or more images to be used for the one or more segmentations of the student network. (Tegzes, ¶0177, “provide a semantic segmentation (e.g., pixel-wise classification) of image data to label each pixel and segment the image into regions corresponding to sets of labels. Similarly, each voxel can be classified according to a region of interest to which it belongs. An example classifier can process image data on a voxel-by-voxel level to provide an output for each voxel based on its neighboring voxels and classify an entire image slice in a single action”)
As to claim 36:
Tegzes in view of Wang discloses all limitations of claim 1, wherein at least one of the teacher neural network and the student neural network are to generate one or more proposals for one or more locations of the one or more annotated bounding boxes. (See Tegzes, ¶0112-0114, proposal of several boundaries/parameters of the current proposed bounding boxes are used to determine precisions.)
Response to Arguments
Applicant’s arguments with respect to the claim(s) have been considered but are moot because a ground of rejection is established with new reference(s) addressed to new limitations and are not specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Price et al. (US 2018/0108137) - Certain aspects involve semantic segmentation of objects in a digital visual medium by determining a score for each pixel of the digital visual medium that is representative of a likelihood that each pixel corresponds to the objects associated with bounding boxes within the digital visual medium. An instance-level label that yields a label for each of the pixels of the digital visual medium corresponding to the objects is determined based, in part, on a collective probability map including the score for each pixel of the digital visual medium. In some aspects, the score for each pixel corresponding to each bounding box is determined by a prediction model trained by a neural network.
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 QUAN M HUA whose telephone number is (571)270-7232. The examiner can normally be reached 10:30-6:30.
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/QUAN M HUA/Primary Examiner, Art Unit 2645
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