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 .
Priority
Receipt is acknowledged that application claims priority to foreign application with application number IN202331054137 dated 08/11/2023. Copies of certified papers required by 37 CFR 1.55 have been received. Priority is acknowledged under 35 USC 119(e) and 37 CFR 1.78.
Information Disclosure Statement
The IDS dated 01/10/2024 has been considered and placed in the application file.
Claim Objections
Claim(s) 1-2, 4, 7-8, 16-17, and 19 are objected to for having illegible equations in each respective claim. The examiner recommends posting a higher quality version of the equations and/or increasing the size of the equation to make them more readable.
Claim 11 is in improper format. All claims must end in a period.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim(s) 11 are rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim(s) 11 recites “The Convolutional Neural network (CNNs) based system for image analysis as claimed in claim 3, wherein in said convolution module said DC processor blocks are included in the first four down-sampling based max-pooling encoder layers and the final four up-sampling decoder layers for gathering ROI-specific data, to lower overlap based error at the segmentation boundary while increasing the accuracy of segmentation and to enable storage of high-level semantic feature based image maps in the decoder; said WG module processor blocks interactive with DC blocks concatenates said high-level semantic feature based image maps stored in the up-sampled decoder arm of DC blocks to focus on the lower-level details in the retrieved encoder feature maps of DC blocks for merging of said up-sampled images with their equivalent encoded representations thereby enhancing significance/weightage of a pixel through said WG module allowing adaptive selection of pixel spatial information by highlighting pixels from the ROI, while suppressing the less important ones, with said last layer of WDU-Net (Deep Weighted Deformable Segmentation Network) involving sigmoid activation function based processer to generate a probabilistic ROI at the system output, as per the block based system architecture below:”.
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The claim limitations states generating a probabilistic ROI using the chart system above. However, it is unclear what the chart is supposed to represent and how it is being used to generate such ROI. The examiner recommends putting the chart/image above into words that can clearly convey how the probabilistic ROI is being generated.
Regarding Claims 1-20
No prior art currently reads on claims 1-20.
References Cited
US 20230005152 A1 to Choi et al. discusses an image segmentation apparatus using a U-net based model.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WAYNE ZHANG whose telephone number is (571) 272-0245. The examiner can normally be reached Monday-Friday 10:00-6:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ms. Sumati Lefkowitz can be reached on (571) 272-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WAYNE ZHANG/Examiner, Art Unit 2672
/GANDHI THIRUGNANAM/ Primary Examiner, Art Unit 2672
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