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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 June 19, 2026 has been entered.
Response to Arguments
Applicant’s arguments and amendment have persuasively overcome the all but one of the 112 rejections and the prior art rejections.
The remaining issues are addressed below.
101
Applicant argues:
please note that in this Response Applicant amends claim 15 to indicate that the "computer program product compris[es] a non-transitory computer-readable …
Examiner responds:
The examiner does not see this language in the claim.
Claim Interpretation
The claims recite both “erase” and “erode.” A review of the art shows that “erode” is a technical term, see, e.g., https://en.wikipedia.org/wiki/Erosion_(morphology). The specification describes erosion as morphological erosion at [0014] and [0042]. “Erase” is understood with its plain meaning, i.e., to remove information.
“Segmentation learning model” is any machine learning model that segments or learns from segments. See, e.g., specification [0041] “For example, the trained segmentation machine learning model 408 may be a neural network, such as … among other suitable networks.” Here, the specification has defined the term to include (at least) neural networks that are suitable, i.e., that perform this function. In other words, the segmentation learning model is defined by what it does, rather than what it is (i.e., it is not defined as a particular neural network architecture).
“Blend” is interpreted under its plain meaning, that is, blending an edge is interpreted as the edge between the two images being aesthetically similar.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 2, and 4-7 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 2, and 4-7 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth the subject matter which the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the applicant regards as the invention.
Claim 1 only requires a processor capable of performing certain instructions, but does not require the presence of the instructions. However, in the present remarks, pages 9 and 10, Applicant discusses their “inventive concept” and specifies that it reduces a need for manual annotation. This does not describe a bare processor, rather, it describes a processor with instructions.
Dependent claims are likewise rejected.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 15 and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because claim 15 recites a “computer program product.” The broadest reasonable interpretation of “computer program product” includes software per se. MPEP 2106.03(I). The most relevant section of the specification is [0016], which discusses transitory signals in the context of a “computer readable storage medium” and a “storage device,” but not a “computer program product.” Claims 17-20 are likewise rejected.
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.
Claims 1, 2, 4-8, 10-15, and 17-20 (all claims) are rejected under 35 U.S.C. 103 as being unpatentable over US20240169622A1 (“Xie”) in view of US20230079774A1 (“Datar”). References are listed in the Notice of Cited References when they were first cited. If a reference is not identifiable (e.g., due to a typo), it can be identified by searching for the quoted text.
As highlighted by the 112 rejection, claim 1 and dependents require only a bare processor. Xie shows a processor 405 in Fig. 4 that appears to be suitable for the intended use of Claim 1-8.
In addition,
1. A system including a computer processor capable of executing computer instructions to reduce a need for manual annotation of datasets to be used for training of one or more segmentation learning models, the computer instructions including instructions to:
receive an annotated data sample comprising an object contained in an annotated mask; (Xie, Fig. 2, step 205)
partially erase the object contained in the annotated mask; (Xie, Fig. 2, step 210. Xie’s partially noisy map teaches the claimed partially erase because the specification uses “erase” broadly (see, e.g., specification [0037] that eroding is a type of erasing), and Xie’s noise means that information has been removed.)
fill out an erased area of the object using a trained generative model to generate an additional annotated data sample, (Xie, Fig. 2, step 215. Xie’s denoising teaches the claimed filling out. If you look closely, you see that the cat in the bottom right is wearing a different hat than the original cat in the upper left. Fig. 12, step 1225. Xie’s training the model teaches the claimed additional annotated data sample.)
Xie is not relied on for the below claim language.
However, Datar teaches the generated additional data sample for use in connection with training one or more segmentation learning models using the generated additional annotated data sample. (Datar, claims 12 and 13, “training the artificial neural network with supervised learning using a loss function penalizing differences between the output data and the label data … subtracting the eroded segmentation mask from the ground truth segmentation mask to generate the label data.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Datar to the teachings of Xie such that Xie’s image editing is used with Datar’s training for the purpose of labeling data (Datar, [0007]) in addition generating more training data (Xie, claim 8).
Based on the above, this is an example of “combining prior art elements according to known methods to yield predictable results.” MPEP 2143.
2. The system of claim 1, wherein the trained generative model comprises a diffusion-based inpainting model. (Xie, [0003] “The multi-modal image editing system performs image inpainting by replacing a region of the image corresponding to the mask with noise and using a diffusion model … ”)
4. The system of claim 1, wherein the generated additional annotated data sample comprises an image-mask pair that comprises the annotated mask of the annotated data sample. (Xie, Fig. 2, step 215. [0171] “The loss function provides a value (a “loss”) for how close the predicted annotation data is to the actual annotation data.” See also, claim 20 “generate a predicted mask based on the composite image map using a mask network.”)
5. The system of claim 1, wherein the processor is to erode the annotated mask to generate an eroded mask and erase an area of the object within the eroded mask to partially erase the object. (Xie, Fig. 2, step 210. Xie’s partially noisy map teaches the claimed partially erase because Xie’s noise is the same as the specification’s eroding (see, specification [0037] that eroding is a type of erasing).)
6. The system of claim 1, wherein the processor is to blend an edge of the filled out area of the object with an original outer portion of the object that was not erased using a Gaussian filter. (Xie, Fig. 7, step 755. This is the same cat image as Fig. 2, but easier to see. That the picture looks relatively unedited teaches the claimed blended edges.)
7. The system of claim 1, wherein the processor is to input an associated class from the annotated mask as a text guidance into a diffusion-based inpainting model. (Xie, Fig. 2, step 205. Xie’s text prompt teaches the claimed associated class because specification [0043] identifies text input as a type of class.)
Claim 8 is rejected as per claim 1.
10. The computer-implemented method of claim 8, wherein the generated additional annotated data sample comprises the annotated mask from the annotated data sample. (Xie, Fig. 12, step 1215.)
11. The computer-implemented method of claim 8, further comprising receiving a text prompt and filling out the erased area using a diffusion-based inpainting model guided by the text prompt. (Xie, Fig. 11, step 1125.)
12. The computer-implemented method of claim 8, wherein partially erasing the object comprises eroding the annotated mask to generate an eroded mask and erasing an area of the object within the eroded mask. (Datar, claim 13, “performing binary erosion on the ground truth segmentation mask to create an eroded segmentation mask;”)
13. The computer-implemented method of claim 8, wherein filling out the erased area comprises blending an edge of the filled out area of the object with an original outer portion of the object that was not erased using a Gaussian filter. (Xie, Fig. 13, step 1325.)
14. The computer-implemented method of claim 8, further comprising filling out, by the processor, the erased area of the object using the generative model to generate a plurality of additional annotated data samples having the same annotation as the annotated data sample. (Xie, Fig. 3. Xie’s composite images (plural) disclose the claimed plurality of samples. Xie, Fig. 12, step 1225. Xie’s training teaches that these are the claimed annotated data samples.)
For claims 15-20, see also, Xie, [0004] “non-transitory computer readable medium.”
Claim 15 is rejected as per claim 1.
Claim 16 is rejected as per claim 9.
Claim 17 is rejected as per claim 11.
Claims 18 is rejected as per claim 12. See also, Xie, [0004] “non-transitory computer readable medium.”
19. The computer program product of claim 15, further comprising program code executable by the processor to receive an erosion kernel and erode the annotated mask based on the erosion kernel. (Datar, [0114] “It has been found by the inventors that a three by three binary erosion kernel yields excellent results.”)
20. The computer program product of claim 15, further comprising program code executable by the processor to fill out the erased area of the object using the generative model to generate a plurality of additional annotated data samples having the same annotation as the annotated data sample. (Xie, Fig. 3. Xie’s composite images (plural) disclose the claimed plurality of samples. Xie, Fig. 12, step 1225. Xie’s training teaches that these are the claimed annotated data samples.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US20210158523A1 – [0220] “The remaining mask was dilated by a kernel size of 6 to reverse the effects of the initial erosion kernel”
US11570398B2 – Fig. 8
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID ORANGE whose telephone number is (571)270-1799. The examiner can normally be reached Mon-Fri, 9-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Gregory Morse can be reached at 571-272-3838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DAVID ORANGE/Primary Examiner, Art Unit 2663
Prosecution Insights