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 .
Response to Arguments
Applicant’s arguments and amendment have persuasively overcome the objection to the drawings, the claim objection, the 112 rejections, and the abstract idea 101 rejections.
The remaining issues are addressed below.
101
Applicant argues:
A close reading, therefore, of Applicant's Specification at Para. [0016] indicates that Applicant's "computer program product" is a "tangible" "storage media" including "storage devices... ."
Examiner responds:
Tangible storage media is insufficient to overcome the rejection for including transitory media, because, for example, a lightning bolt is a tangible storage medium, but also the sort of traveling electromagnetic wave that was denied in Nuijten. Claim 15 does not recite storage device.
102
Applicant argues:
The Office Action indicates that Xie et al. teaches "denoising" but this does not correspond with Applicant's claim language directed to, "fill[ing] out an erased area of the object. ..."
Examiner responds:
The examiner does not understand Applicant’s position. As explained in the non-final, Xie shows the cat with a new hat, hence the area has been filled out with a new hat.
The new limitation is addressed below.
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, 4-8, 10-15, and 17-20 (all claims) 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, 8, and 15 recite “reduce a need,” but the “need” is subjective. MPEP 2173.05(b)(IV). In particular, the need for annotation is tied to the particular training that needs to occur, but the claim does not define the particular need, and thus the scope of the need for annotation is not subject to an objective standard.
Claims 1, 8, and 15 recite “used to train,” but it is unclear whether this is an intended use or a required element. If required, does it require that the training have occurred in the past tense (i.e., before they were generated?). Further, the preambles of the independent claims suggest that the result is data that can be used for training, but this is in conflict with the recitation that the samples are actually used to train.
Dependent claims are likewise rejected.
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 § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 4-8, 10, 11, 13-15, 17 and 20 (all claims except those rejected for 103) are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US20240169622A1 (“Xie”).
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 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; and (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.)
the generated additional data sample used to train one or more segmentation learning models. (Xie, [0092] “the training component compares output image 555 to original image 505 to train the diffusion model.” Xie’s diffusion model teaches the claimed segmentation learning model because it learns from “segmentation data.” See, e.g., claim 8. Additionally, the specification [0041] states “For example, the trained segmentation machine learning model 408 may be a neural network, such as a U-Net (version 1 first released in May 2015), SegFormer (version 1 first released in May 2021), Mask R-CNN (version 1 first released in March 2017), or Swin Transformer (version 1 first released in March 2021), among other suitable networks.” Xie teaches a neural network [0067], a U-Net [0069[ and a Mask R-CNN [0075]. Alternatively, as per the 112 rejection, this limitation is merely intended use.)
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.)
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.
20. The computer program product of claim 15, further comprising program code executable by the processor to fill 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.)
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 12, 18, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over US20240169622A1 (“Xie”) in view of the Wikipedia article Erosion (morphology) as of October 27, 2023, retrieved from https://en.wikipedia.org/w/index.php?title=Erosion_(morphology)&oldid=1182092397 (“Wikipedia”).
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. (Li, [100] “erosion kernel can be preset during the preprocessing stage … Similarly, the mask container table can also be preset in the image material preprocessing stage.”)
(Wikipedia, “The erosion operation usually uses a structuring element for probing and reducing the shapes contained in the input image.” Wikipedia’s reducing teaches the claimed erasing, see also the example under “binary erosion” and “otherwise it gets deleted”. Wikipedia’s input image teaches the claimed mask, further, Wikipedia’s input can be used with Xie’s mask because Xie teaches processing the masked and unmasked images separately.)
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 Wikipedia to the teachings of Xie such that Wikipedia’s erosion is used to implement Li’s erasing for the purpose of a technique for deleting data that is specific to shapes in images (Wikipedia, “reducing the shapes contained in the input image”), simple substitution (MPEP 2143), or art recognized equivalence for the same purpose (MPEP 2144.06(II)) because erosion is a “one of two fundamental operations (the other being dilation) in morphological image processing,” (Wikipedia) and this technique deletes data (i.e., it is a known substitute for other types of removing information).
Based on the above, this is an example of “combining prior art elements according to known methods to yield predictable results.” MPEP 2143.
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. (Wikipedia, Binary erosion, “Example … B is a 3 x 3 matrix.” Wikipedia’s example uses B as an erosion kernel, and B is analogous to specification, [0048] “a conservative erosion kernel of 12x12 pixels.”)
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
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 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.
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/DAVID ORANGE/Primary Examiner, Art Unit 2663
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