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 .
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3 and 13-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by A. Yu, B. Liu, X. Cao, C. Qiu, W. Guo and Y. Quan, "Pixel-Level Self-Supervised Learning for Semi-Supervised Building Extraction From Remote Sensing Images," in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 16 September 2022, Art no. 2507105, doi: 10.1109/LGRS.2022.3207465 (Yu).
As per claim 1, Yu teaches a method for constructing a model, comprising:
training, using a first dataset, a model to be processed to obtain a first model, the first dataset comprising at least one first image data, and the first model comprising a backbone network (Yu:
abstract (shown below): “the backbone is first trained in pixel-level self-supervised learning (SSL) manner without labels”:
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Page 1, col 2, paras 2-3 (shown below) : “we introduce the SSL technique for backbone training without using any labels before tuning the whole model with limited annotations”;
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Page 2 col 1, paras 2:
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Page 2 col 1, paras 3:
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: Pre-train backbone on first dataset. The first dataset is the first set of patched without labels, i.e. unlabeled images.);
constructing, according to the backbone network in the first model, a second model, the second model comprising the backbone network and a first processing network, and the first processing network referring to all or part of other networks other than the backbone network in the second model; and training, using a second dataset, the second model to obtain a model to be used, the model to be used comprising the backbone network and a second processing network, network parameters of the backbone network in the second model kept unchanged during training of the second model, the second processing network referring to a training result of the first processing network in the second model, and the second dataset comprising at least one second image data (Yu:
abstract (shown above): “Next, the pretrained backbone is combined with a prediction head using multiscale features and the whole network is tuned”;
Fig. 1 (shown above);
: Build second model from trained backbone
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Page 3, col 1, para 1: | Page 3, col 2, para 2:
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Page 3, col 2, last para:
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: Freeze backbone while training other networks).
As per claim 2, Yu teaches the method of claim 1, wherein the first processing network is used to process output data of the backbone network so as to obtain an output result of the second model (Yu: See arguments and citations offered in rejecting claim 1 above).
As per claim 3, Yu teaches the method of claim 1, wherein the first image data belongs to single-object image data, and/or (only one alternative is required), the second image data comprises at least two objects (Yu: See arguments and citations offered in rejecting claim 1 above;
Either single object-type being building = single object,
Or
Multiple buildings = multiple objects).
As per claim 13, Yu teaches the method of claim 2, wherein the output result of the second model is a target detection result, a semantic segmentation result, or a key point detection result (Yu: See arguments and citations offered in rejecting claim 2 above).
As per claim 14, Yu teaches the method of claim 1, wherein training, using the first dataset, the model to be processed to obtain a first model, comprises:
performing, using the first dataset, fully-supervised training on the model to be processed to obtain the first model; or,
performing, using the first dataset, self-supervised training on the model to be processed to obtain the first model (Yu: See arguments and citations offered in rejecting claim 1 above).
As per claim 15, Yu teaches the method of claim 1, further comprising:
fine-tuning the model to be used using a preset image dataset to obtain an image processing model, the image processing model comprising a target detection model, a semantic segmentation model, or a key point detection model (Yu: See arguments and citations offered in rejecting claim 1 above).
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.
Claim(s) 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Yu in view of Official Notice.
As per claim(s) 17, arguments made in rejecting claim(s) 1 are analogous. Yu is silent regarding An electronic device, comprising a processor and a memory, wherein the memory is configured to store an instruction or a computer program; and the processor is configured to execute the instruction or the computer program in the memory to cause the electronic device. Examiner provides Official Notice that these limitations were well known prior to filing.
One of ordinary skill in the art, prior to filing, would have recognized the advantage of cost effective development, implementation, and maintenance. The teachings of the prior art could have been incorporated into Yu in that An electronic device, comprising a processor and a memory, wherein the memory is configured to store an instruction or a computer program; and the processor is configured to execute the instruction or the computer program in the memory to cause the electronic device.
As per claim(s) 18, arguments made in rejecting claim(s) 1 are analogous. Yu is silent regarding A non-transitory computer-readable medium, having an instruction or a computer program stored therein, wherein the instruction or the computer program, when run on a device, causes the device. Examiner provides Official Notice that these limitations were well known prior to filing.
One of ordinary skill in the art, prior to filing, would have recognized the advantage cost effective development, implementation, and maintenance. The teachings of the prior art could have been incorporated into Yu in that A non-transitory computer-readable medium, having an instruction or a computer program stored therein, wherein the instruction or the computer program, when run on a device, causes the device.
Allowable Subject Matter
Claims 4-12 and 19-21 are 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.
The following is a statement of reasons for the indication of allowable subject matter: Yu teaches the online model and momentum model in conjunction with the training the backbone network using the unlabeled dataset – not the “second model”, which is comprised of the trained backbone network combined with a “first processing network” trained using the second dataset that is different than the first dataset that was used to pre-train the backbone network – as is required by claims 4 and 19 depending from their respective claims 1 and 17. No other prior art reference remedies these deficiencies.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Atiba Fitzpatrick whose telephone number is (571) 270-5255. The examiner can normally be reached on M-F 10:00am-6pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Bee can be reached on (571) 270-5183. The fax phone number for Atiba Fitzpatrick is (571) 270-6255.
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Atiba Fitzpatrick
/ATIBA O FITZPATRICK/
Primary Examiner, Art Unit 2677
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