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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/17/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et. al. (Zhu, Y., Yang, H., Lu, Y., & Huang, Q. (2023). Simple, Effective and General: A New Backbone for Cross-view Image Geo-localization. ArXiv, abs/2302.01572. (Year: 2023)) in view of Xie (WIPO/PCT WO-2023168613-A1).
Regarding claim 1, Zhu et. al. teaches a cross-view image geo-localization method comprising (Zhu et. al. Introduction i.e., “Cross-view image geo-localization; Fig. 2 depicts the schematic):
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electronically performing with an information processor each of, a first stage operation for acquiring ground-view images and aerial-view images of a geographical position, the aerial-view images are at a first resolution (Fig. 2 above; the aerial-view is at a resolution of Ha x Wa x 3 ); establishing a first training set using each of the ground-view images and its corresponding ground-truth aerial image (Fig. 2, “Convolution Stem” of ground and aerial images respectively; see also P.5, section 3.1 below; );
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a second stage operation for building an attention map of the aerial-view images using the aerial-view image encoder weights (Zhu et. al. Fig. 6 -8);
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accessing at a second resolution of the aerial-view images, the second resolution is higher resolution than the first resolution (Zhu et. al. Table 10 which shows a higher resolution input image size in the second row of data comparison; Fig. 2 serves as stage 1 and stage 2 for two separate image sets; stage 2 is considered the second operation);
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applying the attention map to perform non-uniform cropping of the aerial-view images at the second resolution (Zhu et. al. Fig. 9, 2);
establishing a second training set using each of the first set of area-view image transformer-encoder weights and the aerial-view images at the second resolution (Zhu et. al. Fig. 9);
and training a second aerial-view image transformer-encoder with the second training set (Zhu et. al. Fig. 9).
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Zhu et. al. fails to teach training a ground-view image transformer-encoder with the first training set to produce ground-view image transformer/encoder weights;
training a first aerial-view image transformer-encoder with the first training set to produce a first set of aerial-view image encoder weights. As shown below in Zhu et. al. Table 9, there is a technical distinction made for shared weights, another variable in the transformer method that is emphasized here.
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However, Xie teaches training a ground-view image transformer-encoder with the first training set to produce ground-view image transformer/encoder weights (Xie, [0060]);
training a first aerial-view image transformer-encoder with the first training set to produce a first set of aerial-view image encoder weights (Xie, [0067]-0068]).
Zhu et. al. is analogous to the claimed invention because it pertains to a method for geo-localization via implementing an image transformer algorithm for the image cleaning that is further compared with other types of algorithms that utilize “polar-transformation” or “feature-level partition strategy”.
It would have been obvious to a person skilled in the art before the effective filing date of the claimed invention to have modified the method of cross-view image geo-localization of Zhu et. al. (Introduction) with the teachings of Xie by including the vision transformer algorithm for the first training set to produce ground-view image transformer encoder weights in the image analysis (Xie, [0060], [0067]-[0068]). An enormous number of images and location information can be extracted efficiently with a method that is more computationally affordable as a result of the simplicity and focus of the Simple Attention-Based Image Geo-localization backbone (SAIG). Furthermore, the performance capability of the complex architecture for carrying out the cross-view geo-localization task that relies on Transformer-based models is scalable with less complex structure. This is shown by comparison to previous CNN-based models that rely heavily on area assumptions.
Regarding claim 2, Xie teaches the method of claim 1, wherein the training the ground-view image transformer-encoder further includes training with a first set of class tokens to integrate classification information (Xie [0060]-[0062]).
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Regarding claim 3, Xie teaches the method of claim 2, wherein the training the first aerial-view image transformer-encoder further includes training with a second set of class tokens to integrate classification information (Xie, [0060]-[0062]).
Regarding claim 4, Xie teaches the method of claim 3, wherein the building the attention map of the aerial-view images using the aerial-view image encoder weights includes the second set of class tokens (Xie, Fig. 3, [0069]).
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Regarding claim 5, Zhu et. al. teaches the method of claim 3, wherein the training the second aerial-view image transformer-encoder further includes training with a third set of class tokens to integrate classification information (Zhu et. al. Fig. 8).
Regarding claim 6, Zhu et. al. teaches the method of claim 1, wherein the first stage operation is independent of polar transforms (Zhu et. al. Table 2).
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Regarding claim 7, Zhu et. al. teaches the method of claim 2, wherein the second stage operation is independent of polar transforms (Zhu et. al., Table 2).
Regarding claim 8, Zhu et. al. teaches the method of claim 1, wherein the first stage operation is without data augmentation (Zhu et. al. Fig. 8).
Regarding claim 9, Zhu et. al. teaches the method of claim 8, wherein the second stage operation is without data augmentation (Zhu et. al. Fig 8).
Regarding claim 10, Zhu et. al. teaches the method of claim 1, wherein the aerial images at the first resolution are a down-sampled version of the aerial images at the second resolution (Zhu et. al. Image Retrieval, Table 11, 4.6 Pretraining Results).
Regarding claim 11, Zhu et. al. teaches the method of claim 1, wherein the aerial images at the first resolution are a down-sampled version of the aerial images at the second resolution (Zhu et. al. Table 4, Table 5, Table 6 (4.3.2. Model Analysis).
Regarding claim 12, which is a system claim corresponding to method claim 1. Thus, the rejection analysis of claim 1 is equally applicable here. With respect to the limitations “process” and “memory”, Zhu in view of Xie pertains to processor based convolutional neural network architecture, thus, a process/memory as claimed are inherently necessitated.
Regarding claim 13, rejected based on claim 2.
Regarding claim 14, rejected based on claim 3
Regarding claim 15, rejected based on claim 4.
Regarding claim 16, rejected based on claim 5.
Regarding claim 17, rejected based on claim 6.
Regarding claim 18, rejected based on claims 8 and 9.
Regarding claim 19, rejected based on claim 10
Regarding claim 20, rejected based on claim 11.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Zhou et. al. (US Patent US-20230290135-A1) is relevant to the claimed invention because it claims foreign priority filing date with the prior art of record Xie (Xie WO-2023168613-A1). The inventor’s paper entitled TransGeo: Transformer Is All You Need for Cross-view Image Geo-localization is also acknowledged but does not qualify as prior art due to the grace period disclosure date of March 31, 2022, which falls within 1 year or less of the effective filing date of the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSICA YIFANG LIN whose telephone number is (571)272-6435. The examiner can normally be reached M-F 7:00am-6:15pm, with optional day off.
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/JESSICA YIFANG LIN/Examiner, Art Unit 2668 December 13, 2025
/VU LE/Supervisory Patent Examiner, Art Unit 2668