Prosecution Insights
Last updated: October 23, 2026
Application No. 18/940,140

SYSTEMS AND METHODS FOR IMAGE RETRIEVAL USING SUPER FEATURES

Non-Final OA §103
Filed
Nov 07, 2024
Priority
Oct 05, 2021 — provisional 63/252,223 +1 more
Examiner
FITZPATRICK, ATIBA O
Art Unit
2677
Tech Center
2600 — Communications
Assignee
NAVER Corporation
OA Round
1 (Non-Final)
88%
Grant Probability
Favorable
1-2
OA Rounds
6m
Est. Remaining
94%
With Interview

Examiner Intelligence

Grants 88% — above average
88%
Career Allowance Rate
802 granted / 916 resolved
+25.6% vs TC avg
Moderate +6% lift
Without
With
+6.0%
Interview Lift
resolved cases with interview
Typical timeline
2y 6m
Avg Prosecution
18 currently pending
Career history
934
Total Applications
across all art units

Statute-Specific Performance

§101
16.3%
-23.7% vs TC avg
§103
35.7%
-4.3% vs TC avg
§102
21.0%
-19.0% vs TC avg
§112
19.3%
-20.7% vs TC avg
Black line = Tech Center average estimate • Based on career data from 916 resolved cases

Office Action

§103
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 Interpretation The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “iterative attention module” or “training module” in claims 1-6, 10-15, 19. Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. 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) 1, 3, 4, 14, 16, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over US 20210248421 A1 (Gao) in view of US 20220156939 A1 (Homayounfar). As per claim 1, Gao teaches a training system, comprising: an (Gao: PNG media_image1.png 916 996 media_image1.png Greyscale PNG media_image2.png 703 1376 media_image2.png Greyscale PNG media_image3.png 712 1042 media_image3.png Greyscale PNG media_image4.png 957 996 media_image4.png Greyscale PNG media_image5.png 868 994 media_image5.png Greyscale PNG media_image6.png 878 1250 media_image6.png Greyscale PNG media_image7.png 627 994 media_image7.png Greyscale PNG media_image8.png 776 994 media_image8.png Greyscale PNG media_image9.png 801 1340 media_image9.png Greyscale PNG media_image10.png 768 997 media_image10.png Greyscale PNG media_image11.png 1230 789 media_image11.png Greyscale PNG media_image12.png 1049 993 media_image12.png Greyscale PNG media_image13.png 777 995 media_image13.png Greyscale PNG media_image14.png 322 998 media_image14.png Greyscale Para 62-63, 66, 69-73, 76-77, 81: determine ordered sets of second features based on first features in input images; train attention module (AM) to determine ordered second features based on first features); and a training module configured to: selectively input pairs of matching images to the iterative attention module; selectively input non-matching images to the iterative attention module (Gao: Fig. 3 (shown above): 312-318; PNG media_image15.png 497 994 media_image15.png Greyscale Para 25: “the distance of positive pairs are encouraged to become closer, and the distance of positive pairs are encouraged to become farther”; Para 27: “the CIN pulls positive image pairs closer while pushes negative image pairs away via a CCI module, which exploits channel correlations between samples”; Para 36: “During the training phase, training data (e.g., positive and negative pairs of images) are used to train MLM 138, wherein both SCI module 132 and CCI module 134 are utilized for training the whole CIN.”; Parad 44, 57, 78, 80: positive and negative image pairs;); and based on the ordered sets generated by the iterative attention module based on the input pairs of matching images and the input non-matching images, train the (Gao: para 109 (shown above): “contrastive loss”; paras 44, 79: contrastive loss; equations 9-10; para 55: loss, ground truth; update AM parameters based on one of these loss functions where the inputs to the AM are the matching and non-matching images and the output from the AM are the ordered sets of second features). Gao does not teach using iterative attention over T iterations (emphasis added). Homayounfar teaches a training system, comprising: an iterative attention module configured to, based on first features in input images, determine ordered sets of second features using iterative attention over T iterations, wherein T is an integer greater than or equal to two (Homayounfar: Fig. 4 (shown below): mainly 430, 455, 460 –> 435 –> 470, 355-405: see the recursive arrow on 435; PNG media_image16.png 500 994 media_image16.png Greyscale PNG media_image17.png 774 995 media_image17.png Greyscale PNG media_image18.png 825 994 media_image18.png Greyscale PNG media_image19.png 952 994 media_image19.png Greyscale PNG media_image20.png 312 996 media_image20.png Greyscale PNG media_image21.png 618 1378 media_image21.png Greyscale PNG media_image22.png 774 995 media_image22.png Greyscale PNG media_image23.png 672 893 media_image23.png Greyscale PNG media_image24.png 1336 880 media_image24.png Greyscale PNG media_image25.png 1113 940 media_image25.png Greyscale train iterative attention module (IAM) to determine ordered second features based on first features); and a training module configured to (Homayounfar: Fig. 9 (shown above); paras 134-135 (shown below)): selectively input pairs of matching images to the iterative attention module; selectively input non-matching images to the iterative attention module (Homayounfar: Fig. 4 (shown above): mainly 325, 405; PNG media_image26.png 320 996 media_image26.png Greyscale PNG media_image27.png 546 996 media_image27.png Greyscale PNG media_image28.png 361 1001 media_image28.png Greyscale Para 134 (shown below): “image pairs”); and based on the ordered sets generated by the iterative attention module, train the iterative attention module based on minimizing a loss (Homayounfar: Fig. 9 (shown above): mainly 920-925; PNG media_image29.png 874 995 media_image29.png Greyscale update IAM parameters based on a loss function and the output from the IAM are the ordered sets of second features). Thus, it would have been obvious for one of ordinary skill in the art, prior to filing, to implement the teachings of Homayounfar into Gao since both Gao and Homayounfar suggest a practical solution and field of endeavor of training an attention model to receive pairs of matching and non-matching image pairs and determine ordered sets of second features based on first features in general and Homayounfar additionally provides teachings that can be incorporated into Gao in that the attention model is iterative as to “generate a refined correlation volume 470” (Homayounfar: para 93). The teachings of Homayounfar can be incorporated into Gao in that the attention model is iterative. Furthermore, one of ordinary skill in the art could have combined the elements as claimed by known methods and, in combination, each component functions the same as it does separately. One of ordinary skill in the art would have recognized that the results of the combination would be predictable. As per claim 3, Gao in view of Homayounfar teaches the training system of claim 1 wherein the training module is configured to determine whether a pair of images is matching or not matching based on an annotation included with the pair (Gao: See arguments and citations offered in rejecting claim 1 above; Also see para 44). As per claim 4, Gao in view of Homayounfar teaches the training system of claim 1 wherein the training module is configured to determine matching first features in an input pair of matching images (Gao: See arguments and citations offered in rejecting claim 1 above; Also see: PNG media_image30.png 1018 997 media_image30.png Greyscale Homayounfar: See arguments and citations offered in rejecting claim 1 above; Also see: PNG media_image31.png 804 833 media_image31.png Greyscale ). As per claim(s) 14, 16, and 17, arguments made in rejecting claim(s) 1, 3, and 4 are analogous, respectively. Allowable Subject Matter Claims 2, 5-13, 15, 18-20 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: Limitations added in dependent claims 2, 5, 6, 10, 11, 15, 18, and 19, in conjunction with other limitations present in the corresponding independent and any intervening claim(s), distinguish over the prior art. 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. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. Atiba Fitzpatrick /ATIBA O FITZPATRICK/ Primary Examiner, Art Unit 2677
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Prosecution Timeline

Nov 07, 2024
Application Filed
Sep 28, 2026
Non-Final Rejection mailed — §103 (current)

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Prosecution Projections

1-2
Expected OA Rounds
88%
Grant Probability
94%
With Interview (+6.0%)
2y 6m (~6m remaining)
Median Time to Grant
Low
PTA Risk
Based on 916 resolved cases by this examiner. Grant probability derived from career allowance rate.

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