DETAILED ACTION
Response to Arguments
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 1/07/2026 has been entered.
The application has pending claim(s) 1-20.
Applicant's arguments with respect to claim(s) 1-20 have been considered but are moot in view of the new ground(s) of rejection because of the Request for Continued Examination (RCE).
Applicant's arguments filed 1/07/2026 have been fully considered but they are not persuasive.
The Applicant alleges, “The 35 U.S.C. 103 Rejections …” in pages 8-9, and states respectively that the combination of Pang and Man do not teach or suggest all the elements of amended independent claims 1, 8, and 15 and that neither Zeiler nor Yap alone or in combination with Pang and Man remedy the deficiencies present in the combination of Pang and Man. However the Examiner disagrees because the combination of Pang and Man do not consist of such deficiencies but rather do indeed teach the broadest reasonable claim language interpretation of such an amendment. More specifically, the additional embodiment of Man as depicted in Figs. 3-4 presents a patch analysis for labeling multiple objects / materials / products in the image (see Man, Figs. 3-4, [0058]-[0059], [0061], [0067], [0075], [0084]-[0085], [0089]-[0090], [0092], a label of the material and the color are generated and assigned to each patch of pixels for a plurality of patches of pixels [Fig. 4 depicts an example showing at least a first portion and a second portion], each different label associated with a particular material in terms of color, pattern, and texture, the final segmentation mask of labels is generated by combining the label mappings determined for the patches). Further discussions are addressed in the prior art rejection section below. Therefore claims 1-20 are still not in condition for allowance because they are still not patentably distinguishable over the prior art reference(s).
Claim Rejections - 35 USC § 103
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, 5-8, 10, 12-15, 17, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pang et al (US 2021/0176342 A1, provided by Applicant’s Information Disclosure Statement IDS, as applied in previous Office Action) in view of Man et al (US 2020/0090316 A1, as applied in previous Office Action).
Re Claim 1: Pang discloses a method of image annotation comprising: obtaining a candidate annotation map for an annotation task for an image from each of a plurality of annotation models, the image comprising a plurality of pixels, wherein each of the candidate annotation maps comprises suggested annotations for at least a first portion of the plurality of pixels (see Pang, [0020]-[0026], and more specifically [0028], select machine learning models related to an image annotation job, initial object predictions are generated by the machine learning models, wherein the initial object predictions are initial sets of annotations generated by the machine learning models); receiving, by a processing device, user selections or modifications of at least one of the suggested annotations from one or more of the candidate annotation maps and associations (see Pang, [0020]-[0026], [0028], display such initial object predictions on a user interface to assist the user in making annotation adjustments and/or verifications wherein the user selects which objects/bounding boxes and their associated classifications to view and/or edit, adjust, or otherwise make changes to); and generating, by the processing device, a final annotation map based on the user selections or modifications from the one or more of the candidate annotation maps (see Pang, [0020]-[0026], [0028], save the updated information).
However Pang fails to explicitly disclose where Man discloses wherein each of the candidate annotation maps comprises suggested annotations for at least a first portion of the plurality of pixels, the suggested annotations comprising associating one or more colors with the first portion of the plurality of pixels (see Man, Figs. 3-4, [0058]-[0059], [0061], [0067], [0075], [0084]-[0085], [0089]-[0090], [0092], product materials in an image, a label of the material and the color are generated and assigned by the automatic machine learning process to each patch of pixels for a plurality of patches of pixels [Fig. 4 depicts an example showing at least a first portion], wherein the user either accepts or revises the assigned label(s), each different label associated with a particular material in terms of color, pattern, and texture), the associations between the one or more colors and one or more classes (see Man, Figs. 3-4, [0058]-[0059], [0061], [0067], [0075], [0084]-[0085], [0089]-[0090], [0092], materials include steel, wood, concrete, etc., each different label associated with a particular material in terms of color, pattern, and texture), the final annotation map comprising annotations for at least a second portion of the plurality of pixels and an associated class of the one or more classes for each of the second portion of the plurality of pixels (see Man, Figs. 3-4, [0058]-[0059], [0061], [0067], [0075], [0084]-[0085], [0089]-[0090], [0092], a label of the material and the color are generated and assigned to each patch of pixels for a plurality of patches of pixels [Fig. 4 depicts an example showing at least a second portion], each different label associated with a particular material in terms of color, pattern, and texture, the final segmentation mask of labels is generated by combining the label mappings determined for the patches).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pang’s method using Man’s teachings by including the label mapping between pixels and color labels processing to Pang’s machine learning model, adjustment and verification processing [wherein similarly Pang teaches the user verifies and/or adjusts initial object predictions] in order to improve the label prediction of such machine learning models / algorithms (see Man, Figs. 3-4, [0042], [0059], [0061], [0067], [0075], [0084]-[0085], [0089]-[0090]).
Re Claim 3: Pang further discloses wherein the annotation task comprises identifying and marking locations within the image (see Pang, [0020]-[0026], [0028], image annotation job, wherein the annotations will include e.g. locating objects/bounding boxes and classifying objects).
Re Claim 5: Pang further discloses wherein each of the candidate annotation maps comprises a plurality of categories of suggested annotations (see Pang, [0020]-[0026], [0028], wherein the annotations will include e.g. locating objects/bounding boxes and classifying objects as a car, a person, etc.).
Re Claim 6: Pang further discloses selecting a first candidate annotation map (see Pang, [0020]-[0026], [0028], provide a plurality of such initial object predictions to a user interface of a client device); displaying, the first candidate annotation map comprising suggested annotation via a user interface of a client device (see Pang, [0020]-[0026], [0028], display the plurality of such initial object predictions on the user interface of the client device to assist the user in making annotation adjustments and/or verifications); and receiving user selections or modifications of at least one of the suggested annotations (see Pang, [0020]-[0026], [0028], display the plurality of such initial object predictions and from the displayed initial object predictions, the user then selects which objects/bounding boxes to view and/or edit, adjust, or otherwise make changes to).
Re Claim 7: Pang further discloses wherein the user interface comprises at least one adjustable parameter associated with an annotation model corresponding to the first candidate annotation map (see Pang, [0020]-[0026], [0028], user interface for the requester to specify the requirements for an annotation job, wherein the requester specifies a confidence level threshold, the initial object predictions in the machine learning model output that meet the confidence level threshold are kept and the rest are discarded).
As to claim 8, the claim is the corresponding system claim to claim 1 respectively. The discussions are addressed with regard to claim 1. Further, Pang further discloses a system comprising: a memory; and a processing device, operatively coupled to the memory, to perform the method (see Pang, [0020]-[0026], [0028], and more specifically e.g. [0015], the platform implemented by a processor coupled to a memory, wherein the processor executing the instructions stored by the memory).
As to claim 15, the claim is the corresponding non-transitory computer-readable storage medium claim to claim 1 respectively. The discussions are addressed with regard to claim 1. Further, Pang further discloses a non-transitory computer-readable storage medium having instructions stored thereon that, when executed by a processing device, cause the processing device to perform the method (see Pang, [0020]-[0026], [0028], and more specifically e.g. [0015], the platform implemented by a processor coupled to a memory / storage medium, wherein the processor executing the instructions stored by the memory / storage medium).
As to claims 10 and 17, the discussions are addressed with regard to claim 3 respectively.
As to claims 12 and 19, the discussions are addressed with regard to claim 5 respectively.
As to claims 13 and 20, the discussions are addressed with regard to claim 6 respectively.
As to claim 14, the discussions are addressed with regard to claim 7 respectively.
Claim(s) 2, 9, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pang as modified by Man, and further in view of Zeiler et al (US 10,222,942 B1, provided by Applicant’s Information Disclosure Statement IDS, as applied in previous Office Action). The teachings of Pang as modified by Man have been discussed above.
Re Claim 2: However Pang as modified by Man fails to explicitly disclose where Zeiler discloses providing the final annotation map as additional training data to update at least one of the plurality of annotation models (see Zeiler, col. 5 at lines 61-67, col. 6 at lines 1-9, col. 11 at lines 46-55, the user verification and/or user change of the predicted label generated by the machine learning algorithms are used to update the machine learning algorithms).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Pang’s method, as modified by Man, using Zeiler’s teachings by including the machine learning update process [which is in response to the user verification and/or user change of the predicted labels] to Pang’s [as modified by Man] machine learning models processing [wherein similarly Pang teaches the user verifies and/or adjusts initial object predictions] in order to improve the label prediction of such machine learning models / algorithms (see Zeiler, col. 5 at lines 61-67, col. 6 at lines 1-9, col. 11 at lines 46-55).
As to claims 9 and 16, the discussions are addressed with regard to claim 2 respectively.
Claim(s) 4, 11, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pang as modified by Man, and further in view of Yap et al (“Automated Facial Wrinkles Annotator” – ECCV 2018, pages 1-5, provided by Applicant’s Information Disclosure Statement IDS, as applied in previous Office Action). The teachings of Pang as modified by Man have been discussed above.
Re Claim 4: Although Pang further discloses that the annotation task comprises classifying each object within the image (see Pang, [0020]-[0026], [0028], and more specifically [0027], image annotation job, initial object predictions generated by the machine learning models, wherein the initial object predictions are initial sets of annotations generated by the machine learning models wherein the annotations comprise e.g. label an object as a person, etc.), Pang as modified by Man however fails to explicitly disclose where Yap discloses wherein the annotation task comprises identifying and marking skin wrinkles within the image (see Yap, abstract, first paragraph of Section 4 on page 4, fully automated facial wrinkles annotator).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Pang’s method, as modified by Man, using Yap’s fully automated facial wrinkles annotator to Pang’s [as modified by Man] label prediction processing in order to improve the data labeling for large-scale data annotation tasks and cosmetic applications (see Yap, abstract, first paragraph of Section 4 on page 4).
As to claims 11 and 18, the discussions are addressed with regard to claim 4 respectively.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BERNARD KRASNIC whose telephone number is (571)270-1357. The examiner can normally be reached on Mon. - Thur. and every other Friday from 8am - 4pm.
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/Bernard Krasnic/Primary Examiner, Art Unit 2671 July 24, 2026
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