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 .
Response to Amendment
The Amendment filed 7/24/2026 has been entered. Claims 1-8 and 15-20 remain pending in the application. Applicant’s amendments to the Claims have overcome the objection to claim 16 previously set forth.
Response to Arguments
Applicant's arguments filed with the amendment have been fully considered but they are not persuasive.
Applicant argues that:
Applicant respectfully submits that none of the additional references cited in the Office Action, namely Liu, Dhanuka, Chen, and Aggarwal, nor the references made of record but not relied upon, cure the deficiencies of Price and Liu. None of the cited references, whether taken alone or in combination, cures the deficiencies noted above with respect to independent claims 1 and 15. For example, Dhanuka generates visual image editing guides from a filtered set of edge paths extracted from a "simplified, binary representation of the digital image object" (Dhanuka, [0002]). Dhanuka utilizes those guides to snap, clip, crop, or otherwise align editing operations to the boundaries of a raster object (Dhanuka, [0002], [0024]). Aligning or snapping a user interaction to an edge-path-based guide, as described in Dhanuka, is not using "object identification data that bounds the object for individual selection within the digital image." Dhanuka's guide is also not supporting "a bounding indication of the boundary" presented "to enable the individual selection of the object," as recited in amended claims 1 and 15. Dhanuka, therefore, does not disclose or suggest the subject matter of claims 1 and 15, and does not cure the deficiencies of Price and Liu noted above.
The Examiner cannot concur with the Applicant. Dhanuka discloses this limitation at Figs. 6A-7 with ¶0041, ¶0087, ¶0095. Specifically, the object is identified and a boundary is displayed for selection to edit the object.
The remainder of Applicant’s arguments filed with the Amendment, with respect to rejections under prior art have been fully considered and are moot upon a new ground(s) of rejection, as necessitated by amendment, as outlined below.
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.
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function.
Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
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: “processing device” in claims 1 and 15.
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.
Prior Art
Listed herein below are the prior art references relied upon in this Office Action:
Price et al. (US Patent Application Publication 2020/0364910), referred to as Price herein [previously presented].
Liu et al. (US Patent Application Publication 2023/0260247), referred to as Liu herein [previously presented].
Dhanuka et al. (US Patent Application Publication 2022/0108505), referred to as Dhanuka herein [previously presented].
Chen et al. (US Patent Application Publication 2018/0075290), referred to as Chen herein [previously presented].
Aggarwal et al. (US Patent Application Publication 2021/0368064), referred to as Aggarwal herein [previously presented].
Examiner’s Note
Strikethrough notation in the pending claims has been added by the Examiner.
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, 4, and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Price in view of Dhanuka.
Regarding claim 1, Price discloses a method comprising: determining, by a processing device, edges of an object within a digital image by analyzing gradients from the digital image (Price, Fig. 3 with ¶0032, ¶0047-¶0048, and Fig. 11 with ¶0118-¶0119 – recognized high-level features in an input photograph include edges, gradients, structural elements. ¶0006 – computer with processor executing instructions stored in hardware memory. This element is interpreted under 35 U.S.C. 112(f) as the hardware processor described in Applicant’s Specification ¶0094);
computing, by the processing device, a structure of the object by detecting line segments from the digital image; defining, by the processing device, a boundary of the object based on the edges and the structure, the boundary including object identification data that bounds the object - object edges (from lines) are used to generate output line drawing. In this case, the object edges are the boundary);
and presenting, by the processing device, the object including a bounding indication of the boundary to
However, Price appears not to expressly disclose the limitations in strikethrough above. However, in the same field of endeavor, Dhanuka discloses a digital image editor (Dhanuka, Abstract), including object boundary detection (Dhanuka, Abstract with ¶0002), including
a bounded object for individual selection (Dhanuka, Figs. 6A-7 with ¶0041, ¶0087, ¶0095 – bounding shape is generated for an object. The object can be selected individually for editing such as moving or copying).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the image editing of Price to include generating a bounding shape based on the teachings of Dhanuka. The motivation for doing so would have been to assist users in aligning inputs with image objects, improving input ease and accuracy (Dhanuka, ¶0001).
Regarding claim 4, Price discloses the elements of claim 1 above, and further discloses wherein the edit operation is a snapping operation that identifies a location of the object based on the object identification data and aligns with the location of the object with a location of another entity (Dhanuka, Fig. 5 with ¶0002, ¶0091-¶0093, ¶0121-¶0122, ¶0135-¶0136 – snapping edit operation), and
wherein the computing the structure of the object includes determining a probability that the line segments are part of the object (Price, ¶0048, ¶0069, ¶0120 – probability determination that the image contains an object based on lines. Additionally, pixel probability for a given color is based on recognized features including recognized objects. ¶0046, ¶0124-¶0125 – the lines are constructed from pixels).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the image editing of Price to include a snapping operation based on the teachings of Dhanuka. The motivation for doing so would have been to assist users in aligning inputs with image objects, improving input ease and accuracy (Dhanuka, ¶0001).
Regarding claim 7, Price discloses the elements of claim 1 above, and further discloses wherein the determining the edges of the object includes convolving the digital image with a filter (Price, ¶0063 – convolution with a filter to extract edges).
Claim(s) 2-3, 5, 15-17, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Price in view of Dhanuka in further view of Liu.
Regarding claim 2, Price discloses the elements of claim 1 above, and further discloses generating, by the processing device, a
However, Price appears not to expressly disclose the limitations in strikethrough above. However, in the same field of endeavor, Liu discloses a computer vision system (Liu, ¶0002) for detecting object boundaries (Liu, ¶0003, ¶0076), including
generating, by the processing device, a segmentation map from the digital image, the generating performed by labeling pixels of the digital image and wherein the defining of the boundary is based on the segmentation map, the boundary, and the structure (Liu, ¶0044-¶0046, ¶0051-¶0053 – instance segmentation mapping of the image is used in object identification).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the map of Price to include instance segmentation based on the teachings of Liu. The motivation for doing so would have been to provide more rich, holistic information about a given scene (Liu, ¶0003), aiding in object detection and differentiation.
Regarding claim 3, Price as modified discloses the elements of claim 2 above, and further discloses wherein the generating the segmentation map from the digital image is performed by a machine learning model that includes: a contracting path that performs a plurality of convolutions for down-sampling features of the digital image; and an expanding path that performs a plurality of convolutions for upsampling features of the digital image (Price, ¶0061-0067, ¶0071 – downsampling and upsampling feature maps. Encoder-decoder configuration).
Regarding claim 5, Price as modified discloses the elements of claim 1 above, and further discloses generating an
However, Price appears not to expressly disclose the limitations in strikethrough above. However, in the same field of endeavor, Liu discloses a computer vision system (Liu, ¶0002) for detecting object boundaries (Liu, ¶0003, ¶0076), including
generating, by the processing device, a segmentation map from the digital image, the generating an instance segmentation map by performing instance segmentation using the digital image, and wherein the defining the boundary of the object is based on the instance segmentation map (Liu, ¶0044-¶0046, ¶0051-¶0053 – instance segmentation mapping of the image is used in object identification).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the map of Price to include instance segmentation based on the teachings of Liu. The motivation for doing so would have been to provide more rich, holistic information about a given scene (Liu, ¶0003), aiding in object detection and differentiation.
Regarding claim 15, Price discloses a method comprising: computing, by a processing device, a structure of an object of a digital image by detecting line segments from the digital image (Price, Fig. 3 with ¶0047-¶0048, and Fig. 11 with ¶0118-¶0119 – recognized high-level features in an input photograph include edges, gradients, structural elements. ¶0048, ¶0051 – lines for the object are detected from the image. ¶0052-¶0055, ¶0063 – lines are detected within the image. ¶0006 – computer with processor executing instructions stored in hardware memory. This element is interpreted under 35 U.S.C. 112(f) as the hardware processor described in Applicant’s Specification ¶0094);
generating, by the processing device, a ¶0047-¶0048 – identifying features and generating a feature map to identify edges, objects. ¶0048, ¶0051 – lines for the object are detected from the image. ¶0052-¶0055, ¶0063 – lines are detected within the image. ¶0053, ¶0120, ¶0123-¶124 - object edges (from lines) are used to generate output line drawing. In this case, the object edges are the boundary); and
presenting, by the processing device, the object including a bounding indication of the boundary to
However, Price appears not to expressly disclose the limitations in strikethrough above. However, in the same field of endeavor, Dhanuka discloses a digital image editor (Dhanuka, Abstract), including object boundary detection (Dhanuka, Abstract with ¶0002), including
a bounded object for individual selection (Dhanuka, Fig. 7 with ¶0041, ¶0087, ¶0095 – bounding shape is generated for an object. The object can be selected individually for editing such as moving or copying).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the image editing of Price to include generating a bounding shape based on the teachings of Dhanuka. The motivation for doing so would have been to assist users in aligning inputs with image objects, improving input ease and accuracy (Dhanuka, ¶0001).
However, Price as modified appears not to expressly disclose a segmentation map from the digital image, the generating performed by labeling pixels. However, in the same field of endeavor, Liu discloses a computer vision system (Liu, ¶0002) for detecting object boundaries (Liu, ¶0003, ¶0076), including
generating, by the processing device, a segmentation map from the digital image, the generating performed by labeling pixels of the digital image and wherein the defining of the boundary is based on the segmentation map, the boundary, and the structure (Liu, ¶0044-¶0046, ¶0051-¶0053 – instance segmentation mapping of the pixels of the image is used in object identification).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the map of Price as modified to include instance segmentation based on the teachings of Liu. The motivation for doing so would have been to provide more rich, holistic information about a given scene (Liu, ¶0003), aiding in object detection and differentiation.
Regarding claim 16, Price as modified discloses the elements of claim 15 above, and further discloses determining edges of the object within the digital image by analyzing gradients from the digital image and wherein the defining is based on the edges, the structure, and the segmentation map (Price, Fig. 3 with ¶0047-¶0048, and Fig. 11 with ¶0118-¶0119 – recognized high-level features in an input photograph include edges, gradients, structural elements).
Regarding claim 17, Price as modified discloses the elements of claim 15 above, and further discloses generating an instance segmentation map by performing instance segmentation using the digital image, and wherein the defining the boundary of the object is based on the structure, the segmentation map, and the instance segmentation map (Price, ¶0047-¶0048 – identifying features and generating a feature map to identify edges, objects. Liu, ¶0044-¶0046, ¶0051-¶0053 – instance segmentation mapping of the image is used in object identification).
Regarding claim 20, Price as modified discloses the elements of claim 15 above, and further discloses wherein the generating the segmentation map from the digital image is performed by a machine learning model that includes: a contracting path that performs a plurality of convolutions for down-sampling features of the digital image; and an expanding path that performs a plurality of convolutions for upsampling features of the digital image (Price, ¶0034-¶0036 – neural network learning. ¶0061-0067, ¶0071 – downsampling and upsampling feature maps. Encoder-decoder configuration).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Price in view of Dhanuka in further view of Chen.
Regarding claim 6, Price discloses the elements of claim 1 above, and further discloses generating a feature map that includes a mask identifying the object by performing
However, Price appears not to expressly disclose the limitations in strikethrough above. However, in the same field of endeavor, Chen discloses object identification in images (Chen, Abstract), including
a mask identifying the object by performing patch structure identification on the object of the digital image (Chen, ¶0053-¶0059, ¶0095-¶0097, ¶0104-¶0106 – object identification masking corresponding to image 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 have modified the object recognition of Price to include patch identification based on the teachings of Chen. The motivation for doing so would have been to decrease the computational complexity by reducing the number of candidate regions (Chen, ¶0051).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Price in view of Dhanuka in further view of Aggarwal.
Regarding claim 8, Price discloses the elements of claim 1 above, and further discloses wherein the computing the structure of the object is performed with a machine learning model implementing one or more loss functions selected from
However, Price appears not to expressly disclose the limitations in strikethrough above. However, in the same field of endeavor, Aggarwal discloses machine learning object identification from image data (Aggarwal, Abstract with ¶0002), including
implementing one or more loss functions selected from a distance loss function, a group loss function, or a fuzz loss function (Aggarwal, ¶0049, ¶0065-¶0066 – MSE, Quadratic, L1, L2 , Mean Bias Error distance loss function).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the loss function of Price to distance loss functions based on the teachings of Aggarwal. The motivation for doing so would have been to improve quality, accuracy of the predicted identification (Aggarwal, ¶0066).
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Price in view of Dhanuka in further view of Liu in further view of Chen.
Regarding claim 18, Price discloses the elements of claim 15 above, and further discloses generating a feature map that includes a mask identifying the object by performing
However, Price appears not to expressly disclose the limitations in strikethrough above. However, in the same field of endeavor, Chen discloses object identification in images (Chen, Abstract), including
a mask identifying the object by performing patch structure identification on the object of the digital image (Chen, ¶0053-¶0059, ¶0095-¶0097, ¶0104-¶0106 – object identification masking corresponding to image 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 have modified the object recognition of Price to include patch identification based on the teachings of Chen. The motivation for doing so would have been to decrease the computational complexity by reducing the number of candidate regions (Chen, ¶0051).
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Price in view of Dhanuka in further view of Liu in further view of Aggarwal.
Regarding claim 19, Price as modified discloses the elements of claim 15 above, and further discloses wherein the computing the structure of the object is performed with a machine learning model implementing one or more loss functions selected from
However, Price appears not to expressly disclose the limitations in strikethrough above. However, in the same field of endeavor, Aggarwal discloses machine learning object identification from image data (Aggarwal, Abstract with ¶0002), including
implementing one or more loss functions selected from a distance loss function, a group loss function, or a fuzz loss function (Aggarwal, ¶0049, ¶0065-¶0066 – MSE, Quadratic, L1, L2 , Mean Bias Error distance loss function).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the loss function of Price to distance loss functions based on the teachings of Aggarwal. The motivation for doing so would have been to improve quality, accuracy of the predicted identification (Aggarwal, ¶0066).
Conclusion
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 DANIEL W PARCHER whose telephone number is (303)297-4281. The examiner can normally be reached Monday - Friday, 9:00am - 5:00pm, Mountain Time.
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/DANIEL W PARCHER/Primary Examiner, Art Unit 2174
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