DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . It is responsive to the submission dated 12/20/2025. Claims 1-29 are presented for examination, of which, claims 1, and 16 are independent claims.
Information Disclosure Statement
2. The information disclosure statements (IDSs) submitted on 03/04/2026 are in compliance with the provisions of 37 CFR 1.97 and are being considered by the Examiner.
Claim Rejections - 35 USC § 101
3. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
4. Claims 16-29 are rejected under 35 U.S.C. 101 because the method of claim 16 is directed to a program, as evidenced by recitation of translating a probability distribution to determine target coordinates for placing bounding box within an image, and the use of a machine learning model which are both computer program processes.
None of claims 17-29 cure the deficiency of claim 29.
Claim Rejections - 35 USC § 112
5. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
6. Claims 16-29 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 16 is indefinite for reciting steps written with broadly functional claimed language that only describes the function of the invention as opposed to how it is carried out.
In particular, claim 16 recites a method for determining bounding box coordinates, the method comprising: processing an image to generate a first plurality of tokens associated with the image; processing, using a first machine learning model, the first plurality of tokens and a class token associated with a class of an object to generate a probability distribution associated with coordinates of a bounding box within the image; and determining, based on the probability distribution, target coordinates to position the bounding box within the image.
However, these steps, as claimed, appears to merely recite a concatenation of block box experiments, of which only inputs and outputs are specified. The above stated steps in the claim provide no concrete functional or structural features explaining: how the image is processed to generate the tokens; how the machine learning model is implemented to process the tokens; and also how the target coordinates are determined using the probability distribution
The wording of claim 1 is not only unduly broad with respect to the description of the embodiments and figures but also renders unclear the subject matter for which protection is sought in regard to a technical effect to be achieved. The ordinary skill in the art would not be able to draw a clear boundary between what is and is not covered by the claim. Accordingly, since the functional limitations fail to limit the claims, to make clear the scope of the claims, the applicant, in response to this office action, is suggested to amend the claims such that it expressly recites the corresponding structure, or material for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter(s).
The claims not specifically cited in this rejection are rejected as being dependent upon their rejected base claims.
Claim Rejections - 35 USC § 102
7. 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.
8. Claims 1-14 and 16-29 are rejected under 35 U.S.C. 102(a)(a1) as being anticipated by Kumar et al. (US 20230274478).
Considering claim 1, Kumar discloses an apparatus for image processing, the apparatus (see fig. 8 item 800) comprising: at least one memory (item 810, fig. 8); and
at least one processor (item 805, fig. 8) coupled to the at least one memory (see fig. 8) and configured to:
process an image to generate a first plurality of tokens associated with the image (e.g., Kumar discloses: At operation 405, the system receives an image depicting an object… An object detection component of the system is configured to detect class information and the position information of the object. The object detection component identifies a bounding box corresponding to the object. The sequence of tokens is generated based on the class information and the position information of the object. At operation 410, the system generates a sequence of tokens including a set of tokens corresponding to the object and a set of mask tokens corresponding to an additional object to be inserted into the image. See paras. 50-51);
process, using a first machine learning model, the first plurality of tokens and a class token associated with a class of an object (e.g., Kumar discloses: At operation 405, the system receives an image depicting an object …. the operations of this step refer to, or may be performed by, machine learning model … An object detection component of the system is configured to detect class information and the position information of the object. The object detection component identifies a bounding box corresponding to the object. The sequence of tokens is generated based on the class information and the position information of the object. At operation 410, the system generates a sequence of tokens including a set of tokens corresponding to the object and a set of mask tokens corresponding to an additional object to be inserted into the image. See paras. 50-51. At operation 415, the system generates a placement token value for the set of mask tokens based on the sequence of tokens using a sequence encoder, where the placement token value represents position information of the additional object…. That is, the system predicts a placement token value for each of the mask tokens iteratively. See para. 53) to generate a probability distribution associated with coordinates of a bounding box within the image (e.g., Kumar discloses: According to an embodiment, mask tokens 525 can be inserted at multiple positions in sequence of tokens 510. Mask tokens 525 includes a set of five tokens, they are class token, top-left x coordinate token, top-left y coordinate token, width token, and height token, in an order from left to right. The image generation apparatus can generate x, y, w, h at every index in the sequence of tokens 510, so mask of 5 tokens (i.e., mask tokens 525) may be placed at any suitable location in the flat sequence. Then, all these predictions have likelihood scores associated with them. The image generation apparatus can rank and show the most likely ones based on the likelihood scores. For example, a sequence BOS, c, x, y, w, h, EOS has one bounding box, the possible mask insertions can be: [0064] predict at position 1: BOS, [MASK], [MASK], [MASK], [MASK], [MASK], c, x, y, w, h, EOS). See paras. 63-66); and
determine, based on the probability distribution, target coordinates to position the bounding box within the image (e.g., Kumar discloses: each bounding box generation has an associated output probability, where the probability indicates a score for predicted bounding box x, y, w, h. The score is used for ranking. See para. 145).
As per claim 2, Kumar discloses add a visual representation of the object to the image within the target coordinates of the bounding box. See para. 112.
As per claim 3, Kumar discloses add the visual representation of the object using a second machine learning model. See paras. 106-107.
As per claim 4, Kumar discloses the first machine learning model is a transformer-based neural network model. See fig. 12 and para. 154.
As per claim 5, Kumar discloses the first machine learning model is trained using training data including a plurality of images with preset bounding boxes associated with one or more class of objects. See para. 108.
As per claim 6, Kumar discloses the training is on-device training. See para. 91.
As per claim 7, Kumar discloses generate a second plurality of tokens associated with coordinates of at least one bounding box of the preset bounding boxes (e.g., Kumar discloses: The placement tokens represent position information of the object based on a bounding box corresponding to the object. … Chair, x2, y2, w2, h2 are second object tokens 530 corresponding to the second object in image. See para. 62. training component 820 compares the placement token value with the ground truth information. Training component 820 updates parameters of the sequence encoder based on the comparison of the placement token value with the ground truth information. See para. 108).
As per claim 8, Kumar discloses the second plurality of tokens comprises four tokens associated with the at least one bounding box, including a first x-coordinate token, a second x-coordinate token, a first y-coordinate token, and a second y-coordinate token. See paras. 62-63.
As per claim 9, Kumar discloses at least one class token associated with the at least one bounding box immediately precedes one of the first x-coordinate token or the first y-coordinate token. See para. 63 and fig. 6.
As per claim 10, Kumar discloses process a user selection of the object to generate the class token (e.g., Kumar discloses: the system generates a sequence of tokens including a set of tokens corresponding to the object and a set of mask tokens corresponding to an additional object to be inserted into the image…. the set of tokens includes a class token. … the class token of the additional object is provided by a user. See paras. 51-52), wherein the user selection is represented as a one-hot class embedding vector (e.g., an embedding vector is learned during training for each individual token. See para. 58. Sequence encoder 630 generates a class token value 635 for the set of mask tokens 615 based on the sequence of tokens 600….In some examples, class token value 635 is given or provided from a user, i.e., “chair” object depicted in the query “insert a chair”. See para. 88, wherein by training the embedding vector for each class token using the sequence encoder, based on user input, so as to transform a token label to a token value, Kumar therefore teaches representing the user input as a one-hot class embedding vector. See also para. 126)
As per claim 11, Kumar discloses the probability distribution is a conditional probability distribution representing a probability each coordinate of the bounding box is located at a location within the image based on a location of other coordinates of the bounding box. (See para. 63, wherein the generation of class token ranked based on likelihood scores corresponds to the conditional probability distribution representing a probability. See also para. 77).
As per claim 12, Kumar discloses the coordinates of the bounding box are associated with corners of the bounding box (see para. 63, wherein the representation of the top-left x coordinate token, top-left y coordinate token, width token, and height token, in an order from left to right, respectively, correspond to the corners of the bounding box).
As per claim 13, Kumar discloses the probability distribution is a histogram associated with probabilities of one or more corners of the bounding box being placed at one or more coordinates within the image (e.g., predicting a sequence of token layout based on likelihood scores to generate a scene graph, by attending to all the bounding boxes in the scene of image at once. See paras. 74-77).
As per claim 14, Kumar discloses the target coordinates include x-y coordinates of a coordinate system associated with the image. See paras. 63-68.
The subject-matter of independent claim 16 corresponds in terms of a method to that of independent method claim 1, and the rationale raised above to reject the later also apply, mutatis mutandis, to the former.
Claim 17 is rejected under the same rationale as claim 2.
Claim 18 is rejected under the same rationale as claim 3.
Claim 19 is rejected under the same rationale as claim 4.
Claim 20 is rejected under the same rationale as claim 5.
Claim 21 is rejected under the same rationale as claim 6.
Claim 22 is rejected under the same rationale as claim 7.
Claim 23 is rejected under the same rationale as claim 8.
Claim 24 is rejected under the same rationale as claim 9.
Claim 25 is rejected under the same rationale as claim 10.
Claim 26 is rejected under the same rationale as claim 11.
Claim 27 is rejected under the same rationale as claim 12.
Claim 28 is rejected under the same rationale as claim 13.
Claim 29 is rejected under the same rationale as claim 14.
Claim Rejections - 35 USC § 103
10. 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.
11. Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over
As per claim 15, Kumar fails to teach using at least one camera configured to capture the image. However, such practice is well-known in the art, as evidence of Igal (see para. 59).
Accordingly, it would have been obvious to one of the ordinary skilled in the art, before the effective filling date of the invention was made, to combine the teaching of Kumar with Igal; in order to apply the image editing to a non-synthetic real-world imagery.
Conclusion
12. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Dagley et al. (US 10008045) discloses a system may include a camera, a display, one or more memories, and one or more processors communicatively coupled to the one or more memories. The system may output a bounding shape for presentation on the display. The bounding shape may be superimposed on an image being captured by the camera and presented on the display. The bounding shape may bound an object in the image. The system may determine 3D coordinates of an intersection point associated with the bounding shape. The intersection point may be a point where a projection of the bounding shape into 3D space intersects with a horizontal plane identified in the image. The system may determine 2D coordinates for presentation of an augmented reality object on the display based on the 3D coordinates of the intersection point, and may superimpose a representation of the augmented reality object on the image based on the 2D coordinates.
13. Any inquiry concerning this communication or earlier communications from the examiner should be directed to WESNER SAJOUS whose telephone number is (571) 272-7791. The examiner can normally be reached on M-F 10:00 TO 7:30 (ET).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Said Broome can be reached on 571-272-2931. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WESNER SAJOUS/Primary Examiner, Art Unit 2612
WS
06/23/2026
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