DETAILED ACTION
This action is in response to the original filing on 10/15/2024. Claims 1-20 are pending and have been considered below.
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: a processing device configured to perform operations in claims 13-17.
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 § 102
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 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5, 8, 9, 11-15, and 17-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by He et al. (US 20250097566 A1, published 03/20/2025), hereinafter He.
Regarding claim 1, He teaches the claim comprising:
A method comprising: obtaining, via a user interface, an input image and a user input that indicates a frame for modifying the input image, wherein the frame includes a first region inside of the input image and a second region outside of the input image, and excludes a third region inside of the input image (He Figs. 1-18; [0060], FIG. 3 includes an example representation 302 of apparatus 204 displaying an image 308 (e.g., which may be captured by apparatus 204) and an example representation 314 of apparatus 204 displaying an image 316 (e.g., including generated image content, such as generated pixels 318; [0062], UI 206 may receive a user input (e.g., drag gesture 312) and interpret the user input relative to image 308. For example, UI 206 may determine a starting point of drag gesture 312 and an ending point of drag gesture 312 relative to image 308. Additionally or alternatively, UI 206 may determine a length and direction of drag gesture 312. UI 206 may determine a change (e.g., change 116 of FIG. 1) based on drag gesture 312. The change may be a change to field of view 310. For example, UI 206 may interpret drag gesture 312 as a desire to change field of view 310, for example, by panning field of view 310. UI 206 may determine a direction and length of the desired change to field of view 310; [0063], Apparatus 204 may provide at least a part of image 308 (e.g., as a condition) and instructions based on the change to a generative machine-learning model (e.g., generative machine-learning model 120 and/or generative machine-learning model 121 of FIG. 1) of apparatus 204. For example, based on drag gesture 312, apparatus 204 may determine a part of image 308, for example, the portion out of image 308 that may remain in the frame following the pan, and provide the part to the generative machine-learning model; [0064], FIG. 4 includes an example representation 402 of apparatus 204 displaying an image 408 (e.g., which may be captured by apparatus 204) and an example representation 414 of apparatus 204 displaying an image 416; [0066], UI 206 may receive a user input (e.g., rotate gesture 412) and interpret the user input relative to image 408. For example, UI 206 may interpret a starting point of rotate gesture 412 and an ending point of rotate gesture 412 relative to image 408; [0067], based on drag gesture 312, apparatus 204 may determine a part of image 408, for example, excluding corners that may be cut from the frame by the rotation of image 408);
generating, using an image generation model, a modified image including original content from the input image in the first region and generated content in the second region, and excluding content from the input image in the third region; and presenting the modified image for display in the user interface (He Figs. 1-18; [0063], Apparatus 204 may provide at least a part of image 308 (e.g., as a condition) and instructions based on the change to a generative machine-learning model (e.g., generative machine-learning model 120 and/or generative machine-learning model 121 of FIG. 1) of apparatus 204. For example, based on drag gesture 312, apparatus 204 may determine a part of image 308, for example, the portion out of image 308 that may remain in the frame following the pan, and provide the part to the generative machine-learning model. Alternatively, apparatus 204 may provide all of image 308 to the generative machine-learning model. The generative machine-learning model may generate image 316 based on at least a part of image 308 and the instructions and UI 206 may display image 316 at UI 206 as illustrated in representation 314. Image 316 may include at least a part of image 308 of field of view 310 and generated pixels 318 outside field of view 310. For example, based on drag gesture 312 indicating a desire to pan field of view 310, image 316 may include at least a part of image 308 of field of view 310 and generated pixels 318 on one or more sides of field of view 310. Additionally or alternatively, apparatus 204 may smooth edges between generated pixels 318 and pixels from image 308. Image 316 may appear to be image 308 as if image 308 were captured of a panned field of view. In cases in which image 316 includes a part of image 308, the part of image 308 included in image 316 may be the same the part of image 308 provided by apparatus 204. Alternatively, the part of image 308 included in image 316 may be a subset of what is provided by apparatus 204. For example, apparatus 204 may provide all of image 308 (or a large part of image 308) and image 316 may include a subset of what was provided; [0067], The generative machine-learning model may generate image 416 based on at least a part of image 408 and the instructions and UI 206 may display image 416 at UI 206 as illustrated in representation 414. Image 416 may include at least a part of image 408 of field of view 410 and generated pixels 418 outside field of view 410. For example, based on rotate gesture 412 indicating a desire to rotate field of view 410, image 416 may include at least a part of image 408 of field of view 410 and generated pixels 418 at one or more corners of image 416. Additionally or alternatively, apparatus 204 may smooth edges between generated pixels 418 and pixels from image 408. Image 416 may appear to be image 408 as if image 408 were captured of a rotated field of view. In cases in which image 416 includes a part of image 408, the part of image 408 included in image 416 may be the same the part of image 408 provided by apparatus 204. Alternatively, the part of image 408 included in image 416 may be a subset of what is provided by apparatus 204. For example, apparatus 204 may provide all of image 408 (or a large part of image 408) and image 416 may include a subset of what was provided; [0068], Apparatus 204 may rotate and crop the intermediate image to generate image 416)
Regarding claim 2, He teaches all the limitations of claim 1, further comprising:
wherein obtaining the user input comprises: providing a cropping tool to a user; and receiving a drag input via the cropping tool, wherein the frame is based on the drag input (He Figs. 1-18; [0044], after generating the altered image may display the altered image (or a final image rotated and/or cropped according to the interpreted desire); [0062], UI 206 may receive a user input (e.g., drag gesture 312) and interpret the user input relative to image 308. For example, UI 206 may determine a starting point of drag gesture 312 and an ending point of drag gesture 312 relative to image 308; [0063], Apparatus 204 may provide at least a part of image 308 (e.g., as a condition) and instructions based on the change to a generative machine-learning model (e.g., generative machine-learning model 120 and/or generative machine-learning model 121 of FIG. 1) of apparatus 204. For example, based on drag gesture 312, apparatus 204 may determine a part of image 308, for example, the portion out of image 308 that may remain in the frame following the pan, and provide the part to the generative machine-learning model. Alternatively, apparatus 204 may provide all of image 308 to the generative machine-learning model. The generative machine-learning model may generate image 316 based on at least a part of image 308 and the instructions and UI 206 may display image 316 at UI 206 as illustrated in representation 314. Image 316 may include at least a part of image 308 of field of view 310 and generated pixels 318 outside field of view 310. For example, based on drag gesture 312 indicating a desire to pan field of view 310, image 316 may include at least a part of image 308 of field of view 310 and generated pixels 318 on one or more sides of field of view 310. Additionally or alternatively, apparatus 204 may smooth edges between generated pixels 318 and pixels from image 308. Image 316 may appear to be image 308 as if image 308 were captured of a panned field of view. In cases in which image 316 includes a part of image 308, the part of image 308 included in image 316 may be the same the part of image 308 provided by apparatus 204. Alternatively, the part of image 308 included in image 316 may be a subset of what is provided by apparatus 204. For example, apparatus 204 may provide all of image 308 (or a large part of image 308) and image 316 may include a subset of what was provided; [0067], based on drag gesture 312, apparatus 204 may determine a part of image 408, for example, excluding corners that may be cut from the frame by the rotation of image 408)
Regarding claim 3, He teaches all the limitations of claim 1, further comprising:
further comprising: rotating the input image, wherein the original content from the input image is oriented differently in the modified image than in the input image based on the rotation (He Figs. 1-18; [0041], the systems and techniques may rotate the image and crop the altered image to fit the frame. In some cases, the systems and techniques may rotate the image before providing the image to the generative machine-learning model; [0064], FIG. 4 includes an example representation 402 of apparatus 204 displaying an image 408 (e.g., which may be captured by apparatus 204) and an example representation 414 of apparatus 204 displaying an image 416; [0066], UI 206 may receive a user input (e.g., rotate gesture 412) and interpret the user input relative to image 408. For example, UI 206 may interpret a starting point of rotate gesture 412 and an ending point of rotate gesture 412 relative to image 408; [0066], [0066], UI 206 may receive a user input (e.g., rotate gesture 412) and interpret the user input relative to image 408. For example, UI 206 may interpret a starting point of rotate gesture 412 and an ending point of rotate gesture 412 relative to image 408. Additionally or alternatively, UI 206 may determine a rotational length and/or rotational direction of rotate gesture 412. UI 206 may determine a change (e.g., change 116 of FIG. 1) based on rotate gesture 412. The change may be a change to field of view 410; [0067], Apparatus 204 may provide at least a part of image 408 (e.g., as a condition) and instructions based on the change to a generative machine-learning model (e.g., generative machine-learning model 120 and/or generative machine-learning model 121 of FIG. 1) of apparatus 204. For example, based on drag gesture 312, apparatus 204 may determine a part of image 408, for example, excluding corners that may be cut from the frame by the rotation of image 408, and provide the part to the generative machine-learning model. Alternatively, apparatus 204 may provide all of image 408 to the generative machine-learning model. The generative machine-learning model may generate image 416 based on at least a part of image 408 and the instructions and UI 206 may display image 416 at UI 206 as illustrated in representation 414; [0114], the computing device (or one or more components thereof) may at least one of rotate or crop the image to obtain the at least part of the image to provide to the generative machine-learning model. For example, apparatus 100 may obtain image 114 and rotate or crop image 114 before providing image 114 to generative machine-learning model 120 (or generative machine-learning model 121). Apparatus 100 may provide the rotated and/or cropped image 114 to generative machine-learning model 120 (or generative machine-learning model 121). Generative machine-learning model 120 (or generative machine-learning model 121) may generate image 122 based on the rotated and/or cropped version of image 114. For example, apparatus 100 may obtain image 408. Apparatus 100 may rotate image 408, for example, as illustrated in the frame of image 416, and provide the rotated image 408 to the generative machine-learning model. The generative machine-learning model may generate image 416 based on the rotated image 408)
Regarding claim 4, He teaches all the limitations of claim 3, further comprising:
wherein rotating the input image comprises: identifying a target orientation based on content of the input image, wherein the input image is rotated based on the target orientation (He Figs. 1-18; [0041], the systems and techniques may rotate the image and crop the altered image to fit the frame. In some cases, the systems and techniques may rotate the image before providing the image to the generative machine-learning model; [0064], FIG. 4 includes an example representation 402 of apparatus 204 displaying an image 408 (e.g., which may be captured by apparatus 204) and an example representation 414 of apparatus 204 displaying an image 416; [0066], UI 206 may receive a user input (e.g., rotate gesture 412) and interpret the user input relative to image 408. For example, UI 206 may interpret a starting point of rotate gesture 412 and an ending point of rotate gesture 412 relative to image 408; [0066], [0066], UI 206 may receive a user input (e.g., rotate gesture 412) and interpret the user input relative to image 408. For example, UI 206 may interpret a starting point of rotate gesture 412 and an ending point of rotate gesture 412 relative to image 408. Additionally or alternatively, UI 206 may determine a rotational length and/or rotational direction of rotate gesture 412. UI 206 may determine a change (e.g., change 116 of FIG. 1) based on rotate gesture 412. The change may be a change to field of view 410; [0067], Apparatus 204 may provide at least a part of image 408 (e.g., as a condition) and instructions based on the change to a generative machine-learning model (e.g., generative machine-learning model 120 and/or generative machine-learning model 121 of FIG. 1) of apparatus 204. For example, based on drag gesture 312, apparatus 204 may determine a part of image 408, for example, excluding corners that may be cut from the frame by the rotation of image 408, and provide the part to the generative machine-learning model. Alternatively, apparatus 204 may provide all of image 408 to the generative machine-learning model. The generative machine-learning model may generate image 416 based on at least a part of image 408 and the instructions and UI 206 may display image 416 at UI 206 as illustrated in representation 414; [0114], the computing device (or one or more components thereof) may at least one of rotate or crop the image to obtain the at least part of the image to provide to the generative machine-learning model. For example, apparatus 100 may obtain image 114 and rotate or crop image 114 before providing image 114 to generative machine-learning model 120 (or generative machine-learning model 121). Apparatus 100 may provide the rotated and/or cropped image 114 to generative machine-learning model 120 (or generative machine-learning model 121). Generative machine-learning model 120 (or generative machine-learning model 121) may generate image 122 based on the rotated and/or cropped version of image 114. For example, apparatus 100 may obtain image 408. Apparatus 100 may rotate image 408, for example, as illustrated in the frame of image 416, and provide the rotated image 408 to the generative machine-learning model. The generative machine-learning model may generate image 416 based on the rotated image 408)
Regarding claim 5, He teaches all the limitations of claim 1, further comprising:
further comprising: providing a generative expand element in the user interface; receiving a generative expand input via the generative expand element; and initiating a generative expand mode based on the generative expand input, wherein the modified image is generated based on the generative expand mode (He Figs. 1-18; [0044], after generating the altered image may display the altered image (or a final image rotated and/or cropped according to the interpreted desire); [0062], UI 206 may receive a user input (e.g., drag gesture 312) and interpret the user input relative to image 308. For example, UI 206 may determine a starting point of drag gesture 312 and an ending point of drag gesture 312 relative to image 308; [0063], Apparatus 204 may provide at least a part of image 308 (e.g., as a condition) and instructions based on the change to a generative machine-learning model (e.g., generative machine-learning model 120 and/or generative machine-learning model 121 of FIG. 1) of apparatus 204. For example, based on drag gesture 312, apparatus 204 may determine a part of image 308, for example, the portion out of image 308 that may remain in the frame following the pan, and provide the part to the generative machine-learning model. Alternatively, apparatus 204 may provide all of image 308 to the generative machine-learning model. The generative machine-learning model may generate image 316 based on at least a part of image 308 and the instructions and UI 206 may display image 316 at UI 206 as illustrated in representation 314. Image 316 may include at least a part of image 308 of field of view 310 and generated pixels 318 outside field of view 310. For example, based on drag gesture 312 indicating a desire to pan field of view 310, image 316 may include at least a part of image 308 of field of view 310 and generated pixels 318 on one or more sides of field of view 310. Additionally or alternatively, apparatus 204 may smooth edges between generated pixels 318 and pixels from image 308. Image 316 may appear to be image 308 as if image 308 were captured of a panned field of view. In cases in which image 316 includes a part of image 308, the part of image 308 included in image 316 may be the same the part of image 308 provided by apparatus 204. Alternatively, the part of image 308 included in image 316 may be a subset of what is provided by apparatus 204. For example, apparatus 204 may provide all of image 308 (or a large part of image 308) and image 316 may include a subset of what was provided; [0067], Apparatus 204 may provide at least a part of image 408 (e.g., as a condition) and instructions based on the change to a generative machine-learning model (e.g., generative machine-learning model 120 and/or generative machine-learning model 121 of FIG. 1) of apparatus 204. For example, based on drag gesture 312, apparatus 204 may determine a part of image 408, for example, excluding corners that may be cut from the frame by the rotation of image 408, and provide the part to the generative machine-learning model. Alternatively, apparatus 204 may provide all of image 408 to the generative machine-learning model. The generative machine-learning model may generate image 416 based on at least a part of image 408 and the instructions and UI 206 may display image 416 at UI 206 as illustrated in representation 414)
Regarding claim 8, He teaches all the limitations of claim 1, further comprising:
wherein generating the modified image comprises: generating a multi-layer image including a first layer with the original content and a second layer with the generated content (He Figs. 1-18; [0044], after generating the altered image may display the altered image (or a final image rotated and/or cropped according to the interpreted desire); [0062], UI 206 may receive a user input (e.g., drag gesture 312) and interpret the user input relative to image 308. For example, UI 206 may determine a starting point of drag gesture 312 and an ending point of drag gesture 312 relative to image 308; [0063], Apparatus 204 may provide at least a part of image 308 (e.g., as a condition) and instructions based on the change to a generative machine-learning model (e.g., generative machine-learning model 120 and/or generative machine-learning model 121 of FIG. 1) of apparatus 204. For example, based on drag gesture 312, apparatus 204 may determine a part of image 308, for example, the portion out of image 308 that may remain in the frame following the pan, and provide the part to the generative machine-learning model. Alternatively, apparatus 204 may provide all of image 308 to the generative machine-learning model. The generative machine-learning model may generate image 316 based on at least a part of image 308 and the instructions and UI 206 may display image 316 at UI 206 as illustrated in representation 314. Image 316 may include at least a part of image 308 of field of view 310 and generated pixels 318 outside field of view 310. For example, based on drag gesture 312 indicating a desire to pan field of view 310, image 316 may include at least a part of image 308 of field of view 310 and generated pixels 318 on one or more sides of field of view 310. Additionally or alternatively, apparatus 204 may smooth edges between generated pixels 318 and pixels from image 308. Image 316 may appear to be image 308 as if image 308 were captured of a panned field of view. In cases in which image 316 includes a part of image 308, the part of image 308 included in image 316 may be the same the part of image 308 provided by apparatus 204. Alternatively, the part of image 308 included in image 316 may be a subset of what is provided by apparatus 204. For example, apparatus 204 may provide all of image 308 (or a large part of image 308) and image 316 may include a subset of what was provided; [0067], based on drag gesture 312, apparatus 204 may determine a part of image 408, for example, excluding corners that may be cut from the frame by the rotation of image 408; [0073], UI 206 may receive user input relative to an image including generated content. In such cases, apparatus 204 may generate additional content based on the user input and the image including the already-generated content. For example, after having generated image 216 of FIG. 2 (e.g., by expanding a field of view of image 208), UI 206 may receive a drag gesture relative to image 216. Apparatus 204 may generate another image based on the drag gesture and image 216. As another example, after having generated image 316 of FIG. 3 (e.g., by panning a field of view of image 308), UI 206 may receive a rotate gesture relative to image 316. Apparatus 204 may generate another image based on the rotate gesture and image 316; [0074], UI 206 may receive user input indicating multiple desires. For example, a user input may include a pinch gesture, a drag gesture, and/or a rotate gesture. UI 206 may interpret the user input and generate instructions based on the interpreted desired changes. In some cases, UI 206 may generate one instruction based on all the desired changes and instruct the generative machine-learning model to generate an image once. In other cases, UI 206 may generate on instruction based on each of the desired changes and instruct the generative machine-learning model to generate several images in series to implement the desired changes)
Regarding claim 9, He teaches all the limitations of claim 1, further comprising:
wherein: the original content comprises a pattern and the generated content comprises a repetition of the pattern (He Figs. 1-18; [0044], after generating the altered image may display the altered image (or a final image rotated and/or cropped according to the interpreted desire); [0062], UI 206 may receive a user input (e.g., drag gesture 312) and interpret the user input relative to image 308. For example, UI 206 may determine a starting point of drag gesture 312 and an ending point of drag gesture 312 relative to image 308; [0063], Apparatus 204 may provide at least a part of image 308 (e.g., as a condition) and instructions based on the change to a generative machine-learning model (e.g., generative machine-learning model 120 and/or generative machine-learning model 121 of FIG. 1) of apparatus 204. For example, based on drag gesture 312, apparatus 204 may determine a part of image 308, for example, the portion out of image 308 that may remain in the frame following the pan, and provide the part to the generative machine-learning model. Alternatively, apparatus 204 may provide all of image 308 to the generative machine-learning model. The generative machine-learning model may generate image 316 based on at least a part of image 308 and the instructions and UI 206 may display image 316 at UI 206 as illustrated in representation 314. Image 316 may include at least a part of image 308 of field of view 310 and generated pixels 318 outside field of view 310. For example, based on drag gesture 312 indicating a desire to pan field of view 310, image 316 may include at least a part of image 308 of field of view 310 and generated pixels 318 on one or more sides of field of view 310. Additionally or alternatively, apparatus 204 may smooth edges between generated pixels 318 and pixels from image 308. Image 316 may appear to be image 308 as if image 308 were captured of a panned field of view. In cases in which image 316 includes a part of image 308, the part of image 308 included in image 316 may be the same the part of image 308 provided by apparatus 204. Alternatively, the part of image 308 included in image 316 may be a subset of what is provided by apparatus 204. For example, apparatus 204 may provide all of image 308 (or a large part of image 308) and image 316 may include a subset of what was provided; [0067], The generative machine-learning model may generate image 416 based on at least a part of image 408 and the instructions and UI 206 may display image 416 at UI 206 as illustrated in representation 414. Image 416 may include at least a part of image 408 of field of view 410 and generated pixels 418 outside field of view 410. For example, based on rotate gesture 412 indicating a desire to rotate field of view 410, image 416 may include at least a part of image 408 of field of view 410 and generated pixels 418 at one or more corners of image 416. Additionally or alternatively, apparatus 204 may smooth edges between generated pixels 418 and pixels from image 408. Image 416 may appear to be image 408 as if image 408 were captured of a rotated field of view. In cases in which image 416 includes a part of image 408, the part of image 408 included in image 416 may be the same the part of image 408 provided by apparatus 204. Alternatively, the part of image 408 included in image 416 may be a subset of what is provided by apparatus 204. For example, apparatus 204 may provide all of image 408 (or a large part of image 408) and image 416 may include a subset of what was provided; [0068], Apparatus 204 may rotate and crop the intermediate image to generate image 416; [0073], UI 206 may receive user input relative to an image including generated content. In such cases, apparatus 204 may generate additional content based on the user input and the image including the already-generated content. For example, after having generated image 216 of FIG. 2 (e.g., by expanding a field of view of image 208), UI 206 may receive a drag gesture relative to image 216. Apparatus 204 may generate another image based on the drag gesture and image 216. As another example, after having generated image 316 of FIG. 3 (e.g., by panning a field of view of image 308), UI 206 may receive a rotate gesture relative to image 316. Apparatus 204 may generate another image based on the rotate gesture and image 316; [0074], UI 206 may receive user input indicating multiple desires. For example, a user input may include a pinch gesture, a drag gesture, and/or a rotate gesture. UI 206 may interpret the user input and generate instructions based on the interpreted desired changes. In some cases, UI 206 may generate one instruction based on all the desired changes and instruct the generative machine-learning model to generate an image once. In other cases, UI 206 may generate on instruction based on each of the desired changes and instruct the generative machine-learning model to generate several images in series to implement the desired changes)
Regarding claim 11, He teaches all the limitations of claim 1, further comprising:
further comprising: generating a plurality of modified images using the image generation model based on the input image, wherein the modified image is selected from the plurality of modified images (He Figs. 1-18; [0041], the systems and techniques may rotate the image and crop the altered image to fit the frame. In some cases, the systems and techniques may rotate the image before providing the image to the generative machine-learning model; [0044], after generating the altered image may display the altered image (or a final image rotated and/or cropped according to the interpreted desire); [0062], UI 206 may receive a user input (e.g., drag gesture 312) and interpret the user input relative to image 308. For example, UI 206 may determine a starting point of drag gesture 312 and an ending point of drag gesture 312 relative to image 308; [0063], Apparatus 204 may provide at least a part of image 308 (e.g., as a condition) and instructions based on the change to a generative machine-learning model (e.g., generative machine-learning model 120 and/or generative machine-learning model 121 of FIG. 1) of apparatus 204. For example, based on drag gesture 312, apparatus 204 may determine a part of image 308, for example, the portion out of image 308 that may remain in the frame following the pan, and provide the part to the generative machine-learning model;, apparatus 204 may provide all of image 308 (or a large part of image 308) and image 316 may include a subset of what was provided; [0067], The generative machine-learning model may generate image 416 based on at least a part of image 408 and the instructions and UI 206 may display image 416 at UI 206 as illustrated in representation 414. Image 416 may include at least a part of image 408 of field of view 410 and generated pixels 418 outside field of view 410. For example, based on rotate gesture 412 indicating a desire to rotate field of view 410, image 416 may include at least a part of image 408 of field of view 410 and generated pixels 418 at one or more corners of image 416. Additionally or alternatively, apparatus 204 may smooth edges between generated pixels 418 and pixels from image 408. Image 416 may appear to be image 408 as if image 408 were captured of a rotated field of view. In cases in which image 416 includes a part of image 408, the part of image 408 included in image 416 may be the same the part of image 408 provided by apparatus 204. Alternatively, the part of image 408 included in image 416 may be a subset of what is provided by apparatus 204. For example, apparatus 204 may provide all of image 408 (or a large part of image 408) and image 416 may include a subset of what was provided; [0068], Apparatus 204 may rotate and crop the intermediate image to generate image 416; [0073], UI 206 may receive user input relative to an image including generated content. In such cases, apparatus 204 may generate additional content based on the user input and the image including the already-generated content. For example, after having generated image 216 of FIG. 2 (e.g., by expanding a field of view of image 208), UI 206 may receive a drag gesture relative to image 216. Apparatus 204 may generate another image based on the drag gesture and image 216. As another example, after having generated image 316 of FIG. 3 (e.g., by panning a field of view of image 308), UI 206 may receive a rotate gesture relative to image 316. Apparatus 204 may generate another image based on the rotate gesture and image 316; [0074], UI 206 may receive user input indicating multiple desires. For example, a user input may include a pinch gesture, a drag gesture, and/or a rotate gesture. UI 206 may interpret the user input and generate instructions based on the interpreted desired changes; UI 206 may generate on instruction based on each of the desired changes and instruct the generative machine-learning model to generate several images in series to implement the desired changes)
Regarding claim 12, He teaches all the limitations of claim 11, further comprising:
further comprising: displaying a preview for each of the plurality of modified images, wherein the modified image is selected based on the preview (He Figs. 1-18; [0041], the systems and techniques may rotate the image and crop the altered image to fit the frame. In some cases, the systems and techniques may rotate the image before providing the image to the generative machine-learning model; [0044], after generating the altered image may display the altered image (or a final image rotated and/or cropped according to the interpreted desire); [0062], UI 206 may receive a user input (e.g., drag gesture 312) and interpret the user input relative to image 308. For example, UI 206 may determine a starting point of drag gesture 312 and an ending point of drag gesture 312 relative to image 308; [0063], Apparatus 204 may provide at least a part of image 308 (e.g., as a condition) and instructions based on the change to a generative machine-learning model (e.g., generative machine-learning model 120 and/or generative machine-learning model 121 of FIG. 1) of apparatus 204. For example, based on drag gesture 312, apparatus 204 may determine a part of image 308, for example, the portion out of image 308 that may remain in the frame following the pan, and provide the part to the generative machine-learning model;, apparatus 204 may provide all of image 308 (or a large part of image 308) and image 316 may include a subset of what was provided; [0067], The generative machine-learning model may generate image 416 based on at least a part of image 408 and the instructions and UI 206 may display image 416 at UI 206 as illustrated in representation 414. Image 416 may include at least a part of image 408 of field of view 410 and generated pixels 418 outside field of view 410. For example, based on rotate gesture 412 indicating a desire to rotate field of view 410, image 416 may include at least a part of image 408 of field of view 410 and generated pixels 418 at one or more corners of image 416. Additionally or alternatively, apparatus 204 may smooth edges between generated pixels 418 and pixels from image 408. Image 416 may appear to be image 408 as if image 408 were captured of a rotated field of view. In cases in which image 416 includes a part of image 408, the part of image 408 included in image 416 may be the same the part of image 408 provided by apparatus 204. Alternatively, the part of image 408 included in image 416 may be a subset of what is provided by apparatus 204. For example, apparatus 204 may provide all of image 408 (or a large part of image 408) and image 416 may include a subset of what was provided; [0068], Apparatus 204 may rotate and crop the intermediate image to generate image 416; [0073], UI 206 may receive user input relative to an image including generated content. In such cases, apparatus 204 may generate additional content based on the user input and the image including the already-generated content. For example, after having generated image 216 of FIG. 2 (e.g., by expanding a field of view of image 208), UI 206 may receive a drag gesture relative to image 216. Apparatus 204 may generate another image based on the drag gesture and image 216. As another example, after having generated image 316 of FIG. 3 (e.g., by panning a field of view of image 308), UI 206 may receive a rotate gesture relative to image 316. Apparatus 204 may generate another image based on the rotate gesture and image 316; [0074], UI 206 may receive user input indicating multiple desires. For example, a user input may include a pinch gesture, a drag gesture, and/or a rotate gesture. UI 206 may interpret the user input and generate instructions based on the interpreted desired changes; UI 206 may generate on instruction based on each of the desired changes and instruct the generative machine-learning model to generate several images in series to implement the desired changes)
Regarding claim 13, He teaches the claim comprising:
A system comprising: a memory component; and a processing device coupled to the memory component, the processing device configured to perform operations comprising (He Figs. 1-18; [0006], an apparatus for generating image content is provided that includes at least one memory and at least one processor (e.g., configured in circuitry) coupled to the at least one memory. The at least one processor configured to):
obtaining, using a user interface, a user input that indicates a frame for modifying an input image, wherein the frame includes a first region inside of an input image and a second region outside of the input image, and excludes a third region inside of the input image (He Figs. 1-18; [0060], FIG. 3 includes an example representation 302 of apparatus 204 displaying an image 308 (e.g., which may be captured by apparatus 204) and an example representation 314 of apparatus 204 displaying an image 316 (e.g., including generated image content, such as generated pixels 318; [0062], UI 206 may receive a user input (e.g., drag gesture 312) and interpret the user input relative to image 308. For example, UI 206 may determine a starting point of drag gesture 312 and an ending point of drag gesture 312 relative to image 308. Additionally or alternatively, UI 206 may determine a length and direction of drag gesture 312. UI 206 may determine a change (e.g., change 116 of FIG. 1) based on drag gesture 312. The change may be a change to field of view 310. For example, UI 206 may interpret drag gesture 312 as a desire to change field of view 310, for example, by panning field of view 310. UI 206 may determine a direction and length of the desired change to field of view 310; [0063], Apparatus 204 may provide at least a part of image 308 (e.g., as a condition) and instructions based on the change to a generative machine-learning model (e.g., generative machine-learning model 120 and/or generative machine-learning model 121 of FIG. 1) of apparatus 204. For example, based on drag gesture 312, apparatus 204 may determine a part of image 308, for example, the portion out of image 308 that may remain in the frame following the pan, and provide the part to the generative machine-learning model; [0064], FIG. 4 includes an example representation 402 of apparatus 204 displaying an image 408 (e.g., which may be captured by apparatus 204) and an example representation 414 of apparatus 204 displaying an image 416; [0066], UI 206 may receive a user input (e.g., rotate gesture 412) and interpret the user input relative to image 408. For example, UI 206 may interpret a starting point of rotate gesture 412 and an ending point of rotate gesture 412 relative to image 408; [0067], based on drag gesture 312, apparatus 204 may determine a part of image 408, for example, excluding corners that may be cut from the frame by the rotation of image 408);
and generating, using an image generation model, a modified image including original content from the input image in the first region and generated content in the second region, and excluding content from the input image in the third region (He Figs. 1-18; [0063], Apparatus 204 may provide at least a part of image 308 (e.g., as a condition) and instructions based on the change to a generative machine-learning model (e.g., generative machine-learning model 120 and/or generative machine-learning model 121 of FIG. 1) of apparatus 204. For example, based on drag gesture 312, apparatus 204 may determine a part of image 308, for example, the portion out of image 308 that may remain in the frame following the pan, and provide the part to the generative machine-learning model. Alternatively, apparatus 204 may provide all of image 308 to the generative machine-learning model. The generative machine-learning model may generate image 316 based on at least a part of image 308 and the instructions and UI 206 may display image 316 at UI 206 as illustrated in representation 314. Image 316 may include at least a part of image 308 of field of view 310 and generated pixels 318 outside field of view 310. For example, based on drag gesture 312 indicating a desire to pan field of view 310, image 316 may include at least a part of image 308 of field of view 310 and generated pixels 318 on one or more sides of field of view 310. Additionally or alternatively, apparatus 204 may smooth edges between generated pixels 318 and pixels from image 308. Image 316 may appear to be image 308 as if image 308 were captured of a panned field of view. In cases in which image 316 includes a part of image 308, the part of image 308 included in image 316 may be the same the part of image 308 provided by apparatus 204. Alternatively, the part of image 308 included in image 316 may be a subset of what is provided by apparatus 204. For example, apparatus 204 may provide all of image 308 (or a large part of image 308) and image 316 may include a subset of what was provided; [0067], The generative machine-learning model may generate image 416 based on at least a part of image 408 and the instructions and UI 206 may display image 416 at UI 206 as illustrated in representation 414. Image 416 may include at least a part of image 408 of field of view 410 and generated pixels 418 outside field of view 410. For example, based on rotate gesture 412 indicating a desire to rotate field of view 410, image 416 may include at least a part of image 408 of field of view 410 and generated pixels 418 at one or more corners of image 416. Additionally or alternatively, apparatus 204 may smooth edges between generated pixels 418 and pixels from image 408. Image 416 may appear to be image 408 as if image 408 were captured of a rotated field of view. In cases in which image 416 includes a part of image 408, the part of image 408 included in image 416 may be the same the part of image 408 provided by apparatus 204. Alternatively, the part of image 408 included in image 416 may be a subset of what is provided by apparatus 204. For example, apparatus 204 may provide all of image 408 (or a large part of image 408) and image 416 may include a subset of what was provided; [0068], Apparatus 204 may rotate and crop the intermediate image to generate image 416)
Regarding claim 14, He teaches all the limitations of claim 13, further comprising:
wherein: the user interface comprises a cropping tool configured to receive a drag input, wherein the frame is based on the drag input (He Figs. 1-18; [0044], after generating the altered image may display the altered image (or a final image rotated and/or cropped according to the interpreted desire); [0062], UI 206 may receive a user input (e.g., drag gesture 312) and interpret the user input relative to image 308. For example, UI 206 may determine a starting point of drag gesture 312 and an ending point of drag gesture 312 relative to image 308; [0063], Apparatus 204 may provide at least a part of image 308 (e.g., as a condition) and instructions based on the change to a generative machine-learning model (e.g., generative machine-learning model 120 and/or generative machine-learning model 121 of FIG. 1) of apparatus 204. For example, based on drag gesture 312, apparatus 204 may determine a part of image 308, for example, the portion out of image 308 that may remain in the frame following the pan, and provide the part to the generative machine-learning model. Alternatively, apparatus 204 may provide all of image 308 to the generative machine-learning model. The generative machine-learning model may generate image 316 based on at least a part of image 308 and the instructions and UI 206 may display image 316 at UI 206 as illustrated in representation 314. Image 316 may include at least a part of image 308 of field of view 310 and generated pixels 318 outside field of view 310. For example, based on drag gesture 312 indicating a desire to pan field of view 310, image 316 may include at least a part of image 308 of field of view 310 and generated pixels 318 on one or more sides of field of view 310. Additionally or alternatively, apparatus 204 may smooth edges between generated pixels 318 and pixels from image 308. Image 316 may appear to be image 308 as if image 308 were captured of a panned field of view. In cases in which image 316 includes a part of image 308, the part of image 308 included in image 316 may be the same the part of image 308 provided by apparatus 204. Alternatively, the part of image 308 included in image 316 may be a subset of what is provided by apparatus 204. For example, apparatus 204 may provide all of image 308 (or a large part of image 308) and image 316 may include a subset of what was provided; [0067], based on drag gesture 312, apparatus 204 may determine a part of image 408, for example, excluding corners that may be cut from the frame by the rotation of image 408)
Regarding claim 15, He teaches all the limitations of claim 13, further comprising:
wherein: the user interface comprises a generative expand element configured to receive a generative expand input and initiate a generative expand mode based on the generative expand input, wherein the modified image is generated based on the generative expand mode (He Figs. 1-18; [0044], after generating the altered image may display the altered image (or a final image rotated and/or cropped according to the interpreted desire); [0062], UI 206 may receive a user input (e.g., drag gesture 312) and interpret the user input relative to image 308. For example, UI 206 may determine a starting point of drag gesture 312 and an ending point of drag gesture 312 relative to image 308; [0063], Apparatus 204 may provide at least a part of image 308 (e.g., as a condition) and instructions based on the change to a generative machine-learning model (e.g., generative machine-learning model 120 and/or generative machine-learning model 121 of FIG. 1) of apparatus 204. For example, based on drag gesture 312, apparatus 204 may determine a part of image 308, for example, the portion out of image 308 that may remain in the frame following the pan, and provide the part to the generative machine-learning model. Alternatively, apparatus 204 may provide all of image 308 to the generative machine-learning model. The generative machine-learning model may generate image 316 based on at least a part of image 308 and the instructions and UI 206 may display image 316 at UI 206 as illustrated in representation 314. Image 316 may include at least a part of image 308 of field of view 310 and generated pixels 318 outside field of view 310. For example, based on drag gesture 312 indicating a desire to pan field of view 310, image 316 may include at least a part of image 308 of field of view 310 and generated pixels 318 on one or more sides of field of view 310. Additionally or alternatively, apparatus 204 may smooth edges between generated pixels 318 and pixels from image 308. Image 316 may appear to be image 308 as if image 308 were captured of a panned field of view. In cases in which image 316 includes a part of image 308, the part of image 308 included in image 316 may be the same the part of image 308 provided by apparatus 204. Alternatively, the part of image 308 included in image 316 may be a subset of what is provided by apparatus 204. For example, apparatus 204 may provide all of image 308 (or a large part of image 308) and image 316 may include a subset of what was provided; [0067], Apparatus 204 may provide at least a part of image 408 (e.g., as a condition) and instructions based on the change to a generative machine-learning model (e.g., generative machine-learning model 120 and/or generative machine-learning model 121 of FIG. 1) of apparatus 204. For example, based on drag gesture 312, apparatus 204 may determine a part of image 408, for example, excluding corners that may be cut from the frame by the rotation of image 408, and provide the part to the generative machine-learning model. Alternatively, apparatus 204 may provide all of image 408 to the generative machine-learning model. The generative machine-learning model may generate image 416 based on at least a part of image 408 and the instructions and UI 206 may display image 416 at UI 206 as illustrated in representation 414)
Regarding claim 17, He teaches all the limitations of claim 13, further comprising:
wherein: the image generation model comprises a diffusion U-Net architecture (He Figs. 1-18; [0027], FIG. 15 is a diagram illustrating a U-Net architecture for a diffusion model, in accordance with some aspects of the present disclosure; [0044], after generating the altered image may display the altered image (or a final image rotated and/or cropped according to the interpreted desire); [0037], The systems and techniques described herein may include a user interface (UI) that may allow a user to generate image data using a generative machine-learning model in an easy and convenient way. For example, the systems and techniques may allow a user to quickly and easily alter an image using a generative machine-learning model (e.g., a diffusion neural network model), such as to alter a field of view of the image; [0062-0063], Apparatus 204 may provide at least a part of image 308 (e.g., as a condition) and instructions based on the change to a generative machine-learning model (e.g., generative machine-learning model 120 and/or generative machine-learning model 121 of FIG. 1) of apparatus 204. For example, based on drag gesture 312, apparatus 204 may determine a part of image 308, for example, the portion out of image 308 that may remain in the frame following the pan, and provide the part to the generative machine-learning model. Alternatively, apparatus 204 may provide all of image 308 to the generative machine-learning model. The generative machine-learning model may generate image 316 based on at least a part of image 308 and the instructions and UI 206 may display image 316 at UI 206 as illustrated in representation 314; [0067], Apparatus 204 may provide at least a part of image 408 (e.g., as a condition) and instructions based on the change to a generative machine-learning model (e.g., generative machine-learning model 120 and/or generative machine-learning model 121 of FIG. 1) of apparatus 204. For example, based on drag gesture 312, apparatus 204 may determine a part of image 408, for example, excluding corners that may be cut from the frame by the rotation of image 408, and provide the part to the generative machine-learning model. Alternatively, apparatus 204 may provide all of image 408 to the generative machine-learning model. The generative machine-learning model may generate image 416 based on at least a part of image 408 and the instructions and UI 206 may display image 416 at UI 206 as illustrated in representation 414; [0179], FIG. 15 is a diagram illustrating a U-Net architecture 1500 for a diffusion model, in accordance with some aspects. The initial image 1502 (e.g., of a cat) is provided to the U-Net architecture 1500 which includes a series of residual networks (ResNet) blocks and self-attention layers to represent the network ϵ.sub.θ (x.sub.t, t); [0180], The U-Net architecture 1500 includes a contracting path 1504 and an expansive path 1505 as shown in FIG. 15, which gives it the U-shaped architecture. The contracting path 1504 can be a convolutional network that includes repeated convolutional layers (that apply convolutional operations), each followed by a rectified linear unit (ReLU) and a max pooling operation. When images are being processed (e.g., the image 1502) during the contracting path 1504, the spatial information of the image 1502 is reduced as features are generated. The expansive path 1505 combines the features and spatial information through a sequence of up-convolutions and concatenations with high-resolution features from the contracting path 1504)
Regarding claim 18, He teaches the claim comprising:
A non-transitory computer readable medium storing code for image processing, the code comprising instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising (He Figs. 1-18; [0007], a non-transitory computer-readable medium is provided that has stored thereon instructions that, when executed by one or more processors, cause the one or more processors to):
obtaining an input image and a frame including at least a portion of the input image, wherein the input image is arranged at an oblique angle with respect to the frame (He Figs. 1-18; [0041], the systems and techniques may rotate the image and crop the altered image to fit the frame. In some cases, the systems and techniques may rotate the image before providing the image to the generative machine-learning model; [0064], FIG. 4 includes an example representation 402 of apparatus 204 displaying an image 408 (e.g., which may be captured by apparatus 204) and an example representation 414 of apparatus 204 displaying an image 416; [0066], UI 206 may receive a user input (e.g., rotate gesture 412) and interpret the user input relative to image 408. For example, UI 206 may interpret a starting point of rotate gesture 412 and an ending point of rotate gesture 412 relative to image 408; [0066], UI 206 may receive a user input (e.g., rotate gesture 412) and interpret the user input relative to image 408. For example, UI 206 may interpret a starting point of rotate gesture 412 and an ending point of rotate gesture 412 relative to image 408. Additionally or alternatively, UI 206 may determine a rotational length and/or rotational direction of rotate gesture 412. UI 206 may determine a change (e.g., change 116 of FIG. 1) based on rotate gesture 412. The change may be a change to field of view 410; [0067], Apparatus 204 may provide at least a part of image 408 (e.g., as a condition) and instructions based on the change to a generative machine-learning model (e.g., generative machine-learning model 120 and/or generative machine-learning model 121 of FIG. 1) of apparatus 204. For example, based on drag gesture 312, apparatus 204 may determine a part of image 408, for example, excluding corners that may be cut from the frame by the rotation of image 408, and provide the part to the generative machine-learning model. Alternatively, apparatus 204 may provide all of image 408 to the generative machine-learning model. The generative machine-learning model may generate image 416 based on at least a part of image 408 and the instructions and UI 206 may display image 416 at UI 206 as illustrated in representation 414; [0114], the computing device (or one or more components thereof) may at least one of rotate or crop the image to obtain the at least part of the image to provide to the generative machine-learning model. For example, apparatus 100 may obtain image 114 and rotate or crop image 114 before providing image 114 to generative machine-learning model 120 (or generative machine-learning model 121). Apparatus 100 may provide the rotated and/or cropped image 114 to generative machine-learning model 120 (or generative machine-learning model 121). Generative machine-learning model 120 (or generative machine-learning model 121) may generate image 122 based on the rotated and/or cropped version of image 114. For example, apparatus 100 may obtain image 408. Apparatus 100 may rotate image 408, for example, as illustrated in the frame of image 416, and provide the rotated image 408 to the generative machine-learning model. The generative machine-learning model may generate image 416 based on the rotated image 408);
generating, using an image generation model, a modified image including original content from the input image and generated content within a portion of the frame outside the input image; and presenting the modified image for display in a user interface (He Figs. 1-18; [0041], the systems and techniques may rotate the image and crop the altered image to fit the frame. In some cases, the systems and techniques may rotate the image before providing the image to the generative machine-learning model; [0067], The generative machine-learning model may generate image 416 based on at least a part of image 408 and the instructions and UI 206 may display image 416 at UI 206 as illustrated in representation 414. Image 416 may include at least a part of image 408 of field of view 410 and generated pixels 418 outside field of view 410. For example, based on rotate gesture 412 indicating a desire to rotate field of view 410, image 416 may include at least a part of image 408 of field of view 410 and generated pixels 418 at one or more corners of image 416. Additionally or alternatively, apparatus 204 may smooth edges between generated pixels 418 and pixels from image 408. Image 416 may appear to be image 408 as if image 408 were captured of a rotated field of view. In cases in which image 416 includes a part of image 408, the part of image 408 included in image 416 may be the same the part of image 408 provided by apparatus 204. Alternatively, the part of image 408 included in image 416 may be a subset of what is provided by apparatus 204. For example, apparatus 204 may provide all of image 408 (or a large part of image 408) and image 416 may include a subset of what was provided; [0068], Apparatus 204 may rotate and crop the intermediate image to generate image 416; see also [0114])
Regarding claim 19, He teaches all the limitations of claim 18, further comprising:
wherein: the frame includes a first region inside of the input image and a second region outside of the input image, and excludes a third region inside of the input image (He Figs. 1-18; [0041], the systems and techniques may rotate the image and crop the altered image to fit the frame. In some cases, the systems and techniques may rotate the image before providing the image to the generative machine-learning model; [0067], Apparatus 204 may provide at least a part of image 408 (e.g., as a condition) and instructions based on the change to a generative machine-learning model (e.g., generative machine-learning model 120 and/or generative machine-learning model 121 of FIG. 1) of apparatus 204. For example, based on drag gesture 312, apparatus 204 may determine a part of image 408, for example, excluding corners that may be cut from the frame by the rotation of image 408, and provide the part to the generative machine-learning model. Alternatively, apparatus 204 may provide all of image 408 to the generative machine-learning model. The generative machine-learning model may generate image 416 based on at least a part of image 408 and the instructions and UI 206 may display image 416 at UI 206 as illustrated in representation 414. Image 416 may include at least a part of image 408 of field of view 410 and generated pixels 418 outside field of view 410. For example, based on rotate gesture 412 indicating a desire to rotate field of view 410, image 416 may include at least a part of image 408 of field of view 410 and generated pixels 418 at one or more corners of image 416. Additionally or alternatively, apparatus 204 may smooth edges between generated pixels 418 and pixels from image 408. Image 416 may appear to be image 408 as if image 408 were captured of a rotated field of view. In cases in which image 416 includes a part of image 408, the part of image 408 included in image 416 may be the same the part of image 408 provided by apparatus 204. Alternatively, the part of image 408 included in image 416 may be a subset of what is provided by apparatus 204; [0068], Apparatus 204 may rotate and crop the intermediate image to generate image 416; [0114], the computing device (or one or more components thereof) may at least one of rotate or crop the image to obtain the at least part of the image to provide to the generative machine-learning model. For example, apparatus 100 may obtain image 114 and rotate or crop image 114 before providing image 114 to generative machine-learning model 120 (or generative machine-learning model 121). Apparatus 100 may provide the rotated and/or cropped image 114 to generative machine-learning model 120 (or generative machine-learning model 121). Generative machine-learning model 120 (or generative machine-learning model 121) may generate image 122 based on the rotated and/or cropped version of image 114. For example, apparatus 100 may obtain image 408. Apparatus 100 may rotate image 408, for example, as illustrated in the frame of image 416, and provide the rotated image 408 to the generative machine-learning model. The generative machine-learning model may generate image 416 based on the rotated image 408)
Regarding claim 20, He teaches all the limitations of claim 18, further comprising:
wherein obtaining the input image comprises: receiving a preliminary image; receiving a rotation command indicating the oblique angle; and rotating the preliminary image based on the rotation command to obtain the input image (He Figs. 1-18; [0041], the systems and techniques may rotate the image and crop the altered image to fit the frame. In some cases, the systems and techniques may rotate the image before providing the image to the generative machine-learning model; [0066], UI 206 may receive a user input (e.g., rotate gesture 412) and interpret the user input relative to image 408. For example, UI 206 may interpret a starting point of rotate gesture 412 and an ending point of rotate gesture 412 relative to image 408. Additionally or alternatively, UI 206 may determine a rotational length and/or rotational direction of rotate gesture 412. UI 206 may determine a change (e.g., change 116 of FIG. 1) based on rotate gesture 412. The change may be a change to field of view 410. For example, UI 206 may interpret rotate gesture 412 as a desire to change field of view 410, for example, by rotating field of view 410. UI 206 may determine a rotational length and/or direction of the desired change to field of view 410; [0067], Apparatus 204 may provide at least a part of image 408 (e.g., as a condition) and instructions based on the change to a generative machine-learning model (e.g., generative machine-learning model 120 and/or generative machine-learning model 121 of FIG. 1) of apparatus 204. For example, based on drag gesture 312, apparatus 204 may determine a part of image 408, for example, excluding corners that may be cut from the frame by the rotation of image 408, and provide the part to the generative machine-learning model. Alternatively, apparatus 204 may provide all of image 408 to the generative machine-learning model. The generative machine-learning model may generate image 416 based on at least a part of image 408 and the instructions and UI 206 may display image 416 at UI 206 as illustrated in representation 414. Image 416 may include at least a part of image 408 of field of view 410 and generated pixels 418 outside field of view 410. For example, based on rotate gesture 412 indicating a desire to rotate field of view 410, image 416 may include at least a part of image 408 of field of view 410 and generated pixels 418 at one or more corners of image 416. Additionally or alternatively, apparatus 204 may smooth edges between generated pixels 418 and pixels from image 408. Image 416 may appear to be image 408 as if image 408 were captured of a rotated field of view. In cases in which image 416 includes a part of image 408, the part of image 408 included in image 416 may be the same the part of image 408 provided by apparatus 204. Alternatively, the part of image 408 included in image 416 may be a subset of what is provided by apparatus 204; [0068], Apparatus 204 may rotate and crop the intermediate image to generate image 416; [0114], the computing device (or one or more components thereof) may at least one of rotate or crop the image to obtain the at least part of the image to provide to the generative machine-learning model. For example, apparatus 100 may obtain image 114 and rotate or crop image 114 before providing image 114 to generative machine-learning model 120 (or generative machine-learning model 121). Apparatus 100 may provide the rotated and/or cropped image 114 to generative machine-learning model 120 (or generative machine-learning model 121). Generative machine-learning model 120 (or generative machine-learning model 121) may generate image 122 based on the rotated and/or cropped version of image 114. For example, apparatus 100 may obtain image 408. Apparatus 100 may rotate image 408, for example, as illustrated in the frame of image 416, and provide the rotated image 408 to the generative machine-learning model. The generative machine-learning model may generate image 416 based on the rotated image 408)
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.
Claims 6, 7, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over He in view of Golobokov et al. (US 20240428469 A1, published 12/26/2024), hereinafter Golobokov.
Regarding claim 6, He teaches all the limitations of claim 1. However, He fails to expressly disclose further comprising: receiving a text input, wherein the image generation model generates the modified image based on the text input. In the same field of endeavor, Golobokov teaches:
further comprising: receiving a text input, wherein the image generation model generates the modified image based on the text input (Golobokov Figs. 1-10; [0093], FIG. 5 is a block diagram showing an example image generation system 500, according to some examples. The image generation system 500 includes an image input component 510, an artificial image generation network 530, a text prompt component 520, and a zoomed image generation component 522. Together, these components enable the image generation system 500 to receive a first image depicting a real-world scene including a target object and receive input associated with adjusting a zoom level of the first image and, in response to receiving the input, modify the zoom level associated with the first image to generate a second image having a view of the target object that is different from a view of the target object in the first image; [0098], the text prompt component 520 receives text input from the user defining one or more parameters for generating the artificial image. For example, text input is received from the user that specifies what targets of the first image to improve, modify, and/or focus and/or that specifies the name of one or more landmarks. The text input can also include a parameter that defines what parts of the image not to modify; [0108], the second image 620 is associated with text prompts (automatically generated or supplied by the user) and both the second image 620 and the text prompts are provided to the generative machine learning model that is implemented by the artificial image generation network 530; [0110], The input image 640 is presented to a user in a graphical user interface (GUI) with an extend option 648. In response to receiving input that selects the extend option 648 (or in response to receiving a pinch gesture that zooms out of the input image 640), the image generation system 500 generates a zoomed out image; [0111], the second image 641 is associated with text prompts (automatically generated or supplied by the user) and both the second image 641 and the text prompts are provided to the generative machine learning model that is implemented by the artificial image generation network 530)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated further comprising: receiving a text input, wherein the image generation model generates the modified image based on the text input as suggested in Golobokov into He. Doing so would be desirable because users communicate with each other in a variety of ways. Most of the ways in which users communicate involve the exchange of images or photographs. Ensuring that these images are of high quality is important to conveying the right messages (see Golobokov [0002]). Various communication platforms allow users to share content and create images for transmission to other users. These images can be used to promote products or services and/or to simply represent different real-world objects in simulated or real environments. However, these systems require a user to use expensive equipment and technology to create high-quality, appealing images. Also, users may spend a great deal of effort meticulously placing objects in different environments and manually adjusting lighting and other image attributes to enhance the presentation of the objects in the images. All of these factors can add up to make the creation of high-quality images (e.g., for use in advertising) a significant expense and detract from the overall use and enjoyment of the system. In addition, because users may not have the resources needed to create high-quality images, opportunities to share and present objects in ideal settings are missed. Also, presenting lower quality images of such objects can cause other users to overlook the value of the objects, which wastes the resources used to create and display the objects. In some cases, typical systems allow users to zoom into and out of various portions of images. However, typical systems are unable to fill in the missing detail resulting from the zoom in or zoom out operations which results in blurry or distorted images (see Golobokov [0015]). The disclosed techniques seek to improve the efficiency of using an electronic device by intelligently and automatically generating images that depict real-world objects in a real-world scene in a simple and intuitive manner. For example, a user may wish to zoom out of an image previously captured but then objects in the image are missing since they were not in the original image content. Disclosed techniques address these technical issues by generating artificial image content to fill in the missing features/gaps resulting from the zoom in or zoom out operations. The disclosed techniques create photorealistic images or videos that depict a real-world object in simulated scenes very quickly and efficiently and with minimal user interaction or involvement. This can reduce the overall time and expense incurred to develop high-quality images that feature objects or products, such as shoes, shirts, or other fashion items (see Golobokov [0016]). In this way, the disclosed techniques improve the overall experience of the user in using the electronic device and reduce the overall amount of resources needed to accomplish a task of producing high-quality images (see Golobokov [0017]).
Regarding claim 7, He teaches all the limitations of claim 1. However, He fails to expressly disclose further comprising: providing a context bar in the user interface at a location based on the input image; and receiving a guidance input via the context bar, wherein the generated content is based on the guidance input. In the same field of endeavor, Golobokov teaches:
further comprising: providing a context bar in the user interface at a location based on the input image; and receiving a guidance input via the context bar, wherein the generated content is based on the guidance input (Golobokov Figs. 1-10; [0093], FIG. 5 is a block diagram showing an example image generation system 500, according to some examples. The image generation system 500 includes an image input component 510, an artificial image generation network 530, a text prompt component 520, and a zoomed image generation component 522. Together, these components enable the image generation system 500 to receive a first image depicting a real-world scene including a target object and receive input associated with adjusting a zoom level of the first image and, in response to receiving the input, modify the zoom level associated with the first image to generate a second image having a view of the target object that is different from a view of the target object in the first image; [0107], FIGS. 6A, 6B, and 6C are diagrammatic representations of example inputs and outputs of the image generation system 500, according to some examples. For example, as shown in the sequence of diagrams 600 of FIG. 6A, an input image 610 is received and/or captured that has a first size (e.g., a first bounding box). The input image 610 depicts a real-world or virtual object 614. The input image 610 is to a user in a graphical user interface (GUI) with an extend option 612. In response to receiving input that selects the extend option 612 (or in response to receiving a pinch gesture that zooms out of the input image 610), the image generation system 500 generates a second image 620 that corresponds to zooming the input image 610 by a predetermined or user-specified amount; [0108], The artificial image generation network 530 populates the empty space region 622 with artificial pixel values and can return the artificial image 630; [0110], The input image 640 is presented to a user in a graphical user interface (GUI) with an extend option 648. In response to receiving input that selects the extend option 648 (or in response to receiving a pinch gesture that zooms out of the input image 640), the image generation system 500 generates a zoomed out image; [0111], The second image 641 is then be provided to the artificial image generation network 530 to populate the empty space region with artificial pixels or content; [0113], In response to determining that the input image 650 has been zoomed in by the user input, the image generation system 500 replaces the display of the extend option 654 with an enhance option 656. In response to receiving input that selects the enhance option 656, the second image 655 is provided to the artificial image generation network 530 to enhance or improve portions of the image that have been zoomed into, such as the blurry or noisy portions of the magnified real-world or virtual object 657. The artificial image generation network 530 removes blur or noise from the magnified real-world or virtual object 657 using artificial pixel values and can return the artificial image 658)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated further comprising: providing a context bar in the user interface at a location based on the input image; and receiving a guidance input via the context bar, wherein the generated content is based on the guidance input as suggested in Golobokov into He. Doing so would be desirable because users communicate with each other in a variety of ways. Most of the ways in which users communicate involve the exchange of images or photographs. Ensuring that these images are of high quality is important to conveying the right messages (see Golobokov [0002]). Various communication platforms allow users to share content and create images for transmission to other users. These images can be used to promote products or services and/or to simply represent different real-world objects in simulated or real environments. However, these systems require a user to use expensive equipment and technology to create high-quality, appealing images. Also, users may spend a great deal of effort meticulously placing objects in different environments and manually adjusting lighting and other image attributes to enhance the presentation of the objects in the images. All of these factors can add up to make the creation of high-quality images (e.g., for use in advertising) a significant expense and detract from the overall use and enjoyment of the system. In addition, because users may not have the resources needed to create high-quality images, opportunities to share and present objects in ideal settings are missed. Also, presenting lower quality images of such objects can cause other users to overlook the value of the objects, which wastes the resources used to create and display the objects. In some cases, typical systems allow users to zoom into and out of various portions of images. However, typical systems are unable to fill in the missing detail resulting from the zoom in or zoom out operations which results in blurry or distorted images (see Golobokov [0015]). The disclosed techniques seek to improve the efficiency of using an electronic device by intelligently and automatically generating images that depict real-world objects in a real-world scene in a simple and intuitive manner. For example, a user may wish to zoom out of an image previously captured but then objects in the image are missing since they were not in the original image content. Disclosed techniques address these technical issues by generating artificial image content to fill in the missing features/gaps resulting from the zoom in or zoom out operations. The disclosed techniques create photorealistic images or videos that depict a real-world object in simulated scenes very quickly and efficiently and with minimal user interaction or involvement. This can reduce the overall time and expense incurred to develop high-quality images that feature objects or products, such as shoes, shirts, or other fashion items (see Golobokov [0016]). In this way, the disclosed techniques improve the overall experience of the user in using the electronic device and reduce the overall amount of resources needed to accomplish a task of producing high-quality images (see Golobokov [0017]).
Regarding claim 16, He teaches all the limitations of claim 13. However, He fails to expressly disclose wherein: the user interface comprises a context bar at a location based on the input image and configured to receive a guidance input, wherein the generated content is based on the guidance input. In the same field of endeavor, Golobokov teaches:
wherein: the user interface comprises a context bar at a location based on the input image and configured to receive a guidance input, wherein the generated content is based on the guidance input (Golobokov Figs. 1-10; [0093], FIG. 5 is a block diagram showing an example image generation system 500, according to some examples. The image generation system 500 includes an image input component 510, an artificial image generation network 530, a text prompt component 520, and a zoomed image generation component 522. Together, these components enable the image generation system 500 to receive a first image depicting a real-world scene including a target object and receive input associated with adjusting a zoom level of the first image and, in response to receiving the input, modify the zoom level associated with the first image to generate a second image having a view of the target object that is different from a view of the target object in the first image; [0107], FIGS. 6A, 6B, and 6C are diagrammatic representations of example inputs and outputs of the image generation system 500, according to some examples. For example, as shown in the sequence of diagrams 600 of FIG. 6A, an input image 610 is received and/or captured that has a first size (e.g., a first bounding box). The input image 610 depicts a real-world or virtual object 614. The input image 610 is to a user in a graphical user interface (GUI) with an extend option 612. In response to receiving input that selects the extend option 612 (or in response to receiving a pinch gesture that zooms out of the input image 610), the image generation system 500 generates a second image 620 that corresponds to zooming the input image 610 by a predetermined or user-specified amount; [0108], The artificial image generation network 530 populates the empty space region 622 with artificial pixel values and can return the artificial image 630; [0110], The input image 640 is presented to a user in a graphical user interface (GUI) with an extend option 648. In response to receiving input that selects the extend option 648 (or in response to receiving a pinch gesture that zooms out of the input image 640), the image generation system 500 generates a zoomed out image; [0111], The second image 641 is then be provided to the artificial image generation network 530 to populate the empty space region with artificial pixels or content; [0113], In response to determining that the input image 650 has been zoomed in by the user input, the image generation system 500 replaces the display of the extend option 654 with an enhance option 656. In response to receiving input that selects the enhance option 656, the second image 655 is provided to the artificial image generation network 530 to enhance or improve portions of the image that have been zoomed into, such as the blurry or noisy portions of the magnified real-world or virtual object 657. The artificial image generation network 530 removes blur or noise from the magnified real-world or virtual object 657 using artificial pixel values and can return the artificial image 658)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated wherein: the user interface comprises a context bar at a location based on the input image and configured to receive a guidance input, wherein the generated content is based on the guidance input as suggested in Golobokov into He. Doing so would be desirable because users communicate with each other in a variety of ways. Most of the ways in which users communicate involve the exchange of images or photographs. Ensuring that these images are of high quality is important to conveying the right messages (see Golobokov [0002]). Various communication platforms allow users to share content and create images for transmission to other users. These images can be used to promote products or services and/or to simply represent different real-world objects in simulated or real environments. However, these systems require a user to use expensive equipment and technology to create high-quality, appealing images. Also, users may spend a great deal of effort meticulously placing objects in different environments and manually adjusting lighting and other image attributes to enhance the presentation of the objects in the images. All of these factors can add up to make the creation of high-quality images (e.g., for use in advertising) a significant expense and detract from the overall use and enjoyment of the system. In addition, because users may not have the resources needed to create high-quality images, opportunities to share and present objects in ideal settings are missed. Also, presenting lower quality images of such objects can cause other users to overlook the value of the objects, which wastes the resources used to create and display the objects. In some cases, typical systems allow users to zoom into and out of various portions of images. However, typical systems are unable to fill in the missing detail resulting from the zoom in or zoom out operations which results in blurry or distorted images (see Golobokov [0015]). The disclosed techniques seek to improve the efficiency of using an electronic device by intelligently and automatically generating images that depict real-world objects in a real-world scene in a simple and intuitive manner. For example, a user may wish to zoom out of an image previously captured but then objects in the image are missing since they were not in the original image content. Disclosed techniques address these technical issues by generating artificial image content to fill in the missing features/gaps resulting from the zoom in or zoom out operations. The disclosed techniques create photorealistic images or videos that depict a real-world object in simulated scenes very quickly and efficiently and with minimal user interaction or involvement. This can reduce the overall time and expense incurred to develop high-quality images that feature objects or products, such as shoes, shirts, or other fashion items (see Golobokov [0016]). In this way, the disclosed techniques improve the overall experience of the user in using the electronic device and reduce the overall amount of resources needed to accomplish a task of producing high-quality images (see Golobokov [0017]).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over He in view of Choi et al. (US 20240290019 A1, published 08/29/2024), hereinafter Choi.
Regarding claim 10, He teaches all the limitations of claim 1. However, He fails to expressly disclose further comprising: receiving a pattern expansion selection indicating a pattern expansion option from a plurality of pattern expansion options including a generative expansion option and an algorithmic expansion option. In the same field of endeavor, Choi teaches:
further comprising: receiving a pattern expansion selection indicating a pattern expansion option from a plurality of pattern expansion options including a generative expansion option and an algorithmic expansion option (Choi Figs. 1-20; [0186], the processor 120 may perform image processing for filling the entire image according to the display size through the outpainting technology. In an embodiment, the outpainting technology may include an AI-based (for example, deep learning-based) image generation technology for completing a new image by filling the outside of the image, based on the given image (for example, the original image); [0231], FIG. 15C or 15F illustrates an example of editing and rearranging the original image 1510 or 1530 in accordance with the display size (for example, the aspect ratio of the display) using AI and displaying the edited and rearranged image according to an embodiment of the disclosure rather than simply resizing the original image 1510 or 1530. According to an embodiment, the electronic device 101 may display the edited image 1520 or 1540 in accordance with (for example, more suitable for) the display size (for example, the display ratio) through the display 290 by applying a predetermined image processing technology to the original image 1510 or 1530 using AI for the original image 1510 or 1530. For example, for the original image 1510 or 1530, the electronic device 101 may configure and provide the image 1520 or 1540 (for example, background image) optimized for the display ratio, based on the outpainting technology of filling and rearranging the surroundings of the original image 1510 or 1530 using AI; [0232], the outpainting technology may include an AI-based (for example, deep-learning-based) image generation technology of completing a new image 1520 or 1540 by filling the outside of the image, based on the given image (for example, the original image 1510 or 1530); [0233], the image may be generated through a predetermined image processing technology (for example, the outpainting technology) and may be additionally applied through a generative AI technology; [0234], the electronic device 101 may use generative adversarial networks (GAN) and/or various algorithms such as an auto-encoder (AE) or a variational auto-encoder (VAE); [0235], as illustrated in FIG. 15F, a pattern may be expanded or added. For example, in the prior art, the cropped image was used as illustrated in FIG. 15E, but a similar pattern may be generated (or expanded) through generative AI according to an embodiment. For example, the existing pattern may be expanded or a pattern similar to the existing pattern may be redrawn. This may be changed according to a shape and a policy of the pattern; [0236], According to an embodiment, as illustrated in FIGS. 15C and 15F, the electronic device 101 may use different algorithms according to data characteristics (for example, pattern or actual image) of the original image. For example, when a new pattern is generated, GAN may be used or expansion of the actual image may select and use a predetermined algorithm according to a condition of the original image such as the use of the VAE. The disclosure is not limited thereto, and the use of the algorithm may be changed according to user generation information and a policy of a server (for example, a cloud server))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated further comprising: receiving a pattern expansion selection indicating a pattern expansion option from a plurality of pattern expansion options including a generative expansion option and an algorithmic expansion option as suggested in Choi into He. Doing so would be desirable because when the conventional electronic device provides the wallpaper, fragmentary wallpaper is provided based on an image designated by the user regardless of various form factors of the electronic device and/or the display size according thereto. For example, the electronic device provides a wallpaper configuration fixed to the size of a predetermined image regardless of various display sizes. In other words, the electronic device does not provide wallpaper more suitable for various displays according to form factors of the electronic device. Accordingly, the user experiences the inconvenience of having to perform cumbersome tasks, such as image editing to generate wallpaper more suitable for the electronic device. Accordingly, recent electronic devices have an increasing need to develop user interfaces (UIs) corresponding to various form factors and to operate the same (see Choi [0006]). Embodiments of the disclosure provide, when the electronic device supports image editing, a method of supporting displaying an image, of which the quality is improved and which is most optimized for the display size (see Choi [0010]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Bai (US 20230153965 A1) see Figs. 1-24 and [0073-0080].
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN T REPSHER III whose telephone number is (571)272-7487. The examiner can normally be reached Monday - Friday, 8AM-5PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Welch can be reached at (571) 272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JOHN T REPSHER III/ Primary Examiner, Art Unit 2143
Prosecution Insights