DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This Office Action is in response to Application's amendment/response filed on 05/04/2026, which has been entered and made of record.
Claim 21 has been added.
Claims 1 and 11 have been amended.
Claims 1-21 are pending in the application.
Response to Arguments
Applicant’s argument found in the Remarks Page 8 Paragraph 1 regarding the motivation to combine has been considered, but are not persuasive. Examiner respectfully disagrees that it would not be obvious to combine the arts of Lin, Shah, and Vomweg. To reiterate, the Non-Final Office Action (NFOA) states that Lin and Shah are within the same field of image merging and that there is motivation to combine the arts as Shah teaches how determining feature values for each in image within the set images, as well as identifying a key and first auxiliary image based on the feature values would result in an improved the overall appearance of the merged image. Here, Shah has a suggestion to combine with Lin as improving the appearance of the merged image would be desirable outcome. See MPEP 2143.01, “Obviousness can be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so.” Then, Lin in view Shah would also be obvious to combine with Vomweg. To reiterate, the NFOA states that Shah and Vomweg are within the same field of aligning images and that Vomweg simply teaches a common technique of using optical flow during image alignment. In this case, this would be considered as the use of known technique to improve similar methods. Although not stated within the NFOA, since Lin does teach to generate a composite group photo by merging people from multiple images (See Lin Col 2 Lines 19-30), then implicitly, the images would have to be aligned to facilitate the merging process. Thus, even though as Applicant has shown that Vomweg is within the field of MRIs different from Lin or Shah, the feature that Vomweg teaches, which is registering and aligning images based on optical flow, can still be applied to Lin in view of Shah.
Applicant’s argument found in the Remarks Page 8 Paragraph 2 regarding the limitations taught by Shah being inconsistent with the claim limitations has been considered, but are not persuasive. Applicant states that the scoring system of Shah operates at one granularity (faces) and replacement operations occur at another granularity (face attributes). Examiner respectfully points outs that the NFOA uses Shah to teach the limitations of determining feature values for each target object, identifying a key image based on the feature value, and identifying a first auxiliary image based on the feature value. Even though Shah does teach replacement operations for face attributes, Examiner does not use Shah to teach the claim limitations of generating a synthesized image. Examiner cites Shah to supplement Lin in the usage of determining feature values of the objects and using that to evaluate and identify the key/auxiliary image.
Applicant’s argument found in the Remarks Page 8 Paragraph 3 regarding Lin being inapposite has been considered, but are not persuasive. Applicant states that Lin identifies people that are missing from photos and thus this approach does not work on target objects that are present in both key images and auxiliary images as claimed. Examiner does agree that for the use case in which the same target objects are present in both the key and auxiliary images, that Lin’s approach would not work. However, Examiner would argue that the claim limitations does not have this requirement. Claim 1 broadly recites, “generating a synthesized image including a second target object in the key image and the first target object of the first auxiliary image . . .” The limitation merely requires there to be a first and second target object within the key and auxiliary image, without further specification on what the first and second target objects have to be. Thus, under the broadest reasonable interpretation of the claim language, the first and second target objects can reasonably be different objects, and thus reference Lin would be applicable within this context and would teach the claim limitation.
Lastly, Applicant arguments with respect to claim 1 regarding the newly-added limitation of having the object in the synthesized image be “displayed in front of background content of the key image.” has been fully considered but are moot in view of the new grounds of rejection represented in this Office Action.
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 1, 4-5, 7-8, 11, 14-15, 17-18 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Lin et al. (US 11574392 B2) (Hereinafter referred to as Lin) in view of Shah et al. (US 10475222 B2) (Hereinafter referred to as Shah) and further in view of Vomweg et al. (US 20080292214 A1) (Hereinafter referred to as Vomweg) and further in view of Hegde (US 10582119 B2).
Regarding Claim 1, Lin discloses A method of processing images in a device, comprising: (See Col 2 Lines 22-24, “methods for automatically merging people and objects from multiple digital images into a composite group photo.”)
obtaining a set of images including a plurality of target objects; (See Col 2 Lines 24-27, “For instance, the disclosed systems can utilize a number of models and operations to automatically analyze multiple digital images to identify a missing person from a base image . . .” Also see Col 3 Lines 40-43, “To illustrate, in one or more implementations, the image merging system identifies multiple digital images (or simply “images”), where each of the images includes at least one person.” In this case, each person can be considered as “a plurality of target objects”.)
determining a feature value for each target object of the plurality of target objects in each image of the set of images; (See Col 3 Lines 40-46, “To illustrate, in one or more implementations, the image merging system identifies multiple digital images (or simply “images”), where each of the images includes at least one person. The image merging system can then identify faces in the images and compare the faces between the images (e.g., a base image and a second image) to determine a person that is missing from the base image.”
Also see Col 3 Lines 54-56, “As mentioned above, the image merging system can identify faces of persons within a set of images (e.g., a base image and a second image).”
Also see Col 10 Lines 60-65, “For example, the image merging system analyzes the facial features of each detected face and generates a face descriptor based on the result of the analysis. For instance, as described above, the face descriptor for a detected face includes facial feature values from one or more facial features of the face.” Note that a “face descriptor” corresponds to a “feature value”.)
a key image (See Col 3 Lines 43-46, “The image merging system can then identify faces in the images and compare the faces between the images (e.g., a base image and a second image) to determine a person that is missing from the base image.”)
a first auxiliary image (See Col 3 Lines 43-46, “The image merging system can then identify faces in the images and compare the faces between the images (e.g., a base image and a second image) to determine a person that is missing from the base image.”)
generating a synthesized image including a second target object in the key image and the first target object of the first auxiliary image. (See Col 6 Lines 36-42, “In addition, as shown, the series of acts 100 includes an act 110 of the image merging system generating a merged image. For example, in various implementations, the image merging system generates a composite group photo that merges the missing person from the second image into the base image utilizing the base image, the available location, and the segmented image of the missing person.”
In this case, consider the merged image to correspond to a “synthesized image”, the base image to correspond to a “key image” and second image to correspond to a “first auxiliary image”, missing person from the second image corresponds to “first target object in the first auxiliar image”, and it would be implied there exists a person (second target object) within the base image (key image).)
However, Lin fails to explicitly disclose
identifying a key image from the set of images based on the feature value for each target object;
identifying a first auxiliary image from the set of images based on the feature value associated with a first target object of the plurality of target objects;
aligning the key image and the first auxiliary image based on optical flow between the key image and the first auxiliary image; and
generating a synthesized image including a second target object in the key image and the first target object of the first auxiliary image displayed in front of background content of the key image.
Shah additionally teaches determining a feature value for each target object of the plurality of target objects in each image of the set of images; (See Col 2 Lines 62 – Lines 66, “Embodiments of the invention select a best frame from a short video clip to use as a base image. Each frame in the clip is evaluated with respect to whether the faces are aligned towards the camera, whether the faces have features that are aligned emotionally. . .” Here, a video clip can be considered as “the set of images” and Shah teaches to evaluate each frame in the video clip based on the facial attributes.)
identifying a key image from the set of images based on the feature value for each target object; (See Abstract, “creating a group shot image by intelligently selecting a best frame of a video clip to use as a base frame and then intelligently merging features of other frames into the base frame.”
Also see Cols 2 Line 62 – Col 3 Line 6, “Embodiments of the invention select a best frame from a short video clip to use as a base image. Each frame in the clip is evaluated with respect to whether the faces are aligned towards the camera, whether the faces have features that are aligned emotionally (e.g., happy, sad, neutral, etc.), the quality of the faces with respect to blurriness, lighting, and/or exposure, and/or whether the eyes of the faces are opened or closed. One or more of these evaluations result in scores that are combined or otherwise used to determine a comprehensive score for each of the frames of the clip. The frame having the best score is selected to be used as the base frame for the group shot image.”
Here, Shah teaches to evaluate each frame in the video clip and select a base frame (key image) based on the scores which takes into account the facial features, and thus in combination with Lin already teaching a base image (key image) and teaching facial descriptors (feature values) for each person (target object), the above limitation is taught.)
identifying a first auxiliary image from the set of images based on the feature value associated with a first target object of the plurality of target objects; (See Col 2 Lines 13-19, “The feature merging module then determines replacement features in other frames of the video clip, for example, based on proximity of the other frames of the video clip to the base frame and/or detecting visibility of the replacement features in those frames. Once the replacement features are identified, those features are merged into the base frame to create the group shot image.”
To clarify, Shah teaches to find replacement features in other frames of the video clip. In this situation, the other frame that contains the replacement features can be considered as the “first auxiliary image” and this would be “based on the feature values associated with a first target object”. The feature values associated with a first target object, in the case of Shah, would be the facial attributes (See Shah Cols 2 Line 62 – Col 3 Line 6). Shah is trying to find replacement features in other frames in order to improve the face score, and thus, the process of finding replacement features would means that Shah is using feature values associated with a first target object.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lin with Shah to include determining feature values for each in image within the set images, as well as identifying a key and first auxiliary image.
The motivation to combine Lin with Shah would have been obvious as both Lin and Shah are within the same field of image merging (See Shah abstract). The benefit of using the feature values within each image to select the best fame (key image) and finding replacement features in other frames (first auxiliary image) would be that it improves the overall appearance of the final product. See Shah Col 3 Lines 7-10, “Given a base frame selection, embodiments of the invention additionally or alternatively intelligently merge attributes from other frames into the selected base frame to improve its facial feature attributes.” This would be the motivation to identify the key and auxiliary image using feature values. Additionally, Lin already teaches having a base (key) and second (a first auxiliary) image, Shah simply teaches that they can be identified by the feature value of the target objects.
However, Lin in view of Shah fails to explicitly disclose aligning the key image and the first auxiliary image based on optical flow between the key image and the first auxiliary image; and
generating a synthesized image including a second target object in the key image and the first target object of the first auxiliary image displayed in front of background content of the key image.
Vomweg teaches aligning the key image and the first auxiliary image based on optical flow between the key image and the first auxiliary image; (See [0005], “d) Registering the first and the second image or set of images by applying the inverse optical flow to the pixels or voxels of the second image or set of images.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lin in view of Shah with Vomweg to include aligning the key image and the first auxiliary image based on optical flow.
The motivation to combine Lin in view of Shah with Vomweg would have been obvious as Shah and Vomweg are within the same field of aligning images (See Vomweg). Vomweg is simply teaching the common technique of using optical flow during image alignment. The benefit of using optical flow for image alignment is improved accuracy for alignment.
However, Lin in view of Shah and Vomweg still fails to disclose generating a synthesized image including a second target object in the key image and the first target object of the first auxiliary image displayed in front of background content of the key image.
Hedge teaches generating a synthesized image including a second target object in the key image and the first target object of the first auxiliary image displayed in front of background content of the key image. (See Col 2 Lines 36 – 40, “The image processor may be further configured to generate a first output image in which the portion of the second self-portrait image may be overlaid on a background region of the selected background of the first self-portrait image of the first user in a first image composition.”
Col 5 Lines 14-17, “For example, the first user 108a may select “Your background” option, where an initiator's actual background (such as actual background scene of the first user 108a) is selected for the final composite selfie image.”
In summary, Hedge is an art that teaches combining selfies taken by two different users to generate a composite selfie image. This has a correlation to the claim limitation of generating a synthesized image, as the different users within the images can be considered as the first and second target objects. The main idea though, is that Hedge teaches that one can have a specific background content for the final composite image. Note that since it’s called a background, then obviously the overlaid selfie (the object) would be in front of said background.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lin in view of Shah and Vomweg with Hedge to include having the objects displayed in front of background content of the key image within the generated synthesized image.
The motivation to combine Lin in view of Shah and Vomweg with Hedge would have been obvious as Hedge, Lin, and Shah are all within the same field of combining images and the objects within those images (See Hedge Abstract). Having the background be the background contents of the key image within the final synthesized image is simply an aesthetic design choice in which a person of ordinary skill in the art can easily achieve by having the user be able to designate which background is to be used (See Hedge Col 5 Lines 14-17).
Regarding Claim 4, Lin in view of Shah, Vomweg, and Hedge disclose The method of claim 1, wherein identifying the key image comprises: determining a composite score for each image of the set of images based on the feature value of each target object; and selecting the key image based on the composite score. (See Shah Cols 2 Line 62 – Col 3 Line 6, “Embodiments of the invention select a best frame from a short video clip to use as a base image. Each frame in the clip is evaluated with respect to whether the faces are aligned towards the camera, whether the faces have features that are aligned emotionally (e.g., happy, sad, neutral, etc.), the quality of the faces with respect to blurriness, lighting, and/or exposure, and/or whether the eyes of the faces are opened or closed. One or more of these evaluations result in scores that are combined or otherwise used to determine a comprehensive score for each of the frames of the clip. The frame having the best score is selected to be used as the base frame for the group shot image.” The motivation to combine would have been similar that of Claim 1 rejection motivation.)
Regarding Claim 5, Lin in view of Shah, Vomweg, and Hedge disclose The method of claim 1, further comprising: determining the first target object in the key image is to be modified based on the feature value; and selecting the first auxiliary image from the set of images based on the feature value of the first target object in the first auxiliary image. (See Shah Cols 2 Line 62 – Col 3 Line 6 teaching selecting a best frame (key image) based on features such as eyes of the faces being open or closed, etc.
See Shah Col 11 Lines 12-19, “The technique 500 next involves identifying features of faces in the base frame for replacement based on the face scores, as shown in block 504. In an embodiment of the invention, this involves comparing the face scores with a threshold such as the average face score of all faces in the base frame, and identifying all of the faces that have scores below the threshold for replacement.” Note that one can consider features of faces in the base frame for replacement to be “the first target object in the key image is to be modified” and face scores to correspond to “feature value”.
Also see Shah Col 11 Lines 20-27, “The technique 500 next involves identifying replacement features in other frames of the video clip, as shown in block 505. These features are identified based on various criteria selected to minimize discontinuities and other undesirable visual attributes.”
See Lin Col 3 Lines 43-46, “The image merging system can then identify faces in the images and compare the faces between the images (e.g., a base image and a second image) to determine a person that is missing from the base image.” Note that one can consider the faces, and thus facial descriptors to be “feature value of the first target object” and thus in combination with Shah teaching identifying replacement features, the selection of the second image (the first auxiliary image) can be considered as “selecting the first auxiliary image from the set of images based on the feature value of the first target object”. The motivation to combine would have been similar that of Claim 1 rejection motivation.)
Regarding Claim 7, Lin in view of Shah, Vomweg, and Hedge disclose The method of claim 1, wherein the feature value is associated with a combination of key features associated with each target object, and wherein the key features of a target object include an orientation of the target object with respect to the device and facial features of the target object. (See Shah Cols 2 Line 62 – Col 3 Line 6, “Embodiments of the invention select a best frame from a short video clip to use as a base image. Each frame in the clip is evaluated with respect to whether the faces are aligned towards the camera, whether the faces have features that are aligned emotionally (e.g., happy, sad, neutral, etc.), the quality of the faces with respect to blurriness, lighting, and/or exposure, and/or whether the eyes of the faces are opened or closed. One or more of these evaluations result in scores that are combined or otherwise used to determine a comprehensive score for each of the frames of the clip. The frame having the best score is selected to be used as the base frame for the group shot image.” The motivation to combine would have been similar to that of Claim 1 rejection motivation.)
Regarding Claim 8, Lin in view of Shah and Vomweg disclose The method of claim 1, wherein the set of images are downscaled. (See Lin Col 20 Lines 49-52, “In some instances, blending includes adjusting the contrast, shading, hue, saturation, sharpness, and/or resolution of the segmented image to match the base image.” Here, Lin teaches the ability to adjust the resolution of the images, and thus one of ordinary skill in the art can obviously be able to downscale the images as that would be a well-known and basic function.)
Regarding Claim 8, Lin in view of Shah and Vomweg disclose A computing device for processing images, comprising: at least one memory; and at least one processor coupled to the at least one memory and configured to: (See Lin Col 25 Lines 1-5, “one or more instructions stored on a computer-readable storage medium and executable by processors of one or more computing devices, such as a client device (e.g., a mobile client device) or server device.”)
obtain a set of images including a plurality of target objects; determine a feature value for each target object of the plurality of target objects in each image of the set of images; identify a key image from the set of images based on the feature value for each target object; identify a first auxiliary image from the set of images based on the feature value associated with a first target object of the plurality of target objects; align the key image and the first auxiliary image based on optical flow between the key image and the first auxiliary image; and generate a synthesized image including a second target object in the key image and the first target object of the first auxiliary image displayed in front of background content of the key image. (The above limitations are similar to those of Claim 1 and is therefore rejected under a similar rationale as Claim 1.)
Regarding Claim 14, Claim 14 contains similar limitations as to Claim 4 and is therefore rejected under a similar rationale as Claim 4.
Regarding Claim 15, Claim 15 contains similar limitations as to Claim 5 and is therefore rejected under a similar rationale as Claim 5.
Regarding Claim 17, Claim 17 contains similar limitations as to Claim 7 and is therefore rejected under a similar rationale as Claim 7.
Regarding Claim 18, Claim 18 contains similar limitations as to Claim 8 and is therefore rejected under a similar rationale as Claim 8.
Regarding Claim 21, Lin in view of Shah, Vomweg, and Hedge discloses The computing device of claim 11, wherein the computing device is a consumer electronic device that stores the set of images. (See Lin Col 1 Lines 11-15, “For instance, the hardware on most modern computing devices (e.g., servers, desktops, laptops, tablets, and smartphones) enables digital image editing without significant lag time or processing delays.”)
Claims 2 and 12 is rejected under 35 U.S.C. 103 as being unpatentable over Lin in view of Shah, Vomweg, and Hedge and in further view of Fritz, (“Guide to Image Inpainting: Using machine learning to edit and correct defects in photos”).
Regarding Claim 2, Lin in view of Shah, Vomweg, and Hedge disclose The method of claim 1, wherein generating the synthesized image comprises: inserting pixels of the first target object. (See Lin Col 28 Lines 13-16, “For example, act 1450 can involve generating a merged image by inserting the pixels of the second image representing the missing person into the available location of the first image.”)
However, Lin in view of Shah, Vomweg, and Hedge fails to explicitly disclose generating, using a machine learning model, boundary region pixels of the first target object based on hallucination of pixels at edges of the first target object using the set of images and the machine learning model.
Fritz teaches generating, using a machine learning model, boundary region pixels of the first target object based on hallucination of pixels at edges of the first target object using the set of images and the machine learning model. (See Page 5 teaching “deep learning” for inpainting and training on “huge training datasets”.
Also see Page 9 Paragraph 2, “The first is based on a Fast Marching Method, which starts from the boundary of the region to be inpainted and moves towards the epicenter, gradually filling everything in the boundary first. Each pixel is replaced by a normalized weighed sum of all the know pixels in its neighborhood.” In this case, deep learning implies a machine learning model, and gradually filling everything in the boundary corresponds to generating boundary region pixels based on hallucination of pixels at edges.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lin in view of Shah, Vomweg, and Hedge with Fritz to include using a machine learning model to generate boundary region pixels of the first target object based on hallucination of pixels at edges.
The motivation to combine Lin in view of Shah, Vomweg, and Hedge with Fritz would have been obvious as using a machine learning model for image alignment and merging can have improved speed, accuracy, as well as being automated over traditional methods (See Fritz Page 2).
Regarding Claim 12, Claim 12 contains similar limitations as to Claim 2 and is therefore rejected under a similar rationale as Claim 2.
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Lin in view of Shah, Vomweg, and Hedge and in further view of Hsieh et al. (US 20230132180 A1) (Hereinafter referred to as Hsieh).
Regarding Claim 3, Lin in view of Shah, Vomweg, and Hedge disclose The method of claim 1, further comprising: generating a first mask of the first target object from the first auxiliary image; (See Lin Col 4 Lines 12-23, “Based on identifying a missing person in the second image, the image merging system can create a segmented image of the missing person. . . For example, the segmentation model can create a bounding box around the missing person, generate an object mask of the missing person based on the bounding box, and generate a segmented image of the missing person based on the object mask (e.g., an indication of a plurality of pixels portraying an object such as a binary mask identifying pixels corresponding to an object).”)
However, Lin in view of Shah, Vomweg, and Hedge fails to explicitly disclose upsampling the first mask using a guided upsampling filter for filamentous structures associated with the first target object.
Hsieh teaches upsampling the first mask using a guided upsampling filter for filamentous structures associated with the first target object. (See [0077], “In other words, the segmentation mask refinement and upsampling system 106 utilizes the guided filtering to improve the refined preliminary segmentation mask 504 to recapture details (particularly along borders) from the low-resolution image 500 lost during the generation of the refined preliminary segmentation mask 504.”
Also see [0080], “The segmentation mask refinement and upsampling system 106 then upsamples the refined-filtered preliminary segmentation mask 508 to a higher resolution.” Note that since Lin teaches an object mask of a person, and that mask commonly include filamentous structures such as hair and clothing, thus the upsampling on the masks can be consider as for filamentous structures associated with the first target object.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lin in view of Shah and Vomweg with Hsieh to include upsampling the mask using guided upsampling filter for filamentous structures associated with the first target object.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lin in view of Shah, Vomweg, and Hedge with Hsieh to include upsampling the mask using guided upsampling filter for filamentous structures associated with the first target object.
The motivation to combine Lin in view of Shah, Vomweg, and Hedge with Hsieh would have been obvious as upsampling can improve the quality of the final image by having a higher resolution. Note that Hsieh [0002] teaches that there is a need for high-resolution segmentation masks, “Although conventional segmentation systems generate segmentation masks for digital visual media items, such systems are often inflexibly limited to low-resolutions, are often inaccurate at segmenting fine-grained details in high-resolution images. . . ”
Regarding Claim 13, Claim 13 contains similar limitations as to Claim 3 and is therefore rejected under a similar rationale as Claim 3.
Allowable Subject Matter
Claims 6, 9-10, 16, 19-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding Claim 6, the cited prior art does not disclose or render obvious the combination of elements cited in the claims as a whole. Specifically, the cited prior art fails to disclose or render obvious the limitations: extracting a first background from the key image excluding the plurality of target objects; extracting a second background from the first auxiliary image excluding the plurality of target objects; identifying key points within the first background and the second background; and combining the first background and the second background into a combined background based the optical flow between the key points, wherein the combined background is input into a machine learning model. Thus, Claim 6 contains allowable subject matter.
Regarding Claim 9, the cited prior art does not disclose or render obvious the combination of elements cited in the claims as a whole. Specifically, the cited prior art fails to disclose or render obvious the limitations: generating a first mask based on the first target object in the synthesized image at a first resolution and the first auxiliary image; generating a second mask based on the second target object in the synthesized image at the first resolution and the key image at the first resolution, interpolating the first mask and the second mask to a second resolution higher than the first resolution; and generating the synthesized image at the second resolution. Thus, Claim 9 contains allowable subject matter.
Regarding Claim 10, Claim 10 is dependent upon Claim 9 and thus also contains allowable subject matter.
Regarding Claim 16, Claim 16 contains similar limitations as to Claim 6 and therefore contains similar allowable subject matter.
Regarding Claim 19, Claim 19 contains similar limitations as to Claim 9 and therefore contains similar allowable subject matter.
Regarding Claim 20, Claim 20 contains similar limitations as to Claim 10 and therefore contains similar allowable subject matter.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/T.G.H./Examiner, Art Unit 2611
/KEE M TUNG/Supervisory Patent Examiner, Art Unit 2611
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