DETAILED ACTION
This action is in response to the remarks and amendments filed on April 30th, 2026 and May 18th, 2026. Claims 1-12 and 14-20 are pending and have been examined
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 Arguments
Applicant's arguments filed April 30th, 2026 have been fully considered but they are not persuasive.
On page 12, applicant alleges that "Ning generally describes vector quantization but does not explain how vector quantization would be applied in an image transformation system of the type described by Olszewski. For example, Ning does not specify what the vectors would represent or how to encode/decode the transformed image data." Examiner respectfully disagrees. While Ning does not describe that it would be applied to an image transformation system, one of ordinary skill in the art can appreciate that there is an advantage to using the vector quantization technique described by Ning when performing 2D rendering of 3D volumes. Specifically, Ning describes that their method of quantizing volumes would provide an advantage of decreased use of storage space and fast access to voxels (Ning Section 2). Ning does provide at least one example in which the input volume (and the final rendered image) would represent smoke which has been acoustically excited by an air jet (Ning Section 5). Nevertheless, when considering Olszewski and Ning in combination, one of ordinary skill in the art can appreciate that Olszewski describes that a volume can be made of feature vectors which represent image data, and that the volume would be passed to Ning. Therefore, the feature vectors in Olszewski in view of Ning represent input objects in an image.
On page 13, applicant alleges that “the techniques described by Ning do not teach or suggest the proposed modifications to the system of Olszewski. The examiner has provided a suggestion as to why one skilled in the art would apply vector quantization as taught by Ning to the system of Olszewski but has not established that one skilled in the art would know how to make the specific recited modification by, for example, quantizing feature vectors comprising "multi-dimensional vectors determined during a training process where the vectors define local features of the one or more input objects in the input image that enable disentanglement of respective camera poses during run-time" as now recited in the independent claims.” Examiner respectfully disagrees. Ning describes that the vectors could represent a real world object, as previously discussed with respect to section 5 and the smoke which is acoustically excited by an air jet. Nevertheless, Olszewski is cited to teach that the volume vectors would represent features of the one or more input objects and that the vectors would enable disentanglement of respective camera poses, as taught in the "extracting data from the input image" and "performing a relative pose transformation" steps on pages 9-11 of the non-final rejection date January 30th, 2026.
On page 13 applicant alleges that “Ning does not teach one skilled in the art to modify the encoder-decoder system of Olszewski to include a quantizer that quantizes a transformed volume to "map a feature vector [comprising multi-dimensional vectors determined during a training process where the vectors define local features of the one or more input objects in the input image that enable disentanglement of respective camera poses during run-time] of each cell in three- dimensional (3D) space of the transformed volume to one of a discrete number of quantized feature vector entries in [a] codebook" and a de-quantizer that "de-quantizes the pulled discrete quantized values to produce a quantized 3D representation" and a decoder that "decodes the quantized 3D representation to produce two-dimensional (2D) feature maps and that synthesizes the camera pose of the view of the target image from the 2D feature maps."” Examiner respectfully disagrees. When considering Olszewski in view of Ning, one of ordinary skill in the art could appreciate that the encoder-decoder system of Olszewski could be modified to include a quantization step to reduce storage space and allow for fast access to voxels (Ning Section 2). With his modification, the volume of Olszewski (which contains feature vectors that represent attributes of the input objects) would be input into the vector quantization system of Ning. Then, the quantization, de-quantization, and decoding steps could be performed on the volume of feature vectors representing attributes of the input objects.
On page 14, applicant alleges that “Ning does not provide sufficient teachings for one skilled in the art to modify the methods of Olszewski to enable flexible content manipulation and novel view synthesis by encoding-quantizing-de-quantizing-decoding as claimed. For example, Ning does not suggest quantizing a transformed volume to map a feature vector of each cell in three-dimensional (3D) space of the transformed volume to one of a discrete number of quantized feature vector entries in a codebook where the codebook includes "quantized discrete values of volumetric constituents of input objects learned during training, wherein the feature vectors comprise multi-dimensional vectors determined during a training process where the vectors define local features of the one or more input objects in the input image that enable disentanglement of respective camera poses during run-time and are mapped to the codebook feature vector entries represented as indices to the codebook that are used to look up feature vectors in the codebook to pull corresponding discrete quantized values." Ning merely suggests that an iterative technique may be used to optimize the codebook and teaches that "vectors may be randomly selected from the input or chosen as a uniform distribution in k-dimensional space” Examiner respectfully disagrees. Ning is not cited to teach performing flexible content manipulation and novel view synthesis. Instead, Olszewski teaches performing novel view synthesis by manipulating a volume representing the input image. Ning is cited to teach quantization, de-quantization, and decoding of a volume. Ning suggests that vectors are chosen from the input as the start of the codebook, and iteratively optimized. When considering Olszewski in view of Ning, this would mean that the vectors which are used when constructing the codebook are input from the encoder of Olszewski. These vectors represent features of the input image of Olszewski, and therefore the feature vector entries in the codebook represent multi-dimensional vectors that define local features of the input objects. Furthermore the iterative optimization process of Ning could be considered to be training the codebook, as was previously discussed on page 13 of the non-final rejection dated January 30th, 2026.
On page 15, applicant alleges that “The examiner suggests at page 6 of the Official Action that Ning's teachings of using an iterative technique to optimize the codebook is analogous to the learning technique of the present application. Applicant disagrees. Ning describes in Section 3 that the codebook is updated by replacing codevectors with the centroids of new cells and the steps are repeated until the improvement in overall distortion falls below a threshold. Ning does not further describe that the feature vectors "define local features of the one or more input objects in the input image that enable disentanglement of respective camera poses during run-time" and that creating the spatially disentangled volumetric representation comprises "learning a volumetric feature representation that is resampled under a new camera pose and rendered to a decoder to generate novel view images." As acknowledged by the examiner at page 4 of the Official Action, Ning does not enable novel view synthesis. Simply suggesting a technique for compressing a volume does not enable novel view synthesis using the compressed volume as the examiner implies. Olszewski does not compress a volume or explain how a compressed volume could enable novel view synthesis despite the general difficulties in the art to perform 3-D novel view synthesis.” Examiner respectfully disagrees. As previously discussed, the input volume of Olszewski teaches that the feature vectors represent different aspects of an appearance of the input object. Ning is cited to teach that quantization, de-quantization, and decoding of a volume is possible. Olszewski is cited to teach novel view synthesis and disentanglement of the volumetric representation. Ning suggests a motivation to combine which is reduced storage space and fast voxel access.
For at least the reasons stated above, the rejection of claims 1-12 and 14-20 is maintained.
Applicant's arguments filed May 18th, 2026 have been fully considered but they are not persuasive.
On page 11, applicant alleges that “Ning does not further suggest that the feature vector entries in the codebook include discrete values of "the local features, structures, and colors of one or more input objects that enable the one or more input objects to be represented as volumetric combinations of the local features, structures, and colors learned during training" as now recited, with or without iterations. Instead, Ning describes in Section 4 that for many applications codebooks are designed on an entire class of data but that the codebook may be custom designed for each volume. Ning does not suggest providing vector representations of objects in the codebook.” Examiner respectfully disagrees. Olszewski suggests that feature vectors of a volume can represent the local features, structures, and colors of one or more input objects. Then, Ning suggests that the input volumes vectors can be used as a starting point for the codebook indices. This would suggest that the feature vectors of Olszewski would be used when initially creating the codebook of Ning, and therefore the vectors in the codebook of Ning would represent local features, structures, and colors of one or more input objects.
For at least the reasons stated above, the rejection of claims 1-12 and 14-20 is maintained.
Claim Rejections - 35 USC § 103
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-9 and 12, 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Olszewski (Transformable Bottleneck Networks) in view of Ning (Vector Quantization for Volume Rendering), and Watanabe (US20120251014)
In regards to claim 1, Olszewski teaches a method of transforming an input image to a target image (Olszewski Abstract “We demonstrate that the bottlenecks produced by networks trained for this task contain meaningful spatial structure that allows us to intuitively perform a variety of image manipulations in 3D”), the method comprising: receiving, by an encoder, an input image at an arbitrary camera pose (Olszewski Section 3.1 Step 1 “An encoder network with parameters, that takes in an image Ik” Examiner note: As can be seen in figure 2 , the input image can be at a variety of poses.); extracting, by the encoder, data from the input image describing features representing different aspects of an appearance and shape of one or more objects in the input image to create feature vectors representing a spatially disentangled volumetric representation of relative camera poses of the one or more objects of the input image, the feature vectors comprising multi-dimensional vectors determined during a training process where the vectors define local features, structures, and colors of one or more input objects in the input image that enable definition of the one or more input objects and a shape of the one or more input objects in the input image whereby respective camera poses may be disentangle during run-time (Olszewski Section 3.1 Step 1 “An encoder network, that… outputs a bottleneck representation Xk structured as a volumetric grid of cells, each containing an n-dimensional feature vector”; Section 3.2 “Training” Examiner note: It can be appreciated that feature vectors, by definition, represent different aspects of an appearance and shape of an object. The grid of cells which each contain a feature vector (defined as a bottleneck representation) are analogous to the spatially disentangle volumetric representation of the camera pose of the one or more objects in the input image.), wherein creating the spatially disentangled volumetric representation comprises learning a volumetric feature representation represented by the feature vectors that is resampled under a new camera pose and rendered to generate novel view images (Olszewski Section 3.2.1 “NVS requires a minimum of two images of a given object from different, known viewpoints. Given {Ik,It} and Fk->t we can computer a reconstruction I’t of It using equation (1). Using this, we define several losses in image space with which to train our network parameters.” Examiner note: This section teaches that, during training, the system of Olszewski is given an image from two viewpoints, represented as Ik and It . The system then attempts to recreate It from Ik, this attempted reconstruction from a new viewpoint (or camera pose) is done by the decoder, and is represented as I’t. The error between I’t and It is then used to change (or train) network parameters.); performing a relative pose transformation of the spatially disentangled volumetric representation of the one or more objects of the input image between a camera pose of a view of the input image and a camera pose of a view of the target image to form a transformed volume represented by the feature vectors (Olszewski Section 3.1 Step 2 Examiner note: This step teaches transforming the bottleneck representation using a user provided transform, this transform will change the bottleneck representation from one view of an object to the different, target view.); and decoding, by a decoder, the Olszewski Figure 2 “Decoder”; Section 3.1 Step 3; Abstract “We propose a novel approach to performing fine-grained 3D manipulation of image content via a convolutional neural network, which we call the Transformable Bottleneck Network (TBN).” Examiner note: The transformable bottleneck network described here is implemented through a convolutional neural network. It is known in the art that a convolutional network performs a variety of convolutions which result in feature maps. (See, for example, “A Review of Convolutional Neural Networks” Section II A) Therefore, when the decoder of this disclosure is creating the 2D image from the aggregated volumetric representation, each convolution will result in a feature map, and will eventually result in a 2D image, as can be seen in figure 2(a) “Decoder”.); and synthesizing, by the decoder, the camera pose of the view of the target image from the 2D feature maps. (Olszewski Section 3.1 Step 3).
Olszewski does not teach quantizing the transformed volume to map a feature vector of each cell in the three-dimensional (3D) space of the transformed volume to one of a discrete number of quantized feature vector entries in a codebook including quantized discrete values of the local features, structures, and colors of the one or more input objects that enable the one or more input objects to be represented as volumetric combinations of the local features, structures, and colors learned during training, wherein the feature vectors are mapped to the codebook feature vector entries represented as indices to the codebook that are used to look up feature vectors in the codebook to pull corresponding discrete quantized values; de-quantizing the pulled discrete quantized values to produce a quantized 3D representation; and decoding, by a decoder, the quantized 3D representation.
However, Ning teaches quantizing the Ning Figure 4, Volume and Block; Section 3 “With an 8-bit vector quantizer we can have 256 possible reproduction vectors known as codewords. These are stored in a table known as the codebook. Given a vector to be quantized, the encoder finds the codeword that best represents the input according to some distortion criterion.” Examiner note: This section teaches that each of the blocks in the volume are separately quantized. Once the vectors are quantized, they are represented by the index of the closest codeword (also known as reproduction vector) within the codebook.) including quantized discrete values of the local features, structures, and colors of the one or more input objects that enable the one or more input objects to be represented as volumetric combinations of the local features, structures, and colors learned during training (Ning Section 3 “Perhaps the most interesting aspect of vector quantization is the design of a good codebook for a particular input distribution. One popular design technique (the generalized Lloyd algorithm [7]) exploits the following necessary (but not sufficient) conditions for optimality: 1) given a codebook, the optimal partition is a nearest neighbor assignment; 2) given a partition, the optimal codevector for any cell is the centroid of the input vectors that fall into that cell. The design is an iterative technique that successively optimizes the codebook. First, an initial codebook is determined by some means. For example, vectors may be randomly selected from the input or chose as a uniform distribution in k-dimensional space. Second, the input vectors are partitioned into cells by the nearest neighbors rule applied to the current codebook. Third, the codebook is updated by replacing codevectors with the centroids of the new cells. The second and third steps are then repeated until the improvement in overall distortion falls below some threshold.” Examiner note: The combination of Olszewski and Ning would suggest that the volume of Olszewski, which contains feature vectors representing the local features, structures, and colors of the one or more input objects, would be input into the vector quantization of Ning, and the vectors described in Ning therefore do represent features. Furthermore, this section describes how the system of Ning creates a codebook to adapt to a particular given input. This section describes an iterative technique where the codebook is optimized with each step, until a point where the codebook stops improving by some threshold. In the field of artificial intelligence, training is defined as a system of feedback that takes in data to “learn”, and can generalize that to unseen data. The procedure described by Ning can be reasonably interpreted as training, as the system uses the input data to improve the codebook (thus “learning” how to create a codebook), and can then generalize that codebook training to any unseen data, as the algorithm is generic for any input data.), wherein the feature vectors are mapped to the codebook feature vector entries represented as indices to the codebook that are used to look up feature vectors in the codebook to pull corresponding discrete quantized values (Ning Figure 4 Indices + Codebook; Section 3 “Given a vector to be quantized, the encoder finds the codeword that best represents the input according to some distortion criterion. The binary index of the best codeword is then used to represent the entire vector. A decoder having an identical codebook uses this index to reproduce the vector as a simple table lookup (Fig. 2).” Examiner note: This section teaches that once a vector is quantized, it can be represented by the codeword that represents the closest vector. Then, as shown in figure 4, the decoder can use the indices and the codebook to finally render the volume (which is analogous to dequantizing the vectors).); de-quantizing the pulled discrete quantized values to produce a quantized 3DNing Figure 1, 4 Examiner note: These figures show that once the vectors are quantized, their indices can be pulled from the codebook to either decompress the volume, or to just render the volume into an image. These figures show that this disclosure begins with a volume, and ends with an image (2D representation). Note that in figure 1, the “compressed volume” is the same as the indices + codebook of figure 4.); and decoding, by a decoder, the quantized 3D representation (Ning Figure 1 “Volume Renderer” Examiner note: The quantized (compressed) volume can either be passed directly to a volume renderer for image generation, or it can first be decompressed and then passed to the volume renderer.).
Ning is considered to be analogous to the claimed invention because they are in the same field of using volumes to represent and manipulate images. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Olszewski to include the teachings of Ning, to provide the advantage of requiring less digital storage space, and allowing for fast access of voxels (Ning Section 3 "In consideration of the data compression requirements imposed by volume rendering, it should be evident that vector quantization is particularly suitable. Vectors may be formed from contiguous blocks of voxels in a volume and then quantized according to some codebook. The voxel data is then replaced by a much smaller data set of codebook indices representing the quantized blocks. Decompression consists of simple table lookups into the codebook so fast, on-the-fly voxel access is possible.").
Furthermore, Watanabe teaches quantizing the transformed volume (Watanabe Figure 2, 75-76; Paragraph [0015] “The 3D transformation unit further includes a 3D orthogonal transform coefficient data transformation unit for performing one-dimensional orthogonal transformation on the sorted 2D orthogonal transform coefficient data in the direction the plurality of images are arranged, and transforming the data into 3D orthogonal transform coefficient data, a quantization unit for quantizing the transformed 3D orthogonal transform coefficient data…” Examiner note: This reference specifically shows quantizing a transformed volume, which is not shown by Olszewski in view of Ning.).
Watanabe is considered to be analogous to the claimed invention because they are in the same field of transforming images from their two-dimensional form to a three-dimensional form. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Olszewski in view of Ning to include the teachings of Watanabe, to provide the advantage of allowing for a system that preserves image quality. (Watanabe Paragraph [0024] “According to an embodiment of the present disclosure, deterioration in image quality can be reduced.")
In regards to claim 2, Olszewski in view of Ning, and Watanabe teaches the method of claim 1, and further teaches resampling the transformed volume to correspond to a layout of image content in the view of the target image prior to quantizing the transformed volume. (Olszewski Section 3.1, Step 2)
In regards to claim 3, Olszewski in view of Ning, and Watanabe teaches the method of claim 1, and further teaches interpolating between two views of a same object in the input image, or interpolating two different objects in a same view or a similar view of the input image. (Olszewski Figure 2)
In regards to claim 4, Olszewski in view of Ning, and Watanabe teaches the method of claim 1, and further teaches wherein the input image is a red, green, blue (RGB) image captured in a given camera pose, and wherein creating the spatially disentangled volumetric representation of the input image comprises generating, by the encoder, a volumetric representation of content of the input image using learnable parameters whereby each cell in the volumetric representation contains a feature vector describing a local shape and appearance of a corresponding region in the input image (Olszewski Figure 2, Volume; Section 3.1, Step 1).
In regards to claim 5, Olszewski in view of Ning, and Watanabe teaches the method of claim 4, and further teaches wherein the spatially disentangled volumetric representation is defined within a view space of the input image such that a depth dimension corresponds to a distance from a camera (Olszewski Section 3.1.2 “In this work the grid cells are chosen to be equally spaced, with the volume centered on the target object and axis aligned with the camera coordinate frame.” Examiner note: Since the axis is aligned with the camera coordinates, the depth (and therefore distance from the camera) would correspond to one of the x, y, or z components.).
In regards to claim 6, Olszewski in view of Ning, and Watanabe teaches the method of claim 4, and further teaches wherein performing the relative pose transformation of the spatially disentangled volumetric representation of the one or more objects of the input image between a camera pose of a view of the input image and a camera pose of a view of the target image to form the transformed volume comprises using a trilinear resampling operation with parameters defined based on a transformation between the camera pose of the view of the input image and the camera pose of the view of the target image (Olszewski Section 3.1 Step 2 “transforms the bottleneck via a trilinear resampling operation”).
In regards to claim 7, Olszewski in view of Ning, and Watanabe teaches the method of claim 1, and further teaches receiving information from a number of input views of an object in the input image, transforming the number of input views into the view of the target image, and computing per-cell averages of the feature vectors before decoding the de-quantized image (Olszewski Section 3.1.1 Equation 2 [average]; Section 3.1.1 “our formulation naturally extends to an arbitrary number of inputs”).
In regards to claim 8, Olszewski in view of Ning, and Watanabe teaches the method of claim 1, and further teaches wherein the feature vectors representing the spatially disentangled volumetric representation of poses of the input image comprise 2D feature maps, further comprising reshaping, by the encoder, the 2D feature maps to generate spatially transformed 3D feature maps and reshaping, by the decoder, the spatially transformed 3D feature maps to produce 2D feature maps for synthesis of the target image (Olszewski Figure 2, Encoder and Decoder; Section 3.1 Steps 1 and 3).
In regards to claim 9, Olszewski in view of Ning, and Watanabe teaches the method of claim 1, and further teaches training the codebook using at least one multi-view dataset in which source and target images are randomly selected and a corresponding pose transformation is applied to an encoded source image bottleneck to produce a result that is quantized and decoded to synthesize a synthesized image in the codebook. (Olszewski Section 3.2.1 “NVS requires a minimum of two images of a given object from different, known viewpoints Given {Ik, It} and Fk->i, we can compute a reconstruction, I’t, of It using equation (1). Using this, we define several losses in image space with which to train our network parameters.”)
In regards to claim 12, Olszewski in view of Ning, and Watanabe renders obvious the claim limitations as in the consideration of claim 1.
In regards to claim 14, Olszewski in view of Ning, and Watanabe, teaches a processor and a memory storing computer readable instructions that, when executed by the processor, configure the system to perform operations (Ning Section 1 “Scientific visualization is an emerging discipline that seeks to use computer graphics to extract information and insight from a variety of data.” “This integrated system would allow for efficient use of disk space, main memory, and network bandwidth.” Examiner note: Since this system is being performed with a computer, it is obvious that a processor would need to be used to execute the instructions.) and renders obvious the remaining claim limitations as in the consideration of claims 2 and 12.
In regards to claim 15, Olszewski in view of Ning, and Watanabe renders obvious the claim limitations as in the consideration of claims 3 and 12.
In regards to claim 16, Olszewski in view of Ning, and Watanabe renders obvious the claim limitations as in the consideration of claims 4, 5, and 12.
In regards to claim 17, Olszewski in view of Ning, and Watanabe renders obvious the claim limitations as in the consideration of claims 6 and 12.
In regards to claim 18, Olszewski in view of Ning, and Watanabe renders obvious the claim limitations as in the consideration of claims 7 and 12.
In regards to claim 19, Olszewski in view of Ning, and Watanabe renders obvious the claim limitations as in the consideration of claims 7 and 12.
In regards to claim 20, Olszewski in view of Ning, and Watanabe teaches a non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a processor cause the processor to [perform operations] (Watanabe Paragraph [0129] “The program may be temporarily or permanently stored (recorded) in a removable medium 111 such as a flexible disk, a compact disc read-only memory (CD-ROM), a magnet-optical (MO) disk, a digital versatile disc (DVD), a magnetic disk, a semiconductor memory, or the like. Such a removable medium 111 may be provided as so-called packed software.”) and renders obvious the remaining claim limitations as in the consideration of claims 1 and 12.
Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Olszewski in view of Ning, and Watanabe, and further in view of Löhdefink (Scalar and Vector Quantization for Learned Image Compression: A Study on the Effects of MSE and GAN Loss in Various Space).
In regards to claim 10, Olszewski in view of Ning, and Watanabe teaches the method of claim 9, but fails to further teach wherein training the codebook comprises employing an adversarial loss using a discriminator network with learnable parameters and optimizing the codebook during training using the adversarial loss.
However, Löhdefink teaches wherein training the codebook comprises employing an adversarial loss using a discriminator network with learnable parameters and optimizing the codebook during training using the adversarial loss. (Section III B “We employ a GAN loss LGAN=LMSE+αLFM+βLadv, using a learned discriminator network, combining a standard MSE loss LMSE(1) with a feature matching (FM) loss LFM (2) and an adversarial loss Ladv”)
Löhdefink is considered to be analogous to the claimed invention because they are in the same field of training a neural network to perform vector quantization. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Olszewski in view of Ning, and Watanabe to include the teachings of Löhdefink, to provide the advantage of guaranteeing that the system generates realistic outputs (Löhdefink Section I “Incorporating adversarial loss functions into learned image compression by self-supervised autoencoders constituting the generator of a GAN, see Fig. 1, guarantees to generate realistic outputs even at very low bitrates and has benefits, e.g., in real-time applications dealing with data-intensive machine learning functions.”)
In regards to claim 11, Olszewski in view of Ning, Watanabe, and Löhdefink teaches the method of claim 10, and further teaches wherein training the codebook further comprises selecting an adversarial loss weight applied to the adversarial loss by using a reconstruction loss measured between a ground truth and a reconstructed image and a gradient of an input with respect to a final layer of an image generator. (Löhdefink Equation 1; Section III B “In the loss functions we use the image x=(xi) with pixel index i ∈ I = { 1, 2, H ⋅ W } and I being the set of I=H⋅W pixel indices. The same holds for the reconstructed image x^=(x^i).” Examiner note: In equation 1 they are subtracting the difference between the reconstructed image and the image.)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CALEB LOGAN ESQUINO whose telephone number is (703)756-1462. The examiner can normally be reached M-Fr 8:00AM-4:00PM EST.
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/CALEB L ESQUINO/ Examiner, Art Unit 2677
/ANDREW W BEE/ Supervisory Patent Examiner, Art Unit 2677
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