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 .
Claim Status
Claims 1-20 are pending for examination in the Application No. 19/041,594 filed January 30th, 2025.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed as foreign Patent Application No. KR10-2024-0065444, filed on May 20th, 2024.
Information Disclosure Statement
The information disclosure statement(s) (IDS(s)) submitted on January 30th, 2025, and July 27th, 2026, is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS(s) is/are being considered and attached by the examiner.
Claim Objections
Claims 4, 6, 7, 10, 11, 15, and 17-19 are objected to because of the following informalities failing to comply with 37 CFR 1.71(a) for "full, clear, concise, and exact terms" (see MPEP § 608.01(m)):
In claims 4 and 15, “multiplying the projected difference vector by a weight and the visual feature” should be “multiplying the projected difference vector by a weight [[and]]to the visual feature” as disclosed in para. [0078] of Applicant’s instant Specification;
In claims 6, 10, and 17, the examiner respectfully suggests amending the phrase “the class” to recite “the class of the input image” to maintain consistency in terminology and prevent confusion with the other classes recited in the independent claims;
In claims 7 and 18, the examiner respectfully suggests amending the phrase “at least one of a size, a color, and a pattern” to recite “at least one of a size, a color, [[and]]or a pattern” to prevent interpreting the claims as conjunctive (i.e., ‘at least one of a size, at least one of a color, and at least one of a pattern’), which is inconsistent with Applicant’s instant Specification (See Superguide Corp. v. Direct TV Enterprises, Inc., 358 F.3d 870, 69 USPQ2d 1865 (Fed. Cir. 2004));
In claim 11, the examiner respectfully suggests amending the phrase “at least one of the encoder and the predictor” to recite “at least one of the encoder [[and]]or the predictor” as the claim appears to recite the disjunctive case rather than the conjunctive case (i.e., ‘at least one of a size, at least one of a color, and at least one of a pattern’; See Superguide Corp. v. Direct TV Enterprises, Inc., 358 F.3d 870, 69 USPQ2d 1865 (Fed. Cir. 2004)); and
In claim 19, the examiner respectfully suggests amending the phrase “The apparatus of claim 18…” to recite “The apparatus of claim [[18]]12…” to maintain similar dependencies as similar claim 8 unless intended by Applicant.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim(s) 1, 12, and 20, the term “reflecting” recited in the limitation “an attribute class formed by reflecting attribute information onto the class” of the claims renders the claims indefinite because it is unclear what is meant by “reflecting attribute information onto the class”. For example, it is unclear if the reflection refers to mirroring/copying the class in the attribute information, associating the class with the attribute information, or adding an attribute onto the class. For examination purposes, this limitation in these claims will be read as “an attribute class formed by [[reflecting]]appending attribute information [[onto]]to the class” as disclosed in Fig. 7 of Applicant’s Drawings. Additionally, the phrase “in which the attribute information is reflected” in claims 8 and 19 will be read accordingly as “in which the attribute information is [[reflected]]appended to the class”. Furthermore, claims 2-11 and 13-19 inherit this indefiniteness in view of their dependency to at least one of the claims.
Regarding claim(s) 6 and 17, the term “reflected” recited in the limitation “the attribute information includes visual information reflected in the text information” of the claims renders the claims indefinite because it is unclear what is meant by the phrase “visual information reflected in the text information”. For example, it is unclear if the phrase refers to the text information includes visual information, the visual information is in the form or part of the text information, or that visual information is added to the text information. For examination purposes, this limitation in these claims will be read as “the attribute information includes visual information [[reflected in]]appended to the text information” as disclosed in Fig. 7 of Applicant’s Drawings and paragraph [0072] of Applicant’s instant Specification. Furthermore, claims 7 and 18-19 inherit this indefiniteness in view of their dependency to at least one of the claims.
Regarding claim(s) 9, the phrase “the class corresponding to the input image” in the claim(s) renders the claim(s) indefinite because it is unclear whether the phrase is referring to the “class of the input image” or the “predicted class of the input image” in claim 1. For examination purposes, the phrase will be read as “the class [[corresponding to]]of the input image”.
Regarding claim(s) 11, the claim(s) recites the limitation “the class corresponding to the augmented image” renders the claim indefinite because it is unclear to the examiner if the recited “class” is a new instantiation or referring to the “class of the input image” recited in claim 1. There is insufficient antecedent basis for this limitation in the claim(s). For examination purposes, this limitation in the claim(s) will be read as “[[the]]a class corresponding to the augmented image”.
Regarding claim(s) 11, the phrase “class corresponding to the augmented image as label data” in the claim(s) renders the claim(s) indefinite because it is unclear what is being labeled by the “label data”. For examination purposes, this limitation in the claim(s) will be read as “[a] class corresponding to the augmented image as label data of the augmented image”.
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 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.
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 1-3, 6-14, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lyu et al. (Lyu; “DeltaEdit: Exploring Text-free Training for Text-Driven Image Manipulation,” 2023; cited in Applicant’s IDS filed July 27th, 2026) in view of Tian et al. (Tian; “VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual Recognition,” 2022).
Regarding claim 1, Lyu discloses a method for augmenting a visual feature performed by a visual feature augmentation apparatus (subheading “Text-driven Image Manipulation” in col. 1 of pg. 5, recite(s)
[Text-driven Image Manipulation] “…where the editing direction is subsequently used to generate edited latent embedding
s
'
=
s
+
∆
s
'
. In the final step, we can generate the synthesized image
I
'
with StyleGAN based on
s
'
.”
, where “edited latent embedding
s
'
” is an augmented visual feature), comprising:
extracting a visual feature from an input image (subheading “Text-driven Image Manipulation” in col. 1 of pg. 5—see citation immediately above—, where Fig. 5(C) further recite(s):
PNG
media_image1.png
288
779
media_image1.png
Greyscale
, where embedding vector “s” is a visual feature extracted from input image (
I
));
embedding into a text space respectively, a class of the input image and an attribute class formed by reflecting attribute information onto the class (Fig. 5(C)—see citation immediately above—, where the 2nd para. of col. 1 on pg. 4 and section 3.2 on pg. 4 further recite(s):
[2nd para. of col. 1 on pg. 4] “…Additionally, there is an advantage of leveraging the text embeddings change as conditions, such as “face” as the source text and “face with yellow hair” as the target text. …”
[section 3.2 on pg. 4] “In our DeltaEdit framework, we extract delta image-text features,
∆
i
=
i
2
-
i
1
and
∆
t
=
t
2
-
t
1
, as the condition vectors in the training phase and inference phase, respectively.”
, where the “CLIP joint space” is at least a text space and the text embedding vectors “
t
1
” and “
t
2
” are embedded texts of the class of the input image (e.g., “face”) and the attribute class (e.g., “face with yellow hair”), respectively);
calculating a difference vector between an embedded vector of the class and an embedded vector of the attribute class (Fig. 5(C) and section 3.2 on pg. 4—see citations above—, where subheading “Text Prompts” in col. 1 of pg. 5 further recite(s):
[Text Prompts] “In inference phase pipeline, the crucial issue is how to construct
∆
t
=
t
2
-
t
1
. …”
, where conditional vector “
∆
t
” is a difference vector between an embedded vector of the class (
t
1
) and an embedded vector of the attribute class (
t
2
)); and
augmenting the visual feature corresponding to the input image based on the difference vector (Fig. 5(c)—see citation above—, where subheading “Text-driven Image Manipulation” in col. 1 of pg. 5 further recite(s):
[Text-driven Image Manipulation] “…As shown in Figure 5 (c), given the source image
I
, we first extract its CLIP image embedding
i
and StyleGAN
S
space embedding
s
. Then, we construct ∆t with source and target text prompt, and we can predict the editing direction
∆
s
'
as:
∆
s
'
=
D
e
l
t
a
M
a
p
p
e
r
(
s
,
i
,
∆
t
)
, (6)
where the editing direction is subsequently used to generate edited latent embedding
s
'
=
s
+
∆
s
'
. In the final step, we can generate the synthesized image
I
'
with StyleGAN based on
s
'
.”
, where embedding vector “
s
” is a visual feature augmented (e.g., “edited”) based on at least the difference vector (
Δ
t
) as
Δ
s
'
comprises of
Δ
t
as depicted in Eq(6) above),
wherein the apparatus includes:
an encoder that extracts the visual feature from the input image (Fig. 5(c)—see citation above—, where the StyleGAN Encoder is at least an encoder that extracts the visual feature (e.g., “
s
”) from the input image (
I
))
Where Lyu does not specifically disclose
a predictor that generates predicted class of the input image based on the augmented the visual feature in order to compare whether the predicted class is matched with the class of the input image;
Tian teaches in the same field of endeavor of generating an augmented visual feature
a predictor that generates predicted class of the input image based on the augmented the visual feature in order to compare whether the predicted class is matched with the class of the input image (Fig. 3 and sections 3.1 on pg. 77 and 3.4 on pg. 80, recite(s)
[section 3.1 on pg. 77] “(2) In the second stage, a language-guided recognition (LGR) head is designed to collect the overall linguistic representation of each class to guide the image recognition. As a result, the proposed VL-LTR is able to combine the advantages of visual and linguistic representation and achieve impressive long-tailed recognition performance.”
[section 3.4 on pg. 80] “As mentioned in Sect.3.1, the training process of our method has two stages, namely pre-training and fine-tuning respectively. In the pre-training stage, the visual encoder and linguistic encoder are jointly optimized… In the fine-tuning stage, after computing the classification probabilities PI and PT, we simply calculate their corresponding CrossEntropy loss LCE with the ground truth label y as Eq.9:”
PNG
media_image2.png
611
850
media_image2.png
Greyscale
, where the “language-guided recognition (LGR) head” is a predictor that generates the predicted class (e.g., “image classification” as recited in Fig. 3 above) of the input image based on an augmented visual feature (e.g., the “visual-linguistic representation” or “Select Anchor Sentence” depicted in Fig. 3)).
Since Lyu and Tian each disclose augmenting a visual feature as a visual-linguistic representation, it would have been obvious to one of ordinary skill in the art before the effective filing date of the presently filed invention to try incorporating a predictor that generates a predicted class of the input image based on the augmented visual feature to utilize the augmented visual feature disclosed in Lyu for improving computer vision tasks such as image classification systems by incorporating visual and linguistic information as taught by Tian (last para. of pg. 74, recite(s)
[section 3.1 on pg. 77] “Although there have been some visual-linguistic approaches [15,36,58] for visual recognition, their performance is still not satisfactory, due to the gap between image and text representation and the lack of robustness to noisy text. Recently, the rise of visual-linguistic foundation models [19,37,42] has provided an effective way to learn powerful representation that can connect the image and text modalities. Motivated by this, we present a visual-linguistic framework for long-tailed recognition, termed VL-LTR, which can utilize the advantages of both visual and linguistic representation for visual recognition tasks as shown in Fig.1 (b). Our method mainly consists of two key components, which are (1) a class-wise visual-linguistic pre-training (CVLP) framework for linking images and text descriptions at the class level, and (2) a language-guided recognition (LGR) head designed to perform long-tailed recognition according to the learned visual-linguistic representation.”
).
Regarding claim 2, Lyu in view of Tian discloses the method of claim 1, wherein Lyu further discloses the method of claim 1 further comprising:
projecting the difference vector into a visual space (Fig. 5(C)—see citation in claim 1 above—, where determining the “co-linearity between
Δ
i
and
Δ
t
in the CLIP joint space” is projecting at least the difference vector (
Δ
t
) into a visual space (e.g., “CLIP joint space”)),
wherein, in the augmenting, the visual feature is augmented using the projected difference vector (Fig. 5(c) and subheading “Text-driven Image Manipulation” in col. 1 of pg. 5—see citations in claim 1 limitation “augmenting…” above—, where “edited latent embedding
s
'
” is an augmented visual feature using at least the difference vector (
Δ
t
) as
Δ
s
'
comprises of
Δ
t
as depicted in Eq(6)).
Regarding claim 3, Lyu in view of Tian discloses the method of claim 2, wherein Lyu further discloses, in the projecting, the difference vector is linearly projected into the visual space (Fig. 5(C)—see citation in claim 1 above—, where determining the “co-linearity between
Δ
i
and
Δ
t
in the CLIP joint space” is projecting at least the difference vector (
Δ
t
) linearly (hence the term “co-linearity”) into the visual space).
Regarding claim 6, Lyu in view of Tian discloses the method of claim 1, wherein Lyu further discloses the class includes text information (Fig. 5(C) and the 2nd para. of col. 1 on pg. 4—see citations in claim 1 limitation “embedding into a text space above immediately above—, where the class (e.g., “source text”) includes text information (e.g., text ‘face’)), and
the attribute information includes visual information reflected in the text information (Fig. 5(C) and the 2nd para. of col. 1 on pg. 4—see citations in claim 1 limitation “embedding into a text space above immediately above—, where the attribute information (e.g., “target text”) includes visual information (e.g., ‘yellow hair’) appended to the text information (e.g., text ‘face with yellow hair’)).
Regarding claim 7, Lyu in view of Tian discloses the method of claim 6, wherein Lyu further discloses the visual information includes at least one of a size, a color, and a pattern (Fig. 5(C) and the 2nd para. of col. 1 on pg. 4—see citations in claim 1 limitation “embedding into a text space above immediately above—, where visual information such as ‘yellow hair’ is at least a size and/or pattern).
Regarding claim 8, Lyu in view of Tian discloses the method of claim 1, wherein Lyu further discloses the method of claim 1 further comprising:
prior to the embedding, receiving the attribute class in which the attribute information is reflected (Fig. 5(C) and the 2nd para. of col. 1 on pg. 4—see citations in claim 1 limitation “embedding into a text space above immediately above—, where the attribute class (e.g., “target text”) is received prior to the embedding (e.g., before input into the “CLIP Text Encoder” depicted in Fig. 5(C))).
Regarding claim 9, Lyu in view of Tian discloses the method of claim 1, wherein Lyu further discloses the encoder(section 2.1 on pg. 2 and the 2nd para. of section 3 on pg. 3, recite(s)
[2.1. Vision-Language Representations] “Learning generic vision-language representations is of great importance in the multi-modal field. Following the success of BERT [2], many large scale pre-trained vision language models [13, 18, 27, 30, 40] are proposed. A recent development, CLIP [22], is extremely powerful for joint vision-language learning. It is trained on 400 million (image, text) pairs and learns a joint multi-modal semantic space for both images and texts. …”
[2nd para. of section 3 on pg. 3] “Randomly given two images from the training image dataset, one as the source image
I
1
and the other as the target image
I
2
. Then the CLIP image encoder is used to extract their CLIP image embeddings,
i
1
and
i
2
, and the pre-trained StyleGAN inversion model [31] is adopted as an encoder to extract the latent codes
s
1
and
s
2
in the S space. …”
, where the “pre-trained” encoder (e.g., “StyleGAN”) includes the input image (e.g., “source image
I
1
”) and corresponding class label data (e.g., “text”) is pre-training the encoder using the input image and the class corresponding to the input image as label data).
Where Lyu does not specifically disclose
…the predictor are pre-trained…;
Tian further teaches
…the predictor are pre-trained… (section 3.3 on pg. 79, recite(s)
[3.3 Language-Guided Recognition] “…(2) a language-guided recognition head to effectively use visual and linguistic representation learned in the pre-training stage.”
, where the predictor (e.g., the “language-guided recognition head”) uses the “visual and linguistic representation learned in the pre-training stage” is pre-training the predictor).
Since Lyu discloses that the encoder is at least pre-trained on the input image and the class corresponding to the input image as label data, it would have been obvious to one of ordinary skill in the art before the effective filing date of the presently filed invention to recognize that the system of Lyu comprising the input image and the class corresponding to the input image as label data further incorporating the pre-trained predictor taught by Tian would result in the pre-trained predictor of Tian to be trained on the visual and linguistic representations of the input image and the class corresponding to the input image as label data disclosed by Lyu.
Regarding claim 10, Lyu in view of Tian discloses the method of claim 1, wherein Lyu further discloses the method of claim 1 further comprising:
generating an augmented image based on the augmented visual feature and the class (subheading “Text-driven Image Manipulation” in col. 1 of pg. 5—see citation in claim 1 limitation “augmenting the visual feature…” above—, where the “synthesized image
I
'
” is an augmented image generated based on at least the augmented visual feature (e.g., “edited latent embedding
s
'
”) and the class (i.e.,
s
'
includes the class as
s
'
is calculated based on at least
Δ
s
'
, which includes
Δ
t
as depicted in Eq(6), wherein
Δ
t
includes the class
t
1
as detailed in subheading “Text Prompts” in col. 1 of pg. 5—see citation in claim 1 limitation “calculating a difference vector…” above)).
Regarding claim 11, Lyu in view of Tian discloses the method of claim 10, wherein Lyu further discloses the method of claim 10 further comprising:
training at least one of the encoder and the predictor using the augmented image and the class corresponding to the augmented image as label data (section 4.1 on pg. 5, recite(s)
[4.1. Implementation details] “…we randomly choose 58,000 images from FFHQ [6] dataset and sample 200,000 fake images from the z space in StyleGAN for training.”
, where the encoder (e.g., “StyleGAN”) is trained using at least the augmented image and the class corresponding to the augmented image as label data (e.g., “fake images from the z space of StyleGAN”)).
Regarding claim 12, the claim differs from claim 1 in that the claim is in the form of an apparatus comprising: memory storing instructions, and a processor for executing the instructions to perform the method of claim 1. Lyu discloses said apparatus comprising a processor for executing instructions to perform the method of claim 1 (section 4.1 on pg. 5, recite(s)
[section 4.1 on pg. 8] “…The proposed method is trained on 1 NVIDIA Tesla P40 GPU. ...”
). Therefore, claim 12 recites similar limitations to claim 1 and is rejected for similar rationale and reasoning (see the analysis for claim 1 above).
Regarding claim 13, the claim recites similar limitations to claim 2 and is rejected for similar rationale and reasoning (see the analysis for claim 2 above).
Regarding claim 14, the claim recites similar limitations to claim 3 and is rejected for similar rationale and reasoning (see the analysis for claim 3 above).
Regarding claim 17, the claim recites similar limitations to claim 6 and is rejected for similar rationale and reasoning (see the analysis for claim 6 above).
Regarding claim 18, the claim recites similar limitations to claim 7 and is rejected for similar rationale and reasoning (see the analysis for claim 7 above).
Regarding claim 19, the claim recites similar limitations to claim 8 and is rejected for similar rationale and reasoning (see the analysis for claim 8 above).
Regarding claim 20, the claim differs from claim 1 in that the claim is in the form of a non-transitory computer-readable storage medium storing computer-executable instructions performing the method of claim 1. Therefore, claim 20 recites similar limitations to claim 1 and is rejected for similar rationale and reasoning (see the analysis for claim 1 above).
Claims 4-5 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Lyu in view of Tian as applied to claims 2 and 13, and further in view of Zhou et al. (Zhou; “CLIP-PAE: Projection-Augmentation Embedding to Extract Relevant Features for a Disentangled, Interpretable and Controllable Text-Guided Face Manipulation,” 2023).
Regarding claim 4, Lyu in view of Tian discloses the method of claim 2, wherein Lyu further discloses, in the augmenting, the visual feature is augmented based on a value obtained by(manipulating) the projected difference vector by a weight to the visual feature (subheading “Text-driven Image Manipulation” in col. 1 of pg. 5—see citations in claim 1 limitation “augmenting…” above—, where the para. between pgs. 5-6 further recite(s):
[Text Prompts] “…For a fair comparison, we empirically set β at 0.03 and compare with StyleCLIP-GD under different manipulation strength α in Figure 10. The results demonstrate that StyleGAN-GD cannot be generalized to different image attributes and produce feasible editing results under the same hyper-parameters. For example, the result conditioned on “big eyes” is the most accurate at α = 1 while the result conditioned on “black clothes” is the most correct when α = 4. …”
, where
∆
s
'
in Eq(6) is at least a projected difference vector and is manipulated by a weight “manipulation strength
α
” to the visual feature (
s
) (i.e., ‘big eyes’ and/or ‘black clothes’ are considered projected difference vectors as they are the difference between an embedded vector of the class ‘face’ and an embedded vector of the attribute class ‘face with big eyes’ and/or ‘face with black clothes’ projected in the “StyleGAN
S
space”)).
Where Lyu in view of Tian does not specifically disclose
…the visual feature is augmented based on a value obtained by multiplying the …vector by a weight to the visual feature;
Zhou teaches in the same field of endeavor of augmenting a visual feature
…the visual feature is augmented based on a value obtained by multiplying the …vector by a weight to the visual feature (Eq(6) and para(s). in col. 2 of pg. 5, recite(s)
[Eq(6) and para(s). in col. 2 of pg. 5] “the projected image 𝒘 should be guided by the target text prompt. A simple augmentation operation can be
A
W
,
T
w
,
α
=
w
+
α
e
T
. (6)
… where
α
∈
R
+
controls the augmenting power.”
, where the “projected image 𝒘“ is a visual feature augmented based on a value obtained by multiplying a vector (e.g.,
e
T
or “target text prompt”) by a weight (e.g.,
α
) to the visual feature).
Since Lyu and Zhou each disclose manipulating the augmentation power for a vector by multiplying the vector by a weight to the visual feature, it would have been obvious to one of ordinary skill in the art before the effective filing date of the presently filed invention to modify the system of Lyu to try multiplying the projected difference vector by the weight to the visual feature (i.e., manipulation strength
α
) to guide the augmentation of the visual feature by the projected difference vector as taught by Zhou above while still yielding the predictable result of generating an augmented visual feature.
Regarding claim 5, Lyu, as modified by Tian and Zhou, discloses the method of claim 4, wherein the augmented visual feature is determined as
f
^
I
0
=
f
I
0
+
α
⋅
p
r
o
j
(
Δ
0
→
1
)
here,
f
^
I
0
denotes the augmented visual feature,
f
I
0
denotes the visual feature,
α
denotes the weight, and
p
r
o
j
(
Δ
0
→
1
)
denotes the projected difference vector (Eq(6) in the subheading “Text-driven Image Manipulation” in col. 1 of pg. 5 of Lyu—see citation in claim 6 above—, where
s
'
=
s
+
∆
s
'
is equivalent to
f
^
I
0
=
f
I
0
+
p
r
o
j
(
Δ
0
→
1
)
; wherein Eq(6) and para(s). in col. 2 of pg. 5 of Zhou—see citation in claim 6 above—, teaches multiplying
p
r
o
j
(
Δ
0
→
1
)
by a weight
α
to guide the augmentation of the visual feature by the projected difference vector).
Regarding claim 15, the claim recites similar limitations to claim 4 and is rejected for similar rationale and reasoning (see the analysis for claim 4 above).
Regarding claim 16, the claim recites similar limitations to claim 5 and is rejected for similar rationale and reasoning (see the analysis for claim 5 above).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JULIA Z YAO whose telephone number is (571)272-2870. The examiner can normally be reached Monday - Friday (8:30AM - 5PM).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Emily Terrell can be reached at (571)270-3717. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.Z.Y./Examiner, Art Unit 2666
/MING Y HON/Primary Examiner, Art Unit 2666
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