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 .
Comments
The Preliminary Amendment filed on December 13, 2024 has been entered and made of record.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-16 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-16 of U.S. Patent No. 12,131,521 to Chen et al. Although the claims at issue are not identical, they are not patentably distinct from each other because Chen et al. claims the claimed invention as follows:
Claim 1 Chen et al.
1. An image classification method, comprising:
obtaining an input feature map of an image,
wherein the input feature map comprises a plurality of input sub-feature maps;
performing feature extraction processing on the input feature map based on a feature extraction kernel of a neural network to obtain an output feature map,
wherein the output feature map comprises a plurality of output sub-feature maps,
wherein each of the plurality of output sub-feature maps is determined based on the corresponding input sub-feature map and the feature extraction kernel,
wherein at least one of the output sub-feature maps is determined based on a target matrix obtained after an absolute value is taken, and
wherein the target matrix is obtained by performing matrix addition or matrix subtraction on the input sub-feature map corresponding to the target matrix and the feature extraction kernel; and
classifying the image based on the output feature map to obtain a classification result of the image.
1. An image classification method, comprising:
obtaining an input feature map of a to-be-processed image,
wherein the input feature map comprises a plurality of input sub-feature maps;
performing feature extraction processing on the input feature map based on a feature extraction kernel of a neural network to obtain an output feature map,
wherein the output feature map comprises a plurality of output sub-feature maps,
wherein each of the plurality of output sub-feature maps is determined based on the corresponding input sub-feature map and the feature extraction kernel,
wherein at least one of the output sub-feature maps is determined based on a target matrix obtained after an absolute value is taken, and … (See for example, claim 1 at Col. 27 line 61 through Col. 28 line 11)
wherein the target matrix is obtained by performing matrix addition or matrix subtraction on at least one of the input sub-feature maps and the feature extraction kernel (See for example, claim 2 at Col. 28 lines 15-18)
… classifying the to-be-processed image based on the output feature map to obtain a classification result of the to-be-processed image (See for example, claim 1 at Col. 28 lines 12-14).
Claim 2 Chen et al.
2. The method according to claim 1,
wherein the target matrix is obtained by performing matrix addition or matrix subtraction on at least one of the input sub-feature maps and the feature extraction kernel.
2. The method according to claim 1,
wherein the target matrix is obtained by performing matrix addition or matrix subtraction on at least one of the input sub-feature maps and the feature extraction kernel (i.e., claim 2 at Col. at Col. 28 lines 15-18).
As to claim 3, Chen et al. claims the method according to claim 2,
wherein the at least one of the output sub-feature maps is obtained according to the following equation:
Y
m
,
n
,
t
=
∑
i
=
0
d
-
1
∑
j
=
0
d
-
1
∑
k
=
1
C
-
X
m
+
i
,
n
+
j
,
k
-
F
i
,
j
,
k
,
t
;
o
r
Y
m
,
n
,
t
=
∑
i
=
0
d
-
1
∑
j
=
0
d
-
1
∑
k
=
1
C
-
X
m
+
i
,
n
+
j
,
k
+
F
i
,
j
,
k
,
t
,
wherein
|(
∙
)| represents an operation for taking an absolute value,
∑
(
∙
)
represents a summation operation, Y(m,n,t) represents the at least one of the output sub-feature maps, Y(m,n,t) represents an element in an mth row and an nth column on a tth page in the output feature map, X(m+i,n+j,k) represents an element determined based on an ith row and a jth column on a kth page in the at least one of the input sub-feature maps, F(i,j,k,t) represents an element in an ith row and a jth column on a kth page in the feature extraction kernel, t represents a channel quantity of the feature extraction kernel, d represents a row quantity of the feature extraction kernel, C represents a channel quantity of the input feature map, and d, C, i, j, k, m, n, and t are integers (i.e., claims 7-9 at Col. 29 lines 13-67).
Claim 4 Chen et al.
4. The method according to claim 1,
wherein a gradient of the feature extraction kernel is determined based on the target matrix, and a gradient of the input sub-feature map is determined based on the target matrix.
4. The method according to claim 1,
wherein a gradient of the feature extraction kernel is determined based on the target matrix, and a gradient of the input sub-feature map is determined based on the target matrix (i.e., claim 4 at Col. 28 lines 42-45).
Claim 5 Chen et al.
5. The method according to claim 4,
wherein when a value of T(m,n,i,j,k,t) is within a preset value range, a gradient of F(i,j,k,t) is determined based on the value of T(m,n,i,j,k,t), and a gradient of X(m+i,n+j,k) is determined based on the value of T(m,n,i,j,k,t),
wherein: T(m,n,i,j,k,t)=X(m+i,n+j,k)+F(i,j,k,t), F(i,j,k,t) represents an element in the ith row and the jth column on the kth page in the feature extraction kernel, X(m+i,n+j,k) represents an element determined based on the ith row and the jth column on the kth page in the at least one of the input sub-feature maps, and i, j, k, m, n, and t are integers.
5. The method according to claim 4,
wherein when a value of T(m,n,i,j,k,t) falls within a preset value range, a gradient of F(i,j,k,t) is determined based on the value of T(m,n,i,j,k,t), and a gradient of X(m+i,n+j,k) is determined based on the value of T(m,n,i,j,k,t),
wherein: T(m,n,i,j,k,t)=X(m+i,n+j,k)±F(i,j,k,t), F(i,j,k,t) is an element in the ith row and the jth column on the kth page in the feature extraction kernel, X(m+i, n+j,k) is an element determined based on the ith row and the jth column on the kth page in the at least one of the input sub-feature maps, and i, j, k, m, n, and t are all integers (i.e., claim 5 at Col. 28 lines 46-57).
As to claim 6, Chen et al. claims the method according to claim 5,
wherein the gradient of F(i,j,k,t) is obtained according to the following equation:
∂
Y
(
m
,
n
,
t
)
∂
F
(
i
,
j
,
k
,
t
)
=
-
H
a
r
d
t
a
n
h
(
F
i
,
j
,
k
,
t
±
X
m
+
i
,
n
+
j
,
k
)
; and
the gradient of X(m+i,n+j,k) is obtained according to the following equation:
∂
Y
(
m
,
n
,
t
)
∂
X
(
m
+
i
,
n
+
j
,
k
)
=
-
H
a
r
d
tanh
(
X
(
m
+
i
,
n
+
j
,
k
)
±
(
F
i
,
j
,
k
,
y
)
,
wherein:
H
a
d
r
tanh
x
=
1
x
>
1
-
1
x
<
-
1
x
-
1
≤
x
≤
1
,
∂
Y
(
m
,
n
,
t
)
∂
F
(
i
,
j
,
k
,
t
)
represents the gradient of F(i,j,k,t), and
∂
Y
(
m
,
n
,
t
)
∂
X
(
m
+
i
,
n
+
j
,
k
)
represents the gradient of X(m+i,n+j,k) (i.e., claim 6 at Col. 28 line 28 through Col. 29 line 12).
Claim 7 Chen et al.
7. An image classification apparatus, comprising:
at least one processor;
one or more memories coupled to the at least one processor and storing programming instructions for execution by the at least one processor to:
obtain an input feature map of an image,
wherein the input feature map comprises a plurality of input sub-feature maps;
perform feature extraction processing on the input feature map based on a feature extraction kernel of a neural network to obtain an output feature map,
wherein the output feature map comprises a plurality of output sub-feature maps, each of the plurality of output sub-feature maps is determined based on the corresponding input sub-feature map and the feature extraction kernel, at least one of the output sub-feature maps is determined based on a target matrix obtained after an absolute value is taken, and
the target matrix is obtained by performing matrix addition or matrix subtraction on the input sub-feature map corresponding to the target matrix and the feature extraction kernel; and
classify the image based on the output feature map, to obtain a classification result of the image.
7. An image classification apparatus, comprising:
at least one processor;
one or more memories coupled to the at least one processor and storing programming instructions for execution by the at least one processor to:
obtain an input feature map of a to-be-processed image,
wherein the input feature map comprises a plurality of input sub-feature maps;
perform feature extraction processing on the input feature map based on a feature extraction kernel of a neural network to obtain an output feature map,
wherein the output feature map comprises a plurality of output sub-feature maps, each of the plurality of output sub-feature maps is determined based on the corresponding input sub-feature map and the feature extraction kernel, at least one of the output sub-feature maps is determined based on a target matrix obtained after an absolute value is taken … (See for example, claim 7 at Col. 29 lines 13-34)
the target matrix is obtained by performing matrix addition or matrix subtraction on at least one of the input sub-feature maps and the feature extraction kernel (See for example, claim 8 at Col. 29 lines 38-41)
… classify the to-be-processed image based on the output feature map, to obtain a classification result of the to-be-processed image (See for example, claim 7 at Col. 29 lines 35-37).
Claim 8 Chen et al.
8. The apparatus according to claim 7,
wherein the target matrix is obtained by performing matrix addition or matrix subtraction on at least one of the input sub-feature maps and the feature extraction kernel.
8. The apparatus according to claim 7,
wherein the target matrix is obtained by performing matrix addition or matrix subtraction on at least one of the input sub-feature maps and the feature extraction kernel (i.e., claim 8 at Col. 29 lines 38-41).
As to claim 9, Chen et al. claims the apparatus according to claim 8,
wherein the at least one of the output sub-feature maps is obtained according to the following equation:
Y
m
,
n
,
t
=
∑
i
=
0
d
-
1
∑
j
=
0
d
-
1
∑
k
=
1
C
-
X
m
+
i
,
n
+
j
,
k
-
F
i
,
j
,
k
,
t
;
o
r
Y
m
,
n
,
t
=
∑
i
=
0
d
-
1
∑
j
=
0
d
-
1
∑
k
=
1
C
-
X
m
+
i
,
n
+
j
,
k
+
F
i
,
j
,
k
,
t
,
wherein
|(
∙
)| represents an operation for taking an absolute value,
∑
(
∙
)
represents a summation operation, Y(m,n,t) represents the at least one of the output sub-feature maps, Y(m,n,t) represents an element in an mth row and an nth column on a tth page in the output feature map, X(m+i,n+j,k) represents an element determined based on an ith row and a jth column on a kth page in the at least one of the input sub-feature maps, F(i,j,k,t) represents an element in an ith row and a jth column on a kth page in the feature extraction kernel, t represents a channel quantity of the feature extraction kernel, d represents a row quantity of the feature extraction kernel, C represents a channel quantity of the input feature map, and d, C, i, j, k, m, n, and t are integers (i.e., claim 9 at Col. 29 lines 43-67).
Claim 10 Chen et al.
10. The apparatus according to claim 7,
wherein a gradient of the feature extraction kernel is determined based on the target matrix, and a gradient of the input sub-feature map is determined based on the target matrix.
10. The apparatus according to claim 7,
wherein a gradient of the feature extraction kernel is determined based on the target matrix, and a gradient of the input sub-feature map is determined based on the target matrix (i.e., claim 10 at Col. 30 lines 1-4).
Claim 11 Chen et al.
11. The apparatus according to claim 10,
wherein when a value of T(m,n,i,j,k,t) is within a preset value range, a gradient of F(i,j,k,t) is determined based on the value of T(m,n,i,j,k,t), and a gradient of X(m+i,n+j,k) is determined based on the value of T(m,n,i,j,k,t), wherein:
T(m,n,i,j,k,t)=X(m+i,n+j,k)+F(i,j,k,t),
F(i,j,k,t) represents an element in the ith row and the jth column on the kth page in the feature extraction kernel, X(m+i,n+j,k) represents an element determined based on the ith row and the jth column on the kth page in the at least one of the input sub-feature maps, and i, j, k, m, n, and t are integers.
11. The apparatus according to claim 10,
wherein when a value of T(m,n,i,j,k,t) falls within a preset value range, a gradient of F (i,j,k,t) is determined based on the value of T(m,n,i,j,k,t), and a gradient of X(m+i,n+j,k) is determined based on the value of T(m,n,i,j,k,t), wherein:
T(m,n,i,j,k,t)=X(m+i,n+j,k)±F(i,j,k,t),
F(i,j,k,t) is an element in the ith row and the jth column on the kth page in the feature extraction kernel, X(m+i, n+j,k) is an element determined based on the ith row and the jth column on the kth page in the at least one of the input sub-feature maps, and i, j, k, m, n, and t are all integers (i.e., claim 11 at Col. 30 lines 5-16).
As to claim 12, Chen et al. claims the apparatus according to claim 11,
wherein the gradient of F(i,j,k,t) is obtained according to the following equation:
∂
Y
(
m
,
n
,
t
)
∂
F
(
i
,
j
,
k
,
t
)
=
-
H
a
r
d
t
a
n
h
(
F
i
,
j
,
k
,
t
±
X
m
+
i
,
n
+
j
,
k
)
; and
the gradient of X(m+i,n+j,k) is obtained according to the following equation:
∂
Y
(
m
,
n
,
t
)
∂
X
(
m
+
i
,
n
+
j
,
k
)
=
-
H
a
r
d
tanh
(
X
(
m
+
i
,
n
+
j
,
k
)
±
(
F
i
,
j
,
k
,
y
)
,
wherein:
H
a
d
r
tanh
x
=
1
x
>
1
-
1
x
<
-
1
x
-
1
≤
x
≤
1
,
∂
Y
(
m
,
n
,
t
)
∂
F
(
i
,
j
,
k
,
t
)
represents the gradient of F(i,j,k,t), and
∂
Y
(
m
,
n
,
t
)
∂
X
(
m
+
i
,
n
+
j
,
k
)
represents the gradient of X(m+i,n+j,k) (i.e., claim 12 at Col. 30 lines 16-40).
Claim 13 Chen et al.
13. A non-transitory computer-readable storage medium, wherein the computer-readable storage medium stores program code to be executed by a device, and the program code comprises instructions used to
obtain an input feature map of an image,
wherein the input feature map comprises a plurality of input sub-feature maps;
perform feature extraction processing on the input feature map based on a feature extraction kernel of a neural network to obtain an output feature map,
wherein the output feature map comprises a plurality of output sub-feature maps,
wherein each of the plurality of output sub-feature maps is determined based on the corresponding input sub-feature map and the feature extraction kernel,
wherein at least one of the output sub-feature maps is determined based on a target matrix obtained after an absolute value is taken, and
wherein the target matrix is obtained by performing matrix addition or matrix subtraction on the input sub-feature map corresponding to the target matrix and the feature extraction kernel; and
classify the image based on the output feature map to obtain a classification result of the image.
13. A non-transitory computer-readable storage medium, wherein the computer-readable storage medium stores program code to be executed by a device, and the program code comprises instructions used to
obtain an input feature map of a to-be-processed image,
wherein the input feature map comprises a plurality of input sub-feature maps;
perform feature extraction processing on the input feature map based on a feature extraction kernel of a neural network to obtain an output feature map,
wherein the output feature map comprises a plurality of output sub-feature maps,
wherein each of the plurality of output sub-feature maps is determined based on the corresponding input sub-feature map and the feature extraction kernel,
wherein at least one of the output sub-feature maps is determined based on a target matrix obtained after an absolute value is taken … (See for example, claim 13 at Col. 30 lines 41-59)
wherein the target matrix is obtained by performing matrix addition or matrix subtraction on at least one of the input sub-feature maps and the feature extraction kernel (See for example, claim 14 at Col. 30 lines 63-67)
… classify the to-be-processed image based on the output feature map to obtain a classification result of the to-be-processed image (See for example, claim 13 at Col. 30 lines 60-62).
Claim 14 Chen et al.
14. The non-transitory computer-readable storage medium of claim 13,
wherein the target matrix is obtained by performing matrix addition or matrix subtraction on at least one of the input sub-feature maps and the feature extraction kernel.
14. The non-transitory computer-readable storage medium of claim 13,
wherein the target matrix is obtained by performing matrix addition or matrix subtraction on at least one of the input sub-feature maps and the feature extraction kernel (i.e., claim 14 at Col. 30 lines 63-67).
As to claim 15, Chen et al. claims the non-transitory computer-readable storage medium of claim 14,
wherein the at least one of the output sub-feature maps is obtained according to the following equation:
Y
m
,
n
,
t
=
∑
i
=
0
d
-
1
∑
j
=
0
d
-
1
∑
k
=
1
C
-
X
m
+
i
,
n
+
j
,
k
-
F
i
,
j
,
k
,
t
;
o
r
Y
m
,
n
,
t
=
∑
i
=
0
d
-
1
∑
j
=
0
d
-
1
∑
k
=
1
C
-
X
m
+
i
,
n
+
j
,
k
+
F
i
,
j
,
k
,
t
,
wherein
|(
∙
)| represents an operation for taking an absolute value,
∑
(
∙
)
represents a summation operation, Y(m,n,t) represents the at least one of the output sub-feature maps, Y(m,n,t) represents an element in an mth row and an nth column on a tth page in the output feature map, X(m+i,n+j,k) represents an element determined based on an ith row and a jth column on a kth page in the at least one of the input sub-feature maps, F(i,j,k,t) represents an element in an ith row and a jth column on a kth page in the feature extraction kernel, t represents a channel quantity of the feature extraction kernel, d represents a row quantity of the feature extraction kernel, C represents a channel quantity of the input feature map, and d, C, i, j, k, m, n, and t are integers (i.e., claim 15 at Col. 31 lines 1-27).
Claim 16 Chen et al.
16. The non-transitory computer-readable storage medium of claim 13,
wherein a gradient of the feature extraction kernel is determined based on the target matrix, and a gradient of the input sub-feature map is determined based on the target matrix.
16. The non-transitory computer-readable storage medium of claim 13,
wherein a gradient of the feature extraction kernel is determined based on the target matrix, and a gradient of the input sub-feature map is determined based on the target matrix (i.e., claim 16 at Col. 31 lines 28-33).
Allowable Subject Matter
Claims 1-16 would be allowable upon the filing of a Terminal Disclaimer in order to obviate the non-statutory double patenting rejection as set forth above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSE M TORRES whose telephone number is (571)270-1356. The examiner can normally be reached Monday thru Friday; 10:00 AM to 6:00 PM EST.
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/JOSE M TORRES/Examiner, Art Unit 2664 09/21/2026
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