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 .
Election/Restrictions
Applicant’s election without traverse of Group I, Claims 1-17, in the reply filed on 07/16/2026 is acknowledged. Claims 18-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim.
Status of the Claims
Claims 1-17 are pending and under consideration in this action. Claims 18-20 are withdrawn from consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention.
Priority
The instant application is 371 of PCT/EP2021/061040, filed 04/28/2021, which claims priority to U.S. Provisional Application number 63/018,183, filed 04/30/2020, as reflected in the filing receipt mailed 06/14/2023. The claim for domestic benefit for claims 1-17 is acknowledged. As such, the effective filing date of claims 1-17 is 04/30/2020.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 10/28/2022 and 06/10/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS’s have been considered by the examiner.
It is noted that certain references have not been considered and are lined-through, as they do not comply with the requirements set forth in 37 CFR 1.97. Legible copies of NPL #1-3 from the IDS dated 10/28/2022 were not provided with the submission of the IDS, as all three NPL articles appear to be grayed out and unreadable.
It is further noted that certain references lack appropriate volume numbers (NPL #2 in the IDS dated 06/10/2026). The Examiner has annotated those references herein. Applicant is kindly reminded to provide proper citations in compliance with 37 CFR 1.97 in all future submissions to the office.
Claim Objections
Claim 16 is objected to because of the following informalities:
Claim 16 recites “(a) grouping partitions in the array that are all pairwise connected to one another by a continguous path, wherein the grouping is a cluster” which should be corrected to “(a) grouping partitions in the array that are all pairwise connected to one another by a contiguous path, wherein the grouping is a cluster” to include the appropriate spelling.
Appropriate correction is required.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 5-6, 7, and 10 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The following claims recite variables that are not appropriately defined by the Specification:
Claim 5: i, ch, T, signalChannelch[i], and maxChannelch
Claim 6: d, and
σ
Claim 7: z’, and
i
s
V
a
l
i
d
P
a
r
t
i
t
i
o
n
z
'
Claim 10:
z
y
, and
z
x
For these claim elements, the Specification does not appear to provide an appropriate definition of the variables. The Specification (see Pg. 6, Table 1 and Pg. 10-11, Table 2) provides a summary of the data input in the method and the data output from the method. However, Tables 1 and 2 do not appear to provide the definition for i, ch, T, signalChannelch[i], maxChannelch, d,
σ
, z’,
i
s
V
a
l
i
d
P
a
r
t
i
t
i
o
n
z
'
,
z
y
, and
z
x
as disclosed in claims 5-6, 7, and 10. The Specification (see Pg. 7, Line 4 – Pg. 10, Line 12) further discloses formulas that use each of the variables but also does not appear to provide definitions for i, ch, T, signalChannelch[i], maxChannelch, d,
σ
, z’,
i
s
V
a
l
i
d
P
a
r
t
i
t
i
o
n
z
'
,
z
y
, or
z
x
. Accordingly, the disclosure is not commensurate with the written description scope of the claim.
Claim Rejections - 35 USC § 112(b)
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 5-8, 10-12, and 15 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.
Claim 5 recites the phrase “by a useChannel flag,
s
i
g
n
a
l
S
u
m
i
=
∑
u
s
e
C
h
a
n
n
e
l
c
h
=
=
T
s
i
g
n
a
l
C
h
a
n
n
e
l
c
h
i
/
m
a
x
C
h
a
n
n
e
l
c
h
”. The metes and bounds of the claim are rendered indefinite due to the lack of clarity. The variables i, ch, signalSum[i], useChannel[ch], T, signalChannelch[i], and maxChannelch are lacking the appropriate definition in the claim, and it is therefore unclear what the variables correspond to. The Specification (see Pg. 6, Table 1; Pg. 7, Lines 4-12; and Pg. 10-11, Table 2) provides an explanation of the formula, its use, and the definitions for signalSum, and useChannel, but does not appear to define i, ch, T, signalChannelch and maxChannelch. Examiner further notes that maxChannel is defined in Table 1, but not maxChannelch. Similarly, signalChannel is defined on Pg. 7, Line 10, but not signalChannelch. This rejection can be overcome by amendment of claim 5 to include the specified variable definitions.
Claim 6 recites the limitation “wherein step (a) further comprises applying a kernel function of a distance function comprising:
D
i
s
t
x
1
,
y
1
,
x
2
,
y
2
=
(
x
1
-
x
2
)
2
+
(
y
1
-
y
2
)
2
;
ker
d
=
2
e
-
1
*
σ
*
d
d
=
0
o
t
h
e
r
w
i
s
e
”. The metes and bounds of the claim are rendered indefinite due to the lack of clarity. It is unclear if
D
i
s
t
x
1
,
y
1
,
x
2
,
y
2
corresponds to d recited in the second equation. If it does not correspond, it is unclear what the variable d corresponds to. Additionally, the variable
σ
is not defined by the claim, and the Specification (see Pg. 6, Table 1 and Pg. 7, Lines 13-17) does not appear to provide a definition. This rejection can be overcome by amendment of claim 6 to clarify the variable d and include the appropriate variable definitions. Claims 7-8 are also rejected due to their dependency on claim 6.
Claim 7 recites the limitation “wherein z represents a set of (x,y) coordinates of a first partition and the convolution of z is:
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o
n
v
z
=
∑
i
s
V
a
l
i
d
P
a
r
t
i
t
i
o
n
z
'
∧
D
i
s
t
(
z
,
z
'
)
≤
r
a
d
i
u
s
s
i
g
n
a
l
S
u
m
[
z
'
]
*
k
e
r
(
D
i
s
t
z
,
z
'
)
∑
i
s
V
a
l
i
d
P
a
r
t
i
t
i
o
n
z
'
∧
D
i
s
t
(
z
,
z
'
)
≤
r
a
d
i
u
s
k
e
r
(
D
i
s
t
z
,
z
'
)
”. The metes and bounds of the claim are rendered indefinite due to the lack of clarity. The convolution function recites several variables lacking the appropriate definitions: z’,
i
s
V
a
l
i
d
P
a
r
t
i
t
i
o
n
z
'
, and
s
i
g
n
a
l
S
u
m
[
z
'
]
(as
s
i
g
n
a
l
S
u
m
is defined in claim 5, which is not in the chain of dependency for claim 7). Additionally, it is unclear what the radius refers to, as there is no prior mention of a radius in claim 6, to which this claim depends. The Specification (see Pg. 6, Table 1) recites that the radius is the radius to be used during the convolution, but it is unclear what radius is being measured, e.g., of channels or neighboring valid/void partitions, etc. Additionally, the Specification (see Pg. 6, Table 1; Pg. 7, Lines 18-24; and Pg. 10-11, Table 2) recites the equation but does not appear to define z’, and
i
s
V
a
l
i
d
P
a
r
t
i
t
i
o
n
z
'
. This rejection can be overcome by amendment of claim 7 to include the appropriate variable definitions. Claim 8 is also rejected due to its dependency on claim 7.
Claim 8 recites the limitation “wherein if
i
s
V
a
l
i
d
P
a
r
t
i
t
i
o
n
z
'
∧
D
i
s
t
z
,
z
'
≤
r
a
d
i
u
s
is an empty set, then an output for the empty set is set to a default value outside of the range of the convolution”. There is insufficient antecedent basis for the range of convolution in the claim, since there is no prior mention of this phrase in claim 7, to which this claim depends. This rejection can be overcome by amendment of claim 8 to recite wherein if
i
s
V
a
l
i
d
P
a
r
t
i
t
i
o
n
z
'
∧
D
i
s
t
z
,
z
'
≤
r
a
d
i
u
s
is an empty set, then an output for the empty set is set to a default value outside of a range of the convolution”.
Claim 10 recites the phrase “(a) the vertical reference region, i, is represented by
r
e
f
i
=
z
i
5
×
m
a
x
y
≤
z
y
<
i
+
1
5
×
m
a
x
y
r
e
f
i
=
z
i
5
×
m
a
x
y
≤
z
y
<
i
+
1
5
×
m
a
x
y
; (b) the horizontal reference region, j, is represented by
r
e
f
j
=
z
11
≤
z
y
<
0.5
×
m
a
x
y
∧
j
3
×
m
a
x
x
+
m
a
x
x
6
≤
z
x
≤
j
3
×
m
a
x
x
+
m
a
x
x
2
r
e
f
j
=
z
11
≤
z
y
<
0.5
×
m
a
x
y
∧
j
3
×
m
a
x
x
+
m
a
x
x
6
≤
z
x
≤
j
3
×
m
a
x
x
+
m
a
x
x
2
” in (a) and (b) of the claim. The metes and bounds of the claim are rendered indefinite due to the lack of clarity. It appears that the formula for
r
e
f
i
in (a) and
r
e
f
j
in (b) are each recited twice, as the two formulas in (a) (and likewise the two formulas in (b)) do not appear to be different from each other. Additionally, the variables z, maxy,
z
y
, maxx, and
z
x
are not defined by the claim. The Specification (see Pg. 8, Lines 12-13) discloses that maxx and maxy are the max x and y coordinates of the partitions. However, the Specification (see Pg. 8, Lines 10-20) does not appear to provide definitions for
z
x
and
z
y
. Examiner notes that z is defined in claim 7, however claim 7 is not in the chain of dependency for claim 10. This rejection can be overcome by amendment of claim 10 to clarify duplicate formulas and include appropriate variable definitions. Claims 11-12 are also rejected due to their dependency on claim 10.
Claim 11 recites the phrase “calculating a median absolute deviation (MAD), expressed as
(
m
e
d
i
a
n
m
e
a
n
r
e
f
-
r
e
f
z
)
(
m
e
d
i
a
n
m
e
a
n
r
e
f
-
r
e
f
z
)
and excluding a default convolution value to yield a standard threshold:
v
o
i
d
T
h
r
e
s
=
m
e
a
n
r
e
f
-
d
i
f
f
O
f
f
×
M
A
D
r
e
f
v
o
i
d
T
h
r
e
s
=
m
e
a
n
r
e
f
-
d
i
f
f
O
f
f
×
M
A
D
(
r
e
f
)
”. The metes and bounds of the claim are rendered indefinite due to the lack of clarity. Both the median formula and the
v
o
i
d
T
h
r
e
s
h
formula appear include duplicates, and it is unclear how the duplicate formulas are different from one another. Additionally, it is unclear if ref is referring to
r
e
f
i
or
r
e
f
j
recited in claim 10. Additionally, while the definition of
d
i
f
f
O
f
f
is defined by the Specification (see Pg. 6, Table 1), this definition is not included in the claim. This rejection can be overcome by amendment of claim 11 to clarify duplicate formulas and include appropriate variable definitions. Claim 12 is also rejected due to its dependency on claim 11.
Claim 12 recites the phrase “wherein the method further comprises using an alternative threshold if
m
e
a
n
r
e
f
>
h
i
g
h
C
o
v
o
l
u
t
i
o
n
T
h
r
e
s
h
o
l
d
a
n
d
M
A
D
r
e
f
<
l
o
w
V
a
r
i
a
n
c
e
T
h
r
e
s
h
o
l
d
, wherein the alternative threshold is:
v
o
i
d
T
h
r
e
s
h
=
m
e
a
n
r
e
f
*
t
h
r
e
s
h
o
l
d
A
d
j
u
s
t
m
e
n
t
F
r
a
c
v
o
i
d
T
h
r
e
s
h
=
m
e
a
n
r
e
f
*
t
h
r
e
s
h
o
l
d
A
d
j
u
s
t
m
e
n
t
F
r
a
c
, wherein
t
h
r
e
s
h
o
l
d
A
d
j
u
s
t
m
e
n
t
F
r
a
c
is a fraction of the mean used as the alternative threshold”. The metes and bounds of the claim are rendered indefinite due to the lack of clarity. Analogous to claim 11, it is unclear if ref is referring to
r
e
f
i
or
r
e
f
j
recited in claim 10. Additionally, it appears the
v
o
i
d
T
h
r
e
s
h
is duplicated as it is unclear what the difference is between the two formulas. This rejection can be overcome by amendment of claim 12 to clarify variable definitions and duplicate formulas.
Claim 15 recites the phrase “wherein for a valid partition with coordinate z, the valid partition is designated as a void partition if there exists a void partition z’ with
z
x
-
z
'
x
+
z
y
-
z
'
y
≤
c
l
e
a
n
u
p
R
a
d
i
u
s
z
x
-
z
'
x
+
z
y
-
z
'
y
≤
c
l
e
a
n
u
p
R
a
d
i
u
s
”. The metes and bounds of the claim are rendered indefinite due to the lack of clarity. The claim appears to include a duplicate formula, and it is unclear what the difference is between the two formulas. Additionally, it is unclear what the
c
l
e
a
n
u
p
R
a
d
i
u
s
refers to as an appropriate definition is not provided in the claim. The Specification (see Pg. 6, Table 1) recites that the
c
l
e
a
n
u
p
R
a
d
i
u
s
is the radius used in the dilation cleanup step. Analogous to claim 7 above, it is unclear what radius is being measured during dilation, e.g., of channels or neighboring valid/void partitions, etc. This rejection can be overcome by amendment of claim 15 to clarify duplicate formulas and variable definitions.
Applicant is kindly reminded that any amendment must find adequate support in the Specification as originally filed.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite both (1) mathematical concepts (mathematical relationships, formulas or equations, or mathematical calculations) and (2) mental processes, i.e., concepts performed in the human mind (including observations, evaluations, judgements or opinions) (see MPEP § 2106.04(a)).
Framework with which to evaluate Subject Matter Eligibility as outlined in MPEP § 2106:
Step 1: Are the claims directed to a process, machine, manufacture or composition of matter;
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea;
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework as it pertains to the instant claims:
Step 1:
In the instant application, claims 1-17 are directed towards a method, which falls into one of the categories of statutory subject matter (Step 1: YES).
Step 2A, Prong One:
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong One). The following instant claims recite limitations that equate to one or more categories of judicial exceptions:
Claim 1 recites a mathematical concept (i.e., convolution using a kernel function) in step (a) “combining optical signals across (x, y) coordinates within the array using a convolution with a kernel function, wherein each partition is assigned a convolution value”; a mental process (i.e., an evaluation in comparing convolution value to a threshold) in step (b) “identifying valid partitions and void partitions by comparing the convolution value of each partition to a threshold convolution value”; and a mental process (i.e., an evaluation of data by clustering or image processing) in step (c) “subjecting data collected in step (b) to one or more additional steps comprising: clustering and morphological image processing operations”.
Claim 2 recites a mental process (i.e., an evaluation of the image for processing operations) in “subjecting data collected in step (b) to morphological image processing operations including dilation, erosion, and combinations thereof”.
Claim 3 recites a mental process (i.e., an evaluation of partitions for trimming) in “subjecting data collected in step (b) to clustering comprising valid and/or void trimming”.
Claim 4 recites a mental process (i.e., an evaluation of values below/above threshold values) in “wherein a void partition has a convolution value below the threshold convolution value and a valid partition has a convolution value above the threshold convolution value”.
Claim 5 recites a mental process (i.e., an evaluation of the array) in “wherein the array comprises a plurality of channels” and a mathematical concept in “wherein and step (a) further comprises determining which channel(s) of the plurality of channels to use in the method by a useChannel flag
s
i
g
n
a
l
S
u
m
i
=
∑
u
s
e
C
h
a
n
n
e
l
c
h
=
=
T
s
i
g
n
a
l
C
h
a
n
n
e
l
c
h
i
/
m
a
x
C
h
a
n
n
e
l
c
h
”.
Claim 6 recites a mathematical concept in “wherein step (a) further comprises applying a kernel function of a distance function comprising:
D
i
s
t
x
1
,
y
1
,
x
2
,
y
2
=
(
x
1
-
x
2
)
2
+
(
y
1
-
y
2
)
2
;
ker
d
=
2
e
-
1
*
σ
*
d
d
=
0
o
t
h
e
r
w
i
s
e
”.
Claim 7 recites a mathematical concept in “wherein z represents a set of (x,y) coordinates of a first partition and the convolution of z is:
C
o
n
v
z
=
∑
i
s
V
a
l
i
d
P
a
r
t
i
t
i
o
n
z
'
∧
D
i
s
t
(
z
,
z
'
)
≤
r
a
d
i
u
s
s
i
g
n
a
l
S
u
m
[
z
'
]
*
k
e
r
(
D
i
s
t
z
,
z
'
)
∑
i
s
V
a
l
i
d
P
a
r
t
i
t
i
o
n
z
'
∧
D
i
s
t
(
z
,
z
'
)
≤
r
a
d
i
u
s
k
e
r
(
D
i
s
t
z
,
z
'
)
”.
Claim 8 recites a mathematical concept in “wherein if
i
s
V
a
l
i
d
P
a
r
t
i
t
i
o
n
z
'
∧
D
i
s
t
z
,
z
'
≤
r
a
d
i
u
s
is an empty set, then an output for the empty set is set to a default value outside of the range of the convolution”.
Claim 9 recites a mental process (i.e., an evaluation of convolution values to determine a threshold) in “wherein the convolution threshold is based on a set of convolution values within a selected reference region of the array”.
Claim 10 recites a mathematical concept in “wherein the selected reference region is selected from a vertical reference region, i, a horizonal reference region, j, and combinations thereof, wherein max x and max y are the maximum x and y coordinates of partitions in the vertical and/or horizonal reference regions(s), (a) the vertical reference region, i, is represented by
r
e
f
i
=
z
i
5
×
m
a
x
y
≤
z
y
<
i
+
1
5
×
m
a
x
y
; (b) the horizontal reference region, j, is represented by
r
e
f
j
=
z
11
≤
z
y
<
0.5
×
m
a
x
y
∧
j
3
×
m
a
x
x
+
m
a
x
x
6
≤
z
x
≤
j
3
×
m
a
x
x
+
m
a
x
x
2
”; a mathematical concept in “and for each vertical and/or horizonal reference region, the method further comprises calculating a mean of a valid convolution value of the vertical and/or horizonal reference region”; and a mental process (i.e., an evaluation of values) in “identifying as the selected reference region the vertical and/or horizonal reference region having a second highest mean convolutional value”.
Claim 11 recites a mathematical concept in “calculating a median absolute deviation (MAD), expressed as
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Claim 12 recites a mathematical concept in “wherein the method further comprises using an alternative threshold if
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is a fraction of the mean used as the alternative threshold”.
Claim 13 recites a mental process (i.e., an evaluation of connected arrays and clusters less than a threshold) in “wherein clustering comprises path connectedness including (a) grouping partitions in the array that are all pairwise connected to one another by a contiguous path, wherein the grouping is a cluster, (b) identifying one or more clusters having a size less than a void noise threshold value, and ( c) designating a cluster identified in step (b) as valid”.
Claim 14 recites a mental process (i.e., an evaluation the image to remove boundary voids) in “further comprising dilation to remove boundary voids”.
Claim 15 recites a mathematical concept in “wherein for a valid partition with coordinate z, the valid partition is designated as a void partition if there exists a void partition z’ with
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Claim 16 recites a mental process (i.e., an evaluation of connected arrays and clusters less than a threshold) in “wherein clustering comprises path connectedness including (a) grouping partitions in the array that are all pairwise connected to one another by a contiguous path, wherein the grouping is a cluster, (b) identifying one or more clusters having a size less than a valid noise threshold value, and (c) designating a cluster identified in step (b) as void”.
Claim 17 recites a mental process (i.e., an evaluation of partitions for flagging) in “further comprising flagging partitions identified as void”.
These recitations are similar to the concepts of collecting information and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)), and organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships.
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification, and are determined to be directed to mental processes that in the simplest embodiments are not too complex to practically perform in the human mind. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Specifically, claim 1 involves nothing more than combining optical signals using a convolution with a kernel function, identifying valid and void partitions, and optionally clustering and processing the image. The step reciting combining optical signals using a convolution with a kernel function is, under the BRI, performed using mathematical operations. The instant Specification (see Pg. 7, Lines 13-20) discloses the formulas for the custom exponential kernel used. Additionally, since there are no specifics in the methodology, the steps reciting identifying valid and void partitions, and optionally clustering and processing the image are something that, under the BRI, one could perform mentally. Therefore, the claimed steps are not further defined beyond something that reads on performing a calculation, and merely looking at data and making a determination. As such, said steps are directed to judicial exceptions. The instant claims must therefore be examined further to determine whether they integrate the abstract idea into a practical application (Step 2A, Prong One: YES).
Step 2A, Prong Two:
In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP § 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP § 2106.04(d)(I)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP § 2106.04(d)(III)). The following independent claims recite limitations that equate to additional elements:
Claim 1 recites “wherein the dPCR assay comprises quantifying an amount or concentration of nucleic acid of interest in an array of partitions”.
Regarding the above cited limitation in claim 1 of (i) wherein the dPCR assay comprises quantifying an amount or concentration of nucleic acid of interest in an array of partitions. This limitation equates to insignificant, extra-solution activity of mere data gathering because these limitations gather data before or after the recited judicial exceptions of combining optical signals using a convolution with a kernel function, identifying valid and void partitions, and optionally clustering and processing the image (see MPEP § 2106.04(d)). As such, claims 1-17 are directed to an abstract idea (Step 2A, Prong Two: NO).
Step 2B:
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The instant independent claims recite the same additional elements described in Step 2A, Prong Two above.
Regarding the above cited limitation in claim 1 of (i) wherein the dPCR assay comprises quantifying an amount or concentration of nucleic acid of interest in an array of partitions. The courts have recognized that these limitations equate to laboratory techniques that are well-understood, routine, and conventional activity in the life science arts when they are claimed in a merely generic manner (e.g., at a high level of generality) (see MPEP 2106.05(d)). Detecting DNA or enzymes in a sample is a WURC limitation in Sequenom, 788 F.3d at 1377-78, 115 USPQ2d at 1157, and Cleveland Clinic Foundation 859 F.3d at 1362, 123 USPQ2d at 1088 (Fed. Cir. 2017). Analyzing DNA to provide sequence information or detect allelic variants is a WURC limitation in Genetic Techs. Ltd., 818 F.3d at 1377; 118 USPQ2d at 1546. Amplifying and sequencing nucleic acid sequences is a WURC limitation in University of Utah Research Foundation v. Ambry Genetics, 774 F.3d 755, 764, 113 USPQ2d 1241, 1247 (Fed. Cir. 2014).
These additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the instant claims do not amount to significantly more than the judicial exception itself (Step 2B: NO). As such, claims 1-17 are not patent eligible.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
1. Claims 1, 4, 9, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Betschart et al. (U.S. Patent Application Publication US 2018/0230515 A1; published 08/16/2018; cited in the IDS dated 10/28/2022) in view of Hu et al. (A novel method based on a Mask R-CNN model for processing dPCR images. Anal. Methods 11(27): 3410-3418 (2019); published 06/13/2019).
Regarding claim 1, Betschart et al. teaches a method for reducing quantification errors caused by an optical artefact in digital polymerase chain reaction (dPCR) and to a method for determining the amount or concentration of a nucleic acid in a sample with dPCR (i.e., a method for detecting reaction volume deviations in a digital polymerase chain reaction (dPCR) assay, wherein the dPCR assay comprises quantifying an amount or concentration of nucleic acid of interest in an array of partitions) (Abstract). Betschart et al. further teaches that the method includes a step for determining the distribution of optical signals in each reaction area (Para. [0009]-[0011]). For this, optical signals from various sub-areas of each reaction area are detected and determined. Specifically, the reaction area is subdivided into sub-areas for analysis and an optical signal is obtained for each sub-area, thereby the distribution of optical signals in the reaction area is determined. Subdivision of the reaction area into sub-areas may be done by gridding or rasterizing, wherein the reaction area is subdivided into a generally rectangular grid of pixels, or points of color (i.e., subdivision into (x,y) coordinates). The image is a dot matrix data structure. In the present disclosure, a value characterizing the optical signal for each sub-area is detected, determined and registered for further analysis (i.e., combining optical signals across (x,y) coordinates within the array) (Para. [0043]). Betschart et al. further teaches that as a third step, a reaction area is identified as invalid, if the optical signals in the reaction area determined in the previous step as unequally distributed in the reaction area. It is expected that the dPCR composition, which is usually a liquid, in a reaction area is equally distributed. Accordingly, it can be expected that the optical signals in the sub-areas of the reaction area are essentially identical. An unequal distribution of the signals hints at an artefact. The distribution of the signals is unequal, if a single value differs significantly from any other value or the other values or if the difference between the value and any other value or the other values of the reaction area is above a threshold (i.e., identifying valid and void partitions by comparing to a threshold) (Para. [0046]-[0048]). Betschart et al. further teaches that in fluorescent mode, the value for the optical signal of the sub-areas is determined with a suitable image processing filter operation as for example the 2d-convolution of the image with a suitable filter core. This method is very efficient for the detection of a large number of e.g. pixel-wise relatively small objects. The result of the image processing filter operation is an image in which the center pixels of the partitions have peak values, these are detected then (i.e., subjecting data collected in step (b) to one or more additional steps comprising morphological image processing operations) (Para. [0045] and Fig. 1).
Regarding claim 17, Betschart et al. teaches that the method includes a step of identifying an invalid reaction area if the distribution of optical signals in a reaction area determined in step c) is unequal in the reaction area (i.e., flagging partitions identified as void) (Para. [0018]).
Betschart et al. does not teach combining optical signals using a convolution with a kernel function, wherein each partition is assigned a convolution value (claim 1); identifying valid partitions and void partitions by comparing the convolution value of each partition to a threshold convolution value (claim 1); wherein a void partition has a convolution value below the threshold convolution value and a valid partition has a convolution value above the threshold convolution value (claim 4); and wherein the convolution threshold is based on a set of convolution values within a selected reference region of the array (claim 9).
Regarding claim 1, Hu et al. teaches a deep learning method based on the Mask R-CNN model used for image processing to achieve more accurate quantification of nucleic acids in both microarray and droplet dPCR (Abstract). Hu et al. further teaches that Mask R-CNN is a convolutional neural network which is improved on the basis of Faster R-CNN. The structure of Mask R-CNN can be described as follows: backbone architecture, region proposal networks (RPN), RoiAlign and classifier. ResNet101 consists of 5 parts, including conv1, conv2_x, conv3_x, conv4_x and conv5_x. Taking an input image of 224x224 pixels as an example, it is first extracted into a 64-channel feature map through a convolution kernel with a sliding pane of 2 and a size of 7x7, and computed by 33 building blocks. Each block is 3 layers' convolution, among which the 1x1 convolution kernel plays the role of dimension increase and dimension reduction, thus greatly reducing the introduction of calculation parameters (i.e., combining optical signals using a convolution with a kernel function, wherein each partition is assigned a convolution value) (Pg. 3412, Col.1, Para. 2-4; and Pg. 3412, Fig. 1). Hu et al. further teaches an example where the Mask R-CNN model detects bright spots when processing impure images using parameters determined in the convolutional layers. The positive points class (i.e., valid partitions) is one of the two classes identified by the model by comparing to model thresholds (i.e., identifying valid partitions and void partitions by comparing the convolution value of each partition to a threshold convolution value) (Pg. 3414, Fig. 2; Pg. 3415, Col. 1, Para. 2 – Col. 2, Para. 1; Pg. 3412, Col. 1, Para. 4; and Pg. 3412, Col. 2, Para. 2).
Regarding claim 4, Hu et al. teaches an example using the Mask R-CNN model for impure images. The impure image in from the experiment is shown in Fig. 3A. The detection results of the Mask R-CNN model are shown in Fig. 3D, where validated spots are highlighted in color in the image (Pg. 3415, Fig. 3). The Mask R-CNN model shows excellent robustness in detection and does not label abnormal bright spots (i.e., valid partitions are labeled by the model if they are above the threshold; wherein a void partition has a convolution value below the threshold convolution value and a valid partition has a convolution value above the threshold convolution value) (Pg. 3416, Col. 1, Para. 1 – Col. 2, Para. 1).
Regarding claim 9, Hu et al. teaches that the model structure of Mask R-CNN can be described as follows: backbone architecture, region proposal networks (RPN), RoiAlign and classifier. RPN relies on a sliding window on the feature map to generate a pre-set target frame for each location with a good aspect ratio and an area named anchor (i.e., a selected reference region). After generating the anchor, RPN first judges whether the anchor covers the target, and then corrects the coordinates of the anchor belonging to the foreground. RoiAlign is used to pool the corresponding area into a fixed-size feature map according to the coordinates of the preselected box in the feature map for subsequent classification and bounding box regression operations (i.e., wherein the convolutional threshold is based on a set of convolutional values within a selected reference region of the array) (Pg. 3412, Col. 1, Para. 5 – Col. 2, Para. 4).
Therefore, regarding claims 1, 4, 9, and 17, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of reducing quantification errors caused by optical artifacts in dPCR of Betschart et al. with the method of using convolution with a kernel function of Hu et al. because the method of Hu et al. has higher precision and stronger robustness than the traditional threshold segmentation method in dealing with non-uniform light images and impure images (Hu et al., Pg. 3417, Col. 1, Para. 2). One of ordinary skill in the art would be able to combine the teachings of Betschart et al. with Hu et al. with reasonable expectation of success due to the same nature of the problem to be solved, since both are drawn towards a method for analyzing and quantifying dPCR images. Therefore, regarding claims 1, 4, 9, and 17, the instant invention is prima facie obvious (MPEP § 2142).
2. Claims 2-3 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Betschart et al. in view of Hu et al. as applied to claims 1, 4, 9, and 17 above, and further in view of Huang et al. (Development of an imaging method for quantifying a large digital PCR droplet. Proc. SPIE 10072, Optical Diagnostics and Sensing XVII: Toward Point-of-Care Diagnostics, Vol.100720I (6 pages) (2017); published 02/17/2017).
Betschart et al. in view of Hu et al., as applied to claims 1, 4, 9, and 17 above, does not teach subjecting data collected in step (b) to morphological image processing operations including dilation, erosion, and combinations thereof (claim 2); subjecting data collected in step (b) to clustering comprising valid and/or void trimming (claim 3); and further comprising dilation to remove boundary voids (claim 14).
Regarding claim 2, Huang et al. teaches the development of a computational algorithm to process optical images taken from a digital PCR biochip (Abstract). Huang et al. further teaches the image processing in Fig. 5, which includes morphological operation: erosion & dilation (i.e., subjecting data collected in step (b) to morphological image processing operations include dilation, erosion and combinations thereof) (Pg. 5, Fig. 5).
Regarding claim 3, Huang et al. teaches that during image processing, as shown in Fig. 5, Otsu thresholding and size-filtering are used to remove data (i.e., voids) from the arrays that do not meet the threshold (i.e., subjecting data collected in step (b) to clustering comprising valid and/or void trimming) (Pg. 5, Fig. 5; and Pg. 2, Para. 2-6).
Regarding claim 14, Huang et al. teaches that during image processing, as shown in Fig. 5, morphological operations including dilation are used to remove intensities (i.e., voids) at the boundary between Fig. 5c and 5d (i.e., further comprising dilation to remove boundary voids) (Pg. 5, Fig. 5).
Therefore, regarding claims 2-3 and 14, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of reducing quantification errors caused by optical artifacts in dPCR of Betschart et al. in view of Hu et al. with the image processing operations of Huang et al. because the method of Huang et al. can automatically remove uneven illumination and defects in a step by step process (Huang et al., Pg. 4, Para. 2). One of ordinary skill in the art would be able to combine the teachings of Betschart et al. in view of Hu et al. with Huang et al. with reasonable expectation of success due to the same nature of the problem to be solved, since both incorporate a method for processing dPCR images. Therefore, regarding claims 2-3 and 14, the instant invention is prima facie obvious (MPEP § 2142).
Conclusion
No claims allowed.
Claims 5-8, 10-13, and 15-16 appear to be free from the prior art because the prior art does not fairly suggest or teach the following claim limitations:
Claim 5: “determining which channel(s) of the plurality of channels to use in the method by a useChannel flag:
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Claim 6: “wherein step (a) further comprises applying a kernel function of a distance function comprising
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Claim 10: “wherein the selected reference region is selected from a vertical reference region, i, a horizonal reference region, j, and combinations thereof, wherein max x and max y are the maximum x and y coordinates of partitions in the vertical and/or horizonal reference regions(s), (a) the vertical reference region, i, is represented by
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, and for each vertical and/or horizonal reference region, the method further comprises calculating a mean of a valid convolution value of the vertical and/or horizonal reference region, ad identifying as the selected reference region the vertical and/or horizonal reference region having a second highest mean convolutional value”. The closest prior art is Hu et al. (A novel method based on a Mask R-CNN model for processing dPCR images. Anal. Methods 11(27): 3410-3418 (2019)). Hu et al. discloses a CNN-based deep learning model as described for claim 6 immediately above. Hu et al. further discloses that the model uses a pre-set target frame (i.e., a reference region) for determination of the feature map for subsequent classification (Pg. 3412, Col. 1, Para. 5 – Col. 2, Para. 4). However, Hu et al. does not disclose the specific equations for determining the vertical and horizontal reference regions as defined by the equations above. Claims 11-12 appear to be free of the prior art due to their dependency on claim 10.
Claim 13: “(b) identifying one or more clusters having a size less than a void noise threshold value and (c) designating a cluster identified in step (b) as valid”. The closest prior art is Tai et al. (U.S. Patent Application Publication US 2016/0083787 A1; cited in the IDS dated 10/28/2022). Tai et al. discloses the use of dPCR for non-invasive prenatal testing (Para. [0022]). Tai et al. further discloses clustering of the intensities of all the partitions in 2D space, and subsequent counting of the positive partitions in the channel (Para. [0046]). However, Tai et al. does not disclose the identification of clusters with less than a void noise threshold value as valid.
Claim 15: “wherein for a valid partition with coordinate z, the valid partition is designated as a void partition if there exists a void partition z’ with
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Claim 16: “(b) identifying one or more clusters having a size less than a valid noise threshold value, and (c) designating a cluster identified in step (b) as void”. The closest prior art is Tai et al. (U.S. Patent Application Publication US 2016/0083787 A1; cited in the IDS dated 10/28/2022). Tai et al. discloses the use of dPCR for non-invasive prenatal testing (Para. [0022]). Tai et al. further discloses clustering of the intensities of all the partitions in 2D space, and subsequent counting of the positive partitions in the channel (Para. [0046]). However, Tai et al. does not disclose the identification of clusters with less than a valid noise threshold value as void.
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/D.P.S./Examiner, Art Unit 1687
/Lori A. Clow/Primary Examiner, Art Unit 1687
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