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 Objections
Claim 1 is objected to because of the following informalities: Claim 1 includes expressions in line 11 and 22 which have poor image quality making it hard to interpret. 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 and 5-7 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 1 lines 7 and 17 include the limitation “performing an on-line abnormal working condition detection.” It is unclear and indefinite what on-line means in this context. For the purposes of examination, the limitations will be viewed as “performing an while the industrial production process is online….”
Claim 1 line 20 includes the limitation “if the sample data
X
j
i
in the
i
t
h
view is image data, firstly performing graying processing and normalization processing on the image data, then obtaining an average value {
X
-
i
h
|h=1, 2, . . . , r} of all data under different working conditions according to the acquired sample data, wherein r is the number of working condition categories, and further obtaining the preprocessed sample data:
X
j
'
i
=
[
X
j
i
-
X
-
i
1
,
X
j
i
-
X
i
2
,
…
,
X
j
i
-
X
-
i
r
]
(1)
and if the sample data
X
j
i
∈
i
q
×
i
in the
i
t
h
view is vector data,” It is unclear and indefinite how the method would proceed if the sample data was not image or vector data. For the purposes of examination, the limitation will be viewed as “wherein the sample data is either image data or vector data, if the sample data
X
j
i
in the
i
t
h
view is image data, firstly performing graying processing and normalization processing on the image data, then obtaining an average value {
X
-
i
h
|h=1, 2, . . . , r} of all data under different working conditions according to the acquired sample data, wherein r is the number of working condition categories, and further obtaining the preprocessed sample data:
X
j
'
i
=
[
X
j
i
-
X
-
i
1
,
X
j
i
-
X
i
2
,
…
,
X
j
i
-
X
-
i
r
]
(1)
and if the sample data
X
j
i
∈
i
q
×
i
in the
i
t
h
view is vector data…”
Claims that depend on the above rejected claims are also rejected under 35 U.S.C. 112(b) or 35
U.S.C. 112 (pre-AIA ), second paragraph.
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 and 5-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
With respect to claim 1,
Step 2A Prong One:
The following bold limitations are considered abstract:
“A method for detecting abnormal working conditions of multi-view data based on feature regression, comprising: acquiring sample data under different working conditions in an industrial production process, and preprocessing the acquired sample data;
establishing the method for detecting abnormal working conditions based on the multi-view data by a feature regression method;
and performing an on-line abnormal working condition detection by the method for detecting abnormal working conditions based on the multi-view data;
the method comprising the following steps:
Step 1: acquiring the sample data under different working conditions in an actual industrial production process, denoted as
X
j
i
i
=
1
,
2
.
.
.
,
w
;
j
=
1
,
2
,
.
.
.
,
n
, wherein
X
j
i
is
j
t
h
sample data in an
i
t
h
view, w is the number of views, and n is the number of samples under different views;
Step 2: preprocessing the acquired sample data under different working conditions;
Step 3: after preprocessing the sample data, establishing the method for detecting abnormal working conditions based on the multi-view data by the feature regression method;
and Step 4: performing the on-line abnormal working condition detection by the method for detecting abnormal working conditions based on the multi-view data,
wherein Step 2 comprises: if the sample data
X
j
i
in the
i
t
h
view is image data, firstly performing graying processing and normalization processing on the image data, then obtaining an average value {
X
-
i
h
|h=1, 2, . . . , r} of all data under different working conditions according to the acquired sample data, wherein r is the number of working condition categories, and further obtaining the preprocessed sample data:
X
j
'
i
=
[
X
j
i
-
X
-
i
1
,
X
j
i
-
X
i
2
,
…
,
X
j
i
-
X
-
i
r
]
(1)
and if the sample data
X
j
i
∈
i
q
×
i
in the
i
t
h
view is vector data, the preprocessed sample data is:
X
j
'
i
=
2
wherein
X
j
'
i
represents the preprocessed sample data of the sample data
X
j
i
,
X
j
q
i
represents a
q
t
h
variable of the sample data
X
j
i
,
X
-
q
i
r
and
σ
r
g
represent an average value and a standard deviation of the
q
t
h
variable in the
i
t
h
view under a
r
t
h
working condition, respectively, and {
β
j
|j=1, 2, . . . , r} represents coefficient parameter variables under different working conditions,
wherein in Step 3, an objective function model in the method for detecting abnormal working conditions based on the multi-view data established by the feature regression method is:
L
u
i
j
,
v
i
j
,
p
i
j
=
(
3
)
wherein
X
j
i
∈
i
d
×
m
is
l
t
h
preprocessed sample data in the
i
t
h
view, d and m are dimensions of the sample data
X
j
i
, and
u
i
j
∈
i
l
×
d
and
v
i
j
∈
i
m
×
l
are left and right projection vectors, respectively;
X
f
i
and
X
g
i
are
f
t
h
preprocessed sample data and
g
t
h
preprocessed sample data in the
i
t
h
view, respectively;
p
j
is a regression center of the sample data in all views under a
j
t
h
working condition;
y
l
i
j
is a label of
l
t
h
sample data in the
i
t
h
view, if
X
j
i
belongs to the
j
t
h
working condition,
y
l
i
j
=1, otherwise,
y
l
i
j
=0;
λ
1
and
λ
2
, are both coefficient parameters;
setting
y
l
i
^
=
k
l
i
1
,
k
l
i
2
,
…
k
l
i
r
T
(4)
and
ω
l
=
∑
i
=
1
w
c
o
s
y
l
i
^
,
1
r
×
1
(5)
for l=1, 2 . . . , n, largest t data labels corresponding to
ω
l
constitute a set {Ω}, {
C
f
i
} is a label set of sample data in a same category as the preprocessed sample data
X
f
i
;
defining
G
f
i
=
C
f
i
-
{
Ω
} (6)
And
M
i
=
v
i
1
u
i
1
,
v
i
2
u
i
2
,
…
,
v
i
r
u
i
r
(7) ”
The above bolded limitations are directed to abstract ideas and would fall within the “Mathematical Concept” grouping of abstract ideas. Establishing a method, performing an online abnormal working condition detection, and the subsequent steps 1-4 involved in those processes amount to mathematical concepts as seen later in the claim and in specification paragraphs [0048-0084]. According to MPEP 2106.04(C) “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.”
Step 2A Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements –
“acquiring sample data under different working conditions in an industrial production process, and preprocessing the acquired sample data; Step 1: acquiring the sample data under different working conditions in an actual industrial production process, denoted as
X
j
i
i
=
1
,
2
.
.
.
,
w
;
j
=
1
,
2
,
.
.
.
,
n
, wherein
X
j
i
is
j
t
h
sample data in an
i
t
h
view, w is the number of views, and n is the number of samples under different views;”
Examiner views these limitations amount to generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
As such Examiner does NOT view that the claims
-Improve the functioning of a computer, or to any other technology or technical field
-Apply the judicial exception with, or by use of, a particular machine - see MPEP
2106.05(b)
-Effect a transformation or reduction of a particular article to a different state or thing -
see MPEP 2106.05(c)
-Apply or use the judicial exception in some other meaningful way beyond generally
linking the use of the judicial exception to a particular technological environment, such that the
claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP
2106.05(e) and Vanda Memo.
Moreover, Examiner views the claims to be merely generally linking the use of the judicial exception to industrial data. Furthermore, acquiring sample data is viewed as necessary data gathering as nondescript data is just acquired through some unknown process.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Considering the claim as a whole, one of ordinary skill in the art would not know the practical application of the present invention since the claims do not apply or use the judicial exception in some meaningful way. As currently claimed, Examiner views that the additional elements do not apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, because the claim fails to recite clearly how the judicial exception is applied in a manner that does not monopolize the exception because the limitations “acquiring sample data under different working conditions in an industrial production process, and preprocessing the acquired sample data; Step 1: acquiring the sample data under different working conditions in an actual industrial production process, denoted as
X
j
i
i
=
1
,
2
.
.
.
,
w
;
j
=
1
,
2
,
.
.
.
,
n
, wherein
X
j
i
is
j
t
h
sample data in an
i
t
h
view, w is the number of views, and n is the number of samples under different views;” just tie the claim to some type of industrial data. Examiner further notes that such additional elements are viewed to be well known routine and conventional as evidenced by Li (CN 109978031 A) and Wang (CN 109630095 A; as seen in the IDS dated 6/26/2024).
Dependent claims 5-7 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claims are not directed to an abstract idea, as detailed below:
The dependent claims are directed to solving a model function, solving for other variables using the defined formulas, and further limiting the process for detecting abnormal working conditions. These limitations would fall into mathematical concept groupings of abstract ideas.
Therefore, dependent claims 5-7 further limit the abstract idea with an abstract idea and thus the claims are still directed to an abstract idea without significantly more.
Prior Art Analysis
Claims 1 and 5-7 stand rejected under 35 U.S.C. 112(b) and 35 U.S.C. 101, however, none of the known prior art could be applied to the claims for the following reasons.
With respect to claim 1,
Li (CN 109978031 A) teaches,
a multi-view manifold analysis fault diagnosis method relates to the technical field of industrial smelting process control based on image feature regression. (Abstract) They further teach acquiring industrial data, establishing a method for determining abnormalities based on image feature regression and solving the method to determine if a fault occurred. (Para(s). [0010-0013 and 0039]) They also teach obtaining a grayscale matrix of image data and taking the average value. (Para. [0014]) However, they do not explicitly teach the preprocessed sample data being equal to the claimed matrix nor do they teach an objective model function as claimed.
Wang (CN 109630095 A; as seen in the IDS dated 6/26/2024) teaches,
a pumping well working condition identifying method based on multi-view learning. (Abstract) They further teach acquiring a large amount of multi-source real-time data, performing feature extraction based on mechanism analysis, prior information, and expert knowledge to construct a feature data sample set for each perspective. (Para(s) [0006 and 0013]). Lastly, they teach fault diagnosis based on the data. (Para. [0056]) However they do not explicitly teach the feature regression method or the objective function as claimed.
Tao (Scalable Multi-View Semi-Supervised Classification via Adaptive Regression; 2017) teaches,
an algorithm named multi-view semi-supervised classification via adaptive regression (MVAR) to address the problem with obtaining labels for Multiview data for machine learning applications. (Abstract) They further teach constructing global regression models for each view and formulating a final objective function as the linearly weighted combination of all the loss functions of each view. (Introduction) However, the objective function does not match the claimed objective function and they do not determine abnormalities in an industrial process. (Section III)
As seen above none of the known prior art explicitly teaches and it would be non-obvious to
combine the known prior art to teach,
“wherein in Step 3, an objective function model in the method for detecting abnormal working conditions based on the multi-view data established by the feature regression method is:
L
u
i
j
,
v
i
j
,
p
i
j
=
(
3
)
wherein
X
j
i
∈
i
d
×
m
is
l
t
h
preprocessed sample data in the
i
t
h
view, d and m are dimensions of the sample data
X
j
i
, and
u
i
j
∈
i
l
×
d
and
v
i
j
∈
i
m
×
l
are left and right projection vectors, respectively;
X
f
i
and
X
g
i
are
f
t
h
preprocessed sample data and
g
t
h
preprocessed sample data in the
i
t
h
view, respectively;
p
j
is a regression center of the sample data in all views under a
j
t
h
working condition;
y
l
i
j
is a label of
l
t
h
sample data in the
i
t
h
view, if
X
j
i
belongs to the
j
t
h
working condition,
y
l
i
j
=1, otherwise,
y
l
i
j
=0;
λ
1
and
λ
2
, are both coefficient parameters;
setting
y
l
i
^
=
k
l
i
1
,
k
l
i
2
,
…
k
l
i
r
T
(4)
and
ω
l
=
∑
i
=
1
w
c
o
s
y
l
i
^
,
1
r
×
1
(5)
for l=1, 2 . . . , n, largest t data labels corresponding to
ω
l
constitute a set {Ω}, {
C
f
i
} is a label set of sample data in a same category as the preprocessed sample data
X
f
i
;
defining
G
f
i
=
C
f
i
-
{
Ω
} (6)
And
M
i
=
v
i
1
u
i
1
,
v
i
2
u
i
2
,
…
,
v
i
r
u
i
r
(7)”
Therefore, prior art cannot be applied to claim 1. Prior art cannot be applied to claims 5-7
because they are dependent upon claim 1.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner
should be directed to JOSHUA L FORRISTALL whose telephone number is 703-756-4554. The examiner
can normally be reached Monday-Friday 8:30 AM- 5 PM.
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/JOSHUA L FORRISTALL/Examiner, Art Unit 2857
/ALEXANDER SATANOVSKY/Primary Examiner, Art Unit 2857
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