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 .
Claims 1-20 are presented for examination.
Claim Objections
Claims 5,13 are objected to because of the following informalities:
“determine membership of the one or move vectors” should be “determine membership of the one or more vectors”. 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 7 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 pre-AIA the applicant regards as the invention.
Claim(s) 7 and 15, are dependent on claims 1 and 9 respectively, and each recite(s) “the predetermined threshold”. Claims 1, 7, 9 and 15 each recite “a predetermined threshold”.
It is unclear
whether “the predetermined threshold” of claim 7 is “a predetermined threshold” of claim 1 or claim 7, and
whether “the predetermined threshold” of claim 15 is “a predetermined threshold” of claim 9 or claim 15,
rendering the claim(s) indefinite.
For examination purposes the examiner has interpreted “the predetermined threshold” of claims 7 and 15 to be “a predetermined threshold” of claims 7 and 15 respectively.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim(s) 1-8 is/are method type claim. Claim(s) 9-15 is/are system type claim(s). Claim(s) 17-20 is/are product type claim(s). Therefore, claims 1-20 is/are directed to either a process, machine, manufacture or composition of matter.
Independent claim(s):
Step 2A Prong 1:
Regarding claim(s) 1, 9 and 17 this/these claim(s) recite(s)
classifying one or more sequential data;
generating one or more vectors based on the one or more sequential data;
clustering the one or more vectors into one or more clusters;
determining a membership of the one or more vectors associated with the one or more clusters.
The above limitations of classifying data, generating feature vectors, clustering vectors and determining vector membership appears to be practically implementable in the human mind and is understood to be a recitation of a mental process – a user can mentally evaluate data, generate vectors, evaluate vectors and classify and determine membership for vectors.
Step 2A Prong 2:
Regarding claim(s) 1, 9 and 17, this judicial exception is not integrated into a practical application.
Additional elements:
Regarding claim(s) 9 and 17 this/these claim(s) recite(s) a computer program product storing instructions, and a computer program product storing instructions with a processor executing the instructions respectively, to perform the steps of classifying data, generating feature vectors, clustering vectors and determining vector membership (mere instructions stored in a generic memory component to apply the exception using a generic computer component).
Regarding claim(s) 1, 9 and 17, this/these claim(s) further recite(s)
updating the one or more clusters; and
optimizing the one or more clusters with respect to a predefined threshold.
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of updating and optimizing previously determined clusters.
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Therefore, the claim(s) is/are directed to an abstract idea.
Step 2B:
Regarding claim(s) 1, 9 and 17, this/these claim(s) do/does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
Regarding claim(s) 9 and 17 this/these claim(s) recite(s) a computer program product storing instructions, and a computer program product storing instructions with a processor executing the instructions respectively, to perform the steps of classifying data, generating feature vectors, clustering vectors and determining vector membership (mere instructions stored in a generic memory component to apply the exception using a generic computer component).
Regarding claim(s) 1, 9 and 17, this/these claim(s) further recite(s)
updating the one or more clusters; and
optimizing the one or more clusters with respect to a predefined threshold.
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of updating and optimizing previously determined clusters.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Therefore, the claim(s) is/are not patent eligible.
Step 2A Prong 1, Dependent claims:
Regarding claim(s) 2, 10 and 18 this/these claim(s) recite(s)
classifying of the one or more sequential data... into normal and abnormal sequence data based on ground truth data; and
labeling the one or more sequential data.
The above limitations of classifying and labelling data, appears to be practically implementable in the human mind and is understood to be a recitation of a mental process – a user can mentally evaluate data based on ground truth data and classify it, and can mentally label data.
Regarding claim(s) 4, 12 and 20, these claims depend on claims 3, 11 and 19 respectively, and claims 3, 11 and 19, recite abnormal vectors.
Claim(s) 4, 12 and 20 further recite(s)
clustering the abnormal vectors using K-means method, wherein the .... vectors include one or more parameters; and
initializing the one or more parameters with an estimate.
The above limitations of clustering vectors and initializing vector parameters appears to be practically implementable in the human mind and is understood to be a recitation of a mental process – a user can mentally cluster vectors using k-means and can initialize vector parameters
Regarding claim(s) 5 and 13 this/these claim(s) recite(s)
calculating a probability function to determine membership of the one or move vectors with the one or more clusters (Calculating a probability function understood to be a recitation of a math); and
assigning the membership of the one or more vectors to the one or more clusters based on the calculated result of the probability function (The assignment of cluster membership appears to be practically implementable in the human mind and is understood to be a recitation of a mental process – a user can mentally evaluate vectors based on probability function result(s) and then can assign vector cluster membership).
Regarding claim(s) 7 and 15, this/these claim(s) recite(s)
determining convergence values associated with the membership of the one or more vectors associated with the one or more clusters;
comparing the converge values against the predetermined threshold; and
determining a membership of the one or more vectors until the converge values exceed the predetermined threshold.
The above limitations of determining and comparing convergence values, and determining vector membership appears to be practically implementable in the human mind and is understood to be a recitation of a mental process – a user can mentally evaluate vector membership convergence values, compare convergence values with a threshold and determine memberships until threshold is exceeded).
Step 2A Prong 2, Dependent claims:
Regarding claim(s) 2, 10 and 18, this/these claim(s) recite(s)
classifying of the one or more sequential data by using a multi-variate time series classification model called ROCKET (Random Convolutional Kernel Transform) (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of a ROCKET model).
Regarding claim(s) 3, 11 and 19, these claims depend on claims 1, 9 and 17 respectively, and claims 1, 9 and 17, recite generating ... vectors from .... sequences based on the one or more sequential data.
Claim(s) 3, 11 and 19 further recite(s) generating abnormal vectors from abnormal sequences ... and generating normal vectors from normal sequences ... (These limitations appear to be directed to the specification of information to be evaluated and generated, and is understood to be generally linking the use of the judicial exception to a particular ... field of use, which is not indicative of integration into a practical application. MPEP 2106.05(h)).
Regarding claim(s) 6 and 14 this/these claim(s) recite(s)
wherein updating the one or more clusters is performed by using the M-step of the E-M (Expectation-Maximization) method (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of M-step of the E-M method).
Regarding claim(s) 8 and and 16 this/these claim(s) recite(s)
wherein the predefined threshold further comprises, time duration or a numerical value (These limitations appear to be directed to the specification of information used in the evaluation, and is understood to be generally linking the use of the judicial exception to a particular ... field of use, which is not indicative of integration into a practical application. MPEP 2106.05(h)).
Step 2B, Dependent claims:
Regarding claim(s) 2, 10 and 18, this/these claim(s) recite(s)
classifying of the one or more sequential data by using a multi-variate time series classification model called ROCKET (Random Convolutional Kernel Transform) (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of a ROCKET model).
Regarding claim(s) 3, 11 and 19, these claims depend on claims 1, 9 and 17 respectively, and claims 1, 9 and 17, recite generating ... vectors from .... sequences based on the one or more sequential data.
Claim(s) 3, 11 and 19 further recite(s) generating abnormal vectors from abnormal sequences ... and generating normal vectors from normal sequences ... (These limitations appear to be directed to the specification of information to be evaluated and generated, and is understood to be generally linking the use of the judicial exception to a particular ... field of use, which is not indicative of integration into a practical application. MPEP 2106.05(h)).
Regarding claim(s) 6 and 14 this/these claim(s) recite(s)
wherein updating the one or more clusters is performed by using the M-step of the E-M (Expectation-Maximization) method (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of M-step of the E-M method).
Regarding claim(s) 8 and and 16 this/these claim(s) recite(s)
wherein the predefined threshold further comprises, time duration or a numerical value (These limitations appear to be directed to the specification of information used in the evaluation, and is understood to be generally linking the use of the judicial exception to a particular ... field of use, which is not indicative of integration into a practical application. MPEP 2106.05(h)).
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 3, 4, 5, 8, 9, 11, 12, 13, 16, 17, 19, 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Eskin (US 20150058982 A1).
Regarding claim 1, Eskin teaches a computer-implemented method for an end-to-end anomaly detection and anomalous patterns identification, the computer-method comprising (Eskin [22, 67-78] method for anomaly detection):
classifying one or more sequential data; generating one or more vectors based on the one or more sequential data (Eskin [23, 63, 85] data (instances) is classified, data may be sequential (stream), Eskin [70-73] normalized feature vectors may be generated for data instances);
clustering the one or more vectors into one or more clusters (Eskin [76, 78] normalized feature vectors for data points may be clustered);
determining a membership of the one or more vectors associated with the one or more clusters; updating the one or more clusters (Eskin [ 78, 79] cluster for each vector may be determined and clustered may be updated); and
optimizing the one or more clusters with respect to a predefined threshold (Eskin [91- 93] clusters may be optimized based on minimum distance).
Regarding claim 3, Eskin teaches the invention as claimed in claim 1 above.
Eskin further teaches wherein generating the one or more vectors based on the one or more sequential data further comprises: generating abnormal vectors from abnormal sequences based on the one or more sequential data; and generating normal vectors from normal sequences based on the one or more sequential data (Eskin [77, 78] vectors are generated or sequential data, sequential data may include normal or anomalous data).
Regarding claim 4, Eskin teaches the invention as claimed in claim 1 above.
Eskin further teaches wherein clustering the one or more vectors into one or more clusters further comprises: clustering the abnormal vectors using K-means method, wherein the abnormal vectors include one or more parameters; and initializing the one or more parameters with an estimate (Eskin [195-212, 71, 84] vectors are normalized and then clustered using K-means (KM) clustering, vectors may be normalized using mean and standard deviation).
Regarding claim 5, Eskin teaches the invention as claimed in claim 1 above.
Eskin further teaches wherein determining the membership of the one or more vectors associated with the one or more clusters further comprises: calculating a probability function to determine membership of the one or move vectors with the one or more clusters; and assigning the membership of the one or more vectors to the one or more clusters based on the calculated result of the probability function (Eskin [62-78] probabilities of vectors being in clusters are determined and used to assign vector to a cluster)
Regarding claim 8, Eskin teaches the invention as claimed in claim 1 above.
Eskin further teaches wherein the predefined threshold further comprises... a numerical value (Eskin [91- 93] clusters may be optimized based on minimum distance value).
Claim 9 is directed towards a product storing instructions similar in scope to the instructions performed by the method of claim 1, and is rejected under the same rationale.
Eskin further teaches computer program product for end-to-end anomaly detection and anomalous patterns identification, the computer program product comprising: one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions (Eskin [22, 67-78, 130]).
Claim(s) 11, 12 13, 16 is/are dependent on claim 9 above, is/are directed towards a product storing instructions similar in scope to the instructions performed by the method of claim(s) 3, 4, 5, 8 respectively, and is/are rejected under the same rationale.
Claim 17 is directed towards a system executing instructions similar in scope to the instructions performed by the method of claim 1, and is rejected under the same rationale. Eskin further teaches computer system for end-to-end anomaly detection and anomalous patterns identification, the computer system comprising: one or more computer processors; one or more computer readable storage media; and program instructions stored on the one or more computer readable storage media for execution by at least one of the one or more computer processors, the program instructions (Eskin [22, 67-78, 130]).
Claim(s) 19, 20, is/are dependent on claim 17 above, is/are directed towards a system executing instructions similar in scope to the instructions performed by the method of claim(s) 3, 4, respectively, and is/are rejected under the same rationale.
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.
Claims 2, 10 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Eskin (US 20150058982 A1), in view of Chen (US 20210357680 A1) and Dempster et al “ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels” dated Oct 2019 and retrieved from arXiv:1910.13051v1 .
Dempster was cited in the IDS dated 5/5/2022.
Regarding claim 2, Eskin teaches the invention as claimed in claim 1 above.
Eskin further teaches classifying of the one or more sequential data ... into normal and abnormal sequence data ...; and labeling the one or more sequential data (Eskin [149-156] data may be classified into normal and anomalous, Eskin [23, 85] data is classified, data may be sequential).
Eskin does not specifically teach classifying of the one or more sequential data by using a multi-variate time series classification model called ROCKET (Random Convolutional Kernel Transform) ... based on ground truth data
However Chen teaches classifying of the one or more sequential data... based on ground truth data (Chen [44, 53] streaming (sequential) data may be classified based on ground truth data, anomalies may be determined).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Chen of classifying of the one or more sequential data... based on ground truth data, into the invention suggested by Eskin; since both inventions are directed towards determining anomalies, and incorporating the teaching of Chen into the invention suggested by Eskin would provide the added advantage of leveraging information that is considered to be valid (i.e. ground truth data) while determining classification, and the combination would perform with a reasonable expectation of success
(Chen [44, 53]).
Eskin and Chen do not specifically teach classifying of the one or more sequential data by using a multi-variate time series classification model called ROCKET (Random Convolutional Kernel Transform)
However Dempster teaches classifying of the one or more sequential data by using a multi-variate time series classification model called ROCKET (Random Convolutional Kernel Transform) (Dempster [Introduction Paragraphs 2 and 4, and Section 4.2.1, using ROCKET,
state-of-the-art classification accuracy can be achieved using a fraction of the time required by even ... recent, more scalable methods, by transforming time series using random convolutional kernels, and using the transformed features to train a linear classifier, time series may be multi-variable (features)).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Dempster of classifying of the one or more sequential data by using a multi-variate time series classification model called ROCKET (Random Convolutional Kernel Transform), into the invention suggested by Eskin and Chen; since both inventions are directed towards classifying sequential data, and incorporating the teaching of Dempster into the invention suggested by Eskin and Chen would provide the added advantage of achieving state-of-the-art classification accuracy using a fraction of the time required by even ... recent, more scalable methods, and the combination would perform with a reasonable expectation of success (Dempster [Introduction Paragraphs 2 and 4, and Section 4.2.1)..
Claim(s) 10 is/are dependent on claim 9 above, is/are directed towards a product storing instructions similar in scope to the instructions performed by the method of claim(s) 2, and is/are rejected under the same rationale.
Claim(s) 18 is/are dependent on claim 17 above, is/are directed towards a system executing instructions similar in scope to the instructions performed by the method of claim(s) 2, and is/are rejected under the same rationale.
Claims 6, 14, are rejected under 35 U.S.C. 103 as being unpatentable over Eskin (US 20150058982 A1), in view of Parandehgheibi (US 20160359740 A1).
Regarding claim 6, Eskin teaches the invention as claimed in claim 1 above.
Eskin does not specifically teach wherein updating the one or more clusters is performed by using the M-step of the E-M (Expectation-Maximization) method
However Parandehgheibi teaches wherein updating the one or more clusters is performed by using the M-step of the E-M (Expectation-Maximization) method
(Parandehgheibi [87, 88] data may be clustered using Maximization of E-M method, this computes parameters maximizing the expected log-likelihood found during the E step, E-M method allows finding the maximum likelihood or maximum a posteriori estimates of parameters in a statistical model).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Parandehgheibi of wherein updating the one or more clusters is performed by using the M-step of the E-M (Expectation-Maximization) method, into the invention suggested by Eskin; since both inventions are directed towards classifying anomalies in data, and incorporating the teaching of Parandehgheibi into the invention suggested by Eskin would provide the added advantage of finding the maximum likelihood or maximum a posteriori estimates of parameters in a statistical model and computing parameters maximizing the expected log-likelihood found during the E step, and the combination would perform with a reasonable expectation of success
(Parandehgheibi [87, 88]).
Claim(s) 14 is/are dependent on claim 9 above, is/are directed towards a product storing instructions similar in scope to the instructions performed by the method of claim(s) 6, and is/are rejected under the same rationale.
Claims 7, 15, are rejected under 35 U.S.C. 103 as being unpatentable over Eskin (US 20150058982 A1), in view of Dupont (US 20140096249 A1).
Regarding claim 7, Eskin teaches the invention as claimed in claim 1 above.
Eskin does not specifically teach wherein validating the one or more clusters with respect to a predefined threshold further comprises: determining convergence values associated with the membership of the one or more vectors associated with the one or more clusters; comparing the converge values against the predetermined threshold; and determining a membership of the one or more vectors until the converge values exceed the predetermined threshold
However Dupont teaches wherein validating the one or more clusters with respect to a predefined threshold further comprises: determining convergence values associated with the membership of the one or more vectors associated with the one or more clusters; comparing the converge values against the predetermined threshold; and determining a membership of the one or more vectors until the converge values exceed the predetermined threshold (Dupont [17-22, 1206-1208] clusters may be validated based on vector cluster convergence and a threshold, this allows system to determine whether update is required because clustering is invalid, data anomalies are determined).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Dupont of wherein validating the one or more clusters with respect to a predefined threshold further comprises: determining convergence values associated with the membership of the one or more vectors associated with the one or more clusters; comparing the converge values against the predetermined threshold; and determining a membership of the one or more vectors until the converge values exceed the predetermined threshold, into the invention suggested by Eskin; since both inventions are directed towards determining data anomalies and clustering vectors, and incorporating the teaching of Dupont into the invention suggested by Eskin would provide the added advantage of determining whether update is required because clustering is invalid, and the combination would perform with a reasonable expectation of success (Dupont [17-22, 1206-1208]).
Claim(s) 15, is/are dependent on claim 9 above, is/are directed towards a product storing instructions similar in scope to the instructions performed by the method of claim(s) 7, and is/are rejected under the same rationale.
Conclusion
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SANCHITA . ROY
Primary Examiner
Art Unit 2146
/SANCHITA ROY/Primary Examiner, Art Unit 2146