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 .
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-AI 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.
Claims 1-20 are subject to review.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 10/18/2023 and 6/07/2024 are being considered by the examiner.
Specification
The disclosure is objected to because of the following informalities: Abstract contains a typo – “one or messages” should be “one or more messages”.
Appropriate correction is required.
Claim Objections
Claims 1, 9, objected to because of the following informalities: Typo - "one or messages" should be "one or more messages", and "the plurality of outlier detection models" should be "the plurality of candidate outlier detection models". Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “accurately” in claims 1, 11, 16 is a relative term which renders the claim indefinite. The term “accurately” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. No clear standard of the term “accurately” is defined and becomes unclear to a person having ordinary skill in the art as it cannot be concluded to what standard or measure or correctness is being defined. Dependent claims 2-10 are rejected as being dependent on Claim 1. Dependent claims 12-15 are rejected as being dependent on Claim 11. Dependent claims 17-20 are rejected as being dependent on Claim 16. Clarification is required.
Claims -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.
The term “particular” in claims 11 and 15 is a relative term which renders the claim indefinite. The term “particular” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The term “particular” is subjective and it is unclear to a person having ordinally skill in the art as it cannot be conclusively determined the intended significant or role of the outlier being “particular”. Dependent claims 12-14 are rejected as being dependent on Claim 11. Clarification is required.
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 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 – is the claim directed to a process, machine, manufacture, or composition of matter?
Claims 1-10 are directed to a “method” which describes one of the four statutory categories of patentable subject matter, i.e., a process.
Claims 11-15 are directed to a “a non-transitory, computer-readable medium” which describes one of the four statutory categories of patentable subject matter, i.e., a manufacture.
Claims 16-20 are directed to a “method” which describes one of the four statutory categories of patentable subject matter, i.e., a process.
Regarding Claim 1:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 1 recites an abstract idea, substantially as follows:
As per claim 1, the claim recites limitations of:
“scoring, by the computer system using a first scoring function, a plurality of candidate outlier detection models to evaluate their ability to accurately predict outlier data points in the time-series data;” – is directed to the abstract idea of mathematical concepts (see MPEP 2106.04((2)) as it is describing calculating a model score, which is considered to be a mathematical calculation.
“selecting, by the computer system based on results of the scoring, a selected one of the plurality of candidate outlier detection model that predicts a preliminary set of outlier data points;” – is directed to the abstract idea of a mental process i.e., selecting the best model based on results of the scoring function mirrors the cognitive activity of a person observing the function results, evaluating them, and making a conclusive judgement are concepts performed in the human mind [MPEP 2106.04((2) III. C.]), and may be performed with the aid of pen and paper, or using a computer as a tool.
“evaluating, by the computer system, the time-series data and the preliminary set of outlier data points to generate a final set of outlier data points;” - is directed to the abstract idea of a mental process i.e., evaluating data (concepts performed in the human mind, including observation and evaluation [MPEP 2106.04((2) III. C.]).
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 1 does not include additional elements that integrate the judicial exception into a practical application. The additional elements:
“receiving, at a computer system, time-series data that includes a plurality of data points;” – is merely a recitation of an insignificant extra-solution data gathering (see MPEP 2106.05(g)).
“outputting, by the computer system, one or messages relating to the final set of outlier data points.” – is merely a recitation of an insignificant extra-solution data outputting (see MPEP 2106.05(g)).
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 1 does not include additional limitations that amount to significantly more than the judicial exception. The additional elements:
“receiving, at a computer system, time-series data that includes a plurality of data points;” – the broadest reasonable interpretation of this limitation is found to be merely receiving time-series data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP 2106.05(d) II i.
“outputting, by the computer system, one or messages relating to the final set of outlier data points.” – the broadest reasonable interpretation of this limitation is found to be merely outputting a message related to time series data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP 2106.05(d) II i.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 2:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 2 recites an abstract idea, substantially as follows:
wherein the first scoring function is based on a first plurality of criteria that are measured using aggregated results of the plurality of data points.” – This limitation is directed to the abstract idea of mathematical concepts (see MPEP 2106.04(a)(2)), as it’s considered to be a mathematical calculation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 2 does not include additional elements that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 1 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 3:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 3 recites an abstract idea, substantially as follows:
“using a second scoring function to score the preliminary set of outlier data points;” – is directed to the abstract idea of mathematical concepts (see MPEP 2106.04((2)) as it is describing using a function to score data points, which is considered to be a mathematical calculation.
“identifying any ones of the preliminary set of outlier data points indicated by the second scoring function as weak outliers; and” – is directed to the abstract idea of a mental process as it is describing identifying weak outliers as indicated by a score (concepts performed in the human mind, including observation and evaluation [MPEP 2106.04((2) III. C.]), and may be performed with the aid of pen and paper, or using the computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 3 does not include additional elements that integrate the judicial exception into a practical application. The additional limitation(s):
”removing any identified weak outliers from the preliminary set of outlier data points.” – is merely a recitation of an insignificant extra-solution activity (see MPEP 2106.05(g)). [Examiner Note: The removal of data points identified as outliers amounts to nothing more than “Selecting a particular data source or type of data to be manipulated” under insignificant extra solution activity (see MPEP 2106.05(g)(3)]
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 3 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
”removing any identified weak outliers from the preliminary set of outlier data points.” – is merely a recitation of an insignificant extra-solution data gathering (see MPEP 2106.05(g)). Further, the insignificant extra-solution data gathering is also WURC, see MPEP 2106.05(d)(II) “The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. iii. Electronic recordkeeping”. [Examiner Note: The removal of outlier data points from a set of outlier datapoints amounts to updating and maintaining the data set which falls under electronic recordkeeping]
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 4:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 4 recites an abstract idea, substantially as follows:
wherein the second scoring function is based on a second plurality of criteria that are measured using properties of single points within the plurality of data points. – is directed to the abstract idea of mathematical concepts (see MPEP 2106.04(a)(2)) as it is describing using a function to score data points, which is considered to be a mathematical calculation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 4 does not include additional elements that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 4 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 5:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 5 recites an abstract idea, substantially as follows:
“using a set of rules to evaluate non-outliers in the time-series data to determine any missing outlier data points;” – is directed to the abstract idea of a mental process i.e., evaluating data (concepts performed in the human mind, including observation and evaluation [MPEP 2106.04((2) III. C.])
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 5 does not include additional elements that integrate the judicial exception into a practical application. The additional limitation(s):
“and including any determined missing outlier data points in the final set of outlier data points.” – is merely a recitation of an insignificant extra-solution data gathering (see MPEP 2106.05(g)). [Examiner Note: The addition of missing data points into the data set amounts to nothing more than “Selecting a particular data source or type of data to be manipulated” under insignificant extra solution activity (see MPEP 2106.05(g)(3)]
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 5 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“and including any determined missing outlier data points in the final set of outlier data points.” – is merely a recitation of an insignificant extra-solution data gathering (see MPEP 2106.05(g Further, the insignificant extra-solution data gathering is also WURC, see MPEP 2106.05((II) “The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. iii. Electronic recordkeeping”. [Examiner Note: The addition of missing outlier data points into a set of outlier datapoints amounts to updating and maintaining the data set which falls under electronic recordkeeping.]
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 6:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 6 recites an abstract idea, substantially as follows:
“selecting a filtering rule based on a description of a metric associated with the time-series data;” – is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [MPEP 2106.04((2) III. C.]), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 6 does not include additional elements that integrate the judicial exception into a practical application. The additional limitation(s):
“removing one of the preliminary set of outlier data points based on the selected filtering rule.” – is merely a recitation of an insignificant extra-solution activity (see MPEP 2106.05(g)). [Examiner Note: The removal of data points identified as outliers amounts to nothing more than “Selecting a particular data source or type of data to be manipulated” under insignificant extra solution activity (see MPEP 2106.05(g)(3)]
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 6 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“removing one of the preliminary set of outlier data points based on the selected filtering rule.” – is merely a recitation of an insignificant extra-solution activity (see MPEP 2106.05(g)). Further, the insignificant extra-solution data activity is also WURC, see MPEP 2106.05(d)(II) “The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. iii. Electronic recordkeeping”. [Examiner Note: The removal of outlier data points from a set of outlier datapoints amounts to updating and maintaining the data set which falls under electronic recordkeeping.]
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 7:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 7 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 7 does not include additional elements that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the one or more messages includes at least one message providing an explanation of why a particular outlier data point within the final set of outlier data points was identified.” – is merely a recitation of an insignificant extra-solution data outputting (see MPEP 2106.05(g)).
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 7 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the one or more messages includes at least one message providing an explanation of why a particular outlier data point within the final set of outlier data points was identified.” – the broadest reasonable interpretation of this limitation is found to be merely outputting a message related to time series data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP 2106.05(d) II i.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 8:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 8 recites an abstract idea, substantially as follows:
“identifying trend anomalies in the time-series data with the potential to result in outlier data points.” – is directed to the abstract idea of mental process (concepts performed in the human mind, including observation and evaluation [MPEP 2106.04((2) III. C.]), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 8 does not include additional elements that integrate the mental process into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 8 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 9:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 9 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 9 does not include additional elements that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the one or more messages includes at least one message providing an explanation of why a particular trend was identified.” – is merely a recitation of an insignificant extra-solution data outputting (see MPEP 2106.05(g)).
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 9 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the one or more messages includes at least one message providing an explanation of why a particular trend was identified.” the broadest reasonable interpretation of this limitation is found to be merely outputting a message related to time series data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP 2106.05(d) II i.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 10:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 10 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 10 does not include additional elements that integrate the mental process into a practical application. The additional limitation(s):
“wherein the plurality of outlier detection models includes a first outlier detection model, a second outlier detection model, and a third outlier detection model that is an ensemble model based on the first and second outlier detection models.” – is merely indicating a field of use or technological environment directed towards the technology of outlier/anomaly detection (see MPEP 2106.05(h)) and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 10 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the plurality of outlier detection models includes a first outlier detection model, a second outlier detection model, and a third outlier detection model that is an ensemble model based on the first and second outlier detection models.” – is merely indicating a field of use or technological environment directed towards the technology of outlier/anomaly detection (see MPEP 2106.05(h)) and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 11
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 11 recites an abstract idea, substantially as follows:
“scoring, by the computer system using a first scoring function, a plurality of candidate outlier detection models to evaluate their ability to accurately predict outlier data points in the time-series data;” – is directed to the abstract idea of mathematical concepts (see MPEP 2106.04((2)) as it is describing calculating a model score, which is considered to be a mathematical calculation.
“selecting, by the computer system based on results of the scoring, a selected one of the plurality of candidate outlier detection model that predicts a preliminary set of outlier data points;” – is directed to the abstract idea of a mental process i.e.(concepts performed in the human mind, including observation and evaluation [MPEP 2106.04((2) III. C.]), and may be performed with the aid of pen and paper, or using a computer as a tool.
“evaluating, by the computer system, the time-series data and the preliminary set of outlier data points to generate a final set of outlier data points;” - is directed to the abstract idea of a mental process i.e., evaluating data (concepts performed in the human mind, including observation and evaluation [MPEP 2106.04((2) III. C.]).
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 11 does not include additional elements that integrate the mental process into a practical application. The additional limitation(s):
“receiving, at a computer system, time-series data that includes a plurality of data points;” – is merely a recitation of an insignificant extra-solution data gathering (see MPEP 2106.05(g)).
“outputting, by the computer system, one or messages relating to the final set of outlier data points.” – is merely a recitation of an insignificant extra-solution data outputting (see MPEP 2106.05(g)).
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 11 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“receiving, at a computer system, time-series data that includes a plurality of data points;” – the broadest reasonable interpretation of this limitation is found to be merely receiving time-series data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP 2106.05(d) II i.
“outputting, by the computer system, one or messages relating to the final set of outlier data points.” – the broadest reasonable interpretation of this limitation is found to be merely outputting a message related to time series data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP 2106.05(d) II i.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 12:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 12 recites an abstract idea, substantially as follows:
“wherein the scoring is performed using a scoring function that is based on a first plurality of criteria that are measured using aggregated results of the plurality of data points.” – is directed to the abstract idea of mathematical concepts (see MPEP 2106.04((2)) as it is describing calculating a model score, which is considered to be a mathematical calculation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 12 does not include additional elements that integrate the mental process into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 12 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 13:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 13 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 13 does not include additional elements that integrate the mental process into a practical application. The additional limitation(s):
“wherein the evaluating includes removing weak outliers and adding missing outliers based on pluralities of criteria that are measured using properties of single points within the plurality of data points.” – is merely a recitation of an insignificant extra-solution activity (see MPEP 2106.05(g)). [Examiner Note: The removal and addition of outlier data points amounts to nothing more than “Selecting a particular data source or type of data to be manipulated” under insignificant extra solution activity (see MPEP 2106.05(g)(3)]
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 13 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the evaluating includes removing weak outliers and adding missing outliers based on pluralities of criteria that are measured using properties of single points within the plurality of data points.” – is merely a recitation of an insignificant extra-solution activity (see MPEP 2106.05(g)). Further, the insignificant extra-solution data activity is also WURC, see MPEP 2106.05(d)(II) “The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. iii. Electronic recordkeeping”. [Examiner Note: The removal and addition of outlier data points from a set of outlier datapoints amounts to updating and maintaining the data set which falls under electronic recordkeeping.]
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 14:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 14 recites an abstract idea, substantially as follows:
“selecting a filtering rule based on a description of a metric associated with the time-series data;” – is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [MPEP 2106.04((2) III. C.]), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 14 does not include additional elements that integrate the mental process into a practical application. The additional limitation(s):
“removing one of the preliminary set of outlier data points based on the selected filtering rule.” – is merely a recitation of an insignificant extra-solution activity (see MPEP 2106.05(g)). [Examiner Note: The removal of data points identified as outliers amounts to nothing more than “Selecting a particular data source or type of data to be manipulated” under insignificant extra solution activity (see MPEP 2106.05(g)(3)]
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 14 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
“removing one of the preliminary set of outlier data points based on the selected filtering rule.” – is merely a recitation of an insignificant extra-solution activity (see MPEP 2106.05(g)). Further, the insignificant extra-solution data activity is also WURC, see MPEP 2106.05(d)(II) “The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. iii. Electronic recordkeeping”. [Examiner Note: The removal of outlier data points from a set of outlier datapoints amounts to updating and maintaining the data set which falls under electronic recordkeeping.]
Regarding Claim 15:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 15 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 15 does not include additional elements that integrate the mental process into a practical application. The additional limitation(s):
“wherein the one or more messages includes at least one message providing an explanation of why a particular outlier data point within the final set of outlier data points was identified.” – is merely a recitation of an insignificant extra-solution data outputting (see MPEP 2106.05(g)).
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 15 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the one or more messages includes at least one message providing an explanation of why a particular outlier data point within the final set of outlier data points was identified.” – is merely a recitation of an insignificant extra-solution data outputting (see MPEP 2106.05(g)).
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 16:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 16 recites an abstract idea, substantially as follows:
“scoring, by the computer system using a first scoring function, a plurality of candidate outlier detection models to evaluate their ability to accurately predict outlier data points in the time-series data, wherein the first scoring function is based on a first plurality of criteria that are measured using aggregated results of the plurality of data points;” – is directed to the abstract idea of mathematical concepts (see MPEP 2106.04((2)) as it is describing calculating a model score, which is considered to be a mathematical calculation.
“selecting, by the computer system based on results of the scoring, a selected one of the plurality of candidate outlier detection model that predicts a preliminary set of outlier data points;” – is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [MPEP 2106.04((2) III. C.]), and may be performed with the aid of pen and paper, or using a computer as a tool.
“evaluating, by the computer system, the time-series data and the particular set of outlier data points to generate a final set of outlier data points, wherein the evaluating includes:” - is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [MPEP 2106.04((2) III. C.]).
“identifying, based on a second scoring function, weak outliers in the preliminary set of outlier data points, wherein the second scoring function is based on a second plurality of criteria that are measured using properties of single points within the plurality of data points;” – is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [MPEP 2106.04((2) III. C.]).
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 16 does not include additional elements that integrate the mental process into a practical application. The additional limitation(s):
“receiving, at a computer system, time-series data that includes a plurality of data points;” – is merely a recitation of an insignificant extra-solution data gathering (see MPEP 2106.05(g)).
“outputting, by the computer system, one or messages relating to the final set of outlier data points.” – is merely a recitation of an insignificant extra-solution data outputting (see MPEP 2106.05(g)).
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 16 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“receiving, at a computer system, time-series data that includes a plurality of data points;” – the broadest reasonable interpretation of this limitation is found to be merely receiving time-series data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP 2106.05(d) II i.
“outputting, by the computer system, one or messages relating to the final set of outlier data points.” – the broadest reasonable interpretation of this limitation is found to be merely outputting a message related to time series data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP 2106.05(d) II i.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 17:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 17 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 17 does not include additional elements that integrate the mental process into a practical application. The additional limitation(s):
“wherein the first plurality of criteria includes at least two criteria from the following types of criteria: a first criterion that measures proportions of detected outliers outside different moving average windows for the time-series data; a second criterion that measures a proportion of outliers belonging to spikes; a third criterion that measures distances between outliers and different moving average trends; a fourth criterion that measures lengths of extreme periods for outliers; a fifth criterion that measures lengths of extreme periods for non-outliers; and a sixth criterion that measures a number of similar outliers in proximity to one another.” – is merely indicating a field of use or technological environment directed towards the technology of outlier/anomaly detection (see MPEP 2106.05(h)) and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 17 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the first plurality of criteria includes at least two criteria from the following types of criteria: a first criterion that measures proportions of detected outliers outside different moving average windows for the time-series data; a second criterion that measures a proportion of outliers belonging to spikes; a third criterion that measures distances between outliers and different moving average trends; a fourth criterion that measures lengths of extreme periods for outliers; a fifth criterion that measures lengths of extreme periods for non-outliers; and a sixth criterion that measures a number of similar outliers in proximity to one another.” – is merely indicating a field of use or technological environment directed towards the technology of outlier/anomaly detection (see MPEP 2106.05(h)) and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 18:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 18 recites an abstract idea, substantially as follows:
“determining, based on a set of rules, whether any of the plurality of data points not within the preliminary set of outlier data points, constitutes a missing outlier, wherein the set of rules is based on a third plurality of criteria that are measured using properties of single points within the plurality of data points;” – is directed to the abstract idea of a mental process i.e., evaluating data (concepts performed in the human mind, including observation and evaluation [MPEP 2106.04((2) III. C.])
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 18 does not include additional elements that integrate the mental process into a practical application. The additional limitation(s):
“and adding any determined missing outliers to the preliminary set of outlier data points.” – is merely a recitation of an insignificant extra-solution data gathering (see MPEP 2106.05(g)) [Examiner Note: The addition of missing data points into the data set amounts to nothing more than “Selecting a particular data source or type of data to be manipulated” under insignificant extra solution activity (see MPEP 2106.05(g)(3)]
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 18 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“and adding any determined missing outliers to the preliminary set of outlier data points.” – is merely a recitation of an insignificant extra-solution data gathering (see MPEP 2106.05(g Further, the insignificant extra-solution data gathering is also WURC, see MPEP 2106.05((II) “The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. iii. Electronic recordkeeping”. [Examiner Note: The removal of outlier data points from a set of outlier datapoints amounts to updating and maintaining the data set which falls under electronic recordkeeping]
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 19:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 19 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 19 does not include additional elements that integrate the mental process into a practical application. The additional limitation(s):
“wherein the one or messages include at least one message explaining why one of the final set of outlier data points was identified as an outlier.” – is merely a recitation of an insignificant extra-solution data outputting (see MPEP 2106.05(g)).
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 19 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the one or messages include at least one message explaining why one of the final set of outlier data points was identified as an outlier.” – the broadest reasonable interpretation of this limitation is found to be merely outputting a message related to time series data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP 2106.05(d) II i.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 20:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 20 recites an abstract idea, substantially as follows:
“identifying, from the plurality of data points, a trend that has not given rise to an outlier data point;” – is directed to the abstract idea of mental process (concepts performed in the human mind, including observation and evaluation [MPEP 2106.04((2) III. C.]), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 20 does not include additional elements that integrate the mental process into a practical application. The additional limitation(s):
“outputting, an indication of the identified trend, and a narrative explaining why the trend was identified.” – is merely a recitation of an insignificant extra-solution data outputting (see MPEP 2106.05(g)).
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 20 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“outputting, an indication of the identified trend, and a narrative explaining why the trend was identified.” – the broadest reasonable interpretation of this limitation is found to be merely outputting a message related to time series data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP 2106.05(d) II i.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Claim Rejections - 35 USC § 103
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((2)( for any potential 35 U.S.C. 102((2) prior art against the later invention.
Claim(s) 1-16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Gullikson, Kevin (US 12619226 B2) (hereinafter referred to as “Gullikson”) in view of Vodencarevic, Asmir (US 20230237380 A1) (hereinafter referred to as “Vodencarevic”).
Regarding claim 1,
Gullikson discloses:
“receiving, at a computer system, time-series data that includes a plurality of data points“ (Gullikson at Col 11, lines 5-7, In FIG. 1, sensor data 102 is received and preprocessed at a preprocessor 104. The sensor data 102 includes raw time-series data.)
“scoring, by the computer system using a first scoring function, a plurality of candidate outlier detection models to evaluate their ability to accurately predict outlier data points in the time-series data” (Gullikson at Col 21, lines 4-7, after validation by the model validator 410 one or more models may be scored or ranked by [Examiner Note: mapped to scoring] a model selector 412 to determine which, if any, of the models is to be passed to deployment [Examiner Note: mapped to detection] 414; Col 21, lines 43-46, In a particular aspect, the model selector 412 uses one or more metrics to score the model(s). Metrics to score models generally account for how well a model is able to correctly identify alert conditions in a data set [Examiner Note: mapped to evaluate their ability to accurately predict outlier data points])
“selecting, by the computer system based on results of the scoring, a selected one of the plurality of candidate outlier detection model that predicts a preliminary set of outlier data points (Gullikson at Col 21, lines 4-7, after validation by the model validator 410 one or more models may be scored or ranked by a model selector 412 to determine which, if any, of the models is to be passed to deployment 414; Col 21, Lines 1-4, a model that is sufficiently reliable is passed directly [Examiner Note: mapped to selecting] to deployment 414 where it can be used to monitor one or more assets to detect anomalous operation [Examiner Note: mapped to predicts a preliminary set of outlier data points]).
“outputting, by the computer system, one or messages relating to the final set of outlier data points”. (Gullikson at Col 13, Lines 24-29, If the alert generation model 120 determines to generate an alert indication, the alert indication may include feature importance data [Examiner Note: mapped to messages] indicating which features of the sensor data (or of the input data) [Examiner Note: mapped to relating to the final set of outlier data] have the greatest influence on the determination that the monitored asset(s) are behaving abnormally.)
However, Gullikson fails to disclose:
“selecting, by the computer system based on results of the scoring, a selected one of the plurality of candidate outlier detection model that predicts a preliminary set of outlier data points [Examiner Note: Gulikson teaches selecting an outlier detection that predicts an outlier, but not a set of outliers]
“evaluating, by the computer system, the time-series data and the preliminary set of outlier data points to generate a final set of outlier data points”
“outputting, by the computer system, one or messages relating to the final set of outlier data points” [Examiner Note: Gullikson does not teach messages directed to a set].
On the other hand, Vodencarevic discloses
“selecting, by the computer system based on results of the scoring, a selected one of the plurality of candidate outlier detection model that predicts a preliminary set of outlier data points” (Vodencarevic at [0079] FIG. 3 schematically depicts a method for identifying a set of multivariate outliers in an input dataset;) [Examiner Note: Vodencarevic teaches a set of outliers]
“evaluating, by the computer system, the time-series data and the preliminary set of outlier data points to generate a final set of outlier data points” (Vodencarevic at [0126] The following steps S40A and S40B deal with alternative ways of how a de-identification of the input dataset 1 can be implemented in order to provide the processed dataset PDS.; Vodencarevic at [0121] At optional step S15, the (optionally: anonymize input dataset 1 is processed in order to detect univariate outliers in the input dataset 1. That followed, these univariate outliers may be automatically removed or altered at step S15 so as to generate a “pre-de-identified” input-dataset 1 as an intermediate result at the end of step S15.) [Examiner Note: The modification of the outlier data is mapped to evaluating an initial set of outliers to generate the final set of outliers, since the modification includes removing, rounded, substituting etc.; The modified set is mapped to the final generated set since the output set is resultant of outliers being removed, substituted, etc.;]
“outputting, by the computer system, one or messages relating to the final set of outlier data points” (Vodencarevic at [0123] an explainable AI tool in order to give a user an indication why a certain combination of datapoints [Examiner Note: mapped to set of outlier data] has a given anomaly score. Specifically, the explainable AI module 23 may be applied so as to provide additive explanations [Examiner Note: mapped to messages relating to the final set] for the detection results provided by the multivariate outlier detection algorithm.
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 teachings of Gullikson with the above teachings of Vodencarevic by using a method that selects a best-suited detection model based on an accuracy score to generate a set of outlier data points, as taught by Gullikson, and using a method that that relies on an initial set of outlier data points to generate a final set of outlier data points and output messages relating to the final set, as taught by Vodencarevic. The modification would have been obvious because one of ordinary skill in the art would be motivated to improve outlier detection techniques as suggested by Vodencarevic at [0013]: “to provide improved techniques for detecting multivariate outliers”.
As per claim 11, this is a computer readable medium claim corresponding to method Claim 1, and is rejected for similar reasons.
Regarding Claim 2
Gullikson in view of Vodencarevic recites “The method of claim 1,“ and Gullikson further recites,
“wherein the first scoring function is based on a first plurality of criteria that are measured using aggregated results of the plurality of data points.” (Gullikson at Col 21, Lines 43-49, In a particular aspect, the model selector 412 uses one or more metrics to score the model(s) [Examiner Note: mapped to first scoring function]. Metrics to score [Examiner Note: mapped to plurality of criteria] models generally account for how well a model is able to correctly identify alert conditions in a data set. For purposes of model scoring, each model may be provided input data from a data set [Examiner Note: mapped to plurality of data points] that includes data associated with one or more alert conditions).
As per claim 12, this is a computer readable medium claim corresponding to method Claim 2, and is rejected for similar reasons.
Regarding Claim 3
Gullikson in view of Vodencarevic recites “The method of claim 1, wherein the evaluating includes:” and the limitations are shown in the rejection above. Gullikson fails to disclose:
“using a second scoring function to score the preliminary set of outlier data points;”
“identifying any ones of the preliminary set of outlier data points indicated by the second scoring function as weak outliers; and”
“removing any identified weak outliers from the preliminary set of outlier data points.”
One the other hand, Vodencarevic discloses:
“using a second scoring function to score the preliminary set of outlier data points;” (Vodencarevic at [0047] a plurality of sets of multivariate outliers of datapoints is identified, and the method further comprises displaying a ranking [Examiner Note: mapped to scoring function] of the plurality of sets based on the respective anomaly scores [Examiner Note: mapped to set of outlier data points])
“identifying any ones of the preliminary set of outlier data points indicated by the second scoring function as weak outliers; and” (Vodencarevic at [0086] based on the value of anomaly score [Examiner Note: mapped to as indicated by the second scoring function], datapoints from the dataset (e.g. “rows”) may be sorted from lowest to highest. The datapoint with the lowest score value may get the rank 1 as the most prominent outlier, the next one the rank 2 etc) [Examiner Note: The datapoint with the highest rank 1 is the most prominent outlier, therefore the point with the lowest rank is considered to be least prominent outlier]
“removing any identified weak outliers from the preliminary set of outlier data points.” (Gullikson at Col 11, Lines 31-35, The preprocessor 104 is configured to modify [Examiner Note: mapped to removing] and/or supplement the sensor data 102 to generate preprocessed data for an anomaly detection model 106. Operations performed by the preprocessor 104 include, for example, filtering operations to remove outlying data samples)
The same motivation that was utilized for combining Gullikson with Vodencarevic, as set forth in claim 1, is equally applicable to Claim 3.
Regarding Claim 4
Gullikson in view of Vodencarevic recites “The method of claim 3” and the limitations are shown in the rejection above. Gullikson fails to disclose:
“wherein the second scoring function is based on a second plurality of criteria that are measured using properties of single points within the plurality of data points.”
One the other hand, Vodencarevic discloses:
“wherein the second scoring function is based on a second plurality of criteria that are measured using properties of single points within the plurality of data points.” (Vodencarevic at [0131] performing multivariate outlier detection (22) on the input dataset, comprising computing anomaly scores [Examiner Note: mapped to scoring function] for at least a portion of the plurality of datapoints using a multivariate outlier detection algorithm;)
The same motivation that was utilized for combining Gullikson with Vodencarevic, as set forth in claim 1, is equally applicable to Claim 4.
Regarding Claim 5
Gullikson in view of Vodencarevic recites “The method of claim 1,“ and Gullikson further recites,
“using a set of rules to evaluate non-outliers in the time-series data to determine any missing outlier data points; and” (Gullikson at Col 23, Lines 61-63, several configurable parameters [Examiner Note: mapped to set of rules] are used to determine the scoring window duration, the ideality score, the false negative duration, and the false positive duration; Col 24, Lines 13-17, The configurable parameters may also include a min_lead_time parameter representing a minimum lead time for an alert to be considered useful. Alerts that are issued after this time are ignored and the alert condition is considered missed (e.g., is considered a false negative [Examiner Note: mapped to missing outlier data points]))
“including any determined missing outlier data points in the final set of outlier data points.” (Gullikson at Col 11, Lines 39-42, In some implementations, the preprocessor 104 may also, or in the alternative, add to the sensor data 102, such as imputation to fill in estimated values for missing data samples)
Regarding Claim 6
Gullikson in view of Vodencarevic recites “The method of claim 1, further comprising:” and Gullikson further recites
“selecting a filtering rule based on a description of a metric associated with the time-series data;” (Gullikson at Col 11, Lines 30-39, The preprocessor 104 is configured to modify and/or supplement the sensor data 102 to generate preprocessed data for an anomaly detection model 106. Operations performed by the preprocessor 104 include, for example, filtering operations to remove outlying data samples, to reduce or limit bias (e.g., due to sensor drift or predictable variations), to remove sets of samples associated with particular events [Examiner Note: mapped to description of a metric associated with time-series data] (such as data samples during a start-up period or during a known failure event)
“removing one of the preliminary set of outlier data points based on the selected filtering rule.” (Gullikson at Col 11, Lines 30-39, The preprocessor 104 is configured to modify and/or supplement the sensor data 102 to generate preprocessed data for an anomaly detection model 106. Operations performed by the preprocessor 104 include, for example, filtering operations to remove outlying data samples, to reduce or limit bias (e.g., due to sensor drift or predictable variations), to remove sets of samples associated with particular events (such as data samples during a start-up period or during a known failure event).
As per claim 14, this is computer readable medium claim corresponding to method claim 6, and is rejected for similar reasons.
Regarding Claim 7
Gullikson in view of Vodencarevic recites “The method of claim 1,” and the limitations are shown in the rejection above. Gullikson fails to disclose:
“wherein the one or more messages includes at least one message providing an explanation of why a particular outlier data point within the final set of outlier data points was identified.”
One the other hand, Vodencarevic discloses:
“wherein the one or more messages includes at least one message providing an explanation of why a particular outlier data point within the final set of outlier data points was identified.” (Vodencarevic at [0093] the software tool 2 may also incorporate an explainable AI module 23, e.g. based on a framework such as shapley additive explanations (SHAP). This enables automated reasoning and helps the user to understand why a data point was associated with a higher (or lower) outlier score (e.g. that the combination of values of age, BMI and depression score is unusual)).
The same motivation that was utilized for combining Gullikson with Vodencarevic, as set forth in claim 1, is equally applicable to Claim 7.
As per claim 15, this is a computer readable medium claim corresponding to method claim 7, and is rejected for similar reasons.
Regarding Claim 8
Gullikson in view of Vodencarevic recites “The method of claim 1, further comprising:” and Gullikson further recites
“identifying trend anomalies in the time-series data with the potential to result in outlier data points” (Gullikson at Col 31, Lines 8-12, the alert generation model 120 may determine whether to generate an alert based on the anomaly score 222 using a sequential probability ratio test and historical data, such as the reference anomaly scores 326 or the historical sensor data 234.).
[Examiner Note: Using historical sensor data to determine whether to generate an alert for an anomaly is mapped to identifying trend anomalies.]
Regarding Claim 9
Gullikson in view of Vodencarevic recites “The method of claim 1,” and the limitations are shown in the rejection above. Gullikson further recites
“wherein the one or more messages includes at least one message providing an explanation of why a particular trend was identified.” (Gullikson at Col 31, Lines 8-12, the alert generation model 120 may determine whether to generate an alert based on the anomaly score 222 using a sequential probability ratio test and historical data [Examiner Note: mapped to trend], such as the reference anomaly scores 326 or the historical sensor data 234.).
Gullikson fails to disclose:
“wherein the one or more messages includes at least one message providing an explanation of why a particular trend was identified.”
One the other hand, Vodencarevic discloses:
“wherein the one or more messages includes at least one message providing an explanation of why a particular trend was identified.” (Vodencarevic at [0093] the software tool 2 may also incorporate an explainable AI module 23, e.g. based on a framework such as shapley additive explanations (SHAP))
The same motivation that was utilized for combining Gullikson with Vodencarevic, as set forth in claim 1, is equally applicable to Claim 9.
Regarding Claim 10
Gullikson in view of Vodencarevic recites “The method of claim 1” and Gullikson further recites
“wherein the plurality of outlier detection models includes a first outlier detection model, a second outlier detection model, and a third outlier detection model that is an ensemble model based on the first and second outlier detection models.” (Col 2, Lines 52-55, multiple anomaly detection models can be generated and scored relative to one another to select an anomaly detection model to be deployed; Col 4, Lines 28-30; Additionally, a model can be used in combination with one or more other models to perform a desired analysis)
[Examiner Note: The model used in combination with one or more other models is mapped to ensemble model]
Regarding Claim 13
Gullikson in view of Vodencarevic recites “The non-transitory, computer-readable storage medium of claim 12” and the limitations are shown in the rejection above. Gullikson further discloses:
“wherein the evaluating includes removing weak outliers and adding missing outliers based on pluralities of criteria that are measured using properties of single points within the plurality of data points.” (Gullikson at Col 11, Lines 31-35, The preprocessor 104 is configured to modify [Examiner Note: mapped to removing] and/or supplement the sensor data 102 to generate preprocessed data for an anomaly detection model 106. Operations performed by the preprocessor 104 include, for example, filtering operations to remove outlying data samples [Examiner Note: mapped to removing weak outliers]; Gullikson at Col 11, Lines 39-42, In some implementations, the preprocessor 104 may also, or in the alternative, add to the sensor data 102, such as imputation to fill in estimated values for missing data samples) [Examiner Note: mapped to adding missing outliers]
Regarding Claim 16,
Gullikson in view of Vodencarevic discloses:
“receiving, at a computer system, time-series data that includes a plurality of data points, none of which are labeled as outlier data points; “ (Gullikson at Col 11, lines 5-7, In FIG. 1, sensor data 102 is received and preprocessed at a preprocessor 104. The sensor data 102 includes raw [Examiner Note: mapped to none of which are labeled as outlier data points] time-series data.)
“scoring, by the computer system using a first scoring function, a plurality of candidate outlier detection models to evaluate their ability to accurately predict outlier data points in the time-series data (Gullikson at Col 21, lines 4-7, after validation by the model validator 410 one or more models may be scored or ranked by [Examiner Note: mapped to scoring] a model selector 412 to determine which, if any, of the models is to be passed to deployment [Examiner Note: mapped to detection] 414; Col 21, lines 43-46, In a particular aspect, the model selector 412 uses one or more metrics to score the model(s). Metrics to score models generally account for how well a model is able to correctly identify alert conditions in a data set [Examiner Note: mapped to evaluate their ability to accurately predict outlier data points]), wherein the first scoring function is based on a first plurality of criteria that are measured using aggregated results of the plurality of data points;” (Gullikson at Col 21, lines 4-7, after validation by the model validator 410 one or more models may be scored or ranked by [Examiner Note: mapped to scoring] a model selector 412 to determine which, if any, of the models is to be passed to deployment [Examiner Note: mapped to detection] 414; Col 21, lines 43-46, In a particular aspect, the model selector 412 uses one or more metrics to score the model(s). Metrics to score models generally account for how well a model is able to correctly identify alert conditions in a data set [Examiner Note: mapped to evaluate their ability to accurately predict outlier data points])
“selecting, by the computer system based on results of the scoring, a selected one of the plurality of candidate outlier detection model that predicts a preliminary set of outlier data points;” (Gullikson at Col 21, lines 4-7, after validation by the model validator 410 one or more models may be scored or ranked by a model selector 412 to determine which, if any, of the models is to be passed to deployment 414; Col 21, Lines 1-4, a model that is sufficiently reliable is passed directly [Examiner Note: mapped to selecting] to deployment 414 where it can be used to monitor one or more assets to detect anomalous operation [Examiner Note: mapped to predicts a preliminary set of outlier data points]).
“removing any identified weak outliers from the preliminary set of outlier data points;” (Gullikson at Col 11, Lines 31-35, The preprocessor 104 is configured to modify [Examiner Note: mapped to removing] and/or supplement the sensor data 102 to generate preprocessed data for an anomaly detection model 106. Operations performed by the preprocessor 104 include, for example, filtering operations to remove outlying data samples) [Examiner Note: Gullikson teaches the removal of datapoints, but not “weak outliers”]
“outputting, by the computer system, one or messages relating to the final set of outlier data points”. (Gullikson at Col 13, Lines 24-29, If the alert generation model 120 determines to generate an alert indication, the alert indication may include feature importance data [Examiner Note: mapped to messages] indicating which features of the sensor data (or of the input data) [Examiner Note: mapped to relating to the final set of outlier data] have the greatest influence on the determination that the monitored asset(s) are behaving abnormally.)
However, Gullikson fails to disclose:
“selecting, by the computer system based on results of the scoring, a selected one of the plurality of candidate outlier detection model that predicts a preliminary set of outlier data points [Examiner Note: Gulikson teaches selecting an outlier detection that predicts an outlier, but not a set of outliers]
“evaluating, by the computer system, the time-series data and the preliminary set of outlier data points to generate a final set of outlier data points”
“identifying, based on a second scoring function, weak outliers in the preliminary set of outlier data points, wherein the second scoring function is based on a second plurality of criteria that are measured using properties of single points within the plurality of data points;”
“removing any identified weak outliers from the preliminary set of outlier data points;”
“outputting, by the computer system, one or messages relating to the final set of outlier data points” [Examiner Note: Gullikson does not teach messages directed to a set].
On the other hand, Vodencarevic discloses
“selecting, by the computer system based on results of the scoring, a selected one of the plurality of candidate outlier detection model that predicts a preliminary set of outlier data points” (Vodencarevic at [0079] FIG. 3 schematically depicts a method for identifying a set of multivariate outliers in an input dataset;) [Examiner Note: Vodencarevic teaches a set of outliers]
“evaluating, by the computer system, the time-series data and the preliminary set of outlier data points to generate a final set of outlier data points” (Vodencarevic at [0126] The following steps S40A and S40B deal with alternative ways of how a de-identification of the input dataset 1 can be implemented in order to provide the processed dataset PDS.; Vodencarevic at [0121] At optional step S15, the (optionally: anonymize input dataset 1 is processed in order to detect univariate outliers in the input dataset 1. That followed, these univariate outliers may be automatically removed or altered at step S15 so as to generate a “pre-de-identified” input-dataset 1 as an intermediate result at the end of step S15.) [Examiner Note: The modification of the outlier data is mapped to evaluating an initial ser of outliers to generate the final set of outliers, since the modification includes removing, rounded, substituting etc.; The modified set is mapped to the final generated set since the output set is resultant of outliers being removed, substituted, etc.;]
“removing any identified weak outliers from the preliminary set of outlier data points;” (Vodencarevic at [0086] based on the value of anomaly score, datapoints from the dataset (e.g. “rows”) may be sorted from lowest to highest. The datapoint with the lowest score value may get the rank 1 as the most prominent outlier, the next one the rank 2 etc)
“outputting, by the computer system, one or messages relating to the final set of outlier data points” (Vodencarevic at [0123] an explainable AI tool in order to give a user an indication why a certain combination of datapoints [Examiner Note: mapped to set of outlier data] has a given anomaly score. Specifically, the explainable AI module 23 may be applied so as to provide additive explanations [Examiner Note: mapped to messages relating to the final set] for the detection results provided by the multivariate outlier detection algorithm.
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 teachings of Gullikson with the above teachings of Vodencarevic by using a method that selects a best-suited detection model based on an accuracy score to generate a set of outlier data points, as taught by Gullikson, and using a method that that relies on an initial set of outlier data points to generate a final set of outlier data points and output messages relating to the final set, as taught by Vodencarevic. The modification would have been obvious because one of ordinary skill in the art would be motivated to improve outlier detection techniques as suggested by Vodencarevic at [0013]: “to provide improved techniques for detecting multivariate outliers”.
Regarding Claim 18
Gullikson in view of Vodencarevic recites “The method of claim 16, wherein the evaluating further includes:” and the limitations are shown in the rejection above. Gullikson further discloses:
“determining, based on a set of rules, whether any of the plurality of data points not within the preliminary set of outlier data points, constitutes a missing outlier (Gullikson at Col 23, Lines 61-63, several configurable parameters [Examiner Note: mapped to set of rules] are used to determine the scoring window duration, the ideality score, the false negative duration, and the false positive duration; Col 24, Lines 13-17, The configurable parameters may also include a min_lead_time parameter representing a minimum lead time for an alert to be considered useful. Alerts that are issued after this time are ignored and the alert condition is considered missed (e.g., is considered a false negative [Examiner Note: mapped to missing outlier data points])), “wherein the set of rules is based on a third plurality of criteria that are measured using properties of single points within the plurality of data points;” Metrics to score [Examiner Note: mapped to plurality of criteria] models generally account for how well a model is able to correctly identify alert conditions in a data set. For purposes of model scoring, each model may be provided input data from a data set [Examiner Note: mapped to plurality of data points] that includes data associated with one or more alert conditions)
“and adding any determined missing outliers to the preliminary set of outlier data points.” (Gullikson at Col 11, Lines 39-42, In some implementations, the preprocessor 104 may also, or in the alternative, add to the sensor data 102, such as imputation to fill in estimated values for missing data samples)
Claims 17, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gullikson in view of Vodencarevic and further in view of Filimonov, Vitaly (US 9652354 B2) (hereinafter referred to as “Filimonov”).
Regarding Claim 17
Gullikson in view of Vodencarevic and further in view of Filimonov recites “The method of claim 16” and the limitations are shown in the rejection above.
Gullikson in view of Vodencarevic and further in view of Filimonov does not disclose wherein the first plurality of criteria includes at least two criteria from the following types of criteria:
“wherein the first plurality of criteria includes at least two criteria from the following types of criteria:”
“a first criterion that measures proportions of detected outliers outside different moving average windows for the time-series data;”
“a second criterion that measures a proportion of outliers belonging to spikes;”
“a third criterion that measures distances between outliers and different moving average trends; a fourth criterion that measures lengths of extreme periods for outliers;”
“a fifth criterion that measures lengths of extreme periods for non-outliers;”
“and a sixth criterion that measures a number of similar outliers in proximity to one another.”
On the other hand, Filimonov discloses wherein the first plurality of criteria includes at least two criteria from the following types of criteria:
“a first criterion that measures proportions of detected outliers outside different moving average windows for the time-series data;” (Filimonov at Col 1, Lines 56-61, A Z-test is a statistical test based on calculating the distance between the actual value of the current point and the average value of the corresponding sequence in units of its standard deviation (known as z-score). The outcome of the Z-test is a Boolean value indicating that the current point is an outlier or is not an outlier.; Col 2, Lines 7-9, Control of the frequency of scoring (that is, anomaly detection results) can be based on buffering using time windows of a variable range.)
“a third criterion that measures distances between outliers and different moving average trends; a fourth criterion that measures lengths of extreme periods for outliers;” (Filimonov at Col 4, Lines 23-33, To determine the behavior the data stream exhibits and consequently classify the time series into either one that follows a Gaussian distribution pattern or one that does not follow Gaussian distribution, the standard Z-test algorithm can be applied on the raw data. The standard deviation of the inliers can be estimated and compared to a threshold that is dynamically determined during the warm up phase. For example, for an all-zero sequence interspersed with occasional ones, the standard deviation of the inliers will remain zero, i.e., below the threshold and no anomaly would be raised.)
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 combination of Gullikson and Vodencarevic with the above teachings of Filimonov by using a method that selects a best-suited detection model based on an accuracy score to generate a set of outlier data points, as taught by Gullikson and Vodencarevic, and using a method that that relies on an initial set of outlier data points to generate a final set of outlier data points and output messages relating to the final set, as taught by Filimonov. The modification would have been obvious because one of ordinary skill in the art would be motivated to improve outlier detection techniques using statistical methods as suggested by Filimonov at Col 1, Lines 36-38 “A system and method for unsupervised anomaly detection can enable automatic detection of values that are abnormal to a high degree of probability in any time series sequence”.
Regarding Claim 19
Gullikson in view of Vodencarevic and further in view of Filimonov recites “The method of claim 17,” and the limitations are shown in the rejection above. Gullikson fails to disclose:
“wherein the one or messages include at least one message explaining why one of the final set of outlier data points was identified as an outlier.”
One the other hand, Vodencarevic discloses:
“wherein the one or messages include at least one message explaining why one of the final set of outlier data points was identified as an outlier.” (Vodencarevic at [0093] the software tool 2 may also incorporate an explainable AI module 23, e.g. based on a framework such as shapley additive explanations (SHAP). This enables automated reasoning and helps the user to understand why a data point was associated with a higher (or lower) outlier score (e.g. that the combination of values of age, BMI and depression score is unusual)). [Examiner Note: The messages are interpreted to being directed towards a single outlier]
The same motivation that was utilized for combining Gullikson with Vodencarevic, as set forth in claim 17, is equally applicable to Claim 19.
Regarding Claim 20
Gullikson in view of Vodencarevic and further in view of Filimonov recites “The method of claim 19, further comprising:” and the limitations are shown in the rejection above. Gullikson further discloses:
“identifying, from the plurality of data points, a trend that has not given rise to an outlier data point;” (Col 31, Lines 8-12, the alert generation model 120 may determine whether to generate an alert based on the anomaly score 222 using a sequential probability ratio test and historical data [Examiner Note: the combination of sequential probability ration test and historical data is mapped to trend, specifically in the cases that the alert generation models determines not to generate an alert based on the ration test and data], such as the reference anomaly scores 326 or the historical sensor data 234.)
Gullikson does not disclose:
“outputting, an indication of the identified trend, and a narrative explaining why the trend was identified.”
On the other hand, Vodencarevic discloses:
“outputting, an indication of the identified trend, and a narrative explaining why the trend was identified.” (Vodencarevic at [0093] the software tool 2 may also incorporate an explainable AI module 23, e.g. based on a framework such as shapley additive explanations (SHAP))
The same motivation that was utilized for combining Gullikson with Vodencarevic, as set forth in claim 17, is equally applicable to Claim 20.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20220253426 A1 – recites anomaly detection methods where plurality of outlier detecting machine learning models can be ranked according to a metric. Recites explaining outliers in time-series data.
US 20180176439 A1 – recites methods where missing or outlier pixels i.e. data can be filled in.
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/SAMIYAH KABIR/Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
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