DETAILED ACTION
Remarks
Claims 1-9 have been examined and rejected. This Office Action is responsive to the amendment filed on 04/29/2025, which has been entered in the above identified application.
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 1-9 are presented for examination.
Response to Amendment
Applicant’s amendment filed on 04/29/2025 has been entered. Claims 1, 2, and 5-9 are amended. Claims 1-9 are pending in the application.
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claims
Step 1
Claim 1 is drawn to a concept drift detection device, claim 8 is drawn to a concept drift detection method, and claim 20 is drawn to a concept drift detection system when executed cause a processor to perform the operations of the method of claim 8. Therefore, each of these claim groups falls under one of four categories of statutory subject matter (process/method, machines/product/apparatus, manufactures, and composition of matter).
Step 2A – Prong 1
Claims 1, 8 and 9 are directed to a judicially recognized exception of an abstract idea without significantly more.
Claims 1, 8 and 9 recite a method of dividing the set of past time series data into a set of past windows; dividing the set of current time series data into a set of current windows; and
dividing a set of baseline data created by the baseline model into a set of baseline windows those under their broadest reasonable interpretation enumerates a mental concept. A human can mentally perform, with the physical aid such as pen and paper, to divide a set of time series data into different sets of corresponding windows and to divide a set of baseline data. Therefore, the step of dividing the set of past time series, the set of current time series and the set of baseline data is nothing more than a mental concept (MPEP 2106.04(a)(2)(III)).
Claims 1, 8 and 9 recite further a method of calculating a set of baseline data features from the set of baseline windows; calculating a set of past data features from the set of past windows; calculating a set of current data features from the set of current windows those under their broadest reasonable interpretation enumerates a mathematical concept. A human can mentally perform, with the physical aid such as pen and paper, to calculate data features from the corresponding time windows. Therefore, the step of calculating a set of baseline data features, a set of past data features and a set of current data features is nothing more than a mathematical concept (MPEP 2106.04(a)(2)(I)).
Claims 1, 8 and 9 recite further a method of calculating a baseline distance between a subset of the set of past data features and a subset of the baseline data features that relate to a corresponding time frame; calculating a current distance between a subset of the set of current data features and a subset of the baseline data features that relate to a corresponding time frame those under their broadest reasonable interpretation enumerates a mathematical concept. A human can mentally perform, with the physical aid such as pen and paper, to calculate distance between the subsets. Therefore, the step of calculating a baseline distance and a current distance is nothing more than a mathematical concept (MPEP 2106.04(a)(2)(I)).
Claims 1, 8 and 9 recite further a method of calculating, based on the baseline distance, a baseline statistic that indicates a reference for determining concept drift that under its broadest reasonable interpretation enumerates a mathematical concept. A human can mentally perform, with the physical aid such as pen and paper, to calculate statistic to indicate a concept drift. Therefore, the step of calculating a baseline statistic that indicates reference is nothing more than a mathematical concept (MPEP 2106.04(a)(2)(I)).
Step 2A – Prong 2
Claims 1, 8 and 9 recite further receiving a time series data set that includes a set of past time series data relating to a first time period and a set of current time series data relating to a second time period subsequent to the first time period that fail to integrate the abstract idea into a practical application. The step of receiving time series data is a form of insignificant input and output solution activities, where receiving a time series data set is necessary for all uses of the judicial exception. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Claims 1, 8 and 9 recite further generating a baseline model based on a subset of the set of past time series data that fail to integrate the abstract idea into a practical application. The step of generating a baseline model is a form of insignificant input and output solution activities, where generating a baseline model based on the subset is necessary for all uses of the judicial exception. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Claims 1, 8 and 9 recite further determining, based on the baseline statistic and the current distance, presence or absence of concept drift between the set of current time series data and the set of past time series data that fail to integrate the abstract idea into a practical application. The step of determine whether the concept drift is present or absent is a form of insignificant input and output solution activities, where determining presence or absence of concept drift is necessary for all uses of the judicial exception. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Claims 1, 8 and 9 recite further transmitting a concept drift notification to a client device when the processor determines that concept drift is present that fail to integrate the abstract idea into a practical application. The step of transmitting a notification is a form of insignificant input and output solution activities, where transmitting a concept drift notification is necessary for all uses of the judicial exception. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Step 2B
The additional elements in step 2A-Prong 2 those are forms of insignificant extra-solution activities, do not amount to significantly more than an abstract idea because the court decision have determined that these additional elements of receiving a time series data set; generating a baseline model based on the subset; determining presence or absence of concept drift; and transmitting a concept drift notification to be well-understood, routine, and conventional when claimed in a merely generic manner (MPEP 2106.05(d)(II)).
As such, claims 1, 8 and 9 are not patent eligible.
Dependent claims
Claims 2-7 merely narrow the previously recited abstract idea limitations. For the reasons described above with respect to claim 1, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claims disclose similar limitations described for the independent claims above and do not provide anything more than the mental and mathematical processes that are practically capable of being performed in the human mind with the assistance of pen and paper. Therefore, claims 2-7 also recite abstract ideas that do not integrate into a practical application or amount to significantly more than the judicial exception, and are rejected under U.S.C. 101.
Step 1
Claims 2-7 are drawn to a concept drift detection device. Therefore, this claim group falls under one of four categories of statutory subject matter (process/method, machines/product/apparatus, manufactures, and composition of matter).
Step 2A – Prong 1
Dependent claim 3 recites further the mental process by the baseline model is a trained machine learning model configured to generate, as the baseline data, a set of predicted time series data based on the subset of the set of past time series data that is based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Dependent claim 5 recites further the mental and mathematical processes by calculate, as the baseline statistic, a seasonal baseline that indicates a reference for determining concept drift for each of the set of seasonal time pattern points of the baseline distance; and determine that concept drift exists for a first seasonal time pattern point of the current distance in a case that the first seasonal time pattern point of the current distance exceeds a statistical threshold with respect to the seasonal baseline those are based on one or more features of the ML project (MPEP 2106.04(a)(2)).
Dependent claim 6 recites further the mental process by outputs the concept drift notification when concept drift is determined to exist for a predetermined number of seasonal time pattern points of the current distance that is based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Dependent claim 7 recites further the mental process by update the baseline model using a second time series data set in a case that concept drift is determined to exist for a predetermined number of seasonal time pattern points of the current distance that is based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Step 2A – Prong 2
Dependent claim 2 recites further the insignificant extra solution activities by use an exponentially weighted moving average technique to smooth the baseline distance and the current distance. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 4 recites further the insignificant extra solution activities by the time series data set is seasonal time series data that includes a set of seasonal time patterns that repeat periodically over a defined time period; and each seasonal time pattern of the set of seasonal time patterns includes a set of seasonal time pattern points corresponding to a set of time features. These additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
As such, dependent claims 2-7 are not patent eligible.
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.
Claims 1-3, 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Cavalcante et al (“FEDD: Feature Extraction for Explicit Concept Drift Detection in Time Series”) hereafter Cavalcante, and further in view of Oliner et al (US 20180219889 A1) hereafter Oliner.
Cavalcante was cited in the IDS filed on 03/17/2022.
With respect to claim 1, Cavalcante teaches a concept drift detection device for detecting concept drift in a time series data set (an online explicit drift detection method is proposed to identify concept drifts in time series by monitoring time series features [page 2 & 3, I. Introduction]), the concept drift detection device comprising:
a data input unit configured to receive a time series data set from an external data storage device, wherein the time series data set includes a set of past time series data relating to a first time period and a set of current time series data relating to a second time period subsequent to the first time period (in the approach of Borachi and Roveri that proposed an online concept drift detection for time series, a sequence of fixed size data extracted from the incoming data are compared with a sequence of the most similar previously seen data recovered from memory. If there is a difference between them, a drift is detected [page 3, II. Related Work]);
divide the set of past time series data into a set of past windows; divide the set of current time series data into a set of current windows; and divide a set of baseline data created by the baseline model into a set of baseline windows (FEDD is an explicit drift detection method for time series that monitors statistical features of time series to identify changes of the time series data. FEDD has two modules: feature extraction (FE) and drift detection (DD). The DD monitors the evolution of time series features and tests the occurrence of concept drifts. The DD keeps track of the new time series data by a moving window, such that the DD identifies a concept drift by comparing the feature vectors that the features are computed on the current window [page 3-5, III. The FEDD Approach]); and
calculate a set of baseline data features from the set of baseline windows; calculate a set of past data features from the set of past windows; and calculate a set of current data features from the set of current windows (a moving window slides through the time series data stream to define the training and test data sets. The window size is adjusted to fit the seasonal patterns of the time series. The explicit drift detection method for time series used herein to deal with abrupt and gradual drifts by examining time series features. The DD module keeps a moving window that slides when a new sample is available, and the features are recomputed on the current window [page 3-5, II. Related Work and III. The FEDD Approach]);
calculate a baseline distance between a subset of the set of past data features and a subset of the baseline data features that relate to a corresponding time frame (ECDD is used to measure the distances between an initial feature vector and the current feature vector, when the whole time series data belong to the same context, the distances are expected to be stationary and Zt fluctuates around the distance mean [page 4 & 5, III. The FEDD Approach, B. The DD Module]);
calculate a current distance between a subset of the set of current data features and a subset of the baseline data features that relate to a corresponding time frame (when a change occurs, the distances require a new distance mean and new Zt those are defined based on EWMA. ECDD test was chosen to be used because it monitors the EWMA of the distances instead of monitoring the instantaneous distances or the average distances [page 4 & 5, III. The FEDD Approach, B. The DD Module]);
calculate, based on the baseline distance, a baseline statistic that indicates a reference for determining concept drift (Among the steps of FEDD algorithm, step 3 is repeated every time a new sample from the time series is available, and the feature vector f is used as a reference feature vector for the drift test that it represents the known concept drift [page 5, III. The FEDD Approach, B. The DD Module]);
determine, based on the baseline statistic and the current distance, presence or absence of concept drift between the set of current time series data and the set of past time series data (Step 8 of FEDD indicates the distance between the initial feature vector and the current feature vector is computed. Step 9 indicates the algorithm computes the distance average, the EWMA and the standard deviation, those are the statistics used in concept drift test. A mechanism of FEDD algorithm that determines the warning level whether it is reached when a concept drift is detected [page 5, III. The FEDD Approach, B. The DD Module]).
However, Cavalcante does not disclose a processor that is coupled to the data input unit and is configured to:and transmit a concept drift notification to a client device when the processor determines that concept drift is present.
In the same field of endeavor, Oliner teaches a processor that is coupled to the data input unit and is configured to:(relationships between time series data sets are determined by values associated with the time series data sets, wherein each relationship is generated by a predictive model. The predictive model may be a machine learning model which is trained to predict future values (or data points) associated with the given time series given corresponding values of other time series, and based on prior behavior of multiple time series [par. 0235]); and
transmit a concept drift notification to a client device when the processor determines that concept drift is present (anomalous and/or unusual behavior of a time series can indicate some underlying problem in a computing system or service. When such an anomaly occurs, it is often critical to quickly notify appropriate users or take appropriate actions. The anomaly detection tool may cause an indicator or a push notification of the error to be transmitted to a user or user device [par. 0228, 0280, 0293]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the concept of detecting anomalies based on relationships between multiple time series as suggested by Oliner into the concept of monitoring time series by explicit drift detection methods as suggested by Cavalcante because both of these systems addressing the process of observing the time series data to extract features of the time series. Doing so would be desirable because the system of Cavalcante would be more efficient by preserving a predictive model that determines relationships between time series data sets with associated values of the time series data sets in order to predict future values associated with time series given corresponding values of another time series (Oliner, [par. 0235-0236]).
With respect to claim 2, the combination of Cavalcante and Oliner teaches wherein the processor is further configured to: use an exponentially weighted moving average technique to smooth the baseline distance and the current distance (Cavalcante, the exponentially weighted moving average (EWMA) is used in drift detection test of exponentially concept drift detection (ECDD) to identify changes in the time series values. The DD module uses ECDD to monitor distances between the initial feature vector and the current feature vector. With EWMA, the module estimates the mean of a sequence of values of a variable which gives more important data, whereas the older data is being down weighted [page 3, II. Related Work and page 4 & 5, III. The FEDD Approach]).
With respect to claim 3, the combination of Cavalcante and Oliner teaches wherein: the baseline model is a trained machine learning model configured to generate, as the baseline data, a set of predicted time series data based on the subset of the set of past time series data (Oliner, relationships between time series data sets are determined by values associated with the time series data sets, wherein each relationship is generated by a predictive model. The predictive model may be a machine learning model which is trained to predict future values (or data points) associated with the given time series given corresponding values of other time series, and based on prior behavior of multiple time series [par. 0235]).
With respect to claim 8, it is a concept drift detection method claim that is corresponding to the concept drift device of claim 1. Therefore, it is rejected for the same reason as claimed in claim 1 above.
With respect to claim 9, it is a concept drift detection system claim that is corresponding to the concept drift device of claim 1. Therefore, it is rejected for the same reason as claimed in claim 1 above.
Claims 4-7 are rejected under 35 U.S.C. 103 as being unpatentable over Cavalcante et al (“FEDD: Feature Extraction for Explicit Concept Drift Detection in Time Series”) hereafter Cavalcante, and further in view of Oliner et al (US 20180219889 A1) hereafter Oliner, as applied in claim 1 above, and further in view of Beaver et al (US 20200210393 A1) hereafter Beaver.
Cavalcante was cited in the IDS filed on 03/17/2022.
With respect to claim 4, the combination of Cavalcante and Oliner teaches all limitations as claimed in claim 1 above.
However, the combination of Cavalcante and Oliner does not disclose wherein: the time series data set is seasonal time series data that includes a set of seasonal time patterns that repeat periodically over a defined time period; and each seasonal time pattern of the set of seasonal time patterns includes a set of seasonal time pattern points corresponding to a set of time features.
In the same field of endeavor, Beaver teaches wherein:
the time series data set is seasonal time series data that includes a set of seasonal time patterns that repeat periodically over a defined time period (non-stationary time series may exhibit seasonal behavior, where the seasonal behavior may be related to the level of the time series data. Other behaviors may include trend and concept drift, where the normal behavior changes over time. A concept drift may trigger an anomaly, but the system may determine a new pattern of behavior has occurred [par. 0055, 0065]); and
each seasonal time pattern of the set of seasonal time patterns includes a set of seasonal time pattern points corresponding to a set of time features (Each data point of a time series may include a corresponding time stamp and value of time series. Anomalous determines anomalous time series relative to other time series in some given collection by computing a vector of features for every time series. An anomaly is a pattern that does not conform to past patterns of behavior, non-anomalous data tends to occur in much larger quantities than anomalous data. If there are 100 data points and only 2 anomalies, a classifier can deem every point as non-anomalous and achieve 98% accuracy [par. 0009, 0055, 0119, 0226]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the concept of selecting an anomaly detection method for each of a plurality of class of time series based on characteristics of the time series as suggested by Beaver into the combination of Cavalcante and Oliner because all of these systems addressing the process of observing the time series data to extract features/anomalies from the time series. Doing so would be desirable because the combination of Cavalcante and Oliner would be more efficient by demonstrating a certain anomaly detection method that is more promising given different types of time series characteristics, and filtering a set of possible methods based on the time series class (Beaver, [par. 0011-0013]).
With respect to claim 5, the combination of Cavalcante, Oliner, and Beaver teaches wherein: the processor is further configured to:
calculate, as the baseline statistic, a seasonal baseline that indicates a reference for determining concept drift for each of the set of seasonal time pattern points of the baseline distance (Oliner, to detect concept drift in a predictive model, MAD is used to determine the exceeding threshold value. Predictions from at least one predictive model are used to validate predictions from one or more other predictive models [par. 0289-0291]); and
determine that concept drift exists for a first seasonal time pattern point of the current distance in a case that the first seasonal time pattern point of the current distance exceeds a statistical threshold with respect to the seasonal baseline (Oliner, the predictive models for time series can be used to detect anomalies in values associated with the time series. The anomaly detection tool determines the median absolute deviation (MAD) from the predicted and actual values over a moving window time. An anomaly may be detected based on the MAD exceeding a threshold value [par. 0289-0291]).
With respect to claim 6, the combination of Cavalcante, Oliner and Beaver teaches wherein: the processor outputs the concept drift notification when concept drift is determined to exist for a predetermined number of seasonal time pattern points of the current distance (Beaver, an example of Twitter provides anomalies are point-in-time anomalous data points whereas concept drifts (or breakouts) ramp up from one steady state to another, and a BreakoutDetection library used to determine when a new concept drift has occured [par. 0106]).
With respect to claim 7, the combination of Cavalcante, Oliner and Beaver teaches wherein: the processor is configured to update the baseline model using a second time series data set in a case that concept drift is determined to exist for a predetermined number of seasonal time pattern points of the current distance (Oliner, a predictive model used to predict future values (or data points) associated with a time series given corresponding values (or data points) of at least one other time series based on prior behavior of the multiple time series. Anomaly detection can be performed upon events being created, indexed and stored; and predictive models used in anomaly detection may be updated over time that based on an analysis of subsequently created, indexed and stored events or data points [par. 0235, 0255]).
Response to Arguments
The examiner respectfully acknowledges the applicant’s amendments to claims 1, 2 and 5-9.
Applicant’s amendments filed on 04/29/2025 regarding the claim interpretation under 35 U.S.C. 112(b) to claims 1-3 and 5-7 have been considered and are consequently withdrawn.
Applicant’s arguments filed on 04/29/2025 regarding the rejections to claims 1-9 under 35 U.S.C. 101 have been fully considered but are not persuasive.
Applicant argued that “Step 2A, Prong One. First, according to, MPEP Section 2106.04, Step 2A, Prong One checks whether the claim as a whole is directed to a judicial exception. The Examiner alleges that "a human can mentally perform, with the physical aid such as pen and paper" the claimed operations. Action at 8 and 9. The claimed operations as amended cannot be performed by a human because it requires the use of at least a "processor" which:
…
While one or more of these operations, arguendo, may be capable of being performed by a human with pen in paper, amended Claim 1, as well as amended Claims 8 and 9 as a whole, are not capable of being performed by a human and are not merely mathematical concepts as alleged by the Examiner. The Applicant respectfully submits that the claims are not "directed to" an abstract idea at all for all the foregoing reasons. This is at least demonstrated by the recitations repeated in the above-bulleted list, which recite specific technical processes and address technical problems. As such, the present claims are patent-eligible at the first prong of Step 2A. Further, an invention is not rendered ineligible simply because it involves an abstract concept. In fact, inventions that integrate the building blocks of human ingenuity into something more by applying abstract ideas in a meaningful way are eligible. See Alice, supra, 134 S. Ct. at 2354. Based on the foregoing, the Applicant respectfully submits that none of the claims is "directed to" an abstract idea. Instead, Claims 1-9 merely involve, and only in part, the judicial exceptions that the Examiner identifies, which is insufficient to render an invention patent ineligible.”
Examiner respectfully disagrees.
Based on what is recited in claim 1, the broadest reasonable interpretation (BRI) in view of Specification of the claim limitations “dividing the set of time series data into a set of windows” and “calculating a set of data features from the set of windows” encompass a mental process of dividing a set of time series data and encompass a mathematical process of calculating a set of data features, with the help of pen and paper. According to Applicant, the claimed operations cannot be performed by a human because it requires the use of at least a “processor” is not sufficient when determining the eligibility of such limitations. Simply requiring a processor, particularly a generic one, doesn’t automatically transform an abstract idea into a patent-eligible subject matter. Moreover, the core of the invention remains the mathematical operations of dividing data and calculating features despite the inclusion of a processor. Even if the processor is considered an additional element, the Specification doesn’t adequately provide enough details on how the processing leads to a tangible, real-world improvement or solution to a technical problem.
Applicant argued that “Step 2A, Prong Two. Even assuming that the claims recited a judicial exception in one of the enumerated groups, the claims recite additional elements-the same recitations quoted above-that integrate any exception into a practical application of the exception. As explained in MPEP Section 2106.04(d), in Step 2A, Prong Two, Examiners evaluate whether the claims recite additional elements integrating the exception into a practical application of that exception. If the recited exception is integrated into a practical application of the exception, then the claim is eligible at Prong Two of revised Step 2A, and this concludes the eligibility analysis. In other words, if an abstract idea is integrated into a practical application of that abstract idea, the claim passes muster under Section 101.
Applicant reminds the Examiner that MPEP 2106 explains the proper approach for examining a claim for patent subject matter eligibility and, specifically, MPEP 2106.04(d) provides the proper approach for determining whether the claims integrate an alleged judicial exception into a practical application. First, MPEP 2106.04(d) explains that the claims as a whole must be considered, not simply individual words or phrases. Furthermore, it explains that an alleged judicial exception may be integrated into a practical application if, among other things, it leads to "an improvement in the functioning of a computer, or an improvement to other technology or technical field." The Office Action contends, at pages 9 and 10, that:
The additional elements do "not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea." Applicant traverses this position. The claims, when properly considered as a whole, do not merely recite independently abstract ideas that are not integrated into a practical application with no meaningful limits on practicing the abstract idea, as alleged by the Examiner. As claimed, the claims recite operations performed for detecting concept drift in a time series data set received from an external data storage device, and when concept drift is detected, notify a (separate) client device
…
See amended Claim 1. These claim elements, when taken as a whole, demonstrate how the concept drift detection device of amended Claim 1 improves the previous technology by performing monitoring of the time series data from an external device and notifying an external client device that concept drift is occurring so appropriate corrective actions may be taken. This improves the overall performance of the system using or making the time series data sets. This is different from conventional systems, where no monitoring is performed and concept drift is not detected until it causes a major error.
Thus, at least amended Claim 1 is integrated into a practical application that provides technical improvements to detecting concept drift in seasonal time series data. This holds true whether or not any of the operations are considered "abstract" by the Examiner. See, MPEP 2106.04(d).
To summarize, the MPEP states that "[a] claim is not 'directed to' a judicial exception, and thus is patent-eligible, if the claim as a whole integrates the recited judicial exception into a practical application of that exception." MPEP at 2106.04. Thus, to the extent that the Office Action contends that the claims involve a judicial exception, the claims are still patent-eligible because the claims are integrated into a practical application, and therefore, are not directed to any purported judicial exception.”
Examiner respectfully disagrees.
Applicant always considers the eligibility of a claim after considering the claim as a whole and not simply just individual words or phrases.
Based on what is recited in claim 1, BRI in view Specification of the claim limitations “receiving the time series data set from an external data storage device, wherein the time series data set includes … subsequent to the first time period”, “generating a baseline model based on a subset of the set of past time series data”, “determining, based on the baseline statistic and the current distance, presence or absence of concept drift between the set of current time series data and the set of past timeseries data” are not sufficient for integrating the abstract idea into a practical application. The step of “receiving the time series data” is quite a generic function performed by computers in many applications, and it doesn’t add a meaningful limitation to the abstract idea. “Generating the baseline model” isn’t sufficient defined to go beyond a general instruction to perform the abstract idea on a computer. Additionally, the step of “determining the concept drift” is quite abstract and is not sufficient to be integrated into a practical step. The step of “transmit a concept drift notification to a client device when the processor determines that concept drift is present” might be considered as an inventive step that integrates an abstract idea into a practical application. However, this step is the same as sending an email or displaying a message, as it is merely an environment for implementing the abstract idea, not a transformed invention.
Hence, claim 1 when considered as a whole doesn’t have the ability to integrate the abstract idea into a practical application. The claims, as a whole, describe performing an abstract idea (detecting concept drift) using a generic computer system to receive data, process it according to the abstract idea, and output a notification. This combination is not significantly more than the abstract idea itself. The claim as an ordered combination must amount to significantly more than just the ineligible concept. The combination of steps doesn’t disclose technical improvements in the computer’s functionality, and it doesn’t add enough to the underlying abstract idea to transform it into patent-eligible subject matter.
Applicant argued that “Step 2B As explained in MPEP Section 2106.05, in Step 2B, Examiners evaluate "[d]oes the claim recite additional elements that amount to significantly more than the judicial exception? Examiners should answer this question by first identifying whether there are any additional elements (feature/limitations/steps) recited in the claim beyond the judicial exception(s), and then evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept (i.e., amount to significantly more than the judicial exception(s))" The MPEP in 2106.05 provides the following examples of elements that the courts have found to qualify as "significantly more" when recited in a claim with a judicial exception include:
…
The Examiner further alleges that "the additional elements in step 2A-Prong 2 those are forms of insignificant extra-solution activities, do not amount to significantly more than an abstract idea because the court decision have determined that these additional elements of receiving a time series data set; generating a baseline model based on the subset; and determining presence or absence of concept drift tobe well-understood, routine, and conventional when claimed in a merely generic manner" See Action at 10 and 11. Applicant traverses this position.
First, the Applicant respectfully requests that the Examiner identify the court decisions that have determined that the specific elements of "receiving a time series data set; generating a baseline model based on the subset; and determining presence or absence of concept drift" to be well-understood, routine, and conventional. MPEP 2106.05(d)(II), which the Examiner cites, does not list any of these specific elements. Further MPEP 2106.05(d)(II) states "[t]his listing is not meant to imply that all computer functions are well-understood, routine, conventional activities, or that a claim reciting a generic computer component performing a generic computer function is necessarily ineligible" and "courts have held computer-implemented processes to be significantly more than an abstract idea (an thus eligible), where generic computer components are able in combination to perform functions that are not merely generic."
Amended Claim 1 adds a specific element other than what is a well-understood, routine, conventional activity in the field, which confines the claim to a particular useful application
…
These elements, as well as the claim as a whole, amount to an inventive concept and therefore amount to significantly more than the judicial exception(s). For at least these reasons, the rejections of the amended independent Claims 1, 8, and 9 under 35 U.S.C. § 101 should be withdrawn. For at least these reasons, Applicant respectfully requests the Examiner to withdraw the rejection of Claims 1-9 under 35 U.S.C. § 101 and to allow Claims 1-9. Applicant respectfully requests reconsideration and allowance of all claims.”
Examiner respectfully disagrees.
Based on the reasons listed above under step 2A – prong 2, the steps of “receiving a time series data set”; “generating a baseline model based on the subset”; “determining presence or absence of concept drift”; and “transmitting a concept drift notification” are not sufficient enough to be considered as inventive steps to be integrated into a practical application. These steps are not necessarily the same as the elements listed in MPEP 2106.05(d)(II) that Examiner has listed in the Office Action, but they recite some specific elements those are well-understood, conventional and routine activities.
For example, the step of “receiving the time series data set from an external data storage device, wherein … subsequent to the first time period” is likely considered a generic and conventional activity performed by virtually all computer systems. Receiving data from storage doesn't represent a technical improvement in computing itself, nor does it transform the abstract idea in a meaningful way. Or the step of “transmitting a concept drift notification to a client device when the processor determines that concept drift is present” is a generic, conventional, and routine activity. Merely sending a notification as a result of detecting the abstract idea doesn't transform it into a patent-eligible invention, especially if the notification itself is generic.”
Therefore, claim 1 and its corresponding claims 8 and 9, each claim as a whole does not integrate an abstract idea into a practical application, and the additional elements as listed above are well-understood, routine and conventional activities when claimed in a merely generic manner. Dependent claims 2-7 are not patent eligible as well.
Applicant’s arguments filed on 04/29/2025 regarding the rejections to claims 1-9 under 35 U.S.C. 103 have been fully considered but are not persuasive.
Applicant argued that “D. Claims 1-3, 8, and 9 are allowable over the proposed Cavalcante-Oliner combination.
The Examiner rejects independent Claim 1-3, 8, and 9 under 35 U.S.C. § 103 as being allegedly unpatentable over "FEDD: Feature Extraction for Explicit Concept Drift Detection in Time Series" ("Cavalcante") in view of U.S. Publication No. 2018/0219889 ("Oiner").
Applicant respectfully disagrees with the Examiner.
As an initial matter, the Examiner on page 13 of the Action only indicates that Claims 1-3 are rejected over the Cavalcante-Oliner combination. Later, on page 18 of the Action, the Examiner addresses independent Claims 8 and 9 as being rejected for the same reasons that the Examiner alleges on page 18 for independent Claim 1. The Examiner is respectively requested to verify that Claims 8 and 9 are also being rejected under 35 U.S.C. § 103 as being allegedly unpatentable over the Cavalcante-Oliner combination
…
For at least the reasons stated above, amended Claim 1 is allowable over the Cavalcante and Oliner combination. Amended Claims 8 and 9, recite substantially similar features to amended Claim 1 and are therefore allowable for analogous reasons. Claims 2 and 3 depend on the independent claims shown above to be allowable. Accordingly, Applicant respectfully requests reconsideration and allowance of Claims 1-3.
E. Claims 4-7 are allowable over the proposed Cavalcante-Oliner-Beaver combination.
The Examiner rejects independent Claim 4-7 under 35 U.S.C. § 103 as being allegedly unpatentable over Cavalcante in view of Oliner and further in view of U.S. Publication No.
2020/0210393 ("Beaver"). Applicant respectfully disagrees with the Examiner.
Claims 4-7 depend on the independent claims shown above to be allowable. Beaver does not remedy the deficiencies of Cavalcante. Accordingly, Applicant respectfully requests reconsideration and allowance of Claims 4-7. Applicant respectfully requests reconsideration and allowance of all claims.”
First of all, Examiner appreciates that Applicant has pointed out the error in the Office Action. Examiner mistakenly excluded claims 8 and 9 on page 13 those should be included along with rejection over Cavalcante-Oliner combination.
Besides that, Examiner respectfully disagrees with Applicant’s arguments.
Examiner remains the opinion of the combination of Cavalcante and Oliner teaches the steps of “divide the set of past time series data into a set of past windows;divide the set of current time series data into a set of current windows”; “divide a set of baseline data created by the baseline model into a set of baseline windows.” In the disclosure of Cavalcante [page 3-5], the steps of FEDD algorithm are presented in figure 1, such that an initial subset of time series data is distributed in the window size m. The FEDD method explicitly focuses on detecting concept drift by monitoring the evolution of statistical features extracted from time series data. This inherently involves comparing these features over different time periods, often achieved by processing data in windows, such as a "past window" and a "current window". The differences in feature values extracted from these windows would signal potential drifts. The Feature Extraction (FE) module in FEDD is responsible for extracting features (mean, variance, standard deviation) from segments of the time series data. These segments are essentially the "windows" that the current invention refers to. Finally, the Drift Detection (DD) module will analyze the changes in these extracted features over time to identify concept drift.
Even though Cavalcante does not explicitly teach “transmit a concept drift notification to a client device when the processor determines that concept drift is present”, Oliner does teach the step of transmitting the notification to client device. In fact, in paragraphs 0228, 0280 and 0293, Oliner teaches that anomalous and/or unusual behavior of a time series can indicate some underlying problem in a computing system or service. When such an anomaly occurs, it is often critical to quickly notify appropriate users or take appropriate actions. The anomaly detection tool may cause an indicator or a push notification of the error to be transmitted to a user or user device. Hence, the combination of Cavalcante and Oliner does disclose the amend step of claim 1 as well as its corresponding claims 8 and 9.
Therefore, amended claim 1 and its corresponding claims 8 and 9 are not patent eligible for at least the reasons above. Dependent claims 2-7, those are depended on claim 1, are not patent eligible for the same reasons.
Conclusion
THIS ACTION IS MADE FINAL. See MPEP 706.07(a). Applicant is remined of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Q.L.P./Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143