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 .
The application has been examined. Claims 1 – 20 are pending in this office action.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Based upon consideration of all of the relevant factors with respect to the claims as a whole, claims 1 – 20 are determined to be directed to an abstract idea and not significantly more than the abstract idea itself. The rationale for this determination is explained below:
The representative claim 1 (and other independent claims 11 and 16) recites a computer-implemented method comprising: detecting, by an outlier detector, a first potential outlier in a data structure; determining, by the outlier detector, whether the first potential outlier is a first outlier based on a first threshold; applying, by an outlier corrector, responsive to determining the first potential outlier is a first outlier, a data quality rule to the first outlier; detecting, by the outlier detector, a second potential outlier in the data structure; decreasing, by the outlier detector, the first threshold to a second threshold; determining, by the outlier detector, whether the second potential outlier is a second outlier based on the second threshold and applying, by the outlier corrector, responsive to determining the second potential outlier is a second outlier, the data quality rule to the second outlier.
The claims recite detecting a potential outlier, determining based on a threshold whether the potential outlier is an outlier and adjusting the threshold value, this is grouped under the abstract idea of mental processes as it can be performed in the human mind. The claims also recite applying a corrector outlier, this is grouped under the abstract idea of mathematical concepts. Before computers it would have been obvious for a person who is monitoring data to track if the value seemed to be out of limit or boundary and try to confirm if the value truly was different or deviating from the group of data based on a certain value and if such was the case a correction could be applied by the person. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The claims additionally recite - Claim 1: a computer-implemented, data structure. - Claim 10: a computer program product, computer readable storage media, processor. - Claim 16: a system, a processor, a computer readable storage media, data structure.
This judicial exception is not integrated into a practical application. However, the limitations merely amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f) and generally linking the use of the judicial exception to a particular technological environment or field of use, as discussed in MPEP 2106.05(h). Furthermore, a method for transmitting, receiving, and processing information does not amount to improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a), applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b), effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c), or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e). Accordingly, the 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. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are mere instructions to apply an exception using a generic computer component and thus cannot provide an inventive concept. 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 see MPEP 2106.05 (a) I (iii). The claim is not patent eligible.
Claims 2 – 9, 11 – 15 and 17 – 20 further narrow the abstract idea recited in the independent claims 1, 10 and 16 and are therefore directed towards the same abstract idea. The dependent claims are directed towards further narrowing the abstract idea of detecting outliers, correcting the outliers and adjusting the detection.
Claims 2 – 9, 11 – 15 and 17 – 20 do not recite any additional elements that have not already been analyzed above. Therefore, the claims do not direct the claims to recite a practical application.
Therefore, claims 1 – 20 are rejected under U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter 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 pre-AIA 35 U.S.C. 103(a) 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 under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 1 – 20 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Suzani et al. (US 20220188694 A1) (‘Suzani’ herein after) further in view of Niyazov et al. (US 20240070130 A1) (‘Niyazov’ herein after).
With respect to claim 1, 10, 16,
Suzani discloses a computer-implemented method comprising: detecting, by an outlier detector, a first potential outlier in a data structure (figure 1, 3, 4, paragraph 36, 46 – 48 teaches the anomaly detector which would help in identify the potential outlier, Suzani); determining, by the outlier detector, whether the first potential outlier is a first outlier based on a first threshold (figure 1, 3, 4, paragraphs 37, 42 – 43 teach the threshold is used to determine if a value is an outlier or an anomaly, Suzani); applying, by an outlier corrector, responsive to determining the first potential outlier is a first outlier, a data quality rule to the first outlier (figure 1, 3, 4, paragraph 27 teaches that the moving standard deviation of anomaly scores is adjusted based on a moving average of anomaly scores which is then adjusted based on the anomaly score. The adaptive anomaly threshold is then adjusted based on the moving average of anomaly scores and the moving standard deviation of anomaly scores and paragraph 43, 50, Suzani); detecting, by the outlier detector, a second potential outlier in the data structure (figure 1, 3, 4, paragraph 36, 46 – 48 teaches the anomaly detector which would help in identify the potential outlier, Suzani); decreasing, by the outlier detector, the first threshold to a second threshold (paragraph 25, 26, 60, 99 teaches depending on the flow of the input log data, the dynamic threshold might increase for a brief period of time and then decrease, which usually indicates a temporary change in input log data such as caused by a software update. The system can keep operating properly with no need of retraining the ML model. That saves operating costs and computer resources because retraining the ML model is an expensive operation and is intensive of computational resources. When the dynamic threshold increases without soon decreasing, that indicates that a long-lived concept drift happened, and retraining the ML model is beneficial or necessary, Suzani); determining, by the outlier detector, whether the second potential outlier is a second outlier based on the second threshold (figure 1, 3, 4, paragraphs 37, 42 – 43 teach the threshold is used to determine if a value is an outlier or an anomaly, Suzani) and applying, by the outlier corrector, responsive to determining the second potential outlier is a second outlier, the data quality rule to the second outlier (figure 1, 3, 4, paragraph 27 teaches that the moving standard deviation of anomaly scores is adjusted based on a moving average of anomaly scores which is then adjusted based on the anomaly score. The adaptive anomaly threshold is then adjusted based on the moving average of anomaly scores and the moving standard deviation of anomaly scores and paragraph 43, 50, Suzani).
Suzani teaches applying an adjustment for the correction for the anomaly and outlier but does not specifically teach as claimed the data quality rule.
However, Niyazov teaches the data quality rule and application in paragraphs 51 – 53 teaching the data quality assessment and the rules engine along with a rule learner. Paragraphs 57 – 62 teach data rule definition may be used to develop rule logic for analyzing data, where the rule logic may describe a particular condition in a record with the use of basic syntax where a variable, such as a word or term, is evaluated based on a given condition. Rule logic may evaluate to a true or false value (or may set up pass or fail check, etc.) that evaluates the quality of the data. Data rule definitions may be used as a basis for data rules and quality rule. Similar to data rules, a quality rule may evaluate and validate specific conditions associated with a data source by binding data rule definitions to physical data. A quality rule may be run as part of the data quality analysis operations. The output of a quality rule may be displayed as a rule violation in the data quality score analysis results.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention because both references are directed to the same field of study, namely anomaly/outlier data and corrections. Furthermore, Niyazov’s method adds to Suzani along with identifying data anomalies to generate a compliant data environment, and use the compliant data environment create or run an intelligent analysis and rules recommendation engine that identifies and corrects data anomalies.
With respect to claim 2, 11, 17,
Suzani as modified discloses the method of claim 1, wherein determining whether a potential outlier is an outlier further comprises: determining whether the potential outlier is an additive outlier indicating a deviation at a particular data point; converting, responsive to a determination that the potential outlier is an additive outlier, the potential outlier to a standardized value and determining whether the potential outlier is an outlier by applying a threshold to the standardized value (paragraph 43 and 58 – 61 teaches the deviation of scores and 66, 70 teach the standard deviation, Suzani).
With respect to claim 3, 12, 18,
Suzani as modified discloses the method of claim 1, wherein determining whether a potential outlier is an outlier further comprises: determining whether the potential outlier is a level shift outlier indicating a deviation at a plurality of data points; applying, responsive to a determination that the potential outlier is a level shift outlier, a heuristic to the potential outlier to compute a heuristic value and determining whether the potential outlier is an outlier by applying a threshold to the heuristic value (paragraphs 49 – 50, 60 – 61, Suzani and paragraph 42, 45, 49 – 50, Niyazov).
With respect to claim 4, 13,
Suzani as modified discloses the method of claim 1, further comprising: applying the data quality rule to a plurality of potential outliers in the data structure; determining, based on the applying, whether the data quality rule increases a number of outliers and proposing, responsive to a determination that the data quality rule increases the number of outliers, to remove the data quality rule (paragraphs 51 – 53, 57 – 62, Niyazov).
With respect to claim 5,
Suzani as modified discloses the method of claim 1, further comprising: determining a similarity between the data structure and a second data structure based at least on a data type and a data source; and applying, based on the similarity, the data quality rule to a potential outlier in the second data structure (paragraph 40, 42 and 57 – 62, Niyazov).
With respect to claim 6, 14, 19,
Suzani as modified discloses the method of claim 1, wherein the outlier detector includes a machine learning module configured to detect a potential outlier, and wherein training the machine learning module includes providing the machine learning module a timeseries of historical data (paragraphs 152 – 157, Suzani and paragraphs 48, 65, Niyazov).
With respect to claim 7, 15, 20,
Suzani as modified discloses the method of claim 1, wherein the outlier corrector includes a machine learning module configured to propose a new data quality rule to correct a potential outlier, and wherein training the machine learning module is based on a user feedback on the proposed new data quality rule (paragraphs 152 – 157, Suzani and paragraphs 51 – 53, 57 – 62, Niyazov).
With respect to claim 8,
Suzani as modified discloses the method of claim 1, wherein the data structure includes a table comprising a plurality of columns, and the first potential outlier and the second potential outlier are associated with a column in the plurality of columns (paragraph 164 – 169, Suzani).
With respect to claim 9,
Suzani as modified discloses the method of claim 1, wherein the first threshold is based at least on one of a mean, a median, and a standard deviation (figure 1, 3, 4, paragraph 53, 58 – 60, Suzani).
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20170228432 A1 teaches automated outlier detection system implements an unsupervised set of processes to determine feature subspaces from a dataset; determine candidate exploratory actions, where each candidate exploratory action is a specific combination of a feature subspace and a parameterized instance of an outlier detection algorithm; and identify a set of optimal exploratory actions to recommend for execution on the dataset from among the candidate exploratory actions.
US 20210390455 A1 teaches when significant drift is detected and/or when model accuracy has deteriorated, models can be automatically refreshed with updated training data and/or replaced with one or more other models. A model controller is used to automate model monitoring and management activities across multiple prediction environments where models are deployed and prediction jobs are executed.
US 9262451 B1 teaches data is checked against data quality rules and a corresponding report is generated. The report is provided to an entity, which may be a subscriber. Data correction schema is used to correct stored data. The data quality rules or the data correction schema may be amended or modified according to user input, which may be a subscriber entity.
US 20190370610 A1 teaches data anomaly detection including recommending one or more algorithms from a set of algorithms to process received time series data, wherein the one or more algorithms are recommended based at least in part on a type of workload for processing the received time series data. Assisted parameter tuning is provided for a detected anomaly alert and calibration, and the received time series data is processed based on a user selected algorithm that is parameter tuned, thereby resulting in more efficient and reliable anomaly detection.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAVNEET K GMAHL whose telephone number is (571)272-5636.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SANJIV SHAH can be reached on . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NAVNEET GMAHL/Examiner, Art Unit 2166 Dated: 8/6/2026
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166
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