Detailed Action
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is in response to claims filed on 05/06/2025.
Claims 1-20 are pending; claims 1, 8 and 15 are independent.
Examiner's Notes
The Examiner cites particular sections in the references as applied to the claims below for the convenience of the applicant(s). Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant(s) fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 102 that forms the basis for all the rejections under this section made in this Office Action:
A person shall be entitled to a patent unless—
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5, 8-12 and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Mamaev et al., Pub. No.: US 2024/0333742 A1 (Mamaev).
Claim 1. Mamaev teaches:
A computer-implemented method for generating categorical data for missing values in anomaly detection systems, comprising:
aligning irregular time-series data obtained from cyber-physical systems data into regular time-series data by utilizing a generated timestamp sequence to obtain aligned time-series data; (Mamaev, observation received in irregular time interval is converted to regular time interval in UTG node: ¶¶ 22, “a method for detection of anomalies in a cyber-physical system in real-time is disclosed… obtaining, in real-time, randomly distributed stream of observations of CPS parameters…converting an observation of the CPS parameter to a uniform temporal grid (UTG), when at least a criterion for unloading at least one UTG node of the converted observations is satisfied, unloading the UTG nodes corresponding to the satisfied criterion, for each unloaded UTG node, calculating, for each output parameter of an CPS of a set of output parameters of the CPS, output values of the CPS parameters for the respective unloaded UTG node, and detecting an anomaly in the CPS based on the output values of the CPS parameters)
filling missing values from the aligned time-series data with generated categorical time-series data; (Mamaev, gap/missing value is filled by imputation: ¶ 204, “the calculator 430 does not attempt to interpret the interruption of the thread in a special way, but simply compensates for the gap by imputation”)
performing anomaly detection for a cyber-physical system to obtain system anomalies; and (Mamaev, ¶ 22, “detecting an anomaly in the CPS based on the output values of the CPS parameters”; ¶ 222, “the returned results may further be transmitted to anomaly detector 200 and predictive analyzer”)
performing a corrective action to resolve issues with the cyber-physical system caused by the system anomalies. (Mamaev, ¶ 152, “incidents are sent to one or more modules designed to work with incidents (e.g., a module associated with a service, not specified in the drawing)”; ¶ 268, “The causes of such incidents cannot always be stopped automatically and require investigation by a specialist”)
Claims 8 and 15 are rejected under the same rationale as above.
Claim 2. The computer-implemented method of claim 1, wherein performing the corrective action further comprises generating instruction code to control an autonomous vehicle to resolve issues caused by the detected system anomaly within the autonomous vehicle. (¶ 71, wherein “electronic vehicle systems, smart cars, smart cities, industrial systems, etc.” and ¶ 152, wherein “incidents are sent to one or more modules designed to work with incidents” suggests sending the incident as a code to modules that are designed to work/resolve the incident within the cyber-physical system e.g., an autonomous vehicle)
Claims 9 and 16 are rejected under the same rationale as above.
Claim 3. The computer-implemented method of claim 1, wherein performing the corrective action further comprises generating instruction code to block packets from incoming internet protocol (IP) address detected that caused the system anomaly within a distributed computing system. (¶ 29, wherein “when a frequency of occurrence of “late observation” or “source clock failure” incidents exceeds a specified threshold, overriding the properties of the stream” suggests blocking packets from incoming by overriding its properties)
Claims 10 and 17 are rejected under the same rationale as above.
Claim 4. The computer-implemented method of claim 1, wherein aligning the irregular time-series data further comprises utilizing a fixed time interval to generate the generated timestamp sequence. (¶¶ 78-79, “A cell of a UTG node is a time interval around a UTG node, the length of which is equal to the distance between the nodes (i.e., the timestamps of the UTG nodes)”)
Claims 11 and 18 are rejected under the same rationale as above.
Claim 5. The computer-implemented method of claim 1, wherein filling the missing values further comprises filtering the generated categorical time-series data based on a number of special categories. (specified threshold is used for filtering: ¶ 35, “when a frequency of occurrence of “late observation” or “source clock failure” incidents exceeds a specified threshold, overriding the properties of the stream”, ¶ 232, “an anomaly is detected in the event that the overall forecast error exceeds the threshold value”)
Claims 12 and 19 are rejected under the same rationale as above.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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 6-7, 13-14 and 20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Mamaev as shown above, in view of Medium.com, “Converting an irregular time series to a regular time series: Resampling… Data Imputation Demystified Time Series Data” (Medium).
Claim 6. Mamaev disclosed the computer-implemented method of claim 5; Mamaev did not specifically disclose but Medium discloses wherein filling the missing values further comprises removing categorical time-series data based on a threshold for a proportion of the special categories in a normal time-series data. (Medium.com, wherein a suitable strategy, e.g., deletion strategy can be used for handling missing values: “Various strategies exist to manage missing values, with the most suitable one often dependent on the nature of both the data and missing values themselves… Deletion: This strategy entails eliminating any rows that contain missing values…Constant Imputation: This technique substitutes all missing values with a constant, which might be a common value like zero or an unusual one that effectively establishes a new category for missing values… Mean/Median/Mode Imputation: In this approach, missing values are replaced with the mean (for continuous data), median (for ordinal data), or mode (for categorical data) of the available values”)
It would have been obvious before the effective filling date of the claimed invention to a person having ordinary skill in the art to combine the applied references for disclosing filling the missing values further comprises removing categorical time-series data based on a threshold for a proportion of the special categories in a normal time-series data because doing so would provide for utilizing a deletion strategy for managing missing values based on the nature of both the data and missing values themselves for achieving the same predictable result)
Claims 13 and 20 are rejected under the same rationale as above.
Claim 7. The computer-implemented method of claim 1, wherein filling the missing values further comprises converting numerical data obtained from the cyber-physical systems into categorical time-series data. (Medium, wherein a suitable strategy, e.g., converting numerical into categorical time-series can be used for handling missing values: “Various strategies exist to manage missing values, with the most suitable one often dependent on the nature of both the data and missing values themselves… Deletion: This strategy entails eliminating any rows that contain missing values…Constant Imputation: This technique substitutes all missing values with a constant, which might be a common value like zero or an unusual one that effectively establishes a new category for missing values… Mean/Median/Mode Imputation: In this approach, missing values are replaced with the mean (for continuous data), median (for ordinal data), or mode (for categorical data) of the available values”)
Claim 14 is rejected under the same rationale as above.
Conclusion
The prior arts made of record in PTO-326 and not relied upon are considered pertinent to applicant's disclosure.
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/MOHSEN ALMANI/Primary Examiner, Art Unit 2159