Detailed Action
Applicant amended claims 1, 5-6, 8, 12-13, 15 and 19-20 and presented claims 1-20 on 06/26/2026 for reconsideration.
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 1-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Mamaev et al., Pub. No.: US 2024/0333742 A1 (hereinafter Mamaev), in view of Medium.com, “Converting an irregular time series to a regular time series: Resampling… Data Imputation Demystified Time Series Data” (hereinafter Medium).
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 that includes identical time intervals from the irregular time-series data to obtain aligned time-series data based on similarity of the time-series data; (Mamaev, irregular time-series data is aligned/converted to regular time interval, e.g., regular time-series data by utilizing a generated time stamp, a 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 including common time intervals from the generated timestamp sequence; (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”; note that an imputation, based on definition, is a placeholders based on)
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”)
Mamaev did not specifically disclose but Medium discloses special category placeholders that expose attributes for the missing values. (Medium, “Approaches to Handling Missing Values”, wherein a missing value can be replaced with an unusual value “that effectively establishes a new category for missing values”)
“Various strategies exist to manage missing values, with the most suitable one often dependent on the nature of both the data and the missing values themselves”. (Medium, “Approaches to Handling Missing 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 special category placeholders that expose attributes for the missing values because doing so would provide for utilizing a strategy that best suit a specific need for achieving the same predictable result as Mamaev.
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. (Mamaev, ¶ 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. (Mamaev, ¶ 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. (Mamaev, ¶¶ 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 category placeholders. (Mamaev, 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”; Medium, wherein a special category placeholder is used for representing a missing value)
Claims 12 and 19 are rejected under the same rationale as above.
Claim 6. The computer-implemented method of claim 5, wherein filling the missing values further comprises removing categorical time-series data based on a threshold for a proportion of the special category placeholders in a normal time-series data. (Medium, 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”)
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.
Response to Amendment and Arguments
With respect to interview summary, Applicant noted that: “Applicant tried to conduct a telephone interview to discuss the present application but Examiner decided to postpone such an interview after a response has been filed. Applicant respectfully the Examiner to discuss the patentability of the present invention as soon as Examiner is able.” Remarks, 8.
In response: the interview was denied because “John Christopher Lopez” wan not an Applicant or an authorized person to conduct the interview. Office employees are forbidden to hold either oral or written communication with an unregistered or a suspended or excluded attorney or agent regarding an application unless it is one in which said attorney or agent is the applicant. See MPEP § 105.
With respect to rejected claims, Applicant argued: “there is no ‘generated timestamp sequence” utilized in Mamaev. Remarks, 9.
In response: the argument is not persuasive. The combination of Mamaev and Medium disclose the features as recited in amended claims 1-20 as shown above. Furthermore, a uniform temporal grid (UTG), as disclosed by Mamaev, comprises nodes associated with timestamps. There is a predefined distance between adjacent nodes of UTG: ¶ 75, “A uniform temporal grid (UTG) refers to an infinite sequence of moments in time in which the distance between adjacent elements of the sequence is invariable (the same). See also, Mamaev, ¶¶ 75-78.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. It is suggested that the Applicant review these documents before submitting any amendments.
THIS ACTION IS MADE FINAL. Applicant is reminded 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 extension fee 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Mohsen Almani whose telephone number is (571)270-7722. The examiner can normally be reached on M-F, 9 AM-5 PM, ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ann J. Lo can be reached on 571-272-9767. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHSEN ALMANI/Primary Examiner, Art Unit 2159
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