DETAILED ACTION
Remarks
This office action is issued in response to communication filed on 11/28/23. Claims 1-20 are pending in this Office 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 .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for 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 1-9 and 12-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Muthuswamy et al.,(US Patent Application Publication 2023/0171274 A1, hereinafter “Muthuswamy”)
As to claim 1, Muthuswamy teaches a computer-implemented method, the computer-implemented method comprising: generating, by one or more processors and using a current pipeline version of a data processing pipeline, a time-dependent output for a current data version of a dynamic input dataset at a current time ( Muthuswamy par [0053] teaches pipeline executer executes the SAD pipelines including reading the SAD pipeline configuration, its version, and execute the tasks / steps in the pipeline. The pipeline executer updates the pipeline run configuration and runs the pipeline task-by-task to generate output insights and / or alert) );
generating, by the one or more processors, a current compliance data object that is indicative of the current pipeline version, the current data version, and the time-dependent output (Muthuswamy par [0053] teaches the pipeline executer updates the pipeline run configuration and runs the pipeline task-by-task to generate output insights and / or alert. The alert is the claim “compliance data object”);
identifying, by the one or more processors, a performance anomaly based on a comparison between the current compliance data object and a plurality of historical compliance data objects, ( Muthuswamy par [0083] teaches the Governance View manager checks the pipeline runs for a given claim against historical runs to determine whether the number of alerts generated for the pipeline runs is abnormal)
wherein the plurality of historical compliance data objects corresponds to a plurality of historical times temporally preceding the current time (Muthuswamy par [0083] teaches the Governance View manager checks the pipeline runs for a given claim against historical runs to determine whether the number of alerts generated for the pipeline runs is abnormal) ;
identifying, by the one or more processors, a project segment corresponding to the performance anomaly, wherein the project segment is indicative of at least one of the current pipeline version, the current data version, or the time-dependent output monitored for a set of models (Muthuswamy par [0053] teaches traceability manager can help to identify the pipeline runs for a given alert by trancing the alerts, and what models, programs , filters , data, transform functions, insights and/or events were used to generate the alerts) ; and
initiating, by the one or more processors, the performance of a predictive action based on the project segment. (Muthuswamy par [0083] teaches generating report and/or identifying the reasons for the differences in the pipeline runs )
As to claim 2, Muthuswamy teaches the computer-implemented method of claim , wherein the current data version of the dynamic input dataset based on a time-based priority for one or more time-dependent data objects. (Muthuswamy par [0057] teaches plurality of filters that applied to data 205)
As to claim 3, Muthuswamy teaches the computer-implemented method of claim 2, wherein: (i) the current data version comprises a subset of a plurality of time-dependent data objects that correspond to a current time window, and (ii) the time-based priority is based on the current time window and one or more object attributes of the plurality of time-dependent data objects. (Muthuswamy par [0057] teaches filter 420 process the data 205 or subset 206. A simple function that computes whether the loss date is a weekday or a weekend and whether during business ours or after business hours)
As to claim 4, Muthuswamy teaches the computer-implemented method of claim 3, wherein the plurality of time-dependent data objects are aggregated from a plurality of disparate data sources. (Muthuswamy par [0033] teaches network or website may include a financial institution that records/store information and also include an insurance organization or syndicate that records/store information)
As to claim 5, Muthuswamy teaches the computer-implemented method of claim 1, wherein: (i) the data processing pipeline comprises a plurality of connected data processing models arranged in a directed acyclic graph,( Muthuswamy par [0043] teaches building graph) and (ii) the current pipeline version is indicative of at least a current model version and a current set of weighted parameters for at least one of the plurality of connected data processing models. (Muthuswamy par [0052] teaches for each SAD pipeline run, the pipeline run manager records tag data, version data, snapshot data, model, configuration and results )
As to claim 6, Muthuswamy teaches the computer-implemented method of claim 5, wherein at least one of the plurality of connected data processing models comprises a machine learning model. ( Muthuswamy par [0042] teaches machine learning)
As to claim 7, Muthuswamy teaches the computer-implemented method of claim 1, wherein: (i) a historical compliance data object of the plurality of historical compliance data objects corresponds to a historical time of the plurality of historical times, and (ii) the historical compliance data object is indicative of: (a) a historical pipeline version of the data processing pipeline at the historical time, (b) a historical data version of the dynamic input dataset at the historical time, and (c) a historical time-dependent output previously generated for the historical data version of the dynamic input dataset using the historical pipeline version of the data processing pipeline. (Muthuswamy par [0052] teaches for each SAD pipeline run, the pipeline run manager records tag data, version data, snapshot data, model, configuration and results)
As to claim 8, Muthuswamy teaches the computer-implemented method of claim 7, wherein the performance anomaly is based on a comparison between the time-dependent output, the historical time-dependent output, and an anomaly threshold.( Muthuswamy par [0083] teaches the pipeline runs for the given claim are checked against historical runs to check if the number of alerts generated for the given two pipeline runs are abnormal)
As to claim 9, Muthuswamy teaches the computer-implemented method of claim 8, wherein identifying the project segment corresponding to the performance anomaly comprises: identifying at least one of (i) a model-based anomaly based on a comparison between the historical pipeline version and the current pipeline version, or (ii) a data-based anomaly based on a comparison between the historical data version and the current data version. ( Muthuswamy par [0083] teaches the pipeline runs for the given claim are checked against historical runs to check if the number of alerts generated for the given two pipeline runs are abnormal)
As to claim 12, Muthuswamy teaches the computer-implemented method of claim 1, wherein: (i) the data processing pipeline is associated with an execution workflow comprising an execution frequency, and (ii) the current time and the plurality of historical times are based on the execution frequency. (Muthuswamy par [0046] teaches for a period of ten (10) days the system generated an average of 100 alerts and for the next two days the system generates an average of 250 alerts)
Claims 13-19 merely recites a system to perform the method of claims 1-7 respectively. Accordingly, Muthuswamy teaches every limitation of claims 13-19 as indicates in the above rejection of claims 1-7 respectively.
Claim 20 merely recites one or more non-transitory computer readable storage media including instructions that when executed by one or more processors, perform the method of claim 1. Accordingly, Muthuswamy teaches every limitation of claim 20 as indicates in the above rejection of claims 1.
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 10-11 and are rejected under 35 U.S.C. 103 as being unpatentable over Muthuswamy and further in view of Talagala et al.(US Patent Application Publication 2019/0108417 A1, hereinafter “Talagala”)
As to claim 10, Muthuswamy teaches the computer-implemented method of claim 9, wherein the model-based anomaly is identified, the project segment is indicative of the current pipeline version (Muthuswamy par [0053] teaches traceability manager can help to identify the pipeline runs for a given alert by trancing the alerts, and what models, programs , filters , data, transform functions, insights and/or events were used to generate the alert),
Muthuswamy does not teach the predictive action comprises: initiating the presentation of a model interface for modifying one or more model parameters for the data processing pipeline.
However, Talagala teaches initiating the presentation of a model interface for modifying one or more model parameters for the data processing pipeline. (Talagala par [0087] teaches an interface for monitoring, controlling , modifying , setting, configuring and or the like one or more parameters. The interface may allow for a user to configure various settings of the training pipeline, such as modifying training data used to train the machine learning model, setting the weights or other characteristics of the machine learning model)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Muthuswamy and Talagala to achieve the claimed invention. One would have been motivated to make such combination to allow user to dynamically adjusting machine learning features or settings within the system based (Talagala par [0038])
As to claim 11, Muthuswamy teaches the computer-implemented method of claim 9, wherein the data-based anomaly is identified, the project segment is indicative of the current data version( Muthuswamy par [0053] teaches traceability manager can help to identify the pipeline runs for a given alert by trancing the alerts, and what models, programs , filters , data, transform functions, insights and/or events were used to generate the alert),
Muthuswamy does not teach the predictive action comprises: initiating the presentation of a data interface for modifying one or more data parameters for the dynamic input dataset.
However, Talagala teaches the predictive action comprises: initiating the presentation of a data interface for modifying one or more data parameters for the dynamic input dataset. (Talagala par [0087] teaches an interface for monitoring, controlling , modifying , setting, configuring and or the like one or more parameters. The interface may allow for a user to configure various settings of the training pipeline, such as modifying training data used to train the machine learning model, setting the weights or other characteristics of the machine learning model)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Muthuswamy and Talagala to achieve the claimed invention. One would have been motivated to make such combination to allow user to dynamically adjusting machine learning features or settings within the system based (Talagala par [0038])
Conclusion
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/HIEN L DUONG/Primary Examiner, Art Unit 2147
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