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 .
This office action is responsive to communication filed on 08/12/2024.
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 (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 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 – 3, 9 - 11, and 17 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by BODE et al (US 2024/0129027; hereinafter BODE).
Regarding claim 1, BODE discloses a computer-implemented method for correlation-aware explainable online change point detection, comprising:
transforming collected data metrics (paragraphs [0064], [0089], [0118]; BODE discloses that unified analytics engine 420 is capable of processing petabyte (or greater) scale data from active space communication network's metrics. These metrics can be transformed, processed, indexed, and/or enhanced to produce a rich dataset of features and derived metrics which can describe many aspects of a particular satellite pass, including indicators that can be used to update a quality of service metric. From the processed metrics data, a machine learning model can be trained to predict error conditions for individual satellite passes) from a cloud system to correlation matrices (paragraphs [00], [0051], [0063 - 0064]; BODE discloses that each set of metric data generated by the cloud-computing infrastructure 110 is collected during a specified monitoring time, which may be different for different sets of metric data);
capturing correlation shifts from the correlation matrices as differences of correlation between batches of collected data metrics through determined statistics of the batches of collected data metrics across timesteps (paragraphs [0026], [0107]; BODE discloses that unified analytics engine 420 may be configured to ingest new data sources to continue to analyze broader network performance. An example data source may include the historical records of the underlying Internet traffic/protocol performance. In such cases, unified analytics engine 420 may be configured to generate satellite ground station network performance metrics, look for correlations between failed/flagged satellite passes, and provide other subsystems with actionable information. For example, on a shared network (e.g., one in which an Internet Service Provider line is shared) there may be dips in network latency at specific times of the day. Using unified analytics engine 420, data representing the dips in network latency across various times of the day can be processed by a change point detection model to detect change points and inform a scheduler, network operator, or satellite operator of such connectivity issues);
detecting change points in the cloud system based on the correlation shifts to obtain detected change points (paragraphs [0070], [0072]; BODE discloses that changepoint detection system 702 may be further configured to analyze metrics for change points via machine learning model 710. Some embodiments include changepoint detection system 702 implementing a change point detection model as machine learning model 710. A changepoint detection model may be trained to estimate a number of changepoints included within time series data. A “change point” refers to an event in the dataset which causes a modal shift in the data generating process); and
performing system maintenance autonomously based on the detected change points from identified system entities to optimize the cloud system with an updated configuration (paragraphs [0076 - 0077], [0081]; BODE discloses that [0081] In some embodiments, unified analytics engine 420 may include statistical analysis system 708, which may be configured to answer ad-hoc queries as well as performing formal data analysis. Furthermore, unified analytics engine 420 may be configured to recognize issues in metric data without extensive training or an expert system. This can be done by noting that, when operating correctly, satellite ground stations 120 and satellites 160 that communicate therewith have consistent, reliable, and predictable behavior. Therefore, historical performance can be leveraged to measure mathematical deviations.).
Regarding claim 2, BODE discloses the computer-implemented method of claim 1, wherein performing system maintenance autonomously further comprises performing root cause analysis based on the detected change points (paragraphs [0097], [0126]; BODE discloses that Using unified analytics engine 420, data representing the dips in network latency across various times of the day can be processed by a change point detection model to detect change points and inform a scheduler, network operator, or satellite operator of such connectivity issues. Such notification can enable an entity, such as a network operator, to determine whether to upgrade to a direct network connection or take another course of action to mitigate the connectivity disruptions.).
Regarding claim 3, BODE discloses the computer-implemented method of claim 1, wherein performing system maintenance autonomously further comprises further comprises generating explanations of the change points obtained from a status of the cloud system to assist a decision making of a cloud system professional (paragraphs [0043]; BODE discloses that metric data for every satellite contact (e.g., a communication device connecting to a satellite via a satellite ground station antenna) may be collected and analyzed to detect trends in ground station performance as well as identify whether the source of failed contacts is related to a particular ground station or stations, related to a particular satellite, related to a satellite constellation, or another component, or a combination thereof. Trends/patterns in the metric data may indicate an action or actions should be performed to reduce or eliminate failed operations in the satellite ground station's operations or the satellite's operations).
Allowable Subject Matter
Claims 4 – 8, 12 – 16, and 18 – 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims 9 – 11, and 17 incorporate substantially all the limitations of claims 1 – 3 in system and computer program form rather than method form. The reasons for rejecting claims 1 – 3 apply in claims 9 – 11, and 17. Therefore, claims 9 – 11, and 17 are rejected for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Marvasti et al (US 2016/0323157) discloses methods and systems to manage big data I cloud-computing infrastructures .
Ford et al (US 2024/0333615) discloses a network analysis using dataset shift detection.
Contact Information
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/YVES DALENCOURT/Primary Examiner, Art Unit 2457