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 .
In response to Applicant’s claims filed on May 15, 2025, claims 1-20 are now pending for examination in the application.
Response to Arguments
This office action is in response to amendment filed 11/22/2024. In this action claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ryan et al. (US Pub. No. 20190379589) and Dugger et al. (US Pub. No. 20210241141) and Raghunathan et al. (US Pub. No. 20190205905) in further view of Settle (US Pub. No. 20180357116). The Settle reference has been added to address the amendment of including performing autocorrelation between incremental subsequences of the binary sequence to identify a repeating pattern notwithstanding the missing occurrences, and converting binary sequence back into a time domain to provide a prediction of the time of the next occurrence of the event.
Applicant’s arguments:
In regards to claim 1 on Pages 9, applicant argues the cited art fails to clearly and unequivocally disclose a “Here, the claimed operations involve machine learning models trained on noisy, incomplete event streams to generate real-time predictions. These operations require large-scale data ingestion, flexible error-tolerant thresholds, and real-time autocorrelation across binary-encoded time bins - tasks that the human mind is not practically equipped to perform. Accordingly, the claims do not recite a mental process.” as alleged.
Examiner’s Reply:
Examiner respectfully disagrees. Using a computer as a generic tool, a user would have been able to analyze, identify, and detect events using the human mind to evaluate repeating patterns of event data using various mathematical concepts such as statistical analysis. Machine learning models are additional elements
Applicant’s arguments:
In regards to claim 1 on Pages 9, applicant argues the cited art fails to clearly and unequivocally disclose a “The present claims address a specific technological problem: detecting approximate periodicity in event streams with temporal noise and missing data, and providing real- time predictive notifications. The claimed binary sequence encoding, error-tolerant autocorrelation thresholds, and predictive feedback mechanisms improve the functioning of predictive computing and networking systems by enabling: Real-time forecasting of noisy, incomplete data, Reduction in false positives and missed predictions compared to conventional periodicity detection, and Improved resilience of network monitoring systems under imperfect conditions.” as alleged.
Examiner’s Reply:
Examiner respectfully disagrees. The examiner notes that the computer as recited in the claims are being used for event analysis and prediction (being used a generic tools). The analysis of event data and corresponding predictions does not improve the functioning of a computer. Therefore, the abstract idea recited in the claims is generally linking it to a computer environment, and does not integrate the abstract idea into a practical application.
Applicant’s arguments:
In regards to claim 1 on Pages 9, applicant argues the cited art fails to clearly and unequivocally disclose a “Finally, the Examiner has not provided the factual support required under Berkheimer V. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018), for the assertion that the claimed elements are well-understood, routine, and conventional. On the contrary, the specification expressly teaches that the invention departs from perfect periodicity detection by introducing flexible, error-tolerant thresholds for correlation (Spec. This feature is neither conventional nor generic, but instead represents a technical improvement in event prediction systems..” as alleged.
Examiner’s Reply:
Examiner respectfully disagrees. Pattern recognition using time-series calculations and observation are well-known in the art and the additional elements involved in the storing of data for analysis does not amount to significantly more.
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.
Claim 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the 2019 Revised Patent Subject Matter Eligibility Guidance, hereinafter 2019 PEG.
Step 1. in accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted the claim method (claims 1-10), a medium (claims 11-17), and an (claims 18-20) are directed to one of the eligible categories of subject matter and therefore satisfies Step 1.
Step 2A. In accordance with Step 2A, prong one of the 2019 PEG, it is noted that the independent claims recite an abstract idea falling within the Mental Processes & Mathematical Concepts enumerated groupings of abstract ideas set forth in the 2019 PEG. Examiner is of the position that independent claims 1, 11, and 18 are directed towards the Mental Process Grouping of Abstract Ideas.
Independent claim(s) 1, 11, and 18 recites the following limitations directed towards a Mental Processes & Mathematical Concepts:
monitoring a computing system
analyzing the plurality of records using a machine learning model trained to identify approximate periodicity in event sequences exhibiting temporal noise and missing data to detect a periodic chain of events that is approximately periodic in the data associated with the computing system, wherein the periodic chain of events includes periodicity that is detected based on a plurality of parameters including some missing occurrences therein (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by analyzing records);
responsive to periodic chains of events containing any missing occurrence at one or several period intervals, converting the periodic chain of events into a binary sequence with each bit representing a time bin and having a value based on a presence or absence of an event in the time bin (The limitation recites a mathematical concept of converting data into a sequence);
analyzing the time bins of the binary sequence to recognize a pattern therein, wherein analyzing comprises detecting repeating patterns of event presence and absence, including performing autocorrelation between incremental subsequences of the binary sequence to identify a repeating pattern notwithstanding the missing occurrences, and converting binary sequence back into a time domain to provide a prediction of the time of the next occurrence of the event (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by pattern recognition);
based on a current location of the records of the computing system with respect to the binary sequence, determining a next occurrence of an event, wherein the determination is performed in real-time, and converting the binary sequence back into a time domain to provide a prediction of the time of the next occurrence of the event (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by determining event occurrence).
Step 2A. In accordance with Step 2A, prong two of the 2019 PEG, the judicial exception is not integrated into a practical application because of the recitation in claim(s) 1, 11, and 18:
Step 2B. Similar to the analysis under 2A Prong Two, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Because the additional elements of the independent claims amount to insignificant extra solution activity and/or mere instructions, the additional elements do not add significantly more to the judicial exception such that the independent claims as a whole would be patent eligible.
Therefore, independent claims 1, 11, and 18 are rejected under 35 U.S.C. 101.
With respect to claim(s) 2 and 12:
Step 2A, prong one of the 2019 PEG:
determining a current location of the computing system in the binary sequence, the current location of the computing system in the binary sequence representing a current real-time, location within the detected periodic chain of events (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by determining a location);
determining a next occurrence of the event based on the current location within the detected periodic chain of events (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by determining a location).
Step 2A Prong Two Analysis:
responsive to converting the binary sequence back to a time domain, wherein the time domain includes occurrences of events with timestamps, providing a notification of the next occurrence of the event (recites insignificant extra solution activity that amounts to providing notification data).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 3 and 13:
Step 2A, prong one of the 2019 PEG:
wherein the analyzing the binary sequence to recognize the pattern utilizes autocorrelation between incremental subsequences of the binary sequence (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by to recognize a pattern).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 4:
Step 2A, prong one of the 2019 PEG:
wherein a required autocorrelation value is set to 100% (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by to recognize a pattern).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 5:
Step 2A, prong one of the 2019 PEG:
wherein a required autocorrelation value is set to less than 100% for some or all subsequences (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by to recognize a pattern).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 6 and 14:
Step 2A, prong one of the 2019 PEG:
wherein the analyzing the binary sequence to recognize the pattern utilizes a pattern recognition algorithm (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by to recognize a pattern).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 7 and 15:
Step 2A, prong one of the 2019 PEG:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
wherein the steps include providing a notification of the next occurrence of the event with one or more remediation options to limit a subscriber impact based thereon (recites insignificant extra solution activity that amounts to displaying data).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 8 and 16:
Step 2A, prong one of the 2019 PEG:
determining a quality of a prediction of the next occurrence of the event based on any of a pattern strength and correlation scores (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by to determine quality).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 9 and 17:
Step 2A, prong one of the 2019 PEG:
wherein the analyzing the plurality of records to detect the periodic chain of events includes sorting the plurality of records into one or more queues (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by to sorting records);
analyzing each of the one or more queues to detect approximate periodic chains of events in the plurality of records, wherein the periodic chains of events include periodicity that is detected based on a plurality of parameters including some missing occurrences therein (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by to detecting periodic events)
Step 2A Prong Two Analysis:
one or more of presenting detected periodic chains of events in a user interface, storing the detected periodic events, and transforming the detected periodic chains of events into statistics reflecting period characteristics for use in predictions using a machine learning model (recites insignificant extra solution activity that amounts to storing data).
With respect to claim(s) 10:
Step 2A, prong one of the 2019 PEG:
wherein the events are associated with a network and each has a subscriber impact (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by to analyzing event data).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 19:
Step 2A, prong one of the 2019 PEG:
wherein the binary sequence is analyzed to recognize the pattern via one or more of i) autocorrelation between incremental subsequences of the binary sequence, and ii) a pattern recognition algorithm (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by to recognize a pattern).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 20:
Step 2A, prong one of the 2019 PEG:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
wherein the apparatus is a monitoring system and the system is a network (i.e., as a generic processor/component performing a generic computer function).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ryan et al. (US Pub. No. 20190379589) and Dugger et al. (US Pub. No. 20210241141) and Raghunathan et al. (US Pub. No. 20190205905) in further view of Settle (US Pub. No. 20180357116).
With respect to claim 1, Ryan et al. discloses a method comprising the steps of:
monitoring a computing system and collecting real-time data associated with the computing system, wherein the data includes a plurality of records each including at least a start time and a unique identifier (Paragraph 94 discloses pattern detection and real-time detection. The method 80 includes receiving network measurements (step 82). The network measurements are stored (step 84). Steps 82 and 84 represent a data collection phase and Paragraph 88 discloses the pattern may be mostly localized in window T in this example, the conditional probability of the anomaly or pattern presence is the highest in that window, thus localizing the pattern as starting at time T);
analyzing the plurality of records using a machine learning model trained to identify approximate periodicity in event sequences exhibiting temporal noise and missing data to detect a periodic chain of events that is approximately periodic in the data associated with the computing system (Paragraph 64 discloses data may be analyzed with pattern detection for predicting congestion events 32),
wherein the periodic chain of events includes periodicity that is detected based on a plurality of parameters including some missing occurrences therein (Paragraph 111 discloses Data may be cleaned to handle missing values, time-bin, etc and Paragraph 114 discloses Temporal data often exhibits cyclic patterns that frequently combine with trend and noise);
analyzing the time bins of the binary sequence to recognize a pattern therein, wherein analyzing comprises detecting repeating patterns of event presence and absence, including evaluating sequences of missing and non-missing events using any auto-correlation or pattern matching against a bank of known patterns (Paragraph 51 discloses If there is a correlation between measurements and subsequent threshold crossings, machine learning may be used to discover this correlation and associate the correlation with a pattern. During online usage of new data, pattern detection functions include examining the time-series to find the previously discovered patterns). Ryan et al. does not disclose responsive to periodic chains of events containing any missing occurrence at one or several period intervals, converting the periodic chain of events into a binary sequence with each bit representing a time bin and having a value based on a presence or absence of an event in the time bin
However, Dugger et al. teaches responsive to periodic chains of events containing any missing occurrence at one or several period intervals, converting the periodic chain of events into a binary sequence with each bit representing a time bin and having a value based on a presence or absence of an event in the time bin (Paragraph 42 discloses data samples 126 for an entity indicate the occurrence of the event of interest in a particular time bin and the non-occurrence of the event of interest).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify Ryan et al. with Dugger et al. to include responsive to periodic chains of events containing any missing occurrence at one or several period intervals, converting the periodic chain of events into a binary sequence with each bit representing a time bin and having a value based on a presence or absence of an event in the time bin. This would have provided improved pattern recognition for better event prediction. See Dugger et al. Paragraph(s) 3 and 4.
Ryan et al. as modified by Dugger et al. does not disclose based on a current location of the records of the computing system with respect to the binary sequence, determining a next occurrence of an event, wherein the determination is performed in real-time.
However, Raghunathan et al. teaches based on a current location of the records of the computing system with respect to the binary sequence, determining a next occurrence of an event, wherein the determination is performed in real-time (Paragraph 69 discloses Data from the data sources and via the ingestion and integration engine is provided to a data processing sub-system. Data processing sub-system includes a real-time in-memory processing component which can use machine learning-based models to predict insights in real time and Paragraph 110 discloses A user propensity score is likelihood that a user will purchase an item from a dependent category at a future time. The predictor uses time binned input data, collected from PoS terminal shopping cart data during an analysis period, to predict the propensity score).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify Ryan et al. and Dugger et al. with Raghunathan et al. to include based on a current location of the records of the computing system with respect to the binary sequence, determining a next occurrence of an event, wherein the determination is performed in real-time. This would have provided improved pattern recognition for better event prediction. See Raghunathan et al.. Paragraph(s) 3-8.
Ryan et al. reference as modified by Dugger et al. and Raghunathan et al. does not disclose including performing autocorrelation between incremental subsequences of the binary sequence to identify a repeating pattern notwithstanding the missing occurrences, and converting binary sequence back into a time domain to provide a prediction of the time of the next occurrence of the event.
However, Settle teaches autocorrelation between incremental subsequences of the binary sequence to identify a repeating pattern notwithstanding the missing occurrences (Paragraph 32 discloses employ an approach or concept for identifying a group of related events. Then, for each event occurrence of each event of the group of related events, there may be identified an event type, and one or more resources associated with the event occurrence. Using such information, a pattern or collections of event occurrences of the same event type and using the same resource may be identified), and
converting binary sequence back into a time domain to provide a prediction of the time of the next occurrence of the event (Paragraph 18 discloses may analyze a body or archive of event occurrences, they may provide information about connected events which may predict resource usage, constraints and/or impacts associated with event occurrences that have not previously occurred)
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify Ryan et al. and Dugger et al. and Raghunathan et al. with Settle to include autocorrelation between incremental subsequences of the binary sequence to identify a repeating pattern notwithstanding the missing occurrences, and converting binary sequence back into a time domain to provide a prediction of the time of the next occurrence of the event. This would have provided improved pattern recognition for better event prediction. See Settle Paragraph(s) 4-12.
The Ryan et al. reference as modified by Dugger et al. and Raghunathan et al. and Settle teaches all the limitations of claim 1. With respect to claim 2, Raghunathan et al. teaches the method of claim 1, wherein the steps include
determining a current location of the computing system in the binary sequence, the current location of the computing system in the binary sequence representing a current real-time, location within the detected periodic chain of events (Paragraph 69 discloses Data from the data sources and via the ingestion and integration engine is provided to a data processing sub-system. Data processing sub-system includes a real-time in-memory processing component which can use machine learning-based models to predict insights in real time and Paragraph 110 discloses A user propensity score is likelihood that a user will purchase an item from a dependent category at a future time. The predictor uses time binned input data, collected from PoS terminal shopping cart data during an analysis period, to predict the propensity score);
determining a next occurrence of the event based on the current location within the detected periodic chain of events (Paragraph 69 discloses Data from the data sources and via the ingestion and integration engine is provided to a data processing sub-system. Data processing sub-system includes a real-time in-memory processing component which can use machine learning-based models to predict insights in real time and Paragraph 110 discloses A user propensity score is likelihood that a user will purchase an item from a dependent category at a future time. The predictor uses time binned input data, collected from PoS terminal shopping cart data during an analysis period, to predict the propensity score);
responsive to converting the binary sequence back to a time domain, wherein the time domain includes occurrences of events with timestamps, providing a notification of the next occurrence of the event (Paragraph 83 discloses notifications). The motivation to combine statement previously provided in the rejection of independent claim 1 provided above, combining the Ryan et al. reference and the Raghunathan et al. reference is applicable to dependent claim 4.
The Ryan et al. reference as modified by Dugger et al. and Raghunathan et al. and Settle teaches all the limitations of claim 1. With respect to claim 3, Ryan et al. teaches the method of claim 1, wherein the analyzing the binary sequence to recognize the pattern utilizes autocorrelation between incremental subsequences of the binary sequence (Paragraph 51 discloses If there is a correlation between measurements and subsequent threshold crossings, machine learning may be used to discover this correlation and associate the correlation with a pattern. During online usage of new data, pattern detection functions include examining the time-series to find the previously discovered patterns).
The Ryan et al. reference as modified by Dugger et al. and Raghunathan et al. and Settle teaches all the limitations of claim 3. With respect to claim 4, Ryan et al. teaches the method of claim 3, wherein a required autocorrelation value is set to 100% (Paragraph 51 discloses If there is a correlation between measurements and subsequent threshold crossings, machine learning may be used to discover this correlation and associate the correlation with a pattern. During online usage of new data, pattern detection functions include examining the time-series to find the previously discovered patterns).
The Ryan et al. reference as modified by Dugger et al. and Raghunathan et al. and Settle teaches all the limitations of claim 3. With respect to claim 5, Ryan et al. teaches the method of claim 3, wherein a required autocorrelation value is set to less than 100% for some or all subsequences (Paragraph 51 discloses If there is a correlation between measurements and subsequent threshold crossings, machine learning may be used to discover this correlation and associate the correlation with a pattern. During online usage of new data, pattern detection functions include examining the time-series to find the previously discovered patterns).
The Ryan et al. reference as modified by Dugger et al. and Raghunathan et al. and Settle teaches all the limitations of claim 1. With respect to claim 6, Ryan et al. teaches the method of claim 1, wherein the analyzing the binary sequence to recognize the pattern utilizes a pattern recognition algorithm (Paragraph 51 discloses If there is a correlation between measurements and subsequent threshold crossings, machine learning may be used to discover this correlation and associate the correlation with a pattern. During online usage of new data, pattern detection functions include examining the time-series to find the previously discovered patterns).
The Ryan et al. reference as modified by Dugger et al. and Raghunathan et al. and Settle teaches all the limitations of claim 1. With respect to claim 7, Raghunathan et al. teaches the method of claim 1, wherein the steps include providing a notification of the next occurrence of the event with one or more remediation options to limit a subscriber impact based thereon (Paragraph 83 discloses send sales recommendations, gender context, dynamic pricing, and/or arrival/exit notifications to participating tenants of the physical venue in response to tenant requests. In other implementations, the participating tenants are participating independent retail stores that are not in a tenant-landlord relationship. The distribution sever can use the visitor journey information encoded in the aggregated profile 400 to report to servers representing the participating tenants of arrival of the visitor, accompanied by a profile of the visitor and tenant-specific and aggregate intent propensity information). The motivation to combine statement previously provided in the rejection of independent claim 1 provided above, combining the Ryan et al. reference and the Raghunathan et al. reference is applicable to dependent claim 7.
The Ryan et al. reference as modified by Dugger et al. and Raghunathan et al. and Settle teaches all the limitations of claim 1. With respect to claim 8, Raghunathan et al. teaches the method of claim 1, wherein the steps include determining a quality of a prediction of the next occurrence of the event based on any of a pattern strength and correlation scores (Paragraph 69 discloses Data from the data sources and via the ingestion and integration engine is provided to a data processing sub-system. Data processing sub-system includes a real-time in-memory processing component which can use machine learning-based models to predict insights in real time and Paragraph 110 discloses A user propensity score is likelihood that a user will purchase an item from a dependent category at a future time. The predictor uses time binned input data, collected from PoS terminal shopping cart data during an analysis period, to predict the propensity score). The motivation to combine statement previously provided in the rejection of independent claim 1 provided above, combining the Ryan et al. reference and the Raghunathan et al. reference is applicable to dependent claim 8.
The Ryan et al. reference as modified by Dugger et al. and Raghunathan et al. and Settle teaches all the limitations of claim 1. With respect to claim 9, Ryan et al. teaches the method of claim 1, wherein the analyzing the plurality of records to
detect the periodic chain of events includes sorting the plurality of records into one or more queues (Paragraph 64 discloses data may be analyzed with pattern detection for predicting congestion events 32 (e.g., when traffic volume exceeds a threshold for an extended length of time). Pattern detection is trained with traffic measurements (or CPU utilization measurements) and labeled on graph 30 as patterns 34 that represent a “start of busy period,” which may be indicative of or may result in congestion 32 in the future. One set of data (e.g., queue sizes) can be used for measurements, while another (e.g., end-to-end performance) can be used to generate labels (e.g., “congestion” or “no congestion”));
analyzing each of the one or more queues to detect approximate periodic chains of events in the plurality of records, wherein the periodic chains of events include periodicity that is detected based on a plurality of parameters including some missing occurrences therein (Paragraph 111 discloses network observation data is prepared (block 192). Data may be cleaned to handle missing values, time-bin, etc. Next, optimization or a search is performed for both the hyper-parameters and transformations (block 194)); and
one or more of presenting detected periodic chains of events in a user interface, storing the detected periodic events, and transforming the detected periodic chains of events into statistics reflecting period characteristics for use in predictions using a machine learning model (Paragraph 63 discloses time-series data where Signal-to-Noise Ratio (SNR) measurements are taken over time. A pattern detection model that is modeled from the historical training data can be used with new data for predicting when the SNR curve crosses over a threshold 22).
The Ryan et al. reference as modified by Dugger et al. and Raghunathan et al. and Settle teaches all the limitations of claim 1. With respect to claim 10, Ryan et al. teaches the method of claim 1, wherein the events are associated with a network and each has a subscriber impact (Paragraph 45 discloses in the field of networking applications, pattern detection can be used in the following use cases: for forecasting threshold crossings, for forecasting alarms, for forecasting quality-of-experience (QoE), for network anomaly detection, among others. Pattern detection can also be used in other areas (e.g., forecasting engine failure or tire deflation in cars from engine- or tire-collected information, forecasting bridge failure by detecting patterns in a time-series associated with bridge sensors, detecting earthquakes or tsunamis by detecting patterns in seismological time-series data, recognizing that a person is having a heart-attack from heart rate measurements collected by a smart watch, forecasting traffic congestion on streets by detecting patterns in a time-series from video cameras on streets, cars, or traffic detection sensors, etc.)).
With respect to claim 11, Ryan et al. discloses a non-transitory computer-readable medium having instructions stored thereon for programming a device to perform steps of:
monitoring a computing system and collecting real-time data associated with the computing system, wherein the data includes a plurality of records each including at least a start time and a unique identifier (Paragraph 94 discloses pattern detection and real-time detection. The method 80 includes receiving network measurements (step 82). The network measurements are stored (step 84). Steps 82 and 84 represent a data collection phase and Paragraph 88 discloses the pattern may be mostly localized in window T in this example, the conditional probability of the anomaly or pattern presence is the highest in that window, thus localizing the pattern as starting at time T);
analyzing the plurality of records using a machine learning model trained to identify approximate periodicity in event sequences exhibiting temporal noise and missing data to detect a periodic chain of events that is approximately periodic in the data associated with the computing system (Paragraph 64 discloses data may be analyzed with pattern detection for predicting congestion events 32),
wherein the periodic chain of events includes periodicity that is detected based on a plurality of parameters including some missing occurrences therein (Paragraph 111 discloses Data may be cleaned to handle missing values, time-bin, etc and Paragraph 114 discloses Temporal data often exhibits cyclic patterns that frequently combine with trend and noise);
analyzing the time bins of the binary sequence to recognize a pattern therein, wherein analyzing comprises detecting repeating patterns of event presence and absence, including evaluating sequences of missing and non-missing events using any auto-correlation or pattern matching against a bank of known patterns (Paragraph 51 discloses If there is a correlation between measurements and subsequent threshold crossings, machine learning may be used to discover this correlation and associate the correlation with a pattern. During online usage of new data, pattern detection functions include examining the time-series to find the previously discovered patterns). Ryan et al. does not disclose responsive to periodic chains of events containing any missing occurrence at one or several period intervals, converting the periodic chain of events into a binary sequence with each bit representing a time bin and having a value based on a presence or absence of an event in the time bin
However, Dugger et al. teaches responsive to periodic chains of events containing any missing occurrence at one or several period intervals, converting the periodic chain of events into a binary sequence with each bit representing a time bin and having a value based on a presence or absence of an event in the time bin (Paragraph 42 discloses data samples 126 for an entity indicate the occurrence of the event of interest in a particular time bin and the non-occurrence of the event of interest)
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify Ryan et al. with Dugger et al. to include responsive to periodic chains of events containing any missing occurrence at one or several period intervals, converting the periodic chain of events into a binary sequence with each bit representing a time bin and having a value based on a presence or absence of an event in the time bin. This would have provided improved pattern recognition for better event prediction. See Dugger et al. Paragraph(s) 3 and 4.
Ryan et al. as modified by Dugger et al. does not disclose based on a current location of the records of the computing system with respect to the binary sequence, determining a next occurrence of an event, wherein the determination is performed in real-time.
However, Raghunathan et al. teaches based on a current location of the records of the computing system with respect to the binary sequence, determining a next occurrence of an event, wherein the determination is performed in real-time (Paragraph 69 discloses Data from the data sources and via the ingestion and integration engine is provided to a data processing sub-system. Data processing sub-system includes a real-time in-memory processing component which can use machine learning-based models to predict insights in real time and Paragraph 110 discloses A user propensity score is likelihood that a user will purchase an item from a dependent category at a future time. The predictor uses time binned input data, collected from PoS terminal shopping cart data during an analysis period, to predict the propensity score).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify Ryan et al. and Dugger et al. with Raghunathan et al. to include based on a current location of the records of the computing system with respect to the binary sequence, determining a next occurrence of an event, wherein the determination is performed in real-time. This would have provided improved pattern recognition for better event prediction. See Raghunathan et al.. Paragraph(s) 3-8.
Ryan et al. reference as modified by Dugger et al. and Raghunathan et al. does not disclose including performing autocorrelation between incremental subsequences of the binary sequence to identify a repeating pattern notwithstanding the missing occurrences, and converting binary sequence back into a time domain to provide a prediction of the time of the next occurrence of the event.
However, Settle teaches autocorrelation between incremental subsequences of the binary sequence to identify a repeating pattern notwithstanding the missing occurrences (Paragraph 32 discloses employ an approach or concept for identifying a group of related events. Then, for each event occurrence of each event of the group of related events, there may be identified an event type, and one or more resources associated with the event occurrence. Using such information, a pattern or collections of event occurrences of the same event type and using the same resource may be identified), and
converting binary sequence back into a time domain to provide a prediction of the time of the next occurrence of the event (Paragraph 18 discloses may analyze a body or archive of event occurrences, they may provide information about connected events which may predict resource usage, constraints and/or impacts associated with event occurrences that have not previously occurred)
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify Ryan et al. and Dugger et al. and Raghunathan et al. with Settle to include autocorrelation between incremental subsequences of the binary sequence to identify a repeating pattern notwithstanding the missing occurrences, and converting binary sequence back into a time domain to provide a prediction of the time of the next occurrence of the event. This would have provided improved pattern recognition for better event prediction. See Settle Paragraph(s) 4-12.
With respect to claim 12, it is rejected on grounds corresponding to above rejected claim 2, because claim 12 is substantially equivalent to claim 2.
With respect to claim 13, it is rejected on grounds corresponding to above rejected claim 3, because claim 13 is substantially equivalent to claim 3.
With respect to claim 14, it is rejected on grounds corresponding to above rejected claim 6, because claim 14 is substantially equivalent to claim 6.
With respect to claim 15, it is rejected on grounds corresponding to above rejected claim 7, because claim 15 is substantially equivalent to claim 7.
With respect to claim 16, it is rejected on grounds corresponding to above rejected claim 8, because claim 16 is substantially equivalent to claim 8.
With respect to claim 17, it is rejected on grounds corresponding to above rejected claim 9, because claim 17 is substantially equivalent to claim 9.
With respect to claim 11, Ryan et al. discloses an apparatus comprising: at least one processor and memory storing instructions that, when executed, cause the at least one processor to
monitor a computing system and collecting real-time data associated with the computing system, wherein the data includes a plurality of records each including at least a start time and a unique identifier (Paragraph 94 discloses pattern detection and real-time detection. The method 80 includes receiving network measurements (step 82). The network measurements are stored (step 84). Steps 82 and 84 represent a data collection phase and Paragraph 88 discloses the pattern may be mostly localized in window T in this example, the conditional probability of the anomaly or pattern presence is the highest in that window, thus localizing the pattern as starting at time T);
analyze the plurality of records using a machine learning model trained to identify approximate periodicity in event sequences exhibiting temporal noise and missing data to detect a periodic chain of events that is approximately periodic in the data associated with the computing system (Paragraph 64 discloses data may be analyzed with pattern detection for predicting congestion events 32),
wherein the periodic chain of events includes periodicity that is detected based on a plurality of parameters including some missing occurrences therein (Paragraph 111 discloses Data may be cleaned to handle missing values, time-bin, etc and Paragraph 114 discloses Temporal data often exhibits cyclic patterns that frequently combine with trend and noise);
analyze the time bins of the binary sequence to recognize a pattern therein, wherein analyzing comprises detecting repeating patterns of event presence and absence, including evaluating sequences of missing and non-missing events using any auto-correlation or pattern matching against a bank of known patterns (Paragraph 51 discloses If there is a correlation between measurements and subsequent threshold crossings, machine learning may be used to discover this correlation and associate the correlation with a pattern. During online usage of new data, pattern detection functions include examining the time-series to find the previously discovered patterns). Ryan et al. does not disclose responsive to periodic chains of events containing any missing occurrence at one or several period intervals, converting the periodic chain of events into a binary sequence with each bit representing a time bin and having a value based on a presence or absence of an event in the time bin
However, Dugger et al. teaches responsive to periodic chains of events containing any missing occurrence at one or several period intervals, convert the periodic chain of events into a binary sequence with each bit representing a time bin and having a value based on a presence or absence of an event in the time bin (Paragraph 42 discloses data samples 126 for an entity indicate the occurrence of the event of interest in a particular time bin and the non-occurrence of the event of interest)
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify Ryan et al. with Dugger et al. to include responsive to periodic chains of events containing any missing occurrence at one or several period intervals, converting the periodic chain of events into a binary sequence with each bit representing a time bin and having a value based on a presence or absence of an event in the time bin. This would have provided improved pattern recognition for better event prediction. See Dugger et al. Paragraph(s) 3 and 4.
Ryan et al. as modified by Dugger et al. does not disclose based on a current location of the records of the computing system with respect to the binary sequence, determining a next occurrence of an event, wherein the determination is performed in real-time.
However, Raghunathan et al. teaches based on a current location of the records of the computing system with respect to the binary sequence, determine a next occurrence of an event, wherein the determination is performed in real-time (Paragraph 69 discloses Data from the data sources and via the ingestion and integration engine is provided to a data processing sub-system. Data processing sub-system includes a real-time in-memory processing component which can use machine learning-based models to predict insights in real time and Paragraph 110 discloses A user propensity score is likelihood that a user will purchase an item from a dependent category at a future time. The predictor uses time binned input data, collected from PoS terminal shopping cart data during an analysis period, to predict the propensity score).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify Ryan et al. and Dugger et al. with Raghunathan et al. to include based on a current location of the records of the computing system with respect to the binary sequence, determining a next occurrence of an event, wherein the determination is performed in real-time. This would have provided improved pattern recognition for better event prediction. See Raghunathan et al.. Paragraph(s) 3-8.
Ryan et al. reference as modified by Dugger et al. and Raghunathan et al. does not disclose including performing autocorrelation between incremental subsequences of the binary sequence to identify a repeating pattern notwithstanding the missing occurrences, and converting binary sequence back into a time domain to provide a prediction of the time of the next occurrence of the event.
However, Settle teaches autocorrelation between incremental subsequences of the binary sequence to identify a repeating pattern notwithstanding the missing occurrences (Paragraph 32 discloses employ an approach or concept for identifying a group of related events. Then, for each event occurrence of each event of the group of related events, there may be identified an event type, and one or more resources associated with the event occurrence. Using such information, a pattern or collections of event occurrences of the same event type and using the same resource may be identified), and
converting binary sequence back into a time domain to provide a prediction of the time of the next occurrence of the event (Paragraph 18 discloses may analyze a body or archive of event occurrences, they may provide information about connected events which may predict resource usage, constraints and/or impacts associated with event occurrences that have not previously occurred)
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify Ryan et al. and Dugger et al. and Raghunathan et al. with Settle to include autocorrelation between incremental subsequences of the binary sequence to identify a repeating pattern notwithstanding the missing occurrences, and converting binary sequence back into a time domain to provide a prediction of the time of the next occurrence of the event. This would have provided improved pattern recognition for better event prediction. See Settle Paragraph(s) 4-12.
The Ryan et al. reference as modified by Dugger et al. and Raghunathan et al. and Settle teaches all the limitations of claim 18. With respect to claim 19, Ryan et al. teaches the apparatus of claim 18, wherein the binary sequence is analyzed to recognize the pattern via one or more of i) autocorrelation between incremental subsequences of the binary sequence, and Ii) a pattern recognition algorithm (Paragraph 51 discloses discover patterns during a time interval, associated with values of the time-series dropping below the threshold at a later time. If there is a correlation between measurements and subsequent threshold crossings, machine learning may be used to discover this correlation and associate the correlation with a pattern).
The Ryan et al. reference as modified by Dugger et al. and Raghunathan et al. and Settle teaches all the limitations of claim 18. With respect to claim 20, Ryan et al. teaches the apparatus of claim 18, wherein the apparatus is a monitoring system and the system is a network (Paragraph 65 discloses performance monitoring (PM) and associated alarms over time. The data of graph 40 may be used for predicting alarms before they happen. Pattern detection may be trained with traffic measurements and labeled as patterns (e.g., windows A.sub.1, labeled 42, followed by windows A.sub.2, labeled 44). These changes 46 (e.g., from window A.sub.1 to window A.sub.2) in PM activity may be analyzed in pattern detection analysis to predict a start of congestion in the future, corresponding to alarm A.sub.3, which may be a critical alarm 48. One set of data (e.g., queue sizes) can be used for measurements, while another (e.g., end-to-end performance) can be used to generate labels. Patterns can then be further correlated with the network at the time for root cause analysis).
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Pub. No. 20210116905 is directed to CAUSE DETERMINATION OF ANOMALOUS EVENTS: [Paragraph 055] Determining repeating events spaced according to the interval in a second data set may be effected in act S4. In one embodiment, in this case, one or a plurality of events in the second data set are determined based on the Fourier transform of the second data set. The events may be determined by determining events that are spaced according to the interval (e.g., determined by one or a plurality of anomaly events in the first data set). The anomaly events of a specific periodicity in the first data set may thus be assigned to events having the same periodicity in the second data set. The events in the second data set may be repeating data spaced according to the interval that was determined (e.g., by the first data set). Data in a data set (e.g., the second data set) may thus correspond to events, or events may be determined based on data. By way of example, a relationship may be recognized by a correlation of the Fourier transformations effected. Consequently, the cause research may be effected in the case of anomaly detection, and an automatic cause characterization can be effected. In this case, the periodicity may also be determined based on an extremal value consideration of the data or events in the first data set and/or the second data set. Further, based on a modelling or approximation (e.g., by one or a plurality of periodic functions, such as sine and/or cosine) of the data in the first data set and/or the second data set and a consideration of the arguments of the periodic function, it is possible to determine one or a plurality of intervals based on which one or a plurality of events that are the cause of one or a plurality of anomalies are determined.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
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/N.E.A/Examiner, Art Unit 2154
/SYED H HASAN/Primary Examiner, Art Unit 2154