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 .
Response to Amendment
This office action is responsive to the amendments filed on November 19, 2025. Claims 1, 2, 12 and 20 have been amended. Claims 1-20 are pending in this office action.
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on 10/15/2025 and 04/22/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
New corrected drawings in compliance with 37 CFR 1.121(d) are required in this application because text is figures 1-8D is blurry and illegible. Applicant is advised to employ the services of a competent patent draftsperson outside the Office, as the U.S. Patent and Trademark Office no longer prepares new drawings. The corrected drawings are required in reply to the Office action to avoid abandonment of the application. The requirement for corrected drawings will not be held in abeyance.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The terms “comprehensive” in limitation “maintaining a comprehensive data log…” in claim 1 and “ensuring comprehensive data capture” in claim 13, “state-of-the-art” in limitation “utilizing state-of-the-art machine learning frameworks” in claim 1, “advanced” in limitations “advanced statistical and machine learning techniques” and “including advanced techniques such as wavelet transforms or dynamic time warping” in claim 1, “using an advanced trend detection module” in claim 7 and “using advanced filtering techniques and criteria” in claim 14, “sophisticated” in limitation “using sophisticated algorithms to detect” in claim 7, “pristine” in limitation “ensuring that the dataset is pristine and ready” in claim 15 are all ambiguous and confusing terms which render these claims indefinite. Appropriate correction is required.
Regarding claims 1, 2, 8, 12, 17, 18 and 19 the phrases "such as", “like” “may include”, “may involve” and “Including but not limited to” renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d). Appropriate correction is required.
Claims 3-6, 9-11, 16, and 20 depend from the rejected claims and inherit the deficiencies of the claims from which they depend without curing those deficiencies.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claims are directed to an abstract idea without significantly more.
Regarding claim 1, in Step 1 of the 101 analysis set forth in MPEP 2106, the claim recites “A method for detecting anomalies in time series data exhibiting irregular trends…”. A method is considered a process and is one of the four statutory categories of invention.
In Step 2a Pong 1 of the 101 analysis set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers abstract ideas but for recitation of generic computer components:
“A method for detecting anomalies in time series data exhibiting irregular trends, comprising the steps of: performing preliminary analysis on the extracted data with … to identify any patterns or anomalies and establish a baseline understanding, utilizing statistical methods and visual inspections to detect initial irregularities” is a mental process. A person can mentally perform analysis on data and identify anomalies and patterns.
“cleaning the extracted data … to remove noise and irrelevant information, ensuring that the data used for feature engineering and analysis is accurate and reliable, involving methods such as outlier removal, interpolation, and normalization” is a mental process. A person can mentally clean the data and remove irrelevant data using pen and paper.
“engineering features from the cleaned data by transforming raw data into a structured format that highlights attributes and properties for machine learning algorithms…, which may include creating new variables, aggregating data points, and encoding categorical variables” is a mental process. A person can mentally engineer/transform data from one format to another by use of pen and paper.
“identifying unique trends and patterns within the data by analyzing temporal sequences and external influencers such as holidays or weekends…, applying time-series analysis techniques like moving averages, seasonal decomposition, and Fourier transforms” is a mental process. A person can mentally identify different trends and pattern in a data by using different techniques using pen and paper.
“clustering the data based on identified trend influencers to categorize the data into distinct segments representing different patterns…, which can include methods such as K-means clustering, hierarchical clustering, or Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to group similar data points” is a mental process. A person can mentally cluster the data based on trends and patterns by using different methods using pen and paper.
“developing a specific predictive model for each identified trend cluster, tailored to recognize unique characteristics of each segment…” is a mental process. A person can mentally develop a model for different clusters to recognize unique characteristics.
“serializing the predictive models for efficient storage and retrieval, ensuring that models can be easily accessed and applied to new data…, utilizing formats like PMML (Predictive Model Markup Language) or ONNX (Open Neural Network Exchange) for portability” is a mental process. A person can mentally serialize/convert the models from one format to another using pen and paper.
“classifying new data points to determine a relevant trend cluster by comparing them to historical data patterns” is a mental process. A person can mentally classify new data by comparing to the historical data patterns.
“selecting an appropriate predictive model for the classified data point based on the identified trend cluster…, ensuring a correct model is applied for anomaly detection” is a mental process. A person can mentally select a model for data based on the pattern/trend in the data cluster.
“comparing the classified data point against an expected pattern predicted by the model to detect deviations…, involving calculating residuals and applying threshold criteria to identify anomalies” is a mental process. A person can mentally compare classified data with trends and patterns to detect anomalies.
“determining whether the data point is an anomaly based on the comparison, identifying deviations from expected patterns…” is a mental process. A person can mentally determine anomalies in the data based on comparison of patterns.
“concluding without further action if no anomaly is detected, allowing … to continue monitoring and analyzing incoming data…, ensuring continuous surveillance and anomaly detection capabilities” is a mental process. A person can mentally conclude that an anomaly is not detected and to continue to monitor and analyze the data.
“performing data analysis … to interpret the logged data and extract meaningful insights, applying advanced statistical … techniques to uncover hidden patterns and trends” is a mental process. A person can mentally perform data analysis and uncover patterns and trends in data using different techniques.
“implementing data cleanup to remove any irrelevant or noisy data…, ensuring that the dataset is prepared for further processing, which may involve techniques such as data smoothing, normalization, and imputation of missing values” is a mental process. A person can mentally clean irrelevant data using different techniques by use of pen and paper.
“conducting feature engineering … to identify and create relevant data features that highlight attributes for machine learning algorithms, involving domain-specific transformations and creation of interaction terms” is a mental process. A person can mentally create features and attributes for an algorithm.
“performing trend analysis … to identify unique trends and patterns within the data, including the influence of external factors such as holidays and weekends, employing decomposition methods like STL (Seasonal and Trend decomposition using Loess) or ARIMA (AutoRegressive Integrated Moving Average)” is a mental process. A person can mentally perform analysis on data to identify unique patterns such as holidays and weekends.
“clustering the identified trends … to categorize the data into distinct segments representing different patterns and influencers, ensuring robust grouping…” is a mental process. A person can mentally cluster trends into different segments of different patter and influencers.
“developing pattern-specific predictive models for each trend cluster…” is a mental process. A person can mentally develop models for different patterns using pen and paper.
“serializing the developed predictive models … for efficient storage and retrieval, ensuring easy application to new data, employing serialization formats that support cross-platform deployment and scalability” is a mental process. A person can mentally serialize/convert models from one format to another by use of pen and paper.
“classifying incoming data points to determine their relevant trend cluster…, comparing them to historical data patterns, ensuring accurate classification through techniques like nearest neighbors or support vector machines” is a mental process. A person can mentally classify new data points by comparing them to the historical patterns by using different classification techniques.
“selecting … the appropriate predictive model for the classified data point…, ensuring accurate model application by dynamically selecting a best-fit model based on real-time data characteristics” is a mental process. A person can mentally select a best model based on the data characteristics.
“comparing the classified data point against the expected pattern predicted…, detecting any deviations through robust statistical tests and anomaly detection algorithms” is a mental process. A person can mentally compare the data points against the predicted patterns to detect anomalies.
“determining whether the classified data point is an anomaly based on the comparison…, identifying deviations from expected patterns” is a mental process. A person can mentally determine if the classified data contains anomalies and can also identify deviations.
“concluding the process without further action if no anomaly is detected” is a mental process. A person can mentally conclude the process if an anomaly is not detected.
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as mental processes but for the recitation of generic computer components, then it falls within the mental processes grouping of abstract ideas. According, the claim “recites” an abstract idea.
In Step 2a Prong 2 of the 101-analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
“an analysis module, using a data cleanup module, using a feature engineering module, using a trend analysis module, using a clustering module, using a model development module, using a model serialization module, using a classification module, using a model selection and loading module, using a comparison module, using an anomaly detection module, which may involve statistical tests, machine learning classifiers, or ensemble methods, using a monitoring module, using an analysis module, using a data cleanup module, using a feature engineering module, using a trend analysis module, using a clustering module, using a model development module, using a model serialization module, using a classification module, using a model selection and loading module, by the model using a comparison module, using an anomaly detection module” are mere instructions to apply the judicial exception using generic computer components, see MPEP 2106.05(f).
“collecting data at a data inception point using a data collection module, ensuring data capture from various sources, including but not limited to IoT devices, transaction logs, and sensor networks; logging the collected data in real-time using a real-time logging tool to maintain temporal integrity and allow for accurate sequence analysis, capturing every data point with timestamps to ensure continuity” are insignificant extra solution activities see MPEP 2106.05(g).
“extracting relevant data from the logged data by filtering and isolating key data points necessary for further analysis using an extraction module, which involves removing redundant or irrelevant data entries and focusing on critical metrics; ingesting the engineered features into a storage and primary data source for organized and efficient retrieval using a data ingestion module, ensuring that the data is formatted and stored in a database or data warehouse for easy access” are insignificant extra solution activities see MPEP 2106.05(g).
“retrieving the stored data for detailed trend analysis using algorithms to detect regularities and irregularities with a data retrieval module” are insignificant extra solution activities see MPEP 2106.05(g).
“employing SQL queries or API calls to access specific datasets” is mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
“training machine learning models such as Random Forest, Gradient Boosting, or neural networks on each cluster, employing supervised learning algorithms to assign new data points to a correct cluster, applying machine learning techniques, algorithms like Gaussian Mixture Models or spectral clustering, tailoring the models to recognize the unique characteristics of each segment, utilizing state-of-the-art machine learning frameworks like TensorFlow or PyTorch, employing ensemble methods to enhance detection accuracy” are mere instructions to apply the judicial exception using generic computer components, see MPEP 2106.05(f).
“loading an appropriate predictive model” is mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
“generating an automated response if an anomaly is detected, which may include sending alerts or initiating remedial actions using an automated response module, providing notifications via email, Short Message Service (SMS), or system logs, and triggering automated workflows or corrective actions” are mere instructions to apply the judicial exception using generic computer components, see MPEP 2106.05(f).
“logging the data stream in real-time using a logging tool to ensure accurate temporal data capture, maintaining a comprehensive log of all data interactions and transformations; ingesting the cleaned and engineered data into a primary storage source using a data ingestion module, organizing the data for efficient access and retrieval, ensuring compatibility with big data frameworks like Hadoop or Spark” are insignificant extra solution activities see MPEP 2106.05(g).
“retrieving the ingested data for detailed trend analysis using a data retrieval module, applying algorithms to detect regularities and irregularities, including advanced techniques such as wavelet transforms or dynamic time warping” are insignificant extra solution activities see MPEP 2106.05(g).
“loading the appropriate predictive model” is mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
“generating an automated response if an anomaly is detected using an automated response module, which may include sending alerts or initiating remedial actions, ensuring prompt responses through integration with automated incident management systems” are mere instructions to apply the judicial exception using generic computer components, see MPEP 2106.05(f).
“allowing the system to continue real-time monitoring and analysis using a monitoring module, maintaining a seamless flow of data surveillance and anomaly detection and implementing trend analysis and clustering to continuously update and refine models based on the latest data patterns and trends, ensuring system adaptability and accuracy over time, leveraging continuous learning algorithms to keep the models current and effective” are mere instructions to apply the judicial exception using generic computer components, see MPEP 2106.05(f).
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
In Step 2b of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
“an analysis module, using a data cleanup module, using a feature engineering module, using a trend analysis module, using a clustering module, using a model development module, using a model serialization module, using a classification module, using a model selection and loading module, using a comparison module, using an anomaly detection module, which may involve statistical tests, machine learning classifiers, or ensemble methods, using a monitoring module, using an analysis module, using a data cleanup module, using a feature engineering module, using a trend analysis module, using a clustering module, using a model development module, using a model serialization module, using a classification module, using a model selection and loading module, by the model using a comparison module, using an anomaly detection module” are mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
“collecting data at a data inception point using a data collection module, ensuring data capture from various sources, including but not limited to IoT devices, transaction logs, and sensor networks; logging the collected data in real-time using a real-time logging tool to maintain temporal integrity and allow for accurate sequence analysis, capturing every data point with timestamps to ensure continuity” are insignificant extra solution activities see MPEP 2106.05(g), which are well understood routine and conventional activities, see Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); (MPEP 2106.05(d)).
“extracting relevant data from the logged data by filtering and isolating key data points necessary for further analysis using an extraction module, which involves removing redundant or irrelevant data entries and focusing on critical metrics; ingesting the engineered features into a storage and primary data source for organized and efficient retrieval using a data ingestion module, ensuring that the data is formatted and stored in a database or data warehouse for easy access” are insignificant extra solution activities see MPEP 2106.05(g), which are well understood routine and conventional activities, see Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); (MPEP 2106.05(d)).
“retrieving the stored data for detailed trend analysis using algorithms to detect regularities and irregularities with a data retrieval module” are insignificant extra solution activities see MPEP 2106.05(g), which are well understood routine and conventional activities, see Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); (MPEP 2106.05(d)).
“employing SQL queries or API calls to access specific datasets” is mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
“training machine learning models such as Random Forest, Gradient Boosting, or neural networks on each cluster, employing supervised learning algorithms to assign new data points to a correct cluster, applying machine learning techniques, algorithms like Gaussian Mixture Models or spectral clustering, tailoring the models to recognize the unique characteristics of each segment, utilizing state-of-the-art machine learning frameworks like TensorFlow or PyTorch, employing ensemble methods to enhance detection accuracy” are mere instructions to apply the judicial exception using generic computer components, see MPEP 2106.05(f).
“loading an appropriate predictive model” is mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
“generating an automated response if an anomaly is detected, which may include sending alerts or initiating remedial actions using an automated response module, providing notifications via email, Short Message Service (SMS), or system logs, and triggering automated workflows or corrective actions” are mere instructions to apply the judicial exception using generic computer components, see MPEP 2106.05(f).
“logging the data stream in real-time using a logging tool to ensure accurate temporal data capture, maintaining a comprehensive log of all data interactions and transformations; ingesting the cleaned and engineered data into a primary storage source using a data ingestion module, organizing the data for efficient access and retrieval, ensuring compatibility with big data frameworks like Hadoop or Spark” are insignificant extra solution activities see MPEP 2106.05(g), which are well understood routine and conventional activities, see Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); (MPEP 2106.05(d)).
“retrieving the ingested data for detailed trend analysis using a data retrieval module, applying algorithms to detect regularities and irregularities, including advanced techniques such as wavelet transforms or dynamic time warping” are insignificant extra solution activities see MPEP 2106.05(g), which are well understood routine and conventional activities, see Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); (MPEP 2106.05(d)).
“loading the appropriate predictive model” is mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
“generating an automated response if an anomaly is detected using an automated response module, which may include sending alerts or initiating remedial actions, ensuring prompt responses through integration with automated incident management systems” are mere instructions to apply the judicial exception using generic computer components, see MPEP 2106.05(f).
“allowing the system to continue real-time monitoring and analysis using a monitoring module, maintaining a seamless flow of data surveillance and anomaly detection and implementing trend analysis and clustering to continuously update and refine models based on the latest data patterns and trends, ensuring system adaptability and accuracy over time, leveraging continuous learning algorithms to keep the models current and effective” are mere instructions to apply the judicial exception using generic computer components, see MPEP 2106.05(f).
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 2, in Step 1 of the 101 analysis set forth in MPEP 2106, the claim recites “A method for detecting anomalies in time series data exhibiting irregular trends…”. A method is considered a process and is one of the four statutory categories of invention.
In Step 2a Pong 1 of the 101 analysis set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers abstract ideas but for recitation of generic computer components:
“A method for detecting anomalies in time series data exhibiting irregular trends, comprising the steps of: performing preliminary analysis on the extracted data with … to identify any patterns or anomalies and establish a baseline understanding” is a mental process. A person can mentally detect anomalies in data by performing analysis and identify unusual patterns.
“cleaning the extracted data using … to remove noise and irrelevant information, ensuring that the data used for feature engineering and analysis is accurate and reliable” is a mental process. A person can mentally clean data to remove irrelevant information by use of pen and paper.
“engineering features from the cleaned data by transforming raw data into a structured format that highlights attributes and properties for machine learning algorithms…” is a mental process. A person can mentally serialize/transform data from one format to another by use of pen and paper.
“identifying unique trends and patterns within the data by analyzing temporal sequences and external influencers such as holidays or weekends using…” is a mental process. A person can mentally identify trends and patterns by analyzing sequences in data.
“clustering the data based on identified trend influencers to categorize the data into distinct segments representing different patterns using…” is a mental process. A person can mentally cluster data based on categories representing patterns.
“developing a specific predictive model for each identified trend cluster, tailored to recognize unique characteristics of each segment using…” is a mental process. A person can mentally develop a model to recognize characteristics of each cluster or segment.
“serializing the predictive models for efficient storage and retrieval, ensuring that models can be easily accessed and applied to new data using…” is a mental process. A person can mentally serialize/convert in to a format that is efficient by use of pen and paper.
“classifying new data points to determine a relevant trend cluster by comparing them to historical data patterns using…” is a mental process. A person can mentally classify new data by comparing to past data patterns.
“selecting … an appropriate predictive model for the classified data point based on the identified trend cluster using…” is a mental process. A person can mentally select a model for the identified trend cluster.
“comparing the classified data point against an expected pattern predicted … to detect deviations using…” is a mental process. A person can mentally compare the data again expected patterns.
“determining whether the data point is an anomaly based on the comparison, identifying deviations from expected patterns using…” is a mental process. A person can mentally determine anomalous data based on comparison of data.
“concluding without further action if no anomaly is detected, and continuing monitoring and analyzing incoming data using…” is a mental process. A person can mentally conclude that an anomaly is not detected and to continue to monitor and analyze the data.
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as mental processes but for the recitation of generic computer components, then it falls within the mental processes grouping of abstract ideas. According, the claim “recites” an abstract idea.
In Step 2a Prong 2 of the 101-analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
“an analysis module, a data cleanup module, a feature engineering module, a trend analysis module, a clustering module, a model development module, a model serialization module, a classification module, a model selection and loading module, by the model… a comparison module, an anomaly detection module, and a monitoring module” are mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
“collecting data at a data inception point using a data collection module, ensuring data capture from various sources; logging the collected data in real-time using a real-time logging tool to maintain temporal integrity and allow for accurate sequence analysis” are insignificant extra solution activities see MPEP 2106.05(g).
“extracting relevant data from the logged data by filtering and isolating key data points necessary for further analysis using an extraction module; ingesting the engineered features into a storage and primary data source for organized and efficient retrieval using a data ingestion module” are insignificant extra solution activities see MPEP 2106.05(g).
“retrieving the stored data for detailed trend analysis using algorithms to detect regularities and irregularities with a data retrieval module” are insignificant extra solution activities see MPEP 2106.05(g).
“loading an appropriate predictive model” are mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
“generating an automated response if an anomaly is detected which may include sending alerts or initiating remedial actions using an automated response module” are mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
In Step 2b of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, the additional elements of “an analysis module, a data cleanup module, a feature engineering module, a trend analysis module, a clustering module, a model development module, a model serialization module, a classification module, a model selection and loading module, by the model… a comparison module, an anomaly detection module, and a monitoring module” are mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
“collecting data at a data inception point using a data collection module, ensuring data capture from various sources; logging the collected data in real-time using a real-time logging tool to maintain temporal integrity and allow for accurate sequence analysis” are insignificant extra solution activities see MPEP 2106.05(g), which are well understood routine and conventional activities, see Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); (MPEP 2106.05(d)).
“extracting relevant data from the logged data by filtering and isolating key data points necessary for further analysis using an extraction module; ingesting the engineered features into a storage and primary data source for organized and efficient retrieval using a data ingestion module” are insignificant extra solution activities see MPEP 2106.05(g), which are well understood routine and conventional activities, see Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); (MPEP 2106.05(d)).
“retrieving the stored data for detailed trend analysis using algorithms to detect regularities and irregularities with a data retrieval module” are insignificant extra solution activities see MPEP 2106.05(g), which are well understood routine and conventional activities, see Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); (MPEP 2106.05(d)).
“loading an appropriate predictive model” are mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
“generating an automated response if an anomaly is detected which may include sending alerts or initiating remedial actions using an automated response module” are mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 3, it is dependent upon claim 2, and thereby incorporates the limitations of, and corresponding analysis applied to claim 2. Additionally claim 3 recites “filtering the collected data to isolate the most pertinent information before logging” which is a mental process. A person can mentally filter the pertinent data by use of pen and paper.
Further, under step 2A prong II and step 2B, claim recites “using a data filtering module” which is mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
Since the claim docs not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 4, it is dependent upon claim 3, and thereby incorporates the limitations of, and corresponding analysis applied to claim 3. Additionally claim 4 recites “identifying any apparent patterns or anomalies in the extracted data” which is a mental process. A person can mentally identify anomalies from the extracted data.
Further, under step 2A prong II and step 2B, claim recites “using an initial pattern recognition module” which is mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
Since the claim docs not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 5, it is dependent upon claim 4, and thereby incorporates the limitations of, and corresponding analysis applied to claim 4. Additionally claim 5 recites “removing random fluctuations and extraneous elements from the extracted data” which is a mental process. A person can mentally remove unnecessary data from the extracted data by use of pen and paper.
Further, under step 2A prong II and step 2B, claim recites “using a noise reduction module” which is mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
Since the claim docs not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 6, it is dependent upon claim 5, and thereby incorporates the limitations of, and corresponding analysis applied to claim 5. Additionally claim 6 recites “transforming raw data into a structured format that highlights underlying patterns and trends” which is a mental process. A person can mentally transform data from one format to another by use of pen and paper.
Further, under step 2A prong II and step 2B, claim recites “using a feature transformation module” which is mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
Since the claim docs not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 7, it is dependent upon claim 6, and thereby incorporates the limitations of, and corresponding analysis applied to claim 6. Additionally claim 7 recites “using sophisticated algorithms to detect regularities and irregularities in the data over time” which is a mental process. A person can mentally detect regularities and irregularities in the data.
Further, under step 2A prong II and step 2B, claim recites “using an advanced trend detection module” which is mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
Since the claim docs not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 8, it is dependent upon claim 7, and thereby incorporates the limitations of, and corresponding analysis applied to claim 7. Additionally claim 8 recites “categorizing the data into segments based on specific events such as holidays, weekends, or other non-periodic occurrences” which is a mental process. A person can mentally categorize data based on holidays, weekends or other criteria.
Further, under step 2A prong II and step 2B, claim recites “using a data segmentation module” which is mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
Since the claim docs not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 9, it is dependent upon claim 8, and thereby incorporates the limitations of, and corresponding analysis applied to claim 8. Additionally claim 9 recites “creating models that recognize the unique characteristics of each cluster based on historical data” which is a mental process. A person can mentally come up with models that identify unique data.
Further, under step 2A prong II and step 2B, claim recites “using a historical pattern modeling module” which is mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
Since the claim docs not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 10, it is dependent upon claim 9, and thereby incorporates the limitations of, and corresponding analysis applied to claim 9. Additionally claim 10 recites “initiating remedial actions based on nature and severity of the detected anomaly” which is a mental process. A person can mentally come up with remedial action such as a correction of data and can then correct it by use of pen and paper.
Further, under step 2A prong II and step 2B, claim recites “sending alerts …” which is an insignificant extra solution activity see MPEP 2106.05(g), and is well understood routine and conventional activity, see Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); (MPEP 2106.05(d)) and “using an alert generation and action module” which is mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
Since the claim docs not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 11, it is dependent upon claim 10, and thereby incorporates the limitations of, and corresponding analysis applied to claim 10. Additionally claim 11 recites “storing the models in a structured format that allows for efficient retrieval and application to new data, using a model serialization and storage module” which under step 2A prong II and step 2B, is an insignificant extra solution activity see MPEP 2106.05(g), and is well understood routine and conventional activity, see Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); (MPEP 2106.05(d)).
Since the claim docs not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 12, in Step 1 of the 101 analysis set forth in MPEP 2106, the claim recites “A system for detecting anomalies in time series data exhibiting irregular trends…”. A system is considered a machine and is one of the four statutory categories of invention.
In Step 2a Pong 1 of the 101 analysis set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers abstract ideas but for recitation of generic computer components:
“filter and isolate relevant data from the logged data, necessary for further analysis by removing redundant or irrelevant data entries and focusing on critical metrics” is a mental process. A person can mentally filter and isolate data for analysis by use of pen and paper.
“perform preliminary analysis on the extracted data, identifying any patterns or anomalies and establishing a baseline understanding using statistical methods and visual inspections” is a mental process. A person can mentally perform analysis on data and identify pattern using statistical methods and visual inspections.
“remove noise and irrelevant information from the extracted data, ensuring that the data used for feature engineering and analysis is accurate and reliable through methods such as outlier removal, interpolation, and normalization” is a mental process. A person can mentally remove noise or irrelevant data for accuracy by use of pen and paper.
“transform raw data into a structured format that highlights attributes and properties for machine learning algorithms by creating new variables, aggregating data points, and encoding categorical variables” is a mental process. A person can mentally transform data from one format to another and can aggregate and encode datapoints/variables by use of pen and paper.
“identify unique trends and patterns within the data by analyzing temporal sequences and external influencers such as holidays or weekends using time-series analysis techniques like moving averages, seasonal decomposition, and Fourier transforms” is a mental process. A person can mentally identify trends and patterns in the data such as holidays and weekends.
“categorize the data into distinct segments representing different patterns based on identified trend influencers using clustering methods such as K-means clustering, hierarchical clustering, or Density-Based Spatial Clustering of Applications with Noise (DBSCAN)” is a mental process. A person can mentally categories data into segments based on patterns/trends using different clustering methods.
“develop a specific predictive model for each identified trend cluster, tailored to recognize unique characteristics of each segment” is a mental process. A person can mentally develop a model to recognize unique characteristics of different segments by use of pen and paper.
“serialize the predictive models for efficient storage and retrieval, ensuring that models can be easily accessed and applied to new data using formats like PMML (Predictive Model Markup Language) or ONNX (Open Neural Network Exchange)” is a mental process. A person can mentally serialize/convert in to a format that is efficient by use of pen and paper.
“classify new data points to determine a relevant trend cluster by comparing them to historical data patterns” is a mental process. A person can mentally classify new data by comparing it to historical data.
“select an appropriate predictive model for the classified data point based on the identified trend cluster, ensuring accurate model application” is a mental process. A person can mentally select a model based on different trends.
“compare the classified data point against an expected pattern predicted … to detect deviations, involving calculating residuals and applying threshold criteria to identify anomalies” is a mental process. A person can compare the data to detect deviations/residuals and identify anomalies in data.
“determine whether the data point is an anomaly based on the comparison, identifying deviations from expected patterns” is a mental process. A person can mentally determine an anomaly in data based on patterns in the data.
“conclude without further action if no anomaly is detected, allowing .. to continue monitoring and analyzing incoming data in real-time, ensuring continuous surveillance and anomaly detection capabilities” is a mental process. A person can mentally conclude if an anomaly is detected or not and whether to continue monitoring for anomaly detection.
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as mental processes but for the recitation of generic computer components, then it falls within the mental processes grouping of abstract ideas. According, the claim “recites” an abstract idea.
In Step 2a Prong 2 of the 101-analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
“A system for detecting anomalies in time series data exhibiting irregular trends, the system comprising one or more processors and anon-transitory computer-readable medium storing instructions that, when executed by the one or more processors, cause the system to implement” are either generic computer components being used as a tool or mere instructions to apply the judicial exception using generic computer components, see MPEP 2106.05(f).
“an extraction module configured to, an analysis module configured to, a data cleanup module configured to, a feature engineering module configured to, a trend analysis module configured to, a clustering module configured to, a model development module configured to, a model serialization module configured to, a classification module configured to, using supervised learning algorithms, a model selection and loading module configured to, a comparison module configured to, by the model, an anomaly detection module configured to, using statistical tests, machine learning classifiers, or ensemble methods, a monitoring module configured to” are mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
“training machine learning models such as Random Forest, Gradient Boosting, or neural networks” are mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
“a data collection module configured to collect data at a data inception point, ensuring data capture from various sources, including but not limited to IoT devices, transaction logs, and sensor networks; a real-time logging tool configured to log the collected data in real-time, maintaining temporal integrity and allowing for accurate sequence analysis by capturing every data point with timestamps” are insignificant extra solution activities see MPEP 2106.05(g).
“a data ingestion module configured to ingest the engineered features into a storage and primary data source for organized and efficient retrieval, ensuring the data is formatted and stored in a database or data warehouse for easy access; a data retrieval module configured to retrieve the stored data for detailed trend analysis using algorithms to detect regularities and irregularities, employing SQL queries or API calls to access specific datasets” are insignificant extra solution activities see MPEP 2106.05(g).
“load an appropriate predictive model” are mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
“an automated response module configured to generate an automated response if an anomaly is detected, which may include sending alerts or initiating remedial actions via email, Short Message Service (SMS), or system logs, and triggering automated workflows or corrective actions” are mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
In Step 2b of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
“A system for detecting anomalies in time series data exhibiting irregular trends, the system comprising one or more processors and anon-transitory computer-readable medium storing instructions that, when executed by the one or more processors, cause the system to implement” are either generic computer components being used as a tool or mere instructions to apply the judicial exception using generic computer components, see MPEP 2106.05(f).
“an extraction module configured to, an analysis module configured to, a data cleanup module configured to, a feature engineering module configured to, a trend analysis module configured to, a clustering module configured to, a model development module configured to, a model serialization module configured to, a classification module configured to, using supervised learning algorithms, a model selection and loading module configured to, a comparison module configured to, by the model, an anomaly detection module configured to, using statistical tests, machine learning classifiers, or ensemble methods, a monitoring module configured to” are mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
“training machine learning models such as Random Forest, Gradient Boosting, or neural networks” are mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
“a data collection module configured to collect data at a data inception point, ensuring data capture from various sources, including but not limited to IoT devices, transaction logs, and sensor networks; a real-time logging tool configured to log the collected data in real-time, maintaining temporal integrity and allowing for accurate sequence analysis by capturing every data point with timestamps” are insignificant extra solution activities see MPEP 2106.05(g), which are well understood routine and conventional activities, see Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); (MPEP 2106.05(d)).
“a data ingestion module configured to ingest the engineered features into a storage and primary data source for organized and efficient retrieval, ensuring the data is formatted and stored in a database or data warehouse for easy access; a data retrieval module configured to retrieve the stored data for detailed trend analysis using algorithms to detect regularities and irregularities, employing SQL queries or API calls to access specific datasets” are insignificant extra solution activities see MPEP 2106.05(g), which are well understood routine and conventional activities, see Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); (MPEP 2106.05(d)).
“load an appropriate predictive model” are mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
“an automated response module configured to generate an automated response if an anomaly is detected, which may include sending alerts or initiating remedial actions via email, Short Message Service (SMS), or system logs, and triggering automated workflows or corrective actions” are mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 13, it is dependent upon claim 12, and thereby incorporates the limitations of, and corresponding analysis applied to claim 12. Additionally claim 13 recites “wherein the data collection module is further configured to collect data from IoT devices, transaction logs, and sensor networks, ensuring comprehensive data capture from multiple and diverse sources to provide a holistic view of the system's environment” which under step 2A prong II and step 2B, is an insignificant extra solution activity see MPEP 2106.05(g), and is a well understood routine and conventional activity, see Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); (MPEP 2106.05(d)).
Since the claim docs not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 14, it is dependent upon claim 13, and thereby incorporates the limitations of, and corresponding analysis applied to claim 13. Additionally claim 14 recites “remove redundant or irrelevant data entries and focus on critical metrics for analysis, using advanced filtering techniques and criteria to enhance quality and relevance of the extracted data” which is a mental process. A person can mentally remove irrelevant data by use of pen and paper.
Further, under step 2A prong II and step 2B, claim recites “the extraction module is further configured to” which is mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
Since the claim docs not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 15, it is dependent upon claim 14, and thereby incorporates the limitations of, and corresponding analysis applied to claim 14. Additionally claim 15 recites “perform outlier removal, interpolation, and normalization to refine the data, ensuring that the dataset is pristine and ready for feature engineering by addressing any inconsistencies or gaps in the data” which is a mental process. A person can mentally perform outlier removal and refine the data by use of pen and paper.
Further, under step 2A prong II and step 2B, claim recites “wherein the data cleanup module is further configured to” which is mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
Since the claim docs not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 16, it is dependent upon claim 15, and thereby incorporates the limitations of, and corresponding analysis applied to claim 15. Additionally claim 16 recites “create new variables, aggregate data points, and encode categorical variables to enhance the data for machine learning algorithms, leveraging domain-specific transformations and interactions to extract the most meaningful features” which is a mental process. A person can mentally create, aggregate and encode the data points by use of pen and paper.
Further, under step 2A prong II and step 2B, claim recites “wherein the feature engineering module is further configured to” which is mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
Since the claim docs not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 17, it is dependent upon claim 16, and thereby incorporates the limitations of, and corresponding analysis applied to claim 16. Additionally claim 17 recites “apply time-series analysis techniques such as moving averages, seasonal decomposition, and Fourier transforms to detect unique trends, enabling the system to accurately identify and characterize patterns over time” which is a mental process. A person can mentally apply different techniques to detect trends and pattern in data by use of pen and paper.
Further, under step 2A prong II and step 2B, claim recites “the trend analysis module is further configured to” which is mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
Since the claim docs not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 18, it is dependent upon claim 17, and thereby incorporates the limitations of, and corresponding analysis applied to claim 17. Additionally claim 18 recites “use clustering methods such as K-means clustering, hierarchical clustering, or Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to group similar data points, facilitating the identification of distinct patterns and trends within the data” which is a mental process. A person can mentally use different clustering methods to group similar data based on different patterns and trend in the data.
Further, under step 2A prong II and step 2B, claim recites “wherein the clustering module is further configured to” which is mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
Since the claim docs not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 19, it is dependent upon claim 18, and thereby incorporates the limitations of, and corresponding analysis applied to claim 18. Additionally claim 19 recites “wherein the model development module is further configured to train machine learning models such as Random Forest, Gradient Boosting, or neural networks on each identified trend cluster, ensuring that each model is tailored to the unique characteristics of its respective cluster for optimal predictive accuracy” which under step 2A prong II and step 2B, is mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
Since the claim docs not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 20, it is dependent upon claim 19, and thereby incorporates the limitations of, and corresponding analysis applied to claim 19. Additionally claim 20 recites “wherein the automated response module is further configured to send alerts via email, Short Message Service (SMS), or system logs” which under step 2A prong II and step 2B, is an insignificant extra solution activity see MPEP 2106.05(g), and is well understood routine and conventional activity, see Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and “trigger automated workflows or corrective actions based on the nature and severity of the detected anomaly, ensuring prompt responses to potential issues” which under step 2A prong II and step 2B, is mere instructions to apply the judicial exception using generic computer, see MPEP 2106.05(f).
Since the claim docs not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Response to Arguments
Regarding 112(b), applicant amended the claims to address previous 112(b) issues, however new 112 rejections have been added in this office action for claims 1-20. Please see the above 112(b) rejection for details.
Further, previous 101 rejection have been withdrawn, however examiner has added 101 rejections for claims being directed to an abstract idea without significantly more. See above rejections for details.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Usmaan Saeed whose telephone number is (571)272-4046. The examiner can normally be reached Monday-Friday 9:00am - 5:30pm.
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/USMAAN SAEED/ Supervisory Patent Examiner, Art Unit 2146
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