Detailed Action
This action in response to application on 12/18/2023.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending.
Claims 1-20 are rejected.
Information Disclosure Statement
The information disclosure Statement (IDS) submitted on 12/18/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS statements are being considered by the examiner.
Drawings
The drawings submitted on 12/18/2023 are accepted.
Claim Objections
Claims 1, and 11 recite “the model”. There is insufficient antecedent basis for this limitation in the claims.
Claims 4, and 14 recite “the scores”. There is insufficient antecedent basis for this limitation in the claims.
Claims 7, and 17 recite “a workload class”. It is not clear if this “workload class” is the same or different element “workload class” as recited in the parent claims.
Claims 8, and 18 recites “the workload classes”. There is insufficient antecedent basis for this limitation in the claims.
Appropriate amendments/remarks required.
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 is rejected under 35 U.S.C. 101 as being directed to abstract idea without significantly more.
Representative claim 1 is directed to a method, comprising:
deploying a set of non-degenerate models to a system having a known configuration, wherein each of the non-degenerate models corresponds to a pair that comprises a system configuration and a workload class;
running a workload on the system;
collecting telemetry data generated as a result of the running of the workload;
assessing the telemetry data with each of the non-degenerate models to generate a respective score for each of the models;
identifying, as among the non-degenerate models, which of the non-degenerate models has a best score; and
determining, based on the best score, whether or not a change is needed to hardware and/or software of the known configuration of the system.
Per prong 1, Step 2A, the above emphasized element/concepts are not meaningfully different than those concepts found by the courts to be abstract, namely, mental processes including concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and/or humans using pen and paper (see, October 2019 Patent Eligibility Guidance Update, 84 Fed. Reg. 55,942, hereinafter “PEG”).
For instance, humans can mentally and/or via aid of pen/paper perform a method, comprising: mentally assessing the telemetry data with each of the non-degenerate models to generate a respective score for each of the models; identifying, as among the non-degenerate models, which of the non-degenerate models has a best score; and determining, based on the best score, whether or not a change is needed to hardware and/or software of the known configuration of the system.
Per prong 2, Step 2A, the additional non-emphasized elements as noted above, are mere data gathering/sending steps/insignificant extra-solution activity to the judicial exception; are merely adding words “apply it” (or an equivalent) with the judicial exception/mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea; Generally linking the use of the judicial exception to a particular technological environment or field of use. For instance,
“collecting telemetry data generated as a result of the running of the workload” are mere data gathering/insignificant extra-solution activity to the judicial exception, see MPEP 2106.05(g).
“deploying a set of non-degenerate models to a system having a known configuration, wherein each of the non-degenerate models corresponds to a pair that comprises a system configuration and a workload class; running a workload on the system;” are merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f, h).
Additionally, the recited claim limitations do not improve the functionality of the electronic device or achieve improved technical results and does not sufficiently tie any limitations or combination of limitation to any improvement (if any) to the functionality of the electronic device or achieving improved technical results.
Per Step 2B, the additional non-emphasized elements as noted above, are mere data gathering/sending steps/insignificant extra-solution activity to the judicial exception; are merely adding words “apply it” (or an equivalent) with the judicial exception/mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea; Generally linking the use of the judicial exception to a particular technological environment or field of use; simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d, f, g, h). . For instance,
“collecting telemetry data generated as a result of the running of the workload” are mere data gathering/insignificant extra-solution activity to the judicial exception, see MPEP 2106.05(g).
“deploying a set of non-degenerate models to a system having a known configuration, wherein each of the non-degenerate models corresponds to a pair that comprises a system configuration and a workload class; running a workload on the system;” are merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f, h).
Additionally, the recited claim limitations do not improve the functionality of the electronic device or achieve improved technical results.
Accordingly, claim 1 is rejected under 35 U.S.C. 101 as being directed to an abstract idea without significantly more.
Independent claim 11 is a medium claim corresponding to method claim 1 and is of substantially same scope.
Accordingly, claims 11 is rejected under the same rational as set forth for claim 1.
Dependent claims 2-10, and 12-20 when considered individually or in combination per steps as noted above are rejected under the same rational as set forth above for claims 1, and 11, and the recited claim limitations do not improve the functionality of the electronic device or achieve improved technical results. In particular,
As per claim 2, the rejection of claim 1 further incorporated, further recites wherein one or more of the non-degenerate models comprises a respective trained Riemannian model.
Per prong 1, Step 2A, the above emphasized element/concepts are not meaningfully different than those concepts found by the courts to be abstract, namely, Mental Processes including concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and/or humans using pen and paper.
Per prong 2, Step 2A and 2B, the additional elements (e.g. non-emphasized elements) are mere data gathering/sending steps/insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g) and/or are merely adding words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(d, f, g, h).
As per claim 3, the rejection of claim 1 further incorporated, further recites wherein the non-degenerate models were trained using known workloads and known system configurations.
Per prong 1, Step 2A, the above emphasized element/concepts are not meaningfully different than those concepts found by the courts to be abstract, namely, Mental Processes including concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and/or humans using pen and paper.
Per prong 2, Step 2A and 2B, the additional elements (e.g. non-emphasized elements) are mere data gathering/sending steps/insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g) and/or are merely adding words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(d, f, g, h).
As per claim 4, the rejection of claim 1 further incorporated, further recites wherein the scores are normalized before the identifying of the non-degenerate model with the best score
Per prong 1, Step 2A, the above emphasized element/concepts are not meaningfully different than those concepts found by the courts to be abstract, namely, Mental Processes including concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and/or humans using pen and paper and Mathematical concepts including mathematical relationships, mathematical formulas or equations, and/or mathematical calculations.
Per prong 2, Step 2A and 2B, the additional elements (e.g. non-emphasized elements) are mere data gathering/sending steps/insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g) and/or are merely adding words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(d, f, g, h).
As per claim 5, the rejection of claim 1 further incorporated, further recites wherein assessing the telemetry data comprises identifying a workload classification for the workload.
Per prong 1, Step 2A, the above emphasized element/concepts are not meaningfully different than those concepts found by the courts to be abstract, namely, Mental Processes including concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and/or humans using pen and paper.
Per prong 2, Step 2A and 2B, the additional elements (e.g. non-emphasized elements) are mere data gathering/sending steps/insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g) and/or are merely adding words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(d, f, g, h).
As per claim 6, the rejection of claim 1 further incorporated, further recites wherein each of the non-degenerate models is configured to identify telemetry data that appears anomalous.
Per prong 1, Step 2A, the above emphasized element/concepts are not meaningfully different than those concepts found by the courts to be abstract, namely, Mental Processes including concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and/or humans using pen and paper.
Per prong 2, Step 2A and 2B, the additional elements (e.g. non-emphasized elements) are mere data gathering/sending steps/insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g) and/or are merely adding words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(d, f, g, h).
As per claim 7, the rejection of claim 1 further incorporated, further recites wherein a workload class of the workload is unknown to the non-degenerate models.
Per prong 1, Step 2A, the above emphasized element/concepts are not meaningfully different than those concepts found by the courts to be abstract, namely, Mental Processes including concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and/or humans using pen and paper.
Per prong 2, Step 2A and 2B, the additional elements (e.g. non-emphasized elements) are mere data gathering/sending steps/insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g) and/or are merely adding words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(d, f, g, h).
As per claim 8, the rejection of claim 1 further incorporated, further recites wherein the determining comprises identifying, as among the workload classes respectively associated with each of the non-degenerate models, which of the workload classes most likely corresponds to the workload.
Per prong 1, Step 2A, the above emphasized element/concepts are not meaningfully different than those concepts found by the courts to be abstract, namely, Mental Processes including concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and/or humans using pen and paper.
Per prong 2, Step 2A and 2B, the additional elements (e.g. non-emphasized elements) are mere data gathering/sending steps/insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g) and/or are merely adding words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(d, f, g, h).
As per claim 9, the rejection of claim 1 further incorporated, further recites wherein when the determining indicates that a change is needed to the hardware and/or software, implementing the change to the hardware and/or software.
Per prong 1, Step 2A, the above emphasized element/concepts are not meaningfully different than those concepts found by the courts to be abstract, namely, Mental Processes including concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and/or humans using pen and paper.
Per prong 2, Step 2A and 2B, the additional elements (e.g. non-emphasized elements) are mere data gathering/sending steps/insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g) and/or are merely adding words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(d, f, g, h).
As per claim 10, the rejection of claim 1 further incorporated, further recites wherein the determining is performed as-a- Service to a customer.
Per prong 1, Step 2A, the above emphasized element/concepts are not meaningfully different than those concepts found by the courts to be abstract, namely, Mental Processes including concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and/or humans using pen and paper.
Per prong 2, Step 2A and 2B, the additional elements (e.g. non-emphasized elements) are mere data gathering/sending steps/insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g) and/or are merely adding words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(d, f, g, h).
As per claims 12-20:
Dependent claims 12-20 are medium claims corresponding to method claims 2-10 and are of substantially same scope.
Accordingly, claims 12-20 is rejected under the same rational as set forth for claim 2-10.
Accordingly, claims 1-20 are rejected under 35 U.S.C. 101.
Allowable Subject Matter
Claims 1-20 are allowable if above noted rejections are overcome via amendments and/or arguments. Reasons for allowance will be held in abeyance until all matters in the prosecution are closed.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
FEEDBACK-BASED TUNING OF TELEMETRY COLLECTION PARAMETERS
DOCUMENT ID
US 20230251902 A1
DATE PUBLISHED
2023-08-10
Abstract
A method of tuning telemetry collection parameters may include, with a collector, receiving source data defining at least one application running on a plurality of nodes, the nodes utilizing a finite number of compute resources. With the collector, a number of score models within a scoring agent of the collector may be executed to define telemetry collection parameters used by the collector for source data collection. The method may also include computing, with the scoring agent, a number of scores based on disturbance features and adaptive feedback, and tuning, with the collector, the telemetry collection parameters based on the scores to obtain tuned telemetry collection parameters.
RUN-TIME DETERMINATION OF APPLICATION PERFORMANCE WITH LOW OVERHEAD IMPACT ON SYSTEM PERFORMANCE
DOCUMENT ID
US 20200242000 A1
DATE PUBLISHED
2020-07-30
Abstract
Techniques are disclosed for determining the run-time performance of an application executing on a computing system with low impact on the performance of the computing system. For example, a time series telemetry data stream is obtained for each of a plurality of key performance indicators during run-time execution of the application on a computing system having a given system configuration. One or more statistical features are extracted from each time series telemetry data stream. Model parameters of a machine learning performance score model are populated with values of the extracted statistical features. A run-time performance score of the application is then determined using the model parameters of the machine learning performance score model populated with the values of the extracted statistical features.
A plurality of metrics records, including some records indicating metrics for which anomaly analysis has been performed, is obtained. Using a training data set which includes the metrics records, a machine learning model is trained to predict an anomaly analysis relevance score for an input record which indicates a metric name. Collection of a particular metric of an application is initiated based at least in part on an anomaly analysis relevance score obtained for the particular metric using a trained version of the model.
Machine Learning-based Selection Of Metrics For Anomaly Detection
DOCUMENT ID
US 11748568 B1
DATE PUBLISHED
2023-09-05
Abstract
A plurality of metrics records, including some records indicating metrics for which anomaly analysis has been performed, is obtained. Using a training data set which includes the metrics records, a machine learning model is trained to predict an anomaly analysis relevance score for an input record which indicates a metric name. Collection of a particular metric of an application is initiated based at least in part on an anomaly analysis relevance score obtained for the particular metric using a trained version of the model.
METHOD AND SYSTEM FOR IDENTIFYING ACTIONS TO IMPROVE CUSTOMER EXPERIENCE
DOCUMENT ID
US 20230059500 A1
DATE PUBLISHED
2023-02-23
Abstract
A method for automatically detecting and evaluating experience data associated with an experience journey to identify and adjust an action that is taken to improve customer experience is provided. In some embodiments, the method includes generating performance data associated with the experience journey from the experience data using a machine learning model. The method further includes determining the action to be taken based on analyzing the performance data. The method further includes collecting new experience data responsive to the action having been taken and training the machine learning model using the new experience data. The method further includes updating the performance data and the action to be taken based on training the machine learning model.
MONITORING AND ALERTING SYSTEM BACKED BY A MACHINE LEARNING ENGINE
DOCUMENT ID
US 20230038164 A1
DATE PUBLISHED
2023-02-09
Abstract
A monitoring and alerting system backed by a machine learning engine for anomaly detection and prediction of time series data indicative of health of an application, a system, an environment, or a person. Using any data of interest that is modeled into a time series known as times and values; comparing input data against learned previous patterns; predicting data; identifying anomalies; generating notifications or an alert identifying the deviation, and communicating the alert to users, applications, or devices, applying the action or health functions logic using the significance of the issue to modify/start/stop components of the system or application. The data is received via a metrics server and is cleaned into a unified format and passed through via streaming or push/pull mechanisms. Planned deviations are configured to prevent false positives. A variety of machine learning methods is used and the system has dual function components and disaster recovery.
See form 892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MUSTAFA A AMIN whose telephone number is (571)270-3181. The examiner can normally be reached on Monday-Friday from 8:00 AM to 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kevin Young, can be reached on 571-270-3180. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/MUSTAFA A AMIN/ Primary Examiner, Art Unit 2194
Prosecution Insights