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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on August 13, 2024 has been reviewed considered by the examiner.
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 claimed invention is directed to an abstract idea without significantly more.
Claims 1, 9, and 17 recite determining a traffic indicator of a target object, and obtaining a plurality of traffic indicator values of the traffic indicator from a plurality of databases; determining, based on the plurality of traffic indicator values, a quantile indicator value corresponding to the traffic indicator; obtaining a plurality of first ground-truth indicator values corresponding to an experimental group and a plurality of second ground-truth indicator values corresponding to a control group of a traffic indicator of the plurality of databases; and determining a test result of the experimental parameter based on the plurality of first ground-truth indicator values, the plurality of second ground-truth indicator values and the quantile indicator value, the test result being test passed or test failed.
The limitations above are processes that under the broadest reasonable interpretation fall into the “mathematical concepts” and “mental processes” grouping of abstract ideas –“concepts performed in the human mind by observation, evaluation, judgement, and opinion”, see MPEP 2106.04 (a)(2) (III).
This judicial exception is not integrated into a practical application because the additional elements of “an electronic device, comprising a processor and a memory; the memory storing computer-executable instructions; and the processor executing the computer-executable instructions stored in the memory, causing the processor to perform acts” (claim 9) and “a non-transitory computer-readable storage medium, wherein computer-executable instructions are stored in the computer-readable storage medium, the computer-executable instructions, when executed by a processor, implementing acts” (claim 17) are not indicative of integration into a practical application since they are recited at a high level of generality. Additionally, these additional elements amount to no more than mere instructions to implement the abstract idea on a computer and merely use a computer as a tool to practice the judicial exception. Even when viewed in combination, these additional elements are still mere instructions to implement the judicial exception.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reasons as presented above. The claims as a whole describe how to apply the concept of experimental parameter testing and business indicators using “an electronic device, comprising a processor and a memory; the memory storing computer-executable instructions; and the processor executing the computer-executable instructions stored in the memory, causing the processor to perform acts” (claim 9) and “a non-transitory computer-readable storage medium, wherein computer-executable instructions are stored in the computer-readable storage medium, the computer-executable instructions, when executed by a processor, implementing acts” (claim 17), which are generic computer components, to apply the abstract idea on a computer. Moreover, the additional elements of are known and conventional computing elements as evidenced by the spec at para 0032-0045---describing these elements at a high level of generality. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer, which do not provide an inventive concept. Therefore, the claims are ineligible.
Dependent claims 2-8, 10-16, and 18-20 recite additional details that further narrow the previously recited abstract idea. There are no additional elements that are indicative of integration into a practical application; nor are there additional elements that amount to significantly more that the judicial exception. Thus, even when viewed as a whole, nothing in the claims adds significantly more to the abstract idea. Therefore, the claims are ineligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent Application Publication Number 20230099001 to Harutyunyan et al. (hereafter referred to as Harutyunyan)
Claim 1 rejected, Harutyunyan teaches
determining a traffic indicator of a target object, and obtaining a plurality of traffic indicator values of the traffic indicator from a plurality of databases(Harutyunyan, para [0003-0004], Categories of metrics include CPU usage, memory usage, number of datastores, network throughput, amount of disk space, and summary metrics of the various virtual and physical objects of a data center. para [0086-0090], The operations manager trains inference models for applications running in a distributed computing system. For selected applications, the operations manager collects metrics and KPIs associated with the selected application for a historical time window from a data-storage device. The duration of the historical time window may be preset to an hour, two hours, twelve hours, a day, a week, or a month or even longer. The metrics associated with the selected application are retrieved from the data-storage device and denoted by. Para [0163-0164]]);
determining, based on the plurality of traffic indicator values, a quantile indicator value corresponding to the traffic indicator (Harutyunyan, para [0163-0164], FIGS. 22A-22D show examples of highest ranked metrics associated with different types of performance problems. FIG. 22A shows an example of metrics, importance scores and ranks of metrics with importance scores above 50. The combination of metrics with importance scores greater than 50 are associated with inadequate memory allocated to VMs of an application);
obtaining a plurality of first ground-truth indicator values corresponding to an experimental group and a plurality of second ground-truth indicator values corresponding to a control group of a traffic indicator of the plurality of databases (Harutyunyan, para [0156-0157], FIG. 21 shows an example graphical user interface (“GUI”) 2100 that displays KPIs associated with different applications running in a distributed computing system. The GUI 2100 includes a window 2102 that displays four entries 2104-2107 that list applications identified as Application 1, Application 2, Application 3, and Application 4 and show plots of curves 2108-2111 that represent corresponding KPIs plotted over the same recent run-time interval that ends at the current time denoted by t.sub.c. Horizontal dashed lines represent thresholds between normal and abnormal behavior of the applications. For example, KPI values of Applications 1, 2, and 4 are below a threshold 2112, which indicates the applications are performing normally as represented by normal icons, such as normal icon 2114. On the other hand, KPI values of the Application 3 exceed the threshold 2112, such as KPI value 2114, triggering a warning alert 2116. Threshold 2116 indicates the application exhibits critical behavior that triggers a critical alert icon that is not shown. A user may select “run troubleshooting” by clicking on the button 2118, which begins the automated computer-implemented process of troubleshooting Application 3 described below. Para [0124-0126], a training set and a validating set) ; and
determining a test result of the experimental parameter based on the plurality of first ground-truth indicator values, the plurality of second ground-truth indicator values and the quantile indicator value, the test result being test passed or test failed(Harutyunyan, para [0170-0172], FIG. 26 shows an example GUI 2600 that displays example troubleshooting results for the selected application in FIG. 21. The GUI 2600 displays an alert 2602 indicating that the root cause of the performance problem as inadequate memory allocation to Application 3. In this example, the GUI 2600 displays the rank order metrics in FIG. 22A. The GUI 2600 allows a user, such as a system administrator or software engineer, to perform automated retraining of the inference model with metrics above a minimum score enter in a field 2606 by clicking on button 2608. Based on the combination of ranked metrics, the GUI displays a recommended remedial measure and enables a user to input an additional memory allocation 2610 and cause the operations manager to execute the allocation by clicking on the button 2612.).
Claim 2 is rejected for the reasons set forth hereinabove for claim 1, Harutyunyan teaches the method of claim 1, wherein determining the test result of the experimental parameter based on the plurality of first ground-truth indicator values, the plurality of second ground-truth indicator values and the quantile indicator value comprises (Harutyunyan, para [0170-0172]):
calculating a first permeability indicator associated with the experimental group, to indicate a proportion of first ground-truth indicator values that satisfy a predetermined magnitude relationship with the quantile indicator value (Harutyunyan, fig. 21 and para [0156-0157], KPI values of Applications 1, 2, and 4 are below a threshold 2112, which indicates the applications are performing normally as represented by normal icons, such as normal icon 2114);
calculating a second permeability indicator associated with the control group, to indicate a proportion of second ground-truth indicator values that satisfy a predetermined magnitude relationship with the quantile indicator value (Harutyunyan, para [0156-0157], On the other hand, KPI values of the Application 3 exceed the threshold 2112, such as KPI value 2114, triggering a warning alert 2116. Threshold 2116 indicates the application exhibits critical behavior that triggers a critical alert icon that is not shown. A user may select “run troubleshooting” by clicking on the button 2118, which begins the automated computer-implemented process of troubleshooting Application 3 described below. Para [0170-0175], to perform automated retraining of the inference model with metrics above a minimum score enter in a field 2606 by clicking on button 2608. ); and
determining the test result of the experimental parameter based on the first permeability indicator and the second permeability indicator (Harutyunyan, para [0156-0157], On the other hand, KPI values of the Application 3 exceed the threshold 2112, such as KPI value 2114, triggering a warning alert 2116. Threshold 2116 indicates the application exhibits critical behavior that triggers a critical alert icon that is not shown. A user may select “run troubleshooting” by clicking on the button 2118, which begins the automated computer-implemented process of troubleshooting Application 3 described below.).
Claim 3 is rejected for the reasons set forth hereinabove for claim 2, Harutyunyan teaches the method of claim 2, wherein calculating the first permeability indicator associated with the experimental group comprises (Harutyunyan, fig. 21 and para [0156-0157]):
obtaining the number of first ground-truth indicator values in each database and adding up the numbers of first ground-truth indicator values in the plurality of databases, to obtain a first number (Harutyunyan, para [0083-0085], The operations manager also collects key performance indicators (“Traffic”) for the applications. A KPI is a metric that represents the state, or health, of an application or service provided by the application over time, such as the normal or abnormal behavior of the application. Examples of KPIs include latency, traffic, errors, and saturation. Application latency is the time delay between a time when a client submits a request for an application to perform an operation, or provide a service, and a later time when the application responds to the request. Traffic is the number of requests processed by an application per unit time. Errors are the number of application errors per unit time because of the application processing client requests or accessing resources. Saturation is the percentage, or number, of resources used by the application per unit time.);
determining the number of first target indicator values in each database that satisfy a predetermined magnitude relationship with the quantile indicator value, and adding up the numbers of first target indicator values in the plurality of databases, to obtain a second number(Harutyunyan, para [0083-0085], The operations manager also collects key performance indicators (“Traffic”) for the applications. A KPI is a metric that represents the state, or health, of an application or service provided by the application over time, such as the normal or abnormal behavior of the application. Examples of KPIs include latency, traffic, errors, and saturation. Application latency is the time delay between a time when a client submits a request for an application to perform an operation, or provide a service, and a later time when the application responds to the request. Traffic is the number of requests processed by an application per unit time. Errors are the number of application errors per unit time because of the application processing client requests or accessing resources. Saturation is the percentage, or number, of resources used by the application per unit time.); and
determining a ratio of the second number to the first number as the first permeability indicator (Harutyunyan, para [0156-0157], FIG. 21 shows an example graphical user interface (“GUI”) 2100 that displays KPIs associated with different applications running in a distributed computing system. The GUI 2100 includes a window 2102 that displays four entries 2104-2107 that list applications identified as Application 1, Application 2, Application 3, and Application 4 and show plots of curves 2108-2111 that represent corresponding KPIs plotted over the same recent run-time interval that ends at the current time denoted by t.sub.c. Horizontal dashed lines represent thresholds between normal and abnormal behavior of the applications. For example, KPI values of Applications 1, 2, and 4 are below a threshold 2112, which indicates the applications are performing normally as represented by normal icons, such as normal icon 2114. On the other hand, KPI values of the Application 3 exceed the threshold 2112, such as KPI value 2114, triggering a warning alert 2116. Threshold 2116 indicates the application exhibits critical behavior that triggers a critical alert icon that is not shown. A user may select “run troubleshooting” by clicking on the button 2118, which begins the automated computer-implemented process of troubleshooting Application 3 described below. Figs. 22A-22D, CPU demand entitlement ratio and para [0163-0164].).
Claim 4 is rejected for the reasons set forth hereinabove for claim 2, Harutyunyan teaches the method of claim 2, wherein calculating a second permeability indicator associated with the control group comprises (Harutyunyan, para [0156-0157]):
obtaining the number of second ground-truth indicator values in each database and adding up the numbers of second ground-truth indicator values in the plurality of databases, to obtain a third number (Harutyunyan, para [0083-0085], The operations manager also collects key performance indicators (“Traffic”) for the applications. A KPI is a metric that represents the state, or health, of an application or service provided by the application over time, such as the normal or abnormal behavior of the application. Examples of KPIs include latency, traffic, errors, and saturation. Application latency is the time delay between a time when a client submits a request for an application to perform an operation, or provide a service, and a later time when the application responds to the request. Traffic is the number of requests processed by an application per unit time. Errors are the number of application errors per unit time because of the application processing client requests or accessing resources. Saturation is the percentage, or number, of resources used by the application per unit time.);
determining the number of second target indicator values in each database that satisfy a predetermined magnitude relationship with the quantile indicator value, and adding up the numbers of second target indicator values in the plurality of databases, to obtain a fourth number(Harutyunyan, para [0083-0085], The operations manager also collects key performance indicators (“Traffic”) for the applications. A KPI is a metric that represents the state, or health, of an application or service provided by the application over time, such as the normal or abnormal behavior of the application. Examples of KPIs include latency, traffic, errors, and saturation. Application latency is the time delay between a time when a client submits a request for an application to perform an operation, or provide a service, and a later time when the application responds to the request. Traffic is the number of requests processed by an application per unit time. Errors are the number of application errors per unit time because of the application processing client requests or accessing resources. Saturation is the percentage, or number, of resources used by the application per unit time.); and
determining a ratio of the fourth number to the third number as the second permeability indicator (Harutyunyan, para [0156-0157], FIG. 21 shows an example graphical user interface (“GUI”) 2100 that displays KPIs associated with different applications running in a distributed computing system. The GUI 2100 includes a window 2102 that displays four entries 2104-2107 that list applications identified as Application 1, Application 2, Application 3, and Application 4 and show plots of curves 2108-2111 that represent corresponding KPIs plotted over the same recent run-time interval that ends at the current time denoted by t.sub.c. Horizontal dashed lines represent thresholds between normal and abnormal behavior of the applications. For example, KPI values of Applications 1, 2, and 4 are below a threshold 2112, which indicates the applications are performing normally as represented by normal icons, such as normal icon 2114. On the other hand, KPI values of the Application 3 exceed the threshold 2112, such as KPI value 2114, triggering a warning alert 2116. Threshold 2116 indicates the application exhibits critical behavior that triggers a critical alert icon that is not shown. A user may select “run troubleshooting” by clicking on the button 2118, which begins the automated computer-implemented process of troubleshooting Application 3 described below. Figs. 22A-22D, CPU demand entitlement ratio and para [0163-0164].).
Claim 5 is rejected for the reasons set forth hereinabove for claim 2, Harutyunyan teaches the method of claim 2, wherein determining the test result of the experimental parameter based on the first permeability indicator and the second permeability indicator comprises (Harutyunyan, para [0156-0157]):
processing the first permeability indicator and the second permeability indicator based on hypothesis testing, to obtain a P value associated with the hypothesis testing, the P value indicating whether there is a significant difference between the first permeability indicator and the second permeability indicator(Harutyunyan, para [0119-0124], hypothesis testing); and in
accordance with a determination that the P value indicates that there is a significant difference between the first permeability indicator and the second permeability indicator, determining the test result of the experimental parameter as test passed(Harutyunyan, para [0156-0157], FIG. 21 shows an example graphical user interface (“GUI”) 2100 that displays KPIs associated with different applications running in a distributed computing system. The GUI 2100 includes a window 2102 that displays four entries 2104-2107 that list applications identified as Application 1, Application 2, Application 3, and Application 4 and show plots of curves 2108-2111 that represent corresponding KPIs plotted over the same recent run-time interval that ends at the current time denoted by t.sub.c. Horizontal dashed lines represent thresholds between normal and abnormal behavior of the applications. For example, KPI values of Applications 1, 2, and 4 are below a threshold 2112, which indicates the applications are performing normally as represented by normal icons, such as normal icon 2114.).
Claim 6 is rejected for the reasons set forth hereinabove for claim 1, Harutyunyan teaches the method of claim 1, wherein determining, based on the plurality of traffic indicator values, a quantile indicator value corresponding to the traffic indicator comprises(Harutyunyan, para [0163-0164]):
determining an initial indicator value(Harutyunyan, para [0163-0164], FIGS. 22A-22D show examples of highest ranked metrics associated with different types of performance problems. FIG. 22A shows an example of metrics, importance scores and ranks of metrics with importance scores above 50. The combination of metrics with importance scores greater than 50 are associated with inadequate memory allocated to VMs of an application);
determining, based on the initial indicator value and from the plurality of traffic indicator values, a plurality of target traffic indicator values that satisfy a predetermined magnitude relationship with the initial indicator value(Harutyunyan, para [0162], magnitude);
determining a fifth number of the plurality of target traffic indicator values and a sixth number of the plurality of traffic indicator values (Harutyunyan, para [0100], FIG. 15B shows a plot of metric values synchronized to a general set of uniformly spaced time stamps. Horizontal axis 1520 represents time. Vertical axis 1522 represents a range of metric values. Solid dots represent metric values recorded at irregularly spaced time stamps. Marks located along time axis 1520 represent time stamps of a general set of uniformly spaced time stamps. Note that the metric values are not aligned with the time stamps of the general set of uniformly spaced time stamps. Open dots represent metric values aligned with the time stamps of the general set of uniformly spaced time stamps. Bracket 1524 represents a sliding time window centered at a time stamp t.sub.3 of the general set. The metric values x.sub.1, x.sub.2, x.sub.3, x.sub.4, and x.sub.5 have time stamps within the sliding time window 1524 and are averaged 1526 to obtain synchronized metric value 1528 at the time stamp t.sub.3 of the general set of uniformly spaced time stamps. A synchronized metric value 1530 is interpolated for a missing metric value at the time stamp is by computing an average 1532 of the metric values in the time window 1534.); and
determining the quantile indicator value based on the fifth number, the sixth number, a predetermined quantile, and the initial indicator value(Harutyunyan, para [0159-0161], A threshold for identifying the highest ranked metrics is given by the condition:
I.sub.j.sup.score>Th.sub.score (26)
where Th.sub.score is a user defined threshold. For example, the user-defined threshold may be set to 70%, 60%, 50% or 40%. The importance score computed in Equation (25) is assigned to each corresponding metric. The metrics are rank ordered based on the corresponding importance scores to identify the highest ranked metrics that directly impact the KPI. For example, the highest ranked metrics are metrics with importance scores above the user-defined threshold Th.sub.score. The combination of highest ranked metrics associated with a KPI that indicates a performance problem with an application identify the root cause of the performance problem with the application.).
Claim 7 is rejected for the reasons set forth hereinabove for claim 6, Harutyunyan teaches the method of claim 6, wherein the determining the quantile indicator value based on the fifth number, the sixth number, the predetermined quantile and the initial indicator value comprises (Harutyunyan, para [0159-0161]):
determining a ratio between the fifth number and the sixth number (Harutyunyan, para [0156-0157], FIG. 21 shows an example graphical user interface (“GUI”) 2100 that displays KPIs associated with different applications running in a distributed computing system. The GUI 2100 includes a window 2102 that displays four entries 2104-2107 that list applications identified as Application 1, Application 2, Application 3, and Application 4 and show plots of curves 2108-2111 that represent corresponding KPIs plotted over the same recent run-time interval that ends at the current time denoted by t.sub.c. Horizontal dashed lines represent thresholds between normal and abnormal behavior of the applications. For example, KPI values of Applications 1, 2, and 4 are below a threshold 2112, which indicates the applications are performing normally as represented by normal icons, such as normal icon 2114. On the other hand, KPI values of the Application 3 exceed the threshold 2112, such as KPI value 2114, triggering a warning alert 2116. Threshold 2116 indicates the application exhibits critical behavior that triggers a critical alert icon that is not shown. A user may select “run troubleshooting” by clicking on the button 2118, which begins the automated computer-implemented process of troubleshooting Application 3 described below. Figs. 22A-22D, CPU demand entitlement ratio and para [0163-0164].);
in response to determining that the ratio is equal to the predetermined quantile, determining the initial indicator value as the quantile indicator value(Harutyunyan, para [0163-0164], FIGS. 22A-22D show examples of highest ranked metrics associated with different types of performance problems. FIG. 22A shows an example of metrics, importance scores and ranks of metrics with importance scores above 50. The combination of metrics with importance scores greater than 50 are associated with inadequate memory allocated to VMs of an application); and
in response to determining that the ratio is not equal to the predetermined quantile, updating the initial indicator value until the ratio determined based on the updated initial indicator value is equal to the predetermined quantile, and determining the updated initial indicator value as the quantile indicator value(Harutyunyan, para [0159-0161], A threshold for identifying the highest ranked metrics is given by the condition:
I.sub.j.sup.score>Th.sub.score (26)
where Th.sub.score is a user defined threshold. For example, the user-defined threshold may be set to 70%, 60%, 50% or 40%. The importance score computed in Equation (25) is assigned to each corresponding metric. The metrics are rank ordered based on the corresponding importance scores to identify the highest ranked metrics that directly impact the KPI. For example, the highest ranked metrics are metrics with importance scores above the user-defined threshold Th.sub.score. The combination of highest ranked metrics associated with a KPI that indicates a performance problem with an application identify the root cause of the performance problem with the application.).
Claim 8 is rejected for the reasons set forth hereinabove for claim 1, Harutyunyan teaches the method of claim 1, wherein after determining the test result of the experimental parameter, the method further comprises: if the test result is test failed, generating prompt information indicating a risk of online operation for the experimental parameter (Harutyunyan, fig. 21 and para [0156-0157], KPI values of Applications 1, 2, and 4 are below a threshold 2112, which indicates the applications are performing normally as represented by normal icons, such as normal icon 2114. On the other hand, KPI values of the Application 3 exceed the threshold 2112, such as KPI value 2114, triggering a warning alert 2116. Threshold 2116 indicates the application exhibits critical behavior that triggers a critical alert icon that is not shown. A user may select “run troubleshooting” by clicking on the button 2118, which begins the automated computer-implemented process of troubleshooting Application 3 described below.); and
sending the prompt information to a predetermined device or presenting the prompt information( Harutyunyan, fig. 21 and para [0156-0157]. Harutyunyan, para [0170-0172], FIG. 26 shows an example GUI 2600 that displays example troubleshooting results for the selected application in FIG. 21. The GUI 2600 displays an alert 2602 indicating that the root cause of the performance problem as inadequate memory allocation to Application 3.)
As per claim 9, this is the electronic device claim to method claim 1. Therefore, it is rejected for the same reasons as above.
As per claim 10, this is the electronic device claim to method claim 2. Therefore, it is rejected for the same reasons as above.
As per claim 11, this is the electronic device claim to method claim 3. Therefore, it is rejected for the same reasons as above.
As per claim 12, this is the electronic device claim to method claim 4. Therefore, it is rejected for the same reasons as above.
As per claim 13, this is the electronic device claim to method claim 5. Therefore, it is rejected for the same reasons as above.
As per claim 14, this is the electronic device claim to method claim 6. Therefore, it is rejected for the same reasons as above.
As per claim 15, this is the electronic device claim to method claim 7. Therefore, it is rejected for the same reasons as above.
As per claim 16, this is the electronic device claim to method claim 8. Therefore, it is rejected for the same reasons as above.
As per claim 17, this is the medium claim to method claim 1. Therefore, it is rejected for the same reasons as above.
As per claim 18, this is the medium claim to method claim 2. Therefore, it is rejected for the same reasons as above.
As per claim 19, this is the medium claim to method claim 3. Therefore, it is rejected for the same reasons as above.
As per claim 20, this is the medium claim to method claim 4. Therefore, it is rejected for the same reasons as above.
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DUY KHUONG THANH NGUYEN whose telephone number is (571)270-7139. The examiner can normally be reached Monday - Friday 0800-1630.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lewis Bullock can be reached at 5712723759. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DUY KHUONG T NGUYEN/ Primary Examiner, Art Unit 2199
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