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 .
DETAILED ACTION
Response to Amendment
Claims 1-20 have been amended.
Claims 21-35 have been canceled.
Claims 1-20 are pending.
Response to Arguments
Applicant’s arguments with respect to the pending claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
CLAIM REJECTIONS - 35 USC § 103
I. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
II. CLAIMS 1-5, 7-11, 14, 16 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over MUNIGALA et al (USPN 11,860,727) in view of SWAMINATHAN et al (USPN 11,366,842).
Per claim 1, MUNIGALA et al teach a method performed by a first node in a communications network for satisfying a first intent set for a system in the communications network, wherein the first intent comprises expectations, each expectation corresponding to a Key Performance Indicator, KPI, target, the method comprising:
predicting values of measurable properties that would be observed if a first action were performed in the system, using empirical relationships between actions and the measurable properties (col.1 lines 31-63, col.12 lines 7-58, col.20 lines 13-22, col.23 lines 21-32, col.26 lines 8-33, col.36 line 38-col.37 line 35—predicted replacement values of measurable properties and characteristics, associating inherent properties within the KPI and associating issues with the original data that have a relationship or correlation with a KPI); and
comparing the predicted KPI values to KPI targets in the first intent to predict whether performing the first action would lead to each of the expectations in the first intent being satisfied for the system (col.13 line 43-col.14 line 24, col.18 lines 4-58, col.22 line 39-col.23 line 32, col.25 line 56-col.26 line 33, col.36 line 38-col.37 line 2— comparison of confidence values, imputed KPI values compared to original KPI values in order to attain specific business goals).
MUNIGALA et al teach the limitations, as applied above, and calculating values to be measured in a simulated snapshot, determining performance of actions based on confidence values below a predetermined threshold (col.18 lines 4-40, col.28 lines 30-58, col.35 line 57-col.36 line 37); yet fail to explicitly teach the method “ii) converting the predicted values of the measurable properties into predicted KPI values that are predicted to be measured in the system if the first action were to be performed…the KPI targets corresponding to the expectations in the first intent to predict whether performing the first action would lead to”.
SWAMINATHAN et al teach that data regarding a particular KPI and other KPIs is transformed to predicted future values for the particular KPI over a prediction window, with predicted future KPI scores used to determine a KPI impact score reflecting some measure of the degree to which the KPI can influence the actual future health score (Abstract), with transformation rules including converting the character string into a different format (col.35 lines 28-41, col.62 lines 21-49) and converting recent KPI and health score data (col.70 lines 25-50). SWAMINATHAN et al further teach the KPI and health score data generates feature data instances to train/test with measurable properties or characteristics and service performance, a minimum percentage of expected KPI values recorded in the input data based on KPI production frequency (col.67 lines 1-19, col.67 line 58-col.68 line 47).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed the invention to combine the teachings of MUNIGALA et al with SWAMINATHAN et al for the purpose of the conversion/translation of the predicted values of the measurable values into KPI values, well-known in the art for KPI targets to determine predictions based on the measured values.
Per claim 2, MUNIGALA et al with SWAMINATHAN et al teach the method as in claim 1, MUNIGALA et al further teach the method further comprising: repeating steps i), ii) and iii) for a second action; and selecting an action from the first action and the second action to perform in the system in order to satisfy the first intent, using the predicted KPI values for the first action and the predicted KPI values for the second action; and causing the selected action to be performed (col.16 line 38-col.17 line 6, col.18 lines 24-40, col.26 lines 34-62, col.37 lines 7-35—determination of best actions to select for correcting the erroneous data, subsequent actions may be automated, scored predictions associated with qualified confidence values).
Per claim 3, MUNIGALA et al with SWAMINATHAN et al teach the method as in claim 2, MUNIGALA et al further teach the method wherein causing the selected action to be performed comprises: performing the selected action; or sending a message to a different node in the communications network to cause the different node to perform the selected action (col.27 line 56-col.29 line 9, col.30 lines 37-67—performing best action to correct erroneous data or to accept correct data based on a predetermined remediation technique).
Per claim 4, MUNIGALA et al with SWAMINATHAN et al teach the method as in claim 2, MUNIGALA et al further teach wherein steps i), ii) and iii) are performed in response to: determining a difference between measured KPI values in the system and the KPI targets corresponding to the expectations in the first intent; and raising said difference as a first issue in the system (col.13 lines 54-col.14 line 7, col.22 line 39-col.23 line 9, 26 lines 34-62, col.36 lines 38-61, col.37 lines 14-35—determinations of similarity and if there is insufficient similarity between imputed KPI vales and original KPI values then including issues labels as not relevant to the respective KPIs and an issue determination operation; SWAMINATHAN et al: col.77 line 3-col.78 line 37—difference between the KPI value at the current time used for KPI prediction).
Per claim 5, MUNIGALA et al with SWAMINATHAN et al teach the method as in claim 4, MUNIGALA et al further teach wherein the first action and the second action are different proposals for resolving the first issue in the system (col.28 line 1-col.29 line 9—different remediation methods for resolving the different errors and system issues).
Per claim 7, MUNIGALA et al with SWAMINATHAN et al teach the method as in claim 1, MUNIGALA et al further teach wherein the first intent further comprises penalties to be applied if the expectations in the first intent are not satisfied (col.14 lines 2-46, col.23 lines 21-32, col.26 line 34-col.27 line 10, col.27 lines 26-55—determining if there are any relevant relationship between the data errors in the original data stream, embedded issues will either be ignored or an issue notification will be transmitted, the KPIs are predetermined and described as explicit measurements of success or lack thereof toward attaining specific business goals verifying if the original data is within certain tolerances and determining if there is any association of any potentially erroneous data with one or more KPIs, when the original data is associated, it is impactful).
Claim 20 contains limitations that are substantially equivalent to the limitations of claim 1 and 7 are therefore rejected under the same basis.
Per claim 8, MUNIGALA et al with SWAMINATHAN et al teach the method as in claim 7, MUNIGALA et al further teach wherein the penalties quantify an impact of the KPI targets for the expectations not being satisfied (col.37 lines 21-35--data quality issues are filtered based on the analysis of given KPI and data is modified to mitigate the quality issues considering various scenarios to compute its impact on the measurement of given KPI and additionally measure the confidence of the predicted replacement values).
Per claim 9, MUNIGALA et al with SWAMINATHAN et al teach the method as in claim 7, MUNIGALA et al further teach wherein preceding steps i), ii) and iii), the method comprises: determining a first system penalty, wherein the first system penalty is determined using a penalty formula for aggregating penalties accrued by not meeting one or more of the expectations in the first intent and/or not meeting one or more expectations in other intents set for the system (col.14 line 25-col.15 line 13; col.16 lines 49-67, col.23 line 21-col.24 line 35—determining if there are any relevant relationship between the data errors in the original data stream; discrete “correct” labels and discrete “erroneous” labels and subsequent aggregation of such facilitates further determinations of “best” actions, whether those actions are to correct erroneous data or to accept correct data; embedded issues will either be ignored or an issue notification will be transmitted; determine if the measurements of the collection of business data attain business goals and grouping KPI formulations into two types, “observable box formula” and “unobservable box formula” with respect to data with issues; SWAMINATHAN et al: col.78 line 3-col.79 line 20—predicted KPI information and weighting factor with other formulations).
Per claim 10, MUNIGALA et al with SWAMINATHAN et al teach the method as in claim 9, MUNIGALA et al further teach the method further comprising: using outputs of the step of comparing the predicted KPI values to the KPI targets in the first intent, to predict a second system penalty for the system, if the first action were to be performed (col.13 line 43-col.14 line 24, col.18 lines 4-58, col.22 line 39-col.23 line 32, col.25 line 56-col.26 line 33, col.36 line 38-col.37 line 26—comparison of confidence values, electing a scored prediction of a replacement value for the erroneous data, imputed KPI values compared to original KPI values in order to attain specific business goals; SWAMINATHAN et al: col.72 line 49-col.73 line 32—amount of the time offset may correspond to distance into the future a model is intended to predict).
Per claim 11, MUNIGALA et al with SWAMINATHAN et al teach the method as in claim 10, MUNIGALA et al further teach the method comprising: selecting the first action if the predicted second system penalty is less than or equal to the first system penalty (col.17 line 58-col.18 line 40, col.36 lines 8-37—for confidence values below a predetermined threshold, the respective inferred snapshot values will not be passed for further processing; SWAMINATHAN et al: col.71 lines 7-18—if the predicted future health score falls below a particular threshold, the processing determines that an identification of impactors is needed).
Per claim 14, MUNIGALA et al with SWAMINATHAN et al teach the method as in claim 1, MUNIGALA et al further teach wherein the empirical relationships comprise one or more mathematical formulae derived from experimental data, wherein the experimental data comprises: first test actions and resulting first test values of the measurable properties as performed on the system; and/or second test actions and resulting second test values of the measurable properties as determined using a digital twin of the system (col.15 line 52-col.16 line 20, col.17 lines 3-40, col.21 lines 8-65—KPI formulation characteristics includes inputted values from simulated snapshots of inferred data points; SWAMINATHAN et al: col.21 lines 20-54, col.69 line 26-col.70 line 24—test directory associated with test index and values).
Per claim 16, MUNIGALA et al with SWAMINATHAN et al teach the method as in claim 1, MUNIGALA et al further teach wherein the empirical relationships comprise correlations between actions and the measurable properties, derived using symbolic reasoning (col.26 lines 1-33—correlation between actions and measured characteristics; SWAMINATHAN et al: col.54 lines 13-30, col.77 line 3-col.78 line 9—relationships among various factors that affect the performance of a hierarchically structured computing system).
Per claim 19, MUNIGALA et al with SWAMINATHAN et al teach the method as in claim 1, MUNIGALA et al further teach wherein the first node is an intent manager in the communications system (col.4 lines 62-64—managing done by an organization or third-party).
III. CLAIMS 6, 12-13, 15 and 17-18 are rejected under 35 U.S.C. 103 as being obvious over MUNIGALA et al (USPN 11,860,727) in view of SWAMINATHAN et al (USPN 11,366,842) and PREVITI et al (USPN 11,894,990)*.
*The applied reference has a common Assignee and Inventor with the instant application. Based upon the earlier effectively filed date of the reference, it constitutes prior art under 35 U.S.C. 102(a)(2). This rejection under 35 U.S.C. 103 might be overcome by: (1) a showing under 37 CFR 1.130(a) that the subject matter disclosed in the reference was obtained directly or indirectly from the inventor or a joint inventor of this application and is thus not prior art in accordance with 35 U.S.C.102(b)(2)(A); (2) a showing under 37 CFR 1.130(b) of a prior public disclosure under 35 U.S.C. 102(b)(2)(B); or (3) a statement pursuant to 35 U.S.C. 102(b)(2)(C) establishing that, not later than the effective filing date of the claimed invention, the subject matter disclosed and the claimed invention were either owned by the same person or subject to an obligation of assignment to the same person or subject to a joint research agreement. See generally MPEP § 717.02.
Per claim 6, MUNIGALA et al with SWAMINATHAN et al teach the method as in claim 4, as applied above, yet fail to explicitly teach the method “further comprising: determining that the predicted KPI values for the first action leads to a conflict with a second intent, if the predicted KPI values for the first action would leads to the second intent not being satisfied; and/or determining that the predicted KPI values for the first action lead to a conflict between a first expectation and a second expectation in the first intent, if the predicted KPI values would cause a second issue with the second expectation due to the second expectation not being satisfied”.
PREVITI et al teach determining that predicted KPI values for an action conflict between the KPI targets using the expectation of business intents, BIs (col.1 lines 50-67, col.10 lines 57-67), and if the predicted KPI values cause an issue based on the expectation not being satisfied (col.2 line 61-col.3 line 6, col.11 lines 1-47, col.11 line 65-col.12 line 16). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed the invention to combine the teachings of MUNIGALA et al with SWAMINATHAN et al and PREVITI et al for the purpose of provisioning conflict resolution between multiple intents, KPIs and unsatisfied expectations. These features well-known in the art and obvious with KPI targets to determine resolutions to determined conflicts.
Per claim 12, MUNIGALA et al with SWAMINATHAN et al teach the method as in claim 10, as applied above, along with performing best action to correct erroneous data or to accept correct data based on a predetermined remediation technique (col.27 line 56-col.29 line 9, col.30 lines 37-67), yet fails to explicitly teach selecting the first action if the predicted second system penalty satisfies a system penalty criterion set in a third intent, wherein the system penalty criterion provides an indication of a manner in which to determine, based on the first system penalty and the second system penalty whether the first action should be selected.
PREVITI et al teach multiple business intents, BIs, which are mapped to KPIs (col.5 lines 27-66) and linking business intents, BIs, with the action settings of the KPI targets with changes that increase/decrease (col.6 lines 25-49). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed the invention to combine the teachings of MUNIGALA et al with SWAMINATHAN et al and PREVITI et al for the purpose of provisioning action selection linking the multiple intents and KPIs, which is well-known in the art for applying the necessary remediation actions and techniques.
Per claim 13, MUNIGALA et al with SWAMINATHAN et al and PREVITI et al teach the method as in claim 12, PREVITI et al further teach wherein the system penalty criterion: sets a threshold penalty that the system penalty should not exceed; or indicates that the system penalty should be minimized (col.14 lines 38-46, col.16 lines 21-37, col.17 line 58-col.18 line 40, col.29 lines 10-27, col.36 lines 8-37—any embedded issues will be either ignored and the data processed as is, or an issue notification will be transmitted to the user; for confidence values below a predetermined threshold, the respective inferred snapshot values will not be passed for further processing; respective data points exceeding a threshold value that is based on a probability of what the data point value should be as a function of the established data patterns; one or more instances of the original data may appear to be incorrect due to the respective data elements exceeding a threshold value that is based on a probability of what the data element value should be as a function of the established data patterns).
Per claim 15, MUNIGALA et al with SWAMINATHAN et al teach the method as in claim 14, as applied above, and discloses training models with the simulated snapshot data (col.36 lines 18-25); yet fail to explicitly teach the method wherein the one or more mathematical formulae are comprised in a machine learning, ML, model trained using the experimental data. PREVITI et al teach machine learning process and model training (col.5 line 53-col.6 line 5). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed the invention to combine the teachings of MUNIGALA et al with SWAMINATHAN et al and PREVITI et al for the purpose of provisioning a machine learning model to be trained. Machine learning, ML, models are well-known in the art for receiving input and formulas for training to automate processes using simulated data.
Per claim 17, MUNIGALA et al with SWAMINATHAN et al teach the method as in claim 1, as applied above with service level agreement, SLA, controls allocations and management in the environment (col.6 lines 1-11); yet fail to explicitly teach the method wherein the empirical relationships are expressed as logical inference rules. PREVITI et al teach mapping business intents, BIs, to KPIs based on a set of rules/policies linking each BI to one or more KPIs (col.5 lines 42-45, col.13 lines 4-9). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed the invention to combine the teachings of MUNIGALA et al with SWAMINATHAN et al and PREVITI et al for the purpose of provisioning rules and policies to configure mapping, relationships and correlations between the intents and KPI, which is well-known in the art for applying rule/policy sets.
Claim 18 contains limitations that are substantially equivalent to the limitations of claim 17 and are therefore rejected under the same basis.
Conclusion
IV. The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure: US 2022/0245574; US 2023/0246910; WO 2021/215720; US 2024/0356821; USPN 11063842.
V. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
VI. Any inquiry concerning this communication or earlier communications from the examiner should be directed to KRISTIE D SHINGLES whose telephone number is (571)272-3888. The examiner can normally be reached on Monday-Thursday 10am-7pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamal Divecha can be reached on 571-272-5863. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KRISTIE D SHINGLES/
Primary Examiner, Art Unit 2453
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