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
The amendments filed on May 11, 2026 have been entered. Claims 29-33, 35, 36, 39-41, 43, 46, and 48 have been amended. Claims 1-28 were previously cancelled. Claims 29-48 remain pending in the application.
Response to Arguments
Applicant’s arguments filed on May 11, 2026 in response to the Non-Final Office Action dated December 11, 2025 have been fully considered but they are not persuasive.
Regarding objection to the specification, the Abstract sheet contains other texts like classifications etc. Examiner suggests submitting a newt Abstract sheet with the Abstract only.
Regarding 35 U.S.C. 102(a)(1) rejections:
Applicant argues, in page 12 of the Remarks, “There are several differences between this disclosure and the independent claim features. First, Oliner's models are "predictive models configured to generate predicted values" of a first time series using "values of or associated with" a different second time series. In contrast, the independent claims specify "statistical models representing a corresponding plurality of statistical characteristics of the time series of PM data." Unlike Oliner's "predictive models," the claimed "statistical models" are not used for predicting values of a different time series; instead they are representative of "statistical characteristics" of the time series from which they are computed.”
In response, Examiner respectfully disagrees. The claim does not restrict using second time series. Paragraph 0260 of Oliner discloses different predictive models are generated based different characteristics of the time-series data. Since time-series is a statistical representation of data as explained in paragraph 0226, characteristics of time series correspond to the statistical characteristics. Since the term “projection” is not exclusively defined, the claimed “computing respective projections of plurality of the plurality of statistical models” can be interpreted as computing predicted values using the predictive models.
Applicant argues, in pages 12 and 13 of the Remarks, “Second, Oliner's anomaly detection tool does not "compute projections" of the predictive models onto the same time series of data from which the predictive models were determined. Oliner's anomaly detection tool generates the predictive models used to predict the first time series from the "sequences of time series values," such as by subset grouping and clustering as discussed in [0283]-[0284]. Oliner's anomaly detection tool does not "compute projections of' these predictive models onto the same "sequences of time series values" from which they were determined; rather, the anomaly detection tool applies the predictive models to a different second time series of data for predicting the first time series of data. In contrast, the independent claims specify "computing respective projections of the plurality of statistical models onto the time series of PM data" from which the statistical models were computed.
In response, the claim does define the term “projection”. Under broadest reasonable interpretation, computing projections of models onto the time-series can be interpreted computing predicated values of the time series using predictive models. Since the goal is to choose the best performing model for a given time-series, plurality of models are used to generate plurality of predicted time-series and by comparing each to the actual time-series, performance of models are determined.
Applicant argues, in pages 13 and 14 of the Remarks, “In the OA (at 3), the Examiner also found that Oliner [0307] discloses the independent claim operations of "based on the projections" of the plurality of statistical models onto the time series of data, "selecting one or more of the plurality of statistical models that are most explanatory of the prediction target information." A closer inspection shows error in these findings, particularly in view of the clarifying amendments. Oliner [0307] cited by the Examiner discloses that "at block 1608, ... anomaly detection tool 1316 can select at least one predictive model (e.g., selected model 1416) for anomaly detection based on the determined error of the predictive model," which was determined in [0306] based on the predicted and actual values associated with the first time series. In other words, Oliner's anomaly detection tool selects the model that is most predictive of the first time series based on the respective errors in predicting the same first time series. In contrast, the independent claims specify "selecting one or more of the plurality of statistical models that are most explanatory of the prediction target information" based on the projections of the statistical models "onto the time series of data" - which is different from the "prediction target information." In other words, the projections of the statistical models onto the original "time series of data" are used to determine which of the statistical models is "most of explanatory" of different "prediction target information." This is a very different technique than the technique used by Oliner's anomaly detection tool.”
In response, Examiner respectfully disagree. Applicant unduly narrows the broader interpretations of the terms without defining detail in the claim. The predictive model with least error would generate a predicted time-series that would be closest resemblance of the original time-series. Errors are computed based on the deviation between predicted time series and actual time series. Therefore, selecting a predictive model based on error equates to selecting based on predictive time-series. Examiner suggests defining the terms “prediction target information”, “statistical characteristics”, “projections of plurality of statistical models”, “most explanatory” in specific manners to distinguish over the applied art.
Specification
The abstract of the disclosure is objected to because it contains other text. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Objections
Claim 31 is objected to because of the following informalities: “model”, recited in line 9, should be replaced with “statistical model”. Appropriate correction is required.
Examiner’s Note about the Format of 35 U.S.C. 102/103 Rejections
Generally, limitations of a claim are reproduced identically and followed by examiner’s explanation with citation from prior art in Italic enclosed by a parenthesis, (), for each limitation. In examiner’s explanation, the mapping of the key elements of a limitation to the disclosed elements of prior art is shown by stating the disclosed element immediately followed by the claimed element inside a parenthesis. Specific quotation from prior art is delineated with quotation mark, ““. If primary art fails to teach a limitation or part of the limitation, the limitation or the part of the limitation is placed inside double square brackets, [[ ]], for better understandability, and appropriate secondary art(s) is/are applied later addressing the deficiency of the primary art.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 29, 32-35, 37-42, and 45-48 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Oliner et al. (US PGPUB No. US 20210037037 A1), hereinafter, Oliner.
Regarding claim 29:
Oliner teaches:
A computer-implemented method for identifying communication network performance management (PM) data that is explanatory of prediction target information, the method comprising:
obtaining a time series of PM data representing performance of the communication network at a plurality of periodic time instances over a first duration (paragraph 0304 discloses obtaining time series values (PM data) as stated “At block 1602, sequences of time series values are obtained. For example, anomaly detection tool 1316 may obtain sequences of time series values from data store 1314 and/or indexer 1312. The time series values may correspond to events and may be determined from machine data. Further, each sequence can correspond to a respective time series. The sequences may or may not correspond to one or more streams of source data.”. Paragraph 0069 discloses the time series data represent performance of the network. Fig. 14B shows performance data for yearly period);
based on the time series of PM data, computing a plurality of statistical models representing a corresponding plurality of statistical characteristics of the time series of PM data (paragraph 0305 discloses based on the time series values, generating plurality of predictive models as stated “At block 1604, a plurality of predictive models is generated for a first time series from the sequences. For example, anomaly detection tool 1316 can generate predictive models 1410 for a first time series from the sequences of time series values.”. Paragraphs 0283 and 0284 disclose grouping time series based on statistical characteristics);
computing respective projections of the plurality of statistical models onto the time series of PM data (paragraph 306 discloses computing predicted values using the predictive models. Also see Fig. 14B); and
based on the projections, selecting one or more of the plurality of statistical models that are most explanatory of the prediction target information (paragraph 0307 discloses based on the errors of predicted values, selecting a predictive model as stated “At block 1608, a predictive model is selected for anomaly detection. For example, anomaly detection tool 1316 can select at least one predictive model (e.g., selected model 1416) for anomaly detection based on the determined error of the predictive model. In some embodiments, this may include iterative training, evaluating, and filtering of predictive models.”).
As to claim 32, the rejection of claim 29 is incorporated. Oliner teaches all the limitations of claim 29 as shown above.
Oliner further teaches wherein computing the plurality of statistical models is further based on data representative of factors external to the communication network (paragraphs 0264 and 0265 disclose considering additional factors like time the day, day of the week).
As to claim 33, the rejection of claim 32 is incorporated. Oliner teaches all the limitations of claim 32 as shown above.
Oliner further teaches wherein: computing the plurality of statistical models comprises scaling or transforming the time series of PM data using the data representative of the external factors; and the plurality of statistical models are computed based on the scaled or transformed time series of PM data (paragraphs 0264 discloses excluding periodic spike in particular time of the day).
As to claim 34, the rejection of claim 33 is incorporated. Oliner teaches all the limitations of claim 33 as shown above.
Oliner further teaches wherein the time series of PM data is scaled or transformed based on a function representative of effects of the external factors on a relation between the time series of PM data and the prediction target information (paragraph 0264 discloses excluding periodic spike in particular time of the day).
As to claim 35, the rejection of claim 29 is incorporated. Oliner teaches all the limitations of claim 29 as shown above.
Oliner further teaches wherein selecting one or more of the statistical models based on the projections comprises: for each projection, calculating interaction information for data including the projection and the prediction target information; and selecting a subset of the plurality of statistical models corresponding to a subset of the projections whose calculated interaction information meets one or more criteria (paragraph 0271 discloses calculating residual error for each predication model and selecting a subset of prediction models based on the residual error).
As to claim 37, the rejection of claim 35 is incorporated. Oliner teaches all the limitations of claim 35 as shown above.
Oliner further teaches wherein selecting one or more of the models based on the projections further comprises separating the projections into first and second subsets, with projections of the first subset having greater interaction information than projections of the second subset, wherein the first subset is selected based on having greater interaction information (Fig. 14A shows different set of prediction models. Paragraph 0260 discloses different sets of models with different characteristics paragraph 0278 discloses iterative process to find out the best prediction model).
As to claim 38, the rejection of claim 35 is incorporated. Oliner teaches all the limitations of claim 35 as shown above.
Oliner further teaches wherein the interaction information for each projection includes: first interaction information for the projection; second interaction information for the projection and the prediction target information; and information gain from the first interaction information to the second interaction information (paragraph 0270 discloses calculating residual error between predicted time series values and actual time series values. Paragraph 0272 discloses, besides residual error, multiple factors can be considered for selecting a model. Paragraph 0274 discloses considering model’s explanatory value for model selection).
As to claim 39, the rejection of claim 38 is incorporated. Oliner teaches all the limitations of claim 38 as shown above.
Oliner further teaches wherein selecting one or more of the plurality of statistical models based on the projections further comprises separating the projections into first and second subsets, with projections of the first subset having greater information gain than projections of the second subset, wherein the first subset is selected based on having greater information gain (paragraph 0257 disclose iterative process with grouping predictive models and Fig. 14A shows as iteration progresses, subset of models are more accurate than the previous one ).
As to claim 40, the rejection of claim 29 is incorporated. Oliner teaches all the limitations of claim 29 as shown above.
Oliner further teaches wherein selecting one or more of the plurality of statistical models based on the projections comprises: for each projection, calculating a correlation between the projection and the prediction target information; and selecting a subset of the plurality of statistical models corresponding to a subset of the projections whose calculated correlation meets one or more criteria (paragraph 0270 discloses calculating residual error between predicted time series values and actual time series values. Paragraph 0271 discloses selecting models based on the residual errors meeting a threshold criteria).
As to claim 41, the rejection of claim 40 is incorporated. Oliner teaches all the limitations of claim 40 as shown above.
Oliner further teaches wherein selecting one or more of the plurality of statistical models based on the projections further comprises separating the projections into first and second subsets, with projections of the first subset having greater correlation than projections of the second subset, wherein the first subset is selected based on having greater correlation (paragraph 0257 disclose iterative process with grouping predictive models and Fig. 14A shows as iteration progresses, subset of models are more accurate than the previous one).
As to claim 42, the rejection of claim 37 is incorporated. Oliner teaches all the limitations of claim 37 as shown above.
Oliner further teaches wherein the first and second subsets are separated based on a predetermined one of the following: number of projections to be included in the first subset, interaction information threshold, information gain threshold, or correlation threshold (paragraph 0271 discloses selecting models based on the residual errors meeting a threshold criteria).
As to claim 45, the rejection of claim 29 is incorporated. Oliner teaches all the limitations of claim 29 as shown above.
Oliner further teaches wherein: the time series of PM data includes samples of key performance indicators (KPIs) for each of a plurality of network nodes or network functions (NFs) of the communication network and for each of the plurality of periodic time instances over the first duration; and the prediction target information is one or more of the following for the communication network: end-to-end (E2E) latency, E2E throughput, and energy usage (paragraph 0069 discloses the time series data represent performance of the nodes of the network. Paragraph 0177 discloses energy-usage).
As to claim 46, the rejection of claim 45 is incorporated. Oliner teaches all the limitations of claim 45 as shown above.
Oliner further teaches wherein: obtaining the time series of PM data comprises grouping the time series of PM data according to geo-location of the respective network nodes or NFs; and the plurality of statistical models are computed based on the time series of PM data grouped according to geo-location (paragraph 0095 discloses performance data include geographic location).
As to claim 47, the rejection of claim 32 is incorporated. Oliner teaches all the limitations of claim 32 as shown above.
Oliner further teaches wherein the data representative of factors external to the communication network includes data representative of one or more of the following over the first duration: forecast or actual weather, days of the week, month of the year, season of the year, public events or demonstrations, road usage or traffic, public transportation usage, power outages, public health, and shopping or other commerce (paragraphs 0264 and 0265 disclose considering additional factors like time the day, day of the week).
Regarding claim 48:
Claim 48 is directed towards computing apparatus performing the method of claim 29. Accordingly, it is rejected under similar rationale.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 30 and 31 are rejected under 35 U.S.C. 103 as being unpatentable over Oliner in view of Gopalakrishnan et al. (US PGPUB No. US 20160285700 A1), hereinafter, Gopalakrishnan.
As to claim 30, the rejection of claim 29 is incorporated. Oliner teaches all the limitations of claim 29 as shown above.
Oliner does not teach wherein each statistical model is computed as one of the following: a Hidden Markov Model (HMM) with a plurality of emission states having distributions according to Gaussian Mixture Models (GMMs); a Dirichlet distribution; a von Mises-Fisher distribution.
Gopalakrishnan teaches wherein each statistical model is computed as one of the following: a Hidden Markov Model (HMM) with a plurality of emission states having distributions according to Gaussian Mixture Models (GMMs); a Dirichlet distribution; a von Mises-Fisher distribution (paragraph 0043 discloses HMM for predicting. Fig. 7 shows emission states of the HMM as explained paragraph 0071 and 0072.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Oliner to incorporate the teaching of Gopalakrishnan about HMM for predicting. One would be motivated to use HMM that since it is a powerful and sophisticated algorithm which combines the past and the present for time series prediction (see paragraph 0063 of Gopalakrishnan).
As to claim 31, the rejection of claim 30 is incorporated. Oliner in view of Gopalakrishnan teaches all the limitations of claim 30 as shown above.
Oliner does not teach wherein: the plurality of emission states of the HMM correspond to a respective plurality of clusters of the time series of PM data; and computing projections of the plurality of statistical models onto the time series of PM data comprises computing the projection of each statistical model based on a product of the following at each time instance over the first duration: the time series of PM data, and the posterior probabilities of the respective emission states of the HMM for the model.
Gopalakrishnan wherein: the plurality of emission states of the HMM correspond to a respective plurality of clusters of the time series of PM data; and computing projections of the plurality of statistical models onto the time series of PM data comprises computing the projection of each statistical model based on a product of the following at each time instance over the first duration: the time series of PM data, and the posterior probabilities of the respective emission states of the HMM for the model (paragraph 0043 discloses HMM for predicting. Fig. 7 shows emission states of the HMM as explained paragraph 0071 and 0072).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Oliner to incorporate the teaching of Gopalakrishnan about HMM for predicting. One would be motivated to use HMM that since it is a powerful and sophisticated algorithm which combines the past and the present for time series prediction (see paragraph 0063 of Gopalakrishnan).
Claim 36 is rejected under 35 U.S.C. 103 as being unpatentable over Oliner in view of Woo (US PGPUB No. US 20230142131 A1), hereinafter, Woo.
As to claim 36, the rejection of claim 35 is incorporated. Oliner teaches all the limitations of claim 35 as shown above.
Oliner further teaches wherein: each projection represents a temporal pattern of the time series of PM data that is associated with the corresponding statistical model (paragraph 0316 and 317 discloses clustering the time series. Paragraph 0318 discloses corresponding models for respective cluster).
Oliner does not teach the interaction information for each projection is calculated based on a joint entropy among the temporal pattern of the time series of PM data and the prediction target information.
Woo teaches the interaction information for each projection is calculated based on a joint entropy among the temporal pattern of the time series of PM data and the prediction target information (paragraph 0006 discloses joint entropy between model parameters and predicted output).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Oliner to incorporate the teaching of Woo about joint entropy. One would be motivated to do that to improve the performance of the prediction (see paragraph 0006 of Woo).
Claim 43 is rejected under 35 U.S.C. 103 as being unpatentable over Oliner in view of Zhang et al. (US PGPUB No. US 20170337326 A1), hereinafter, Zhang.
As to claim 43, the rejection of claim 29 is incorporated. Oliner teaches all the limitations of claim 29 as shown above.
Oliner does not teach wherein: the time series of PM data includes samples of a plurality PM counters for each a plurality of base stations at different locations in the communication network and for each of the plurality of periodic time instances over the first duration; and the prediction target information is patients admitted to hospital.
Zhang teaches wherein: the time series of PM data includes samples of a plurality PM counters for each a plurality of base stations at different locations in the communication network and for each of the plurality of periodic time instances over the first duration; and the prediction target information is patients admitted to hospital (paragraph 0036 discloses user has gone to hospital based on cell tower identifier in this area. Also see Fig. 3 showing prediction of user visiting the hospital).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Oliner to incorporate the teaching of Zhang about prediction of user visiting the hospital. One would be motivated to do that to automatically determiner when a user will seek a treatment (see paragraph 0001 of Zhang).
Claim 44 is rejected under 35 U.S.C. 103 as being unpatentable over Oliner in view of Zhang and further in view of Chou et al. (US PGPUB No. US 20160007216 A1), hereinafter, Chou.
As to claim 44, the rejection of claim 43 is incorporated. Oliner in view of Zhang teach all the limitations of claim 43 as shown above.
Oliner does not teach wherein the plurality of PM counters include any of the following: number of active users in uplink, number of active users in downlink, total number of handovers, and total duration of all UE sessions in an area during a time interval.
Chou teaches wherein the plurality of PM counters include any of the following: number of active users in uplink, number of active users in downlink, total number of handovers, and total duration of all UE sessions in an area during a time interval (paragraph 0026 discloses performance data include number of active UEs in a given area).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Oliner and Zang to incorporate the teaching of Chou about number of active UEs in a given area. One would be motivated to do that to automatically determiner when a user will seek a treatment (see paragraph 0001 of Zhang).
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAMAL M HOSSAIN whose telephone number is (571)270-3070. The examiner can normally be reached 9:30-5:30 M-F.
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May 29, 2026
/KAMAL M HOSSAIN/ Primary Examiner, Art Unit 2444
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