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 .
Status of the Claims
The Amendment filed on 06/03/2026 has been entered. Claims 1-40 are pending in the instant patent application. Claims 1-2, 5, 17, 19-22, 37 and 39-40 have been amended. Claim 41 is canceled.
Response to Claim Amendments
Applicant’s amendments to the claims are insufficient to overcome the 35 U.S.C. §101 rejections. The rejections remain pending and are updated and addressed below in light of the amendments and per guidelines for 101 analysis (PEG 2019).
Applicant’s amendments have also necessitated new grounds of rejection under 35 U.S.C. §112.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 21-40 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Independent Claim 21 recites the limitation "the training module" in the first "training" limitation. There is insufficient antecedent basis for this limitation in the claim and the dependent claims do not cure this deficiency.
Response to 35 U.S.C. §101 Arguments
Applicant’s arguments regarding 35 U.S.C. §101 rejection of the claims have been fully considered but are not persuasive.
Regarding Applicant’s arguments that the limitations of “the training module trains a forward model as part of a machine learning training phase, wherein the training module trains the forward model on standardized values, and wherein the machine learning training phase includes analyzing hidden patterns in data; the training module trains a backward model as part of the machine learning training phase” are not abstract, Examiner agrees that they are not abstract and would not analyzed under Step 2A Prong 1. However, in light of Step 2A Prong Two, Examiner asserts that the amended language does not integrate the abstract idea into a practical application. The training modules are merely being used as tools to implement the abstract idea and generally links the use of the abstract idea to a particular technological environment or field of use.
Regarding Applicant’s arguments that the claim limitations reflect an improvement to machine-learning technology, Examiner respectfully disagrees. As noted in Ex Parte Desjardins, the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. There were clear improvements noted and further reflected in the claim language. The same cannot be said of the current claims in light of Ex Parte Desjardins. In review, the additional elements presented in the claim language are still performing functions in their generic capacity and do not reflect any improvement to the technical field, computer components or computer system. Furthermore, Examiner reminds Applicant, regardless of the complexity and/or granularity of the type of data, computational data analysis without meaningful limitations within the claims that amount to significantly more, is an abstract idea.
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.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 1, claim 1 recites to use target metrics, data, and metadata and schema to generate important supporting metrics, supporting metrics meta information, and best grouping columns; uses the important supporting metrics, the supporting metrics meta information, and the best grouping columns to generate the forward model and the backward model; obtains an analysis period, an agent-specified target metrics value, and an agent-specified supporting metrics value to generate a predicted target metrics value and predicted supporting metrics value that distributes error between an actual target metrics value and a forward-model prediction across supporting metrics in proportion to feature-importance scores, computes adjusted supporting-metric values, and synchronizes the forward model and backward model using the adjusted supporting metric values; uses the analysis period, historical data of target and supporting metrics, and anomaly scan direction to generate a target metrics anomaly score and anomaly reasoning; uses the best grouping columns and timeseries data to generate predicted values of target metrics for future periods with breakup; provides suggestions how to achieve expected targets.
These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be performed in the human mind (including an observation, evaluation, judgment) and/or with pen/paper as described in MPEP 2106.04(a)(2)(III). Examiner will also note that the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind. In addition, falling into the Mathematical Concepts grouping of abstract ideas due to the mathematical relationships/calculations taking place.
Accordingly, the claim recites an abstract idea and dependent claims 2-20 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of a training module, a deep metrics discovery engine, a target metrics/supporting metrics (TMSM) association modeling engine, a strategy planning engine, a descriptive analytics engine, a predictive analytics engine, a prescriptive analytics engine, the training module trains a forward model as part of a machine learning training phase, wherein the training module trains the forward model on standardized values, and wherein the machine learning training phase includes analyzing hidden patterns in data; the training module trains a backward model as part of the machine learning training phase forward model, backward model and TMSM sync algorithm. The training module, deep metrics discovery engine, a target metrics/supporting metrics (TMSM) association modeling engine, a strategy planning engine, a descriptive analytics engine, a predictive analytics engine, a prescriptive analytics engine, the training module trains a forward model as part of a machine learning training phase, wherein the training module trains the forward model on standardized values, and wherein the machine learning training phase includes analyzing hidden patterns in data; the training module trains a backward model as part of the machine learning training phase forward model, backward model and TMSM sync algorithm are merely generic computing devices and do not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claims 1-17 and 19-20 includes various elements that are not directed to the abstract idea under 2A. These elements include a training module, a deep metrics discovery engine; a target metrics/supporting metrics (TMSM) association modeling engine; a strategy planning engine; a descriptive analytics engine; a predictive analytics engine; a prescriptive analytics engine, the training module trains a forward model as part of a machine learning training phase, wherein the training module trains the forward model on standardized values, and wherein the machine learning training phase includes analyzing hidden patterns in data; the training module trains a backward model as part of the machine learning training phase, a server engine, a target metrics datastore, an important supporting metrics datastore, a supporting metrics meta information datastore, a best grouping columns datastore, a forward model datastore, a backward model datastore, a data sampler engine, a preprocess engine, an eligibility engine, a transform engine, a supporting metrics synthesis engine, an important supporting metrics ranking engine, a meta enrichment engine, an important categorical claims discovery engine, a forward modeling engine, a backward modeling engine, a timeseries engine, a TMSM sync engine, a target metrics anomaly detection engine, a univariate timeseries anomaly detector algorithm, an anomaly reason finder engine, backward model, forward model, TMSM sync algorithm and the generic computing elements described in the Applicant's specification in at least Para 0055-0060. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions.
Therefore, Claims 1-17 and 19-20, alone or in combination, are not drawn to eligible subject matter as they are directed to abstract ideas without significantly more.
Regarding Claims 21-40, they are directed to a method, however the claims are directed to a judicial exception without significantly more. Claims 21-40 are directed to the abstract idea of finding insights based on metrics.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 21, claim 21 recites generating important supporting metrics, supporting metrics meta information, and best grouping columns using target metrics, data, and metadata and schema; generating the forward model and the backward model using the important supporting metrics, the supporting metrics meta information, and the best grouping columns; generating one or both of predicted target metrics value and predicted supporting metrics value using one or more of an analysis period, an agent-specified target metrics value, and an agent- specified supporting metrics value, wherein the predicted target metrics value is generated using the forward model which is fed the supporting metrics, and the predicted supporting metrics value is generated using the backward model that distributes error between an actual target metrics value and a forward-model prediction across supporting metrics in proportion to feature-importance scores, computes adjusted supporting-metric values and synchronizes the forward model and backward model using the adjusted supporting metrics value; generating a target metrics anomaly score and anomaly reasoning using the analysis period, historical data of target and supporting metrics, and anomaly scan direction; generating predicted values of target metrics for future periods with breakup using the best grouping columns and timeseries data; providing suggestions how to achieve expected targets.
These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be performed in the human mind (including an observation, evaluation, judgment) and/or with pen/paper as described in MPEP 2106.04(a)(2)(III). Examiner will also note that the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind. In addition, falling into the Mathematical Concepts grouping of abstract ideas due to the mathematical relationships/calculations taking place.
Accordingly, the claim recites an abstract idea and dependent claims 22-40 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of a training module, training a forward model as part of a machine learning training phase, wherein the training module trains the forward model on standardized values, and wherein the machine learning training phase includes analyzing hidden patterns in data; training a backward model as part of the machine learning training phase; a forward model, backward model and TMSM sync algorithm. The training module, training a forward model as part of a machine learning training phase, wherein the training module trains the forward model on standardized values, and wherein the machine learning training phase includes analyzing hidden patterns in data; training a backward model as part of the machine learning training phase; forward model, backward model and TMSM sync algorithm are merely generic computing devices and do not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claims 21-23, 34 and 36 includes various elements that are not directed to the abstract idea under 2A. These elements include a training module, training a forward model as part of a machine learning training phase, wherein the training module trains the forward model on standardized values, and wherein the machine learning training phase includes analyzing hidden patterns in data; training a backward model as part of the machine learning training phase; deep metrics discovery engine, server engine, target metrics datastore, important supporting metrics datastore, supporting metrics meta information datastore, best grouping columns datastore, forward model datastore, backward model datastore, univariate timeseries predictor algorithm, univariate timeseries anomaly detector algorithm, forward model, backward model, TMSM sync algorithm and the generic computing elements described in the Applicant's specification in at least Para 0055-0060. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions.
Therefore, Claims 21-23, 34 and 36, alone or in combination, are not drawn to eligible subject matter as they are directed to abstract ideas without significantly more.
Conclusion
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.
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/T.E.S./Examiner, Art Unit 3625
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625
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