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
Claims 1-20 are pending.
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 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.
Response to Arguments
The Double Patenting Rejections have been withdrawn in view of the approved Terminal Disclaimer dated 1/13/2026.
Applicant's arguments filed 1/13/2026 with respect to the prior art rejections have been fully considered but they are not persuasive. Although the examiner indicated in the Interview dated 1/13/2026 the amendments initially may have overcome the previously applied rejections, after further review, and in light of the applicant’s specific arguments, the rejections were not overcome. For example, the amendment to claim 1 seems to change the claim from media to a method while also changing the tense and grammar of the claim are not particularly significant after more careful review.
The applicant substantially argues with respect to claim 1 that Walczyk does not disclose any elements that the examiner has alleged Walczyk to disclose. For example, the applicant at least contends:
“Walczyk does not disclose or suggest ‘a model pipeline ... wherein an output of one of the sub-models determines an input of another of the sub-models,’ as in claim 1.” Page 7
“Further, Walczyk fails to disclose or suggest ‘a model pipeline that... (outputs) a model-pipeline prediction,’ as in claim 1.” Page 7
“However, Walczyk does NOT disclose and is silent regarding ‘files containing formatted datasets,’ as in claim 1, and further is silent regarding ‘retrieving’ such files, as in claim 1.” Page 8
“However, "utiliz[ing] the clustering methods to identify predictions and models with relatively high confidence scores," as described in Walczyk, is clearly not functionally equivalent to ‘accessing (another) sub-model and utilizing the (other) sub-model with the set of sub-models in the model pipeline,’ as set forth in claim 1.” Page 8
The examiner disagrees. While it is clear that Walczyk does not disclose the exact language of the claim 1, Walczyk discloses (emphasis added by examiner) a computer implemented method performed by one or more hardware processors, the computer implemented method comprising (Abstract):
inputting data to a model pipeline that includes sub-models, wherein an output of one of the sub-models determines an input of another of the sub-models ([0016], particularly, “Program 150 is a cognitive multi-pipeline control system controlling multiple parallel-operating machine learning pipelines where feature evaluation, model selection, and confidence scoring are performed in reduced time and with reduced computational resources…Feature controller 152 records feature readiness and controls model calculations. Model controller 154 evaluates the aggregation of a plurality of model predictions. In an embodiment, model controller 154 utilizes heuristics and rule prediction structures to record a model readiness and determine an ensemble prediction utilizing evaluated aggregations. In this embodiment, model controller 154 utilizes ensemble methods to obtain increased predictive performance than could be obtained from any constituent models. Program 150 is depicted and described in further detail with respect to FIG. 2.”);
detecting, as an output from the model pipeline in response to inputting the data, a model-pipeline prediction computed based on the sub-models ([0016], particularly, “Program 150 is a cognitive multi-pipeline control system controlling multiple parallel-operating machine learning pipelines where feature evaluation, model selection, and confidence scoring are performed in reduced time and with reduced computational resources…Feature controller 152 records feature readiness and controls model calculations. Model controller 154 evaluates the aggregation of a plurality of model predictions. In an embodiment, model controller 154 utilizes heuristics and rule prediction structures to record a model readiness and determine an ensemble prediction utilizing evaluated aggregations. In this embodiment, model controller 154 utilizes ensemble methods to obtain increased predictive performance than could be obtained from any constituent models. Program 150 is depicted and described in further detail with respect to FIG. 2.”);
retrieving one or more files containing formatted data corresponding to the model pipeline, wherein the formatted data comprises information associated with the model-pipeline prediction ([0023], particularly, “Program 150 calculates prediction confidence (step 210). In an embodiment, program 150 calculates a prediction confidence value or score for the aggregated predictions from step 208. Program 150 generates a confidence score with any set of aggregated model predictions allowing program 150 to mitigate missing model predictions. In this embodiment, program 150 determines whether a sufficient accuracy is obtained by utilizing test/validation sets and the associated test labels. In another embodiment, program 150 utilizes cross-entropy (e.g., Kullback-Leibler (KL) divergence, etc.) as a loss function to determine the level of prediction accuracy.”);
computing one or more sub-model performance metrics based on the formatted data ([0023], particularly, “In a further embodiment, program 150 generates prediction, global ensemble and local model, statistics including, but not limited to, predictive accuracy (e.g., Brier scores, Gini coefficients, discordant ratios, C-statistic values, net reclassification improvement indexes, receiver operating characteristics, generalized discrimination measures, Hosmer-Lemeshow goodness of fit values, etc.), error rates (e.g., root mean squared error (RMSE), mean absolute error, mean absolute percentage error, mean percentage error, etc.), precision, overfitting considerations, model fitness, and related system statistics (e.g., memory utilization, CPU utilization, storage utilization, etc.).”); and
based on computing the one or more sub-model performance metrics, accessing a sub-model and utilizing the sub-model pipeline with the sub-models in the model pipeline in association with an operation of the model pipeline ([0023]-[0025], particularly, “In an embodiment, program 150 utilizes one or more clustering methods and/or algorithms (e.g., binary classifiers, multi-class classifiers, multi-label classifiers, Naïve Bayes, k-nearest neighbors, random forest, etc.) to create a plurality of clusters representing a high level view of the predictions and associated models. In this embodiment, program 150 utilizes the clustering methods to identify predictions and models with relatively high confidence scores. For example, program 150 utilizes clustering to group models that have accurate predictions even though the aggregated prediction was inaccurate.”).
The flaws in the applicant’s arguments are looking for a literal word for word recitation in Walczyk of the instant claim while failing to appreciate or even attempt to define the claim with respect to its broadest reasonable interpretation. With regard to the later point, the applicant has essentially only attempted to define the instant claim limitations with respect the Walczyk reference rather than giving them the broadest reasonable interpretation in light of the instant specification. In other words, the applicant has effectively argued, whatever the broadest reasonable interpretation of the instant claims, the only thing clear is that it is not recited in Walczyk.
For example, with regard to point (a) above, “Walczyk does not disclose or suggest ‘a model pipeline ... wherein an output of one of the sub-models determines an input of another of the sub-models,’ as in claim 1.” Walczyk explicitly discloses machine learning pipelines and pipelines, by definition, have inputs determine the outputs later in the pipeline so it is unclear how the applicant has reached their conclusions aside from noticing that Walczyk is not using the same language as the claims. For points (b)-(d), see the above citations from Walczyk by the examiner where further emphasis has been provided where necessary.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Walczyk, III et al (US Pub. No. 2022/0198320; cited on IDS), hereafter, “Walczyk,” in view of Hamilton et al (US Pub. No. 2023/0177369; cited on IDS), hereafter, “Hamilton.”
As to claim 1, Walczyk discloses a computer implemented method performed by one or more hardware processors, the computer implemented method comprising (Abstract):
inputting data to a model pipeline that includes sub-models, wherein an output of one of the sub-models determines an input of another of the sub-models ([0016], particularly, “Program 150 is a cognitive multi-pipeline control system controlling multiple parallel-operating machine learning pipelines where feature evaluation, model selection, and confidence scoring are performed in reduced time and with reduced computational resources…Feature controller 152 records feature readiness and controls model calculations. Model controller 154 evaluates the aggregation of a plurality of model predictions. In an embodiment, model controller 154 utilizes heuristics and rule prediction structures to record a model readiness and determine an ensemble prediction utilizing evaluated aggregations. In this embodiment, model controller 154 utilizes ensemble methods to obtain increased predictive performance than could be obtained from any constituent models. Program 150 is depicted and described in further detail with respect to FIG. 2.”);
detecting, as an output from the model pipeline in response to inputting the data, a model-pipeline prediction computed based on the sub-models ([0016], particularly, “Program 150 is a cognitive multi-pipeline control system controlling multiple parallel-operating machine learning pipelines where feature evaluation, model selection, and confidence scoring are performed in reduced time and with reduced computational resources…Feature controller 152 records feature readiness and controls model calculations. Model controller 154 evaluates the aggregation of a plurality of model predictions. In an embodiment, model controller 154 utilizes heuristics and rule prediction structures to record a model readiness and determine an ensemble prediction utilizing evaluated aggregations. In this embodiment, model controller 154 utilizes ensemble methods to obtain increased predictive performance than could be obtained from any constituent models. Program 150 is depicted and described in further detail with respect to FIG. 2.”);
retrieving one or more files containing formatted data corresponding to the model pipeline, wherein the formatted data comprises information associated with the model-pipeline prediction ([0023], particularly, “Program 150 calculates prediction confidence (step 210). In an embodiment, program 150 calculates a prediction confidence value or score for the aggregated predictions from step 208. Program 150 generates a confidence score with any set of aggregated model predictions allowing program 150 to mitigate missing model predictions. In this embodiment, program 150 determines whether a sufficient accuracy is obtained by utilizing test/validation sets and the associated test labels. In another embodiment, program 150 utilizes cross-entropy (e.g., Kullback-Leibler (KL) divergence, etc.) as a loss function to determine the level of prediction accuracy.”);
computing one or more sub-model performance metrics based on the formatted data ([0023], particularly, “In a further embodiment, program 150 generates prediction, global ensemble and local model, statistics including, but not limited to, predictive accuracy (e.g., Brier scores, Gini coefficients, discordant ratios, C-statistic values, net reclassification improvement indexes, receiver operating characteristics, generalized discrimination measures, Hosmer-Lemeshow goodness of fit values, etc.), error rates (e.g., root mean squared error (RMSE), mean absolute error, mean absolute percentage error, mean percentage error, etc.), precision, overfitting considerations, model fitness, and related system statistics (e.g., memory utilization, CPU utilization, storage utilization, etc.).”); and
based on computing the one or more sub-model performance metrics, accessing a sub-model and utilizing the sub-model pipeline with the sub-models in the model pipeline in association with an operation of the model pipeline ([0023]-[0025], particularly, “In an embodiment, program 150 utilizes one or more clustering methods and/or algorithms (e.g., binary classifiers, multi-class classifiers, multi-label classifiers, Naïve Bayes, k-nearest neighbors, random forest, etc.) to create a plurality of clusters representing a high level view of the predictions and associated models. In this embodiment, program 150 utilizes the clustering methods to identify predictions and models with relatively high confidence scores. For example, program 150 utilizes clustering to group models that have accurate predictions even though the aggregated prediction was inaccurate.”).
However, Walcyzk does not explicitly disclose the data is healthcare data.
But, Hamilton discloses inputting healthcare data to a model pipeline that includes sub-models, wherein an output of one of the sub-models determines an input of another of the sub-models (Abstract, Fig. 3, [0001], and [0030], particularly, “In one example, the user 102 may seek an explanation of the outcome produced by the server system 106 in any field such as, business, healthcare, education and the like.”).
Therefore it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the application to combine the teachings of Walczyk and Hamilton in order to extend the system to more specific inputs so as to create a broader market for the system overall.
As to claims 8 and 15, they are rejected by a similar rationale by that set forth in claim 1’s rejection.
As to claim 2, 9, and 16, the teachings of Walczyk and Hamilton as combined for the same reasons set forth in claim 1’s rejection further disclose prior to accessing the sub-model, the submodel is not included in the model pipeline (Walczyk, [0023]-[0025], particularly, “In an embodiment, program 150 utilizes one or more clustering methods and/or algorithms (e.g., binary classifiers, multi-class classifiers, multi-label classifiers, Naïve Bayes, k-nearest neighbors, random forest, etc.) to create a plurality of clusters representing a high level view of the predictions and associated models. In this embodiment, program 150 utilizes the clustering methods to identify predictions and models with relatively high confidence scores. For example, program 150 utilizes clustering to group models that have accurate predictions even though the aggregated prediction was inaccurate.”).
As to claim 3, 10, and 17, the teachings of Walczyk and Hamilton as combined for the same reasons set forth in claim 1’s rejection further disclose the sub-model is configured: (a) to operate with the model pipeline, and (b) based on an operation of a first sub-model of the sub-models models (Walczyk, [0016], particularly, “Program 150 is a cognitive multi-pipeline control system controlling multiple parallel-operating machine learning pipelines where feature evaluation, model selection, and confidence scoring are performed in reduced time and with reduced computational resources…Feature controller 152 records feature readiness and controls model calculations. Model controller 154 evaluates the aggregation of a plurality of model predictions. In an embodiment, model controller 154 utilizes heuristics and rule prediction structures to record a model readiness and determine an ensemble prediction utilizing evaluated aggregations. In this embodiment, model controller 154 utilizes ensemble methods to obtain increased predictive performance than could be obtained from any constituent models. Program 150 is depicted and described in further detail with respect to FIG. 2.”)
As to claim 4, 11, and 18, the teachings of Walczyk and Hamilton as combined for the same reasons set forth in claim 1’s rejection further disclose the sub-model is configured as a second version of a first sub-model of the sub-models (Walczyk, [0023]-[0025], particularly, “In an embodiment, program 150 utilizes one or more clustering methods and/or algorithms (e.g., binary classifiers, multi-class classifiers, multi-label classifiers, Naïve Bayes, k-nearest neighbors, random forest, etc.) to create a plurality of clusters representing a high level view of the predictions and associated models. In this embodiment, program 150 utilizes the clustering methods to identify predictions and models with relatively high confidence scores. For example, program 150 utilizes clustering to group models that have accurate predictions even though the aggregated prediction was inaccurate.”).
As to claim 5, 12, and 19, the teachings of Walczyk and Hamilton as combined for the same reasons set forth in claim 1’s rejection further disclose the operations further comprise: determining one or more performance metrics associated with the model pipeline, and updating the model pipeline via the sub-model based on the one or more performance metrics (Walczyk, [0023]-[0025], particularly, “In an embodiment, program 150 utilizes one or more clustering methods and/or algorithms (e.g., binary classifiers, multi-class classifiers, multi-label classifiers, Naïve Bayes, k-nearest neighbors, random forest, etc.) to create a plurality of clusters representing a high level view of the predictions and associated models. In this embodiment, program 150 utilizes the clustering methods to identify predictions and models with relatively high confidence scores. For example, program 150 utilizes clustering to group models that have accurate predictions even though the aggregated prediction was inaccurate.”).
As to claim 6, 13, and 20, the teachings of Walczyk and Hamilton as combined for the same reasons set forth in claim 1’s rejection further disclose the sub-model corresponds at least partially to a first sub-model of the sub-models, and wherein the sub-model is deployed into the model pipeline to influence one or both of a performance metric associated with the first sub-model and an accuracy metric associated with the first sub-model (Walczyk, [0023]-[0025], particularly, “In an embodiment, program 150 utilizes one or more clustering methods and/or algorithms (e.g., binary classifiers, multi-class classifiers, multi-label classifiers, Naïve Bayes, k-nearest neighbors, random forest, etc.) to create a plurality of clusters representing a high level view of the predictions and associated models. In this embodiment, program 150 utilizes the clustering methods to identify predictions and models with relatively high confidence scores. For example, program 150 utilizes clustering to group models that have accurate predictions even though the aggregated prediction was inaccurate.”).
As to claim 7 and 14, the teachings of Walczyk and Hamilton as combined for the same reasons set forth in claim 1’s rejection further disclose the sub-model is configured to replace a first sub-model of the sub-models (Walczyk, [0023]-[0025], particularly, “In an embodiment, program 150 utilizes one or more clustering methods and/or algorithms (e.g., binary classifiers, multi-class classifiers, multi-label classifiers, Naïve Bayes, k-nearest neighbors, random forest, etc.) to create a plurality of clusters representing a high level view of the predictions and associated models. In this embodiment, program 150 utilizes the clustering methods to identify predictions and models with relatively high confidence scores. For example, program 150 utilizes clustering to group models that have accurate predictions even though the aggregated prediction was inaccurate.”)
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 THOMAS J DAILEY whose telephone number is (571)270-1246. The examiner can normally be reached 9:30am-6:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Umar Cheema can be reached on 571-270-3037. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THOMAS J DAILEY/ Primary Examiner, Art Unit 2458
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