DETAILED ACTION
This action is in response to claims filed 21 April 2026 for application 17401002 filed 12 August 2021. Currently claims 1-12, and 14-15 are pending.
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 .
Claim Rejections - 35 USC § 103
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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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 1-12, 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Jannink et al. (US 2020/0184273 A1) in view of Barratt et al. (Optimizing for Generalization in Machine Learning with Cross-Validation Gradients) further in view of Aaron et al. (US 2017/0295374 A1).
Regarding claim 1, Jannink discloses: A computing device [0094], comprising:
a training platform at the computing device, the computing device including a processor to execute instructions associated with the training platform (Fig 5 and [0071] model training, [0094]); and
an inference platform coupled to the training platform (Fig 5 processing), wherein based upon an updating of hyperparameters in the training platform (Fig 5 hyperparam search), an optimized inference model is configured to be deployed to the inference platform (Fig 5 Deploy).
Jannink does not explicitly disclose, however, Barratt teaches: wherein: (ii) a gradient of the … of each hyperparameter of the hyperparameters are calculated and utilized as joint prediction criteria by the training platform to perform a cross-validation prediction to select the optimized inference model (Algorithm 1 shows a cross-validation gradient method for selecting optimal hyperparameters (optimized inference model), hyperparameter metric α and gradient of α are used for the joint prediction.)
Jannink and Barratt are in the same field of endeavor of hyperparameter/ML optimization and are analogous. Jannink discloses a machine learning training and hyperparameter optimization platform for continually updating models. Barratt discloses a method of model optimization using gradients of hyperparameters. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the known hyperparameter optimization as disclosed by Jannink with the known hyperparameter gradient optimization method as taught by Barratt to yield predictable results.
Jannink does not explicitly disclose, however, Aaron teaches: (i) a metric score and … the metric score not being equivalent to a hyperparameter (“After the fold generator 230 generates the folds 240, the pre-training subsystem 250 selects a “hyperparameter combination,” and performs cross-validation operations based on the selected hyperparameter combination. As referred to herein, a hyperparameter combination is a set of values for the hyperparameters included in the perceptual quality model. Further, values for hyperparameters are also referred to herein as hyperparameter values. For each of the folds 240, the pre-training subsystem 250 trains the perceptual quality model that includes the selected hyperparameter combination based on the fold training metric values. The result of the training is a fold-specific set of values for the parameters included in the perceptual quality model, referred to herein as “fold-specific parameter values.”” [0044]).
Jannink, Barratt and Aaron are in the same field of endeavor of hyperparameter/ML optimization and are analogous. Jannink discloses a machine learning training and hyperparameter optimization platform for continually updating models. Barratt discloses a method of model optimization using gradients of hyperparameters. Aaron teaches cross-validation using hyperparameters and metrics for the hyperparameters. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the known hyperparameter optimization and gradients of hyperparameters as taught by Jannink and Barratt with the known cross-validation using metrics and hyperparameters as taught by Aaron to yield predictable results.
Regarding claim 2, Jannink discloses: The computing device of claim 1, wherein: the training platform updates the hyperparameters using the metric score generated in the training platform (“A further benefit of incorporating the model retraining and improvement into the overall system is that it becomes possible to continuously generate new subject models by varying model hyper parameters. Taking multiple candidate subject models in this way, training them with those new parameters, and validating them against existing models, enables a directed optimization search through the model parameter space. At the end of each training cycle we preserve only the best of the candidates, if they supersede the existing models in accuracy.” [0023]).
Regarding claim 3, Jannink discloses: The system of claim 2, wherein: the metric score is indicative of an accuracy of a candidate inference model (“A further benefit of incorporating the model retraining and improvement into the overall system is that it becomes possible to continuously generate new subject models by varying model hyper parameters. Taking multiple candidate subject models in this way, training them with those new parameters, and validating them against existing models, enables a directed optimization search through the model parameter space. At the end of each training cycle we preserve only the best of the candidates, if they supersede the existing models in accuracy.” [0023]).
Regarding claim 4, Jannink discloses: The computing device of claim 3, wherein: the optimized inference model is generated in the training platform by using a feedback of the updated hyperparameters (“This data collection provides an initial input to the feedback loop in which the first representations of the physical sensor environment are stored and deployed. Subsequently, the feedback enables improved versions of analysis algorithms and machine learning model hyperparameters to be selected for the system model. The refinement process is called the training cycle and a separate training pipeline performs all of the functions necessary to complete this function.” [0032]).
Regarding claim 5, Jannink discloses: The computing device of claim 4, wherein: the inference platform updates the optimized inference model to generate a second version of the optimized inference model (“Returning to FIG. 2, at 62, dataset annotation occurs. Thereafter, training sets 64 are created to be used in the creation of trained models to recognize the subjects of data capture. These training sets are used in model training 66 to produce different versions of the model of the environment 68. The repeated process of training and validating models, and the loops 68 of further dataset generation, and annotation needed to produce an updated model tend to make each successively trained model superior to the previous one.” [0038]).
Regarding claim 6, Jannink discloses: The computing device of claim 5, wherein: the inference platform updates the optimized inference model by using a client observation and a first prediction response (“Returning to FIG. 2, at 62, dataset annotation occurs. Thereafter, training sets 64 are created to be used in the creation of trained models to recognize the subjects of data capture. These training sets are used in model training 66 to produce different versions of the model of the environment 68. The repeated process of training and validating models, and the loops 68 of further dataset generation, and annotation needed to produce an updated model tend to make each successively trained model superior to the previous one.” [0038]).
Regarding claim 7, Jannink discloses: The computing device of claim 6, wherein: an observation difference is generated by the inference platform by calculating a difference between the client observation and the first prediction response ([0037], [0090-91] disclose difference analysis of collected data vs existing data).
Regarding claim 8, Jannink discloses: The computing device of claim 7, wherein: the second version of the optimized inference model is used to generate a second prediction response ([0042] discloses validating and release of optimized models).
Regarding claim 9, Jannink discloses: The computing device of claim 8, wherein: the second prediction response is generated in response to a second prediction request [0070].
Regarding claim 10, Jannink discloses: A training platform in a computing device [0094], comprising:
a model training unit at the computing device, the computing device including a processor to execute instructions associated with the computing device (Fig 5);
a model validation unit coupled to the model training unit at the computing device (Fig 5, [0070], [0094]); and
a hyperparameter updating unit at the computing device coupled to the model validation unit (Fig 5), wherein based upon an updating of hyperparameters associated with an inference model generated in the model training unit (Fig 5 hyperparam search), an optimized inference model is output by the training platform (Fig 5 deploy).
However, Jannink does not explicitly disclose: Barratt teaches: wherein: (ii) a gradient of the … of each hyperparameter of the hyperparameters are calculated and utilized as joint prediction criteria by the training platform to perform a cross-validation prediction to select the optimized inference model (Algorithm 1 shows a cross-validation gradient method for selecting optimal hyperparameters (optimized inference model), hyperparameter metric α and gradient of α are used for the joint prediction.)
Jannink does not explicitly disclose, however, Aaron teaches: (i) a metric score and … the metric score not being equivalent to a hyperparameter (“After the fold generator 230 generates the folds 240, the pre-training subsystem 250 selects a “hyperparameter combination,” and performs cross-validation operations based on the selected hyperparameter combination. As referred to herein, a hyperparameter combination is a set of values for the hyperparameters included in the perceptual quality model. Further, values for hyperparameters are also referred to herein as hyperparameter values. For each of the folds 240, the pre-training subsystem 250 trains the perceptual quality model that includes the selected hyperparameter combination based on the fold training metric values. The result of the training is a fold-specific set of values for the parameters included in the perceptual quality model, referred to herein as “fold-specific parameter values.”” [0044]).
Regarding claim 11, Jannink discloses: The training platform of claim 10, wherein: the hyperparameter updating unit uses the metric score to update the hyperparameters (“A further benefit of incorporating the model retraining and improvement into the overall system is that it becomes possible to continuously generate new subject models by varying model hyper parameters. Taking multiple candidate subject models in this way, training them with those new parameters, and validating them against existing models, enables a directed optimization search through the model parameter space. At the end of each training cycle we preserve only the best of the candidates, if they supersede the existing models in accuracy.” [0023]).
Regarding claim 12, Jannink discloses: The training platform of claim 11, wherein: the metric score used to update the hyperparameters is indicative of an accuracy of the inference model (“A further benefit of incorporating the model retraining and improvement into the overall system is that it becomes possible to continuously generate new subject models by varying model hyper parameters. Taking multiple candidate subject models in this way, training them with those new parameters, and validating them against existing models, enables a directed optimization search through the model parameter space. At the end of each training cycle we preserve only the best of the candidates, if they supersede the existing models in accuracy.” [0023]).
Regarding claim 14, Jannink discloses: The training platform of claim 12, further comprising: a separation unit coupled to the model training unit, wherein the separation unit provides a first data set to the model training unit and a second data set to the model validation unit (“In FIG. 2, the sandbox testing 70 of the output model prior to its release and replacement of existing models allows for validation. This testing may compare the output of the new model against a previously validated “golden data set” output of the existing models.” [0042]).
Regarding claim 15, Jannink discloses: The training platform of claim 14, wherein: the model validation unit uses a candidate inference model and the second data set to generate the metric score (“In FIG. 2, the sandbox testing 70 of the output model prior to its release and replacement of existing models allows for validation. This testing may compare the output of the new model against a previously validated “golden data set” output of the existing models. Assuming the new model is deemed acceptable in that it accurately reflects the instrumented environment (perhaps within predetermined tolerances), the updated model is released 72, and the now-deployed new ML model 74 assumes the place of the original model 52 for further data collection and analysis. As should be evident, the end of one training cycle is also the start of the next one.” [0042], see also [0023]).
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-12 and 14-15 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. Please see the addition of new reference Aaron for how the amended claim language is taught.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC NILSSON whose telephone number is (571)272-5246. The examiner can normally be reached M-F: 7-3.
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/ERIC NILSSON/Primary Examiner, Art Unit 2151
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