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
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on June 15, 2026 has been entered.
REMARKS
On pages 8-10, Applicant’s argument by pointing to [0141] and [0144]-[0146] for discussion of the improvement over the technology to overcome the 35 USC 101 rejection as applied to claims 1-18 is persuasive. Therefore, the 35 USC 101 rejection as applied to claims 1-18 is withdrawn.
On pages 10-16, Applicant argues by claim amendment overcomes the 35 U.S.C. 102(a)(1) rejection in view of Andoni et al. (US 2019/0073591 A, provided in the IDS filed October 07, 2022) as applied to claims 1-5, 7, 9, 13, 17, and 18 is persuasive. The 35 U.S.C. 102(a)(1) rejection in view of Andoni et al. (US 2019/0073591 A, provided in the IDS filed October 07, 2022) as applied to claims 1-5, 7, 9, 13, 17, and 18 is withdrawn. The 35 U.S.C. 103 rejection in view of Andoni et al. (US 2019/0073591 A, provided in the IDS filed October 07, 2022) and Radoswvovic as applied to claim 8 is withdrawn. The 35 U.S.C. 103 rejection in view of Andoni et al. (US 2019/0073591 A, provided in the IDS filed October 07, 2022) and Indarapu et al. as applied to claims 10-12 is withdrawn. The 35 U.S.C. 103 rejection in view of Andoni et al. (US 2019/0073591 A, provided in the IDS filed October 07, 2022) and Chen et al. as applied to claims 14 and 15 is withdrawn. The 35 U.S.C. 103 rejection in view of Andoni et al. (US 2019/0073591 A, provided in the IDS filed October 07, 2022) and Otte et al. as applied to claim 16 is withdrawn. The new limitations have been addressed by the addition of VerMilyea et al. (Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF, April 02, 2020).
Claim 19 is new.
Claims 1-19, filed June 15, 2026, are examined on the merits.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-19 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. NEW MATTER
Claim 1, lines 5-11, the new limitation of “two or more of the plurality of epochs…” has not been found in the instant. It is noted that the specification describes the claimed invention using “one or more epochs…” throughout, which is distinct from the new limitation. Further, [0204] discloses “generating AI models based on confidence metrics have been described. These methods train a plurality of AI models on a common validation dataset over many epochs. A confidence metric as the best epoch (over all the epochs) is saved to allow comparison of the different AI models. A final AI model can then be selected using these AI models, for example using ensemble, distillation or other selection methods. In the case of an ensemble model, a confidence based voting strategy may be used.” The new limitation “two or more of the plurality of epochs…” has a specific lower limit, while, the disclosure of “many epochs…” in [0204] does not specify any lower limit. The same issue is present in claims 17 and 18.
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)(2) 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.
Claim(s) 1-7, 9, 13, and 17-19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by VerMilyea et al. (VerMilyea hereafter, Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF, April 02, 2020).
Claim 1, VerMilyea discloses a computational method for generating an Artificial Intelligence (AI) models model, the method comprising:
training a plurality of Artificial Intelligence (AI) models using a common validation dataset (page 775, e.g. stable the accuracy value was on the validation set over the training process) that is fixed over a plurality of epochs, wherein during training of each model, at least one confidence metric is calculated at two or more epochs (page 776, e.g. Well-performing individual models that exhibited different methodologies, or extracted different biases from the features obtained through machine learning, were combined using arrange of voting strategies based on the confidence of each model), and, for each model, the at least one confidence metric for each of the two or more of the plurality of epochs is stored (page 776, e.g. train-validate cycle was carried out for2–100 epochs until a sufficiently stable model was developed with low loss function), and a best confidence metric value over the plurality of epochs, wherein the at least one confidence metric is calculated at an end of an epoch and is based on a plurality of prediction scores generated by the AI model over the common validation dataset, wherein the at least one confidence metric is a distribution-sensitive metric that evaluates a distribution of prediction scores associated with correct and incorrect classification predictions (page 775, e.g. compute the difference between the prediction and the actual outcome (loss)), and an associated epoch number at which the best confidence metric occurs, are identified (page 775, e.g. select the best model types, and page 776, e.g. the highest performing models were combined into a final ensemble model);
generating an AI model comprising:
retrospectively selecting at least one of the plurality of trained AI models based on the stored best confidence metric (page 778, Figure 3, e.g. selection methodology);
calculating a confidence metric for the selected at least one trained AI model applied to a blind test set (page 776, e.g. Well-performing individual models that exhibited different methodologies, or extracted different biases from the features obtained through machine learning, were combined using arrange of voting strategies based on the confidence of each model); and
deploying the AI model if the best confidence metric exceeds an acceptance threshold (page 778, Figure 3, e.g. the highest performing individual models were considered candidates for inclusion in the final ensemble model, and the final ensemble model was selected based using majority mean voting strategy).
Claim 2, VerMilyea discloses wherein the at least one confidence metric is calculated at each epoch (page 776, e.g. Well-performing individual models that exhibited different methodologies ,or extracted different biases from the features obtained through machine learning, were combined using arrange of voting strategies based on the confidence of each model).
Claim 3, VerMilyea discloses wherein generating an AI model comprises generating an ensemble AI model using at least two of the plurality of trained AI models based on the stored best confidence metrics, and the ensemble model uses a to a confidence based voting strategy (page 778, Figure 3, e.g. the highest performing individual models were considered candidates for inclusion in the final ensemble model, and the final ensemble model was selected based using majority mean voting strategy).
Claim 4, VerMilyea discloses wherein generating an ensemble AI model comprises selecting at least two of the plurality of trained AI models based on the stored best confidence metric;
generating a plurality of distinct candidate ensemble models wherein each candidate ensemble model combines the results of the selected at least two of the plurality of trained AI models according to a confidence based voting strategy (page 776, e.g. train-validate cycle was carried out for 2–100 epochs until a sufficiently stable model was developed with low loss function. At the conclusion of the series of train-validate cycles, the highest performing models were combined into a final ensemble model);
calculating the confidence metric (page 776, e.g. Well-performing individual models that exhibited different methodologies ,or extracted different biases from the features obtained through machine learning, were combined using arrange of voting strategies based on the confidence of each model) for each candidate ensemble model applied to a common ensemble validation dataset (page 775, e.g. stable the accuracy value was on the validation set over the training process);
selecting a candidate ensemble model from the plurality of distinct candidate ensemble models and calculating a confidence metric for the selected candidate ensemble model applied to a blind test set (page 776, e.g. tested on blind test datasets as described in the results section, and page 778, Figure 3, e.g. the highest performing individual models were considered candidates for inclusion in the final ensemble model, and the final ensemble model was selected based using majority mean voting strategy).
Claim 5, VerMilyea discloses wherein the common ensemble validation dataset is the common validation dataset (page 776, e.g. train-validate cycle was carried out for 2–100 epochs until a sufficiently stable model was developed with low loss function. At the conclusion of the series of train-validate cycles, the highest performing models were combined into a final ensemble model).
Claim 6, VerMilyea discloses the common ensemble validation dataset is an intermediate test set not used in training the plurality of Artificial Intelligence (AI) models (page 772, e.g. Criteria for inclusion/exclusion were established prospectively, and images not matching the criteria were excluded from analysis).
Claim 7, VerMilyea discloses wherein the confidence based voting strategy is selected from the group consisting of maximum confidence, mean confidence, majority-mean confidence, majority-max confidence, median confidence, or weighted mean confidence (page 776, e.g. tested on blind test datasets as described in the results section, and page 778, Figure 3, e.g. the highest performing individual models were considered candidates for inclusion in the final ensemble model, and the final ensemble model was selected based using majority mean voting strategy).
Claim 9, VerMilyea discloses selecting at least one of the plurality of trained AI models based on the stored best confidence metric comprises: selecting at least two of the plurality of trained AI models, comparing each of the at least two of the plurality of trained AI models using a confidence based metric, and selecting the best trained AI models based on the comparison (page 776, e.g. tested on blind test datasets as described in the results section, and page 778, Figure 3, e.g. the highest performing individual models were considered candidates for inclusion in the final ensemble model, and the final ensemble model was selected based using majority mean voting strategy).
Claim 13, VerMilyea discloses wherein the plurality of AI models comprise a plurality of distinct model configurations, wherein each model configuration comprises a model type (page 776, e.g. After shortlisting model types), a model architecture, and one or more pre-processing methods.
Claims 17 and 18 are directed to a system comprising the same steps a as claim 1. VerMilyea discloses a system (page 781, e.g. development of AI-based systems for classification of embryo quality) for implementing the above cited method. These claims are similarly rejected under the same rationale as claim 1, supra.
Claim 19, the validation dataset comprises a plurality of healthcare or medical images (page 772, e.g. , images were required to be of embryos on Day5 of culture taken using a standard optical light microscope mounted camera).
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.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over VerMilyea et al. (VerMilyea hereafter, Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF, April 02, 2020), as applied to claims 1-7, 9, 13, and 17-19 above, in view of Radosavovic (Data Distillation: Towards Omni-Supervised Learning, 2018).
Claim 8, VerMilyea discloses the claimed invention except for the limitation of a distillation method to train the model. Radosavovic discloses a distillation method to train the model (page 2, column 2, Section 3, e.g. propose data distillation, a general method for omnisupervised learning that distills knowledge from unlabeled data without the requirement of training a large set of models).
Radosavovic discloses the new knowledge generated from unlabeled data can be used to improve the model (page 3, column 1). One of ordinary skill in the art at the time prior to the effective filing date of the instant invention would have been motivated by Radosavovic to improve the model of VerMilyea. Therefore, it would have been obvious for one of ordinary skill in the art to use the method of VerMilyea with the distillation method of Radosavovic. The benefit would be to improve the model.
Claim(s) 10-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over VerMilyea et al. (VerMilyea hereafter, Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF, April 02, 2020), as applied to claims 1-7, 9, 13, and 17-19 above, in view of Indarapu et al. (Indarapu hereafter, US 20170286997 A1).
Claim 10, VerMilyea discloses the claimed invention except for at least one confidence metric comprises one or more of Log loss. Indarapu discloses training comprising at least one confidence metric comprises one or more of Log loss ([0036], e.g. Since the log-loss function 510 depends on positive and negative instances, the ML trainer can use the log-loss function 510 as a training algorithm for training the classifier model if the positive and negative instances are certain).
Indarapu discloses log-loss function is an objective function measuring the accuracy of the classifier model. The less value of the log-loss function, the better accuracy has the classifier model ([0040]). One of ordinary skill in the art at the time prior to the effective filing date of the instant invention would have been motivated by Indarapu to improve the model of VerMilyea. Therefore, it would have been obvious for one of ordinary skill in the art to use the method of VerMilyea with the Log loss metric of Indarapu. The benefit would be to improve the accuracy of the classifier model.
Claim 11, VerMilyea as modified discloses wherein a plurality of assessment metrics are calculated and are selected from the group consisting of accuracy, Mean class accuracy, sensitivity, specificity, a confusion matrix, Sensitivity-to-specificity ratio, precision, negative predictive value, balanced accuracy, Log loss (Indarapu, [0036], e.g. Since the log-loss function 510 depends on positive and negative instances, the ML trainer can use the log-loss function 510 as a training algorithm for training the classifier model if the positive and negative instances are certain), combined class Log loss, combined data-source Log loss, combined class and data-source Log loss, tangent score, bounded tangent score, per-class ratio of tangent score vs Log Loss, Sigmoid score, epoch number, mean of square error (MSE), root MSE, mean of average error, mean average precision (mAP), confidence score, Area-Under-the-Curve (AUC) threshold, Receiver Operating Characteristic (ROC) curve threshold, Precision-Recall curve.
Claim 12, VerMilyea as modified discloses wherein the plurality of assessment metrics comprises a primary metric and at least one secondary metric, wherein the primary metric is a confidence metric, and the at least one secondary metric are used as tiebreaker metrics (Andoni, [0063], e.g. if the fitness value is between the first threshold and the second threshold, the genetic algorithm 110 may be producing acceptable results for the amount of processing resources used by the system 100. If the fitness value fails to satisfy both the first and second thresholds, the system 100 may determine to increase the epoch size as compared to the prior epoch).
Claim(s) 14 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over VerMilyea et al. (VerMilyea hereafter, Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF, April 02, 2020), as applied to claims 1-7, 9, 13, and 17-19 above, in view of Chen et al. (Chen hereafter, US 20200320769 A1).
Claim 14, VerMilyea discloses the claimed invention except for the limitation of wherein the one or more pre- processing methods comprises segmentation, and the plurality of AI models comprises at least one AI model applied to unsegmented images, and at least one AI model applied to segmented images. Chen discloses wherein the one or more pre- processing methods comprises segmentation, and the plurality of AI models comprises at least one AI model applied to unsegmented images, and at least one AI model applied to segmented images ([0185]-[0187], e.g. Preparing Training Data for Deep Learning In the context of the prediction of garment attributes, the image data used for model training can be in the format of: unsegmented mannequin photos of the garment, either in a single frontal view, or in multiple distinct camera views; segmented garment texture sprites from the mannequin photos).
Chen discloses an invention to improve the capability and generality of visual feature extraction and hence enhance the accuracy of classification or regression ([0007]). One of ordinary skill in the art at the time prior to the effective filing date of the instant invention would have been motivated by Chen to improve the method of VerMilyea. Therefore, it would have been obvious for one of ordinary skill in the art to use the method of VerMilyea with the segmentation of Chen. The benefit would be to improve the capability and generality of visual feature extraction.
Claim 15, VerMilyea as modified discloses wherein the one or more pre- processing methods comprises one or more computer vision pre-processing methods (Chen, [0007], the capability and generality of visual feature extraction and hence enhance the accuracy of classification or regression).
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over VerMilyea et al. (VerMilyea hereafter, Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF, April 02, 2020), as applied to claims 1-7, 9, 13, and 17-19 above, in view of Otte et al. (Otte hereafter, US 11514289 B1).
Claim 16, VerMilyea discloses the claimed invention except for the limitation of a plurality of healthcare images. Otte discloses a plurality of healthcare images (column 4, lines 44-49, e.g. training samples can correspond to samples having measured properties of the sample (e.g., genomic data and other subject data, such as images or health records), as well as known classifications/labels (e.g., phenotypes or treatments) for the subject).
Otte discloses an improvement that addresses the problems of the prior art by providing an apparatuses for generating and using machine learning models using genetic data (column 1, lines 29-34). One of ordinary skill in the art at the time prior to the effective filing date of the instant invention would have been motivated by Otte to improve the method of VerMilyea. Therefore, it would have been obvious for one of ordinary skill in the art to use the method of VerMilyea with the healthcare images of Ottie. The benefit would be to address the problems of the prior art.
PERTINENT PRIOR ART
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Milletari et al. (US 11804050 B1) discloses each iteration may include training performed by each model trainer 110 occurring for a predetermined period of time, such as a training epoch, which may be a same or different training epoch used by different model trainers 110. In at least one embodiment, each iteration may include after a predetermined period of time, machine learning models 106 and/or portions thereof being provided to interface manager 120 of training aggregator 104 by a corresponding model manager 112 (column 5, lines 13-32).
CONCLUSION
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to C. Dune Ly, whose telephone number is (571) 272-0716. The examiner can normally be reached on Monday-Friday from 8 A.M. to 4 PM ET.
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/Cheyne D Ly/
Primary Examiner, Art Unit 2152
6/26/2026
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