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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/20/2026 has been entered.
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, 3-4, 6, 21, 23-24, 26-27, 29-30, and 32-35 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.
The Specification does not provide support for the limitation “without user modification”.
Per MPEP 2173.05(i), any negative limitation or exclusionary proviso must have basis in the original disclosure. Support for such limitations can be found through the exclusions of disclosed alternatives or through explicit disclosure of such a negative limitation. Furthermore, it is recognized that there may be situations in which it can be established that a skilled artisan would understand that a negative limitation is necessarily present in the disclosure, that is not the case in the instant application. A skilled artisan would not recognize that Applicant’s invention, as disclose in the Specification, includes the exclusionary proviso that the datasets are used “without user modification”. The silence as to the negative limitation in the original disclosure establishes a lack of written description.
Furthermore, “training…the machine learning model…using directly the first dataset and the model predicted third dataset without user modification” lacks written description because there is no support for the training of a model which utilizes a dataset without somehow altering that dataset, i.e. the training of the model necessarily alters the dataset by taking in inputs, altering them, and producing outputs so there is no support for a model which does not alter the dataset.
Any claim depending from a claim which lacks written description inherits the lack of written description.
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 1, 3-4, 6, 21, 23-24, 26, 29-30, and 32-35 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.
Regarding claims 1, “using directly the first dataset and the model predicted third dataset without user modification” renders the claim indefinite because it is unclear what falls within the scope of “user modification” given the context in which it appears in the claim. For example, the machine learning model is trained via the first and third dataset, which necessarily alters the dataset as it is input and run through the model to create an output. If this were not the case, the input would be the same as the output and no training would occur. Therefore, “user modification” appears not to apply to the machine learning model and therefore a user initiating the training of the model appears not to fall under the scope of “without user modification” which renders the claim indefinite because a user initiating the training of the model would be user initiated modification.
Any claims depending from an indefinite claim is indefinite by virtue of dependency.
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) 1, 3, 6, 21, 23, 26, 29, and 32-35 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guo (US20190073447A1) in view of Shteingart (US20160379135A1).
Regarding claim 1, Guo teaches a training method for a machine learning model for physiological analysis (¶21), comprising:
receiving training data comprising a first dataset of labeled data of a physiological-related parameter and a second dataset of weakly-labeled data of the physiological-related parameter (¶14 provides a labeled dataset and an unlabeled (i.e., weakly-labeled) dataset);
training, by at least one processor, an initial machine learning model for predicting the physiological-related parameter using the first dataset (¶14, a classifier is trained on the labeled data);
applying, by the at least one processor, the initial machine learning model to the second dataset to predict values of the physiological-related parameter corresponding to data samples in a subset of the weakly-labeled data in the second dataset and generate a third dataset of pseudo-labeled data by labeling the subset of the weakly-labeled data with the predicted values of the physiological-related parameter (¶15, M0 is used to label S1);
training, by the at least one processor, the machine learning model for predicting the physiological-related parameter using directly first dataset and the model predicted third dataset without user modification; and providing the trained machine learning model for predicting the physiological-related parameter (¶17 where the labeled data is unioned with the third dataset to create a new model or retrain the model, see ¶21 where the model is used for classifying ultrasound images – directly before training, the unioned dataset does not undergo user modification).
Guo does not teach wherein the physiological-related parameter includes blood pressure.
It is known to utilize blood pressure as a physiological-related parameter in a dataset in the context of machine learning (¶20, ¶56 of Shteingart).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize blood pressure as the physiological-related parameter in Guo in order to perform analyses in classification which utilize blood pressure values in the dataset, thereby providing the benefits of accurate classification of Guo (¶27 of Guo).
Regarding claim 3, Guo teaches all of the limitations of claim 1, further comprising:
selecting pseudo-labeled data satisfying a first preset condition at least associated with a confidence level to be included in the third dataset (¶16).
Regarding claim 6, Guo teaches all of the limitations of claim 1, further comprising:
labeling another subset of the weakly-labeled data in the second dataset using prior information of the physiological-related parameter to form additional pseudo-labeled data and adding the additional pseudo-labeled data in the third dataset, wherein the prior information of the physiological-related parameter includes a vessel with a first stenosis degree or more severe stenosis being functional significant (¶18, ¶26).
Regarding claim 21, Guo as modified teaches a non-transitory computer readable medium having computer instructions stored thereon, where in the computer instructions, when executed by at least one processor, cause the at least one processor to perform a training method for a machine learning model for physiological analysis (¶39-42, see rejection of claim 1) and Guo as modified further teaches the training method of claim 21, see the rejection of claim 1.
Regarding claims 23 and 26, Guo as modified teaches all of the limitations of claim 21 and the rejection claims 3 and 6 is analogous to claims 23 and 26 respectively.
Regarding claim 27, Guo as modified teaches an apparatus comprising at least one processor and a non-transitory computer readable medium having computer instructions stored thereon, where in the computer instructions, when executed by the at least one processor, cause the at least one processor to perform a training method for a machine learning model for physiological analysis (¶39-42, see rejection of claim 1) and Guo as modified further teaches the training method of claim 27, see the rejection of claim 1.
Regarding claims 29 and 32, Guo teaches all of the limitations of claim 27 and the rejection claims 3 and 6 is analogous to claims 29 and 32 respectively.
Regarding claim 33, Guo teaches all of the limitations of claim 6, wherein training the machine learning model for predicting the physiological related parameter based on the first dataset and the third dataset further comprises:
training the machine learning model for predicting the physiological-related parameter using directly first dataset and the model predicted third dataset without user intervention, wherein the third dataset includes the pseudo-labeled data labeled with the values of the physiological-related parameter predicted by the initial machine learning model and the additional pseudo-labeled data labeled using the prior information of the physiological-related parameter (¶18, as data is labeled it is added into the training data - directly before training, the unioned dataset does not undergo user modification).
Regarding claims 34-35, Guo teaches all of the limitations of claims 6, 26, and 32 and the rejection claim 33 is analogous to claims 34-35.
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.
Claim(s) 4, 24, and 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guo (US20190073447A1) in view of Shteingart (US20160379135A1), further in view of Li (Li Y, Chen J, Xie X, Ma K, Zheng Y. Self-loop uncertainty: A novel pseudo-label for semi-supervised medical image segmentation. InMedical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I 23 2020 (pp. 614-623). Springer International Publishing.)
Regarding claim 4, Guo as modified teaches all of the limitations of claim 1, but does not teach wherein training the initial machine learning model uses a first regression loss term formulated by the labeled data in the first dataset, and training the machine learning model uses the first regression loss term and a second regression loss term formulated by the pseudo- labeled data in the third dataset.
Li teaches training the initial machine learning model using a first regression loss term formulated by the labeled data in the first dataset, and training the machine learning model using the first regression loss term and a second regression loss term formulated by the pseudo-labeled data in the third dataset (§2.3, Equation (2), which includes segmentation loss, i.e. a first regression loss term, and guided uncertainty loss, i.e. a second regression loss term formulated by the pseudo-labeled data) which boosts accuracy (§4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the training in Guo to include training the initial machine learning model using a first regression loss term formulated by the labeled data in the first dataset, and training the machine learning model using the first regression loss term and a second regression loss term formulated by the pseudo-labeled data in the third dataset to boost accuracy.
Regarding claims 24 and 30, Guo as modified teaches all of the limitations of claims 21 and 27 and the rejections of claims 24 and 30 are analogous to the rejection of claim 4.
Response to Arguments
Applicant’s remarks filed 12/16/2025 have been fully considered.
Applicant has argued that Guo does not predict a physiological related parameter because stenosis is “simply not a value of any physiological-related parameter” among those claimed. It is not purported that stenosis is a physiological parameter among those in the amended limitations and therefore the argument is moot.
Applicant has argued that Guo does not with “without user modification” because Guo discloses manual annotation. As noted herein, directly before training, there is no user modification because the user does not intervene once data is input into the model.
Conclusion
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/SCHYLER S SANKS/ Primary Examiner, Art Unit 2129