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 .
Response to Amendment
In the previous Office Action issued 10-20-2025 (hereinafter “the previous Office Action”), claims 1-20 were pending.
This action is in response to the amendment and remarks filed 1-13-2026. In the amendment, claims 1-4, 6-19 were amended, claim 20 was canceled, and claim 21 was added. Thus, claims 1-19 and 21 are pending.
The rejection of claim 20 under 35 U.S.C. § 101, set forth in the previous Office Action, has been withdrawn in view of claim 20’s cancelation.
The rejections of claims 1-7, 10-12, and 15-18 under 35 U.S.C. § 102 and 103, set forth in the previous Office Action, have been withdrawn in view of Applicant’s amendments and remarks.
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.
Claims 8-9, 13-14, and 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 8-9 are directed to a method [process]. Claims 13-14 are directed to an information processing system [machine]. Claim 19 is directed to a computer program product [machine].
Regarding Claim 8:
Step 2A, Prong 1: The following limitations are directed to the abstract idea of a mathematical concept [see MPEP 2106.04(a)(2) I. C.].
wherein the loss-objective function is defined as:
PNG
media_image1.png
145
1013
media_image1.png
Greyscale
where
λ
is a parameter of the second ML model, N is the total number of data points,
E
is the expected value,
l
is a decision cost function, M is a nonnegative number, c is a decision generated by the second ML model on the dataset having a highest utility according to the utility function represented as
l
(
c
n
,
y
)
M
, D’ is a calibration dataset, y is a model prediction, x is a data point from the first dataset,p is the first posterior predictive distribution, q is the second posterior predictive distribution, D is training data,
θ
0
is a set of hyperparameters, and KL is Kullback-Leibler divergence
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements does not meaningfully limit the judicial exception because they are recited at a high level of generality and they merely linked the use of the abstract idea to a particular technological environment (i.e., ‘implementation via computers’) [see MPEP 2106.05(e)]. Therefore, the additional element does not integrate the abstract ideas into a practical application.
wherein training the second ML model includes: configuring the second ML model with parameters optimizing a loss-objective function that concurrently maximizes utility of the second posterior predictive distribution according to the utility function and a log-likelihood of the first dataset
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
The following additional elements does not meaningfully limit the judicial exception because they are recited at a high level of generality and they merely linked the use of the abstract idea to a particular technological environment (i.e., ‘implementation via computers’) [see MPEP 2106.05(e)]. Therefore, the additional element does not amount to significantly more than the judicial exception.
wherein training the second ML model includes: configuring the second ML model with parameters optimizing a loss-objective function that concurrently maximizes utility of the second posterior predictive distribution according to the utility function and a log-likelihood of the first dataset
Regarding Claim 9:
Step 2A, Prong 1: The following limitations are directed to the abstract idea of a mathematical concept [see MPEP 2106.04(a)(2) I. C.].
wherein the loss-objective function is defined as:
PNG
media_image2.png
159
978
media_image2.png
Greyscale
where
λ
is a parameter of the second ML model, N is the total number of data points,
E
is the expected value,
l
is a decision cost function, M is a nonnegative number, c is a decision generated by the second ML model on the first dataset having a highest utility according to the utility function represented as
l
(
c
n
,
y
)
M
, D’ is a calibration dataset, y is a model prediction, x is a data point from the first dataset, S is an amortized approximation of the first posterior predictive distribution, q is the second posterior predictive distribution, KL is Kullback-Leibler divergence, and
ω
is a weight of the amortized approximation S
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements does not meaningfully limit the judicial exception because they are recited at a high level of generality and they merely linked the use of the abstract idea to a particular technological environment (i.e., ‘implementation via computers’) [see MPEP 2106.05(e)]. Therefore, the additional element does not integrate the abstract ideas into a practical application.
wherein training the second ML model includes: configuring the second ML model with parameters from a set of parameters optimizing a loss-objective function that concurrently maximizes utility of the second posterior predictive distribution according to the utility function and a log-likelihood on the first dataset
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
The following additional elements does not meaningfully limit the judicial exception because they are recited at a high level of generality and they merely linked the use of the abstract idea to a particular technological environment (i.e., ‘implementation via computers’) [see MPEP 2106.05(e)]. Therefore, the additional element does not amount to significantly more than the judicial exception.
wherein training the second ML model includes: configuring the second ML model with parameters from a set of parameters optimizing a loss-objective function that concurrently maximizes utility of the second posterior predictive distribution according to the utility function and a log-likelihood on the first dataset
Regarding Claims 13-14:
Claims 13-14 correspond to claims 8-9. In particular, 13:8, 14:9.
Step 2A, Prong 1: Claims 13-14 recite the same abstract ideas as in claims 8-9.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application. The analysis of claims 13-14 at this step mirror that of claims 8-9.
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception. The analysis of claims 13-14 at this step mirror that of claims 8-9.
Regarding Claims 19:
Claim 19 correspond to claim 8.
Step 2A, Prong 1: Claim 19 recites the same abstract ideas as in claim 8.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application. The analysis of claim 19 at this step mirror that of claim 8.
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception. The analysis of claim 19 at this step mirror that of claim 8.
Response to Arguments
Applicant's arguments filed 1-13-2026 (“Remarks”) have been fully considered, but they are not persuasive.
35 U.S.C. § 101:
Remarks, pg. 15. No arguments are present regarding claims 8-9, 13-14, and 19-20 under 35 U.S.C. § 101.
35 U.S.C. § 102 and 103:
Remarks, pg. 15-17. Applicant’s arguments with respect to claims 1, 10, and 15 have been considered but are moot because the rejections have been withdrawn
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon, is considered pertinent to applicant's disclosure.
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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/S.H.P./Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125