DETAILED ACTION
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 .
Information Disclosure Statement
Information disclosure statement (IDS) was submitted on 19 January 2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
This action is in response to the submission filed 06 March 2026 for application 17/155,335. Claims 1 and 8 have been amended. Currently claims 1-14 are pending and have been examined.
Response to Arguments
Regarding applicant’s arguments, filed 06 March 2026, see page 8, with regards to the rejection of claims 1-14 under 35 U.S.C. §101, Applicant specifically argues on Page 8 that MPEP 2106.04(a)(1)(vii) states that training neural networks do not recite an abstract idea. Here, similar to MPEP 2106.04(a)(1)(vii), the present embodiments train a generative neural network to perform discriminative inference with generative models for covariate incompletion. Thus, similar to MPEP 2106.04(a)(1)(vii), the present embodiments train neural networks and do not recite an abstract idea. Therefore, the present embodiments are not merely directed to an abstract idea.
Examiners response: Applicant’s arguments have been fully considered but they are not persuasive. Examiner respectfully disagrees because as stated in the previous Non-Final rejection dated 12/18/2025, “training the neural network” is not identified as an abstract idea. In fact, it is identified as an additional element in Step 2A, prong 2. The limitations of computing a predictive distribution and minimizing an objective function are identified as abstract ideas because they very clearly recite Mathematical calculations.
Regarding applicant’s arguments, filed 06 March 2026, see pages 8-10, with regards to the rejection of claims 1-14 under 35 U.S.C. §101, Applicant specifically argues on Page 8 that even assuming, arguendo, that the present embodiments contain an abstract idea, which Applicant respectfully submits they do not, the present embodiments integrate the abstract idea into practical applications. Applicant continues to argue on Page 9 that the present embodiments improve computer technology by improving training of machine learning models. Here, similar to Desjardins, the present embodiments improve computer technology by resolving a technical problem when training machine learning models for discriminative inference with generative models. Applicant continues on Page 10 that similar to Desjardins, the present embodiments resolve these issues construct a generic approximation of the transformation from a joint density to a conditional density, and utilize the generic approximation for the training of neural networks to perform DIGM. See claim 1; at least para. [0030]-[0034] of the Specification as filed; compare, determining a respective measure step of Application No. 16/319,040 in p. 2-3 of Desjardins. Additionally, similar to Desjardins, the present embodiments reduce the system complexity when training neural networks to perform DIGM. See id. Thus, similar to Desjardins, the present embodiments improve computer technology by resolving a technical problem when training machine learning models for discriminative inference with generative models. Therefore, claim 1 satisfies the requirements of 35 U.S.C. § 101 at least due to the reasons set forth above.
Examiners response: Applicant’s arguments have been fully considered but they are not persuasive. Examiner respectfully disagrees because firstly, here the improvement is in the abstract idea of the mathematical calculations (in Step 2A, prong 1 of the 101 analysis) that are recited in the claims and not in the training of machine learning itself. Secondly, Applicant themselves mention that the present embodiment constructs a generic approximation of the transformation from a joint density to a conditional density, and utilize the generic approximation for the training of neural networks to perform DIGM. So the neural network is merely using the abstract idea. An improvement in the abstract idea is still an abstract idea and as disclosed in MPEP 2106.05(a) it is important to note, the judicial exception alone cannot provide the improvement. Lastly, as explained in the previous Non-Final rejected dated 12/18/2025 and Final rejection dated 3/4/2025, and as explained in the detailed rejection below, although training is mentioned in the claims, there are no details of the actual training steps. The claim simply recites “training the generative neural network” to do something, making training to be a black box with no details of what that training actually entails. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application. The claims are not patent eligible.
Hence, the rejection of independent claim 1 and similarly independent claim 8 is maintained. The rest of the claims depend from one of these claims and are therefore rejected for the same reason.
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 1 - 14 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards abstract ideas without significantly more.
Regarding claims 1-7:
According to the first step (Step 1) of the 101 analysis, claim 1 is directed to a computer-implemented method (process) and falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Regarding claim 1:
In the next step (Step 2A, prong 1) of the analysis, the limitations of:
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under the broadest reasonable interpretation, the above limitations are process steps that recite mathematical relationships and calculations but for the recitation of generic computer components. If a claim, under its broadest reasonable interpretation covers mathematical concepts but for the recitation of generic computer components, then it falls within the “Mathematical concepts” grouping of abstract ideas.
In the same step (Step 2A, prong 1) of the analysis, the limitations of:
predicting a progression of a disease using the incomplete set of covariates from the electronic health record of the patient;
and generating the missing entries from the electronic health record of the patient based on the progression of the disease.
Under the broadest reasonable interpretation, the above limitations are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, the limitation,
acquiring an incomplete set of covariates x including incomplete features x~ and an incomplete pattern m indicating missing entries of the incomplete features x~ from an electronic health record of a patient;
is considered to be an additional element and as recited represent insignificant extra-solution activity because it is mere data gathering. See MPEP 2106.05(g), discussing limitations that the Federal Circuit has considered to be insignificant extra-solution activity.
In the same step (Step 2A, prong 2) of the analysis, the limitations,
from a generative neural network,
through training the generative neural network
to perform discriminative inference with generative models for covariate incompletion.
The above limitations are considered to be additional elements and do not integrate the abstract idea into a practical application because the additional elements are recited so generically. No details whatsoever are provided other than using an incomplete set of covariates and a parameter “from a generative neural network”. Next, although training is mentioned there are no details of the actual training steps. The claim recites that learning the parameter is performed “through training the generative neural network” making training to be a black box. Lastly, “to perform discriminative inference with generative models for covariate incompletion” just explains the desirable result and does not limit the claim further. As discussed in MPEP 2106.05(f), the recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". Hence, these limitations represent no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
In the last step (Step 2B) of the analysis, as discussed above the additional element of acquiring an incomplete set of covariates, which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and do not amount to significantly more.
In the same step (Step 2B) of the analysis, as discussed above the additional elements of:
from a generative neural network,
through training the generative neural network
to perform discriminative inference with generative models for covariate incompletion.
do not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, these limitations are at best the equivalent of merely adding the words “apply it” to the judicial exception because of the lack of details regarding the generative neural network and its training and reciting only the desirable result. Mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception. See MPEP 2106.05(f). The claim is not patent eligible.
Regarding claim 2:
In the step (Step 2A, prong 1) of the analysis, the limitation of:
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under the broadest reasonable interpretation, the above limitation is a process step that recites mathematical relationships and calculations but for the recitation of generic computer components. If a claim, under its broadest reasonable interpretation covers mathematical concepts but for the recitation of generic computer components, then it falls within the “Mathematical concepts” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, it does not integrate into a practical application because it does not add any additional elements that integrate the abstract idea into practical application.
In the last step (Step 2B) of the analysis, it does not add any additional elements that amount to significantly more than the abstract idea and thus fails to add an inventive concept. The claim is not patent eligible.
Regarding claim 3:
In the step (Step 2A, prong 1) of the analysis, the limitation of:
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771
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under the broadest reasonable interpretation, the above limitation is a process step that recites mathematical relationships and calculations but for the recitation of generic computer components. If a claim, under its broadest reasonable interpretation covers mathematical concepts but for the recitation of generic computer components, then it falls within the “Mathematical concepts” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, it does not integrate into a practical application because it does not add any additional elements that integrate the abstract idea into practical application.
In the last step (Step 2B) of the analysis, it does not add any additional elements that amount to significantly more than the abstract idea and thus fails to add an inventive concept. The claim is not patent eligible
Regarding claim 4:
In the step (Step 2A, prong 1) of the analysis, the limitation of:
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775
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under the broadest reasonable interpretation, the above limitation is a process step that recites mathematical relationships and calculations but for the recitation of generic computer components. If a claim, under its broadest reasonable interpretation covers mathematical concepts but for the recitation of generic computer components, then it falls within the “Mathematical concepts” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, it does not integrate into a practical application because it does not add any additional elements that integrate the abstract idea into practical application.
In the last step (Step 2B) of the analysis, it does not add any additional elements that amount to significantly more than the abstract idea and thus fails to add an inventive concept. The claim is not patent eligible.
Regarding claim 5:
In the step (Step 2A, prong 1) of the analysis, the limitation of wherein a discriminative variational autoencoder (DVAE) performs discriminative inference with generative models (DIGM) with the incomplete set of covariates x, under the broadest reasonable interpretation, the above limitation is a process step that recites mathematical relationships and calculations but for the recitation of generic computer components. If a claim, under its broadest reasonable interpretation covers mathematical concepts but for the recitation of generic computer components, then it falls within the “Mathematical concepts” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, it does not integrate into a practical application because it does not add any additional elements that integrate the abstract idea into practical application.
In the last step (Step 2B) of the analysis, it does not add any additional elements that amount to significantly more than the abstract idea and thus fails to add an inventive concept. The claim is not patent eligible.
Regarding claim 6:
In the next step (Step 2A, prong 2) of the analysis, the limitation, wherein the DVAE includes a generative network, two variational networks, and a surrogate network, is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional element is recited so generically (no details whatsoever are provided other than that it is a computer implemented method) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
In the last step (Step 2B) of the analysis, the additional element does not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the computer implemented method is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 7:
In the step (Step 2A, prong 1) of the analysis, the limitation of wherein stochastic gradient-based optimization is employed to minimize the objective function, under the broadest reasonable interpretation, the above limitation is a process step that recites mathematical relationships and calculations but for the recitation of generic computer components. If a claim, under its broadest reasonable interpretation covers mathematical concepts but for the recitation of generic computer components, then it falls within the “Mathematical concepts” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, it does not integrate into a practical application because it does not add any additional elements that integrate the abstract idea into practical application.
In the last step (Step 2B) of the analysis, it does not add any additional elements that amount to significantly more than the abstract idea and thus fails to add an inventive concept. The claim is not patent eligible.
Regarding claims 8-14:
According to the first step (Step 1) of the 101 analysis, claim 8 is directed to a computer program product, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to do some steps (manufacture) and falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Regarding claim 8:
In the next step (Step 2A, prong 1) of the analysis, the limitations of:
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under the broadest reasonable interpretation, the above limitations are process steps that recite mathematical relationships and calculations but for the recitation of generic computer components. If a claim, under its broadest reasonable interpretation covers mathematical concepts but for the recitation of generic computer components, then it falls within the “Mathematical concepts” grouping of abstract ideas.
In the same step (Step 2A, prong 1) of the analysis, the limitations of:
predict a progression of a disease using an incomplete set of covariates from the electronic health record of the patient;
and generate the missing entries from the electronic health record of the patient based on the progression of the disease.
Under the broadest reasonable interpretation, the above limitations are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, the limitation,
acquire an incomplete set of covariates x including incomplete features x~ and an incomplete pattern m indicating missing entries of the incomplete features x~ from an electronic health record of a patient;
is considered to be an additional element and as recited represent insignificant extra-solution activity because it is mere data gathering. See MPEP 2106.05(g), discussing limitations that the Federal Circuit has considered to be insignificant extra-solution activity.
In the same step (Step 2A, prong 2) of the analysis, the limitations,
from a generative neural network,
through training the generative neural network
to perform discriminative inference with generative models for covariate incompletion.
The above limitations are considered to be additional elements and do not integrate the abstract idea into a practical application because the additional elements are recited so generically. No details whatsoever are provided other than using an incomplete set of covariates and a parameter “from a generative neural network”. Next, although training is mentioned there are no details of the actual training steps. The claim recites that learning the parameter is performed “through training the generative neural network” making training to be a black box. Lastly, “to perform discriminative inference with generative models for covariate incompletion” just explains the desirable result and does not limit the claim further. As discussed in MPEP 2106.05(f), the recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". Hence, these limitations represent no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
In the last step (Step 2B) of the analysis, as discussed above the additional element of acquiring an incomplete set of covariates, which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and do not amount to significantly more.
In the same step (Step 2B) of the analysis, as discussed above the additional elements of:
from a generative neural network,
through training the generative neural network
to perform discriminative inference with generative models for covariate incompletion.
do not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, these limitations are at best the equivalent of merely adding the words “apply it” to the judicial exception because of the lack of details regarding the generative neural network and its training and reciting only the desirable result. Mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception. See MPEP 2106.05(f). The claim is not patent eligible.
Regarding claim 9:
In the step (Step 2A, prong 1) of the analysis, the limitation of:
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under the broadest reasonable interpretation, the above limitation is a process step that recites mathematical relationships and calculations but for the recitation of generic computer components. If a claim, under its broadest reasonable interpretation covers mathematical concepts but for the recitation of generic computer components, then it falls within the “Mathematical concepts” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, it does not integrate into a practical application because it does not add any additional elements that integrate the abstract idea into practical application.
In the last step (Step 2B) of the analysis, it does not add any additional elements that amount to significantly more than the abstract idea and thus fails to add an inventive concept. The claim is not patent eligible.
Regarding claim 10:
In the step (Step 2A, prong 1) of the analysis, the limitation of:
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778
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under the broadest reasonable interpretation, the above limitation is a process step that recites mathematical relationships and calculations but for the recitation of generic computer components. If a claim, under its broadest reasonable interpretation covers mathematical concepts but for the recitation of generic computer components, then it falls within the “Mathematical concepts” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, it does not integrate into a practical application because it does not add any additional elements that integrate the abstract idea into practical application.
In the last step (Step 2B) of the analysis, it does not add any additional elements that amount to significantly more than the abstract idea and thus fails to add an inventive concept. The claim is not patent eligible
Regarding claim 11:
In the step (Step 2A, prong 1) of the analysis, the limitation of:
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under the broadest reasonable interpretation, the above limitation is a process step that recites mathematical relationships and calculations but for the recitation of generic computer components. If a claim, under its broadest reasonable interpretation covers mathematical concepts but for the recitation of generic computer components, then it falls within the “Mathematical concepts” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, it does not integrate into a practical application because it does not add any additional elements that integrate the abstract idea into practical application.
In the last step (Step 2B) of the analysis, it does not add any additional elements that amount to significantly more than the abstract idea and thus fails to add an inventive concept. The claim is not patent eligible.
Regarding claim 12:
In the step (Step 2A, prong 1) of the analysis, the limitation of wherein a discriminative variational autoencoder (DVAE) performs discriminative inference with generative models (DIGM) with the incomplete set of covariates x, under the broadest reasonable interpretation, the above limitation is a process step that recites mathematical relationships and calculations but for the recitation of generic computer components. If a claim, under its broadest reasonable interpretation covers mathematical concepts but for the recitation of generic computer components, then it falls within the “Mathematical concepts” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, it does not integrate into a practical application because it does not add any additional elements that integrate the abstract idea into practical application.
In the last step (Step 2B) of the analysis, it does not add any additional elements that amount to significantly more than the abstract idea and thus fails to add an inventive concept. The claim is not patent eligible.
Regarding claim 13:
In the next step (Step 2A, prong 2) of the analysis, the limitation, wherein the DVAE includes a generative network, two variational networks, and a surrogate network, is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional element is recited so generically (no details whatsoever are provided other than that it is a computer implemented method) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
In the last step (Step 2B) of the analysis, the additional element does not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the computer implemented method is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 14:
In the step (Step 2A, prong 1) of the analysis, the limitation of wherein stochastic gradient-based optimization is employed to minimize the objective function, under the broadest reasonable interpretation, the above limitation is a process step that recites mathematical relationships and calculations but for the recitation of generic computer components. If a claim, under its broadest reasonable interpretation covers mathematical concepts but for the recitation of generic computer components, then it falls within the “Mathematical concepts” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, it does not integrate into a practical application because it does not add any additional elements that integrate the abstract idea into practical application.
In the last step (Step 2B) of the analysis, it does not add any additional elements that amount to significantly more than the abstract idea and thus fails to add an inventive concept. The claim is not patent eligible.
Prior Art
No prior art was found that teaches the claimed method for computing an objective function of discriminative inference with generative models with incomplete data in which some of entries are missing.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jebara (Discriminative, Generative and Imitative Learning, 2001) discloses a common framework that combines three different paradigms in machine learning: generative, discriminative and imitative learning. The final result is a distribution that is a good generator of novel exemplars.
Azoury et al (Relative loss bounds for on-line density estimation with the exponential family of distributions, 2001) discloses an on-line density estimation with a parameterized density from the exponential family. The on-line algorithm receives one example at a time and maintains a parameter that is essentially an average of the past examples. After receiving an example the algorithm incurs a loss, which is the negative log-likelihood of the example with respect to the current parameter of the algorithm. An off-line algorithm can choose the best parameter based on all the examples. We prove bounds on the additional total loss of the on-line algorithm over the total loss of the best off-line parameter. These relative loss bounds hold for an arbitrary sequence of examples. The goal is to design algorithms with the best possible relative loss bounds. We use a Bregman divergence to derive and analyze each algorithm. These divergences are relative entropies between two exponential distributions. We also use our methods to prove relative loss bounds for linear regression.
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 CHAITANYA RAMESH JAYAKUMAR whose telephone number is (571)272-3369. The examiner can normally be reached Mon-Fri 9am-1pm.
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, Omar Fernandez Rivas can be reached on (571)272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/C.R.J./Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
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