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
The information disclosure statement(s) (IDS) submitted on the following dates is/are entered and considered by Examiner:
* 19 February 2026
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “prediction model trainer”, “prediction analyzer” and “prediction model performance monitor” in claim 13-14.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claim(s) 1-13,15-17,19 and 21-23 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Claim 1 recites:
A system comprising:
a database storing a source dataset comprising previous patient hospital stay data for a plurality of patients and storing a target dataset associated with a current patient cohort;
a memory storing instructions; and
a processor communicatively coupled to the memory and the database, the processor configured to execute the instructions to:
train a transfer classifier based on the source dataset, the training performed using only features common to the source dataset and the target dataset;
apply the trained transfer classifier to the target dataset using the common features as inputs to generate prediction scores;
add only the prediction scores to the target dataset as a new feature column to generate an augmented target dataset;
train a prediction model using the augmented target dataset;
receive current patient hospital stay data for a current patient;
generate a risk score of health deterioration for the current patient based on the prediction model and the current patient hospital stay data; and
determine a likelihood of the current patient being transferred to an intensive care unit (ICU) within a selected period based on the risk score,
wherein the source dataset is associated with a cohort different from the current patient cohort.
Step 1:
The claim as a whole falls within at least one statutory category, i.e. a process, machine, manufacture, or composition of matter.
Step 2A Prong One:
The highlighted portion, as drafted, is a process that, under its broadest reasonable interpretation, falls under “Certain methods of organizing human activity” because the step of determining a likelihood of a patient being transferred to an ICU is traditionally performed by a physician and/or a nurse when treating a patient, such as during triage, i.e. managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). MPEP 2106.04(a)(2)(II)
The highlighted portion, as drafted, is a process that, under its broadest reasonable interpretation, falls under “Mental processes”.
But for a computer recited with a high level of generality in a post hoc manner to implement the abstract idea, the highlighted steps may be performed in the human mind either mentally or with pen and paper.
Accordingly, these limitations have been found to be directed towards concepts performed in the human mind (including an observation, evaluation, judgment, opinion). MPEP 2106.04(a)(2)(III)
The different categories of abstract ideas are being considered together as one single abstract idea. MPEP 2106.04(II)(B)
Dependent claim(s) recite(s) additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claim(s) 2-12 reciting limitations further defining the abstract idea, which may be performed in the mind but for recitation of generic computer components, and/or may be a method of managing relationship or interactions between people).
Step 2A Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claim recites the following additional element(s), if any:
a database storing a source dataset comprising previous patient hospital stay data for a plurality of patients and storing a target dataset associated with a current patient cohort;
a memory storing instructions; and
a processor communicatively coupled to the memory and the database, the processor configured to execute the instructions to:
train a transfer classifier based on the source dataset, the training performed using only features common to the source dataset and the target dataset;
apply the trained transfer classifier;
train a prediction model using the augmented target dataset;
receive current patient hospital stay data for a current patient.
The additional element(s) do(es) not integrate the abstract idea into a practical application, other than the abstract idea per se.
Regarding the processor, the Specification as originally filed on 19 November 2024 discloses a generic/general-purpose computer (page 35 paragraph 0146 to page 36 paragraph 0147), and amount(s) to mere instructions to apply an exception (invoking computers as a tool to perform the abstract idea). MPEP 2106.05(f))
Regarding the database, memory, and the steps of training a prediction model and receiving data, these limitations merely add(s) insignificant extra-solution activity to the abstract idea (mere data gathering, selecting a particular data source or type of data to be manipulated, insignificant application). MPEP 2106.05(g))
Dependent claim(s) recite(s) additional subject matter which amount to limitation(s) consistent with the additional element(s) in the independent claims.
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Accordingly, the additional elements do not integrate the judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Accordingly, the claim recites an abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use.
The additional elements, as discussed above and incorporated herein, amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use, as discussed above and incorporated herein.
Mere instructions to apply an exception, insignificant extra-solution activity, and linking to a particular technological environment using a generic computer component cannot provide an inventive concept.
The database and associated memory have been recited with a high level of generality to perform a basic computer function of storing data, and amount(s) to element(s) that have been recognized as well-understood, routine, and conventional (WURC) activity in particular fields (e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii); e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv)).
Regarding the step of training a prediction model, Hartman (20230081372) discloses training a typical machine learning model in a manner that would be WURC in the pertinent arts (page 2 paragraph 0029).
The step of receiving data amount(s) to element(s) that have been recognized as WURC activity in particular fields (e.g., electronic recordkeeping e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i)). MPEP 2106.05(d)(II)(ii))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims.
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
The claim is not patent eligible.
Claim 13 recites:
A system comprising:
a data processor configured to generate training data for a prediction model based on previous patient hospital stay data for a plurality of patients and to generate a risk score for health deterioration for a current patient based on the prediction model and current patient hospital stay data;
a prediction model trainer configured to train the prediction model based on the training data;
a prediction analyzer configured to generate an uncertainty score for the risk score and to generate a clinical measurement recommendation for the current patient based on the uncertainty score; and
a prediction model performance monitor configured to monitor a performance of the prediction model over time,
wherein the prediction model performance monitor comprises a Kalman filter-based framework configured to:
receive, via a processing system, prediction outputs of the prediction model and corresponding ground-truth outcome labels defining positive samples mt and negative samples nt for a plurality of patients produced during a current time window ending at a time t;
compute, via the processing system, a sample performance metric value zt for the current time window, wherein the sample performance metric value zt comprises an area under a receiver operating curve (AUROC) computed using the prediction outputs and the ground-truth outcome labels;
estimate, via the processing system, a sample variance rt associated with the sample performance metric value zt comprising:
estimating the sample variance rt via a statistical variance estimation technique in response to the number of positive samples mt in the current time window exceeding a predefined threshold, and
assigning the sample variance rt to a conservative upper-bound variance based on the number of positive samples mt and the number of negative samples nt in response to the number of positive samples mt not exceeding the predefined threshold;
extrapolate, via the processing system, a prior estimation variance pt,t-1 from a previously stored estimated variance using at least the number of positive samples and the number of negative samples in the current time window;
compute, via the processing system, a Kalman gain Kt based on the sample variance rt and the prior estimation variance pt,t-1;
update, via the processing system, an estimated performance state Ot by combining a prior performance estimate Ot-1 with the sample performance metric value zt weighted by the Kalman gain Kt; and
update, via the processing system, an estimation variance pt.t corresponding to the estimated performance state.
Step 1:
The claim as a whole falls within at least one statutory category, i.e. a process, machine, manufacture, or composition of matter.
The highlighted portion, as drafted, is a process that, under its broadest reasonable interpretation, falls under “Mathematical concepts” because the claim recites various mathematical equation, including at least a Kalman filter, i.e. mathematical relationships, mathematical formulas or equations, mathematical calculations. MPEP § 2106.04(a)(2)(I)
The highlighted portion, as drafted, is a process that, under its broadest reasonable interpretation, falls under “Certain methods of organizing human activity” because the step of determining a risk score for a patient is traditionally performed by a physician and/or a nurse when treating a patient, such as during triage, i.e. managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). MPEP 2106.04(a)(2)(II)
The highlighted portion, as drafted, is a process that, under its broadest reasonable interpretation, falls under “Mental processes”.
But for a computer recited with a high level of generality in a post hoc manner to implement the abstract idea, the highlighted steps may be performed in the human mind either mentally or with pen and paper.
Accordingly, these limitations have been found to be directed towards concepts performed in the human mind (including an observation, evaluation, judgment, opinion). MPEP 2106.04(a)(2)(III)
The different categories of abstract ideas are being considered together as one single abstract idea. MPEP 2106.04(II)(B)
Dependent claim(s) recite(s) additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claim(s) 15, 21 reciting limitations further defining the abstract idea, which may be performed in the mind but for recitation of generic computer components, and/or may be a method of managing relationship or interactions between people).
Step 2A Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claim recites the following additional element(s), if any:
a data processor;
a prediction model trainer configured to train the prediction model based on the training data;
a prediction analyzer; and
a prediction model performance monitor;
receive, via a processing system, prediction outputs of the prediction model and corresponding ground-truth outcome labels defining positive samples mt and negative samples nt for a plurality of patients produced during a current time window ending at a time t.
The additional element(s) do(es) not integrate the abstract idea into a practical application, other than the abstract idea per se.
Regarding the processor, the Specification as originally filed discloses a generic/general-purpose computer (page 35 paragraph 0146 to page 36 paragraph 0147), and amount(s) to mere instructions to apply an exception (invoking computers as a tool to perform the abstract idea). MPEP 2106.05(f))
Regarding the processing system and remaining structural elements, the broadest reasonable interpretation would include a generic/general-purpose computer invoked with a high level of generality in a post hoc manner to implement the abstract idea, and amount(s) to mere instructions to apply an exception (invoking computers as a tool to perform the abstract idea). MPEP 2106.05(f))
Regarding the step(s) of training a prediction model and receiving data, these limitations merely add(s) insignificant extra-solution activity to the abstract idea (mere data gathering, selecting a particular data source or type of data to be manipulated, insignificant application). MPEP 2106.05(g))
Dependent claim(s) recite(s) additional subject matter which amount to limitation(s) consistent with the additional element(s) in the independent claims.
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Accordingly, the additional elements do not integrate the judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Accordingly, the claim recites an abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use.
The additional elements, as discussed above and incorporated herein, amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use, as discussed above and incorporated herein.
Mere instructions to apply an exception, insignificant extra-solution activity, and linking to a particular technological environment using a generic computer component cannot provide an inventive concept.
Regarding the step of training a prediction model, Hartman discloses training a typical machine learning model in a manner that would be WURC in the pertinent arts (page 2 paragraph 0029).
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims.
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
The claim is not patent eligible.
Claim(s) 16-17, 19, 22-23 recite(s) substantially similar limitations as those of claim(s) 1-12, 21 above, and are therefore rejected for substantially similar rationale as applied above, and incorporated herein.
Subject Matter Free of Prior Art
Claim(s) 1-13,15-17,19 and 21-23 distinguish(es) over the prior art for the following reasons.
The following is a statement of reasons for the subject matter free of prior art:
Claim 1: the primary reason for the indication of subject matter free of prior art is the inclusion of the following limitations in the combination as recited in the abstract concept and not found in the closest available prior art of record:
train a transfer classifier based on the source dataset, the training performed using only features common to the source dataset and the target dataset;
apply the trained transfer classifier to the target dataset using the common features as inputs to generate prediction scores;
add only the prediction scores to the target dataset as a new feature column to generate an augmented target dataset;
train a prediction model using the augmented target dataset;
receive current patient hospital stay data for a current patient;
generate a risk score of health deterioration for the current patient based on the prediction model and the current patient hospital stay data; and
determine a likelihood of the current patient being transferred to an intensive care unit (ICU) within a selected period based on the risk score,
wherein the source dataset is associated with a cohort different from the current patient cohort.
The closest available prior art of record are as follows:
Mortazavi (20180315507) discloses predicting a patient’s risk of a post-op complication from a procedure (paragraph 0004), but does not fairly disclose or suggest training a model in the manner claimed.
Based on the evidence presented above, none of the closest available prior art of record fairly discloses or suggests the claimed invention. For this reason, claim 1 would be found to be subject matter free of prior art.
Claim(s) 2-12, 21: this/these claim(s) would also be found to be subject matter free of prior art for at least the same rationale as applied to parent claim 1 above, and incorporated herein.
Claim 13: the primary reason for the indication of subject matter free of prior art is the inclusion of the following limitations in the combination as recited in the abstract concept and not found in the closest available prior art of record:
receive, via a processing system, prediction outputs of the prediction model and corresponding ground-truth outcome labels defining positive samples mt and negative samples nt for a plurality of patients produced during a current time window ending at a time t;
compute, via the processing system, a sample performance metric value zt for the current time window, wherein the sample performance metric value zt comprises an area under a receiver operating curve (AUROC) computed using the prediction outputs and the ground-truth outcome labels;
estimate, via the processing system, a sample variance rt associated with the sample performance metric value zt comprising:
estimating the sample variance rt via a statistical variance estimation technique in response to the number of positive samples mt in the current time window exceeding a predefined threshold, and
assigning the sample variance rt to a conservative upper-bound variance based on the number of positive samples mt and the number of negative samples nt in response to the number of positive samples mt not exceeding the predefined threshold;
extrapolate, via the processing system, a prior estimation variance pt,t-1 from a previously stored estimated variance using at least the number of positive samples and the number of negative samples in the current time window;
compute, via the processing system, a Kalman gain Kt based on the sample variance rt and the prior estimation variance pt,t-1;
update, via the processing system, an estimated performance state Ot by combining a prior performance estimate Ot-1 with the sample performance metric value zt weighted by the Kalman gain Kt; and
update, via the processing system, an estimation variance pt.t corresponding to the estimated performance state.
The closest available prior art of record are as follows:
Papageorgiou (A Hybrid SEIHCRDV-UKF Model for COVID-19 Prediction. Application on real-time data, previously mailed on 19 November 2025) discloses a Kalman based framework (page 14 Section 2.3 paragraph 1); but does not fairly disclose or suggest training a model in the manner claimed.
Based on the evidence presented above, none of the closest available prior art of record fairly discloses or suggests the claimed invention. For this reason, claim 13 would be found to be subject matter free of prior art.
Claim(s) 15: this/these claim(s) would also be found to be subject matter free of prior art for at least the same rationale as applied to parent claim 13 above, and incorporated herein.
Claim(s) 16-17, 22-23: this/these claim(s) would also be found to be subject matter free of prior art for substantially similar rationale as applied to claim(s) 1-12, 21 above, and incorporated herein.
Response to Arguments
In the Remarks filed on 19 February 2026, Applicant makes numerous arguments. Examiner will address these arguments in the order presented.
Applicant’s arguments, see page 9, with respect to the objection to all pending claims for minor informalities have been carefully considered and are persuasive. The objection to all pending claims has been withdrawn.
On page 9 Applicant argues that the claims recite eligible subject matter.
While Applicant’s arguments have been carefully considered, they are not found persuasive for the reasons stated in the section above, and incorporated herein.
Applicant’s arguments, see page 10-12, with respect to the rejection to all pending claims under Section 102/103(a) have been carefully considered and are persuasive. The rejection of all pending claims under Section 102/103(a) has been withdrawn.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cox (20220310267) discloses monitoring patient conditions and adjusting treatment actions based thereon (Abstract) in a manner similar to those disclosed in the instant pending Specification as originally filed.
Edmondson (20200013489) discloses modifying patient treatment based on readmission risk (Abstract) in a manner similar to those disclosed in the instant pending Specification as originally filed.
THIS ACTION IS MADE FINAL. 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 extension fee 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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/T.N.N./ Examiner, Art Unit 3685
/KAMBIZ ABDI/ Supervisory Patent Examiner, Art Unit 3685
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