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 .
Election/Restrictions
Applicant’s election without traverse of Invention I (claims 1-10) in the reply filed on 06/10/2026 is acknowledged. Claims 11-16 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 06/10/2026.
Remarks
This action is in response to the remarks filed 06/10/2026.
Claims 1-10 are examined below.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a method for training a prediction model. To determine whether a claim satisfies the criteria for subject matter eligibility, the claim is evaluated according to a stepwise process as described in MPEP 2106(III) and 2106.03-2106.04. The instant claims are evaluated according to such analysis.
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Claim 1 is directed towards a method and meets the requirements for step 1.
Step 2A (Prong 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Claim 1 is directed towards a method for training a prediction model, comprising training an architecture with a training data set that comprises control training data and emergency training data, wherein the training input comprises static variables and time series data of a target variable in an observation window, and the training ground truth comprises time series data of the target variable in a forecast window. The limitation of method for training a prediction model, as drafted in claims 1-10, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind or using pen and paper or generic computer components. A claim that requires a computer can still recite a mental process. See MPEP 2164.04(a)(2)(III)(C). For example, a user could train a model with a training data that has a training input and a training ground truth.
Step 2A (Prong 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
The additional element of the model being an attention-based architecture is recited at a high level of generality (i.e., as generic model) such that they amount to no more than mere instructions to apply the exception using a generic computer component.
Accordingly, these additional elements do no integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.04(a)(2)(III)(C).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
The additional elements when considered individually and in combination is not enough to qualify as significantly more than the abstract idea. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of the model being an attention-based architecture amounts to no more than mere instructions to apply the exception using generic data analysis. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Furthermore, the additional elements do not amount to more than generically linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Therefore, the claims are not patent eligible.
Claims 2-10 depend on claim 1 and recite the same abstract idea as claim 1 from which they depend. Further, these claims only contain recitations that further limit the abstract idea (that is, the claims only recite limitations that further limit the mental process). For example, the additional limitations recited in claims 2-10 (i.e. providing details about the data used) are further data gathering steps. The additional elements individually do not amount to significantly more than the judicial exception explained above (the abstract idea). Looking at the limitations as a whole adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves any technology or includes a particular solution to a computer-based problem or a particular way to achieve a computer-based outcome. Rather, the collective functions of the claimed invention merely provides a conventional computer implementation, i.e. the computer (processor) is simply a tool to perform the claimed invention.
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)(1) 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.
Claims 1-10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ravishankar et al. (US Patent Application Publication 2023/0238134), hereinafter Ravishankar.
Regarding claim 1, Ravishankar discloses a method for training a prediction model to predict future vital signs of a subject within a predetermined future time interval, comprising training an attention-based architecture with a vital sign training dataset comprising multiple vital sign training data, each of which has a training input and a training ground truth (e.g. Abstract; Pars. [0049]-[0050]: training the model), wherein: the multiple vital sign training data comprise multiple control training data of multiple first patients without critical conditions, and multiple emergency training data of multiple second patients with critical conditions (e.g. Pars. [0049]-[0050]: training data comprises data from both arrhythmia and non-arrhythmia patients); the training input comprises one or more static variables and time series data of a target variable and one or more time-dependent unknown variables in an observation window (e.g. Par. [0024]; Pars. [0049]-[0050]); and the training ground truth comprises time series data of the target variable in a forecast window (e.g. Pars. [0049]-[0050]).
Regarding claim 2, Ravishankar further discloses wherein the training input further comprises time series data of one or more time-dependent known variables in the observation window and the forecast window (e.g. Par. [0038]: ECG is known time-dependent variable).
Regarding claim 3, Ravishankar further discloses wherein the attention-based architecture is temporal fusion transformer (e.g. Par. [0040]).
Regarding claim 4, Ravishankar further discloses wherein the multiple second patients have critical conditions occur in the observation window or the forecast window (e.g. Par. [0049]).
Regarding claim 5, Ravishankar further discloses wherein the critical conditions comprise cardiac arrest, shock, and/or respiratory failure (e.g. Abstract: arrhythmia; Par. [0049]).
Regarding claim 6, Ravishankar further discloses wherein the one or more static variables comprise at least one of a comorbidity label, a BMI value, an oxygen supply status, and a state of consciousness (e.g. Par. [0024]: oxygen saturation).
Regarding claim 7, Ravishankar further discloses wherein the target variable is selected from a group of vital signs consisting of heart rate, respiratory rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, and blood oxygen saturation (e.g. Par. [0024]; Par. [0047]).
Regarding claim 8, Ravishankar further discloses wherein the one or more time-dependent unknown variables comprises the group of vital signs which are not selected as the target variable (e.g. Par. [0047]).
Regarding claim 9, Ravishankar further discloses wherein the observation window comprises 24 consecutive past time points (e.g. Par. [0049]; Par. [0070]).
Regarding claim 10, Ravishankar further discloses wherein the forecast window comprises 12 consecutive future time points (e.g. Par. [0070]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Srinivasan et al. (WO 2025/231190) is directed towards machine learning techniques for predicting physiological changes and adverse events.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHREYA P ANJARIA whose telephone number is (571)272-9083. The examiner can normally be reached M-F: 8:00-5:00 EST.
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/SHREYA ANJARIA/Examiner, Art Unit 3796
/ALLEN PORTER/Primary Examiner, Art Unit 3796
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