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 .
Status of Application
This action is in reply to the reply received January 28, 2026 (hereinafter “Reply”) and the accompanying request for continued examination.
Claims 1, 8, 11, and 18 are amended.
Claims 1-20 are pending.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 January 28, 2026 has been entered.
Claim Rejections - 35 U.S.C. § 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 1-7, 9-17, 19, and 20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. Claims 1-7, 9-17, 19, and 20 are directed to an abstract idea without significantly more as required by the Alice test as discussed below.
Step 1
Claims 1-20 are directed to a process, machine, manufacture, or composition of matter.
Step 2A
Claims 1-20 are directed to abstract ideas, as explained below.
Prong one of the Step 2A analysis requires identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea; and determining whether the identified limitation(s) falls within at least one of the groupings of abstract ideas of mathematical concepts, mental processes, and certain methods of organizing human activity.
The claims recite the following limitations that are directed to abstract ideas. Claim 1 recites encoding a time series with a time series encoder of a joint variational encoder trained to detect anomalies from both time series and event sequences; encoding an event sequence with an event sequence encoder of the joint variational encoder; generating a latent code from outputs of the time series encoder and the event sequence encoder by fusing information from different timestamps of sequences with hidden states of the time series encoder and the event sequence encoder; reconstructing the time series from the latent code using a time series decoder of the joint variational autoencoder; reconstructing the event sequence from the latent code using an event sequence decoder of the joint variational autoencoder; and determining, with the joint variational autoencoder, an anomaly score based on a reconstruction loss of the reconstructed time series and a reconstruction loss of the reconstructed event sequence. Claim 11 recites similar features as claim 1. Claims 2-10 and 12-20 further specify features of the identified abstract ideas.
These limitations describe abstract ideas that correspond to concepts identified as abstract ideas by the courts as mental processes—such as concepts performed in the human mind (including an observation, evaluation, judgment, or opinion)—because the claimed features identified above are concepts performed in the human mind (including an observation, evaluation, judgment, or opinion).
These limitations describe abstract ideas that correspond to concepts identified as abstract ideas by the courts as certain methods of organizing human activity—such as fundamental economic principles or practices (including hedging, insurance, mitigating risk), commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations), managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)—the claimed features identified above are manage personal behavior or relationships or interactions between people including following rules or instructions.
Thus, the concepts set forth in claims 1-20 recite abstract ideas.
Prong two of the Step 2A requires identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the exception into a practical application of the exception. “Integration into a practical application” requires an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. Further, “integration into a practical application” uses the considerations laid out by the Supreme Court and the Federal Circuit to evaluate whether the judicial exception is integrated into a practical application, such as considerations discussed in M.P.E.P. § 2106.05(a)-(h).
The claims recite the following additional elements beyond those identified above as being directed to an abstract idea. Claim 1 recites in its preamble that its method is computer implemented and transmitting instructions to another system (with an intended result that parameters of the other system be altered). Claim 11 recites similar features as claim 1 and adds a hardware processor and a memory. Claims 8 and 18 recite that the corrective action includes a treatment action … to automatically administer an adjusted dosage of a blood pressure treatment to the patient having blood pressure issues.
The identified judicial exception(s) are not integrated into a practical application for the following reasons.
First, evaluated individually, the additional elements do not integrate the identified abstract ideas into a practical application. The additional computer elements identified above—the computer, hardware processor, and memory—are recited at a high level of generality. Inclusion of these elements amounts to mere instructions to implement the identified abstract ideas on a computer. See M.P.E.P. § 2106.05(f). The use of conventional computer elements to transmit instructions to another system is the insignificant, extra-solution activity of mere data gathering or outputting in conjunction with a law of nature or abstract idea. See M.P.E.P. § 2106.05(g). To the extent that the claims transform data, the mere manipulation of data is not a transformation. See M.P.E.P. § 2106.05(c). Inclusion of computing system in the claims amounts to generally linking the use of the judicial exception to a particular technological environment or field of use. See M.P.E.P. § 2106.05(h). Thus, taken alone, the additional elements do not amount to significantly more than a judicial exception.
Second, evaluating the claim 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. See M.P.E.P. § 2106.05(a). Their collective functions merely provide an implementation of the identified abstract ideas on a computer system in the general field of use of healthcare decision making. See M.P.E.P. § 2106.05(h).
Thus, claims 1-7, 9-17, 19, and 20 recite mathematical concepts, mental processes, or certain methods of organizing human activity without including additional elements that integrate the exception into a practical application of the exception.
Accordingly, claims 1-7, 9-17, 19, and 20 are directed to abstract ideas.
Step 2B
Claims 1-7, 9-17, 19, and 20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
The analysis above describes how the claims recite the additional elements beyond those identified above as being directed to an abstract idea, as well as why identified judicial exception(s) are not integrated into a practical application. These findings are hereby incorporated into the analysis of the additional elements when considered both individually and in combination. Additional features of these analyses are discussed below.
Evaluated individually, the additional elements do not amount to significantly more than a judicial exception. In addition to the factors discussed regarding Step 2A, prong two, these additional computer elements also provide conventional computer functions that do not add meaningful limits to practicing the abstract idea. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. The use of generic computer components to transmit instructions to another system is the well-understood, routine, and conventional computer functions of receiving or transmitting data over a network, e.g., the Internet, and does not impose any meaningful limit on the computer implementation of the identified abstract ideas. See M.P.E.P. § 2106.05(d)(II). Thus, taken alone, the additional elements do not amount to significantly more than a judicial exception.
Evaluating the claim limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. In addition to the factors discussed regarding Step 2A, prong two, there is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely amount to mere instructions to implement the identified abstract ideas on a computer.
Thus, claims 1-7, 9-17, 19, and 20, taken individually and as an ordered combination of elements, are not directed to eligible subject matter since they are directed to an abstract idea without significantly more.
Statement Regarding the Prior Art
The independent claim recites features for, inter alia, encoding a time series with a time series encoder of a joint variational encoder trained to detect anomalies from both time series and event sequences; encoding an event sequence with an event sequence encoder of the joint variational encoder; generating a latent code from outputs of the time series encoder and the event sequence encoder by fusing information from different timestamps of sequences with hidden states of the time series encoder and the event sequence encoder; reconstructing the time series from the latent code using a time series decoder of the joint variational autoencoder; reconstructing the event sequence from the latent code using an event sequence decoder of the joint variational autoencoder; and determining, with the joint variational autoencoder, an anomaly score based on a reconstruction loss of the reconstructed time series and a reconstruction loss of the reconstructed event sequence.
Adoni et al. (U.S. Pub. No. 2023/0186053 A1) discloses machine learning based behavior modeling, in which a portion of time-series data is processed using a trained encoder network to generate a dimensionally reduced encoding of the portion of the time-series data; to process the dimensionally reduced encoding using a trained decoder network to determine decoder output data; and to set parameters of a predictive machine-learning model based on the decoder output data, wherein the predictive machine-learning model is configured to, based on the parameters, determine a predicted future value of the time-series data. However, Adoni does not disclose all of the features of the independent claims of the present application.
Filho et al. (U.S. Pub. No. 2023/0094355A1) teaches techniques for training a neural network with enforced monotonicity. One technique includes providing a data model representing a neural network for predicting an outcome based on input data, receiving a feature data as input data; predicting an outcome based on the input data using the neural network; computing a loss function based on the predicted outcome and an expected outcome associated with the input data, the loss function custom-character being dependent on a monotonicity penalty Ω computed based on a set of training data including the feature data and on a set of random data; and updating weights of the neural network based on the loss function. However, Filho does not teach all of the features of the independent claims of the present application.
Sun et al. (U.S. Pat. No. 11,545,255 B2) teaches systems and methods for classifying an anomaly medical image using a variational autoencoder. An example method in Sun includes inputting a medical image into a recognition model, the recognition model configured to: generate one or more attribute distributions that are substantially Gaussian when inputted with a normal image; and generate one or more attribute distributions that are substantially non-Gaussian when inputted with an abnormal image; generating, by the recognition model, one or more attribute distributions corresponding to medical image; generating a marginal likelihood corresponding to the likelihood of a sample image substantially matching the medical image, the sample image generated by sampling, by a generative model, the one or more attribute distributions; and generating a classification by at least: if the marginal likelihood is greater than or equal to a predetermined likelihood threshold, determining the image to be normal; and if the marginal likelihood is less than the predetermined likelihood threshold, determining the image to be abnormal. However, Sun does not teach all of the features of the independent claims of the present application.
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. The following references have been cited to further show the state of the art with respect to techniques for automated healthcare decision making anomaly detection.
Francesco et al. (U.S. Pub. No. 2021/0349897 A1) (anomaly detection system);
Shvetsova et al. (“Anomaly Detection in Medical Imaging With Deep Perceptual Autoencoders,” in IEEE Access, vol. 9, pp. 118571-118583, 2021).
The closest art of record, including the references discussed above, each fail to teach, suggest, or render obvious each and every element of the claims as presently arranged in the claims. Further, based on the evidence of record, it appears as though one of ordinary skill in the art at the time of invention would not look to combine these references, or the closest art of record, to arrive at the present claims, without using impermissible hindsight.
Allowable Subject Matter
Claims 8 and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Response to Arguments
The arguments submitted with the Reply have been fully considered. The amendments obviate the rejections under § 101 as claims 8 and 18. The remaining arguments are not persuasive.
Applicant argues that the claims are not directed to mental processes because “A human mind cannot perform this process at least because a human mind is not equipped to generate a latent code from output of the time series encoder and the event sequence encoder.” Reply, p. 8. Examiner disagrees, because a human could look at a list of times and events (i.e., outputs from time series and event sequence encoders) and determine that a lull in mid-day was due to a lunch break (i.e., generating a latent code). Although this claim features uses language that sound esoteric, the broadest reasonable interpretation of this claim feature includes rudimentary data processing that a person can perform, whether completely mentally or with the aid of paper and pencil.
Applicant also argues that the “there are no features of the present embodiments that are directed to managing personal behavior or relationships or interactions between people.” Reply, p. 8. Examiner disagrees, because insofar as the data processing in the claims involves receiving health data and recommending a corresponding treatment, this amounts to the managing personal behavior or relationships or interactions between people (e.g., a doctor-patient relationship) including following rules or instructions (e.g., rules or instructions for how to analyze the health information to arrive at a recommended course of action).
Applicant argues that the “present embodiments improve the functioning of machine learning models for anomaly detection and decision making the in the medical field with anomaly detection using joint variational autoencoder (VAE) model.” Reply, p. 10. Applicant expands on the arguments on pages 11-12 of the Reply.
Examiner disagrees because an improvement to a mathematical or algorithmic model for making a healthcare decision (e.g., as embodied in a neural network data structure), would be an improvement to features identified as part of the abstract idea—not to a particular technology or technological field. In other words, the alleged improvement would be to the abstract idea, not anything relating to the technical aspects of the claimed invention. See SAP Am., Inc. v. InvestPic, LLC, No. 2017-2081, slip op. at 14 (Fed. Cir. Aug. 2, 2018) (“What is needed is an inventive concept in the non-abstract application realm. … [L]imitation of the claims to a particular field of information … does not move the claims out of the realm of abstract ideas.”). Moreover, “[A] claim for a new abstract idea is still an abstract idea.” Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016) (emphasis added). “[U]nder the Mayo/Alice framework, a claim directed to a newly discovered law of nature (or natural phenomenon or abstract idea) cannot rely on the novelty of that discovery for the inventive concept necessary for patent eligibility ….” Genetic Techs. Ltd. v. Merial L.L.C., 818 F.3d 1369, 1376 (Fed. Cir. 2016) (citations omitted).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Christopher Tokarczyk, whose telephone number is 571-272-9594. The examiner can normally be reached Monday-Thursday between 6:00 AM and 4:00 PM Eastern.
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/CHRISTOPHER B TOKARCZYK/ Primary Examiner, Art Unit 3687