DETAILED ACTION
Notice to Applicant
This communication is in response to the Request for Continued Examination (RCE) submitted June 18, 2026. Claims 1, 14, and 17 are amended. Claims 4 and 8 were previously cancelled. Claims 21 – 22 are new. Claims 1 – 3, 5 – 7, and 9 - 22 are pending.
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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 June 18, 2026 has been entered.
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 – 3, 5 – 7, and 9 - 22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step One
Claims 1 – 3, 5 – 7, and 9 - 22 are drawn to a method, system ,and non-transitory computer-readable media, which is/are statutory categories of invention (Step 1: YES).
Step 2A Prong One
Independent claims 1, 14, and 17 recite a method for comprising: receiving a first reading for a first biometric parameter for a first patient; applying a plurality of algorithms that determine a plurality of first scores, respectively, for the first reading, wherein each of the plurality of algorithms uses different logic; determining an aggregate score based on the determined plurality of first scores and on a weighting of the plurality of algorithms; applying the plurality of algorithms to historical patient data of a plurality of individuals other than the first patient and apply to medical events associated with the plurality of individuals; apply the weighting to the plurality of first scores such that the aggregate score is indicative of whether an alert should be raised and reduces the false positive alerts relative to application of any individual algorithm of the plurality of algorithms; comparing the aggregate score to a threshold; and providing an alert to a user.
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity, as reflected in the specification, which states that “present disclosure relate generally to systems and methods for remote patient monitoring, and more particularly to, systems, computer implemented methods, and non-transitory computer readable mediums for balancing alerting algorithms” (paragraph 1 of the published specification). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The present claims cover certain methods of organizing human activity because they “provide a personalized, automated, and explainable alerting system that reduces health care provider's alert fatigue through combining different algorithms, each of which is designed to extract different types of risky patterns from data.” (paragraph 46 of the published specification). Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).”
Step 2A Prong Two
This judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including:
Claim 1: “computer implemented”, “one or more processors”, “machine learning model”, “learned”, “trained”, “training outputs”, “training ground truth labels”, “controlling generation of the alert, wherein: based on determining the aggregate score does not satisfy the threshold, suppressing the generation of the alert; or based on determining the aggregate score satisfies the threshold, generating the alert”, “extracting the learned weighting of the plurality of algorithms by applying a feature extractor to the machine learning model’, “determining, by the one or more processors, an explanation for the alert, the explanation being based on the extracted learned weighting of the plurality of algorithms and indicative of a reasoning process of the machine learning model”, “visual indication”
Claim 5: “a plurality of training readings, wherein the ground truth labels are indicative of whether the training reading occurred during a predetermined period of time before a medical event”
Claims 9 – 11: “one or more processors”
Claim 12: “one or more processors”, “machine learning model”
Claim 14: “system”, “memory having processor-readable instructions stored therein”, “processors configured to access the memory and execute the processor-readable instructions to perform operations”, “machine learning model”, “trained”, “training outputs”, “training ground truth labels”, “controlling generation of the alert, wherein: based on determining the aggregate score does not satisfy the threshold, suppressing the generation of the alert; or based on determining the aggregate score satisfies the threshold, generating the alert”, “extracting the learned weighting of the plurality of algorithms by applying a feature extractor to the machine learning model’, “determining, by the one or more processors, an explanation for the alert, the explanation being based on the extracted learned weighting of the plurality of algorithms and indicative of a reasoning process of the machine learning model”, “visual indication”
Claims 15 – 16: “system”
Claim 17: “non-transitory computer-readable media storing a set of instructions that, when executed by one or more processors, perform operations”, “machine learning model”, “trained”, “training outputs”, “training ground truth labels”, “controlling generation of the alert, wherein: based on determining the aggregate score does not satisfy the threshold, suppressing the generation of the alert; or based on determining the aggregate score satisfies the threshold, generating the alert”, “extracting the learned weighting of the plurality of algorithms by applying a feature extractor to the machine learning model’, “determining, by the one or more processors, an explanation for the alert, the explanation being based on the extracted learned weighting of the plurality of algorithms and indicative of a reasoning process of the machine learning model”, “visual indication”
Claims 18 – 19: “computer-readable media”
Claim 20: “computer-readable media”, “a plurality of training readings, wherein the ground truth labels are indicative of whether the training reading occurred during a predetermined period of time before a medical event”
Claim 22: “one or more processors”
These features are additional elements that are recited at a high level of generality such that they amount to no more than mere instruction to apply the exception using generic computer components. See: MPEP 2106.05(f).
The additional elements are merely incidental or token additions to the claim that do not alter or affect how the process steps or functions in the abstract idea are performed. Therefore, the claimed additional elements do not add meaningful limitations to the indicated claims beyond a general linking to a technological environment. See: MPEP 2106.05(h).
The combination of these additional elements is no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Hence, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea (Step 2A Prong Two: NO).
Step 2B
The claims do 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, using the additional elements to perform the abstract idea amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using a generic components cannot provide an inventive concept. See MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are not integrated into the claim because they are merely incidental or token additions to the claim that do not alter or affect how the process steps or functions in the abstract idea are performed. Therefore, the claimed additional elements do not add meaningful limitations to the indicated claims beyond a general linking to a technological environment. See: MPEP 2106.05(h).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See: MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea. The published specification supports this conclusion as follows:
[0054] As used herein, a "machine-learning model" generally encompasses instructions, data, and/or a model configured to receive input, and apply one or more of a weight, bias, classification, or analysis on the input to generate an output. The output may include, for example, an analysis based on the input, a prediction, suggestion, or recommendation associated with the input, a dynamic action performed by a system, or any other suitable type of output. A machine-learning model is generally trained using training data, e.g., experiential data and/or samples of input data, which are fed into the model in order to establish, tune, or modify one or more aspects of the model, e.g., the weights, biases, criteria for forming classifications or clusters, or the like. Aspects of a machine-learning model may operate on an input linearly, in parallel, via a network ( e.g., a neural network), or via any suitable configuration.
[0055] The execution of the machine-learning model may include deployment of one or more machine-learning techniques, such ask-nearest neighbors, linear regression, logistical regression, random forest, gradient boosted machine (GBM), support-vector machine, deep learning, text classifiers, image recognition classifiers, You Only Look Once (YOLO), a deep neural network, greedy matching, propensity score matching, and/or any other suitable machine learning technique that solves problems specifically addressed in the current disclosure. Supervised, semi-supervised, and/or unsupervised training may be employed. For example, supervised learning may include providing training data and labels corresponding to the training data, e.g., as ground truth. Unsupervised approaches may include clustering, classification, principal component analysis (PCA) or the like. K-means clustering or K-Nearest Neighbors may also be used, which may be supervised or unsupervised. Combinations of K-Nearest Neighbors and an unsupervised cluster technique may also be used. Other models for detecting objects in contents/files, such as documents, images, pictures, drawings, and media files may be used as well. Any suitable type of training may be used, e.g., stochastic, gradient boosted, random seeded, recursive, epoch or batch-based, etc.
[0062] The user device 104 may include any electronic equipment, controlled by a processor (e.g., central processing unit (CPU)), for inputting information or data and displaying a user interface. A computing device or user device can send or receive signals, such as via a wired or wireless network, or can process or store signals, such as in memory as physical memory states. A user device may include, for example: a desktop computer; a mobile computer ( e.g., a tablet computer, a laptop computer, or a notebook computer); a smartphone; a wearable computing device (e.g., smart watch); or the like, consistent with the computing devices shown in FIG. 19.
[0063] The server device 106 may include a service point which provides, e.g., processing, database, and communication facilities. By way of example, and not limitation, the term "server device" can refer to a single, physical processor with associated communications and data storage and database facilities, or it can refer to a networked or clustered complex of processors, such as an elastic computer cluster, and associated network and storage devices, as well as operating software and one or more database systems and application software that support the services provided by the server. The server device 106, for example, can be a cloud-based server, a cloud-computing platform, or a virtual machine. Server devices 106 can vary widely in configuration or capabilities, but generally a server can include one or more central processing units and memory. A server device 106 can also include one or more mass storage devices, one or more power supplies, one or more wired or wireless network interfaces, one or more input/output interfaces, or one or more operating systems, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, or the like.
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea with routine, conventional activity specified at a high level of generality in a particular technological environment.
Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea (Step 2B: NO).
Dependent claim(s) 2 – 3, 5 – 7, 9 – 13, 15 – 16, and 18 – 22 when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
Claim Rejections - 35 USC § 102
The rejection of Claim(s) 1 – 3, 7, 11, and 14 – 19 under 35 U.S.C. 102(a)(1) as being anticipated by Zhan et al., herein after Zhan (U.S. Patent Number 11,610,679 B1) were withdrawn in the Office Action mailed March 20, 2026.
Claim Rejections - 35 USC § 103
The rejection of Claim(s) 9 – 10 and 12 under 35 U.S.C. 103 as being unpatentable over Zhan et al., herein after Zhan (U.S. Patent Number 11,610,679 B1) in view of Daniels (WO 2023/196805 A2) were withdrawn in the Office Action mailed March 20, 2026.
The rejection of Claim(s) 5, 6, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhan et al., herein after Zhan (U.S. Patent Number 11,610,679 B1) in view of Daniels (WO 2023/196805 A2) further in view of Gederi et al., herein after Gederi (U.S. Patent Number 12,154,270 B2) were withdrawn in the Office Action mailed March 20, 2026.
Response to Arguments
Applicant's arguments filed June 18, 2026 have been fully considered but they are not persuasive. The Applicant’s arguments have been addressed in the order in which they were presented.
Section 101 Rejections
The Applicant argues the claims do not recite a judicial exception. The Examiner disagrees. Under its broadest reasonable interpretation, the Applicant’s claims are an abstract idea that falls into the grouping of “Certain Methods of Organizing Human Activity” which covers fundamental economic principles or practices, commercial or legal interactions, or managing personal behavior or relationships or interactions between people. The Examiner respectfully submits that the PEG (Patent Eligibility Guidelines) of January 2019 recite that “Certain Methods of Organizing Human Activity” include managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions. The present claims recite the abstract idea of reducing health care provider's alert fatigue through combining different algorithms, each of which is designed to extract different types of risky patterns from data. The present claims recite receiving a first reading for a first biometric parameter for a first patient; applying a plurality of algorithms that determine a plurality of first scores, respectively, for the first reading, wherein each of the plurality of algorithms uses different logic; determining an aggregate score based on the determined plurality of first scores and on a weighting of the plurality of algorithms; applying the plurality of algorithms to historical patient data of a plurality of individuals other than the first patient and apply to medical events associated with the plurality of individuals; apply the weighting to the plurality of first scores such that the aggregate score is indicative of whether an alert should be raised and reduces the false positive alerts relative to application of any individual algorithm of the plurality of algorithms; comparing the aggregate score to a threshold; and providing an alert to a user. These features describe interactions with people, thus “Certain Methods of Organizing Human Activity”. Thus, if a claim limitation, under its broadest reasonable interpretation, covers interactions with people, but for the recitation of generic components, then it is still in the “Certain Methods of Organizing Human Activity” grouping.
The Applicant argues the claims integrate the Judicial Exception into a practical application. The Examiner respectfully disagrees. The additional elements of the present claims fail to integrate the exception into a practical application of the exception. The 2019 PEG defines the phrase “integration into a practical application” to require 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 it is more than a drafting effort designed to monopolize the exception. For example, the 2019 PEG guidelines recite limitations that are indicative of integration into a practical application when recited in a claim with a judicial exception include:
Improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e) and the Vanda Memo issued in June 2018.
The present claims fail to demonstrate an improvement to the functioning of a computer or to any other technology or technical field. Thus, Applicant’s argument is not persuasive, and the rejection is maintained.
The Applicant argues the present claims recite a specific ordered combination of operations that changes how a computer system performs alert generation and analysis in a patient monitoring system. The Examiner respectfully disagrees. The Examiner respectfully disagrees. The claim fails to improve the recited technological field; the steps of the claim are directed to reducing health care provider's alert fatigue through combining different algorithms, each of which is designed to extract different types of risky patterns from data. The focus of the claims is not on such an improvement in computers as tools, but on abstract ideas that use computers as tools. The claims here do not require any nonconventional computer, network or display components, or even a "non-conventional and non-generic arrangement of known, conventional pieces”. This conclusion is supported by the Applicant’s published specification:
[0054] As used herein, a "machine-learning model" generally encompasses instructions, data, and/or a model configured to receive input, and apply one or more of a weight, bias, classification, or analysis on the input to generate an output. The output may include, for example, an analysis based on the input, a prediction, suggestion, or recommendation associated with the input, a dynamic action performed by a system, or any other suitable type of output. A machine-learning model is generally trained using training data, e.g., experiential data and/or samples of input data, which are fed into the model in order to establish, tune, or modify one or more aspects of the model, e.g., the weights, biases, criteria for forming classifications or clusters, or the like. Aspects of a machine-learning model may operate on an input linearly, in parallel, via a network ( e.g., a neural network), or via any suitable configuration.
[0055] The execution of the machine-learning model may include deployment of one or more machine-learning techniques, such ask-nearest neighbors, linear regression, logistical regression, random forest, gradient boosted machine (GBM), support-vector machine, deep learning, text classifiers, image recognition classifiers, You Only Look Once (YOLO), a deep neural network, greedy matching, propensity score matching, and/or any other suitable machine learning technique that solves problems specifically addressed in the current disclosure. Supervised, semi-supervised, and/or unsupervised training may be employed. For example, supervised learning may include providing training data and labels corresponding to the training data, e.g., as ground truth. Unsupervised approaches may include clustering, classification, principal component analysis (PCA) or the like. K-means clustering or K-Nearest Neighbors may also be used, which may be supervised or unsupervised. Combinations of K-Nearest Neighbors and an unsupervised cluster technique may also be used. Other models for detecting objects in contents/files, such as documents, images, pictures, drawings, and media files may be used as well. Any suitable type of training may be used, e.g., stochastic, gradient boosted, random seeded, recursive, epoch or batch-based, etc.
[0062] The user device 104 may include any electronic equipment, controlled by a processor (e.g., central processing unit (CPU)), for inputting information or data and displaying a user interface. A computing device or user device can send or receive signals, such as via a wired or wireless network, or can process or store signals, such as in memory as physical memory states. A user device may include, for example: a desktop computer; a mobile computer ( e.g., a tablet computer, a laptop computer, or a notebook computer); a smartphone; a wearable computing device (e.g., smart watch); or the like, consistent with the computing devices shown in FIG. 19.
[0063] The server device 106 may include a service point which provides, e.g., processing, database, and communication facilities. By way of example, and not limitation, the term "server device" can refer to a single, physical processor with associated communications and data storage and database facilities, or it can refer to a networked or clustered complex of processors, such as an elastic computer cluster, and associated network and storage devices, as well as operating software and one or more database systems and application software that support the services provided by the server. The server device 106, for example, can be a cloud-based server, a cloud-computing platform, or a virtual machine. Server devices 106 can vary widely in configuration or capabilities, but generally a server can include one or more central processing units and memory. A server device 106 can also include one or more mass storage devices, one or more power supplies, one or more wired or wireless network interfaces, one or more input/output interfaces, or one or more operating systems, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, or the like.
The claims use existing computers as tools in aid of processes focused on abstract Ideas. Taken alone or as an ordered combination, these additional elements (“computer implemented”, “one or more processors”, “machine learning model”, “learned”, “trained”, “training outputs”, “training ground truth labels”, “controlling generation of the alert, wherein: based on determining the aggregate score does not satisfy the threshold, suppressing the generation of the alert; or based on determining the aggregate score satisfies the threshold, generating the alert”, “extracting the learned weighting of the plurality of algorithms by applying a feature extractor to the machine learning model’, “determining, by the one or more processors, an explanation for the alert, the explanation being based on the extracted learned weighting of the plurality of algorithms and indicative of a reasoning process of the machine learning model”, “visual indication”) do not amount to a claim as a whole that is significantly more than the exception.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KRISTINE K RAPILLO whose telephone number is (571)270-3325. The examiner can normally be reached Monday - Friday 7:30 - 4 pm.
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KRISTINE K. RAPILLO
Examiner
Art Unit 3626
/KRISTINE K RAPILLO/Examiner, Art Unit 3682
Prosecution Insights