DETAILED ACTION
Notice to Applicant
This communication is in response to the application submitted March 10, 2023. Claims 1 – 20 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 .
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 – 20 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 – 20 are drawn to a method, system ,and non-transitory computer-readable medium, which is/are statutory categories of invention (Step 1: YES).
Step 2A Prong One
Independent claims 1, 14, and 17 recite a method for improved provision of health alerts associated with patients, the method 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; comparing the aggregate score to a threshold; and providing an alert to a user based on the comparing.
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”
Claim 2: “wherein the machine learning model was trained based at least in part on hospitalization events”
Claim 4: “wherein the machine learning model was trained based at least in part on medical events”
Claim 5: “wherein the machine learning model was trained using a plurality of training readings, wherein each training reading was assigned a ground truth label based on whether the training reading occurred during a predetermined period of time before a medical event”
Claims 8 – 11: “one or more processors”
Claim 12: “one or more processors”, “machine learning model”
Claim 14: “system”, “a memory having processor-readable instructions stored therein”, “a processor configured to access the memory and execute the processor-readable instructions to perform operations”, “machine learning model”
Claim 15: “system”, “wherein the machine learning model was trained based at least in part on medical events”
Claim 16: “system”
Claim 17: “non-transitory computer-readable medium storing a set of instructions that, when executed by a processor, perform operations”, “machine learning model”
Claim 18: “computer-readable medium”, “wherein the machine learning model was trained based at least in part on medical events”
Claim 19: “computer-readable medium”
Claim 20: “computer-readable medium”, “wherein the machine learning model was trained using a plurality of training readings, wherein each training reading was assigned a ground truth label based on whether the training reading occurred during a predetermined period of time before a medical event”
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 – 13, 15 – 16, and 18 – 20 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
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.
Claim(s) 1 – 4, 7, 11, and 14 – 19 is/are rejected 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).
Claim 1. Zhan teaches a computer-implemented method for improved provision of health alerts associated with patients, the method comprising:
receiving, by one or more processors, a first reading for a first biometric parameter for a first patient (Column 14, lines 33 – 35 discloses the plurality of features comprises medical chart information (e.g., most recent heart rates, blood pressures));
applying, by the one or more processors, a plurality of algorithms that determine a plurality of first scores, a plurality of algorithms respectively, for the first reading, wherein each of the plurality of algorithms uses different logic (column 16, line 33 through column 17, line 11 discloses one or more models for obtaining a risk value for a medical event (e.g., model(s) for predicting whether an ED visit will occur), one or more models for obtaining an interceptability value for the medical event (e.g., model(s) for predicting whether the ED) visit is preventable), one or more models for obtaining clinical factors underlying the medical event (e.g., model(s) for predicting clinical factors underlying the ED visit));
determining, by the one or more processors and using a machine learning model, an aggregate score based on the determined plurality of first scores and on a learned weighting of the plurality of algorithms (column 19, lines 9 – 21 discloses if the aggregation method is a weighted average, the weights can be uniform (i.e., each model gets the same weight), or weights can be determined by factors such as the performance of the models, with better-performing models receiving greater weight and can be implemented using a deep learning approach);
comparing, by the one or more processors, the aggregate score to a threshold (column 16, lines 23 – 26 discloses the plurality of outcomes includes percent change in utilization, absolute change in utilization, whether or not these changes were above a certain threshold, and the main health issue driving the utilization increase; column 17, line 65 through column 18, line discloses the thresholds are different for each ranking of reason (e.g. the top most likely reason has a threshold that is lower than the second most likely reason) so that the system is more likely to output at least one reason, and only outputs additional reasons if the system is more confident in their likelihoods); and
providing, by the one or more processors, an alert to a user based on the comparing (column 9, line 50 through column 10, line 28 discloses a user interface integrated with existing platforms (e.g. alert systems) and databases (e.g. EMR), a risk value obtained based on the patient’s health parameters and one or more machine learning algorithms is displayed on a user interface; column 21, lines 65 – 67 discloses an output device which can be any suitable device that provides output, such as a touch screen, haptics device, or speaker).
Claim 2. Zhan teaches the method of claim 1, wherein the machine learning model was trained based at least in part on hospitalization events (column 7, lines 6 – 15 discloses using machine learning to provide personalized prediction and prevention of various types of medical events, including hospitalization).
Claim 3. Zhan teaches the method of claim 1, wherein each first score indicates a probability of hospitalization based on the first reading (column 12, lines 9 – 12 discloses the data obtained may include biometric data; column 17, lines 12 – 26 discloses a model is trained to receive a set of values and output a probabilistic value indicating whether the patient is likely to make an emergency department (hospitalization) visit).
System and storage claims 16 and 19 repeat the subject matter of claim 3. As the underlying processes of claims 16 and 19 have been shown to be fully disclosed by the teachings of Zhan in the above rejections of claim 3; as such, these limitations (16 and 19) are rejected for the same reasons given above for claim 3 and incorporated herein.
Claim 4. Zhan teaches the method of claim 1, wherein the machine learning model was trained based at least in part on medical events (column 7, lines 6 – 15 discloses using machine learning to provide personalized prediction and prevention of various types of medical events, including hospitalization).
System and storage claims 15 and 18 repeat the subject matter of claim 4. As the underlying processes of claims 15 and 18 have been shown to be fully disclosed by the teachings of Zhan in the above rejections of claim 4; as such, these limitations (15 and 18) are rejected for the same reasons given above for claim 4 and incorporated herein.
Claim 7. Zhan teaches the method of claim 1, wherein the user is the first patient or a health care provider (column 9, lines 64 – 66 discloses the user can be nurses, doctors, population health managers, actuaries, and administrators).
Claim 11. Zhan teaches the method of claim 1, further comprising receiving, by the one or more processors, additional information for the first patient, wherein the aggregate score is based on the received additional information (column 20, lines 56 – 58 discloses the system automatically refreshes the displayed predictions on the user interface as updated data (additional data) on the patients is collected).
Claim 14. Zhan teaches a system for improved provision of health alerts associated with patients, the system comprising:
a memory having processor-readable instructions stored therein (column 3, lines 5 – 10 discloses a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions); and
a processor configured to access the memory and execute the processor-readable instructions to perform operations (column 3, lines 5 – 10 discloses a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions) comprising:
receiving a first reading for a first biometric parameter for a first patient (column 14, lines 33 – 35 discloses the plurality of features comprises medical chart information (e.g., most recent heart rates, blood pressures));
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 (column 16, line 33 through column 17, line 11 discloses one or more models for obtaining a risk value for a medical event (e.g., model(s) for predicting whether an ED visit will occur), one or more models for obtaining an interceptability value for the medical event (e.g., model(s) for predicting whether the ED) visit is preventable), one or more models for obtaining clinical factors underlying the medical event (e.g., model(s) for predicting clinical factors underlying the ED visit));
determining, using a machine learning model, an aggregate score based on the determined plurality of first scores and on a learned weighting of the plurality of algorithms (column 19, lines 9 – 21 discloses if the aggregation method is a weighted average, the weights can be uniform (i.e., each model gets the same weight), or weights can be determined by factors such as the performance of the models, with better-performing models receiving greater weight and can be implemented using a deep learning approach);
comparing the aggregate score to a threshold (column 16, lines 23 – 26 discloses the plurality of outcomes includes percent change in utilization, absolute change in utilization, whether or not these changes were above a certain threshold, and the main health issue driving the utilization increase; column 17, line 65 through column 18, line discloses the thresholds are different for each ranking of reason (e.g. the top most likely reason has a threshold that is lower than the second most likely reason) so that the system is more likely to output at least one reason, and only outputs additional reasons if the system is more confident in their likelihoods); and
providing an alert to a user based on the comparing (column 9, line 50 through column 10, line 28 discloses a user interface integrated with existing platforms (e.g. alert systems) and databases (e.g. EMR), a risk value obtained based on the patient’s health parameters and one or more machine learning algorithms is displayed on a user interface; column 21, lines 65 – 67 discloses an output device which can be any suitable device that provides output, such as a touch screen, haptics device, or speaker).
Claim 17. Zhan teaches a non-transitory computer-readable medium storing a set of instructions that, when executed by a processor, perform operations (column 3, lines 22 – 26 discloses non-transitory computer-readable storage medium stores one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of an electronic device having a display) for improved provision of health alerts associated with patients, the operations comprising:
receiving a first reading for a first biometric parameter for a first patient (column 14, lines 33 – 35 discloses the plurality of features comprises medical chart information (e.g., most recent heart rates, blood pressures));
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 (column 16, line 33 through column 17, line 11 discloses one or more models for obtaining a risk value for a medical event (e.g., model(s) for predicting whether an ED visit will occur), one or more models for obtaining an interceptability value for the medical event (e.g., model(s) for predicting whether the ED) visit is preventable), one or more models for obtaining clinical factors underlying the medical event (e.g., model(s) for predicting clinical factors underlying the ED visit));
determining, using a machine learning model, an aggregate score based on the determined plurality of first scores and on a learned weighting of the plurality of algorithms (column 19, lines 9 – 21 discloses if the aggregation method is a weighted average, the weights can be uniform (i.e., each model gets the same weight), or weights can be determined by factors such as the performance of the models, with better-performing models receiving greater weight and can be implemented using a deep learning approach);
comparing the aggregate score to a threshold (column 16, lines 23 – 26 discloses the plurality of outcomes includes percent change in utilization, absolute change in utilization, whether or not these changes were above a certain threshold, and the main health issue driving the utilization increase; column 17, line 65 through column 18, line discloses the thresholds are different for each ranking of reason (e.g. the top most likely reason has a threshold that is lower than the second most likely reason) so that the system is more likely to output at least one reason, and only outputs additional reasons if the system is more confident in their likelihoods); and
providing an alert to a user based on the comparing (column 9, line 50 through column 10, line 28 discloses a user interface integrated with existing platforms (e.g. alert systems) and databases (e.g. EMR), a risk value obtained based on the patient’s health parameters and one or more machine learning algorithms is displayed on a user interface; column 21, lines 65 – 67 discloses an output device which can be any suitable device that provides output, such as a touch screen, haptics device, or speaker).
Claim Rejections - 35 USC § 103
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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 8 – 10 and 12 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).
Claim 8. Zhan teaches the method of claim 1.
Zhan fails to explicitly teach the following limitations met by Daniels as cited:
further comprising providing, by the one or more processors, an explanation for the alert based on the learned weighting of the plurality of algorithms and the aggregate score (paragraph 100 discloses if a change in patient parameters exceeds a threshold, and if so, an alert is sent; paragraph 101 discloses the notification (alert) may include the suggested diagnosis and current treatment options (explanation)).
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to expand the method of Zhan to further include a wearable electronic that detects a change in a physical condition of a patient and compares it with a baseline biometric.as disclosed by Daniels.
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to expand the method of Zhan in this way to provide allows for real-time monitoring and personalized treatment for patients, leading to improved patient outcomes and reduced healthcare costs (Daniels: paragraph 26).
Claim 9. Zhan teaches the method of claim 1.
Zhan fails to explicitly teach the following limitations met by Daniels as cited:
further comprising: ranking, by the one or more processors, the plurality of algorithms based on a contribution of each algorithm to the aggregate score; and providing a list of algorithms based on the ranking (Figure 1; paragraph 90 discloses an algorithm for activating an action based on comparison of a baseline biometric versus monitored biometrics; paragraph 103 discloses determining the at least one patient-specific threshold may further comprise applying a statistical weighting to each of the one or more monitored biometric parameters, where the statistical weighting is dependent on a predetermined value of a ranking of importance in detecting each of the at least one physiological change for said each of the one or more monitored biometric parameters relative to others of the one or more monitored biometric parameters; paragraph 126 discloses Fig 6 illustrates an algorithm for applied probabilistic analysis to determine a concerning physiological change; paragraph 136 discloses Fig 7 illustrates an algorithm for an early warning system with applied probabilistic analysis of multiple biometric parameters; paragraph 143 discloses the threshold for triggering an alert (e.g. score) is predetermined or calculated for each biometric reading and alerts are sent if any analyzed change exceeds the threshold; paragraph 146 discloses Fig 8 illustrates an algorithm for a single parameter early warning system).
The motivation to combine the teachings of Zhan and Daniels is discussed in the rejection of claim 8, and incorporated herein.
Claim 10. Zhan teaches the method of claim 1. Zhan teaches a method further comprising: receiving, by the one or more processors, a second reading for a second biometric parameter for the first patient (column 2, lines 33 – 35 discloses a second single model for predicting a risk, a reason, and interceptability for a second medical event).
Zhan fails to explicitly teach the following limitations met by Daniels as cited:
applying, by the one or more processors, the plurality of algorithms to determine a plurality of second scores, respectively, for the second reading, wherein the determined aggregate score is further based on the plurality of second scores (Figure 1; paragraph 90 discloses an algorithm for activating an action based on comparison of a baseline biometric versus monitored biometrics; paragraph 126 discloses Fig 6 illustrates an algorithm for applied probabilistic analysis to determine a concerning physiological change; paragraph 136 discloses Fig 7 illustrates an algorithm for an early warning system with applied probabilistic analysis of multiple biometric parameters; paragraph 143 discloses the threshold for triggering an alert (e.g. score) is predetermined or calculated for each biometric reading and alerts are sent if any analyzed change exceeds the threshold; paragraph 146 discloses Fig 8 illustrates an algorithm for a single parameter early warning system).
The motivation to combine the teachings of Zhan and Daniels is discussed in the rejection of claim 8, and incorporated herein.
Claim 12. Zhan teaches the method of claim 1. Zhan teaches a method further comprising: receiving, by the one or more processors, a second reading for a second patient (column 4, lines 36 – 37 discloses a second feature set corresponding to a second patient);
determining, by the one or more processors and using the machine learning model, a secondary aggregate score for the second patient based on the determined plurality of second scores (column 6, lines 11 – 18 discloses train a second machine-learning model based on the set of training data, wherein the second machine learning model is configured to receive the plurality of features of the given patient and output a probabilistic value);
ranking, by the one or more processors, the aggregate score and the secondary aggregate score (column 17, line 62 through column 18, line 3 discloses outputting the top reasons based on these probabilistic values, potentially omitting reasons whose associated probabilities are below a specific threshold. In some embodiments, the thresholds are different for each ranking of reason ( e.g., the top most likely reason has a threshold that is lower than the second most likely reason) so that the system is more likely to output at least one reason, and only outputs additional reasons if the system is more confident in their likelihoods); and
providing, by the one or more processors, the aggregate score and the secondary aggregate score based on the ranking (column 18, lines 1 – 3 discloses outputting (providing) at least one reason, and only outputs additional reasons if the system is more confident in their likelihoods).
Zhan fails to explicitly teach the following limitations met by Daniels as cited:
applying, by the one or more processors, the plurality of algorithms that determine a plurality of second scores, respectively, to the second reading (Figure 1; paragraph 90 discloses an algorithm for activating an action based on comparison of a baseline biometric versus monitored biometrics; paragraph 126 discloses Fig 6 illustrates an algorithm for applied probabilistic analysis to determine a concerning physiological change; paragraph 136 discloses Fig 7 illustrates an algorithm for an early warning system with applied probabilistic analysis of multiple biometric parameters; paragraph 143 discloses the threshold for triggering an alert (e.g. score) is predetermined or calculated for each biometric reading and alerts are sent if any analyzed change exceeds the threshold; paragraph 146 discloses Fig 8 illustrates an algorithm for a single parameter early warning system).
The motivation to combine the teachings of Zhan and Daniels is discussed in the rejection of claim 8, and incorporated herein.
Claim 13. Zhan teaches the method of claim 1.
Zhan fails to explicitly teach the following limitations met by Daniels as cited:
wherein the threshold is based on a user input and/or a predetermined alert frequency (paragraph 27 discloses a device determines patient-specific thresholds for monitored biometric parameters (user input) based on the stored baseline data).
The motivation to combine the teachings of Zhan and Daniels is discussed in the rejection of claim 8, and incorporated herein.
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).
Claim 5. Zhan teaches the method of claim 1.
Zhan fails to explicitly teach the following limitations met by Daniels as cited:
wherein the machine learning model was trained using a plurality of training readings (paragraph 128 specific biometric data from a human or animal body is used for training artificial intelligence and machine learning (AI/ML) type of algorithms).
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to expand the method of Zhan to further include a wearable electronic that detects a change in a physical condition of a patient and compares it with a baseline biometric.as disclosed by Daniels.
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to expand the method of Zhan in this way to provide allows for real-time monitoring and personalized treatment for patients, leading to improved patient outcomes and reduced healthcare costs (Daniels: paragraph 26).
Zhan and Daniels fail to explicitly teach the following limitations met by Gederi as cited:
wherein each training reading was assigned a ground truth label based on whether the training reading occurred during a predetermined period of time before a medical event (Figure 4; column 5, lines 29 – 42 discloses training a Machine Learning model to recognize different respiratory states comprising the steps of providing a training dataset of input images with corresponding ground truth labels; column 5, lines 42 – 44 discloses providing at least one image from the training set to a respiratory state predictor to output a respiratory state prediction, which is interpreted as performed prior to a medical event).
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to expand the method of Zhan and Daniels to further include Computer Aided Diagnosis (CADx) systems and methods for assisting the interpretation of medical images to support clinicians in healthcare as disclosed by Gederi.
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to expand the method of Zhan and Daniels in this way to provide a method of training a Machine Learning model to recognize different respiratory states comprising the steps of providing a training dataset of input images with corresponding ground truth labels by providing at least one image from the training set to a respiratory state predictor to output a respiratory state prediction, comparing the respiratory state prediction with the corresponding ground truth label to determine the accuracy of the machine learning model, repeating the above steps until the variation between the prediction and the ground truth level reaches a pre-set level (Gederi: column 5, lines 38 - 48).
Claim 6. Zahn, Daniels, and Gederi teach the method of claim 5.
Daniels fails to explicitly teach the following limitations met by Zhan as cited:
wherein the predetermined period of time is a calculated admission window, and the medical event is an admission date to a hospital (column 2, lines 51 – 53 discloses a probabilistic value indicative of the likelihood of the future medical event occurring within a predetermined time period; column 4, lines 13 – 14 discloses a medical event is hospital admission; column 14, lines 21 – 28 discloses the length of inpatient admissions).
The motivation to combine the teachings of Zhan, Daniels and Gederi is discussed in the rejection of claim 5, and incorporated herein.
Storage claim 20 repeats the subject matter of claim 5. As the underlying processes of claim 20 has been shown to be fully disclosed by the teachings of Daniels, Zhan, and Gederi in the above rejections of claim 5; as such, these limitations (claim 20) is rejected for the same reasons given above for claim 5 and incorporated herein.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Sprigg et al. (U.S. Publication Number 2013/0278414 A1) discloses a system, methods and server for monitoring health and safety of individuals in a population and sending alert notifications when exceptions are detected include comparing biometric data obtained from the individuals to a biometric model generated for the individual through computer-learning methods .
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