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 .
Notice to Applicant
Receipt of Applicant’s amendments filed March 3, 2025 is acknowledged.
Response to Amendment
Claims 3-5, 7, and 12-14 have been amended. Claims 1-2, 6, and 8-11 have not been modified. Claims 16-20 have been added. Claims 1-20 are pending and are provided to be examined upon their merits.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on July 14, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
However, in view of the excessive volume of references cited, Examiner was only able to give a cursory review of the documents. If Applicant desires any particular references to be specifically considered, Examiner suggests providing a concise list and/or brief explanation of the references considered most relevant.
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.
Subject Matter Eligibility Criteria – Step 1:
The claims recite subject matter within a statutory category as a process and a machine
(claims 1-20). Accordingly, claims 1-20 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria – Step 2A – Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP §2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and /or c) mathematical concepts. MPEP §2106.04(a).
The Examiner has identified device claim 1 as the claim that represents the claimed invention for analysis; method claim 16 being similar to device claim 1.
Claim 1:
A computing device comprising:
communication circuitry configured to wirelessly communicate with a sensor device on a patient or implanted within the patient;
one or more output devices; and
processing circuitry configured to:
receive episode data for an acute health event detected by the sensor device via the communication circuitry, the episode data transmitted by the sensor device in response to detecting the acute health event;
segment the episode data into a plurality of segments, wherein each segment of the plurality of segments consists of a respective portion of the episode data associated with a respective portion of the acute health event;
apply one or more machine learning models to each segment of the plurality of segments of the episode data to determine a respective classification of a plurality of predetermined classifications for each segment of the plurality of segments;
determine a classification of the acute health event from the plurality of predetermined classifications based on the respective classifications of the plurality of segments; and
determine whether to control the one or more output devices to output an alarm based on the classification.
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity under managing personal behaviors of people. The claim elements are directed towards “receiv[ing] episode data for an acute health event”, “segment[ing] the episode data into a plurality of segments”, “determin[ing] a classification of the acute health event from the plurality of predetermined classifications”, and “determin[ing] whether to control the one or more output devices to output an alarm”. Segmenting and classifying health data into health events are human activities typically performed by physicians for their patients. For example, cardiologists regularly analyze and determine arrhythmia types from patient electrocardiogram signals ([0004] of Applicant specification). Under the broadest reasonable interpretation, the claims may provide a set of instructions for a physician to follow when segmenting and classifying received health data.
These claims further recite: mental processes. The claims recite elements, underlined above, that can be performed in the mind of a person, with pen and paper, or using a generic computer. See also MPEP 2106.04(a)(2) III C that teaches generic computer performing an abstract idea can also fall under mental processes. These encompass “receiv[ing] episode data for an acute health event”, “segment[ing] the episode data into a plurality of segments”, “determin[ing] a classification of the acute health event from the plurality of predetermined classifications”, and “determin[ing] whether to control the one or more output devices to output an alarm”.
Accordingly, the claim recites at least one abstract idea.
Claim 16 is found to be abstract for the same reasons.
Subject Matter Eligibility Criteria – Step 2A – Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the idea into a practical application. As noted at MPEP §2106.04 (ID)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
Additional elements cited in the claims:
computing device (1-14); communication circuitry (1-2,16); sensor device (1-2,14-17); one or more output devices (1,16); processing circuitry (1-7,9-10,16); one or more machine learning models (1,5,10-11,16,20); long short-term memory network (11); smartphone (12); Internet of Things device (13); implantable medical device (15)
Any computing devices that would be able to perform the method (computing device, processing circuitry, smartphone, Internet of Things device) are taught at a high level of generality such that the claim elements amounts to no more than mere instructions to apply the exception using any generic component capable of performing the claim limitations. [0204] of Applicant specification recites: “Computing devices 12 retrieve event data and other sensed physiological data from IMD 10 that was collected and stored by the IMD. In some examples, computing devices 12 take the form of personal computing devices of patient 4. For example, computing device 12A may take the form of a smartphone of patient 4, and computing device 12B may take the form of a smartwatch or other smart apparel of patient 4. In some examples, computing devices 12 may be any computing device configured for wireless communication with IMD 10, such as a desktop, laptop, or tablet computer.” [0052] further recites: “environment 28 may include one or more Internet of Things (loT) devices, such as loT devices 30A-30D (collectively “loT devices 30”) illustrated in the example of FIG. 1. loT devices 30 may include, as examples, so called “smart” speakers, cameras, televisions, lights, locks, thermostats, appliances, actuators, controllers, or any other smart home (or building) devices.” No specific, technical improvements are being made to computing devices as generic devices are merely being applied to perform the abstract idea of analyzing patient vital sign data.
Machine learning models (long short-term memory network) are also taught at a high level of generality. [0097] recites: “Example machine learning techniques that may be employed to generate rules 250 (as well as rules 84 and/or 196) can include various learning styles, such as supervised learning, unsupervised learning, and semi-supervised learning. Example types of algorithms include Bayesian algorithms, Clustering algorithms, decision-tree algorithms, regularization algorithms, regression algorithms, instance-based algorithms, artificial neural network algorithms, deep learning algorithms, dimensionality reduction algorithms and the like. Various examples of specific algorithms include Bayesian Linear Regression, Boosted Decision Tree Regression, and Neural Network Regression, Back Propagation Neural Networks, Convolution Neural Networks (CNN), Long Short Term Networks (LSTM), the Apriori algorithm, K-Means Clustering, k-Nearest Neighbour (kNN), Learning Vector Quantization (LVQ), Self-Organizing Map (SOM), Locally Weighted Learning (LWL), Ridge Regression, Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net, and Least-Angle Regression (LARS), Principal Component Analysis (PCA) and Principal Component Regression (PCR).” No specific, technical improvements are being made to the technology of machine learning as generically trained machine learning algorithms are merely applied to perform the abstract idea of classifying patient vital sign data.
Communication circuitry is also taught at a high level of generality. [0097] recites: “Communication circuitry 60 may include any suitable hardware, firmware, software, or any combination thereof for wirelessly communicating with another device, such as computing devices 12 and/or loT devices 30.” No specific, technical improvements are being made to the technology of machine learning as generic communication circuitry is merely applied to perform an insignificant extra-solution activity of transmitting data; MPEP 2106.05(g).
Sensor devices (sensor device, implantable medical device) is also taught at a high level of generality. [0097] recites: “One or more sensors 138 of computing device 12 may sense physiological parameters or signals of patient 4. Sensor(s) 138 may include electrodes, accelerometers (e.g., 3-axis accelerometers), an optical sensor, impedance sensors, temperature sensors, pressure sensors, heart sound sensors (e.g., microphones), and other sensors, and sensing circuitry (e.g., including an ADC), similar to those described above with respect to IMD 10 and FIG. 2.” No specific, technical improvements are being made to the technology of sensor device as generic sensor devices are merely applied to perform an insignificant extra-solution activity of data gathering; MPEP 2106.05(g).
Output devices are also taught at a high level of generality. [0078] recites: “One or more output devices 136 of computing device 12 may generate output, e.g., to patient 4 or another user. Examples of output are tactile, haptic, audio, and visual output. Output devices 134 of computing device 12 may include a presence-sensitive screen, sound card, video graphics adapter card, speaker, cathode ray tube (CRT) monitor, liquid crystal display (LCD), light emitting diodes (LEDs), or any type of device for generating tactile, audio, and/or visual output.” No specific, technical improvements are being made to the technology of output device as generic output devices are merely applied to perform an insignificant extra-solution activity of outputting alarms; MPEP 2106.05(g).
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the limitations reciting the at least one abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of 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 does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(IID)(A)(2).
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claims 2 and 17: These claims recite wherein the processing circuitry is configured to control the communication circuitry to request additional episode data from the sensor device based on the classification; which teaches the processing and communication circuitry at a high level of generality such that it is only applied to perform the insignificant extra-solution activities of gathering and transmitting data.
Claims 3 and 18: These claims recite wherein the processing circuitry is configured to determine the classification of the acute health event based on a number of the segments determined to have the classification; which only serves to narrow the abstract idea of determining the classification.
Claims 4 and 19: These claims recite wherein the processing circuitry is configured to determine the classification of the acute health event based on a time location of one or more segments determined to have the classification within the episode data; which only serves to narrow the abstract idea of determining the classification.
Claims 5 and 20: These claims recite wherein the processing circuitry is configured to: apply the one or more machine learning models to each segment of the plurality of segments to determine a respective probability associated with the respective classification for each segment of the plurality of segments; and determine the classification of the acute health event based on the respective probabilities; which teaches an abstract idea of mathematical concepts by determining probabilities to perform the abstract idea of classification of health events.
Claim 6: This claim recites wherein, to determine the classification of the acute health event based on the respective probabilities, the processing circuitry is configured to compare the respective probabilities to a threshold; which only serves to narrow the abstract idea of mathematical concepts by utilizing a probability threshold.
Claim 7: This claim recites wherein, to determine the classification of the acute health event based on the respective probabilities, the processing circuitry is configured to compare a combination of probabilities to a threshold; which only serves to narrow the abstract idea of mathematical concepts by utilizing a probability threshold for a combination of probabilities.
Claim 8: This claim recites wherein the combination comprises a weighted combination; which only serves to narrow the abstract idea of mathematical concepts by specifying the type of combination.
Claim 9: This claim recites wherein the processing circuitry weights the probabilities based on a time location of the corresponding segments within the episode data; which teaches an abstract idea of mathematical concepts by teaching how to weight the probabilities.
Claim 10: This claim recites wherein the one or more machine learning models comprise a first one or more machine learning models, and wherein, to determine the classification of acute health event from the plurality of predetermined classifications based on the respective classifications of the plurality of segments, the processing circuitry is configured to apply the respective classifications to a second one or more machine learning models; which teaches the machine learning models at a high level of generality such that they are merely applied to perform an abstract idea of classification of health events.
Claim 11: This claim recites wherein the second one or more machine learning models comprise a long short-term memory network; which only serves to limit the type of second machine learning model.
Claim 12: This claim recites wherein the computing device comprises a smartphone; which only serves to narrow the type of computing device with no specific, technical improvements to computing devices.
Claim 13: This claim recites wherein the computing device comprises an Internet of Things device; which only serves to narrow the type of computing device with no specific, technical improvements to computing devices.
Claim 14: This claim recites a system comprising: the sensor device; and the computing device of claim 1; which teaches a system at a high level of generality with no specific, technical improvements to systems.
Claim 15: This claim recites wherein the sensor device comprises an implantable medical device; which only serves to narrow the type of sensor device with no specific, technical improvements to sensor devices.
Subject Matter Eligibility Criteria – Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
Amount to elements that have been recognized as known activities in particular fields (such as Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), MPEP §2106.05(d)(II)(i);storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv)).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2-15 and 17-20 additional limitations which amount to elements that have been recognized as known activities in particular fields, claims 2-15 and 17-20 e.g., performing repetitive calculations, Flook, MPEP §2106.05(d)(II)(ii); claims 2-15 and 17-20, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 5-8, 12-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Charles (US 20190378617) in view of Govari (US 20200054292).
Regarding claim 1, Charles teaches a computing device ([0043], “a computer comprising:”) comprising:
communication circuitry configured to wirelessly communicate with a sensor device on a patient or implanted within the patient ([0053], “The system may further comprise a wearable ECG device for obtaining the ECG signals from the subject.” [0054], “The system may further comprise a mobile device configured to wirelessly receive the ECG signals from the wearable ECG device, and to transmit the ECG signals to the computer.”);
one or more output devices ([0069], “the mobile device 310 may be configured to perform a full analysis of the ECG data locally using a neural network, and may comprise a reporting module for transmitting a report including the analysis to the server, or to a user (e.g. via a display of the mobile device).”); and
processing circuitry configured ([0066], “A microprocessor or microcontroller may be provided, configured to perform some processing on the electrocardiogram data (e.g. normalisation, filtering etc).”) to:
receive episode data for an acute health event detected by the sensor device via the communication circuitry (col. 5, lines 10-14, “The mobile device 310 is configured to receive the ECG data from the ECG device 305 via a data reception module (e.g. a wireless communication module).” [0020], “a method of detecting abnormalities in ECG signals”);
segment the episode data into a plurality of segments, wherein each segment of the plurality of segments consists of a respective portion of the episode data associated with a respective portion of the acute health event ([0045], “classification estimate corresponding with a time segment of the ECG signal” [0008], “FIG. 1 illustrates a basic electrocardiograph (ECG) signal.”). Fig. 1 depicts the different segments of a signal.
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apply one or more machine learning models to each segment of the plurality of segments of the episode data to determine a respective classification of a plurality of predetermined classifications for each segment of the plurality of segments ([0045], “a neural network for determining a final classification comprising a plurality of final classification estimates, each final classification estimate corresponding with a time segment of the ECG signal”);
determine a classification of the acute health event from the plurality of predetermined classifications based on the respective classifications of the plurality of segments ([0035], “The final classification estimates may include an estimate as to the probability of each time segment comprising at least one of: “noise”, a “normal” ECG and “other”.”); and
determine whether to control the one or more output devices to output an alarm based on the classification ([0068], “The mobile device 310 may be responsive to the analysis, for example to send an alert or message to the cardiologist in the case of a significant abnormality.”).
Charles does not teach wherein the episode data transmitted by the sensor device in response to detecting the acute health event.
However, Govari does teach wherein the episode data transmitted by the sensor device in response to detecting the acute health event ([0118], “As shown at block 706 in FIG. 7, the method 700 includes transmitting (e.g., via a LAN), upon detection of an event, all or a portion of the patient biometric data stored and maintained at block 704. The all or portion of the patient biometric data that is transmitted may be patient biometric data that is collected or that occurs within a time range before and/or after the detection of the event.”).
Charles in view of Govari are considered analogous to the claimed invention because they are in the field of episode monitoring. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Charles with Govari for the advantage of “allowing to transmit and store a fraction of the monitored data that may have diagnostic significance” which provides a system wherein “significant power may be saved when compared to conventional sensing devices” (Govari; [0082]).
Regarding claim 2, Charles in view of Govari teaches the computing device of claim 1. Charles does not teach wherein the processing circuitry is configured to control the communication circuitry to request additional episode data from the sensor device based on the classification.
However, Govari does teach wherein the processing circuitry is configured to control the communication circuitry to request additional episode data from the sensor device based on the classification ([0005], “An additional sensing apparatus may be provided and may be configured to receive a request for an additional data from the local computing device, sense the additional data and transmit, via the first network, the additional data.” [0150], “The message 1215 informs the user 802 that the arrhythmia has occurred. In addition, the message 1215 instructs the user to provide additional data via the blood cuff.”).
Charles in view of Govari are considered analogous to the claimed invention because they are in the field of episode monitoring. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Charles with Govari for the advantage of providing a system wherein “additional information may then be transmitted back to the cloud database for further storage and analysis” (Govari; [0148]).
Regarding claim 3, Charles in view of Govari teaches the computing device of claim 1. Charles further teaches wherein the processing circuitry is configured to determine the classification of the acute health event based on a number of the segments determined to have the classification ([0075], “The classification output from the neural network may comprises a set of probabilities corresponding with each of a plurality of classifications for each time period of the input ECG signal. For example, the neural network may use classifications of “AF” and “not-AF”, and the classification output may provide an estimate of the probability for each 0.2 s period of the input signal (i.e. at 5 Hz) being “AF” and “not-AF”. Since these example classifications are mutually exclusive, the sum of their probabilities will be equal to 1” [0078], “regions classified as “AF” with a probability of greater than a predetermined threshold (e.g. 0.8) may be flagged or highlighted for further review by a cardiologist.”). Examiner notes that because the final classification is determined based on a sum of all the classifications for each segment, the final classification, which depends on a threshold compared to the sum of positive health events, comprises a classification based on the number of segments determined to have the classification.
Regarding claim 5, Charles in view of Govari teaches the computing device of claim 1. Charles further teaches wherein the processing circuitry is configured to:
apply the one or more machine learning models to each segment of the plurality of segments to determine a respective probability associated with the respective classification for each segment of the plurality of segments ([0045], “a neural network for determining a final classification comprising a plurality of final classification estimates, each final classification estimate corresponding with a time segment of the ECG signal” [0034], “The final classification estimates may include an estimate as to the probability of each time segment comprising atrial fibrillation.”); and
determine the classification of the acute health event based on the respective probabilities ([0045], “a neural network for determining a final classification comprising a plurality of final classification estimates, each final classification estimate corresponding with a time segment of the ECG signal” [0075], “For example, the neural network may use classifications of “AF” and “not-AF”, and the classification output may provide an estimate of the probability for each 0.2 s period of the input signal (i.e. at 5 Hz) being “AF” and “not-AF”. Since these example classifications are mutually exclusive, the sum of their probabilities will be equal to 1,”).
Regarding claim 6, Charles in view of Govari teaches the computing device of claims 1 and 5. Charles further teaches wherein, to determine the classification of the acute health event based on the respective probabilities, the processing circuitry is configured to compare the respective probabilities to a threshold ([0075], “For example, the neural network may use classifications of “AF” and “not-AF”, and the classification output may provide an estimate of the probability for each 0.2 s period of the input signal (i.e. at 5 Hz) being “AF” and “not-AF”. Since these example classifications are mutually exclusive, the sum of their probabilities will be equal to 1,” [0078], “regions classified as “AF” with a probability of greater than a predetermined threshold (e.g. 0.8) may be flagged or highlighted for further review by a cardiologist.”).
Regarding claim 7, Charles in view of Govari teaches the computing device of claims 1 and 5. Charles further teaches wherein, to determine the classification of the acute health event based on the respective probabilities, the processing circuitry is configured to compare a combination of probabilities to a threshold [0075], “For example, the neural network may use classifications of “AF” and “not-AF”, and the classification output may provide an estimate of the probability for each 0.2 s period of the input signal (i.e. at 5 Hz) being “AF” and “not-AF”. Since these example classifications are mutually exclusive, the sum of their probabilities will be equal to 1,” [0078], “regions classified as “AF” with a probability of greater than a predetermined threshold (e.g. 0.8) may be flagged or highlighted for further review by a cardiologist.”).
Regarding claim 8, Charles in view of Govari teaches the computing device of claims 1, 5, and 7. Charles further teaches wherein the combination comprises a weighted combination ([0075], “For example, the neural network may use classifications of “AF” and “not-AF”, and the classification output may provide an estimate of the probability for each 0.2 s period of the input signal (i.e. at 5 Hz) being “AF” and “not-AF”. Since these example classifications are mutually exclusive, the sum of their probabilities will be equal to 1,”). Under the broadest reasonable interpretation, a sum of all parts having equal weight of x1 comprises a weighted combination.
Regarding claim 12, Charles in view of Govari teaches the computing device of claim 1. Charles further teaches wherein the computing device comprises a smartphone ([0050], “The computer may be a mobile device, and the data reception module may comprise a wireless receiver.”).
Regarding claim 13, Charles in view of Govari teaches the computing device of claim 1. Charles does not teach wherein the computing device comprises an Internet of Things device.
However, Govari does teach wherein the computing device comprises an Internet of Things device ([0114], “When used in a networking environment, computer system 610 may include modem 672 for establishing communications over a network 120, such as the Internet. Modem 672 may be connected to system bus 621 via network interface 670, or via another appropriate mechanism.”).
Charles in view of Govari are considered analogous to the claimed invention because they are in the field of episode monitoring. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Charles with Govari for the advantage of providing a system wherein “using any one of various long-range wireless communication protocols” (Govari; [0063]).
Regarding claim 14, Charles in view of Govari teaches the computing device of claim 1. Charles further teaches a system comprising: the sensor device; and the computing device of claim 1 ([0053], “The system may further comprise a wearable ECG device for obtaining the ECG signals from the subject.” [0054], “The system may further comprise a mobile device configured to wirelessly receive the ECG signals from the wearable ECG device, and to transmit the ECG signals to the computer.”) [0050], “The computer may be a mobile device, and the data reception module may comprise a wireless receiver.”).
Regarding claim 15, Charles in view of Govari teaches the computing device of claims 1 and 14. Charles does not teach wherein the sensor device comprises an implantable medical device.
However, Govari does teach wherein the sensor device comprises an implantable medical device ([0057], “a monitoring and processing apparatus 102 may be an apparatus that is internal to the patient's body (e.g., subcutaneously implantable). The monitoring and processing apparatus 102 may be inserted into a patient via any applicable manner including orally injecting, surgical insertion via a vein or artery, an endoscopic procedure, or a laparoscopic procedure.”).
Charles in view of Govari are considered analogous to the claimed invention because they are in the field of episode monitoring. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Charles with Govari for the advantage of providing a system wherein “monitor[ing] and process[ing] patient biometrics internal (e.g., subcutaneous) to the patient” (Govari; [0074]).
Regarding claims 16, 17, 18, and 20, these claims are rejected for the same reasons as claims 1, 2, 3, and 5, respectively.
Claims 4, 9, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Charles (US 20190378617) in view of Govari (US 20200054292) further in view of Zhong (US 20180277246).
Regarding claim 4, Charles in view of Govari teaches the computing device of claim 1. Charles in view of Govari does not teach wherein the processing circuitry is configured to determine the classification of the acute health event based on a time location of one or more segments determined to have the classification within the episode data.
However, Zhong does teach wherein the processing circuitry is configured to determine the classification of the acute health event based on a time location of one or more segments determined to have the classification within the episode data ([0142], “the earlier portions of the ensemble glucose prediction 1204 are weighted more heavily toward outputs of prediction models that are more reliable in the short-term while portions of the ensemble glucose prediction 1204 further into the future are weighted more heavily toward outputs of prediction models having better longer term reliability. For example, the portion of the ensemble glucose prediction 1204 from 10 AM to 11 AM may be composed of 60% of the output of the patient's ARIMA model and 40% of the patient's hourly forecasting model, while the subsequent portion of the ensemble glucose prediction 1204 from 11 AM to 12 PM may be composed of 40% of the output of the patient's ARIMA model and 60% of the patient's hourly forecasting model, the portion of the ensemble glucose prediction 1204 from 12 PM to 1 PM may be composed of 30% of the output of the patient's ARIMA model and 70% of the patient's hourly forecasting model, and so on.”). Under the broadest reasonable interpretation, Examiner interprets “time location” to refer to the location of segments to each other with respect to time. Thus, weighing segments/portions based on those segments being earlier or later encompasses the claim limitation.
Charles in view of Govari further in view of Zhong are considered analogous to the claimed invention because they are in the field of episode monitoring. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Charles in view of Govari with Zhong for the advantage of “identify[ing] predictive relationships and corresponding influences on patient outcomes for improved modeling” (Zhong; [0158]).
Regarding claim 9, Charles in view of Govari teaches the computing device of claims 1, 5, and 7-8. Charles in view of Govari does not teach wherein the processing circuitry weights the probabilities based on a time location of the corresponding segments within the episode data.
However, Zhong does teach wherein the processing circuitry weights the probabilities based on a time location of the corresponding segments within the episode data ([0142], “the earlier portions of the ensemble glucose prediction 1204 are weighted more heavily toward outputs of prediction models that are more reliable in the short-term while portions of the ensemble glucose prediction 1204 further into the future are weighted more heavily toward outputs of prediction models having better longer term reliability. For example, the portion of the ensemble glucose prediction 1204 from 10 AM to 11 AM may be composed of 60% of the output of the patient's ARIMA model and 40% of the patient's hourly forecasting model, while the subsequent portion of the ensemble glucose prediction 1204 from 11 AM to 12 PM may be composed of 40% of the output of the patient's ARIMA model and 60% of the patient's hourly forecasting model, the portion of the ensemble glucose prediction 1204 from 12 PM to 1 PM may be composed of 30% of the output of the patient's ARIMA model and 70% of the patient's hourly forecasting model, and so on.”).
Charles in view of Govari further in view of Zhong are considered analogous to the claimed invention because they are in the field of episode monitoring. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Charles in view of Govari with Zhong for the advantage of “identify[ing] predictive relationships and corresponding influences on patient outcomes for improved modeling” (Zhong; [0158]).
Regarding claim 19, this claim is rejected for the same reasons as claim 4, as described above.
Claims 10 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Charles (US 20190378617) in view of Govari (US 20200054292) further in view of Ravishankar (US 12694980).
Regarding claim 10, Charles in view of Govari teaches the computing device of claim 1. Charles further teaches wherein the one or more machine learning models comprise a first one or more machine learning models ([0021], “providing an ECG signal to a neural network;”).
Charles in view of Govari does not teach wherein, to determine the classification of acute health event from the plurality of predetermined classifications based on the respective classifications of the plurality of segments, the processing circuitry is configured to apply the respective classifications to a second one or more machine learning models.
However, Ravishankar does teach wherein, to determine the classification of acute health event from the plurality of predetermined classifications based on the respective classifications of the plurality of segments, the processing circuitry is configured to apply the respective classifications to a second one or more machine learning models (col. 13, lines 10-12, “The resulting outputs from each arm of the tri-net deep learning model may be combined and input into a second MLP, which may output an arrhythmia prediction.” Col. 8, lines 5-14, “The multi-arm neural network 218 includes transformers for inter-beat temporal dynamics and multilayer perceptron (MLP) combined with a convolutional neural network (CNN) for short-term and long-term modeling, as will be further described below with respect to FIG. 3”).
Charles in view of Govari further in view of Ravishankar are considered analogous to the claimed invention because they are in the field of episode monitoring. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Charles in view of Govari with Ravishankar for the advantage of providing a system wherein “providing additional classification of the summarized input data” (Ravishankar; col. 11, lines 10-11).
Regarding claim 11, Charles in view of Govari teaches the computing device of claims 1 and 10. Charles in view of Govari does not teach wherein the second one or more machine learning models comprise a long short-term memory network.
However, Ravishankar does teach wherein the second one or more machine learning models comprise a long short-term memory network (Col. 8, lines 5-14, “The multi-arm neural network 218 includes transformers for inter-beat temporal dynamics and multilayer perceptron (MLP) combined with a convolutional neural network (CNN) for short-term and long-term modeling, as will be further described below with respect to FIG. 3”).
Charles in view of Govari further in view of Ravishankar are considered analogous to the claimed invention because they are in the field of episode monitoring. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Charles in view of Govari with Ravishankar for the advantage of providing a system wherein “construct an accurate prediction model that is capable of analyzing short-term temporal dynamics (e.g., rhythm changes) as well as long-term dynamics (e.g., heart rate variability)” (Ravishankar; col. 2, lines 28-31).
Conclusion
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/D.C./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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