DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the amendment filed on 08/10/26.
Clams 1, 28, 29 have been amended and are hereby entered.
Claim 2 has previously been canceled.
Claim 10 has been canceled.
Claims 11-20 were previously withdrawn by Applicant.
Claims 31-34 have been added.
Claims 1, 3-9, 21-34 are currently pending and have been examined.
This action is made final.
Priority
Applicant’s claim to the benefit of and priority to US Provisional Applications 63/308,887, filed 2/10/22 and 63/418,329, filed 10/21/22, is acknowledged. Accordingly, a priority date of 02/10/22 has been given to this application.
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, 3-9, 21-34 are rejected under 35 U.S.C.101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more.
Step 1
Claims 1, 3-9, 21-34 are drawn to a method, which is within the four statutory categories. Claims 1, 3-9, 21-34 are further directed to an abstract idea on the grounds set out in detail below.
Step 2A Prong 1
Claim 1 recites implementing the steps of:
identifying sensory data when PPG data differs from a prototypical waveform pattern by a configured threshold;
analyzing the sensory data that was not identified to generate a recommendation for an activity level of the patient, wherein the recommendation is generated by models/algorithms; and
supplying the recommendation to at least one of a patient, a clinician, or an agent
receiving an indication of adoption or rejection of the recommendation to refine the models and/or algorithms
These steps amount to managing personal behavior or relationships or interactions
between people and therefore recite certain methods of organizing human activity. Analyzing sensory and PPG data of a patient to determine a recommendation for an activity level to provide to a patient, clinician or agent and using an indication of adoption or rejection of the recommendation to refine a model are a personal behaviors that may be performed by healthcare providers.
Independent claim 28 recites similar limitations and also recites an abstract idea under the same analysis.
Claim 29 recites implementing the steps of:
identifying sensory data when PPG data differs from a prototypical waveform pattern by a configured threshold;
analyzing the sensory data that was not identified to generate a recommendation, wherein the recommendation is generated by models and/or algorithms; and
displaying the recommendation to at least one of a patient, a clinician, or an agent, wherein the displayed recommendation comprises a percentage blood oxygen saturation, a heartrate, a perfusion index (PI), and a PPG signal measured by the one or more sensors
receiving an indication of adoption or rejection of the recommendation to refine the models and/or algorithms
These steps amount to managing personal behavior or relationships or interactions
between people and therefore recite certain methods of organizing human activity. Analyzing sensory and PPG data of a patient to determine a recommendation to provide to a patient, clinician or agent and using an indication of adoption/rejection of the recommendation to refine a model are personal behaviors that may be performed by healthcare providers.
The above claims are therefore directed to an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application because the additional
elements within the claims only amount to:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
Claims 1, 28 additionally recite:
an external device as implementing the steps of supplying the recommendation to at least one of a patient, a clinician, or an agent and receiving an indication of adoption or rejection of the recommendation
machine learning (ML) models and/or algorithms as the types of models/algorithms to perform the step of analyzing the sensory data that was not identified to generate a recommendation of an activity level of a patient
training set to refine the ML models/algorithms
Claim 29 additionally recites:
a display as implementing the steps of displaying the recommendation to at least one of a patient, a clinician, or an agent and receiving an indication of adoption or rejection of the recommendation
machine learning (ML) models and/or algorithms as the types of models/algorithms to perform the step of analyzing the sensory data that was not identified to generate a recommendation of an activity level of a patient
training set to refine the ML models/algorithms
The broad recitation of general purpose computing elements such as an “external device” at a high level of generality only amounts to mere instructions to implement the abstract idea using computing components as tools. Regarding the external device and display, these are understood to be general purpose computing devices (computerized device, including a desktop computer system, a laptop computer, a tablet, a smartphone, or a smartwatch per [0047]; a desktop computer system, a laptop computer, a tablet, or a smartphone per [0048]) which are functioning in their ordinary capacity. Regarding the ML models and/or algorithms, no particulars of the machine learning models/algorithms are disclosed. This is therefore understood to be a known ML model/algorithm implemented on a generic computing device used to apply the abstract idea. Regarding the training set to refine the ML models, no particulars of how the model is trained using the training set is provided. This is therefore understood to be a known training method used on a generic computing device.
B. Insignificant Extra-Solution Activity. MPEP 2106.05(g)
Claim 1 additionally recites
collecting sensory data and photoplethysmography (PPG) data from a patient via a patient device coupled to a patient, wherein the sensory data and the PPG data are separate from one another and received from one or more sensors within the patient device, wherein the one or more sensors are placed in contact with the patient, wherein the sensory data comprises pulse rate and blood oxygen saturation, wherein the one or more sensors comprises at least one optical sensor, wherein the one or more sensors are placed in contact with a finger of the patient to measure blood within the finger;
Claim 28 additionally recites
collecting sensory data and photoplethysmography (PPG) data from a patient, wherein the sensory data and the PPG data are separate from one another and are received from two or more sensors placed in contact with the patient, wherein the two or more sensors comprises at least one optical sensor, wherein the two or more sensors are each placed in contact with a finger, a head, an arm, or a foot of the patient, wherein the two or more sensors are configured to measure a percentage blood oxygen saturation, a heartrate, a perfusion index (PI), and a PPG signal
transmitting the sensory data to a sensor device
Claim 29 additionally recites
collecting sensory data and photoplethysmography (PPG) data from a patient via a patient device coupled to a patient, wherein the sensory data and the PPG data are received from two or more sensors placed in contact with the patient and are separate from one another, wherein the two or more sensors comprises at least one optical sensor;
transmitting the sensory data to a sensor device worn by the patient
In the present claims 1, 28, 29, the functions of collecting sensory data and photoplethysmography (PPG) data from a patient, in which the data is received from sensor(s) contacting the patient comprising at least one optical sensor, only amounts to insignificant extra-solution activity in the form of mere data gathering – e.g., this step only serves as a pre-solution data gathering step to obtain the data which is used during performance of the abstract idea to ultimately generate a recommendation, and accordingly constitutes insignificant extra-solution activity.
Regarding “transmitting the sensory data” in Claims 28-29, these steps only amount to insignificant extra-solution activity. As stated in MPEP 2106.05(g), "[t]he term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim." The step of transmitting the sensory data in claims 28-29 is only nominally or tangentially related to the process of analyzing sensory and PPG data of a patient to determine a recommendation for the patient to provide to the patient, clinician or agent, and accordingly constitutes insignificant extra-solution activity.
These elements are therefore not sufficient to integrate the abstract idea into a practical application. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Claims 1, 28, 29 as a whole, are therefore directed to an abstract idea.
Step 2B
The present claims do not include additional elements that are sufficient to amount to
more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
As explained above, the independent claims only recite the aforementioned computing elements as tools for performing the steps of the abstract idea, and mere instructions to perform the abstract idea using a computer is not sufficient to amount to significantly more than the abstract idea. MPEP 2106.05(f).
B. Insignificant Extra-Solution Activity. MPEP 2106.05(g)
As explained above, the steps of collecting sensory data and photoplethysmography (PPG) data from a patient…, and the step of transmitting the sensory data to a sensor device only amount to insignificant extra-solution activity.
C. Well-Understood, Routine and Conventional Activities. MPEP 2106.05(d)
In addition to amounting to insignificant extra-solution activity the elements in Section A above constitute well-understood, routine and conventional activity. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements in Claims 1, 28 and 29 pertaining to collecting sensory data and photoplethysmography (PPG) data from a patient were considered extra-solution activity. This has been re-evaluated under the “significantly more” analysis and determined to be well-understood, routine, conventional activity in the field of computerized healthcare. As evidenced by the prior art of record, collecting sensory data and photoplethysmography (PPG) data from a patient via a patient device coupled to a patient, wherein the sensory data and the PPG data are received from sensor(s) placed in contact with the patient, wherein the one or more sensors comprises at least one optical sensor is well-understood, routine, and conventional elements in the field of computerized healthcare. Please see the following references:
Gelissen, at least paras. [0005], [0006], [0059], [0072], [0077], [0087]-[0097]
Banet ‘916, at least Abstract, paras. [0002], [0005], [0015], [0022]
Shemesh, at least Abstract, paras. [0006]-[0007], [0053], [0059], [0086], [0128], [0131], [0140]
Well-understood, routine, conventional activity cannot provide an inventive concept (“significantly more”). As such the claim is not patent eligible.
Regarding the limitations in Claims 28-29 pertaining to “transmitting the sensory data to a sensor device”, these also constitute well-understood, routine and conventional activity in the form of transmitting data over a network. These types of activities have been recognized by the courts as well-understood, routine and conventional activity when claimed as insignificant extra-solution activity. See MPEP 2106.05(d).
Thus, taken alone, the additional elements do not amount to significantly more than the
above-identified judicial exception. Looking at the limitations as an ordered combination adds
nothing that is not already present when looking at the elements taken individually. Their
collective functions merely provide conventional computer implementation.
Depending Claims
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims or further recite certain methods of organizing human activity. For example, Claims 3-9, 21-27, 30-34 recite limitations which further narrow the scope of the independent claims.
Dependent claims 3-9, 21-27, 30-34 when analyzed as a whole, 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. 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. 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.
The dependent claims have been given the full two-part analysis including analyzing the additional limitations both individually and in combination. The dependent claims, when analyzed individually, and in combination, are also held to be patent ineligible under 35 U.S.C. 101 as they include all of the limitations of claim 1 or 29 respectively. The additional recited limitations of the dependent claims fail to establish that the claims do not recite an abstract idea because the additional recited limitations of the dependent claims merely further narrow the abstract idea. Beyond the limitations which recite the abstract idea, the claims recite additional elements consistent with those identified above with respect to the independent claims which encompass adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claims 3-9, 21-27, 30-34, when analyzed as a whole, 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.
For the reasons stated, Claims 1, 3-9, 21-34 fail the Subject Matter Eligibility Test and are consequently rejected under 35 U.S.C. 101.
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.
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.
Claim(s) 1, 3-5, 9, 10, 21-23, 25-27, 32 is/are rejected under 35 U.S.C. 103 as being unpatentable over McGonigle et. al. (US Publication 20140012109 A1) in view of Dos Santos et. al. (US Publication 20230253103A1), and further in view of Oktay et. al. (US Publication 20230102428A1).
Regarding Claim 1, McGonigle discloses:
collecting sensory data and photoplethysmography (PPG) data from a patient via a patient device coupled to a patient, wherein the sensory data and the PPG data are separate from one another and are received from one or more sensors [coupled to] the patient device, (Fig. 1; [0043] teaches on determining physiological parameters such as SpO2 and pulse rate (sensory data) based on the value of the received signals and/or data corresponding to the light received by the detector, per [0022] “signals” is understood to be PPG signal; [0067] teaches on determining respiration rate based on the PPG signal – also interpreted as “sensory data”; Fig. 5 and para. [0066] teaching on a PPG signal obtained from a patient; Fig. 1; para. [0028] teaching on the pulse ox system which includes sensor 12 for detecting light emanating from patient’s tissue after passing through patient’s tissue which may be used to calculate sensory data, e.g., pulse rate per [0022]; per [0029] sensor 12 may be a single sensor or a “sensor array” in lieu of a single sensor; sensor 12 is communicatively coupled to monitor 14 via cable or may be wireless; monitor 14 is connected to patient monitor 26 – both can be interpreted as “patient device”), wherein the one or more sensors are placed in contact with the patient ([0033] teaches on patient monitor 26 (Fig. 1) displaying a patient’s SpO2 and pulse rate information from monitor 14 – interpreted as sensory data; per 112(b) section above, Examiner interprets “separate from one another” to apply to the sensory data and the PPG data; [0043] teaches on obtaining pulse rate and SpO2 and Fig. 5/ [0066] specifically teach on obtaining the PPG data; Examiner interprets pulse rate and SpO2 to be separate data than the PPG data shown in Fig. 5), wherein the sensory data comprises pulse rate and blood oxygen saturation ([0033] teaches on patient monitor 26 (Fig. 1) displaying a patient’s SpO2 and pulse rate information from monitor 14); [0043] teaches on determining physiological parameters such as SpO2 and pulse rate), wherein the one or more sensors comprises at least one optical sensor ([0022] teaches on a “light sensor” being placed on a patient which passes a light source through blood perfused tissue and photoelectrically senses the absorption of light in the tissue, which is interpreted as reading on “optical sensor”), wherein the one or more sensors are placed in contact with a finger of the patient to measure blood within the finger ([0021]-[0022] teach on a pulse oximeter measuring blood via a light sensor that is placed at a site on the patient such as the fingertip);
identifying the sensory data when the PPG data differs from a prototypical waveform pattern by a configured threshold (Abstract teaches on determining if the PPG signal is “consistent”, it may be further analyzed to determine an underlying signal parameter such as a patient respiration rate, however, if the PPG signal is determined to be “inconsistent”, the “inconsistent portion of the signal” may be removed from the overall signal – e.g., identifying where PPG data differs from prototypical pattern as discussed below with respect to [0068]; further using the segment to identify respiration rate corresponding to the “consistent” signal is understood to indicate that sensory data corresponding to where the PPG data differs from prototypical is identified and removed from analysis; [0068] teaches on selecting consistent parts of PPG signal, where the consistent parts of PPG signal exhibit statistical regularity or other features that match closely or identically the features used to derive signal processing algorithms (“prototypical patterns”); to select consistent parts of PPG signals, several features of PPG signal may be used: signal peaks, signal troughs, and interpeak distances, which are interpreted as making up a “waveform pattern”); the process can be used for selecting a “consistent part of PPG signal” prior to determining an underlying patient parameter; e.g., respiration rate (interpreted as reading on broadest reasonable interpretation of sensory data); per Abstract, the “inconsistent” portion is identified as it is removed; per [0005], consistent portions of PPG signals may be identified via upper and lower thresholds);
[determining] sensory data that was not identified (Abstract teaches on determining if the PPG signal is “consistent”, it may be further analyzed to determine an underlying signal parameter such as a patient respiration rate (interpreted as not differing from prototypical waveform), however, if the PPG signal is determined to be “inconsistent”, the “inconsistent portion of the signal” may be removed from the overall signal – interpreted as teaching that the inconsistent data has been removed, so the consistent data (e.g., the data not identified as differing from prototypical waveform) has been determined and remains; [0068] teaches on selecting the “consistent parts” (the part not identified as being inconsistent) to determine an underlying parameter of the patient such as respiration rate; Respiration rate is understood to be a physiological parameter and is interpreted as being a type of sensory data; While the cited examples refer to “respiration rate” as the sensory data that is identified as corresponding to portions of a PPG signal that either differ from or conform to a prototypical waveform pattern, respiration rate, SpO2 and pulse ox are all understood to be physiological parameters that may be determined from a PPG signal (see at least paras. [0043], [0067]. It would have been obvious to substitute SpO2 and pulse ox for respiration rate since the combination is merely simple substitution of one known element for another producing a predictable result (KSR B). Since each individual element is shown within the teachings of McGonigle (e.g., respiration rate, pulse ox, and SpO2 are all physiological parameters that can be determined from PPG signals), the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself—that is, in the substitution of the physiological parameters of SpO2 and pulse ox as taught in [0043] for the physiological parameter of respiration rate as taught in [0086]). Thus, the simple substitution of one known element (SpO2 and pulse rate) for another (respiration rate) producing a predictable result renders the claim obvious).
McGonigle does not disclose, but Dos Santos, which is directed to systems and methods for monitoring user activity, teaches:
one or more sensors within a patient device ([0063] teaches on one or more sensors being integrated or embedded in a smartwatch).
analyzing the sensory data to generate a recommendation for an activity level of the patient ([0079] teaches on determining whether a physiological parameter exceeds a predetermined threshold which may be an unsafe or unhealthy level, e.g., a threshold can be indicative of a high heart rate that could pose a risk to the user; [0081] teaches on, responsive to determination that the physiological parameter has exceeded the predetermined threshold during an activity, generating an alert and communicating the alert to the user or third party; the alert communicated to the user can “encourage the user to reduce their exertion, e.g., slow down” (interpreted as “a recommendation for an activity level”)), wherein the recommendation is generated by machine learning (ML) models and/or algorithms ([0089] teaching on selecting activity modules within an activity session for a user; [0090] teaches on the activity modules comprising the activity session being selected from the plurality of modules using a trained machine learning algorithm) and
supplying the recommendation to at least one of a patient, a clinician, or an agent via an external device ([0081] teaches on, responsive to determination that the physiological parameter has exceeded the predetermined threshold during an activity, generating an alert and communicating the alert to the user or third party via a display device; the alert communicated to the user can “encourage the user to reduce their exertion, e.g., slow down”; [0100] teaching on recommendations can be communicated to a user during the activity session or during a pause in an activity module; an activity module may be paused due to a physiological parameter such as heart rate exceeding a threshold, and a recommendation can be communicated to the user during the pause to aid in modifying the parameter, e.g., sit down, do breathing exercises).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify McGonigle with these teachings of Dos Santos, to use one or more sensors within a patient device as taught by Dos Santos, such as a smart watch, with the motivation of providing sensors that can be worn by the patient ([0063]), and to analyze the sensor data of McGonigle (e.g., the “consistent” data that was not removed/identified) to generate a recommendation for an activity level of the patient and provide the recommendation to the patient’s device, wherein the recommendation is generated by a ML model, with the motivation of automatically monitoring patients for whom overexertion can pose a serious health or safety risk and pause or stop an activity when physiological parameters exceed a threshold and aid in modifying the physiological parameter so that it no longer exceeds the threshold and to use a machine learning model that can be trained to learn and adapt to the current user (Dos Santos [0024], [0081], [0090]).
McGonigle/Dos Santos do not teach the following, but Oktay, which is directed to generating medical reports, teaches:
receiving, via the external device, an indication of adoption or rejection of the recommendation, wherein the indication of adoption or rejection is used as part of a training set to refine the ML models and/or algorithms ([0035] teaches on using a machine learning model to generate suggestions; [0040] teaches on training of a machine learning model, based on the user accepting or rejecting the model’s suggestions in each iteration; per abstract, the suggestions(recommendation) are output to an interface which is interpreted as being part of an external device).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify McGonigle/Dos Santos with these teachings of Oktay, to receive an indication of an adoption/rejection of the ML model of McGonigle/Dos Santos, wit the motivation of refining the training of the machine learning model with each iteration (Oktay [0040]).
Regarding Claim 3, McGonigle/Dos Santos/Oktay teach the limitations of Claim 1. McGonigle further discloses wherein the sensory data includes at least one of a perfusion index (PI) percentage, blood pressure, electrocardiogram ([0046], [0061] teaches on the present disclosure including other biosignals including electrocardiogram).
Regarding Claim 4, McGonigle/Dos Santos/Oktay teach the limitations of Claim 1. McGonigle does not disclose, but Dos Santos further teaches wherein the recommendation is generated using historical patient profile and sensory data ([0090] teaches on using a machine learning algorithm to generate activity recommendations; the ML algorithm receives as input, the user profile associated with the user and previously recorded physiological data associated with the user; per [0028] the user profile includes demographic and biometric information associated with the user, family history of conditions, medical information of the user including medical conditions associated with the user, medication usage, or both – all interpreted as reading on “historical patient profile”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to further modify the combined teachings of McGonigle/Dos Santos/Oktay with these teachings of Dos Santos, to use historical patient profile information with sensor data to generate the recommendation, with the motivation of providing an activity recommendation that is adapted for the current user (Dos Santos [0090]).
Regarding Claim 5, McGonigle/Dos Santos/Oktay teach the limitations of Claim 1. McGonigle does not disclose, but Dos Santos further teaches wherein the recommendation is provided to the agent as an alert ([0081] teaches on responsive to determining that the physiological parameter has exceeded a threshold, generating and communicating the alert to a third party such as a family member or medical provider; the alert can encourage the user to reduce their exertion/slow down (recommendation)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify McGonigle/Dos Santos/Oktay with these teachings of Dos Santos, to communicate the recommendation to an agent as an alert, with the motivation of prompting the agent (family member/medical provider) to check on the patient (Dos Santos [0081]).
Regarding Claim 9, McGonigle/Dos Santos/Oktay teach the limitations of Claim 1. McGonigle does not disclose, but Dos Santos further teaches wherein the recommendation is generated by a set of rules comparing the collected sensory data to target values ([0079] teaches on determining that the physiological parameter has exceeded a predetermined threshold, e.g., a heart rate that exceeds a value such as over 120bpm, over 130 bpm, over 150 bpm, etc., which could pose a health risk to the user; [0081] teaches on determining that a threshold has been exceeded and providing an alert (recommendation) to the patient to encourage them to reduce exertion/slow down to aid in modifying the physiological parameter so that it no longer exceeds the threshold).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to further modify McGonigle/Dos Santos/Oktay with these teachings of Dos Santos, generate the recommendation by comparing the collected sensory data of McGonigle to target values using rules, with the motivation of automatically monitoring patients for whom overexertion can pose a serious health or safety risk and pause or stop an activity when physiological parameters exceed a threshold (Dos Santos [0024]).
Regarding Claim 10, McGonigle/Dos Santos/Oktay teach the limitations of Claim 1. McGonigle does not disclose, but Dos Santos further teaches wherein the recommendation is generated by machine learning (ML) models and/or algorithms ([0103] teaches on training a machine learning algorithm used for determining when and how to modify a user’s activity session).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to further modify McGonigle/Dos Santos/Oktay with these teachings of Dos Santos, to use a machine learning algorithm for making a recommendation regarding the user’s activity level, with the motivation of improving the effectiveness of future modifications of an activity session (Dos Santos [0103]).
Regarding Claim 21, McGonigle/Dos Santos/Oktay teach the limitations of claim 1. McGonigle further discloses wherein the one or more sensors comprise at least one infrared sensor ([0035] teaches on pulse oximetry system using emitter to emit RED and IR wavelengths of light; [0036] teaches on the light emitted from a sensor being “infrared”).
Regarding Claim 22, McGonigle/Dos Santos/Oktay teach the limitations of claim 1. McGonigle further discloses wherein the external device comprises a display (Fig. 1 and para. [0033] teach on “display 28”).
Regarding Claim 23, McGonigle/Dos Santos/Oktay teach the limitations of claim 1. McGonigle further discloses wherein the one or more sensors are configured to pulse infrared light to the finger ([0035], teaching on sensor 12 having a light emitting source which may be an IR source such as IR LED for emanating light into the patient’s tissue), wherein the one or more sensors are configured to detect the infrared light ([0035] teaches on sensor 12 having a detector; [0036] teaches on choosing a detector specifically sensitive to the chosen targeted energy spectrum and emitter; [0037] teaches on detector 18 detecting light of IR wavelengths; light may enter detector after passing through the patient’s tissue).
Regarding Claim 25, McGonigle/Dos Santos/Oktay teach the limitations of claim 1. McGonigle further discloses, wherein the one or more sensors are coupled to a user device (Fig. 1 showing sensor 12 connected (“coupled”) to patient monitor 14 which is coupled to the computer via cable 34 with relevant discussion at paras. [0032] and [0034]), wherein the user device is configured to analyze signals from the sensory data ([0031] teaches on monitor 14 calculating physiological parameters based on data received from sensor 12).
Regarding Claim 26, McGonigle/Dos Santos/Oktay teach the limitations of claim 25. McGonigle further discloses, wherein the one or more sensors wirelessly communicate with the user device ([0031], teaching on sensor being wirelessly connected to the monitor).
Regarding Claim 27, McGonigle/Dos Santos/Oktay teach the limitations of claim 25. McGonigle further discloses, wherein the one or more sensors communicate with the user device via a wired connection (Fig. 1, showing sensor 12 is coupled to the patient monitor 14 and ultimately computer via cables 24 and 32, e.g., wired connections).
Regarding Claim 32, McGonigle/Dos Santos/Oktay teach the limitations of Claim 1. McGonigle does not disclose, but Dos Santos further teaches: wherein the one or more sensors comprise an accelerometer and/or a gyroscope configured to sense movement of the patient ([0062] teaches on including one or more accelerometers or gyroscopes).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to further modify McGonigle/Dos Santos/Oktay with these teachings of Dos Santos, to use a gyroscope or accelerometer, with the motivation of determining motion or movement of a user who is holding/wearing it (Dos Santos [00-62]).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over McGonigle et. al. (US Publication 20140012109 ) in view of Dos Santos et. al. (US Publication 20230253103A1) and further in view of Oktay et. al. (US Publication 20230102428A1) as applied to Claim 1 above, and further in view of Charlap US Publication 20170300648A1).
Regarding Claim 6, McGonigle/Dos Santos/Oktay teach the limitations of Claim 1 but do not teach the following. Charlap, which is directed to a method and system for patient data collection and analysis, teaches wherein the agent is a virtual agent ([0017] teaches on displaying an embodied virtual agent on a monitor to a patient which engages in a conversational interview with the patient).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the combined teachings of McGonigle/Dos Santos/Oktay with these teachings of Charlap, to incorporate a virtual agent within the system of McGonigle/Santos/Oktay, with the motivation of automating information presented to the user via the agent (Charlap [0028]).
Claim(s) 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over McGonigle et. al. (US Publication 20140012109 ) in view of Dos Santos et. al. (US Publication 20230253103A1) and further in view of Oktay et. al. (US Publication 20230102428A1) as applied to Claim 1 above, and further in view of Georgiev et al. article “Identification of Sine, Squire, Triangle and Sawtooth Waveforms with Uniform White and Inverse F Noises by Adaptive Neuro-Fuzzy Interface System" (hereinafter Georgiev).
Regarding Claim 7, McGonigle/Dos Santos/Oktay teach the limitations of Claim 1 but do not teach the following. Georgiev, which is directed to a method and system for patient data collection and analysis, teaches, wherein the prototypical waveform pattern is a saw-toothed shape (Page 2, Col 1-2 approach to identifying noise in analog and digital signals; “sawtooth” waveforms with superimposed noise).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to further modify the combined teachings of McGonigle/Dos Santos/Oktay with these teachings of Georgiev, to use a sawtoothed shape as the waveform pattern, with the motivation of accurately identifying and filtering artifacts (Page 1, Col 2, first paragraph).
Regarding Claim 8, McGonigle/Dos Santos/Oktay teach the limitations of Claim 1 but do not teach the following. Georgiev, which is directed to a method and system for patient data collection and analysis, teaches, wherein the PPG data differing from the prototypical waveform pattern is calculated by a least means squared algorithm (page 1, col 2- The system combines an adaptive filter, integrating the Least Mean Square (LMS); reduce the noise present in the pure respiratory signal is carried out a comparison of an adaptive filtering based on ANFIS and filtering through LMS).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to further modify the combined teachings of McGonigle/Dos Santos/Oktay with these teachings of Georgiev, to use a least mean square algorithm, with the motivation of accurately identifying and filtering artifacts (Page 1, Col 2, first paragraph).
Claim(s) 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over McGonigle et. al. (US Publication 20140012109 ) in view of Dos Santos et. al. (US Publication 20230253103A1) and further in view of Oktay et. al. (US Publication 20230102428A1) as applied to Claim 23 above, and further in view of Lange (US Publication 20190015014A1).
Regarding Claim 24, McGonigle/Dos Santos/Oktay teach the limitations of Claim 23. McGonigle further discloses wherein the one or more sensors are configured to detect blood oxygen saturation, heart rate, of the patient ([0043] teaches on determining SpO2 and pulse rate).
McGonigle/Dos Santos/Oktay do not teach the following but Lange, which is directed to a system and method for monitoring respiratory rate and oxygen saturation, teaches:
perfusion index of the patient ([0076]-[0077] teach on determining a perfusion index for a patient based on obtained PPG data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to further modify the combined teachings of McGonigle/Dos Santos/Oktay with these teachings of Lange to determine perfusion index of the patient, as it can be used to indicate the quality of the PPG signal ([0077]).
Claim(s) 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over McGonigle et. al. (US Publication 20140012109 ) in view of Lange (US Publication 20190015014A1), further in view of Dos Santos et. al. (US Publication 20230253103A1), and further in view of Oktay et. al. (US Publication 20230102428A1).
Regarding Claim 28, McGonigle discloses:
collecting sensory data and photoplethysmography (PPG) data from a patient ([0043] teaches on determining physiological parameters such as SpO2 and pulse rate (sensory data) based on the value of the received signals and/or data corresponding to the light received by the detector, per [0022] “signals” is understood to be PPG signal; [0067] teaches on determining respiration rate based on the PPG signal – also interpreted as “sensory data”; Fig. 5 and para. [0066] teaching on a PPG signal obtained from a patient), wherein the sensory data and the PPG data are separate from one another ([0033] teaches on patient monitor 26 (Fig. 1) displaying a patient’s SpO2 and pulse rate information from monitor 14 – interpreted as sensory data; [0043] teaches on obtaining pulse rate and SpO2 and Fig. 5/ [0066] specifically teach on obtaining the PPG data; Examiner interprets pulse rate and SpO2 to be separate data than the PPG data shown in Fig. 5) and are received from two or more sensors placed in contact with the patient (Fig. 1; para. [0028] teaching on the pulse ox system which includes sensor 12 for detecting light emanating from patient’s tissue after passing through patient’s tissue; per [0029] sensor 12 may be a single sensor or a “a plurality of sensors forming a sensor array” in lieu of a single sensor – interpreted as being “two or more sensors”; [0021]-[0022] teach on a pulse oximeter measuring blood via a light sensor that is placed at a site on the patient such as the fingertip which is interpreted as sensors placed in contact with the patient), wherein the two or more sensors comprises at least one optical sensor ([0022] teaches on a “light sensor” being placed on a patient which passes a light source through blood perfused tissue and photoelectrically senses the absorption of light in the tissue, which is interpreted as reading on “optical sensor”), wherein the two or more sensors are each placed in contact with a finger, a head, an arm, or a foot of the patient ([0021]-[0022] teach on a pulse oximeter measuring blood via a light sensor that is placed at a site on the patient such as the fingertip), wherein the two or more sensors are configured to measure a percentage blood oxygen saturation, a heartrate ([0033] teaches on patient monitor 26 (Fig. 1) displaying a patient’s SpO2 (“blood oxygen saturation”) and pulse rate (“heartrate”) information from monitor 14); [0043] teaches on determining physiological parameters such as SpO2 and pulse rate), , and a PPG signal (Fig. 5 and para. [0066] teaching on a PPG signal obtained from a patient);
transmitting the sensory data to a sensor device (Fig. 1; [0028], teaching on system 10 including sensor 12 and pulse ox monitor 14; sensor 12 emits light into patient’s tissue and detects light emanating from tissue after passing through; [0031], sensor 12 connects to monitor 14 to receives data from sensor 12 relating to light emission/detection and can calculate physiological parameters received from the sensor);
identifying the sensory data when the PPG data differs from a prototypical waveform pattern by a configured threshold (Abstract teaches on determining if the PPG signal is “consistent”, it may be further analyzed to determine an underlying signal parameter such as a patient respiration rate, however, if the PPG signal is determined to be “inconsistent”, the “inconsistent portion of the signal” may be removed from the overall signal – e.g., identifying where PPG data differs from prototypical pattern as discussed below with respect to [0068]; further using the segment to identify respiration rate corresponding to the “consistent” signal is understood to indicate that sensory data corresponding to where the PPG data differs from prototypical is identified and removed from analysis; [0068] teaches on selecting consistent parts of PPG signal, where the consistent parts of PPG signal exhibit statistical regularity or other features that match closely or identically the features used to derive signal processing algorithms (“prototypical patterns”); to select consistent parts of PPG signals, several features of PPG signal may be used: signal peaks, signal troughs, and interpeak distances, which are interpreted as making up a “waveform pattern”); the process can be used for selecting a “consistent part of PPG signal” prior to determining an underlying patient parameter; e.g., respiration rate (interpreted as reading on broadest reasonable interpretation of sensory data); per Abstract, the “inconsistent” portion is identified as it is removed; per [0005], consistent portions of PPG signals may be identified via upper and lower thresholds);
[determining] sensory data that was not identified (Abstract teaches on determining if the PPG signal is “consistent”, it may be further analyzed to determine an underlying signal parameter such as a patient respiration rate (interpreted as not differing from prototypical waveform), however, if the PPG signal is determined to be “inconsistent”, the “inconsistent portion of the signal” may be removed from the overall signal – interpreted as teaching that the inconsistent data has been removed, so the consistent data (e.g., the data not identified as differing from prototypical waveform) has been determined and remains; [0068] teaches on selecting the “consistent parts” (the part not identified as being inconsistent) to determine an underlying parameter of the patient such as respiration rate; Respiration rate is understood to be a physiological parameter and is interpreted as being a type of sensory data; While the cited examples refer to “respiration rate” as the sensory data that is identified as corresponding to portions of a PPG signal that either differ from or conform to a prototypical waveform pattern, respiration rate, SpO2 and pulse ox are all understood to be physiological parameters that may be determined from a PPG signal (see at least paras. [0043], [0067]. It would have been obvious to substitute SpO2 and pulse ox for respiration rate since the combination is merely simple substitution of one known element for another producing a predictable result (KSR B). Since each individual element is shown within the teachings of McGonigle (e.g., respiration rate, pulse ox, and SpO2 are all physiological parameters that can be determined from PPG signals), the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself—that is, in the substitution of the physiological parameters of SpO2 and pulse ox as taught in [0043] for the physiological parameter of respiration rate as taught in [0086]). Thus, the simple substitution of one known element (SpO2 and pulse rate) for another (respiration rate) producing a predictable result renders the claim obvious);
McGonigle does not teach the following but Lange, which is directed to a system and method for monitoring respiratory rate and oxygen saturation, teaches:
perfusion index of the patient ([0076]-[0077] teach on determining a perfusion index for a patient based on obtained PPG data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to further modify the McGonigle with these teachings of Lange to determine perfusion index of the patient, as it can be used to indicate the quality of the PPG signal ([0077]).
McGonigle/Lange do not teach, but Dos Santos, which is directed to systems and methods for monitoring user activity using a PPG sensor, teaches:
analyzing the sensory data to generate a recommendation for an activity level of the patient ([0079] teaches on determining whether a physiological parameter exceeds a predetermined threshold which may be an unsafe or unhealthy level, e.g., a threshold can be indicative of a high heart rate that could pose a risk to the user; [0081] teaches on, responsive to determination that the physiological parameter has exceeded the predetermined threshold during an activity, generating an alert and communicating the alert to the user or third party; the alert communicated to the user can “encourage the user to reduce their exertion, e.g., slow down” (interpreted as “a recommendation for an activity level”) wherein the recommendation is generated by machine learning (ML) models and/or algorithms ([0089] teaching on selecting activity modules within an activity session for a user; [0090] teaches on the activity modules comprising the activity session being selected from the plurality of modules using a trained machine learning algorithm); and
supplying the recommendation to at least one of a patient, a clinician, or an agent via an external device ([0081] teaches on, responsive to determination that the physiological parameter has exceeded the predetermined threshold during an activity, generating an alert and communicating the alert to the user or third party via a display device; the alert communicated to the user can “encourage the user to reduce their exertion, e.g., slow down”; [0100] teaching on recommendations can be communicated to a user during the activity session or during a pause in an activity module; an activity module may be paused due to a physiological parameter such as heart rate exceeding a threshold, and a recommendation can be communicated to the user during the pause to aid in modifying the parameter, e.g., sit down, do breathing exercises).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of McGonigle/Lange to analyze the sensor data of McGonigle (e.g., the “consistent” data that was not removed/identified) to generate a recommendation for an activity level of the patient and provide the recommendation to the patient’s device, with the motivation of automatically monitoring patients for whom overexertion can pose a serious health or safety risk and pause or stop an activity when physiological parameters exceed a threshold and aid in modifying the physiological parameter so that it no longer exceeds the threshold (Dos Santos [0024], [0081]) and to use a machine learning model for generating recommendations with the motivation of enabling the system to be trained to learn and adapt to the current user (Dos Santos [0090]).
McGonigle/Lange/Dos Santos do not teach the following, but Oktay, which is directed to generating medical reports, teaches:
receiving, via the display, an indication of adoption or rejection of the recommendation, wherein the indication of adoption or rejection is used as part of a training set to refine the ML models and/or algorithms ([0035] teaches on using a machine learning model to generate suggestions; [0040] teaches on training of a machine learning model, based on the user accepting or rejecting the model’s suggestions in each iteration; per abstract, the suggestions(recommendation) are output to an interface which is interpreted as being part of an external device).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify McGonigle/Lange/Dos Santos with these teachings of Oktay, to receive an indication of an adoption/rejection of the ML model of McGonigle/Dos Santos, with the motivation of refining the training of the machine learning model with each iteration (Oktay [0040]).
Claim(s) 29-30 is/are rejected under 35 U.S.C. 103 as being unpatentable over McGonigle et. al. (US Publication 20140012109 ) in view of Dos Santos et. al. (US Publication 20230253103A1), further in view of Lange (US Publication 20190015014A1), and further in view of Oktay et. al. (US Publication 20230102428A1).
Regarding Claim 29, McGonigle discloses
collecting sensory data and photoplethysmography (PPG) data from a patient via a patient device coupled to a patient (Fig. 1; [0043] teaches on determining physiological parameters such as SpO2 and pulse rate (sensory data) based on the value of the received signals and/or data corresponding to the light received by the detector, per [0022] “signals” is understood to be PPG signal; [0067] teaches on determining respiration rate based on the PPG signal – also interpreted as “sensory data”; Fig. 5 and para. [0066] teaching on a PPG signal obtained from a patient), wherein the sensory data and the PPG data are received from two or more sensors (Fig. 1; para. [0028] teaching on the pulse ox system which includes sensor 12 for detecting light emanating from patient’s tissue after passing through patient’s tissue; per [0029] sensor 12 may be a single sensor or a “a plurality of sensors forming a sensor array” in lieu of a single sensor – interpreted as being “two or more sensors”) placed in contact with the patient ([0021]-[0022] teach on a pulse oximeter measuring blood via a light sensor that is placed at a site on the patient such as the fingertip) and are separate from one another ([0033] teaches on patient monitor 26 (Fig. 1) displaying a patient’s SpO2 and pulse rate information from monitor 14 – interpreted as sensory data; per 112(b) section above, Examiner interprets “separate from one another” to apply to the sensory data and the PPG data; [0043] teaches on obtaining pulse rate and SpO2 and Fig. 5/ [0066] specifically teach on obtaining the PPG data; Examiner interprets pulse rate and SpO2 to be separate data than the PPG data shown in Fig. 5), wherein the two or more sensors comprises at least one optical sensor ([0022] teaches on a “light sensor” being placed on a patient which passes a light source through blood perfused tissue and photoelectrically senses the absorption of light in the tissue, which is interpreted as reading on “optical sensor”);
transmitting the sensory data to a sensor device (Fig. 1; [0028], teaching on system 10 including sensor 12 and pulse ox monitor 14; sensor 12 emits light into patient’s tissue and detects light emanating from tissue after passing through; [0031], sensor 12 connects to monitor 14 to receives data from sensor 12 relating to light emission/detection and can calculate physiological parameters received from the sensor);
identifying the sensory data when the PPG data differs from a prototypical waveform pattern by a configured threshold (Abstract teaches on determining if the PPG signal is “consistent”, it may be further analyzed to determine an underlying signal parameter such as a patient respiration rate, however, if the PPG signal is determined to be “inconsistent”, the “inconsistent portion of the signal” may be removed from the overall signal – e.g., identifying where PPG data differs from prototypical pattern as discussed below with respect to [0068]; further using the segment to identify respiration rate corresponding to the “consistent” signal is understood to indicate that sensory data corresponding to where the PPG data differs from prototypical is identified and removed from analysis; [0068] teaches on selecting consistent parts of PPG signal, where the consistent parts of PPG signal exhibit statistical regularity or other features that match closely or identically the features used to derive signal processing algorithms (“prototypical patterns”); to select consistent parts of PPG signals, several features of PPG signal may be used: signal peaks, signal troughs, and interpeak distances, which are interpreted as making up a “waveform pattern”); the process can be used for selecting a “consistent part of PPG signal” prior to determining an underlying patient parameter; e.g., respiration rate (interpreted as reading on broadest reasonable interpretation of sensory data); per Abstract, the “inconsistent” portion is identified as it is removed; per [0005], consistent portions of PPG signals may be identified via upper and lower thresholds);
sensory data that was not identified (Abstract teaches on determining if the PPG signal is “consistent”, it may be further analyzed to determine an underlying signal parameter such as a patient respiration rate (interpreted as not differing from prototypical waveform), however, if the PPG signal is determined to be “inconsistent”, the “inconsistent portion of the signal” may be removed from the overall signal – interpreted as teaching that the inconsistent data has been removed, so the consistent data (e.g., the data not identified as differing from prototypical waveform) has been determined and remains; [0068] teaches on selecting the “consistent parts” (the part not identified as being inconsistent) to determine an underlying parameter of the patient such as respiration rate; Respiration rate is understood to be a physiological parameter and is interpreted as being a type of sensory data; While the cited examples refer to “respiration rate” as the sensory data that is identified as corresponding to portions of a PPG signal that either differ from or conform to a prototypical waveform pattern, respiration rate, SpO2 and pulse ox are all understood to be physiological parameters that may be determined from a PPG signal (see at least paras. [0043], [0067]. It would have been obvious to substitute SpO2 and pulse ox for respiration rate since the combination is merely simple substitution of one known element for another producing a predictable result (KSR B). Since each individual element is shown within the teachings of McGonigle (e.g., respiration rate, pulse ox, and SpO2 are all physiological parameters that can be determined from PPG signals), the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself—that is, in the substitution of the physiological parameters of SpO2 and pulse ox as taught in [0043] for the physiological parameter of respiration rate as taught in [0086]). Thus, the simple substitution of one known element (SpO2 and pulse rate) for another (respiration rate) producing a predictable result renders the claim obvious);
displaying [information] , wherein the display comprises a percentage blood oxygen saturation, a heartrate, and a PPG signal measured by the two or more sensors ([0031], monitor 14 may include a display 20 configured to display the physiological parameters which are understood to include SpO2 and pulse rate per [0032]; Fig. 5/[0066] teaching on displaying a PPG signal plot using any suitable display device such as a monitor or display of a computing device).
McGonigle does not teach, but Dos Santos, which is directed to systems and methods for monitoring user activity using a PPG sensor, teaches:
a sensor device worn by the patient ([0063] teaches on one or more sensors being integrated or embedded in a smartwatch which is interpreted as being a sensor device worn by the patient).
analyzing the sensory data that was not identified to generate a recommendation ([0079] teaches on determining whether a physiological parameter exceeds a predetermined threshold which may be an unsafe or unhealthy level, e.g., a threshold can be indicative of a high heart rate that could pose a risk to the user; [0081] teaches on, responsive to determination that the physiological parameter has exceeded the predetermined threshold during an activity, generating an alert and communicating the alert to the user or third party; the alert communicated to the user can “encourage the user to reduce their exertion, e.g., slow down” (interpreted as “a recommendation for an activity level”), wherein the recommendation is generated by machine learning (ML) models and/or algorithms ([0089] teaching on selecting activity modules within an activity session for a user; [0090] teaches on the activity modules comprising the activity session being selected from the plurality of modules using a trained machine learning algorithm); and
displaying the recommendation to at least one of a patient, a clinician, or an agent with a display ([0081] teaches on, responsive to determination that the physiological parameter has exceeded the predetermined threshold during an activity, generating an alert and communicating the alert to the user or third party via a display device; the alert communicated to the user can “encourage the user to reduce their exertion, e.g., slow down”; [0100] teaching on recommendations can be communicated to a user during the activity session or during a pause in an activity module; an activity module may be paused due to a physiological parameter such as heart rate exceeding a threshold, and a recommendation can be communicated to the user during the pause to aid in modifying the parameter, e.g., sit down, do breathing exercises), wherein the display comprises a percentage blood oxygen saturation, a heartrate, a perfusion index (PI), and a PPG signal measured by the one or more sensors.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify McGonigle with these teachings of Dos Santos, to use one or more sensors within a patient device as taught by Dos Santos, such as a smart watch, with the motivation of providing sensors that can be worn by the patient ([0063]), and to analyze the sensor data of McGonigle (e.g., the “consistent” data that was not removed/identified) to generate a recommendation for an activity level of the patient and provide the recommendation to the patient’s device, wherein the recommendation is generated by a ML model, with the motivation of automatically monitoring patients for whom overexertion can pose a serious health or safety risk and pause or stop an activity when physiological parameters exceed a threshold and aid in modifying the physiological parameter so that it no longer exceeds the threshold and to use a machine learning model that can be trained to learn and adapt to the current user (Dos Santos [0024], [0081], [0090]).
McGonigle/Dos Santos do not teach the following but Lange, which is directed to a system and method for monitoring respiratory rate and oxygen saturation, teaches:
perfusion index of the patient ([0076]-[0077] teach on determining a perfusion index for a patient based on obtained PPG data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to further modify McGonigle/Dos Santos with these teachings of Lange to determine perfusion index of the patient and include it with the displayed information of McGonigle/Dos Santos, as it can be used to indicate the quality of the PPG signal ([0077]).
McGonigle/Dos Santos/Lange do not teach the following, but Oktay, which is directed to generating medical reports, teaches:
receiving, via the display, an indication of adoption or rejection of the recommendation, wherein the indication of adoption or rejection is used as part of a training set to refine the ML models and/or algorithms ([0035] teaches on using a machine learning model to generate suggestions; [0040] teaches on training of a machine learning model, based on the user accepting or rejecting the model’s suggestions in each iteration; per abstract, the suggestions(recommendation) are output to an interface which is interpreted as being a display).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify McGonigle/Dos Santos with these teachings of Oktay, to receive an indication of an adoption/rejection of the ML model of McGonigle/Dos Santos/Lange, with the motivation of refining the training of the machine learning model with each iteration (Oktay [0040]).
Regarding Claim 30, McGonigle/Dos Santos/Lange teach the limitations of Claim 29. McGonigle further discloses wherein the PPG signal includes a waveform (Para. [0066] and Fig. 5, teaching on/showing a plot of a PPG signal as a waveform).
Claim(s) 31 is/are rejected under 35 U.S.C. 103 as being unpatentable over McGonigle et. al. (US Publication 20140012109 ) in view of Dos Santos et. al. (US Publication 20230253103A1), and further in view of Oktay et. al. (US Publication 20230102428A1) as applied to Claim 1 above, and further in view of Blomquist US Publication 20080300534 A1).
Regarding Claim 31, McGonigle/Dos Santos/Oktay teach the limitations of Claim 1 but do not teach the following. Blomquist, which is directed to an insulin pump system, teaches:
wherein the recommendation for [an action] of the patient is generated based on a rate of change of the sensory data over time relative to a target range ([0045] teaches on using a rate of change of blood level to determine one or more recommended actions for display, where [0022] teaches on having a specified/acceptable range (“target range”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to further modify the combined teachings of McGonigle/Dos Santos/Oktay with these teachings of Blomquist, to generate the recommendation for the activity level of McGonigle/Dos Santos/Oktay based on a rate of change of sensory data over time relative to a target range, with the motivation of using the rate of change of the sensory data as an indicator of risk when the individual sensory data points themselves may be normal (Blomquist [0045]).
Claim(s) 33 is/are rejected under 35 U.S.C. 103 as being unpatentable over McGonigle et. al. (US Publication 20140012109 ) in view of Dos Santos et. al. (US Publication 20230253103A1), and further in view of Oktay et. al. (US Publication 20230102428A1) as applied to Claim 1 above, and further in view of Parthasarathy et. al. (US Publication 20100210924A1).
Regarding Claim 33, McGonigle/Dos Santos/Oktay teach the limitations of Claim 1 but do not teach the following. Parthasarathy, which is directed to an oximeter device, teaches: wherein the patient device comprises a wrist- mounted device, wherein the optical sensor comprises a finger sleeve coupled to the wrist- mounted device (see Fig. 1, a wrist-mounted device coupled to device 10A which is worn over the finger; see paras. [0044]-[0045], the sensor element 12A within the finger tip sleeve includes an optical detector).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to further modify the combined teachings of McGonigle/Dos Santos/Oktay with these teachings of Parthasarathy, to use a wrist-mounted device with an optical sensor in a finger sleeve coupled to the wrist-mounted device, with the motivation of using a sensor module that has a flexible structure to be conformed to provide a close fit on a patient to provide medical data (Parthasarathy [0005]).
Claim(s) 34 is/are rejected under 35 U.S.C. 103 as being unpatentable over McGonigle et. al. (US Publication 20140012109 ) in view of Dos Santos et. al. (US Publication 20230253103A1) and further in view of Oktay et. al. (US Publication 20230102428A1) as applied to Claim 1 above, and further in view of Georgiev et al. article “Identification of Sine, Squire, Triangle and Sawtooth Waveforms with Uniform White and Inverse F Noises by Adaptive Neuro-Fuzzy Interface System" (hereinafter Georgiev), and further in view of Wu et. al. (US Publication 20210386307A1).
Regarding Claim 34, McGonigle/Dos Santos/Oktay/Georgiev teach the limitations of Claim 8 but do not teach the following. Wu, which is directed for detecting heart rate measurement, teaches: wherein peaks and valleys of the prototypical waveform pattern are aligned to peaks and valleys of the PPG data ([0110] teaching on aligning the filter pulse signal with a reference signal), wherein peak heights of the aligned PPG data are normalized prior to calculation of the least means squared algorithm ([0076] teaches on using a normalized least mean squared filter).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to further modify the combined teachings of McGonigle/Dos Santos/Oktay/Georgiev with these teachings of Wu, to align the prototypical waveform pattern to PPG data and normalize the signal data prior to calculating least means squared algorithm, with the motivation of mitigating motion artifacts in the pre-processed signal (Wu [0076]).
Response to Applicant’s Remarks/Arguments
Please note: When referencing page numbers of Applicant’s response, references are to page numbers as printed.
35 USC 112(b)
The rejection of Claims 1, 3-9, 21-27, 29-30 under 35 USC 112(b) for indefiniteness are withdrawn in view of Applicant’s amendments to Claims 1 and 29.
35 USC 101 Rejections
Regarding the rejection of 1, 3-9, 21-27, 29-30, Applicant has canceled Claim 10, rendering the 35 USC 101 rejection of Claim 10 moot. Regarding the remaining claims, including new claims 31-34, the Examiner has considered Applicant’s arguments; however, the arguments are not persuasive. Applicant argues:
Independent Claim 1 integrates any alleged abstract idea into a practical application (step 2A) (page 9)
Regarding (B), the Examiner respectfully disagrees. Applicant asserts (page 9) that independent claim 1 as a whole “transforms the alleged abstract ideas into a practical application since the additional elements when working together with the alleged abstract ideas achieve a meaningful limit on the abstract ideas”. However, Applicant has not provided support, nor can Examiner find, evidence of a meaningful limit placed on the abstract idea.
Regarding Applicant’s remarks to “practical application” , MPEP 2106.04(d)(I) provides relevant considerations for evaluating whether additional elements integrate a judicial exception into a practical application:
• An improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
• Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
• Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, 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).
Examiner submits that Applicant has not explained or provided evidence of, nor can Examiner find evidence in the instant specification, of how the instant invention satisfies any of the above considerations. Regarding remarks at page 10 pertaining to “additional features” such as “receiving, via the external device, an indication of adoption or rejection of a recommendation generated by machine learning (ML) models and/or algorithms, wherein the indication of adoption or rejection is used as part of a training set to refine the ML models and/or
Algorithms”, Examiner respectfully disagrees that this feature transforms the abstract ideas into a practical application. Examiner submits that the “external deice” only amounts to mere instructions to apply the abstract idea of receiving an indication of adoption or rejection of a recommendation generated by ML models/algorithms, and using the indication to refine the models/algorithms. Regarding remarks pertaining to the claim providing a “mechanism by which the output of the ML models and/or algorithms is iteratively refined”, Examiner submits that this is how all machine learning models are optimized (i.e., select training data, train the model, compare the output to validation data, receive feedback, adjust the parameters of the training data according to the comparison/feedback, and repeat until an accuracy threshold is met). Put another way, the particular way the machine learning model of applicant’s invention uses the data to train itself is not improved, which is the holding of Desjardins. Applicant is merely improving the accuracy of the model by iteratively retraining the model with user feedback. Improving the accuracy of a model is not an improvement by any measure in MPEP 2106. Further, Examiner submits that the broad recitation of a machine learning model, in this case to determine an activity level recommendation, only amounts to using the machine learning model as a tool to apply data to a model and generate a result (see MPEP 2106.05(f)(2)). This argument is not persuasive.
Regarding remarks pertaining to August 2025 updates and Applicant’s remarks and “the way in which the additional elements use or interact with the exception”, and MPEP 2106.04(d)(1), the additional elements are understood to be general purpose computing components functioning in their ordinary capacities. Applicant has not provided, nor can Examiner find, how any of the additional elements integrate the judicial exception into a practical application, amount to significantly more than the judicial exception, or amount to anything other than well understood, routine and conventional activity in the healthcare field.
MPEP 2106.04(d)(I) further states:
The courts have also identified limitations that did not integrate a judicial exception into a practical application:
• Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
• Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
• Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
Regarding remarks at bottom of page 10 pertaining to “no particulars of machine learning model are disclosed”, Examiner maintains the position that the ML models and/or algorithms are understood to be known ML models functioning on a generic computing device. See above remarks pertaining to “transforming” a judicial exception into a practical application. The ML model/algorithm is used to generally apply the abstract idea without placing any limits on how the ML model/algorithm actually functions. MPEP 2106.05(f). This argument is not persuasive. This argument is not persuasive.
For the above reasons, the rejections of Claims 1, 3-10, 21-30 under 35 USC 101 are maintained.
35 USC 103 Rejections
Applicant’s arguments with respect to claim(s) 1 have been considered but are moot because the new grounds of rejection do not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. The same rationale applies to newly added independent claims 28 and 29 which are substantially similar to Claim 1.
Regarding the rejection of dependent claims, the Applicant has not offered any arguments with respect to these claims other than to reiterate the argument(s) present for the claims from which they depend. As such, the rejection of these claims is also maintained.
The rejections of Claims 1, 3-9, 21-34 under 35 USC 103 are maintained.
Conclusion
In the interest of expediting prosecution, Examiner respectfully requests that Applicant provides citations to relevant paragraphs of specification for support for amendments in future correspondence.
The following relevant prior art not cited is made of record:
US Publication US 20220273269 A1, teaching on cycle-based workout coaching which provides a recommendation for activity level intensity based on user sensor data
US Publication 20210008413 A1, teaching on an interactive personal training device
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANNE-MARIE K ALDERSON whose telephone number is (571)272-3370. The examiner can normally be reached on Mon-Fri 9:00am-5:00pm EST, and generally schedules interviews in the timeframe of 2:00-5:00pm EST.
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/ANNE-MARIE K ALDERSON/Primary Examiner, Art Unit 3682
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