DETAILED ACTION
Applicant’s arguments, filed on 10/07/2025, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed on 10/07/2025, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1-30 are the current claims hereby under examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 13-18 and 27 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 13, the claim recites the limitation “at least one of a plurality of predicted oxygen saturation levels” in lines 9-10. It is unclear if this limitation is meant to refer to the predicted oxygen saturation level from claim 1, or a different predicted oxygen saturation level. If it is meant to refer to the predicted oxygen saturation level from claim 1, it needs to refer back to it. If it is referring to a different predicted oxygen saturation level, it needs to be distinguished from the predicted oxygen saturation level from claim 1. For purposes of examination, it is being interpreted as referring to the predicted oxygen saturation level from claim 1. Claims 14-18 are also rejected due to their dependency on claim 13.
Regarding claim 27, the claim recites the limitation “output the indication of the patient experiencing the oxygen saturation event”. It is unclear how the method involves both refraining from outputting the indication, as stated in claim 19, and also outputting the indication. It is not clear how the method is able to do both of these processes, as they conflict and cannot be performed at the same time. The broad and indefinite scope of the limitation fails to inform a person of ordinary skill in the art with reasonable certainty of the metes and bounds of the claimed invention, therefore the claim is rendered indefinite. For purposes of examination, it is being interpreted as being two separate iterations of the method not occurring in the same embodiment.
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.
Claims 1, 9, 12, and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Al-Ali (US 20140128696) in further view of Mannheimer (US 20080183058), Yu (US 12507961), and Meger (CN 102113034). Citations to CN 102113034 will refer to the English Machine Translation that accompanies this Office Action.
Regarding independent claim 1, Al-Ali teaches a method comprising:
receiving, by processing circuitry of a patient monitoring device (Abstract: “A processor has an input responsive to the sensor signal and a physiological parameter output”), a signal indicative of a monitored oxygen saturation level of a patient ([0017]: “A pulse oximeter sensor (not shown) provides a signal input 301 that is responsive to arterial oxygen saturation”);
determining, by the processing circuitry, that the signal indicates the monitored oxygen saturation level is at or below a threshold ([0004]: “FIG. 2 illustrates the operation of a conventional threshold detector 120 (FIG. 1) utilizing a graph 200 of oxygen saturation 201 versus time 202. The graph 200 displays a particular oxygen saturation measurement 210 corresponding to the measurement output 103 (FIG. 1) and a predetermined alarm threshold 206”);
in response to determining the monitored oxygen saturation level is at or below the threshold, initiating a calculation period ([0004]: “During an alarm time period 270 when the measured oxygen saturation 210 is below the threshold 206, an alarm output 105 (FIG. 1) is generated”).
However, Al-Ali does not disclose determining the oxygen saturation in response to determining the oxygen saturation level is at or below the desaturation threshold.
Mannheimer discloses a method and apparatus for controlling alarms in a medical diagnostic apparatus. Specifically, Mannheimer teaches determining the oxygen saturation in response to determining the monitored oxygen saturation level is at or below the desaturation threshold at the end of the calculation period ([0026]: “saturation signal 70 is compared to a low sat threshold 74. Also illustrated is an integral threshold 78. An excursion 80 produces an integral value 82 that can exceed the integral threshold 78. The value 82 is a product of the amount of time and the amount by which the measured value of oxygen saturation exceeds the threshold … As the instantaneous readings of SpO.sub.2 (or other variable) wander beyond these thresholds, the product 82 of time and extent beyond the threshold is calculated.”). Al-Ali and Mannheimer are analogous arts as they are both related to systems that generate alarms based on oxygen saturation measurements.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include determining the oxygen saturation in response to determining the oxygen saturation level is at or below the desaturation threshold from Mannheimer into the method from Al-Ali as it allows the method to start determining the oxygen saturation at the end of the calculation period only if it is at or below the threshold, which means it only does the analysis when necessary and not at all times.
The Al-Ali/Mannheimer combination discloses initiating a prediction period ([0018]: “The predictor SPO.sub.2 processor 420, advantageously, provides a curve-fitting or a predictive measurement of oxygen saturation that detects trends in oxygen saturation”), however the combination does not teach initiating the prediction period after determining that the monitored oxygen saturation is at or below the desaturation threshold.
Yu discloses systems and method for monitoring fluid of a user. Specifically, Yu teaches in response to determining that the monitored parameter of the patient is at or below the threshold at the end of the calculation period, initiating a prediction period (Column 5, lines 56-60: “the controller may be configured to predict an infection score of a patient based at least in part on the signal. In some variations, the controller may be configured to predict an infection state of a patient in response to any one or more of the following: the infection score exceeding a predetermined threshold during each of one or more successive measurement time periods”). Al-Ali, Mannheimer, and Yu are analogous arts as they are all related to systems that predict the health state of a user based on monitored parameters.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the steps of initiating the prediction period after it is determined that the monitored parameter is outside a threshold from Yu into the Al-Ali/Mannheimer combination as it allows the combination to predict the oxygen saturation only after it is determined to be outside the threshold, which ensures the method is not doing unnecessary calculations and only determining the predictions when necessary.
The Al-Ali/Mannheimer/Yu combination teaches predicting, by the processing circuitry and using an oxygen saturation prediction model, a predicted oxygen saturation level of the patient will increase above the desaturation threshold by the end of the prediction period (Al-Ali, [0018]: “The predictor SPO.sub.2 processor 420, advantageously, provides a curve-fitting or a predictive measurement of oxygen saturation that detects trends in oxygen saturation”; [0007]: “In various embodiments, a threshold is input to the pattern extractor. The identified features comprise at least the number of times the physiological measurement crosses the threshold within a predetermined time period. The identified features comprise at least the duration of each time the physiological measurement crosses the threshold. The physiological measurement comprises a predictive oxygen saturation measurement.”; [0017]: “A predictor SpO.sub.2 processor 420 outputs a fast SPO.sub.2 measurement 422”; Figs 4-5); and
in response to predicting that the predicted oxygen saturation level of the patient will increase above the desaturation threshold by the end of the prediction period, refraining from outputting an indication of the patient experiencing an oxygen desaturation event (Al-Ali, Fig. 5, alarm is only generated if threshold detector is below, and slope detector is not positive; [0019]: “the trend recognition alarm indicator 320 reduces false alarms by suppressing a threshold-based alarm on the slow SpO.sub.2 measurement 412 when the fast SpO.sub.2 measurement 422 determines that a patient's oxygen saturation is in recovery”; Figs. 6A-6B).
However, the Al-Ali/Mannheimer/Yu combination is silent on how long the method refrains from outputting the alert.
Meger discloses a method and system to monitor and predict clinical episodes. Specifically, Meger teaches refraining for at least a delay period from outputting an indication of the patient experiencing an event ([0393]: “if the value of a clinical parameter indicates that the system should issue an alert, the system will delay issuing the alert if the confidence level is low. During the delay period, the system continuously measures the clinical parameters and assesses whether to issue an alert.”). Al-Ali, Mannheimer, Yu, and Meger are analogous arts as they are all related to systems that predict the health state of a user based on monitored parameters.
Regarding claim 9, the Al-Ali/Mannheimer/Yu/Meger combination teaches the method of claim 1, further comprising: after predicting that the predicted oxygen saturation level of the patient will increase above the desaturation threshold within the prediction period, determining, by the processing circuitry and prior to the prediction period ending, the monitored oxygen saturation level of the patient is continuing to decrease; and in response to determining that the oxygen saturation level of the patient is continuing to decrease, outputting, by the processing circuitry after the delay period, the indication of the patient experiencing the oxygen desaturation event (Al-Ali, Fig. 6A-6B. Figure 6B shows that when both the measure and prediction of SP02 levels are decreasing, the alarm is presented, in contrast to Figure 6A,where when the measured and predicted oxygen saturation is increasing, the alarm is suppressed.).
Regarding claim 12, the Al-Ali/Mannheimer/Yu/Meger combination teaches the method of claim 1, further comprising: determining, by the processing circuitry and at the end of the prediction period, the monitored oxygen saturation level of the patient is not above the desaturation threshold; and in response to determining that the monitored oxygen saturation level of the patient is not above the desaturation threshold, outputting, by the processing circuitry, the indication of the patient experiencing the oxygen desaturation event (Al-Ali, Fig. 6A-6B. Figure 6B shows that when both the measure and prediction of SP02 levels are decreasing, the alarm is presented, in contrast to Figure 6A,where when the measured and predicted oxygen saturation is increasing, the alarm is suppressed.).
Regarding independent claim 30, Al-Ali teaches receiving a signal indicative of a monitored oxygen saturation level of a patient ([0017]: “A pulse oximeter sensor (not shown) provides a signal input 301 that is responsive to arterial oxygen saturation”);
Determine that the signal indicates the monitored oxygen saturation level is at or below a threshold ([0004]: “FIG. 2 illustrates the operation of a conventional threshold detector 120 (FIG. 1) utilizing a graph 200 of oxygen saturation 201 versus time 202. The graph 200 displays a particular oxygen saturation measurement 210 corresponding to the measurement output 103 (FIG. 1) and a predetermined alarm threshold 206”);
in response to determining the monitored oxygen saturation level is at or below the threshold, initiating a calculation period ([0004]: “During an alarm time period 270 when the measured oxygen saturation 210 is below the threshold 206, an alarm output 105 (FIG. 1) is generated”).
However, Al-Ali does not disclose determining the oxygen saturation in response to determining the oxygen saturation level is at or below the desaturation threshold.
Mannheimer discloses a method and apparatus for controlling alarms in a medical diagnostic apparatus. Specifically, Mannheimer teaches determining the oxygen saturation in response to determining the monitored oxygen saturation level is at or below the desaturation threshold at the end of the calculation period ([0026]: “saturation signal 70 is compared to a low sat threshold 74. Also illustrated is an integral threshold 78. An excursion 80 produces an integral value 82 that can exceed the integral threshold 78. The value 82 is a product of the amount of time and the amount by which the measured value of oxygen saturation exceeds the threshold … As the instantaneous readings of SpO.sub.2 (or other variable) wander beyond these thresholds, the product 82 of time and extent beyond the threshold is calculated.”). Al-Ali and Mannheimer are analogous arts as they are both related to systems that generate alarms based on oxygen saturation measurements.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include determining the oxygen saturation in response to determining the oxygen saturation level is at or below the desaturation threshold from Mannheimer into the method from Al-Ali as it allows the method to start determining the oxygen saturation at the end of the calculation period only if it is at or below the threshold, which means it only does the analysis when necessary and not at all times.
The Al-Ali/Mannheimer combination discloses initiating a prediction period ([0018]: “The predictor SPO.sub.2 processor 420, advantageously, provides a curve-fitting or a predictive measurement of oxygen saturation that detects trends in oxygen saturation”), however the combination does not teach initiating the prediction period after determining that the monitored oxygen saturation is at or below the desaturation threshold.
Yu discloses systems and method for monitoring fluid of a user. Specifically, Yu teaches in response to determining that the monitored parameter of the patient is at or below the threshold at the end of the calculation period, initiating a prediction period (Column 5, lines 56-60: “the controller may be configured to predict an infection score of a patient based at least in part on the signal. In some variations, the controller may be configured to predict an infection state of a patient in response to any one or more of the following: the infection score exceeding a predetermined threshold during each of one or more successive measurement time periods”). Al-Ali, Mannheimer, and Yu are analogous arts as they are all related to systems that predict the health state of a user based on monitored parameters.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the steps of initiating the prediction period after it is determined that the monitored parameter is outside a threshold from Yu into the Al-Ali/Mannheimer combination as it allows the combination to predict the oxygen saturation only after it is determined to be outside the threshold, which ensures the method is not doing unnecessary calculations and only determining the predictions when necessary.
The Al-Ali/Mannheimer/Yu combination teaches predicting, using an oxygen saturation prediction model, a predicted oxygen saturation level of the patient will increase above the desaturation threshold by the end of the prediction period (Al-Ali, [0018]: “The predictor SPO.sub.2 processor 420, advantageously, provides a curve-fitting or a predictive measurement of oxygen saturation that detects trends in oxygen saturation”; [0007]: “In various embodiments, a threshold is input to the pattern extractor. The identified features comprise at least the number of times the physiological measurement crosses the threshold within a predetermined time period. The identified features comprise at least the duration of each time the physiological measurement crosses the threshold. The physiological measurement comprises a predictive oxygen saturation measurement.”; [0017]: “A predictor SpO.sub.2 processor 420 outputs a fast SPO.sub.2 measurement 422”; Figs 4-5); and
in response to predicting that the predicted oxygen saturation level of the patient will increase above the desaturation threshold by the end of the prediction period, refrain from outputting an indication of the patient experiencing an oxygen desaturation event (Al-Ali, Fig. 5, alarm is only generated if threshold detector is below, and slope detector is not positive; [0019]: “the trend recognition alarm indicator 320 reduces false alarms by suppressing a threshold-based alarm on the slow SpO.sub.2 measurement 412 when the fast SpO.sub.2 measurement 422 determines that a patient's oxygen saturation is in recovery”; Figs. 6A-6B).
However, the Al-Ali/Mannheimer/Yu combination is silent on how long the method refrains from outputting the alert.
Meger discloses a method and system to monitor and predict clinical episodes. Specifically, Meger teaches refraining for at least a delay period from outputting an indication of the patient experiencing an event ([0393]: “if the value of a clinical parameter indicates that the system should issue an alert, the system will delay issuing the alert if the confidence level is low. During the delay period, the system continuously measures the clinical parameters and assesses whether to issue an alert.”). Al-Ali, Mannheimer, Yu, and Meger are analogous arts as they are all related to systems that predict the health state of a user based on monitored parameters.
However, the Al-Ali/Mannheimer/Yu/Meger combination does not disclose a non-transitory computer readable storable medium comprising instructions.
Yu disclose a non-transitory computer readable storable medium comprising instructions (Column 29, lines 14-19: “Some variations described herein relate to a computer storage product with a non-transitory computer-readable medium (also may be referred to as a non-transitory processor-readable medium) having instructions or computer code thereon for performing various computer-implemented operations.”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the storage as it allows the system to have a storage location suitable for storing the instructions for the method.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over the Al-Ali/Mannheimer/Yu/Meger combination as applied to claim 1 above, and further in view of King (US 9649073) and Gross (US 10264968).
Regarding claim 2, the Al-Ali/Mannheimer/Yu/Meger combination teaches the method of claim 1.
However, the Al-Ali/Mannheimer/Yu/Meger combination does not teach wherein the oxygen saturation prediction model executes at an external device that is communicably coupled to the patient monitoring device, and wherein a latency period is between the calculation period and the prediction period to account for communications latency between the patient monitoring device and the external device.
King discloses a system used to manage patient alarms. Specifically, King teaches wherein the oxygen saturation prediction model executes at an external device that is communicably coupled to the patient monitoring device (Column 21, lines 32-40: “the input/output (“I/O”) interface 2060 may be configured to couple to one or more external devices, to receive data from the one or more external devices, and to send data to the one or more external devices. Such external devices along with the various internal devices may also be known as peripheral devices. The I/O interface 2060 may include both electrical and physical connections for operably coupling the various peripheral devices to the computing machine 2000 or the processor 2010”). Al-Ali, Mannheimer, and King are analogous arts as they are all related to devices used to monitor patient’s health conditions and initiate alarms.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the external device from King into the Al-Ali/Mannheimer/Yu/Meger combination as it allows the combination to have a separate device that can be used to predict the oxygen saturation, which can allow the external device to be used in a different location than the patient monitoring device, which can allow it to be more mobile and viewed by different users.
However, the Al- Al-Ali/Mannheimer/Yu/Meger/King combination does not teach wherein a latency period is between the calculation period and the prediction period to account for communications latency between the patient monitoring device and the external device.
Gross discloses sensors used to monitor a physiological condition of a user. Specifically, Gross teaches wherein a latency period occurs between the calculation period and the prediction period to account for communications latency between the patient monitoring device and the external device (Column 12, lines 28-40: “One reason such synchronization is valuable is that different sensor data streams may have different latency times between the acquisition of the common continuous physiological parameter and the availability of the indicative sensor data stream to the coordination function … Such latencies can result from signal transmission delays (e.g., capacitive or inductive delays), data processing delays, or so forth, delays introduced by communication protocols, or so forth.”). Al-Ali, Mannheimer, King, and Gross are analogous arts as they are all related to devices used to monitor patient’s health conditions and initiate alarms.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the latency period from Gross into the Al-Ali/Mannheimer/Yu/Meger/King combination as it allows the combination to account for the period of time needed for communication between the devices and for the calculation time, which can ensure a more accurate result.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over the Al-Ali/Mannheimer/Yu/Meger combination as applied to claim 1 above, and further in view of Hu (US 9600990).
Regarding claim 3, the Al-Ali/Mannheimer/Yu/Meger combination teaches the method of claim 1.
However, the Al-Ali/Mannheimer/Yu/Meger combination does not teach further comprising: setting, by the processing circuitry, a prediction threshold associated with the oxygen saturation prediction model to correspond to a sensitivity associated with the oxygen saturation prediction model.
Hu discloses a system and method for monitoring physiological data and generating alarms. Specifically, Hu teaches further comprising: setting, by the processing circuitry, a first prediction threshold value associated with the oxygen saturation prediction model to correspond to a sensitivity associated with the oxygen saturation prediction model (Column 3, lines 18-20: “FIG. 10 is a graph showing sensitivity curves of four super-alarm sets obtained using the optimal algorithm parameters under four different false positive ratio thresholds.”; Column 9, lines 58-64: “we determine the optimal algorithm parameters for a given FPR threshold and then use the full training data to obtain the super-alarm set. Table 3 lists the optimal parameters for each choice of FPR threshold, the total number of super-alarm patterns, the total number of super-alarms per each pattern length, and the average sensitivity obtained at the specified FPR threshold.”). Al-Ali, Mannheimer, and Hu are analogous arts as they are all related to devices used to monitor patient’s health conditions and initiate alarms.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to account for sensitivity from Hu into the Al-Ali/Mannheimer/Yu/Meger combination as it allows the combination to factor in the sensitivity of the model, which can ensure a more accurate result and produce the correct alarm that is necessary.
Claims 4-7 are rejected under 35 U.S.C. 103 as being unpatentable over the Al-Ali/Mannheimer/Yu/Meger/Hu combination as applied to claim 3 above, and further in view of Ray (WO 2022067125).
Regarding claim 4, the Al-Ali/Mannheimer/Yu/Meger/Hu combination teaches the method of claim 3, further comprising: generating, by the processing circuitry, a receiver operator characteristic (ROC) curve associated with the oxygen saturation prediction model (Hu, Column 7, lines 21-23: “a receiver operator characteristic (ROC) curve is generated for each 10-fold CV data set following the conventional cross-validation analysis.”).
However, the Al-Ali/Mannheimer/Yu/Meger/Hu combination does not teach determining, by the processing circuitry and based at least in part on the ROC curve, a prediction level that corresponds to the sensitivity; and adjusting, by the processing circuitry and based at least in part on the prediction level that corresponds to the sensitivity, the prediction threshold associated with the oxygen saturation prediction model to maintain the sensitivity associated with the oxygen saturation prediction model over time.
Ray discloses a system and method for developing a fetal oximetry model. Specifically, Ray teaches determining, by the processing circuitry and based at least in part on the ROC curve, a prediction level that corresponds to the sensitivity ([000103]-[000104]: “models (e.g., simulated fetal oximetry models) generated by process 400 may incorporate K-fold cross-validation to, for example, generate the expected error, receiver operating characteristic (ROC), and/or area under the curve (AUC) values for the model. In some embodiments, execution of step 410 may include selection of one or more types of outputs that may be incorporated into the machine learning architecture. Exemplary outputs include predicted fetal oximetry (e.g., SpO2 and/or fetal tissue oxygen saturation) values”); and adjusting, by the processing circuitry and based at least in part on the prediction level that corresponds to the sensitivity, the first prediction threshold value to a second prediction threshold value associated with the oxygen saturation prediction model to maintain the sensitivity associated with the oxygen saturation prediction model over time ([00059]: “the present invention may be used to perform sensitivity analysis, which may allow for changing multiple variables/parameters used to generate the models and/or simulated light transmission data sets so that, for example, the results (e.g., calculated fetal SpO2 values) may be evaluated for accuracy and/or to determine how multiple variables may interact with one another to vary calculated fetal SpO2 values.”; [000113]: “process 400 and/or portions thereof may be repeated on a periodic, as-needed, and/or continuous basis to, for example, improve the accuracy of the predictions the model yields, perform perturbation analysis, and/or perform sensitivity analysis”). Al-Ali, Mannheimer, and Hu are analogous arts as they are all related to devices used to monitor patient’s health conditions.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the analysis using the ROC curve from Ray into the Al-Ali/Mannheimer/Yu/Meger/Hu combination as it allows the combination to further analyze the ROC curve and determine parameters from the curve, which can provide further information to the model to determine a more accurate prediction.
Regarding claim 5, the Al-Ali/Mannheimer/Yu/Meger/Hu/Ray combination teaches the method of claim 4.
However, the Al-Ali/Mannheimer/Yu/Meger/Hu/Ray combination does not teach further comprising: updating, by the processing circuitry, the ROC curve based on updated data that includes patient data associated with the patient; and redetermining, by the processing circuitry and based at least in part on the updated ROC curve, the prediction level that corresponds to the sensitivity.
Ray teaches further comprising: updating, by the processing circuitry, the ROC curve based on updated data that includes patient data associated with the patient ([000111]: “the third set of predicted output values may be compared with the corresponding measured output values to determine differences between them (step 428). Results of the comparison may then be evaluated (step 430) and used to update the in vivo fetal oximetry model”); and redetermining, by the processing circuitry and based at least in part on the updated ROC curve, a redetermined prediction level that corresponds to the sensitivity ([00059]: “the present invention may be used to perform sensitivity analysis, which may allow for changing multiple variables/parameters used to generate the models and/or simulated light transmission data sets so that, for example, the results (e.g., calculated fetal SpO2 values) may be evaluated for accuracy and/or to determine how multiple variables may interact with one another to vary calculated fetal SpO2 values.”; [000113]: “process 400 and/or portions thereof may be repeated on a periodic, as-needed, and/or continuous basis to, for example, improve the accuracy of the predictions the model yields, perform perturbation analysis, and/or perform sensitivity analysis”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the updating from Ray into the Al-Ali/Mannheimer/Yu/Meger/Hu/Ray combination as it allows the combination to update over time and be more accurate when more information is provided, which ensures the most accurate result.
Regarding claim 6, the Al-Ali/Mannheimer/Yu/Meger/Hu/Ray combination teaches the method of claim 4, further comprising: outputting, by the processing circuitry and for display at a display, the ROC curve associated with the oxygen saturation prediction model (Al-Ali, [0003]: “The oxygen saturation measurement output 103 is typically a digital signal that is then communicated to a display.”); receiving, by the processing circuitry, user input that corresponds to a point on ROC curve (Al-Ali, [0016]: “The alarm conditions 305 define particular limits with respect to one or more of the measurements 312. The alarm conditions 305 may be predefined, such as by user input, or determined by a separate process, such as a measurement of sensor signal quality or data confidence”); mapping, by the processing circuitry, the point on the ROC curve to a third prediction threshold value; and adjusting, by the processing circuitry, the second prediction threshold value associated with the oxygen saturation prediction model according to the prediction threshold value (Hu, Column 7, lines 44-48: “Based on a ROC curve, an operating point 212 is picked by first specifying the maximally tolerated FPR (FPRmax) and then the operating point is determined at the location where the TPR is the maximized while the corresponding FPR is below the specified FPRmax.”; Column 9, lines 58-64: “we determine the optimal algorithm parameters for a given FPR threshold and then use the full training data to obtain the super-alarm set. Table 3 lists the optimal parameters for each choice of FPR threshold, the total number of super-alarm patterns, the total number of super-alarms per each pattern length, and the average sensitivity obtained at the specified FPR threshold.”).
Regarding claim 7, the Al-Ali/Mannheimer/Yu/Meger/Hu/Ray combination teaches the method of claim 3, wherein setting the prediction threshold associated with the oxygen saturation prediction model further comprises: receiving, by the processing circuitry, a false positive rate that corresponds to the sensitivity associated with the oxygen saturation prediction model (Hu, Column 3, lines 18-20: “FIG. 10 is a graph showing sensitivity curves of four super-alarm sets obtained using the optimal algorithm parameters under four different false positive ratio thresholds.”).
Claims 10-11 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over the Al-Ali/Mannheimer/Yu/Meger combination as applied to claim 1 above, and further in view of Ray.
Regarding claim 10, the Al-Ali/Mannheimer/Yu/Meger combination teaches the method of claim 1.
However, the Al-Ali/Mannheimer/Yu/Meger combination does not teach wherein the oxygen saturation prediction model comprises a neural network algorithm trained via machine learning over training data that includes one or more of: sets of blood oxygen level of a population of patients, sets of blood pressure values of the population of patients, or metrics derived from sets of PPG signals of the population of patients.
Ray teaches wherein the oxygen saturation prediction model comprises a neural network algorithm trained via machine learning over training data that includes one or more of: sets of blood oxygen levels of a population of patients, sets of blood pressure values of the population of patients, or metrics derived from sets of PPG signals of the population of patients ([000103]: “inputs to the machine learning architecture and/or software program for determining fetal oximetry values may be selected … the machine learning architecture may be a neural network, an artificial neural network”; [0007]: “In order to train a fetal oximetry model using only measured in vivo data, a sufficient number (e.g., 5,000 - 10,000,000) of measured oximetry values in a healthy state (e.g., fetal oxygenation levels are sufficient) and a disease state (e.g., fetal hypoxia and/or fetal hypoxemia) and corresponding light transmission data must be measured and input into the machine learning/model training architecture to train a fetal oximetry model that outputs sufficiently accurate predictions of fetal oximetry values using light transmission data measured in a clinical setting”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the neural network from Ray into the Al-Ali/Mannheimer/Yu/Meger combination as it allows the combination to process the information and create the predictions using a trained model, which can provide a more efficient analysis.
Regarding claim 11, the Al-Ali/Mannheimer/Yu/Meger combination teaches the method of claim 1.
However, the Al-Ali/Mannheimer/Yu/Meger combination does not teach further comprising: determining, by the processing circuitry, offsets between oxygen saturation levels predicted using the oxygen saturation prediction model and actual oxygen saturation levels of the patient; and calibrating, by the processing circuitry, the oxygen saturation prediction model based at least in part on the determined offsets.
Ray teaches further comprising: determining, by the processing circuitry, an offset between the predicted oxygen saturation level predicted using the oxygen saturation prediction model and an actual oxygen saturation level of the patient; and calibrating, by the processing circuitry, the oxygen saturation prediction model based at least in part on the determined offset ([000111]: “the third set of predicted output values may be compared with the corresponding measured output values to determine differences between them (step 428). Results of the comparison may then be evaluated (step 430) and used to update the in vivo fetal oximetry model (step 432).”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the offset and calibration from Ray into the Al-Ali/Mannheimer/Yu/Meger combination as it allows the combination to calibrate itself, which ensures that it is providing the most accurate result.
Regarding claim 13, the Al-Ali/Mannheimer/Yu/Meger combination teaches the method of claim 1.
However, the Al-Ali/Mannheimer/Yu/Meger combination does not teach using multiple prediction models.
Ray teaches using multiple prediction models ([000106]: “The second version of the simulated fetal oximetry model may be used to predict a second set of outputs. In some embodiments, the second version of the simulated fetal oximetry model may be similar, or identical to, the first version of the fetal oximetry model.”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the multiple prediction models from Ray into the Al-Ali/Mannheimer/Yu/Meger combination as it allows the combination to create multiple predictions which can provide more information for the analysis and provide a more accurate result.
The Al-Ali/Mannheimer/Yu/Meger/Ray combination teaches wherein the predicting comprises using a plurality of oxygen saturation prediction models, wherein the oxygen saturation prediction model is a first oxygen saturation prediction model, and wherein the plurality of oxygen saturation prediction models includes at least the first oxygen saturation prediction model and a second oxygen saturation prediction model, the predicting comprising: determining, by the processing circuitry and using the plurality of oxygen saturation prediction models, at least one of a plurality of predicted oxygen saturation levels of the patient will increase above the desaturation threshold by the end of the prediction period, wherein the predicted oxygen saturation level is a first predicted oxygen saturation level, and wherein the plurality of predicted oxygen saturation levels includes at least the first predicted oxygen saturation level and a second predicted oxygen saturation level; and predicting, by the processing circuitry and based at least in part on the plurality of predicted oxygen saturation levels, whether the at least one predicted oxygen saturation level of the patient will increase above the desaturation threshold by the end of the prediction period (Al-Ali, [0018]: “The predictor SPO.sub.2 processor 420, advantageously, provides a curve-fitting or a predictive measurement of oxygen saturation that detects trends in oxygen saturation”; Figs 4-5; Fig. 5, alarm is only generated if threshold detector is below, and slope detector is not positive; [0019]: “the trend recognition alarm indicator 320 reduces false alarms by suppressing a threshold-based alarm on the slow SpO.sub.2 measurement 412 when the fast SpO.sub.2 measurement 422 determines that a patient's oxygen saturation is in recovery”; Figs. 6A-6B; Ray, [000106]: “The second version of the simulated fetal oximetry model may be used to predict a second set of outputs. In some embodiments, the second version of the simulated fetal oximetry model may be similar, or identical to, the first version of the fetal oximetry model.”).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over the Al-Ali/Mannheimer/Yu/Meger combination as applied to claim 1 above, and further in view of Hoegh (US 20150094962).
Regarding claim 8, the Al-Ali/Mannheimer/Yu/Meger combination teaches the method of claim 1.
However, the Al-Ali/Mannheimer/Yu/Meger combination does not teach further comprising: in response to predicting that the oxygen saturation level of the patient will increase above the desaturation threshold by the end of the prediction period, determining, by the processing circuitry and prior to the end of the prediction period, the oxygen saturation level of the patient has decreased below a deep desaturation threshold; and in response to determining that the oxygen saturation level of the patient has decreased below the deep desaturation threshold, outputting an indication of the patient experiencing a deep oxygen desaturation event.
Hoegh discloses a screening method for sleep apnea that measures oxygen saturation. Specifically, Hoegh teaches further comprising: after predicting that the predicted oxygen saturation level of the patient will increase above the desaturation threshold by the end of the prediction period, determining, by the processing circuitry and prior to the end of the prediction period, the monitored oxygen saturation level of the patient has decreased below a deep desaturation threshold; and in response to determining that the monitored oxygen saturation level of the patient has decreased below the deep desaturation threshold, outputting an indication of the patient experiencing a deep oxygen desaturation event (several methods available to characterize these severe desaturations. For example, the calculation of a mean oxygen desaturation value can provide an estimate of the average severity of oxygen desaturations. A low threshold (e.g. 3%) may be used to account for a plethora of small fluctuations in oxygen level (e.g. 0.5%) that may overwhelm the data set.”; [0176]: “Given the plethora of methods to characterize oxygen desaturation severity, other thresholds and criteria are available. One embodiment is mean oxygen desaturation associated with respiratory events. As expected, the higher the mean oxygen desaturation threshold, the more likely that challenging subjects with severe oxygen desaturations will satisfy the criteria. Example severity thresholds include values equal to, greater than, or less than 3%, 4%, 5%, 6%, 7%, and 8%, and can further include thresholds of values equal to, greater than, or less than 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, and 25%. Based on empirical data and with reference to FIG. 17, subjects with mean oxygen desaturations (associated with respiratory events) below 5% are associated with the "borderline" or "strong" sub-groups”). Al-Ali, Mannheimer, and Hoegh are analogous arts as they are all related to devices used to monitor patient’s health conditions.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the deep desaturation threshold from Hoegh into the Al-Ali/Mannheimer/Yu/Meger combination as it allows the combination to alert when an extreme level of desaturation occurs, which could be harmful to the patient, so an alert would be helpful to inform the patient.
Claims 14-15 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over the Al-Ali/Mannheimer/Yu/Meger/Ray combination as applied to claim 13 above, and further in view of Taghvaeeyan (WO 2020008339).
Regarding claim 14, the Al-Ali/Mannheimer/Yu/Meger/Ray teaches the method of claim 13.
However, the Al-Ali/Mannheimer/Yu/Meger/Ray combination does not teach wherein predicting, based at least in part on the plurality of predictions, whether the oxygen saturation level of the patient will increase above the desaturation threshold by the end of the prediction period comprises: determining, by the processing circuitry, an average predicted oxygen saturation level by the end of the prediction period from two or more of the plurality of predictions.
Taghvaeeyan discloses a system for monitoring signals from a biological sensor. Specifically, Taghvaeeyan teaches wherein predicting, based at least in part on the plurality of predicted oxygen saturation levels, whether the at least one predicted oxygen saturation level of the patient will increase above the desaturation threshold by the end of the prediction period comprises: determining, by the processing circuitry, an average predicted oxygen saturation level by the end of the prediction period from two or more of the plurality of predicted oxygen saturation levels ([0157]: “One example is to use majority voting and take the average of predictions from classifiers previously trained on historical data.”). Al-Ali, Mannheimer, Ray, and Taghvaeeyan are analogous arts as they are all related to devices used to monitor patient’s health conditions.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the average of the predictions from Taghvaeeyan into the Al-Ali/Mannheimer/Yu/Meger/Ray combination as it allows the combination to take all of the predictions into account in the analysis, which can provide a more accurate result.
The Al-Ali/Mannheimer/Yu/Meger/Ray/Taghvaeeyan combination teaches predicting, by the processing circuitry and based at least in part on the average predicted oxygen saturation level by the end of the prediction period, whether the at least one predicted oxygen saturation level of the patient will increase above the desaturation threshold by the end of the prediction period (Al-Ali, Fig. 5, alarm is only generated if threshold detector is below, and slope detector is not positive; [0019]: “the trend recognition alarm indicator 320 reduces false alarms by suppressing a threshold-based alarm on the slow SpO.sub.2 measurement 412 when the fast SpO.sub.2 measurement 422 determines that a patient's oxygen saturation is in recovery”; Figs. 6A-6B).
Regarding claim 15, the Al-Ali/Mannheimer/Yu/Meger/Ray/Taghvaeeyan combination teaches the method of claim 14, wherein determining the average predicted oxygen saturation level by the end of the prediction period from the two or more of the plurality of predicted oxygen saturation levels comprises: determining, by the processing circuitry, a weighted average predicted oxygen saturation level by the end of the prediction period from the two or more of the plurality of predicted oxygen saturation levels (Taghvaeeyan, [0164]: “the final prediction is a weighted average of all the M predictions from source-domain classifiers”), wherein predicting whether the at least one predicted oxygen saturation level of the patient will increase above the desaturation threshold by the end of the prediction period comprises predicting, by the processing circuitry and based at least in part on the weighted average predicted oxygen saturation level, whether the at least one predicted oxygen saturation level of the patient will increase above the desaturation threshold by the end of the prediction period (Al-Ali, Fig. 5, alarm is only generated if threshold detector is below, and slope detector is not positive; [0019]: “the trend recognition alarm indicator 320 reduces false alarms by suppressing a threshold-based alarm on the slow SpO.sub.2 measurement 412 when the fast SpO.sub.2 measurement 422 determines that a patient's oxygen saturation is in recovery”; Figs. 6A-6B).
Regarding claim 18, the Al-Ali/Mannheimer/Yu/Meger/Ray/Taghvaeeyan combination teaches the method of claim 14.
However, the Al-Ali/Mannheimer/Yu/Meger/Ray/Taghvaeeyan combination does not teach further comprising: determining, by the processing circuitry, one or more outlier predictions from the plurality of predictions; and refraining, by the processing circuitry, from including the one or more outlier predictions in the two or more of the plurality of predictions.
Taghvaeeyan teaches further comprising: determining, by the processing circuitry, one or more outlier predicted oxygen saturation levels from the plurality of predicted oxygen saturation levels; and refraining, by the processing circuitry, from including the one or more outlier predicted oxygen saturation levels in the two or more of the plurality of predicted oxygen saturation levels ([0217]: “eliminating outliers in the extracted data based on an expected outlier ratio … eliminating outliers in the extracted feature data”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the outlier determination from Taghvaeeyan into the Al-Ali/Mannheimer/Yu/Meger/Ray/Taghvaeeyan combination as it allows the combination to remove any unwanted values, which ensures a more accurate result.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over the Al-Ali/Mannheimer/Yu/Meger/Ray/Taghvaeeyan combination as applied to claim 14 above, and further in view of Clifford (WO 2013036718).
Regarding claim 16, the Al-Ali/Mannheimer/Yu/Meger/Ray/Taghvaeeyan combination teaches the method of claim 14.
However, the Al-Ali/Mannheimer/Yu/Meger/Ray/Taghvaeeyan combination does not teach wherein determining the average predicted oxygen saturation level by the end of the prediction period from the two or more of the plurality of predictions comprises: adding, by the processing circuitry, a bias to the average predicted oxygen saturation level to determine a biased average predicted oxygen saturation level, wherein predicting whether the oxygen saturation level of the patient will increase above the desaturation threshold by the end of the prediction period comprises predicting, by the processing circuitry and based at least in part on the biased average predicted oxygen saturation level, whether the oxygen saturation level of the patient will increase above the desaturation threshold by the end of the prediction period.
Clifford discloses a system and method for determining the acceptability of physiological signals. Specifically, Clifford teaches wherein determining the average predicted oxygen saturation level by the end of the prediction period from the two or more of the plurality of predicted oxygen saturation levels comprises: adding, by the processing circuitry, a bias to the average predicted oxygen saturation level to determine a biased average predicted oxygen saturation level ([00133]: “Since there is, at most, 1000 training examples in the training set and it is desired that a number of free parameters (weights in the MLP) to be approximately one tenth of this or less (See Bishop), then the number of hidden nodes must be restricted to about 13 (< 1000/74 if we include the bias weights)”). Al-Ali, Mannheimer, Ray, Taghvaeeyan, and Clifford are analogous arts as they are all related to devices used to monitor patient’s health conditions.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to account for bias from Clifford into the Al-Ali/Mannheimer/Yu/Meger/Ray/Taghvaeeyan combination as it allows the combination to account for the bias in the averages in the calculations, which can provide a more accurate result.
The Al-Ali/Mannheimer/Yu/Meger/Ray/Taghvaeeyan/Clifford combination teaches wherein predicting whether the at least one predicted oxygen saturation level of the patient will increase above the desaturation threshold by the end of the prediction period comprises predicting, by the processing circuitry and based at least in part on the biased average predicted oxygen saturation level, whether the at least one predicted oxygen saturation level of the patient will increase above the desaturation threshold by the end of the prediction period (Al-Ali, Fig. 5, alarm is only generated if threshold detector is below, and slope detector is not positive; [0019]: “the trend recognition alarm indicator 320 reduces false alarms by suppressing a threshold-based alarm on the slow SpO.sub.2 measurement 412 when the fast SpO.sub.2 measurement 422 determines that a patient's oxygen saturation is in recovery”; Figs. 6A-6B).
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over the Al-Ali/Mannheimer/Yu/Meger/Ray/Taghvaeeyan combination as applied to claim 14 above, and further in view of Lafon (WO 2022035646).
Regarding claim 17, the Al-Ali/Mannheimer/Yu/Meger/Ray/Taghvaeeyan combination teaches the method of claim 14.
However, the Al-Ali/Mannheimer/Yu/Meger/Ray/Taghvaeeyan combination does not teach further comprising: selecting, by the processing circuitry, the two or more of the plurality of predictions based at least in part on the two or more of the plurality of predictions being within a specified percentile of the plurality of predictions.
Lafon discloses a temperature detection and assessment device. Specifically, Lafon teaches further comprising: selecting, by the processing circuitry, the two or more of the plurality of predicted oxygen saturation levels based at least in part on the two or more of the plurality of predicted oxygen saturation levels being within a specified percentile of the plurality of predicted oxygen saturation levels ([0044]: “different readings are evaluated, including average, median, 75.sup.th percentile, 90.sup.th percentile, and mode”). Al-Ali, Mannheimer, Ray, Taghvaeeyan, and Lafon are analogous arts as they are all related to devices used to monitor patient’s health conditions.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the percentile analysis from Lafon into the Al-Ali/Mannheimer/Yu/Meger/Ray/Taghvaeeyan combination as it allows the combination to only use a specific desired percentile, which can ensure a specific desired result and can provide a more accurate result.
Claims 19 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Al-Ali (US 20140128696) in further view of Mannheimer (US 20080183058) and Yu (US 12507961).
Regarding independent claim 19, Al-Ali teaches a system comprising:
an oxygen saturation sensing device configured to sense an oxygen saturation level of a patient ([0005]: “One aspect of a physiological trend monitor comprises transmitting light into a patient tissue site, generating a sensor signal, detecting a blood parameter trend according to the sensor signal and generating an alarm according to the blood parameter trend”); and
processing circuitry (Abstract: “A processor has an input responsive to the sensor signal and a physiological parameter output”) configured to perform operations, comprising:
receive a signal indicative of the sensed oxygen saturation level of the patient ([0017]: “A pulse oximeter sensor (not shown) provides a signal input 301 that is responsive to arterial oxygen saturation”);
determine that the signal indicates the sensed oxygen saturation level is at or below a threshold ([0004]: “FIG. 2 illustrates the operation of a conventional threshold detector 120 (FIG. 1) utilizing a graph 200 of oxygen saturation 201 versus time 202. The graph 200 displays a particular oxygen saturation measurement 210 corresponding to the measurement output 103 (FIG. 1) and a predetermined alarm threshold 206”);
in response to determining the sensed oxygen saturation level is at or below the threshold, initiating a calculation period ([0004]: “During an alarm time period 270 when the measured oxygen saturation 210 is below the threshold 206, an alarm output 105 (FIG. 1) is generated”).
However, Al-Ali does not disclose determining the oxygen saturation in response to determining the oxygen saturation level is at or below the desaturation threshold.
Mannheimer discloses a method and apparatus for controlling alarms in a medical diagnostic apparatus. Specifically, Mannheimer teaches determining the oxygen saturation in response to determining the sensed oxygen saturation level is at or below the desaturation threshold at the end of the calculation period ([0026]: “saturation signal 70 is compared to a low sat threshold 74. Also illustrated is an integral threshold 78. An excursion 80 produces an integral value 82 that can exceed the integral threshold 78. The value 82 is a product of the amount of time and the amount by which the measured value of oxygen saturation exceeds the threshold … As the instantaneous readings of SpO.sub.2 (or other variable) wander beyond these thresholds, the product 82 of time and extent beyond the threshold is calculated.”). Al-Ali and Mannheimer are analogous arts as they are both related to systems that generate alarms based on oxygen saturation measurements.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include determining the oxygen saturation in response to determining the oxygen saturation level is at or below the desaturation threshold from Mannheimer into the method from Al-Ali as it allows the system to start determining the oxygen saturation at the end of the calculation period only if it is at or below the threshold, which means it only does the analysis when necessary and not at all times.
The Al-Ali/Mannheimer combination discloses initiating a prediction period ([0018]: “The predictor SPO.sub.2 processor 420, advantageously, provides a curve-fitting or a predictive measurement of oxygen saturation that detects trends in oxygen saturation”), however the combination does not teach initiating the prediction period after determining that the monitored oxygen saturation is at or below the desaturation threshold.
Yu discloses systems and method for monitoring fluid of a user. Specifically, Yu teaches in response to determining that the sensed parameter of the patient is at or below the threshold at the end of the calculation period, initiating a prediction period (Column 5, lines 56-60: “the controller may be configured to predict an infection score of a patient based at least in part on the signal. In some variations, the controller may be configured to predict an infection state of a patient in response to any one or more of the following: the infection score exceeding a predetermined threshold during each of one or more successive measurement time periods”). Al-Ali, Mannheimer, and Yu are analogous arts as they are all related to systems that predict the health state of a user based on monitored parameters.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the steps of initiating the prediction period after it is determined that the monitored parameter is outside a threshold from Yu into the Al-Ali/Mannheimer combination as it allows the combination to predict the oxygen saturation only after it is determined to be outside the threshold, which ensures the system is not doing unnecessary calculations and only determining the predictions when necessary.
The Al-Ali/Mannheimer/Yu combination teaches the step of predicting, using an oxygen saturation prediction model, a predicted oxygen saturation level of the patient will increase above the desaturation threshold by the end of the prediction period (Al-Ali, [0018]: “The predictor SPO.sub.2 processor 420, advantageously, provides a curve-fitting or a predictive measurement of oxygen saturation that detects trends in oxygen saturation”; [0007]: “In various embodiments, a threshold is input to the pattern extractor. The identified features comprise at least the number of times the physiological measurement crosses the threshold within a predetermined time period. The identified features comprise at least the duration of each time the physiological measurement crosses the threshold. The physiological measurement comprises a predictive oxygen saturation measurement.”; [0017]: “A predictor SpO.sub.2 processor 420 outputs a fast SPO.sub.2 measurement 422”; Figs 4-5); and
in response to predicting that the predicted oxygen saturation level of the patient will increase above the desaturation threshold by the end of the prediction period, refraining from outputting an indication of the patient experiencing an oxygen desaturation event (Al-Ali, Fig. 5, alarm is only generated if threshold detector is below, and slope detector is not positive; [0019]: “the trend recognition alarm indicator 320 reduces false alarms by suppressing a threshold-based alarm on the slow SpO.sub.2 measurement 412 when the fast SpO.sub.2 measurement 422 determines that a patient's oxygen saturation is in recovery”; Figs. 6A-6B).
Regarding claim 27, the Al-Ali/Mannheimer/Yu combination teaches the system of claim 19, wherein the processing circuitry is further configured to: after predicting that the predicted oxygen saturation level of the patient will increase above the desaturation threshold within the prediction period, determine, prior to the prediction period ending, the sensed oxygen saturation level of the patient is continuing to decrease; and in response to determining that the sensed oxygen saturation level of the patient is continuing to decrease, output the indication of the patient experiencing the oxygen desaturation event (Al-Ali, Fig. 6A-6B. Figure 6B shows that when both the measure and prediction of SP02 levels are decreasing, the alarm is presented, in contrast to Figure 6A,where when the measured and predicted oxygen saturation is increasing, the alarm is suppressed.).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over the Al-Ali/Mannheimer/Yu combination as applied to claim 19 above, and further in view of King and Gross.
Regarding claim 20, the Al-Ali/Mannheimer/Yu combination teaches the system of claim 19.
However, the Al-Ali/Mannheimer/Yu combination does not teach wherein the oxygen saturation prediction model executes at an external device that is communicably coupled to the patient monitoring device, and wherein a latency period is between the calculation period and the prediction period to account for communications latency between the patient monitoring device and the external device.
King discloses a system used to manage patient alarms. Specifically, King teaches wherein the oxygen saturation prediction model executes at an external device that is communicably coupled to the patient monitoring device (Column 21, lines 32-40: “the input/output (“I/O”) interface 2060 may be configured to couple to one or more external devices, to receive data from the one or more external devices, and to send data to the one or more external devices. Such external devices along with the various internal devices may also be known as peripheral devices. The I/O interface 2060 may include both electrical and physical connections for operably coupling the various peripheral devices to the computing machine 2000 or the processor 2010”). Al-Ali, Mannheimer, and King are analogous arts as they are all related to devices used to monitor patient’s health conditions and initiate alarms.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the external device from King into the Al-Ali/Mannheimer/Yu combination as it allows the combination to have a separate device that can be used to predict the oxygen saturation, which can allow the external device to be used in a different location than the patient monitoring device, which can allow it to be more mobile and viewed by different users.
However, the Al-Ali/Mannheimer/Yu/King combination does not teach wherein a latency period is between the calculation period and the prediction period to account for communications latency between the patient monitoring device and the external device.
Gross teaches wherein a latency period occurs between the calculation period and the prediction period to account for communications latency between the patient monitoring device and the external device (Column 12, lines 28-40: “One reason such synchronization is valuable is that different sensor data streams may have different latency times between the acquisition of the common continuous physiological parameter and the availability of the indicative sensor data stream to the coordination function … Such latencies can result from signal transmission delays (e.g., capacitive or inductive delays), data processing delays, or so forth, delays introduced by communication protocols, or so forth.”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the latency period from Gross into the Al- Al-Ali/Mannheimer/Yu/King combination as it allows the combination to account for the period of time needed for communication between the devices and for the calculation time, which can ensure a more accurate result.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over the Al-Ali/Mannheimer/Yu combination as applied to claim 19 above, and further in view of Hu.
Regarding claim 21, the Al-Ali/Mannheimer/Yu combination teaches the system of claim 19.
However, the Al-Ali/Mannheimer/Yu combination does not teach wherein the processing circuitry is further configured to: set a prediction threshold associated with the oxygen saturation prediction model to correspond to a sensitivity associated with the oxygen saturation prediction model.
Hu teaches wherein the processing circuitry is further configured to: set a prediction threshold associated with the oxygen saturation prediction model to correspond to a sensitivity associated with the oxygen saturation prediction model (Column 3, lines 18-20: “FIG. 10 is a graph showing sensitivity curves of four super-alarm sets obtained using the optimal algorithm parameters under four different false positive ratio thresholds.”; Column 9, lines 58-64: “we determine the optimal algorithm parameters for a given FPR threshold and then use the full training data to obtain the super-alarm set. Table 3 lists the optimal parameters for each choice of FPR threshold, the total number of super-alarm patterns, the total number of super-alarms per each pattern length, and the average sensitivity obtained at the specified FPR threshold.”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to account for sensitivity from Hu into the Al-Ali/Mannheimer/Yu combination as it allows the combination to factor in the sensitivity of the model, which can ensure a more accurate result and produce the correct alarm that is necessary.
Claims 22-25 are rejected under 35 U.S.C. 103 as being unpatentable over the Al-Ali/Mannheimer/Yu/Hu combination as applied to claim 21 above, and further in view of Ray.
Regarding claim 22, the Al-Ali/Mannheimer/Yu/Hu combination teaches the system of claim 21, wherein the processing circuitry is further configured to: generate a receiver operator characteristic (ROC) curve associated with the oxygen saturation prediction model (Hu, Column 7, lines 21-23: “a receiver operator characteristic (ROC) curve is generated for each 10-fold CV data set following the conventional cross-validation analysis.”).
However, the Al-Ali/Mannheimer/Yu/Hu combination does not teach determining, by the processing circuitry and based at least in part on the ROC curve, a prediction level that corresponds to the sensitivity; and adjusting, by the processing circuitry and based at least in part on the prediction level that corresponds to the sensitivity, the prediction threshold associated with the oxygen saturation prediction model to maintain the sensitivity associated with the oxygen saturation prediction model over time.
Ray teaches determine, based at least in part on the ROC curve, a prediction level that corresponds to the sensitivity ([000103]-[000104]: “models (e.g., simulated fetal oximetry models) generated by process 400 may incorporate K-fold cross-validation to, for example, generate the expected error, receiver operating characteristic (ROC), and/or area under the curve (AUC) values for the model. In some embodiments, execution of step 410 may include selection of one or more types of outputs that may be incorporated into the machine learning architecture. Exemplary outputs include predicted fetal oximetry (e.g., SpO2 and/or fetal tissue oxygen saturation) values”); and adjust, based at least in part on the prediction level that corresponds to the sensitivity, the first prediction threshold value to a second prediction threshold value associated with the oxygen saturation prediction model to maintain the sensitivity associated with the oxygen saturation prediction model over time ([00059]: “the present invention may be used to perform sensitivity analysis, which may allow for changing multiple variables/parameters used to generate the models and/or simulated light transmission data sets so that, for example, the results (e.g., calculated fetal SpO2 values) may be evaluated for accuracy and/or to determine how multiple variables may interact with one another to vary calculated fetal SpO2 values.”; [000113]: “process 400 and/or portions thereof may be repeated on a periodic, as-needed, and/or continuous basis to, for example, improve the accuracy of the predictions the model yields, perform perturbation analysis, and/or perform sensitivity analysis”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the analysis using the ROC curve from Ray into the Al-Ali/Mannheimer/Yu/Hu combination as it allows the combination to further analyze the ROC curve and determine parameters from the curve, which can provide further information to the model to determine a more accurate prediction.
Regarding claim 23, the Al-Ali/Mannheimer/Yu/Hu/Ray combination teaches the system of claim 22.
However, the Al-Ali/Mannheimer/Yu/Hu/Ray combination does not teach further comprising: updating, by the processing circuitry, the ROC curve based on updated data that includes patient data associated with the patient; and redetermining, by the processing circuitry and based at least in part on the updated ROC curve, the prediction level that corresponds to the sensitivity.
Ray teaches wherein the processing circuitry is further configured to: update the ROC curve based on updated data that includes patient data associated with the patient ([000111]: “the third set of predicted output values may be compared with the corresponding measured output values to determine differences between them (step 428). Results of the comparison may then be evaluated (step 430) and used to update the in vivo fetal oximetry model”); and redetermine, based at least in part on the updated ROC curve, a redetermined prediction level that corresponds to the sensitivity ([00059]: “the present invention may be used to perform sensitivity analysis, which may allow for changing multiple variables/parameters used to generate the models and/or simulated light transmission data sets so that, for example, the results (e.g., calculated fetal SpO2 values) may be evaluated for accuracy and/or to determine how multiple variables may interact with one another to vary calculated fetal SpO2 values.”; [000113]: “process 400 and/or portions thereof may be repeated on a periodic, as-needed, and/or continuous basis to, for example, improve the accuracy of the predictions the model yields, perform perturbation analysis, and/or perform sensitivity analysis”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the updating from Ray into the Al-Ali/Mannheimer/Yu/Hu/Ray combination as it allows the combination to update over time and be more accurate when more information is provided, which ensures the most accurate result.
Regarding claim 24, the Al-Ali/Mannheimer/Yu/Hu/Ray combination teaches the system of claim 22, wherein the processing circuitry is further configured to: output, for display at a display, the ROC curve associated with the oxygen saturation prediction model (Al-Ali, [0003]: “The oxygen saturation measurement output 103 is typically a digital signal that is then communicated to a display.”); receive user input that corresponds to a point on the ROC curve (Al-Ali, [0016]: “The alarm conditions 305 define particular limits with respect to one or more of the measurements 312. The alarm conditions 305 may be predefined, such as by user input, or determined by a separate process, such as a measurement of sensor signal quality or data confidence”); map the point on the ROC curve to a third prediction threshold value; and adjust the second prediction threshold value associated with the oxygen saturation prediction model according to the third prediction threshold value (Hu, Column 7, lines 44-48: “Based on a ROC curve, an operating point 212 is picked by first specifying the maximally tolerated FPR (FPRmax) and then the operating point is determined at the location where the TPR is the maximized while the corresponding FPR is below the specified FPRmax.”; Column 9, lines 58-64: “we determine the optimal algorithm parameters for a given FPR threshold and then use the full training data to obtain the super-alarm set. Table 3 lists the optimal parameters for each choice of FPR threshold, the total number of super-alarm patterns, the total number of super-alarms per each pattern length, and the average sensitivity obtained at the specified FPR threshold.”).
Regarding claim 25, the Al-Ali/Mannheimer/Yu/Hu/Ray combination teaches the system of claim 21, wherein to set the first prediction threshold value associated with the oxygen saturation prediction model, the processing circuitry is further configured to: receive a false positive rate that corresponds to the sensitivity associated with the oxygen saturation prediction model (Hu, Column 3, lines 18-20: “FIG. 10 is a graph showing sensitivity curves of four super-alarm sets obtained using the optimal algorithm parameters under four different false positive ratio thresholds.”).
Claims 28-29 are rejected under 35 U.S.C. 103 as being unpatentable over the Al-Ali/Mannheimer/Yu combination as applied to claim 19 above, and further in view of Ray.
Regarding claim 28, the Al-Ali/Mannheimer/Yu combination teaches the system of claim 19.
However, the Al-Ali/Mannheimer/Yu combination does not teach wherein the oxygen saturation prediction model comprises a neural network algorithm trained via machine learning over training data that includes one or more of: sets of blood oxygen level of a population of patients, sets of blood pressure values of the population of patients, or metrics derived from sets of PPG signals of the population of patients.
Ray teaches wherein the oxygen saturation prediction model comprises a neural network algorithm trained via machine learning over training data that includes one or more of: sets of blood oxygen levels of a population of patients, sets of blood pressure values of the population of patients, or metrics derived from sets of PPG signals of the population of patients ([000103]: “inputs to the machine learning architecture and/or software program for determining fetal oximetry values may be selected … the machine learning architecture may be a neural network, an artificial neural network”; [0007]: “In order to train a fetal oximetry model using only measured in vivo data, a sufficient number (e.g., 5,000 - 10,000,000) of measured oximetry values in a healthy state (e.g., fetal oxygenation levels are sufficient) and a disease state (e.g., fetal hypoxia and/or fetal hypoxemia) and corresponding light transmission data must be measured and input into the machine learning/model training architecture to train a fetal oximetry model that outputs sufficiently accurate predictions of fetal oximetry values using light transmission data measured in a clinical setting”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the neural network from Ray into the Al-Ali/Mannheimer/Yu combination as it allows the combination to process the information and create the predictions using a trained model, which can provide a more efficient analysis.
Regarding claim 29, the Al-Ali/Mannheimer/Yu combination teaches the system of claim 19.
However, the Al-Ali/Mannheimer/Yu combination does not teach wherein the processing circuitry is further configured to: determine offsets between oxygen saturation levels predicted using the oxygen saturation prediction model and actual oxygen saturation levels of the patient; and calibrate the oxygen saturation prediction model based at least in part on the determined offsets.
Ray teaches wherein the processing circuitry is further configured to: determine an offset between the predicted oxygen saturation level predicted using the oxygen saturation prediction model and an actual oxygen saturation level of the patient; and calibrate the oxygen saturation prediction model based at least in part on the determined offset ([000111]: “the third set of predicted output values may be compared with the corresponding measured output values to determine differences between them (step 428). Results of the comparison may then be evaluated (step 430) and used to update the in vivo fetal oximetry model (step 432).”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the offset and calibration from Ray into the Al-Ali/Mannheimer/Yu combination as it allows the combination to calibrate itself, which ensures that it is providing the most accurate result.
Claim 26 is rejected under 35 U.S.C. 103 as being unpatentable over the Al-Ali/Mannheimer/Yu combination as applied to claim 19 above, and further in view of Hoegh (US 20150094962).
Regarding claim 26, the Al-Ali/Mannheimer/Yu combination teaches the system of claim 19.
However, the Al-Ali/Mannheimer/Yu combination does not teach wherein the processing circuitry is further configured to: in response to predicting that the oxygen saturation level of the patient will increase above the desaturation threshold by the end of the prediction period, determine, prior to the end of the prediction period, the oxygen saturation level of the patient has decreased below a deep desaturation threshold; and in response to determining that the oxygen saturation level of the patient has decreased below the deep desaturation threshold, output an indication of the patient experiencing a deep oxygen desaturation event.
Hoegh teaches wherein the processing circuitry is further configured to: after predicting that the predicted oxygen saturation level of the patient will increase above the desaturation threshold by the end of the prediction period, determine, prior to the end of the prediction period, the sensed oxygen saturation level of the patient has decreased below a deep desaturation threshold; and in response to determining that the sensed oxygen saturation level of the patient has decreased below the deep desaturation threshold, output an indication of the patient experiencing a deep oxygen desaturation event (several methods available to characterize these severe desaturations. For example, the calculation of a mean oxygen desaturation value can provide an estimate of the average severity of oxygen desaturations. A low threshold (e.g. 3%) may be used to account for a plethora of small fluctuations in oxygen level (e.g. 0.5%) that may overwhelm the data set.”; [0176]: “Given the plethora of methods to characterize oxygen desaturation severity, other thresholds and criteria are available. One embodiment is mean oxygen desaturation associated with respiratory events. As expected, the higher the mean oxygen desaturation threshold, the more likely that challenging subjects with severe oxygen desaturations will satisfy the criteria. Example severity thresholds include values equal to, greater than, or less than 3%, 4%, 5%, 6%, 7%, and 8%, and can further include thresholds of values equal to, greater than, or less than 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, and 25%. Based on empirical data and with reference to FIG. 17, subjects with mean oxygen desaturations (associated with respiratory events) below 5% are associated with the "borderline" or "strong" sub-groups”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the deep desaturation threshold from Hoegh into the Al-Ali/Mannheimer/Yu combination as it allows the combination to alert when an extreme level of desaturation occurs, which could be harmful to the patient, so an alert would be helpful to inform the patient.
Response to Arguments
All of applicant’s argument regarding the rejections and objections previously set forth have been fully considered and are persuasive unless directly addressed subsequently.
Applicant has amended the claims, however, the amendments have introduced new 112(b) rejections due to indefiniteness issues brought up in the rejections above. Additionally, the 112(b) rejection of claim 27 is maintained, as this limitation has not been amended and still contains the indefiniteness issue introduced in the last office action.
Applicant's arguments filed 10/07/2025 with respect to the prediction of oxygen saturation levels in prior art rejections of the independent claims have been fully considered but they are not persuasive. Applicant argues that Al-Ali does not disclose predicting the oxygen saturation levels, however as shown in the 103 rejection above, Al-Ali does disclose predicting the oxygen saturation levels (Al-Ali, [0018]: “The predictor SPO.sub.2 processor 420, advantageously, provides a curve-fitting or a predictive measurement of oxygen saturation that detects trends in oxygen saturation”; [0007]: “In various embodiments, a threshold is input to the pattern extractor. The identified features comprise at least the number of times the physiological measurement crosses the threshold within a predetermined time period. The identified features comprise at least the duration of each time the physiological measurement crosses the threshold. The physiological measurement comprises a predictive oxygen saturation measurement.”; [0017]: “A predictor SpO.sub.2 processor 420 outputs a fast SPO.sub.2 measurement 422”; Figs 4-5).
Applicant’s arguments with respect to the prior art rejections relating to the calculation period prediction period, and the delay period of the independent claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIN K MCCORMACK whose telephone number is (703)756-1886. The examiner can normally be reached Mon-Fri 7:30-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason Sims can be reached at 5712727540. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/E.K.M./Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791
Prosecution Insights