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 .
Response to Amendment
Applicant’s amendments, filed 11/03/2025, has been received. Claims 1, 4, 5, 7-12, 14, 15 remain pending.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 5, 7-12, 14, 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gholami et al. (US20200261674), hereafter Gholami, in view of Errico (WO2021110576), hereafter Errico, further in view of Kahl et al. (20220330837), hereafter Kahl.
Regarding Claim 1, Gholami discloses a medical device for treating an associated patient (Abstract, system shown in Fig. 1, 2), comprising: an electronic processing device (Fig. 1, communication module, data processing module, recommendation module) configured to receive ventilation waveform data during mechanical ventilation of the associated patient (par. 0058, “Acquire waveform data, including at least the flow and pressure from the ventilator.”) and to perform a patient-ventilator asynchrony monitoring method (par. 0005, “The present invention provides methods and systems for detecting patient-ventilator asynchrony.”) including: detecting initial patient-ventilator asynchrony events during a training period (par. 0056, “…through a training process”) of the mechanical ventilation by analysis of measurements of the associated patient acquired during the training period (par. 0005, “training a machine learning algorithm to perform mapping between features and patient-ventilator asynchrony types. Training data typically involves assigning labels associated with different asynchrony types to waveforms from each breath”; par. 0115, “In some embodiments, training data for training the machine learning classifier includes both labeled data from previously collected patient data and synthetic data”) training a machine learning (ML) component to analyze ventilation waveform data to detect patient-ventilator asynchrony events using the ventilation waveform data received during the training period with labels indicating the initial patient-ventilator asynchrony events (par. 0005, “and training a machine learning algorithm to perform mapping between features and patient-ventilator asynchrony types. Training data typically involves assigning labels associated with different asynchrony types to waveforms from each breath.”).
Gholami discloses the measurements used during the training period includes diaphragm activity (par. 0005, “Training data typically involves assigning labels… such labels can be generated by clinical experts reviewing the waveforms or by clinical experts reviewing waveforms as well as other appropriate clinical signals such as esophageal pressure or diaphragm activity.”) but is silent on wherein the measurements comprise non-invasive diaphragmatic or lung sliding ultrasound measurements.
However, Errico teaches a known technique of measuring diaphragm activity for machine learning purposes, wherein the measurements comprise non-invasive diaphragmatic ultrasound measurements (Abstract; pg. 2 line 9-14). This known technique is applicable to the device of Gholami as they both share characteristics and capabilities, namely, they are directed to use the measurements to train a machine learning model.
One of ordinary skilled in the art would have recognized that applying the known technique of Errico would have yielded predictable results and resulted in an improved system before the effective filing date of the claimed invention, because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate diaphragm activity measurements into similar systems. Further, applying diaphragmatic ultrasound measurements to Gholami would have been recognized by those of ordinary skill in the art as resulting in an improved device as it provides a non-invasive procedures.
The modified Gholami is still silent on using posture measurements from a posture sensor configured to detect a posture of the associated patient as a function of time during the training period.
However, Kahl teaches a medical device for treating an associated patient (par. 0005, “A possible application of the present invention is the control of a mechanical ventilator. This ventilator assists the spontaneous breathing of a patient”), wherein a component is trained during a training phase (par. 0021-0022, “The process according to the present invention comprises a training phase…
During the training phase the signal processing unit receives measured values from a sum signal sensor device comprising at least one sum signal sensor”), and a use phase (par. 0094), wherein patient-ventilator asynchrony is detected during the use phase (par. 0102, “A ventilator signal is measured… In case of a deviation above a threshold, asynchrony is detected”). Kahl further teaches using posture measurements from a posture sensor (par. 0080, “a mechanical or pneumatic or optical sensor measures an indicator for the body geometry… An optical sensor comprises especially an image recording device and an image analysis unit, which employs an imaging method.”) configured to detect a posture of the associated patient as a function of time during the training period (par. 0081, “the current posture or body position of the patient is used as the transmission channel parameter or as a transmission channel parameter, for example, the position of the patient in a bed or whether the upper body of the patient is upright or curved”; par. 0082 discloses the posture over a time interval affects the sum signal which is used in the training period). Therefore, it would have been obvious for one of ordinary skilled in the art, to modify the known device of Gholami, and use posture of the patient during the training period, as the posture affects the flow of breathing air as taught by Kahl (Kahl, par. 0080-0082).
The modified Gholami further discloses the trained ML component forming a patient- specific ML component that is specific to the associated patient and the posture of the associated patient (See Gholami, par. 0003 and 0005, the system is designed to be patient specific; Kahl, par. 0080-0082); applying the patient-specific ML component to the ventilation waveform data and the posture measurements received after the training period to detect patient-ventilator asynchrony events occurring after the training period (Gholami, par. 0072, “Use the extracted features from the Delta waveform as an input to a statistical (machine learning) classifier, and use the class assignment provided by the classifier to classify the breath into premature termination, delayed termination…”; Kahl, par. 0095-0102); determining one or more optimized settings of a mechanical ventilator based on the detected patient-ventilator asynchrony events (Gholami, par. 0005, “…optimize or improve ventilator performance (e.g., triggering and cycling timings), or identify other relevant parameters that affect ventilator function, and/or to allow for intervention including changes to a patient's sedation level.”); controlling the mechanical ventilator by adjusting settings of the mechanical ventilator with the determined one or more optimized settings (Gholami, par. 0005, “…adjust ventilator settings to avoid asynchrony, assess a patient experiencing asynchrony and make an appropriate action, alert hospital staff to, and/or change internal settings automatically, optimize or improve ventilator performance (e.g., triggering and cycling timings)”) and a display device configured to display an indication of patient-ventilator asynchrony events detected by the applying of the patient-specific ML component (Gholami, par. 0006, “a graphical user interface (GUI) for communicating detected patient-ventilator asynchrony whereby the system can be used as a clinical decision support system”).
Regarding Claim 5, the modified Gholami discloses the device of claim 1, wherein the electronic processing device is further configured to perform the step of: determining a proposed adjustment to the mechanical ventilation to reduce or eliminate the patient-ventilator asynchrony events detected by the applying of the patient-specific ML component (Gholami, par. 0005, “The systems and methods can also be configured to include recommendation… adjust ventilator settings to avoid asynchrony, assess a patient experiencing asynchrony and make an appropriate action… optimize or improve ventilator performance (e.g., triggering and cycling timings), or identify other relevant parameters that affect ventilator function, and/or to allow for intervention including changes to a patient's sedation level.”); wherein the display device is further configured to display the proposed adjustment (Gholami, par. 0119, Fig. 23, “the GUI can display one or more recommendations for mitigating detected asynchronies…”).
Regarding Claim 7, the modified Gholami discloses The device of claim 1, but is silent on wherein the ML component is further trained using imaging data of the associated patient.
However, Errico further teaches a method and system for ventilation treatment control (Abstract), comprising of an imaging device that records imaging data (Fig. 1, an ultrasound probe system 190), a machine learning component (Abstract, a machine-learning algorithm) wherein the ML component is further trained using imaging data of the associated patient (Abstract, “One or more ultrasound images (of the patient) are processed using a machine-learning algorithm to derive health information of the patient”; pg. 2 line 23, “processing the one or more ultrasound images, using a machine-learning algorithm (i.e. deep learning)”). Therefore, it would have been obvious for one of ordinary skilled in the art to modify the known device of Gholami, with the method and system of Errico, and include diaphragm ultrasound imaging measurements to facilitate the ventilation by providing aids in the visualization of the patient’s state as taught by Errico (Errico, pg. 3, line 5-8).
Regarding Claim 8, the modified Gholami discloses the device of claim 7, further comprising: an imaging device configured to acquire the imaging data (Errico, Fig. 1, an ultrasound probe system 190).
Regarding Claim 9, the modified Gholami discloses the device of claim 1, wherein the indication of patient-ventilator asynchrony events comprises an asynchrony index (Gholami, par. 0118, the asynchrony index); and wherein the asynchrony index is displayed on the display device (Gholami, Fig. 23, the asynchrony index is displayed).
Regarding Claim 10, the modified Gholami discloses The device of claim 9, wherein the electronic processing device is further configured to perform the step of: calculate the asynchrony index as a ratio of a number of asynchronous breaths of the patient in labelled waveforms over a predetermined time period and a total breath count of the patient over the predetermined time period (Gholami, par. 0118, “The asynchrony index is the fraction of breaths (including triggered and un-triggered attempted breaths) with one or more detected asynchronies over a period of time.”; fraction of breaths includes the asynchronous breath count and the total breath count).
Regarding Claim 11, the modified Gholami discloses the device of claim 1, wherein the electronic processing device is further configured to perform the step of: determining one or more optimized settings of the mechanical ventilator based on the detected patient-ventilator asynchrony events (Gholami, par. 0005, “…optimize or improve ventilator performance (e.g., triggering and cycling timings), or identify other relevant parameters that affect ventilator function, and/or to allow for intervention including changes to a patient's sedation level.”); and displaying the one or more optimized settings of the mechanical ventilator on the display device (Gholami, par. 0119, Fig. 23, “the GUI can display one or more recommendations for mitigating detected asynchronies…”).
Regarding Claim 12, the modified Gholami discloses the device of claim 1, wherein the electronic processing device is further configured to perform the step of: determining one or more optimized settings of the mechanical ventilator; (Gholami, par. 0005, “…optimize or improve ventilator performance (e.g., triggering and cycling timings), or identify other relevant parameters that affect ventilator function, and/or to allow for intervention including changes to a patient's sedation level.”); and displaying the one or more optimized settings of the mechanical ventilator on the display device (Gholami, par. 0119, Fig. 23, “the GUI can display one or more recommendations for mitigating detected asynchronies…”).
Gholami is silent on that the optimized settings are determined based on receiving imaging data.
However, Errico teaches a method and system for ventilation treatment control (Abstract), comprising of an imaging device receiving imaging data (Fig. 1, an ultrasound probe system 190), and a displaying device (Fig. 2, display 200). Therefore, it would have been obvious for one of ordinary skilled in the art to modify the known device of Gholami, with the system of Errico, and determine optimized settings based on receiving imaging data, to facilitate the ventilation treatment by providing aids in the visualization of the patient’s state as taught by Errico (Errico, pg. 3, line 5-8).
Regarding Claim 14, the modified Gholami discloses the device of claim 13, wherein the electronic processing device is further configured to perform the step of: tracking a progression of a patient-ventilator interaction (Gholami, par. 0115, “In some embodiments, training data for training the machine learning classifier includes both labeled data from previously collected patient data and synthetic data”); and predicting a future patient-ventilator asynchrony event based on the tracking (Gholami, par. 0115, “Once the classifier is trained on the training set and the internal parameters of the classifier (e.g., random forests) are determined, the classifier can be used to predict class labels for new breath cycles.”).
Regarding Claim 15, the modified Gholami discloses a mechanical ventilation method (Gholami, Abstract, “a framework that uses pressure, flow, and volume waveforms to detect patient-ventilator asynchrony ”) comprising: receiving ventilation waveform data during mechanical ventilation of an associated patient (Gholami, par. 0058, “Acquire waveform data, including at least the flow and pressure from the ventilator.”); detecting initial patient-ventilator asynchrony events during a training period (Gholami, par. 0056, “…through a training process”) of the mechanical ventilation by analysis of measurements of the associated patient acquired during the training period (Gholami, par. 0005, “training a machine learning algorithm to perform mapping between features and patient-ventilator asynchrony types. Training data typically involves assigning labels associated with different asynchrony types to waveforms from each breath”; par. 0115, “In some embodiments, training data for training the machine learning classifier includes both labeled data from previously collected patient data and synthetic data”); training a machine learning (ML) component to analyze ventilation waveform data to detect patient-ventilator asynchrony events using the ventilation waveform data received during the training period with labels indicating the initial patient-ventilator asynchrony events (Gholami, par. 0005, “and training a machine learning algorithm to perform mapping between features and patient-ventilator asynchrony types. Training data typically involves assigning labels associated with different asynchrony types to waveforms from each breath.”)
Gholami discloses the measurements used during the training period includes diaphragm activity (par. 0005, “Training data typically involves assigning labels… such labels can be generated by clinical experts reviewing the waveforms or by clinical experts reviewing waveforms as well as other appropriate clinical signals such as esophageal pressure or diaphragm activity.”) but is silent on wherein the measurements comprise non-invasive diaphragmatic or lung sliding ultrasound measurements.
However, Errico teaches a known technique of measuring diaphragm activity for machine learning purposes, wherein the measurements comprise non-invasive diaphragmatic ultrasound measurements (Abstract; pg. 2 line 9-14). This known technique is applicable to the device of Gholami as they both share characteristics and capabilities, namely, they are directed to use the measurements to train a machine learning model.
One of ordinary skilled in the art would have recognized that applying the known technique of Errico would have yielded predictable results and resulted in an improved system before the effective filing date of the claimed invention, because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate diaphragm activity measurements into similar systems. Further, applying diaphragmatic ultrasound measurements to Gholami would have been recognized by those of ordinary skill in the art as resulting in an improved device as it provides a non-invasive procedures.
The modified Gholami is still silent on using posture measurements from a posture sensor configured to detect a posture of the associated patient as a function of time during the training period.
However, Kahl teaches a mechanical ventilation method (par. 0005), wherein a component is trained during a training phase (par. 0021-0022, “The process according to the present invention comprises a training phase… During the training phase the signal processing unit receives measured values from a sum signal sensor device comprising at least one sum signal sensor”), and a use phase (par. 0094), wherein patient-ventilator asynchrony is detected during the use phase (par. 0102, “A ventilator signal is measured… In case of a deviation above a threshold, asynchrony is detected”). Kahl further teaches using posture measurements from a posture sensor (par. 0080, “a mechanical or pneumatic or optical sensor measures an indicator for the body geometry… An optical sensor comprises especially an image recording device and an image analysis unit, which employs an imaging method.”) configured to detect a posture of the associated patient as a function of time during the training period (par. 0081, “the current posture or body position of the patient is used as the transmission channel parameter or as a transmission channel parameter, for example, the position of the patient in a bed or whether the upper body of the patient is upright or curved”; par. 0082 discloses the posture over a time interval affects the sum signal which is used in the training period). Therefore, it would have been obvious for one of ordinary skilled in the art, to modify the known device of Gholami, and use posture of the patient during the training period, as the posture affects the flow of breathing air as taught by Kahl (Kahl, par. 0080-0082).
The modified Gholami further discloses the trained ML component forming a patient-specific ML component that is specific to the associated patient and the posture of the associated patient(Gholami, See par. 0003 and 0005, the system is designed to be patient specific; Kahl, par. 0080-0082); applying the patient-specific ML component to the ventilation waveform data and the posture measurements received after the training period to detect patient-ventilator asynchrony events occurring after the training period (Gholami, par. 0072, “Use the extracted features from the Delta waveform as an input to a statistical (machine learning) classifier, and use the class assignment provided by the classifier to classify the breath into premature termination, delayed termination…”; Kahl, par. 0095-0102); and displaying an indication of patient-ventilator asynchrony events detected by the applying of the patient-specific ML component (Gholami, par. 0006, “a graphical user interface (GUI) for communicating detected patient-ventilator asynchrony whereby the system can be used as a clinical decision support system”).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gholami, in view of Errico, in view of Kahl, further in view of Ma et al. (US12073945), hereafter Ma.
Regarding Claim 4, the modified Gholami discloses the device of claim 1, but does not specifically disclose the training period is in a range of 1 minute to 20 minutes inclusive
However, Ma teaches a machine learning patient ventilator asynchrony detection system (Abstract, “…machine learning algorithms to identify instances of patient ventilator asynchrony”), wherein the training period is in a range of 1 minute to 20 minutes inclusive (Fig. 20, col. 12, line 44-46, “In some embodiments, the historical measurements comprise measurements obtained within a recent timeframe such as the previous 20 minutes”).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the training period of Gholami to 20 minutes, as applicant appears to have placed no criticality on the claimed range (See Applicant’s disclosure, par. 0032, “The training period can be relatively brief, for example in a range of 1 minute to 20 minutes inclusive in some non-limiting embodiments”) and since it has been held that “[i]n the case where the claimed ranges ‘overlap or lie inside ranges disclosed by the prior art' a prima facie case of obviousness exists”. In re Wertheim, 541 F.2d 257, 191 USPQ 90 (CCPA 1976); In re Woodruff, 919 F.2d 1575, 16 USPQ2d 1934 (Fed. Cir. 1990).
Response to Arguments
Applicant’s arguments filed 11/03/2025 with respect to claim(s) 1 and 15 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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KRIS HANYU GONG whose telephone number is (703)756-5898. The examiner can normally be reached M-F 8:30-4:30.
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, Brandy Lee can be reached at 571-270-7410. 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.
/KRIS HANYU GONG/Examiner, Art Unit 3785
/BRANDY S LEE/Supervisory Patent Examiner, Art Unit 3785