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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/16/2025 has been entered.
Response to Amendment
This office action is in response to the amendment filed on 11/24/2025. As directed by the amendment, claims 1 and 15 were amended and claims 7 and 9-12 have been cancelled. As such, claims 1-6 and 13-16 are pending in the instant application.
Applicant has cancelled claim 11; the 112(a) rejection to claim 11 has been withdrawn.
Applicant has cancelled claim 7; the 112(b) rejection to claim 7 has been withdrawn.
Response to Arguments
Applicant's arguments, see page 5 of Remarks, filed 11/24/2025, pertaining to the
newly amended limitations have been noted. However, a new ground(s) of rejection has been
provided below to address the newly added limitations.
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) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 3, 5 and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Errico (WO 2021110576 A1) in view of Habashi (US 20080295839 A1), Ozaki (US 20070151563 A1) and Jones (US 20050061321 A1).
Regarding claim 1, Errico teaches a mechanical ventilation device (ultrasound imaging system 1, see Fig. 1) comprising at least one electronic controller (processor 110, see Fig. 1) configured to:
receive ultrasound data related to a thickness of a diaphragm of a patient during inspiration and expiration while the patient undergoes mechanical ventilation therapy with a mechanical ventilator (ventilator 195, see Fig. 1) (“For example, if the health information comprises a time series of indicators of the patient’s health derive from sets of one or more ultrasound image(s) (e.g. a time series of the patient’s diaphragm thickness)…” see page 13, lines 31-34; Errico teaches diaphragm examinations of the diagraph thickness throughout continuous breathing cycles which includes both inspiration and expiration as seen on page 13, lines 8-12), the mechanical ventilation therapy including a mechanical ventilation training program (Errico teaches a ventilator 195 with an information provisions system 100 wherein the system is to provide information for assisting in the treatment of a ventilated and sedated patient as seen in Figs. 1 and 6 and page 7, lines 16-19);
calculate a diaphragm thickness metric based on at least the ultrasound data (Errico teaches health information such as the thickness of a patient’s diaphragm to see if the patient may face a medical complication if they were removed from the ventilation (see page 9, lines 3-15), wherein the patient’s diaphragm thickness is derived from an ultrasound image as seen in page 13, lines 31-34);
detect when the calculated diaphragm thickness metric does not satisfy an acceptance criterion of the mechanical ventilation training program (Errico teaches a ventilator 195 with an information provisions system 100 wherein the system is to provide information for assisting in the treatment of a ventilated and sedated patient as seen in Figs. 1 and 6 and page 7, lines 16-19. The information includes health information which include one or more health indicators (taken as acceptance criterion) that are responsive to changes in the patient’s health and used to indicate the probability of the patient having further health problems if ventilation is removed as seen on page 3, lines 9-19. Such a health indicator includes diaphragm thickness, wherein a health indicator is a score responsive to a difference between a measured diaphragm thickness (e.g. a current diaphragm thickness) and a historic/baseline diaphragm thickness as seen on page 4, lines 16-21 and page 3, line 29 to page 4, line 8. Errico further teaches taking ultrasound images, processing the ultrasound images and using a machine-learning algorithm to obtain a value for one or more health indicators responsive to a change in the patient’s health as seen on page 5, lines 6-14. As such, if the health indicator shows the measured diaphragm thickness will have a high likelihood of complications if ventilation is removed (does not satisfy an acceptance criteria), it would be detected).
But does not teach adjust a level-of-support parameter of the mechanical ventilation training program until the calculated diaphragm thickness metric satisfies the acceptance criterion;
multiply the level-of-support parameter by a respiratory muscle pressure to determine an airway ventilation pressure value; and
continuously perform the mechanical ventilation training program until the airway ventilation pressure value falls below a predetermined training program threshold.
However, Habashi teaches adjust a level-of-support parameter of the mechanical ventilation training program until the critical pulmonary physiological conditions satisfies the acceptance criterion (Habashi teaches a weaning module 250 and a weaning failure criteria 710 as a parameter evaluation (taken as acceptance criterion), wherein P(high) (see [0050]) (taken as level-of-support parameter) is adjusted based on whether or not the weaning failure criteria is met as seen in Fig. 9 and [0112]-[0113]. The criteria evaluates the patient against a more rigorous series of critical pulmonary physiological conditions to ensure the subject can withstand the added stresses of substantially less gradual changes in the ventilation mode of operation as seen in [0112]. Habashi further teaches continuously adjusting and/or decreasing assistance until the weaned patient can be extubated as seen in Fig. 10 and [0114]).
Errico teaches using ultrasound images to aid in making a decision to wean patients off the ventilator, especially as diaphragm dysfunction can be caused by ventilator and sedative dosages as seen on page 2, line 30 to page 3, line 4. Habashi teaches input devices including ultrasound equipment (see [0036]) and weaning a patient off a ventilator based on criteria that evaluates a patient against a series of critical pulmonary physiological conditions (see [0112]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the device taught by Errico to include the weaning module taught by Habashi to aid in weaning patients off by adjusting the ventilator parameter (see [0112]-[0114]). Furthermore, Errico in view of Habashi teaches adjusting a level-of-support parameter of the mechanical ventilation training program until the calculated diaphragm thickness metric satisfies the acceptance criterion (Habashi teaches adjusting P(high) against a weaning failure criterion which evaluates the patient against a series of critical pulmonary physiological conditions as seen in [0112]. Errico teaches the health information to comprise of a measurement for evaluating diaphragmatic dysfunction (see page 3, lines 25-28) and one or more health indicators that are responsive to a change in the patient’s health, such as a diaphragm thickness (page 4, lines 16-21 and page 3, line 29 to page 4, line 8). As such, Errico in view of Habashi teaches a weaning failure criteria using diaphragm thickness as part of the pulmonary physiological conditions evaluations to adjust P(high)).
However, Ozaki teaches multiply the level-of-support parameter by a respiratory muscle pressure to determine an airway ventilation pressure value ([0039] of applicant’s specification reads “For example, in proportional assist ventilation (PAV or PAV+), the degree of assistance is set by the percentage level-of-support parameter K which scales the airway pressure (Paw) delivered to the patient…” Ozaki teaches the gas-delivery mechanism 20 controlled by the control apparatus 21 discharging the assisting gas at the assisting gas pressure P.sub.vent to attain the respiratory airway pressure P.sub.aw (=P.sub.mus.beta.) amplified proportionally to the respiratory effort pressure P.sub.mus of the patient and on the basis of the present amplified gain.beta. as seen in Fig. 2 and [0151] and [0219]. P.sub.mus is the patient’s respiratory effort pressure induced by the respiratory muscles such as patient's diaphragm and is acted to the patient's respiratory system as seen in [0116]. Ozaki further teaches a flow rate amplification gain Beta.sub.FG and a volume amplification gain Beta.sub.VG, such that when the flow rate amplification gain and volume amplification gain is set to the same value, the gains are known as amplification gain Beta as seen in [0142]-[0143]. Furthermore, Ozaki teaches setting adequate values of flow-rate-assist gain K.sub.fa and volume-assist gain K.sub.Va to prevent injuries to a patient’s lung and/or respiratory airway due to PAV technology as seen in [0011]-[0012] and [0022]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the device taught by Errico in view of Habashi to include the control apparatus 21 as taught by Ozaki to discharge the assisting gas based on respiratory effort pressure and gain wherein the gain is more stable/prevent runaway to prevent injury to a patient’s lungs and/or respiratory airway (see [0011]-[0012], [0174]-[0175] and [0179]-[0181]).
However, Jones teaches an acceptable range of measured pressured values and when the value falls outside of the acceptable ranges, controller 18 will display a warning and/or shut down the ventilation device as seen in [0037].
Ozaki teaches if a runaway occurs when a flow-rate-assist gain or volume-assist gain is inadequately set, it can cause injuries of patient’s lung and/or respiratory airway and that it is obliged to stop ventilatory support during assist as seen in [0011]. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the device taught by modified Errico to include the controller and stop the ventilation when the pressure falls outside of the acceptable ranges as taught by Jones to allow the patient to take appropriate action (see [0037]) and to protect the patient (see [0008] and [0070]). Modified Errico teaches continuously perform the mechanical ventilation training program until the airway ventilation pressure value falls below a predetermined training program threshold (Modified Errico teaches discharging the assisting gas at the assisting gas pressure P.sub.vent to attain the respiratory airway pressure P.sub.aw (see Fig. 2 and [0151] and [0219] of Ozaki) until pressures is outside of an acceptable range as taught by [0037] of Jones, especially for the respiratory airway pressure, especially since it is known if the gain is not set properly, injuries can occur in a patient’s lungs and/or respiratory airway).
Regarding claim 3, modified Errico teaches the device of claim 1, and Errico further teaches wherein the diaphragm thickness metric includes a mean diaphragm thickness over a respiratory cycle (“Example health indicators may comprise the diaphragm thickness itself or a trend/ differential of the diaphragm thickness that indicates how the diaphragm thickness changes over time. For example, the health information may comprise an indicator of variance diaphragm thickness over a period of time.” See page 4, lines 3-6; Errico teaches the health information to indicate how the diaphragm thickness changes/varies over time and therefore teaches a mean diaphragm thickness over a respiratory cycle)
Regarding claim 5, modified Errico teaches the device of claim 1, and further teaches wherein the at least one electronic controller is configured to:
control the mechanical ventilator to adjust one or more parameters of the mechanical ventilation therapy delivered to the patient (modified Errico teaches a ventilator 195 with an information provisions system 100 as seen in Figs. 1 and 6 and page 7, lines 16-19 of Errico. Modified Errico further taught a weaning module 250 (taught by Habashi), wherein the weaning module makes adjustments to P(high) to reduce ventilator support while assessing patient response as seen in Fig. 9 and [0047] and [0112]-[0113] of Habashi); and
iteratively repeat the receive, calculate, and control operations to provide feedback control of the mechanical ventilator based at least on whether the calculated diaphragm thickness metric satisfies the acceptance criterion (Habashi teaches iteratively adjusting P(high) against a weaning failure criterion 710 which evaluates the patient against a series of critical pulmonary physiological conditions for feedback seen in Fig. 9 and [0112]-[0113] and [0047]. Errico teaches the health information to comprise of a measurement for evaluating diaphragmatic dysfunction (see page 3, lines 25-28) and one or more health indicators that are responsive to a change in the patient’s health, such as a diaphragm thickness (page 4, lines 16-21 and page 3, line 29 to page 4, line 8). As such, modified Errico teaches a weaning failure criteria using diaphragm thickness as part of the pulmonary physiological conditions evaluations to iteratively adjust P(high)).
Regarding claim 14, modified Errico teaches the device of claim 1, and further teaches wherein the at least one electronic controller configured to is configured to:
control the mechanical ventilator to adjust one or more parameters of the mechanical ventilation therapy delivered to the patient (modified Errico teaches a ventilator 195 with an information provisions system 100 as seen in Figs. 1 and 6 and page 7, lines 16-19 of Errico. Modified Errico further taught a weaning module 250 (taught by Habashi), wherein the weaning module makes adjustments to P(high) to reduce ventilator support while assessing patient response as seen in Fig. 9 and [0047] and [0112]-[0113] of Habashi).
Regarding claim 15, Errico teaches a mechanical ventilation method (“...there is provided a computer-implemented method of providing information for assisting in the treatment of a ventilated and sedated patient.” See page 2, lines 18-20) comprising, with at least one electronic controller (processor 110, see Fig. 1):
receiving ultrasound data related to a thickness of a diaphragm of patient during inspiration and expiration while the patient undergoes mechanical ventilation therapy with a mechanical ventilator (ventilator 195, see Fig. 1) (“For example, if the health information comprises a time series of indicators of the patient’s health derive from sets of one or more ultrasound image(s) (e.g. a time series of the patient’s diaphragm thickness)…” see page 13, lines 31-34; Errico teaches diaphragm examinations of the DTF throughout continuous breathing cycles which includes both inspiration and expiration as seen on page 13, lines 8-12), the mechanical ventilation therapy including a mechanical ventilation training program (Errico teaches a ventilator 195 with an information provisions system 100 wherein the system is to provide information for assisting in the treatment of a ventilated and sedated patient as seen in Figs. 1 and 6 and page 7, lines 16-19);
calculating a diaphragm thickness metric based on at least the ultrasound data (Errico teaches health information such as the thickness of a patient’s diaphragm to see if the patient may face a medical complication if they were removed from the ventilation (see page 9, lines 3-15), wherein the patient’s diaphragm thickness is derived from an ultrasound image as seen in page 13, lines 31-34);
detecting when the calculated diaphragm thickness metric does not satisfy an acceptance criterion of the mechanical ventilation training program (Errico teaches a ventilator 195 with an information provisions system 100 wherein the system is to provide information for assisting in the treatment of a ventilated and sedated patient as seen in Figs. 1 and 6 and page 7, lines 16-19. The information includes health information which include one or more health indicators (taken as acceptance criterion) that are responsive to changes in the patient’s health and used to indicate the probability of the patient having further health problems if ventilation is removed as seen on page 3, lines 9-19. Such a health indicator includes diaphragm thickness, wherein a health indicator is a score responsive to a difference between a measured diaphragm thickness (e.g. a current diaphragm thickness) and a historic/baseline diaphragm thickness as seen on page 4, lines 16-21 and page 3, line 29 to page 4, line 8. Errico further teaches taking ultrasound images, processing the ultrasound images and using a machine-learning algorithm to obtain a value for one or more health indicators responsive to a change in the patient’s health as seen on page 5, lines 6-14. As such, if the health indicator shows the measured diaphragm thickness will have a high likelihood of complications if ventilation is removed (does not satisfy an acceptance criteria), it would be detected)
But does not teach adjusting a level-of-support parameter of the mechanical ventilation training program until the calculated diaphragm thickness metric satisfies the acceptance criterion;
multiplying the level-of-support parameter by a respiratory muscle pressure to determine an airway ventilation pressure value; and
continuously perform the mechanical ventilation training program until the airway ventilation pressure value falls below a predetermined training program threshold.
However, Habashi teaches adjusting a level-of-support parameter of the mechanical ventilation training program until the critical pulmonary physiological conditions satisfies the acceptance criterion (Habashi teaches a weaning module 250 and a weaning failure criteria as a parameter evaluation 710 (taken as acceptance criterion), wherein P(high) (see [0050]) (taken as level-of-support parameter) is adjusted based on whether or not the weaning failure criteria is met as seen in Fig. 9 and [0112]-[0113]. The criteria evaluates the patient against a more rigorous series of critical pulmonary physiological conditions to ensure the subject can withstand the added stresses of substantially less gradual changes in the ventilation mode of operation as seen in [0112]. Habashi further teaches continuously adjusting and/or decreasing assistance until the weaned patient can be extubated as seen in Fig. 10 and [0114]).
Errico teaches using ultrasound images to aid in making a decision to wean patients off the ventilator, especially as diaphragm dysfunction can be caused by ventilator and sedative dosages as seen on page 2, line 30 to page 3, line 4. Habashi teaches input devices including ultrasound equipment (see [0036]) and weaning a patient off a ventilator based on criteria that evaluates a patient against a series of critical pulmonary physiological conditions (see [0112]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the device taught by Errico to include the weaning module taught by Habashi to aid in weaning patients off by adjusting the ventilator parameter (see [0112]-[0114]). Furthermore, Errico in view of Habashi teaches adjusting a level-of-support parameter of the mechanical ventilation training program until the calculated diaphragm thickness metric satisfies the acceptance criterion (Habashi teaches adjusting P(high) against a weaning failure criterion which evaluates the patient against a series of critical pulmonary physiological conditions as seen in [0112]. Errico teaches the health information to comprise of a measurement for evaluating diaphragmatic dysfunction (see page 3, lines 25-28) and one or more health indicators that are responsive to a change in the patient’s health, such as a diaphragm thickness (page 4, lines 16-21 and page 3, line 29 to page 4, line 8). As such, Errico in view of Habashi teaches a weaning failure criteria using diaphragm thickness as part of the pulmonary physiological conditions evaluations to adjust P(high)).
However, Ozaki teaches multiplying the level-of-support parameter by a respiratory muscle pressure to determine an airway ventilation pressure value ([0039] of applicant’s specification reads “For example, in proportional assist ventilation (PAV or PAV+), the degree of assistance is set by the percentage level-of-support parameter K which scales the airway pressure (Paw) delivered to the patient…” Ozaki teaches the gas-delivery mechanism 20 controlled by the control apparatus 21 discharging the assisting gas at the assisting gas pressure P.sub.vent to attain the respiratory airway pressure P.sub.aw (=P.sub.mus.beta.) amplified proportionally to the respiratory effort pressure P.sub.mus of the patient and on the basis of the present amplified gain.beta. as seen in Fig. 2 and [0151] and [0219]. P.sub.mus is the patient’s respiratory effort pressure induced by the respiratory muscles such as patient's diaphragm and is acted to the patient's respiratory system as seen in [0116]. Ozaki further teaches a flow rate amplification gain Beta.sub.FG and a volume amplification gain Beta.sub.VG, such that when the flow rate amplification gain and volume amplification gain is set to the same value, the gains are known as amplification gain Beta as seen in [0142]-[0143]. Furthermore, Ozaki teaches setting adequate values of flow-rate-assist gain K.sub.fa and volume-assist gain K.sub.Va to prevent injuries to a patient’s lung and/or respiratory airway due to PAV technology as seen in [0011]-[0012] and [0022]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the device taught by Errico in view of Habashi to include the control apparatus 21 as taught by Ozaki to discharge the assisting gas based on respiratory effort pressure and gain wherein the gain is more stable/prevent runaway to prevent injury to a patient’s lungs and/or respiratory airway (see [0011]-[0012], [0174]-[0175] and [0179]-[0181]).
However, Jones teaches an acceptable range of measured pressured values and when the value falls outside of the acceptable ranges, controller 18 will display a warning and/or shut down the ventilation device as seen in [0037].
Ozaki teaches if a runaway occurs when a flow-rate-assist gain or volume-assist gain is inadequately set, it can cause injuries of patient’s lung and/or respiratory airway and that it is obliged to stop ventilatory support during assist as seen in [0011]. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the device taught by modified Errico to include the controller and stop the ventilation when the pressure falls outside of the acceptable ranges as taught by Jones to allow the patient to take appropriate action (see [0037]) and to protect the patient (see [0008] and [0070]). Modified Errico teaches continuously perform the mechanical ventilation training program until the airway ventilation pressure value falls below a predetermined training program threshold (Modified Errico teaches discharging the assisting gas at the assisting gas pressure P.sub.vent to attain the respiratory airway pressure P.sub.aw (see Fig. 2 and [0151] and [0219] of Ozaki) until pressures is outside of an acceptable range as taught by [0037] of Jones, especially for the respiratory airway pressure, especially since it is known if the gain is not set properly, injuries can occur in a patient’s lungs and/or respiratory airway).
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Errico (WO 2021110576 A1) in view of Habashi (US 20080295839 A1), Ozaki (US 20070151563 A1) and Jones (US 20050061321 A1), as applied to claim 1 above, and further in view of “Diaphragm ultrasound as a new index of discontinuation from mechanical ventilation” (hereinafter, known as “Ferrari”).
Regarding claim 2, modified Errico teaches the device of claim 1, and Errico further teaches “diaphragm examinations such as diaphragm thickness (DTF) can be performed during quiet tidal breathing and maximum inspiration throughout continuous breathing cycles. DTF measurement is an indicator of muscle thickening which reflects the ventilation burden and if it has been properly selected for the subject” as seen in page 13, lines 8-12 but does not teach wherein the diaphragm thickness metric includes a diaphragm thickening ratio indicative of a diaphragm thickness during inspiration relative to a diaphragm thickness during expiration.
However, Ferrari teaches the diaphragm thickness metric includes a diaphragm thickening ratio indicative of a diaphragm thickness during inspiration relative to a diaphragm thickness during expiration (the diaphragmatic thickening fraction (DTF) ([0033] of applicant’s specification discusses a diaphragm thickening ratio or fraction) is used to assess diaphragmatic functions and its contribution to respiratory workload (see page 2, first column, second paragraph) and is calculated as (Thickness at end inspiration – thickness at end expiration / thickness at end expiration (see page 2, second column, last paragraph)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the device taught by modified Errico to include the diaphragm thickening ratio indicative of a diaphragm thickness during inspiration relative to a diaphragm thickness during expiration as taught by Ferrari as it is helpful to access diaphragmatic functions and its contribution to respiratory workload (see page 2, first column, second paragraph) and an easy to obtain index (see page 5, second column, last paragraph).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Errico (WO 2021110576 A1) in view of Habashi (US 20080295839 A1), Ozaki (US 20070151563 A1) and Jones (US 20050061321 A1), as applied to claim 1 above, and further in view of Wernerth (US 20160192902 A1).
Regarding claim 4, modified Errico teaches the device of claim 1, and Errico further teaches further comprising: an ultrasound transducer (ultrasound transducer 191, see Fig. 1) from which the at least one electronic controller receives the ultrasound data (“The information provision system 100 is adapted to obtain one or more ultrasound images 152 at the one or more processors 110. In the illustrated example, these are directly provided by the ultrasound probe system 190, but the skilled person would appreciate that the one or more ultrasound images may be obtained from a storage or memory (e.g. memory 130).” See page 7, lines 28-32)
but does not teach a wearable ultrasound transducer.
However, Wernerth teaches a wearable ultrasound transducer (Wernerth teaches ultrasound transducers coupled to different wearable garments such as a vest, shirt, bib, and more as seen in [0158]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the device taught by modified Errico to replace the ultrasound transducer with the wearable ultrasound transducer as taught by Wernerth to maintain the transducer in close contact with the patient’s torso/body part for a monitorable signal (see [0021]).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Errico (WO 2021110576 A1) in view of Habashi (US 20080295839 A1), Ozaki (US 20070151563 A1) and Jones (US 20050061321 A1), as applied to claim 1 above, and further in view of Li (US 20160235931 A1).
Regarding claim 6, modified Errico teaches the device of claim 1, and Errico further teaches further comprising:
an ultrasound imaging device (ultrasound probe system 190, see Fig. 1) configured to generate the ultrasound data (“The ultrasound probe system 190 is adapted to generate one or more ultrasound images responsive to a user’s control.” See page 7, lines 20-21) and further teaches a processor 110
but does not teach wherein the at least one electronic controller is implemented in the ultrasound imaging device.
However, Li teaches a breathable controller 413 for the ventilator 101 and an ultrasound controller 423 for the ultrasound scanning machine 42 as seen in Fig. 4 and [0033] and [0035].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the device taught by modified Errico to replace the processor with two separate processors for each of the ventilator and ultrasound machine as taught by Li as an alternative design choice that is known in the art to have a processor in each medical device rather than a single processor to control both devices. Furthermore, Errico teaches the processing system can be implemented as a combination of dedicated hardware (see page 20, lines 27-30).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Errico (WO 2021110576 A1) in view of Habashi (US 20080295839 A1), Ozaki (US 20070151563 A1), Jones (US 20050061321 A1) and Li (US 20160235931 A1), as applied to claim 9 above, and further in view of Sanborn (US 20070272242 A1).
Regarding claim 13, modified Errico teaches the device of claim 9, and Errico further teaches the alert being indicative of the calculated diaphragm thickness metric failing to satisfy the acceptance criterion (“The diaphragm thickness may be compared to patients’ baseline values and threshold values derived either from population of patients with same medical history, or guidelines for clinically acceptable diaphragm thicknesses, and an alarm may be generated if the diaphragm thickness exceeds this threshold value.” See page 11, line 34 to page 12, line 3) and further teaches user interface 120 with a display (see page 10, lines 20-25) and ventilation information 153 to be obtained from ventilator 195 (see page 8, lines 19-23) but does not teach wherein the second electronic controller is configured to:
output an alert on a display device of the mechanical ventilator.
However, Sanborn teaches a controller (“WOB calculation module 22 may include a processor 62, memory 64, and any other suitable hardware or software.” See [0045] and Fig. 1) outputting an alert on a display device (WOB graphic 16 and display device 20, see Fig. 1) of the ventilator (ventilator 14, see Fig. 1) (“However, display device 100 may additionally or alternatively be operable to visually represent patient data, alarm conditions, various charts, graphs, tables, and/or other such information as may be appropriate or useful to a caregiver in assessing a patient's respiratory or other vital functions.” See [0050]; WOB calculation module may include techniques for calculated estimated WOB measures that may be displayed on WOB graphic 16).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the device taught by modified Errico to include a display on the ventilator to output patient data and an alert as taught by Sanborn to give useful or appropriate information to a caregiver (see [0050]).
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Errico (WO 2021110576 A1) in view of Habashi (US 20080295839 A1), Ozaki (US 20070151563 A1) and Jones (US 20050061321 A1), as applied to claim 1 above, and further in view of Jafari (US 20140230818 A1).
Regarding claim 16, modified Errico teaches the device of claim 1, and Errico further teaches wherein the at least one electronic controller configured to is configured to:
when the calculated diaphragm thickness metric does not satisfy an acceptance criterion, at least one of:
output an alert indicative of the calculated diaphragm thickness metric failing to satisfy the acceptance criterion (Errico teaches health information comprising a numerical measure of diaphragm thickness, including patient’s baseline values and threshold values as seen on page 12, lines 4-7 and page 4, lines 16-21. Errico further teaches an alarm may be generated if the diaphragm thickness exceeds the threshold value as seen in page 11, line 34 to page 12, line 3)
but does not teach output a recommended adjustment to one or more parameters of the mechanical ventilation therapy delivered to the patient.
However, Jafari teaches output an alert indicative of the metric failing to satisfy the acceptance criterion (Jafari teaches if the monitored chest wall movement and/or calculated local tidal volume displacement of one or both of the lungs, lung lobes, and/or the patient's abdomen does not meet a specified criterion and/or changes by a pre-determined threshold, the ventilator 202 may trigger an alarm as seen in Fig. 4 and [0030]);
output a recommended adjustment to one or more parameters of the mechanical ventilation therapy delivered to the patient (“…the ventilator 202 may trigger an alarm (Step S460) and provide to a clinician relevant data and/or recommendations, e.g., by smart prompt module 226, relating to the change.” See [0030]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the device taught by modified Errico to include the smart prompt module taught by Jafari to provide relevant data and recommendations related to the alarm to allow the clinician to spend less time at the patient’s bed side and to increase efficacy (see [0006]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tina Zhang whose telephone number is (571)272-6956. The examiner can normally be reached Monday - Friday 9:00AM-5:00PM.
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/TINA ZHANG/Examiner, Art Unit 3785
/BRANDY S LEE/Supervisory Patent Examiner, Art Unit 3785