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 .
Information Disclosure Statement
The information disclosure statement (IDS) dated 06/06/2023 has been received and considered.
Election/Restrictions
Claims 39-55 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected group, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 04/01/2026.
Claim Interpretation
Claim 37 limitation “a patient stay on ventilation” and claim 38 limitation “a patient stay in critical care, emergency areas, and theatres” has been interpreted to mean a patient’s treatment duration on ventilation (in the case of claim 37) or critical care, emergency areas, and theaters (in the case of claim 38).
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 36-38 are rejected under 35 U.S.C. 103 as being unpatentable over Loncar et al (US 9199056 B1), hereafter Loncar in view of Rehman et al. (US 2020/0261675 A1), hereafter Rehman.
Regarding Claim 36, Loncar discloses a portable ventilator module (fig. 1, breathing apparatus 100 is a ventilator, col. 6 lines 61-64); wherein the portable ventilator module is detachably attachable to an anaesthesia module (fig. 1, anesthetic aggregate 1 is modular to be attached, or not attached, to the ventilator, col. 4 lines 35-38 and col. 6 lines 66-67) to enable a single device to collect data from a patient in critical care and theatre environments (col. 4 lines 48-52).
Loncar does not disclose that the data collection is done by a data module capable of artificial intelligence (AI), machine learning (ML), or a combination thereof.
However, Rehman teaches that an anesthesia machine and a ventilator ([0046]) may collect patient data using a data module capable of artificial intelligence ([0048] the machine learning data mining algorithm may be stored in a memory device coupled to the monitoring device 10 and/or processor 20). The machine learning assists medical professionals in making real-time clinical decisions for patients under anesthesia ([0049]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include an AI or machine learning module in Loncar’s device for the benefit of assisting clinicians in making real-time decisions for patient health (Rehman [0049]).
Regarding Claim 37, Loncar as modified discloses a portable ventilator module of claim 36, wherein: the data module comprises an Artificial Intelligence (AI) module for the acquisition of data from a patient stay on ventilation (as modified by Rehman, the machine learning module may acquire data such as clinical decisions made, chosen airway device, respiratory function pattern associated with the airway device [0048]); wherein the Al module is capable of performing analysis of the data (Rehman [0048-0049]); wherein the Al module can be inserted into or form part of the portable ventilator to provide life-sustaining ventilation support to the patient in critical care ([0048] the machine learning algorithm stored in memory may be coupled to or stored in the processor 20); and wherein the portable ventilator can dock with the anaesthesia module to provide anaesthesia during operations in theatre (Loncar fig. 1, anesthesia gasifier unit 110 is coupled to the breathing apparatus 100, col. 6 lines 66-67; this device is capable of providing anesthesia during an operation).
Regarding Claim 38, Loncar as modified discloses a portable ventilator module of claim 37, wherein: the Al module is configured for unbroken acquisition of data (Loncar provides continuous monitoring of pressure, col. 8 lines 52-64; as modified by Rehman, both anesthesia machines and ventilators are known to provide continuous patient monitoring [0044]) from a patient stay in critical care, emergency areas, and theatres (Loncar col. 5 lines 31-33, the invention is used with existing intensive care ventilators); and a machine change from an ICU ventilator to a separate anaesthesia machine and back again is not required, thereby avoiding data loss (Loncar fig. 4 col. 11 lines 45-52, the anesthesia unit 110 is added into the ventilator as a side stream configuration, and the patient does not need to be transferred to a separate anesthesia machine, col. 5 lines 5-12) .
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2002/0017299 A1 discloses modular anesthesia, pneumatic systems, and patient monitoring ([0163]) and mathematical modeling via neural network for predicting effects of drugs delivered to a patient ([0116])
US 2007/0101993 A1 discloses modular anesthesia monitoring modules ([0010])
US 9549687 B2 discloses a modular patient transport apparatus having an anesthesia module (fig. 2, 114) a ventilator (fig. 2 118) a monitoring module (122; col. 4 lines 38-55)
US 2019/0104919 A1 discloses using machine learning for a pattern recognition system that obtains data from modular devices ([0307]) that includes anesthesia and ventilators ([0327]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SARA K. TOICH whose telephone number is (703)756-1450. The examiner can normally be reached M-Th 7:30 am - 4:30 pm, every other F 7:30-3:30 ET.
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/SARA K TOICH/Examiner, Art Unit 3785
/BRANDY S LEE/Supervisory Patent Examiner, Art Unit 3785
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