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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-5, and 11-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-2 of copending Application No. 18/532,991 (reference application).
Regarding claims 1, 11 and 16, although the claims at issue are not identical, they are not patentably distinct from each other because instant claims 1, 11 and 16 all recite a system comprising medical equipment, a processor, and a non-transitory computer-readable medium including software comprising artificial intelligence, the software configured to cause the processor to: receive an input comprising an quantifiable parameter from the medical equipment, analyze the input using artificial intelligence, and modify performance of the medical equipment resulting in a quantifiable improvement in the quantifiable parameter – with copending App. #18/532,991 similarly reciting a system comprising a ventilator (therefore a piece of medical equipment), a processor, and a non-transitory computer-readable medium including software comprising artificial intelligence, the software configured to cause the processor to: receive an input comprising an quantifiable parameter from the medical equipment, analyze the input using artificial intelligence, and modify performance of the medical equipment resulting in a quantifiable improvement in the quantifiable parameter.
Regarding claim 2, claim 1 of the ‘991 copending application corresponds to this claim.
Regarding claim 3, claim 2 of the ‘991 copending application corresponds to this claim.
Regarding claim 4, claim 1 of the ‘991 copending application corresponds to this claim.
Regarding claim 5, claim 4 of the ‘991 copending application corresponds to this claim.
Regarding claim 11, claim 1 of the ‘991 copending application corresponds to this claim.
Regarding claim 12, claim 1 of the ‘991 copending application corresponds to this claim.
Regarding claim 13, claim 2 of the ‘991 copending application corresponds to this claim.
Regarding claim 14, claim 12 of the ‘991 copending application corresponds to this claim.
Regarding claim 15, claim 8 of the ‘991 copending application corresponds to this claim.
Regarding claim 16, claim 1 of the ‘991 copending application corresponds to this claim.
Regarding claim 17, claim 1 of the ‘991 copending application corresponds to this claim.
Regarding claim 18, claim 13 of the ‘991 copending application corresponds to this claim.
Regarding claim 19, claim 13 of the ‘991 copending application corresponds to this claim.
Regarding claim 20, claim 13 of the ‘991 copending application corresponds to this claim.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 6 and 8-10 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 18/532,991 (reference application) in view of Gutierrez (US 2019/0371460 A1).
Regarding claims 6, and 8-10, although the claims at issue are not identical, they are not patentably distinct from each other because instant claim 6 recites a system comprising medical equipment, a processor, and a non-transitory computer-readable medium including software comprising artificial intelligence, the software configured to cause the processor to: receive an input comprising an quantifiable parameter from the medical equipment, analyze the input using artificial intelligence, and modify performance of the medical equipment resulting in a quantifiable improvement in the quantifiable parameter – with copending App. #18/532,991 similarly reciting a system comprising a ventilator (therefore a piece of medical equipment), a processor, and a non-transitory computer-readable medium including software comprising artificial intelligence, the software configured to cause the processor to: receive an input comprising an quantifiable parameter from the medical equipment, analyze the input using artificial intelligence, and modify performance of the medical equipment resulting in a quantifiable improvement in the quantifiable parameter. However, the copending App. ‘991 does not recite receiving input from a remote location. However, Gutierrez teaches a data acquisition system configured to acquire medical equipment data and send such data to remote locations/devices such as smartphones, tablets, laptops etc. for data processing (Paragraph 0450). Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filling date of the claimed invention to modify the claims of the copending application with the remote data locations/devices feature, as taught by Gutierrez, as providing a remote location and/or device for a system to store and manage data is an art-recognized means of system components communicating to one another, and allows the user to store his/her data at a location convenient to themselves and/or their caregiver for quick access. Claims 8-10 similarly recite limitations directed towards features of the remote data location, which are found in Gutierrez’s Paragraphs 0450 and 0446 and Figure 26 describing the system using remote devices such as smartphones, tablets, laptops etc. for data processing and control of ventilator.
Regarding claim 7, claim 1 of the ‘991 copending application corresponds to this claim.
This is a provisional nonstatutory double patenting rejection.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, and 6-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gutierrez (US 2019/0371460 A1).
Regarding claim 1, Gutierrez discloses a system, the system comprising: (a) medical equipment (system 100 comprising a patient 10 coupled to a ventilator 20, Paragraph 0052 and Figure 1); (b) a processor (CPU 60 can be any suitable processor capable of obtaining data and performing analysis, Paragraph 0056 and Figure 1); and (c) a non-transitory computer-readable medium including software (embodiments of the invention may be implemented in one or a combination of hardware, firmware, software, machine-readable medium which may be read and executed by a computing platform such as the processor, Paragraph 0438) comprising artificial intelligence (embodiments may use artificial intelligence or machine learning to predict system interventions, Abstract and Paragraph 0260), the software configured to cause the processor to: (i) receive an input comprising a quantifiable parameter from the medical equipment (data acquisition system 30 acquires data such as air flow data from the air flow sensor 40, air pressure data from the pressure sensor 50 and/or data from other data sources such as the respiratory monitor 85, and sensors capable of sensing any periodic signal produced by breathing, Paragraph 0052 and Figure 1); (ii) analyze the input using the artificial intelligence (artificial intelligence and/or machine learning algorithms may be used for the analysis of the monitored data of the mechanical ventilator, Paragraph 0260); and (iii) modify performance of the medical equipment resulting in a quantifiable improvement in the quantifiable parameter (the use of the artificial intelligence and/or machine learning algorithms may be used to predict interventions predicted to result in positive outcomes, such as the proper amount of sedation to achieve positive outcomes, Paragraph 0260 and Claim 20).
Regarding claim 2, Gutierrez further discloses wherein the medical equipment comprises a ventilator (ventilator 20, Paragraph 0052 and Figure 1).
Regarding claim 3, Gutierrez further discloses wherein the quantifiable parameter comprises ventilator data (data acquisition system 30 acquires data such as air flow data from the air flow sensor 40, air pressure data from the pressure sensor 50 and/or data from other data sources such as the respiratory monitor 85, and sensors capable of sensing any periodic signal produced by breathing, Paragraph 0052 and Figure 1; see also Paragraph 0309 describing the monitoring of ventilator sedation levels).
Regarding claim 6, Gutierrez discloses system, the system comprising: (a) medical equipment (system 100 comprising a patient 10 coupled to a ventilator 20, Paragraph 0052 and Figure 1); (b) a processor (CPU 60 can be any suitable processor capable of obtaining data and performing analysis, Paragraph 0056 and Figure 1); and (c) a non-transitory computer-readable medium including software (embodiments of the invention may be implemented in one or a combination of hardware, firmware, software, machine-readable medium which may be read and executed by a computing platform such as the processor, Paragraph 0438) comprising artificial intelligence (embodiments may use artificial intelligence or machine learning to predict system interventions, Abstract and Paragraph 0260), the software configured to cause the processor to: (i) receive, at a remote location, an input comprising a quantifiable parameter from a patient receiving support from the medical equipment (data acquisition system 30 acquires data such as air flow data from the air flow sensor 40, air pressure data from the pressure sensor 50 and/or data from other data sources such as the respiratory monitor 85, and sensors capable of sensing any periodic signal produced by breathing, Paragraph 0052 and Figure 1; see Paragraph 0450 and Figure 26 describing the system using remote devices such as smartphones, tablets, laptops etc. for data processing); (ii) analyze, at the remote location, the input using the artificial intelligence (artificial intelligence and/or machine learning algorithms may be used for the analysis of the monitored data of the mechanical ventilator, Paragraph 0260); and (iii) send instructions, from the remote location, to modify performance of the medical equipment resulting in a quantifiable improvement in the quantifiable parameter (see Paragraph 0213 and Figure 26 describing step 1604 of collecting ventilator data and sending messages and instructions; see claim 1 stating the process of electronically obtaining, by at least one processor, a flow or pressure signal representative of respiratory movement of a patient on ventilatory support of a mechanical ventilator, captured by at least one respiratory sensor; electronically modifying, by the at least one processor, the flow or pressure signal; see also Paragraph 0260 and claim 20 stating the use of the artificial intelligence and/or machine learning algorithms may be used to predict interventions predicted to result in positive outcomes, such as the proper amount of sedation to achieve positive outcomes, Paragraph 0260 and Claim 20).
Regarding claim 7, Gutierrez further discloses wherein the medical equipment comprises a ventilator (ventilator 20, Paragraph 0052 and Figure 1).
Regarding claim 8, Gutierrez further discloses wherein the remote location controls the medical equipment see Paragraph 0450 and Figure 26 describing the system using remote devices such as smartphones, tablets, laptops etc. for data processing and control of ventilator).
Regarding claim 9, Gutierrez further discloses wherein the medical equipment provides audio or visual capabilities at the remote location (an audio and/or video output device may be provided to the system to output a sound and/or other data in the form of a voice, Paragraph 0446).
Regarding claim 10, Gutierrez further discloses a display at the remote location, the display showing the quantifiable parameter captured by the medical equipment (an audio and/or video output device may be provided to the system to output data, Paragraph 0446).
Regarding claim 11, Gutierrez discloses a system for providing support to a patient , the system comprising: (a) medical equipment (system 100 comprising a patient 10 coupled to a ventilator 20, Paragraph 0052 and Figure 1); (b) a processor (CPU 60 can be any suitable processor capable of obtaining data and performing analysis, Paragraph 0056 and Figure 1); and (c) a non-transitory computer-readable medium including software (embodiments of the invention may be implemented in one or a combination of hardware, firmware, software, machine-readable medium which may be read and executed by a computing platform such as the processor, Paragraph 0438) comprising artificial intelligence (embodiments may use artificial intelligence or machine learning to predict system interventions, Abstract and Paragraph 0260), the software configured to cause the processor to: (i) receive an input comprising a quantifiable parameter from a patient receiving support from the medical equipment (data acquisition system 30 acquires data such as air flow data from the air flow sensor 40, air pressure data from the pressure sensor 50 and/or data from other data sources such as the respiratory monitor 85, and sensors capable of sensing any periodic signal produced by breathing, Paragraph 0052 and Figure 1); (ii) model the input using the artificial intelligence (artificial intelligence and/or machine learning algorithms may be used for the analysis of the monitored data of the mechanical ventilator, Paragraph 0260); and (iii) modify performance of the medical equipment using the model to result in optimized performance of the medical equipment (the use of the artificial intelligence and/or machine learning algorithms may be used to predict interventions predicted to result in positive outcomes, such as the proper amount of sedation to achieve positive outcomes, Paragraph 0260 and Claim 20).
Regarding claim 12, Gutierrez further discloses wherein the medical equipment comprises a ventilator (ventilator 20, Paragraph 0052 and Figure 1).
Regarding claim 13, Gutierrez further discloses wherein the model optimizes performance of the ventilator (the use of the artificial intelligence and/or machine learning algorithms may be used to predict interventions predicted to result in positive outcomes, such as the proper amount of sedation to achieve positive outcomes, Paragraph 0260 and Claim 20).
Regarding claim 14, Gutierrez further discloses wherein the optimized performance is achieved by comparing the input to the model (the use of the artificial intelligence and/or machine learning algorithms may be used to predict interventions predicted to result in positive outcomes, such as the proper amount of sedation to achieve positive outcomes, Paragraph 0260 and Claim 20).
Regarding claim 15, Gutierrez further discloses wherein the software is further configured to cause the processor to train the model with a data set of parameters from patients, wherein the model is a machine learning model (the use of the artificial intelligence and/or machine learning algorithms may be used to predict interventions predicted to result in positive outcomes, such as the proper amount of sedation to achieve positive outcomes, Paragraph 0260 and Claim 20).
Regarding claim 16, Gutierrez discloses a system for providing support to a patient, the system comprising: (a) medical equipment (system 100 comprising a patient 10 coupled to a ventilator 20, Paragraph 0052 and Figure 1); (b) a processor (CPU 60 can be any suitable processor capable of obtaining data and performing analysis, Paragraph 0056 and Figure 1); and (c) a non-transitory computer-readable medium including software (embodiments of the invention may be implemented in one or a combination of hardware, firmware, software, machine-readable medium which may be read and executed by a computing platform such as the processor, Paragraph 0438) comprising artificial intelligence (embodiments may use artificial intelligence or machine learning to predict system interventions, Abstract and Paragraph 0260), the software configured to cause the processor to: (i) receive an input comprising a quantifiable parameter from a patient receiving support from the medical equipment (data acquisition system 30 acquires data such as air flow data from the air flow sensor 40, air pressure data from the pressure sensor 50 and/or data from other data sources such as the respiratory monitor 85, and sensors capable of sensing any periodic signal produced by breathing, Paragraph 0052 and Figure 1); (ii) analyze the input using the artificial intelligence (artificial intelligence and/or machine learning algorithms may be used for the analysis of the monitored data of the mechanical ventilator, Paragraph 0260); and (iii) modify performance of the medical equipment using a defined mode of operation that results in a quantifiable improvement in the quantifiable parameter (the use of the artificial intelligence and/or machine learning algorithms may be used to predict interventions predicted to result in positive outcomes, such as the proper amount of sedation to achieve positive outcomes, therefore modifying the performance of the ventilator using a defined mode of operation, Paragraph 0260 and Claim 20).
Regarding claim 17, Gutierrez further discloses wherein the medical equipment comprises a ventilator (ventilator 20, Paragraph 0052 and Figure 1).
Regarding claim 18, Gutierrez further discloses wherein the defined mode of operation is an open loop operation, wherein all changes to the performance of the medical equipment are suggested by the artificial intelligence (the use of the artificial intelligence and/or machine learning algorithms may be used to predict interventions predicted to result in positive outcomes, such as the proper amount of sedation to achieve positive outcomes, therefore the artificial intelligence is fully capable of performing an open loop operation, Paragraph 0260 and Claim 20).
Regarding claim 19, Gutierrez further discloses wherein the defined mode of operation is a semi-open loop operation, wherein some changes to the performance of the medical equipment are suggested by the artificial intelligence, and wherein other changes to the performance of the medical equipment are made by the artificial intelligence (the use of the artificial intelligence and/or machine learning algorithms may be used to predict interventions predicted to result in positive outcomes, such as the proper amount of sedation to achieve positive outcomes, therefore the artificial intelligence if fully capable of being a semi-open loop operations, making some suggestions in some instances, and performing modifications in other instances, Paragraph 0260 and Claim 20).
Regarding claim 20, Gutierrez further discloses wherein the defined mode of operation is a closed loop operation, wherein all changes to the performance of the medical equipment are made by the artificial intelligence (the use of the artificial intelligence and/or machine learning algorithms may be used to predict interventions predicted to result in positive outcomes, such as the proper amount of sedation to achieve positive outcomes, therefore the artificial intelligence is fully capable of being closed loop operation, Paragraph 0260 and Claim 20).
Claim Rejections - 35 USC § 103
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Gutierrez (US 2019/0371460 A1) in view of Crow et al. (US 2019/0099582 A1).
Regarding claim 4, Gutierrez discloses system of claim 1, and although further teaches medical equipment such as a ventilator (Figure 1), Gutierrez is silent wherein the medical equipment comprises at least one of: a cardiac monitor; an intravenous pump; a blood gas analyzer; medical imaging equipment; an electrocardiogram; intermittent pneumatic compression equipment; bed settings; and thermal pads and blankets.
However, Crow teaches a patient monitoring system (Abstract, system 10, Figure 1) that utilities artificial intelligence (the system 10 may use machine learning and artificial intelligence, to conduct comparisons of personal results versus various cohorts, personal baselines, genetic factors, etc., Paragraph 0164) wherein the system comprises medical equipment such as a cardiac monitor or ECG (the data acquisition device 12 can be configured to detect, measure, monitor, and record brain activity using electroencephalography (EEG), eye activity using electrooculography (EOG), muscle activity using electromyography (EMG), cardiac activity using electrocardiography (ECG), respiration rate (e.g., using respiratory inductance plethysmography (RIP), pressure sensor, and/or a temperature sensor), oxygen saturation (e.g., using pulse oximetry, Paragraph 0109)).
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filling date of the claimed invention to modify Gutierrez’s device by including additional medical equipment, such as a cardiac monitor or ECG, as taught by Crow, as such medical equipment components are well-known patient monitoring components that would provide a more robust assessment of the patient’s condition by providing more patient data.
Regarding claim 5, Crow further teaches wherein the quantifiable parameter comprises data from the medical equipment (the system 10 may use machine learning and artificial intelligence, to conduct comparisons of personal results versus various cohorts, personal baselines, genetic factors, etc., Paragraph 0164) wherein the system comprises medical equipment such as a cardiac monitor or ECG (the data acquisition device 12 can be configured to detect, measure, monitor, and record brain activity using electroencephalography (EEG), eye activity using electrooculography (EOG), muscle activity using electromyography (EMG), cardiac activity using electrocardiography (ECG), respiration rate (e.g., using respiratory inductance plethysmography (RIP), pressure sensor, and/or a temperature sensor), oxygen saturation (e.g., using pulse oximetry, Paragraph 0109)).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: McCormick et al. (US 2019/0344032 A1) and Milne (US 2014/0150796 A1).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SARAH B LEDERER whose telephone number is 571-272-7274. The examiner can normally be reached on Monday - Friday, 7:30 AM - 4:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brandy Lee can be reached on (571)-270-7410. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SARAH B LEDERER/Examiner, Art Unit 3785
/BRANDY S LEE/Supervisory Patent Examiner, Art Unit 3785
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