DETAILED ACTION
Acknowledgements
This office action is in response to the claims filed December 29, 2024
Claims 1-12, 14, 16, 30, and 31 are pending
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 .
Drawing Objection(s)
The drawings are objected to because figures 4, 5, 6A, 6B, 6C, 6D, 6E, 7, 10, an 11 are unreadable. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejection - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-12, 14, 16, 30, and 31 are rejected to under 35 U.S.C 101 as not being directed to eligible subject matter the grounds set out in detail below:
Independent Claims 1 and 16:
Eligibility Step 1 (does the subject matter fall within a statutory category?):
Independent claim 1 falls within the statutory category of method.
Independent claim 16 falls within the statutory category of machine.
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Independent claims 1 and 16 claimed invention are directed to a judicial exception.
The claim elements in the independent claim 1 which set forth the abstract idea are:
A method of mechanical ventilation therapy data management, wherein the method comprises:
putting patient information and real-time received mechanical ventilation therapy data into a standard data structure object respectively, to form standardized data;
the mechanical ventilation therapy data comprises ventilator data;
performing data cleaning on the standardized data to obtain a medical data set;
performing data labeling on the mechanical ventilation therapy data in the medical data set;
the data labeling comprises: marking amplitudes, frequencies, and shapes of waveform data, marking measurement values of numerical data, and marking abnormal events;
when a report generation instruction is received, generating a mechanical ventilation therapy data analysis report based on the medical data set with the data labeling.
which falls within “certain methods of organizing human activity” as following rules and instructions to aggregate data, analyze data, and generate report related to mechanical ventilation data See MPEP § 2106.04(a)(2).
The claim elements in the independent claim 16 which set forth the abstract idea are:
put the patient information and the real-time received mechanical ventilation therapy data into the standard data structure object respectively to form standardized data;
the mechanical ventilation therapy data comprises ventilator data;
perform data cleaning on the standardized data to obtain a medical data set;
data labeling on the mechanical ventilation therapy data in the medical data set;
the data labeling comprises: marking amplitudes, frequencies, and shapes of waveform data, marking measurement values of numerical data, and marking abnormal events;
generate a mechanical ventilation therapy data analysis report based on the medical data set with the data labeling when a report generation instruction is received;
issue the report generation instructions, and further configured to receive and display the mechanical ventilation therapy data analysis reports;
store the standardized data, the medical data sets, and the mechanical ventilation therapy data analysis reports in partitions.
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For Independent Claims 1 and 16 this judicial exception is not integrated into a practical application.
In Claim 1 there are no additional elements recited thus purely treated as the abstract idea.
In Claim 16 the additional elements are:
an application software system
clinical data terminal devices
a database server;
a data acquisition and standardization unit
a data cleaning unit
a data labeling unit
a report generation unit
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, (a), is recited as executing the abstract idea as “apply-it”
The additional elements, (b), (c), (d), (e), (f), and (g) are using computer elements as a tool to apply the abstract idea as “apply-“it for gathering, analyzing, and outputting data
Accordingly, claims 1 and 16 do not integrate the abstract idea into a practical application.
Eligibility Step 2B (Does the claim amount to significantly more?): The independent claims 1 and 16 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as analyzed above in step 2A prong 2 above, these additional elements, whether viewed individually or as an ordered combination, amount to no more than applying the abstract idea thus insufficient to provide “significantly more”. Therefore, the claims do not amount to significantly more and the claims are ineligible.
Dependent Claims 2-12, 14, 30, and 31:
Eligibility Step 1 (does the subject matter fall within a statutory category?):
The dependent claims 2-12 and 14 fall within the statutory category of method.
The dependent claims 30-31 fall within the statutory category of machine.
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Dependent claims 2-12, 14, 30, and 31 claimed invention is directed to an abstract idea without significantly more. The claims continue to limit the independent claims 1 and 16 abstract idea by (1) further limiting the types of data and structure of data and (2) further limiting the analysis report format which falls within “certain methods of organizing human activity” as following rules and instructions to aggregate data, analyze data, and generate report related to mechanical ventilation data See MPEP § 2106.04(a)(2).
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For claims 2-12, 14, 30, and 31 this judicial exception is not integrated into a practical application.
The dependent claims recite the additional claim elements below not previously cited in the independent claims:
Ventilator devices
a streaming media management unit
bedside audio video devices
a multi-mode gateway
a master control server
multi-parameter monitors
clinical information systems
a multi-task operating system
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The noted above additional claim elements, (a)-(h) are applying the abstract idea as “apply-it” to gather and analyze data
Accordingly, the dependent claims as a whole do not integrate the recited abstract idea into a practical application (MPEP 2106.05(f) and 2106.04(d)(1).
Eligibility Step 2B (Does the claim amount to significantly more?): The dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as analyzed above in step 2A prong 2, are applying the abstract idea and therefore insufficient to amount to significantly more. The claims are patent ineligible.
Claim Interpretation - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations in claim 16 are: “a data acquisition and standardization unit, configured to …[…]…a data cleaning unit is configured to …[…]…a data labeling unit is configured to …[…]…a report generation unit is configured to .” Such claim limitations in claim 30 are: “a streaming media management unit, the streaming media management unit is configured to…[…]…” Such claim limitations in claim 31 are: “the data acquisition and standardization unit”
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
Claim Rejection(s) - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 11 recites the limitation " the report generation instruction " in line 2. There is insufficient antecedent basis for this limitation in the claim.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 14, 16, and 30 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Seely (US8924235B2).
As per claim 1, Seely teaches:
A method of mechanical ventilation therapy data management, wherein the method comprises: (Col. 8 lines 1-9 discloses, “As will be explained below, the evaluation of a patients variability has many uses, e.g. in detecting the onset of dis ease, both in real-time and retrospectively. Another Such clinical application is the evaluation of change in variability in response to an intervention. For example, this enables the system described below, and/or parts thereof, to assist clinicians in the safety and timing of liberation from medical apparatus such as mechanical ventilation in critically ill patients.”)
putting patient information and real-time received mechanical ventilation therapy data into a standard data structure object respectively, to form standardized data; (see Fig. 9 and Fig. 10C Col. 8 lines 1-26 discloses, “As will be explained below, the evaluation of a patients variability has many uses, e.g. in detecting the onset of dis ease, both in real-time and retrospectively. Another Such clinical application is the evaluation of change in variability in response to an intervention. For example, this enables the system described below, and/or parts thereof, to assist clinicians in the safety and timing of liberation from medical apparatus such as mechanical ventilation in critically ill patients. In order to take advantage of the power of variability analysis over time for the above reasons and many more, an under lying framework has been developed that can handle multiple variability analyses over multiple intervals of time, across a distributed system in a consistent manner. This is accomplished, in part, by constructing and storing a standard wave form data file as well as a separate variability data file for each variable being analyzed, that includes a comprehensive characterization of the underlying data acquired using variability monitoring. The consistent and standard data files, along with the underlying framework enables a user to make use of a set of convenient variability display tools, while a central entity can provide connectivity to the distributed environment and provide away to update the equipment and Software to ensure consistent and relevant analyses. The system can be extended into many environments, including in-patient, out-patient and completely mobile/stand-alone.” And see Col. 18 lines 33-63 and Col. 20 lines 40-67 / examiner notes the standard data structure object is defined in the instant application in para. [0063] as an abstraction of the ventilation therapy data therefore the disclosed standard interfaces with specific structures of data objects shown are interpreted to be abstractions of the data into data sets of standardized data objects)
the mechanical ventilation therapy data comprises ventilator data; (e.g. see Fig. 10C and see table 1 and Col. 14 lines 4-36 discloses, an example of ventilator data waveforms which can be analyzed to be output into a standardized data object on interface for review)
performing data cleaning on the standardized data to obtain a medical data set; (Col. 16 lines 23-67 and Col. 17 lines 1-17 discloses, “This output is also fed to the display toolkit 72, which can output the raw data on the display 74, and is also fed to a data cleaner 66. The data cleaner 66 identifies and removes artefacts and other noise from the raw data such that it is suitable for use by a variability analysis module 68. It may be noted that there are many techniques that can be used to quantify artefacts at each interval in the data, e.g. a Pointcaré Plot. Also, different variability analysis techniques (e.g. wavelet, frequency domain etc.) have different thresh olds for how much artefact can be handled without compromising the variability analysis. For example, the data cleaner 66 first determines how much artefact is present and then determines which technique(s) can handle that amount of artefact. For example, a particular set of data may have too much artefact for performing a fast Fourier transform, but could be handled by a wavelet analysis. More discussion of these techniques is provided later. The variability analysis module 68 performs the variability analysis and receives and processes the update data 22 and any other inputs necessary to perform the variability analysis. As can also be seen, the threshold data 20 is obtained by the variability analysis server 24 and used as appropriate. The variability analysis module 68 may output variability data (i.e. separate from the data packages 18) if desired, which can be used by the display toolkit 72 to output on the display 74. The variability data file builder 70 also receives the results of the variability analysis as an input for building the variability portion(s) of the data packages 18, and receives additional patient information 48 if applicable. Prior to transmitting the data packages 18 to the central service 10, a data conditioning stage 78 is used to filter, amplify, compress and otherwise prepare the data for transmission. It can be seen in FIG. 6 that at any stage, the output data is preferably stored in the data storage device 76 Such that it may be accessed, processed and viewed at a later time or during the variability analysis. It may be noted that the variability analysis module 68 can be configured for and programmed to perform any type of variability analysis. Similarly, the data cleaner 66 can be programmed to perform any desirable data cleaning or conditioning. The following provides more detail on how the data cleaning and variability analysis may be performed. The first step in variability analysis is typically to select data points. This can be done at the data cleaning stage 66 or upon execution of the variability analysis module 68. Real data measurement systems often acquire spurious signals that are not relevant to the required analysis. As discussed above, these spurious data points are referred to as artefacts, and it is desirable to remove them in order to make analysis more 18 meaningful. There are many acceptable methods for finding and removing artefacts from sequences of data collected from a wide variety of medical devices. A plurality of methods may be used. As also noted above, one technique is to use a Pointcaré plot. A Pointcaré plot represents differences between consecutive data points. The absolute value of a difference between a data point and the preceding data point (X-X) is plotted on the X-axis, and the absolute value of a difference between the same data point and the Subsequent data point IX-X, is plotted on the y-axis. A visual evaluation may be used to eliminate artefact data.”)
performing data labeling on the mechanical ventilation therapy data in the medical data set; (see Fig. 9 and Fig. 10C and fig. 24 and fig. 25 where data is labelled which pertains to the mechanical ventilation)
the data labeling comprises: marking amplitudes, frequencies, and shapes of waveform data, marking measurement values of numerical data, and marking abnormal events; (see Fig. 9 and Fig. 10C and see Fig. 23 see and see fig. 24 and see Col. 10 lines 40-67 and Col. 11 lines 1-3 discloses, “The patient interfaces 28 monitor physiological parameters of the patient 26 using one or more sensors 30. The data or patient parameters can include any variable that can be accurately measured in real time or intermittently. The data may be obtained from a continuous waveform (at a certain frequency level, e.g. 100 Hz for a CO2 capnograph or 500 Hz for an EKG), or taken as absolute measurements at certain intervals, e.g. temperature measurements. The sensors 30 and patient interfaces 28 may include, for example, an electrocardiogram (ECG), a CO capnograph, a temperature sensor, a proportional assist ventilator, an optoelectronic plethymography, a urometer, a pulmonary arterial catheter, an arterial line, an O. saturation device and others. To provide more meaning to the data acquired through the sensors 30, clinical events are associated with the data, through an act of recording time stamped events 32, which are typically entered by a heath care worker 34 in the hospital (bedside) environment. Clinical (time stamped) events can be physical activity, administration of medication, diagnoses, life Support, washing, rolling over, blood aspiration etc. The clinical events are associated with a specific time, which is then also associated with the data that is acquired at the same specific time using the sensors 30. It will be appreciated that the clinical events can also be recorded in an automated fashion, e.g. by utilizing algorithms which detect events electronically and process such events to designate them as clinical events or noise. In this example, the patient interface 28 is configured to gather the time stamped event data 32 concurrently with the sensor data 30, further detail being provided below. It may be noted that additional non-time-stamped information (e.g. demographics) can also be recorded for each patient.” And see Col. 14 lines 14-52 discloses, “It may therefore be possible to extract information on respiratory variability using Such ventilators. However, other ventilators exist which provide dynamic alteration of both pressure and volume, which improves the significance of the respiratory variability. Specifically, a proportional assist ventilator permits the breath-to-breath alteration and measurement of multiple respiratory parameters, including airway resistance, pulmonary compliance, tidal Volume, peak airway pressure. Therefore, one use for the proportional assist ventilator is where useful data to evaluate respiratory variability is provided. Numerous other parameters, as shown in Table 1 (above), may be measured and the resulting data stored for a Subsequent variability analysis. It is important to note that the patient parameters described do not form an exclusive list of patient parameters that can be analyzed using the variability analysis server 24. Rather, the variability analysis server 24 can accommodate any number of patient parameters that are subject to real-time, accurate measurement. Thus, when technology becomes available to measure other patient parameters, related data may be input along with the variables described, in order to provide an even more complete analysis of physiologic or pathologic variability. In the variability analysis server 24, a variability time series is created for each patient physiological parameter. First, the user can set the interval and step for data monitoring over a period of time. That is, the variability analysis is performed on an interval and moves stepwise through the data in time. Collecting the data involves retrieving or accepting measured data points acquired by patient interfaces 28, for example, and storing the data points for Subsequent analysis. The data collecting step also includes monitoring a quantity of data collected. For example, initial analysis may begin after approximately 1000 data points (for example 15 minutes of heart rate measurement) have been collected. For each patient parameter V, a user, typically an attending physician, may select the number of data points m to collect in order to perform the variability analysis. Recommended settings may be provided by the central service 10 as well.” And see Col. 18 / examiner notes someone of ordinary skill in the art would understand for the waveforms to be shown representing mechanical ventilation parameters and variability that amplitude is marked for data analysis to determine e.g. PEEP or PIP )
when a report generation instruction is received, generating a mechanical ventilation therapy data analysis report based on the medical data set with the data labeling. (e.g. Fig. 24 and Fig. 25 and see Col. 15 lines 23-34 discloses, “FIG. 24 is an example interface 300 that illustrates one way in which to obtain clinical events 32 for the time stamped event recorder 82. It can be seen that several selection boxes 302 can be provided to enable clinical event data to be recorded before the data is uploaded to the variability analysis server 24. The ability to “upload waveform data and clinical data simultaneously enables, among other things, the following: comparison of clinical data and variability data, provision of a “report encompassing both clinical and variability data, and performance of Standardized multi-center research trials where variability is compared to standard clinical criteria.” And see Col. 16 lines 16-28 discloses, “For example, as shown in FIG. 25, an GUI 304 can be provided using the interface 75 for configuring the variability analysis type 306 and other parameters. It will be appreciated that the GUI 304 can be customized for specific trials or studies or can provide a generic interface. The output from the raw data builder 64 is fed to a variability data file builder 70, which creates the data packages 18 and appends any other files or related data thereto. This output is also fed to the display toolkit 72, which can output the raw data on the display 74, and is also fed to a data cleaner 66. The data cleaner 66 identifies and removes artefacts and other noise from the raw data such that it is suitable for use by a variability analysis module 68.”)
As per claim 2, Seely teaches:
The method of mechanical ventilation therapy data management according to claim 1, wherein the mechanical ventilation therapy data further comprises one or more of the following data: physiological parameters data, blood gas analysis data, laboratory diagnostic data, imaging data, clinical diagnosis and treatment information, and the ID of the device for outputting the mechanical ventilation therapy data; wherein, the physiological parameters data is output by multi-parameter monitors; (Col. 7 lines 27-28 discloses, “FIG. 15 shows a multi-parameter respiratory rate and heart rate variability analyses.” And see Col. 25 lines 6-67 and see Col. 26 lines 1-26 discloses, multi parameter monitors for various mechanical ventilation therapy parameters which can also be considered more broadly physiological parameters data / (the claim recites “or” between the series of elements. Per MPEP § 2143.03, Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).)
the patient information, the blood gas analysis data, the laboratory diagnostic data, the imaging data, and the clinical diagnosis and treatment information are all output by a clinical information system; ([The Examiner notes that “a phrase” was optional in the previous limitation. Because that option was not taken, the phrase is not required and the Examiner declines to address this limitation.]
the mechanical ventilation data is output by ventilator devices. (Col. 7 lines 27-28 discloses, “FIG. 15 shows a multi-parameter respiratory rate and heart rate variability analyses.” And see Col. 25 lines 6-67 and see Col. 26 lines 1-26 discloses, multi parameter monitors for various mechanical ventilation therapy parameters which can also be considered more broadly physiological parameters data / (the claim recites “or” between the series of elements. Per MPEP § 2143.03, Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).)
As per claim 14, Seely teaches:
The method of mechanical ventilation therapy data management according to any of claim 1, wherein, in the standard data structure object, a corresponding data structure object is configured for each item of the mechanical ventilation therapy data and the patient information respectively; each mechanical ventilation therapy data decoupled and the patient information are placed into the corresponding data structure object to form standardized data. (see Fig. 9 and Fig. 10C and see Fig. 24 and Fig. 25 and see Col. 8 lines 1-26 discloses, “As will be explained below, the evaluation of a patients variability has many uses, e.g. in detecting the onset of dis ease, both in real-time and retrospectively. Another Such clinical application is the evaluation of change in variability in response to an intervention. For example, this enables the system described below, and/or parts thereof, to assist clinicians in the safety and timing of liberation from medical apparatus such as mechanical ventilation in critically ill patients. In order to take advantage of the power of variability analysis over time for the above reasons and many more, an under lying framework has been developed that can handle multiple variability analyses over multiple intervals of time, across a distributed system in a consistent manner. This is accomplished, in part, by constructing and storing a standard wave form data file as well as a separate variability data file for each variable being analyzed, that includes a comprehensive characterization of the underlying data acquired using variability monitoring. The consistent and standard data files, along with the underlying framework enables a user to make use of a set of convenient variability display tools, while a central entity can provide connectivity to the distributed environment and provide away to update the equipment and Software to ensure consistent and relevant analyses. The system can be extended into many environments, including in-patient, out-patient and completely mobile/stand-alone.” And see Col. 18 lines 33-63 and Col. 20 lines 40-67 / examiner notes the standard data structure object is defined in the instant application in para. [0063] as an abstraction of the ventilation therapy data therefore the disclosed standard interfaces with specific structures of data objects shown are interpreted to be abstractions of the data into data sets of standardized data objects)
As per claim 16, Seely teaches:
A system of mechanical ventilation therapy data management, wherein, the system comprises an application software system, clinical data terminal devices and a database server; (Col. 8 lines 1-9 discloses, “As will be explained below, the evaluation of a patients variability has many uses, e.g. in detecting the onset of dis ease, both in real-time and retrospectively. Another Such clinical application is the evaluation of change in variability in response to an intervention. For example, this enables the system described below, and/or parts thereof, to assist clinicians in the safety and timing of liberation from medical apparatus such as mechanical ventilation in critically ill patients.” And see Col. 29 lines 34-49 discloses, “The screens 150, 160, 180 and 190 can optionally be provided in one application and/or consolidated display Screen (not shown), which enables the user to quickly move between the different tools and have both the waveform data 104 and variability data 103 loaded and available to them at the same time. It can be appreciated that the Vmovie 133 and Vcam 128 tools are preferably provided as extensions to the Vcorder tool 130 such that a user can Zoom or panthrough the data, select a region and display the four plots as shown in FIG. 18 at any point in the time series or can generate movies of change in variability over time within a certain interval of time. This can be done to offer a more intuitive master tool that provides all the features in a single application for the user's convenience.. The functionality of the tools in the display toolkit 72 can be upgraded and refined by having regular update data 22 sent to each server 24 at each monitoring site 16.” And see Fig. 2, 4, and 8)
wherein, the application software system comprises: a data acquisition and standardization unit, configured to put the patient information and the real-time received mechanical ventilation therapy data into the standard data structure object respectively to form standardized data; (Col. 11 lines 16-34 discloses, “The variability analysis server 24 can be embodied as a fixed unit or a moveable unit such as on a cart, in order to facilitate movement about the hospital site 16a to serve multiple patients 26 in multiple locations. Similarly, the variability analysis server 24 can be a proprietary apparatus or can be embodied as an add-on to existing beside or centralized equipment to minimize space. The variability analysis server 24 can also interact with a bedside monitor 40, which may be made available to or otherwise represent a nurse or other personnel that monitors the patient 26 at the bedside. Similarly, the variability analysis server 24 can also interact with sensor displays 44, which are associated with other medical equipment Such as ECGs, blood pressure sensors, temperature sensors etc. As noted above, the variability analysis server 24 can be a separate, stand-alone unit but may also be integrated as a plug-in or additional module that in this case could be used or integrated with existing bedside monitoring equipment, displays and sensors.” And see Fig. 9 and Fig. 10C Col. 8 lines 1-26 discloses, “As will be explained below, the evaluation of a patients variability has many uses, e.g. in detecting the onset of dis ease, both in real-time and retrospectively. Another Such clinical application is the evaluation of change in variability in response to an intervention. For example, this enables the system described below, and/or parts thereof, to assist clinicians in the safety and timing of liberation from medical apparatus such as mechanical ventilation in critically ill patients. In order to take advantage of the power of variability analysis over time for the above reasons and many more, an under lying framework has been developed that can handle multiple variability analyses over multiple intervals of time, across a distributed system in a consistent manner. This is accomplished, in part, by constructing and storing a standard wave form data file as well as a separate variability data file for each variable being analyzed, that includes a comprehensive characterization of the underlying data acquired using variability monitoring. The consistent and standard data files, along with the underlying framework enables a user to make use of a set of convenient variability display tools, while a central entity can provide connectivity to the distributed environment and provide away to update the equipment and Software to ensure consistent and relevant analyses. The system can be extended into many environments, including in-patient, out-patient and completely mobile/stand-alone.” And see Col. 18 lines 33-63 and Col. 20 lines 40-67 / examiner notes the standard data structure object is defined in the instant application in para. [0063] as an abstraction of the ventilation therapy data therefore the disclosed standard interfaces with specific structures of data objects shown are interpreted to be abstractions of the data into data sets of standardized data objects)
the mechanical ventilation therapy data comprises ventilator data; (e.g. see Fig. 10C and see table 1 and Col. 14 lines 4-36 discloses, an example of ventilator data waveforms which can be analyzed to be output into a standardized data object on interface for review)
a data cleaning unit is configured to perform data cleaning on the standardized data to obtain a medical data set; (Col. 16 lines 23-67 and Col. 17 lines 1-17 discloses, “This output is also fed to the display toolkit 72, which can output the raw data on the display 74, and is also fed to a data cleaner 66. The data cleaner 66 identifies and removes artefacts and other noise from the raw data such that it is suitable for use by a variability analysis module 68. It may be noted that there are many techniques that can be used to quantify artefacts at each interval in the data, e.g. a Pointcaré Plot. Also, different variability analysis techniques (e.g. wavelet, frequency domain etc.) have different thresh olds for how much artefact can be handled without compromising the variability analysis. For example, the data cleaner 66 first determines how much artefact is present and then determines which technique(s) can handle that amount of artefact. For example, a particular set of data may have too much artefact for performing a fast Fourier transform, but could be handled by a wavelet analysis. More discussion of these techniques is provided later. The variability analysis module 68 performs the variability analysis and receives and processes the update data 22 and any other inputs necessary to perform the variability analysis. As can also be seen, the threshold data 20 is obtained by the variability analysis server 24 and used as appropriate. The variability analysis module 68 may output variability data (i.e. separate from the data packages 18) if desired, which can be used by the display toolkit 72 to output on the display 74. The variability data file builder 70 also receives the results of the variability analysis as an input for building the variability portion(s) of the data packages 18, and receives additional patient information 48 if applicable. Prior to transmitting the data packages 18 to the central service 10, a data conditioning stage 78 is used to filter, amplify, compress and otherwise prepare the data for transmission. It can be seen in FIG. 6 that at any stage, the output data is preferably stored in the data storage device 76 Such that it may be accessed, processed and viewed at a later time or during the variability analysis. It may be noted that the variability analysis module 68 can be configured for and programmed to perform any type of variability analysis. Similarly, the data cleaner 66 can be programmed to perform any desirable data cleaning or conditioning. The following provides more detail on how the data cleaning and variability analysis may be performed. The first step in variability analysis is typically to select data points. This can be done at the data cleaning stage 66 or upon execution of the variability analysis module 68. Real data measurement systems often acquire spurious signals that are not relevant to the required analysis. As discussed above, these spurious data points are referred to as artefacts, and it is desirable to remove them in order to make analysis more 18 meaningful. There are many acceptable methods for finding and removing artefacts from sequences of data collected from a wide variety of medical devices. A plurality of methods may be used. As also noted above, one technique is to use a Pointcaré plot. A Pointcaré plot represents differences between consecutive data points. The absolute value of a difference between a data point and the preceding data point (X-X) is plotted on the X-axis, and the absolute value of a difference between the same data point and the Subsequent data point IX-X, is plotted on the y-axis. A visual evaluation may be used to eliminate artefact data.”)
a data labeling unit is configured to perform data labeling on the mechanical ventilation therapy data in the medical data set; (see Fig. 9 and Fig. 10C and fig. 24 and fig. 25 where data is labelled which pertains to the mechanical ventilation and see Col. 15 lines 3-51 and Col. 16 lines 3-64 and see Col. 23 lines 61-67 discloses, the data transformation models and display format modules which standardized data for display / examiner notes the )
the data labeling comprises: marking amplitudes, frequencies, and shapes of waveform data, marking measurement values of numerical data, and marking abnormal events; (see Fig. 9 and Fig. 10C and see Fig. 23 see and see fig. 24 and see Col. 10 lines 40-67 and Col. 11 lines 1-3 discloses, “The patient interfaces 28 monitor physiological parameters of the patient 26 using one or more sensors 30. The data or patient parameters can include any variable that can be accurately measured in real time or intermittently. The data may be obtained from a continuous waveform (at a certain frequency level, e.g. 100 Hz for a CO2 capnograph or 500 Hz for an EKG), or taken as absolute measurements at certain intervals, e.g. temperature measurements. The sensors 30 and patient interfaces 28 may include, for example, an electrocardiogram (ECG), a CO capnograph, a temperature sensor, a proportional assist ventilator, an optoelectronic plethymography, a urometer, a pulmonary arterial catheter, an arterial line, an O. saturation device and others. To provide more meaning to the data acquired through the sensors 30, clinical events are associated with the data, through an act of recording time stamped events 32, which are typically entered by a heath care worker 34 in the hospital (bedside) environment. Clinical (time stamped) events can be physical activity, administration of medication, diagnoses, life Support, washing, rolling over, blood aspiration etc. The clinical events are associated with a specific time, which is then also associated with the data that is acquired at the same specific time using the sensors 30. It will be appreciated that the clinical events can also be recorded in an automated fashion, e.g. by utilizing algorithms which detect events electronically and process such events to designate them as clinical events or noise. In this example, the patient interface 28 is configured to gather the time stamped event data 32 concurrently with the sensor data 30, further detail being provided below. It may be noted that additional non-time-stamped information (e.g. demographics) can also be recorded for each patient.” And see Col. 14 lines 14-52 discloses, “It may therefore be possible to extract information on respiratory variability using Such ventilators. However, other ventilators exist which provide dynamic alteration of both pressure and volume, which improves the significance of the respiratory variability. Specifically, a proportional assist ventilator permits the breath-to-breath alteration and measurement of multiple respiratory parameters, including airway resistance, pulmonary compliance, tidal Volume, peak airway pressure. Therefore, one use for the proportional assist ventilator is where useful data to evaluate respiratory variability is provided. Numerous other parameters, as shown in Table 1 (above), may be measured and the resulting data stored for a Subsequent variability analysis. It is important to note that the patient parameters described do not form an exclusive list of patient parameters that can be analyzed using the variability analysis server 24. Rather, the variability analysis server 24 can accommodate any number of patient parameters that are subject to real-time, accurate measurement. Thus, when technology becomes available to measure other patient parameters, related data may be input along with the variables described, in order to provide an even more complete analysis of physiologic or pathologic variability. In the variability analysis server 24, a variability time series is created for each patient physiological parameter. First, the user can set the interval and step for data monitoring over a period of time. That is, the variability analysis is performed on an interval and moves stepwise through the data in time. Collecting the data involves retrieving or accepting measured data points acquired by patient interfaces 28, for example, and storing the data points for Subsequent analysis. The data collecting step also includes monitoring a quantity of data collected. For example, initial analysis may begin after approximately 1000 data points (for example 15 minutes of heart rate measurement) have been collected. For each patient parameter V, a user, typically an attending physician, may select the number of data points m to collect in order to perform the variability analysis. Recommended settings may be provided by the central service 10 as well.” And see Col. 18 / examiner notes someone of ordinary skill in the art would understand for the waveforms to be shown representing mechanical ventilation parameters and variability that amplitude is marked for data analysis to determine e.g. PEEP or PIP )
a report generation unit is configured to generate a mechanical ventilation therapy data analysis report based on the medical data set with the data labeling when a report generation instruction is received; (see Fig. 24 and 25 and fig. 9 and 10C and see Col. 25 lines 5-23 discloses, “It may be noted that the variability analysis server 24 can be local or remote and thus the acquisition site can represent any location or entity that is capable of receiving and/or storing and/or processing the data to be uploaded. The variability analysis server 24 may then process the data retrospectively according 10 to the principles exemplified above and a report 314 generated pertaining to the variability analysis of the data that was uploaded. It will be appreciated that, as shown in FIG. 26, the reports 314 can also be sent to or downloaded by the central service 10 and stored in a central database 96 (see also FIG. 15 12). Similarly, the data packages 18 comprising the data files, detail of which is provided below, can also be provided to the central service 10 by the variability analysis server 24. It can therefore be seen that the acquisition of patient data, Subsequent variability analysis and storage, processing and reporting of results can be accomplished in any Suitable physical configuration and the stages shown can be temporally spaced if appropriate.”)
the clinical data terminal devices are configured to issue the report generation instructions, and further configured to receive and display the mechanical ventilation therapy data analysis reports; (Col. 11 lines 52-67 and Col. 12 lines 3-53 discloses, “Turning now to FIG. 3, a clinic site 16b is shown. An example of a clinic site 16b is a bone marrow transplant clinic. Similar to the hospital site 16a discussed above, the clinic site 16b includes a variability analysis server 24, that obtains data from one or more patient interfaces 28, and connects to the Internet 14 for facilitating data transfer (i.e. to send data packages 18 and to receive threshold data 20 and update data 22). In the clinic site 16b, the patients 26 are referred to as outpatients as they are not admitted to a hospital. The sensors 30, clinical events recorded as time stamped events 32 and patient data 48 is acquired and used in a manner similar to that discussed above and thus further details need not be reiterated. Similarly, the variability analysis server 24 can provide data and interact with medical professionals 42 at the clinic site 16b, as well as local scientists 50, if applicable. The clinic site 16b may include one or more variability analysis servers…[…]…The monitoring centre 52 enables the clinic’s variability analysis server 24 to be monitored from a remote location and allows personnel to monitor several servers 24 if several are present in the clinic. In this way, a central monitoring centre 52 can be used to service several clinic sites 16b…[…]…In all cases, variability can be monitored over time and analyzed on an individual basis for any patient 26 Such that the resultant data is specific to that individual. Using the wider system 12 allows the central service 10 to take advantage of the individual results for many patients 26 and ascertain further and more complete information. The mobile site 16c generally represents any site that includes a variability analysis server 24, which connects to the central service 10 and can communicate with one or more patients 26, whether they are patients in the traditional sense or another type of user. In the example shown in FIG. 4, the user 26 generally includes a mobile device 54 and has a number of sensors 30 that are in communication with a variability analysis server 24. The mobile device 54 can also be used to provide inputs, e.g. for the time stamped event data 32, as well as to provide a display to the user 26 for entering parameters or to view display data 60 acquired by the sensors 30 and/or processed by the server 24. The connections between the mobile device 54 and the server 24, as well as between the sensors 30 and patient interface 28 can be wired or wireless and the variabil ity analysis server 24 can be a fixed unit at a base station or a portable unit Such as on a cart at a monitoring centre. The mobile device 54 can be a personal digital assistant (PDA), mobile telephone, laptop computer, personal computer or any other device that can provide an input device, a display and some form of connectivity for interacting with the variability analysis server 24, preferably in a completely mobile manner. As noted above, each monitoring site 16 includes a vari ability analysis server 24.”)
the database server is configured to store the standardized data, the medical data sets, and the mechanical ventilation therapy data analysis reports in partitions. (Col 30 lines 11-15 discloses, “As can be seen in FIG. 6, the waveforms 62 are stored in their native form in the data storage device 76 at the server 24 as well as being fed into the raw data builder 64 to create the time series used by the variability analysis module 68 for conducting variability analyses.”)
As per claim 30, Seely teaches:
The system of mechanical ventilation therapy data management according to claim 16, wherein the application software system further comprises a streaming media management unit, the streaming media management unit is configured to receive streaming media data of the treatment scenes in real time; the streaming media data is output by bedside audio video devices; (Col. 9 lines 7-23 discloses, “As shown in FIG. 1, the Internet 14 provides a medium for transferring data between the central service 10 and the monitoring sites 16. Data packages 18 that are created at the monitoring sites 16 can be uploaded to the central service 10 by the monitoring sites 16 as shown, or may also be down loaded or pulled from the monitoring sites 16 by the central service 10, e.g. using a periodic poll, transfer or batch process. In either case, the data packages 18 are of a Suitable format to be transferred over the intermediary network, e.g. one or more data packets, email attachments, streaming data, etc., when using the Internet 14. The data packages 18 may also be text files, or a combination of several file types such as text, graphics, audio etc. It will be appreciated that the data packages 18 need not be embodied as discrete portions or packets during transmission but instead may be sent as continuous or semi-continuous data streams that are received and processed at the central service 10.” And see Col. 11 lines 23-34 discloses, “The variability analysis server 24 can also interact with a bedside monitor 40, which may be made available to or otherwise represent a nurse or other personnel that monitors the patient 26 at the bedside. Similarly, the variability analysis server 24 can also interact with sensor displays 44, which are associated with other medical equipment Such as ECGs, blood pressure sensors, temperature sensors etc. As noted above, the variability analysis server 24 can be a separate, stand-alone unit but may also be integrated as a plug-in or additional module that in this case could be used or integrated with existing bedside monitoring equipment, displays and sensors.” )
the database server is further configured to store the streaming media data in partitions and associate the streaming media data with the medical data sets and the mechanical ventilation therapy data analysis reports; (Fig. 24 and Fig. 25 and Fig. 9 and Fig. 10C and see Col. 9and Col. 10 and see Col 30 lines 11-15 discloses, “As can be seen in FIG. 6, the waveforms 62 are stored in their native form in the data storage device 76 at the server 24 as well as being fed into the raw data builder 64 to create the time series used by the variability analysis module 68 for conducting variability analyses.” )
the clinical data terminal devices are further configured to live broadcast in real time of the waveform data, the numeric data, and the treatment scenes in the mechanical ventilation therapy data of the medical data set as well as the mechanical ventilation therapy data analysis reports of some or all patients in one or more wards. (Col. 30 lines 39-67 and Col. 31 lines 1-6 discloses, “The central service 10 can, at any time, either periodically or on a need-to basis, prepare and distribute threshold data 20 and update data 22 according to the discussion above. It will be appreciated that the data 20, 22 can be pushed to the monitoring sites 16 or pulled down using any Suitable and known data transfer mechanism and should not be limited to any particular one. Similarly, the research programs 94 and statistics engine 100 can be utilized “off-line' or can be regimented to conduct regular refinements or data mining sessions. The administration interface 92 can also be used periodically or on a need-to basis. The update data 22 and threshold data 20 can be built manually, automatically using prepared algorithms or a combination of both. The connec tivity provided by the system 12 also provides a framework for sending alerts between monitoring sites, e.g. by way of emails. This may be useful where outpatients move from a hospital site 16a to a clinic site 16b or mobile site 16c and information should be shared with a regular practitioner. The data flows above may be done in real time or at any interval that Suits the particular application and environment. In this way, regular monitoring can be done at the site and alerts created locally, which are then added as appended data to data packages 18 for a particular patient, which are then uploaded or transmitted in bulk exchanges. This enables the data packages 18 to be analysed locally and annotated when appropriate rather then immediately sending data directly to the central service 10. However, if a particular environment does not have local monitoring, e.g. certain mobile sites, the central service 10 can be used to either do the monitoring or redirect data to an appropriate monitoring centre (similar to the arrangement in a clinic site 16b). It can therefore be seen that the underlying theory behind variability analysis over time has a widespread application in many environments, e.g. for treatment, early diagnosis, real time prognosis and overall health monitoring.” And see Col. 10 lines 31-43 discloses, “The variability analysis server 24 gathers data acquired from one or more patients 26 through individual patient interfaces 28, computes the measures of variability (i.e. conducts variability analyses) for one or more patient parameters, and connects to the central server 10 through the Internet 14 for facilitating the transfer and/or receipt of data packages 18, threshold data 20 and update data 22. As shown, there can be different types of patients 26 such as those in the ICU or in a regular hospital ward. The patient interfaces 28 monitor physiological parameters of the patient 26 using one or more sensors 30. The data or patient parameters can include any variable that can be accurately measured in real time or intermittently.”)
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 3 is rejected under 35 U.S.C. 103 as being unpatentable over Seely (US8924235B2) in view of Daniels et. al (hereinafter Daniels) (US6471658B1)
As per claim 3, Seely teaches:
The method of mechanical ventilation therapy data management according to claim 2, wherein in the mechanical ventilation data, the waveform data comprises: mechanical ventilation pressure waveform data, volume waveform data, flow waveform data and end-tidal carbon dioxide waveform data; the numerical data comprises: airway resistance, work of breathing, compliance, ventilation parameters and respiratory rate;(see table 1 and see Fig. 19 and see Col. 14 lines 18-27 discloses, “Specifically, a proportional assist ventilator permits the breath-to-breath alteration and measurement of multiple respiratory parameters, including airway resistance, pulmonary compliance, tidal Volume, peak airway pressure. Therefore, one use for the proportional assist ventilator is where useful data to evaluate respiratory variability is provided. Numerous other parameters, as shown in Table 1 (above), may be measured and the resulting data stored for a Subsequent variability analysis.” And see Col. 26 lines 59-67 and Col. 27 lines 1-9 discloses, the work of breathing during SBT determined)
However, Seely does not teach:
in the data labeling process for the mechanical ventilation data, the shapes of the labeled waveform data comprise: beginning of inspiratory phase, inspiratory phase, end of inspiratory phase, beginning of expiratory phase, expiratory phase, end of expiratory phase, peak airway pressure, and peak expiratory flow rate of each mechanical ventilation waveform, as well as inspiratory baseline, expiratory plateau height, beginning and end of expiratory plateau of the end-tidal carbon dioxide waveform;
the labeled abnormal events comprise: patient-ventilator asynchronous event, high/low airway pressure, high/low positive end-expiratory pressure, high/low expired tidal volume, high/low minute ventilation, high/low inspired oxygen concentration, high/low oxygen concentration, high/low respiratory rate, high/low partial pressure of end-tidal carbon dioxide, high/low tidal volume, ventilators stop working, asphyxia and increasing airway pressure.
However, Daniels does teach:
in the data labeling process for the mechanical ventilation data, the shapes of the labeled waveform data comprise: beginning of inspiratory phase, inspiratory phase, end of inspiratory phase, beginning of expiratory phase, expiratory phase, end of expiratory phase, (see fig. 4 and see Col. 5 lines 66-67 and Col. 6 discloses, “waveform labeling of inspiratory and expiratory phases of breathing) peak airway pressure, and peak expiratory flow rate of each mechanical ventilation waveform, as well as inspiratory baseline, expiratory plateau height, beginning and end of expiratory plateau of the end-tidal carbon dioxide waveform; (see fig. 4 and see fig. 16 and fig. 17 and see Col. 6 lines 13-67 and Col. 7 lines 1-14)
the labeled abnormal events comprise: patient-ventilator asynchronous event, (Col. 9 lines 1-38 discloses, ineffective tidal volume a type of asynchronous event) high/low airway pressure, high/low positive end-expiratory pressure, high/low expired tidal volume, high/low minute ventilation, high/low inspired oxygen concentration, high/low oxygen concentration, high/low respiratory rate, high/low partial pressure of end-tidal carbon dioxide, high/low tidal volume, ventilators stop working, asphyxia and increasing airway pressure. (see fig. 4 and see fig. 16 and fig. 17 and Col. 7 lines 55-65 )
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Seely’s teachings of mechanical ventilator data and other physiological data and waveforms as previously cited with Daniel’s teachings of labelled data as previously cited, the motivation being mere simple substitution of one known element for another producing a predictable result (KSR rationale B). Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself—that is, in the substitution of the certain labelled physiological data of the secondary reference for the labelled physiological data of the primary reference. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious. .
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Seely (US8924235B2) in view of Daniels et. al (hereinafter Daniels) (US6471658B1) in view of Kaufman et. al (hereinafter Kaufman) (US20190374428A1) and in further view of ANDERS (WO0176471A1)
As per Claim 4, Seely teaches:
The method of mechanical ventilation therapy data management according to claim 3, wherein in the physiological parameters data, the waveform data comprises: electrocardiogram waveform data, respiratory waveform data, photoplethysmogram waveform data and invasive arterial pressure waveform data; (Col. 10 lines 40-56 and see table 1 and see Col. 5 lines 63-67)
the numerical data comprises: heart rate, blood pressure, blood oxygen saturation and body temperature; (Col. 18 lines 33-51 and see Col. 10 lines 40-52 and see table 1)
…[…]…high/low body temperature (Col. 3 lines 26-34 discloses, “This panel of variability analysis techniques was developed to help characterize biologic signals. They have been applied to heart rate, respiratory rate, blood pressure, neutrophil count, temperature and more; investigations have consistently demonstrated the following: (1) patterns of variability provide additional clinically useful information regarding the absolute value of that parameter, (2) altered variation is present in association with age and illness, and (3) degree of alteration correlates with severity of illness.”)
However, Seely does not teach:
in the data labeling process for the physiological parameters data, the shapes of the labeled electrocardiogram waveform data comprise: P wave, QRS complex, T wave, ST segment, PR interval, RR interval and QT interval of each ECG cycle waveform;
the shapes of the labeled respiratory waveform data comprise: peaks and valleys of each respiratory cycle waveform;
the shapes of the labeled photoplethysmogram waveform data comprise: peaks and valleys of each pulse cycle waveform;
the shapes of the labeled invasive arterial pressure waveform data comprise: peaks and valleys of each arterial systolic-diastolic cycle waveform;
the labeled abnormal events comprise: tachycardia, bradycardia, arrhythmia, cardiac arrest, atrial flutter, atrial fibrillation, ventricular flutter, ventricular fibrillation, atrioventricular block, ST-segment elevation/depression, high/low heart rate, high/low blood pressure,
and low blood oxygen saturation.
However, Daniels does teach:
the shapes of the labeled respiratory waveform data comprise: peaks and valleys of each respiratory cycle waveform; (Fig . 16 and Fig. 17)
and low blood oxygen saturation. (see Col. 12 lines 63-65 discloses, “The System of the present invention may also display parameters Such as blood oxygen Saturation (SpO) and pulse rate. “ and see Col. 13 lines 55-59 discloses, “Each displayed value (i.e., a bar 262 of the bar graph 261) is representative of all of the corresponding measured respiratory profile parameter Val ues over a particular time interval (e.g., an average value, median value, low value, high value, etc.).”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Seely’s teachings of mechanical ventilator data and other physiological data and waveforms as previously cited with Daniel’s teachings as previously cited for the same reasons given in claim 3.
However, Daniels doesn’t teach:
in the data labeling process for the physiological parameters data, the shapes of the labeled electrocardiogram waveform data comprise: P wave, QRS complex, T wave, ST segment, PR interval, RR interval and QT interval of each ECG cycle waveform;
the shapes of the labeled photoplethysmogram waveform data comprise: peaks and valleys of each pulse cycle waveform;
the shapes of the labeled invasive arterial pressure waveform data comprise: peaks and valleys of each arterial systolic-diastolic cycle waveform;
However, Kaufman does teach:
in the data labeling process for the physiological parameters data, the shapes of the labeled electrocardiogram waveform data comprise: P wave, QRS complex, T wave, ST segment, PR interval, RR interval and QT interval of each ECG cycle waveform; (see Fig. 5A and 5B and see [0120])
the labeled abnormal events comprise: tachycardia, bradycardia, arrhythmia, cardiac arrest, atrial flutter, atrial fibrillation, ventricular flutter, ventricular fibrillation, atrioventricular block, ST-segment elevation/depression, high/low heart rate, high/low blood pressure, ([ 0118 ] discloses, “Returning to step 308 , if the patient 101 is not in asystole , then the processor 123 of the medical device 114 analyzes the received ECG waveform to determine if the waveform is indicative of a shockable cardiac rhythm ( e.g. , ventricular fibrillation or ventricular tachycardia ) in step 312.” And see [0048] discloses, “FIG . 5B illustrates an example of a QRS complex of an ECG waveform for a patient with unconscious hypotension with an organized ECG , which may have similar characteristics to pulseless electrical activity ( PEA ) or pseudo pulseless electrical activity.” And see [0067] discloses, “Following the state of extreme shock is pulseless electrical activity ( PEA ) cardiac arrest…[…]…” / examiner notes some arrhythmias cited include high/low pressure, high/low heart rate and ST-segment elevation/depression)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Seely’s teachings of mechanical ventilator data and other physiological data and waveforms as previously cited and Daniel’s teachings as previously cited with Kaufman’s teachings as previously cited for the same reasons given in claim 3.
However, Kaufman also doesn’t teach:
the shapes of the labeled photoplethysmogram waveform data comprise: peaks and valleys of each pulse cycle waveform;
the shapes of the labeled invasive arterial pressure waveform data comprise: peaks and valleys of each arterial systolic-diastolic cycle waveform;
However, Anders does teach:
the shapes of the labeled photoplethysmogram waveform data comprise: peaks and valleys of each pulse cycle waveform; (see Fig. 7 and see page 1 para. 6 and 7)
the shapes of the labeled invasive arterial pressure waveform data comprise: peaks and valleys of each arterial systolic-diastolic cycle waveform; (see fig.7)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Seely’s teachings of mechanical ventilator data and other physiological data and waveforms as previously cited and Daniel’s teachings as previously cited and Kaufman’s teachings with Ander’s teachings as previously cited for the same reasons given in claim 3.
As per claim 5, Examiner notes:
The method of mechanical ventilation therapy data management according to claim 4, wherein, in the blood gas analysis data, the numerical data comprises: partial pressure of oxygen, partial pressure of carbon dioxide, total carbon dioxide, potassium ion concentration, sodium ion concentration, bicarbonate ion concentration and pH value; in the data labeling process for the blood gas analysis data, the labeled abnormal events comprise: high/low partial pressure of oxygen, high/low partial pressure of carbon dioxide, high/low total carbon dioxide, high/low potassium ion concentration, high/low sodium ion concentration and high/low pH value. ([The Examiner notes that “a phrase” was optional in the previous limitation. Because that option was not taken, the phrase is not required and the Examiner declines to address this limitation. Examiner also notes this language is non-functional language]
As per claim 6, Examiner notes:
The method of mechanical ventilation therapy data management according to claim 5, wherein in the laboratory diagnostic data, the numerical data comprises: white blood cell count, red blood cell count, platelet count, hemoglobin, hematocrit, blood glucose, prothrombin time and activated partial thrombin time; in the data labeling process for the laboratory diagnostic data, the labeled abnormal events comprise: high/low white blood cell count, high/low red blood cell count, high/low hemoglobin, high/low hematocrit, high/low blood glucose, high/low platelet count, long/short prothrombin time, and long/short activated partial thrombin time. ([The Examiner notes that “a phrase” was optional in the previous limitation. Because that option was not taken, the phrase is not required and the Examiner declines to address this limitation. Examiner also notes this language is non-functional language]
Claim 7, 8, 11, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Seely (US8924235B2) in view of Daniels et. al (hereinafter Daniels) (US6471658B1) in view of Kaufman et. al (hereinafter Kaufman) (US20190374428A1) and in further view of ANDERS (WO0176471A1) and in even further view of Zhang et. al (hereinafter Zhang) (US9743889B2)
As per claim 7, Seely teaches:
The method of mechanical ventilation therapy data management according to claim 6, wherein the reporting framework of the mechanical ventilation therapy data analysis report comprises a report overview page and a report analysis page; (see Fig. 24 and fig. 25)
wherein the report overview page is divided into a basic information area (see fig. 24 “patient and trial information”), a mechanical ventilation data area (see fig. 24 “pre-SBT parameters”), and an analysis conclusion area; (see Fig. 24 “airway compromise”)
the basic information area comprises at least the patient information and key time point information; (see fig. 24 “patient and trial information”)
and further comprises the following options: the clinical diagnosis information, (see Fig. 24 “angina or infarction”) the ID of the device for outputting the mechanical ventilation therapy data as well as mechanical ventilation mode and setting information; ( examiner notes that “the following options” is interpreted as one or more of the items listed as being required Per MPEP § 2143.03, Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. )
the mechanical ventilation data area comprises at least respiratory analysis results, (see Fig. 24 “airway compromise”)…[…]… compliance change analysis results, and lung injury early warning analysis results; (see table 1 and see Fig. 19 and see Col. 14 lines 18-27 discloses, “Specifically, a proportional assist ventilator permits the breath-to-breath alteration and measurement of multiple respiratory parameters, including airway resistance, pulmonary compliance, tidal Volume, peak airway pressure. Therefore, one use for the proportional assist ventilator is where useful data to evaluate respiratory variability is provided. Numerous other parameters, as shown in Table 1 (above), may be measured and the resulting data stored for a Subsequent variability analysis.” And see Col. 26 lines 59-67 and Col. 27 lines 1-9 discloses, the work of breathing during SBT determined)
each of the analysis results of the mechanical ventilation data area is obtained based on the mechanical ventilation data and its data labeling; (see Fig. 9 and Fig 10C and Fig. 24 and Fig. 25 and see table 1 and see Fig. 19 and see Col. 14 lines 18-27 discloses, “Specifically, a proportional assist ventilator permits the breath-to-breath alteration and measurement of multiple respiratory parameters, including airway resistance, pulmonary compliance, tidal Volume, peak airway pressure. Therefore, one use for the proportional assist ventilator is where useful data to evaluate respiratory variability is provided. Numerous other parameters, as shown in Table 1 (above), may be measured and the resulting data stored for a Subsequent variability analysis.” And see Col. 26 lines 59-67 and Col. 27 lines 1-9 discloses, the work of breathing during SBT determined)
the analysis conclusion area is configured to display the analysis descriptions and conclusions of the mechanical ventilation therapy data; (see Fig. 24 and see Fig. 25)
the report analysis page is configured to display a detailed analysis report of one or more mechanical ventilation therapy data separately in graphical form. (Col. 15 lines 26-34 discloses, “It can be seen that several selection boxes 302 can be provided to enable clinical event data to be recorded before the data is uploaded to the variability analysis server 24. The ability to “upload waveform data and clinical data simultaneously enables, among other things, the following: comparison of clinical data and variability data, provision of a “report encompassing both clinical and variability data, and performance of Standardized multi-center research trials where variability is compared to standard clinical criteria.” And see Fig. 10C)
However, Seely does not teach:
patient-ventilator asynchronous events analysis results,
airway resistance change analysis results,
the respiratory analysis results comprise a rapid shallow breathing index
However, Daniels does teach:
patient-ventilator asynchronous events analysis results, (Col. 9 lines 1-38 discloses, ineffective tidal volume a type of asynchronous event)
airway resistance change analysis results, (see fig. 4 and see fig. 16 and fig. 17 and see Col. 6 lines 13-67 and Col. 7 lines 1-14)
However, Daniels, Kaufman, and Anders also do not teach:
the respiratory analysis results comprise a rapid shallow breathing index
However, Zhang does teach:
the respiratory analysis results comprise a rapid shallow breathing index; (Col. 17 lines 24-41 discloses, shallow breathing index shown in figure results analysis)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Seely’s teachings of mechanical ventilator data and other physiological data and waveforms as previously cited and Daniel’s teachings as previously cited and Kaufman’s teachings and Ander’s teachings as previously cited with Zhang’s teachings for the same reasons given in claim 3.
As per claim 8, Seely does not teach:
The method of mechanical ventilation therapy data management according to claim 7, wherein the report analysis page comprises a trend graph page; the trend graph page comprises: trend analysis areas of a pressure, volume, and ventilation volume of the mechanical ventilation data;
The method of mechanical ventilation therapy data management according to claim 7, wherein the report analysis page comprises a trend graph page; the trend graph page comprises: trend analysis areas of a pressure, volume, and ventilation volume of the mechanical ventilation data;
However, Daniels does teach:
The method of mechanical ventilation therapy data management according to claim 7, wherein the report analysis page comprises a trend graph page; the trend graph page comprises: trend analysis areas of a pressure, volume, and ventilation volume of the mechanical ventilation data; (Col. 13 lines 33-51 discloses, “When the “Trend” key is depressed, detected at inquiry 220, the system of the present invention inquires whether any of the trend displays are being shown (examples are described below). If not, the system shows the trend display that was last shown. If So, the System shows the next trend display. A first trend display Shows, as depicted at 221, a bar graph illustrating the trend of alveolar minute ventilation and the trend of CO2 production, each over a set time duration. Preferably, the alveolar minute ventilation trend graph illustrates both the Spontaneous and mechanical ventilator components thereof. An exemplary time duration for illustration of Such trends is twenty minutes. A Second trend display shows bar graphs which illustrate the trends, or recent histories, of tidal volume attributable to ventilator induced breathing and tidal Volume generated by a patient's Spontaneous breathing, as shown at 223. All of the respiratory parameters may be Stored for a Set duration of time by processing unit 18 (see FIG. 1), which generates and displays trend bar graphs.”)
a trend analysis area of a respiratory frequency; And see a trend analysis area of an inhaled oxygen concentration; and a trend analysis description and conclusion area is configured to synthesize the trend direction of each trend analysis area, to provide trend analysis descriptions and corresponding conclusions; the contents of each trend analysis area in the trend graph page are obtained based on the mechanical ventilation data and its data labeling. (Col. 8 lines 15-23 discloses, “Referring to FIG. 8, the respiratory rates, also referred to as respiratory frequency, for all breaths, for ventilator breaths, and for Spontaneous breaths are measured as shown at 146, 147 and 148, respectively. The frequency of each breath is equal to 60/T, which Supplies a value in units of breaths per minute. Preferably, each of the respiratory frequency values is determined by averaging the frequency values for the last eight breaths of each respective breath type (i.e., total, ventilator and spontaneous breaths). And see (Col. 13 lines 33-51 discloses, “When the “Trend” key is depressed, detected at inquiry 220, the system of the present invention inquires whether any of the trend displays are being shown (examples are described below). If not, the system shows the trend display that was last shown. If So, the System shows the next trend display. A first trend display Shows, as depicted at 221, a bar graph illustrating the trend of alveolar minute ventilation and the trend of CO2 production, each over a set time duration. Preferably, the alveolar minute ventilation trend graph illustrates both the Spontaneous and mechanical ventilator components thereof. An exemplary time duration for illustration of Such trends is twenty minutes. A Second trend display shows bar graphs which illustrate the trends, or recent histories, of tidal volume attributable to ventilator induced breathing and tidal Volume generated by a patient's Spontaneous breathing, as shown at 223. All of the respiratory parameters may be Stored for a Set duration of time by processing unit 18 (see FIG. 1), which generates and displays trend bar graphs. With reference to FIG. 20, the trend display 260 illustrates the trend of a particular respiratory profile parameter over a Set duration of time (e.g., 20 minutes, 1, 4, 8, 12 or 24 hours) as a set number of values (e.g., 10, 20). Each displayed value (i.e., a bar 262 of the bar graph 261) is representative of all of the corresponding measured respiratory profile parameter Values over a particular time interval (e.g., an average value, median value, low value, high value, etc.). Preferably, all of the displayed values (i.e., bars 262) represent the corresponding measured respiratory profile parameter values over consecutive time intervals of equal length. For example, if the bar graph 261 includes twenty bars 262 which represent a respiratory profile parameter trend over the past 24 hours, each displayed value (i.e., a bar 262 of bar graph 261) represents all of the corresponding actual respiratory profile parameter values of each consecutive 1.2 hour interval.” And see Col. 14 lines 1-24)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Seely’s teachings of mechanical ventilator data and other physiological data and waveforms as previously cited and Daniel’s teachings as previously cited for the same reasons given in claim 3.
As per claim 11, Seely teaches:
The method of mechanical ventilation therapy data management according to claim 7, wherein, the report generation instruction is a customized report generation instruction; the customized report generation instruction comprises one or more customized parameters, is configured to generate the report overview page, and the report analysis page matching the customized parameters; the customized parameters comprise the trend graph page, the data page, or the graphic page. (see Fig. 24 and 25 and fig. 9 and 10C)
As per claim 12, Seely and Daniels do not teach:
The method of mechanical ventilation therapy data management according to claim 7, wherein the method further comprises: in the data labeling process, a first aid instruction is issued when one or more abnormal events of ventilators stop working, asphyxia, increasing airway pressure, cardiac arrest, tachycardia, bradycardia, ventricular flutter, ventricular fibrillation and hypotension occur, the first aid instruction carries the information about the abnormal events that occur;
in the data labeling process, a ventilator parameters adjustment instruction is issued when one or more abnormal events of patient-ventilator asynchronous event, high airway pressure, low airway pressure, high positive end expiratory pressure, low positive end expiratory pressure, high minute ventilation, low minute ventilation, high tidal volume, low tidal volume, high respiratory rate, low respiratory rate, high oxygen concentration and low oxygen concentration occur, wherein the ventilator parameters adjustment instruction carries the information about the abnormal events that occur.
However, Kaufman teaches:
The method of mechanical ventilation therapy data management according to claim 7, wherein the method further comprises: in the data labeling process, a first aid instruction is issued when one or more abnormal events of ventilators stop working, asphyxia, increasing airway pressure, cardiac arrest, tachycardia, bradycardia, ventricular flutter, ventricular fibrillation and hypotension occur, the first aid instruction carries the information about the abnormal events that occur; ([0116] discloses, “Next , in step 308 , the processor 123 determines whether the patient is in asystole ( i.e. , the absence of any cardiac activity ) . If the patient is in asystole , then the medical device 114 transmits a signal to the automated chest compressor 108 to immediately begin delivering chest com pressions and / or the medical device 114 may also provide pacing in step 310. While the embodiment is described with respect to an automated chest compressor 108 , the chest compressions could be provided manually . In such a case , the medical device 114 displays one or more prompts that provide an indication of when to provide the chest com pressions . Likewise , the medical device 114 may further provide visual and / or audible feedback to the rescuer to provide step - by - step instructions on how to adjust the chest compressions ( e.g. , faster , slower , deeper , shallower”)
in the data labeling process, a ventilator parameters adjustment instruction is issued when one or more abnormal events of patient-ventilator asynchronous event, high airway pressure, low airway pressure, high positive end expiratory pressure, low positive end expiratory pressure, high minute ventilation, low minute ventilation, high tidal volume, low tidal volume, high respiratory rate, low respiratory rate, high oxygen concentration and low oxygen concentration occur, wherein the ventilator parameters adjustment instruction carries the information about the abnormal events that occur. ([0108] discloses, “Additionally , the medical device 114 may further include one or more speakers 118 capable of providing audible feedback . For example , the speaker 118 may provide an alarm in response to a deteriorating heartbeat , deteriorating ventilations , or deteriorating end tidal CO2 . The speaker may also provide verbal instructions for the rescuer 105 to carry out one or more shock therapy protocols.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Seely’s teachings of mechanical ventilator data and other physiological data and waveforms as previously cited and Daniel’s teachings as previously cited with Kaufman’s teachings for the same reasons given in claim 3.
Claims 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Seely (US8924235B2) in view of Daniels et. al (hereinafter Daniels) (US6471658B1) in view of Kaufman et. al (hereinafter Kaufman) (US20190374428A1) and in further view of ANDERS (WO0176471A1) and in even further view of Zhang et. al (hereinafter Zhang) (US9743889B2) and in even further view of Robinson et. al (hereinafter Robinson) (US20180103898A1)
As per claim 9, Seely, Daniels, Kaufman, Anders, and Zhang do not explicitly teach:
The method of mechanical ventilation therapy data management according to claim 8, wherein the report analysis page further comprises a data page; the data page comprises at least: a mechanical ventilation setting parameters data sub-page comprises: a mechanical ventilation setting parameters record sheet, ventilation modes of mechanical ventilation and a change trend graph of setting parameters;a mechanical ventilation monitoring parameters data sub-page comprises a mechanical ventilation monitoring parameters record sheet;the contents of the mechanical ventilation setting parameters data sub-page and the mechanical ventilation monitoring parameters data sub-page are obtained based on the mechanical ventilation data and its data labeling;
the data page further comprises the following optional data sub-pages:
a 24-hour physiological parameters data sub-page comprises a 24-hour vital signs monitoring record sheet and a heart rate/respiratory rate/blood pressure trend graph; the contents of the 24-hour physiological parameters data sub-page are obtained based on the physiological parameters data and its data labeling;a 24-hour blood gas analysis and laboratory diagnostic data sub-page comprises a 24-hour blood gas analysis monitoring record sheet, a partial pressure of oxygen/partial pressure of carbon dioxide/bicarbonate trend graph, and a laboratory diagnostic data sheet; the contents of the 24-hour blood gas analysis and laboratory diagnostic data sub-page are obtained based on the blood gas analysis data, the laboratory diagnostic data, and their data labeling; an alarm events data sub-page comprises: an alarm events record sheet, a 24-hour alarm events distribution diagram, and an alarm events comprehensive analysis sheet; the alarm events data sub-page is obtained based on the abnormal events labeled by the mechanical ventilation therapy data; an extended image data sub-page is configured to display the image data.
However, Robinson does teach:
The method of mechanical ventilation therapy data management according to claim 8, wherein the report analysis page further comprises a data page; the data page comprises at least: a mechanical ventilation setting parameters data sub-page comprises: a mechanical ventilation setting parameters record sheet, ventilation modes of mechanical ventilation and a change trend graph of setting parameters;a mechanical ventilation monitoring parameters data sub-page comprises a mechanical ventilation monitoring parameters record sheet;the contents of the mechanical ventilation setting parameters data sub-page and the mechanical ventilation monitoring parameters data sub-page are obtained based on the mechanical ventilation data and its data labeling; (see Fig. 2-9 and see [0024] and see [0032] )
the data page further comprises the following optional data sub-pages:
a 24-hour physiological parameters data sub-page comprises a 24-hour vital signs monitoring record sheet and a heart rate/respiratory rate/blood pressure trend graph; the contents of the 24-hour physiological parameters data sub-page are obtained based on the physiological parameters data and its data labeling;a 24-hour blood gas analysis and laboratory diagnostic data sub-page comprises a 24-hour blood gas analysis monitoring record sheet, a partial pressure of oxygen/partial pressure of carbon dioxide/bicarbonate trend graph, and a laboratory diagnostic data sheet; the contents of the 24-hour blood gas analysis and laboratory diagnostic data sub-page are obtained based on the blood gas analysis data, the laboratory diagnostic data, and their data labeling; an alarm events data sub-page comprises: an alarm events record sheet, a 24-hour alarm events distribution diagram, and an alarm events comprehensive analysis sheet; the alarm events data sub-page is obtained based on the abnormal events labeled by the mechanical ventilation therapy data; an extended image data sub-page is configured to display the image data. (Per MPEP § 2143.03, Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009) [The Examiner notes that “a phrase” was optional in the limitation. Because that option was not taken, the phrase is not required and the Examiner declines to address this limitation. Examiner also notes this language is non-functional language])
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Seely’s teachings of mechanical ventilator data and other physiological data and waveforms as previously cited and Daniel’s teachings as previously cited and Kaufman’s teachings as previously cited, and Anders teachings as previously cited, and Zhang’s teachings as previously with Robinson’s teachings for the same reasons given in claim 3 as it is all simple non-functional substitution of data shown on a report.
As per claim 10, Seely, Daniels, Kaufman, Anders, and Zhang do not teach:
The method of mechanical ventilation therapy data management according to claim 9, wherein, the report analysis page further comprises a graphic page; the graphic page is divided into a mechanical ventilation waveform data area, a mechanical ventilation loop chart area, and a physiological parameters waveform data area;
wherein the mechanical ventilation waveform data area comprises waveform graphs of continuous normal mechanical ventilation data and waveform graphs of abnormal mechanical ventilation events data;
the mechanical ventilation loop chart area comprises a pressure-volume loop chart and a flow-volume loop chart drawn by the combination of the mechanical ventilation pressure, volume, and flow data; the contents of the mechanical ventilation waveform data area and the mechanical ventilation loop chart area are obtained based on the mechanical ventilation data and its data labeling;
the physiological parameters waveform data area comprises waveform graphs of continuous physiological parameters data synchronized with the mechanical ventilation data waveform graphs; the content of the physiological parameters waveform data area is obtained based on the physiological parameters data and its data labeling.
However, Robinson does teach:
The method of mechanical ventilation therapy data management according to claim 9, wherein, the report analysis page further comprises a graphic page; the graphic page is divided into a mechanical ventilation waveform data area, a mechanical ventilation loop chart area, and a physiological parameters waveform data area; (see Fig. 9)
wherein the mechanical ventilation waveform data area comprises waveform graphs of continuous normal mechanical ventilation data and waveform graphs of abnormal mechanical ventilation events data; (see Fig. 3 and 4 and see [0039])
the mechanical ventilation loop chart area comprises a pressure-volume loop chart and a flow-volume loop chart drawn by the combination of the mechanical ventilation pressure, volume, and flow data; the contents of the mechanical ventilation waveform data area and the mechanical ventilation loop chart area are obtained based on the mechanical ventilation data and its data labeling; (see [0049] and see Fig. 9)
the physiological parameters waveform data area comprises waveform graphs of continuous physiological parameters data synchronized with the mechanical ventilation data waveform graphs; the content of the physiological parameters waveform data area is obtained based on the physiological parameters data and its data labeling. (see e.g. fig. 8 and see [0048])
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Seely’s teachings of mechanical ventilator data and other physiological data and waveforms as previously cited and Daniel’s teachings as previously cited and Kaufman’s teachings as previously cited, and Anders teachings as previously cited, and Zhang’s teachings as previously with Robinson’s teachings for the same reasons given in claim 3 as it is all simple non-functional substitution of data shown on a report.
Claim 31 is rejected under 35 U.S.C. 103 as being unpatentable over Seely (US8924235B2) in view of Kelly et. al (hereinafter Kelly) (US20150182712A1)
As per claim 31, Seely does not teach:
The system of mechanical ventilation therapy data management according to claim16, wherein the system further comprises a multi-mode gateway and a master control server;
wherein the multi-mode gateway is configured to connect ventilator devices, multi-parameter monitors, bedside audio video devices, clinical information systems and clinical data terminal devices from different manufacturers in each ward of the hospital through the network;
the multi-mode gateway is embedded with a variety of communication protocols to realize the data communication between the ventilator devices, the multi-parameter monitors, the bedside audio video devices, the clinical information systems and the data acquisition and standardization unit, and configured to realize the data communication between the application software system and the clinical data terminal devices where the patients are located;
the master control server is embedded with a multi-task operating system and an application software system running on the multi-task operating system, is configured to control the operation of the application software system.
However, Kelly does teach:
The system of mechanical ventilation therapy data management according to claim16, wherein the system further comprises a multi-mode gateway and a master control server; (see fig. 1 and see [0030] discloses, “For example, ventilators 212 and 214 might include various different types of ventilators that are manufactured by various different vendors. As such, components of FIG. 2 might communicate with bus 216 via a gateway (e.g., device gateway or internal gateway), an adapter, or by any other means” and see [0018] discloses, “With continued reference to FIG. 1, the computing environment 100 includes a general purpose computing device in the form of a control server 102. Exemplary com ponents of the control server 102 include a processing unit, internal system memory, and a Suitable system bus for cou pling various system components, including database cluster 104, with the control server 102. The system bus might be any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, and a local bus, using any of a variety of bus architectures. Exemplary architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronic Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus, also known as Mezzanine bus.”)
wherein the multi-mode gateway is configured to connect ventilator devices, multi-parameter monitors, bedside audio video devices, clinical information systems and clinical data terminal devices from different manufacturers in each ward of the hospital through the network; ([0033] discloses, “Ventilator manager 224 communicates with bus 216 and functions to document, display and manage ventilator information received from ventilators 212 and 214.” And see [0030] discloses, “As previously indicated, and as depicted in FIG. 2, each of medical devices 210, ventilators 212 and 214, health care information system 228, and clinical user devices 226 may be in communication with bus 216. Bus 216 generally provides a connection framework for these components by creating and managing all connections, providing a messaging architecture to facilitate an exchange of information between the various components of FIG. 2, and providing general operational and management capabilities for connected devices. In one embodiment, medical device 210, ventilators 212 and 214, clinical user devices 226, and health care information system 228 communicate with bus 216.” And see [0030] discloses, “For example, ventilators 212 and 214 might include various different types of ventilators that are manufactured by various different vendors.”)
the multi-mode gateway is embedded with a variety of communication protocols to realize the data communication between the ventilator devices, the multi-parameter monitors, the bedside audio video devices, the clinical information systems and the data acquisition and standardization unit, and configured to realize the data communication between the application software system and the clinical data terminal devices where the patients are located; ([0022] discloses, “Exemplary computer networks 106 include local area networks (LANs) and/or wide area networks (WANs). Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets, and the Inter net. When utilized in a WAN networking environment, the control server 102 might include a modem or other means for establishing communications over the WAN, such as the Internet. In a networked environment, program modules or portions thereof might be stored in association with the control server 102, the database cluster 104, or any of the remote computers 108. For example, various application programs may reside on the memory associated with any one or more of the remote computers 108. It will be appreciated by those of ordinary skill in the art that the network connections shown are exemplary and other means of establishing a communications link between the computers (e.g., control server 102 and remote computers 108) might be utilized.” Examiner notes under BRI that LAN and WAN utilize communication protocols)
the master control server is embedded with a multi-task operating system and an application software system running on the multi-task operating system, is configured to control the operation of the application software system.([0022] discloses, “For example, various application programs may reside on the memory associated with any one or more of the remote computers 108. It will be appreciated by those of ordinary skill in the art that the network connections shown are exemplary and other means of establishing a communications link between the computers (e.g., control server 102 and remote computers 108) might be utilized.” And see fig. 1 and see [0030] discloses, “For example, ventilators 212 and 214 might include various different types of ventilators that are manufactured by various different vendors. As such, components of FIG. 2 might communicate with bus 216 via a gateway (e.g., device gateway or internal gateway), an adapter, or by any other means” and see [0018] discloses, “With continued reference to FIG. 1, the computing environment 100 includes a general purpose computing device in the form of a control server 102. Exemplary com ponents of the control server 102 include a processing unit, internal system memory, and a Suitable system bus for cou pling various system components, including database cluster 104, with the control server 102. The system bus might be any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, and a local bus, using any of a variety of bus architectures. Exemplary architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronic Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus, also known as Mezzanine bus.”) / examiner notes a multi task operating system is under BRI one of ordinary skill would understand to be hardware which can run multiple applications)
Prior Art Cited But Not Relied Upon
US20220265944A1 - MULQUEENY et. al (hereinafter MULQUEENY)
A controller or processor ( s ) implements detection of respi ratory related conditions , such as asynchrony , associated with use of a respiratory treatment apparatus or ventilator . Based on data derived from sensor signals associated with 2 1110 1108 1112 the respiratory treatment , the detector may evaluate a feature set of detection values to determine whether or not an asynchrony occurs in a breath of the patient's respiratory cycle such as by comparing the values against a set of thresholds . Different events may also be identified based on the particular feature set and threshold ( s ) involved in the detection processing . Automated determination of feature sets may also be implemented to design different asynchrony event classifiers . The methodologies may be implemented by computers or by respiratory treatment apparatus . The detection of such asynchrony events can then also serve as part of control logic for automated adjustments to the control parameters of the respiratory treatment generated by the respiratory treatment apparatus .
US20220148701A1 – Cipollone et. al (hereinafter Cipollone)
The present technology is directed to respiratory therapy data management systems , device , and methods . The systems can collect , store , monitor , report , and / or analyze patient treatment data associated with patient use of one or more respiratory therapy devices . The patient treatment data can include therapy data related to the use of multiple respiratory therapies , such as ventilation , oxygen , cough assistance , suction , and nebulization . The patient treatment data may be collected from a plurality of respiratory devices associated with a particular patient , or from a single respiratory device associated with a particular patient . The system can generate customizable reports detailing the patient treatment data . The reports can summarize patient use , illustrate therapy trends , and / or provide therapy recommendations
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ashley Elizabeth Evans whose telephone number is (571) 270-0110. The examiner can normally be reached Monday – Friday 8:00 AM – 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached on (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned 571-273-8300.
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/ASHLEY ELIZABETH EVANS/Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687
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