DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending and have been examined.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Should applicant desire to obtain the benefit of foreign priority under 35 U.S.C. 119(a)-(d) prior to declaration of an interference, a certified English translation of the foreign application must be submitted in reply to this action. 37 CFR 41.154(b) and 41.202(e).
Failure to provide a certified translation may result in no benefit being accorded for the non-English application.
Application Data Sheet (ADS)
Applicant needs to file an amended ADS. Applicant’s ADS filed March 5, 2025, has the filing date for PCT/CN2023/112988 as 2024-08-14, where the filing date is 2023-08-14.
Claim Rejections - 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-20 are directed to a system or method, which are statutory categories of invention. (Step 1: YES).
The Examiner has identified method Claim 12 as the claim that represents the claimed invention for analysis and is similar to system claim 1 and method claim 17.
Claim 12 recites the limitations of:
A method for processing vital information, comprising
obtaining multiple types of patient data of a patient;
analyzing the patient data to obtain multiple analysis results, wherein the multiple analysis results at least comprise a first trend of change for first information and a second trend of change for second information, which respective information is extracted from the patient data;
wherein the first information and the second information are related to a same patient state of the patient;
determining whether the first trend of change and the second trend of change satisfy a preset condition; and
displaying the first trend of change and the second trend of change, when the first trend of change and the second trend of change are determined to satisfy the preset condition.
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. The claim recites elements, in bold above, which covers performance of the limitation as managing interactions between people. Obtaining patient data, analyzing the data to obtain analysis results, determining a first and second trend change satisfy a preset condition is following rules of instructions of obtaining and analyzing patient data. Displaying the first and second trend change, would be providing at least teaching of patient trends. Also, giving the claim it’s broadest reasonable interpretation in light of the specification, monitoring and reporting patient data would be performed by a health professional (e.g., para. [0007]). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as managing interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Claims 1 and 17 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
In as much as the claims are obtaining data, analyzing the data, and providing a result by displaying the trends, the claims are also abstract as a mental process. A person can in their mind read (collect) information, mentally analyze data, determine a trend, and draw trend lines with pen and paper. Further, the claim does not recite a computer, therefore the claim apparently is being performed by a person.
This judicial exception is not integrated into a practical application. In particular, the claims only recite: memory, processor, display (Claim 1). The computer hardware is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 1, 12, and 17 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See Applicant’s specification para. [00235] about implantation using general purpose computing devices and MPEP 2106.05(f) where applying a computer as a tool is not indicative of significantly more. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Steps such as obtaining (receiving) are steps that are considered insignificant extra solution activity and mere instructions to apply the exception using general computer components (see MPEP 2106.05(d), II). Thus claims 1, 12, and 17 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 2-11, 13-16, and 18-20 further define the abstract idea that is present in their respective independent claims 1, 12, and 17 and thus correspond to Certain Methods of Organizing Human Activity and Mental Processes and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Claims 3, 5, 8, 10 recite generic processor at a high level of generality. Claims 4 and 9 recite generic memory at a high level of generality. Claims 3, 5, 8, and 10 recite controlling a generic display at a high level of generality. Therefore, the claims 2-11, 13-16, and 18-20 are directed to an abstract idea. Thus, the claims 1-20 are not patent-eligible.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 U.S.C. §112(a) or §112 1st paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Pub. No. US 2007/0208263 to John et al. in view of Pub. No. US 2021/0059616 to Abrol et al.
Regarding claim 1
A system for processing vital information, comprising a memory, a processor, and a display;
wherein the memory is configured to store an executable program, and the processor is configured to execute the executable program to perform following operations:
John et al. teaches:
Memory and processor…
“FIG. 1B illustrates in block diagram form an embodiment of the cardiotrend system 500, which includes a sensing module 510, a storage module 526 with an associated random access memory, a diagnostic module 516, a control module 518, an alarm module 522, a signal routing module 530 an intervention module 524, a stimulation module 528, a communication module 521, and a power supply 520. The control module 518, diagnostic module 516, storage module 526 will typically be implemented by a digital processor (or different digital processors) and associated software. The sensing module 510 controls the sensing of signals (including patient state related data) from the human patient and typically includes amplifiers, multiplexers, and other electronic circuitry related to communication with the sensors 506 and contacts 509A, 509B and 509C. The diagnostic module 516 is designed to analyze monitored data including sensed data provided by the sensing module 510, as well as data from the storage module 526 in order to produce "monitoring results" which are used by the control program 519 of the control module 518 to responsively provide alerting or other CardioTrend operations. The control module 518 and the rest of the cardiotrend system 500 is powered by the power supply 520 which may be a rechargeable battery. The system 500 communicates with other implanted devices and the external system (CTE) 500B using its communication module 521.” [0043]
Example of display…
“When most of the system 500 is implanted, the (CTI) 500A may communicate with the CTE 500B as well as with other implanted devices (not shown). For example, the system 500 can communicate with a physician programmer 501, a portable patient external device (EXD) 502A, or a home patient data transmission device (DTD) 502B which may be a limited version of the physician programmer 501. The programmer 501, EXD 502A and DTD 502B may have an input module 503 that may include a keyboard, mice, various control buttons, microphones and communication transceivers (e.g., telemetry circuitry) hardware. The programmer, EXD 502A and DTD 502B may have an output module 504 that may include various displays, alarm transducers, a (wireless) modem, and other communication equipment. The programmer 501, EXD 502A, and DTD 502B can communicate with a central station 505, which may be fully automated or may include a staff of medical personnel who can assist the patient if the system 500 has alerted the patient that medically significant activity has been detected.”[0040]
obtaining patient data of a patient;
Medical devices for sensing and recording (obtaining) biological activity (patient data)…
“The invention described herein relates to medical devices for monitoring biological activity. More particularly it describes a system incorporating a device capable of sensing and recording long segments of biological activity, providing early detection and/or prediction of medical conditions and possibly alarming the patient in response thereto. When the invention is preferably applied to the monitoring of cardiac status it is referred to as the "CardioTrend system".” [0007]
Example of patient state values (patient data)…
“A "patient state value" pertains to the physiological, emotional, mental, periodic (e.g., circadian), or environmental state of a patient. "Patient state values" include, without limitation, a patient's mental state (e.g. angry, confused, etc.), physical state (e.g. walking, supine, sleeping etc.), time of day, patient input, evaluation of sensed data related to body temperature, blood pressure, or other available measures. "Patient state values" may also include heart related features such as heart rate, the presence of arrhythmias, as well as acoustic or chemical measures related to cardiac function, which may be obtained.” [0036]
analyzing the patient data to obtain multiple analysis results, wherein the multiple analysis results at least comprise a first trend of change for first information and a second trend of change for second information, which respective information is extracted from the patient data;
Analyzing patient data and acquiring values based on awake or asleep…
“The above described operations of acquiring and analyzing patient data, and performing an ameliorative response (e.g. issuing and alarm or providing therapy) are executed in accordance with patient state information. For example, an alarm threshold value (detection operation) may be set according to whether a patient is awake or asleep.” [0010]
One or more (multiple) cardiotrend (trends) operations and adjust the detection of abnormal cardiac activity (changing information)…
“FIG. 9A shows a method by which a patient's state is used to adjust one or more of the cardiotrend operations by modifying the operation of the detection algorithm, or the operation of the modules of an alarm module, in order to adjust the detection of abnormal cardiac activity or the generation of alarms, respectively;” [0028]
Two or more measures (first and second trends) and joint evaluation (multiple analysis or multivariate results), related to (respective information) such as temperature, electrical activity, etc. …
“The diagnostic module 516 and its related methods provide monitoring results that can cause the cardiotrend system 500 to provide a number of CTO's. The monitoring results can be based upon the joint evaluation of 2 or more measures computed by modules embodied within the diagnostic module 516. In other words, rather than examining two or more measures independently, and comparing each of these to some selected criteria measures may be combined, for example, using the multivariate analysis module 562. Abnormal cardiac activity can be computed from the monitored signals that are being monitored. These can be related to temperature, electrical activity, a reflected optical signal, pressure or other measure and two or more measures, used to detect an anomalous cardiac event, can be obtained which are within or across these sensed signals. It is an important and novel feature of the cardiotrend system 500 that feature, trend and histogram data can all be used to increase the accuracy of the monitoring result provided by the diagnostic module 516. In other words trend data are not merely used for storage, but are also used to provide the monitoring result. This is a valuable feature since histogram data may have a number of characteristics that do not lend themselves to providing certain types of diagnostic information. For example, histogram data do not weight current data more heavily than temporally more remote data as long as these are both within the period covered by the histogram. Additionally, as the amount of data which is incorporated into the histogram increases, the effect each new data value on the histogram's distribution decreases.” [0082]
Example of excessive ST shift and acute change in amplitude of ST level (changes in trends)…
“In the present invention excessive ST shift may also be identified using only two occurrences of 6 out of 8 beats (or 5 out of 8 beats) provided that the ST shift was more than a second higher threshold. This higher second threshold could be pre-set or based on some percentage (e.g. 120%) of the standard detection threshold. Such a sliding scale function that inversely weights two heart signal parameters, e.g. magnitude and duration, provides an advantage of more rapid and comprehensive abnormal event detection (while maintaining a similar level of specificity), since an acute change in amplitude of ST level can require less beats in order to trigger an alert, as long as these events are relatively more abnormal. Additionally, the values of the thresholds or weighting factors may be altered based upon the rate of onset of a cardiac feature. For example, if the amount of S-T deviation has grown rapidly in the recent past (as may be assessed using the trend data 560) then the values of the thresholds or weighting factors can be adjusted to address this rapid onset in a desired manner.” [0131]
determining whether the first trend of change and the second trend of change satisfy a preset condition;
Pre-set threshold value…
“The comparison module 564 can be used to compare (e.g., using a statistical comparison) current data to a specific reference value or to any reference data in the reference data module 552. Comparisons can be made with respect to a monitoring criterion such as a pre-set threshold value which may be a statistical criterion. The outcome of this comparison provides a monitoring result. If the comparison is statistical, then this can occur using the statistics module 559, which is located in the summary statistics module 558 which is designed to provide statistical computational functionality to any of the other modules of the system 500. For example, the comparison module 564 can compare the current average ST interval voltage of a current segment of data to values computed upon data measured earlier during a baseline data collection period (e.g., a self norm) in order to determine if an ST elevation event has occurred.” [0059]
Example of exceeds a threshold value (preset condition)…
“The feature analysis module 554 and trend analysis module 560 of the diagnostic module 516 of the cardiotrend system 500 can monitor trends which may occur over time. For example, if over the period of a month there is a slow but steady increase in the number of pacing treatments provided by an implanted device, the trend analysis module 560 can issue an alert signal. The alert may be triggered, for instance, if a feature of the trend, such as the most current value, or the slope or variability of at least a portion of the trend, exceeds a threshold value.” [0070]
“In the present invention excessive ST shift may also be identified using only two occurrences of 6 out of 8 beats (or 5 out of 8 beats) provided that the ST shift was more than a second higher threshold. This higher second threshold could be pre-set or based on some percentage (e.g. 120%) of the standard detection threshold. Such a sliding scale function that inversely weights two heart signal parameters, e.g. magnitude and duration, provides an advantage of more rapid and comprehensive abnormal event detection (while maintaining a similar level of specificity), since an acute change in amplitude of ST level can require less beats in order to trigger an alert, as long as these events are relatively more abnormal. Additionally, the values of the thresholds or weighting factors may be altered based upon the rate of onset of a cardiac feature. For example, if the amount of S-T deviation has grown rapidly in the recent past (as may be assessed using the trend data 560) then the values of the thresholds or weighting factors can be adjusted to address this rapid onset in a desired manner.” [0131]
determining a patient state of the patient, which state is related to the preset condition, when the first trend of change and the second trend of change are determined to satisfy the preset condition;
Example of first and second thresholds (preset conditions) being exceeded (satisfied)…
“The patient state index parameter may also be used to adjust the alarm protocol 906b implemented by the alarm module 522 using a `repeating-alarm PS-rule`. For example, if an alert signal has been triggered and the patient has turned the warning signal off then a subsequent alarm may not be issued, even if a subsequent abnormal event is detected, as long as the patient state index has remained constant. The repeating-alarm PS-Rule may dictate, in this example, that if the patient state index hasn't changed, then another alarm is not provided in response to subsequent detected abnormal events that fall within a specified time period (e.g., 2 minutes) unless these subsequent abnormal events exceed a secondary threshold…” [0142]
wherein the patient state at least comprises a state of a physiological system of the patient; wherein the physiological system of the patient comprises at least one of:
a nervous system, a circulatory system, and a respiratory system, of the patient; and
Example of vagal/cranial nerve stimulators (nervous system)…
“…The improved treatment benefit realized by the methods and systems described here could be applied to therapy directed towards treatment with other types of implanted devices such as neurostimulators, vagal/cranial nerve stimulators, and drug pumps.” [0088]
Example of blood being supplied by arteries (circulatory system)…
“The cardiotrend system 500 as described herein may be primarily oriented towards monitoring of cardiac electrical activity. Other embodiments, however, are no less useful and may be preferred, instead of, or in addition to, sensed electrical activity in the monitoring of different disorders. For example, optical data can be obtained which is related to SAO2 levels, in order to detect ischemic status of different vessels supplying the heart. Abnormalities can be localized by computing difference between measures from different sensors, especially with respect to SA02 levels or flow velocity. While cardiac electrical activity may reflect the functional perfusion of the heart, this measure is an indirect measure of the actual flow rates and oxygen saturation of the blood being supplied by arteries to the heart. When SAO2 levels are measured optically, for both arterial and venous passages, functional oxygen availability and usage (the difference in input and output oxygen levels) can be computed…” [0202]
Breaths (respiratory system)…
“FIG. 12 is a flow chart that shows a detection scheme that may be implemented by the control module 518, sensing module 510 and diagnostic module 516, all of which are shown in FIG. 1B. The scheme shown in FIG. 12 provides a framework for interaction amongst these different modules. The scheme shown in FIG. 12 involves partitioning data into segments that may be time based or based on physiological events (heart beats, breaths etc.) Any type of data may be partitioned. For example, the data to be partitioned may be an electrocardiogram or may consist of mechanical data such as measures of left ventricular pressure, volume or strain. In the following discussion, for purposes of explanation, it will be assumed that that the data has the same periodicity as the cardiac cycle.” [0159]
Example of blood being supplied by arteries (circulatory system)…
“The cardiotrend system 500 as described herein may be primarily oriented towards monitoring of cardiac electrical activity. Other embodiments, however, are no less useful and may be preferred, instead of, or in addition to, sensed electrical activity in the monitoring of different disorders. For example, optical data can be obtained which is related to SAO2 levels, in order to detect ischemic status of different vessels supplying the heart. Abnormalities can be localized by computing difference between measures from different sensors, especially with respect to SA02 levels or flow velocity. While cardiac electrical activity may reflect the functional perfusion of the heart, this measure is an indirect measure of the actual flow rates and oxygen saturation of the blood being supplied by arteries to the heart. When SAO2 levels are measured optically, for both arterial and venous passages, functional oxygen availability and usage (the difference in input and output oxygen levels) can be computed…” [0202]
controlling the display to display indication information which indicates the patient state, and to display the first trend of change and the second trend of change, wherein the indication information at least comprises an overall assessment of the state of the physiological system; wherein:
[No Patentable Weight is given to non-functional descriptive claim language of “display to display indication information which indicates the patient state, and to display the first trend of change and the second trend of change, wherein the indication information at least comprises an overall assessment of the state of the physiological system; wherein:” as there is no functional interaction with the display. Also, “to display” is indented use language given no patentable weight.]
Plotting (display) trend data…
“The present invention cardiotrend 500 provides improved methods of choosing appropriate normative data records and utilizing multivariate and/or classification schemes. In one embodiment, the method entails: computing a clinical state vector as a combination of two or more features related to the heart status of a patient, where each feature is associated with a particular weighting factor; computing a difference score, which may be represented as a vector, between the clinical state vector and a reference state vector; plotting this difference score as trend data, wherein if the magnitude of the vector increases over time then this indicates a worsening of cardiac status; and modifying CTOs according to evaluation of this trend data, for example, modifying CTOs when the trend, or slope of the trend, exceeds a threshold criterion.” [0078]
“…The '705 application of Fischell demonstrates a number of advantageous schemes for histogram (and trend graph) segregation such as generating histograms of various features of the cardiac activity according to different rates of heart-beats. For example, QRS voltage deviations, expressed as a percentage of a baseline self-norm reference, are grouped for 3 heart-rate ranges of 50-80, 81-100, and 101-120.” [0108]
“When a patient transitions from exercising to resting, the PS-rule can dictate that the threshold level for detecting ST-deviation is changed over time according to a function which calculated using self- or population-normative data. Such a function is generated from hypothetical data of FIG. 9C. FIG. 9C has an upper graph which represents the level of ST-deviation for an individual across 3 repeated runs of a stress test. The x-axis comprises categorical items which represent different test conditions: a baseline measure of ST-deviation; two time-points acquired during exercise (E1 and E2) which may be times at 5 and 10 minutes through the activity; and, 4 time-points acquired during the recovery period (R1, R2, R3, R4) which may have occurred at approximately times 1, 2, 3, and 4 minutes after the termination of exercise. The y-axis is ST-deviation, and is in arbitrary units. The stress test may simply consist of the patient ascending several flights of stairs. This data may then be used to generate the mean functions for both exercise and recovery as well as confidence limits, such as the 95% (or 99%) confidence limits, as is shown in the lower graph of FIG. 9C. In this case the graph in the lower panel has been computed upon self-norm data from the upper panel, but it could just as easily have been computed from population normative data.” [0153]
See Display below.
when the physiological system of the patient comprises the nervous system of the patient, at least one of the first trend of change and the second trend of change comprises an indicator which is related to a cranial nerve;
[No Patentable Weight is given to non-functional descriptive claim language of “when the physiological system of the patient comprises the nervous system of the patient, at least one of the first trend of change and the second trend of change comprises an indicator which is related to a cranial nerve” as there is no functional interaction with the display.]
See Display below.
when the physiological system of the patient comprises the circulatory system of the patient, at least one of the first trend of change and the second trend of change comprises an indicator which is related to hemodynamics or perfusion; and
[No Patentable Weight is given to non-functional descriptive claim language of “when the physiological system of the patient comprises the circulatory system of the patient, at least one of the first trend of change and the second trend of change comprises an indicator which is related to hemodynamics or perfusion; and” as there is no functional interaction with the display.]
See Display below.
when the physiological system of the patient comprises the respiratory system of the patient, at least one of the first trend of change and the second trend of change comprises an indicator which is related to oxygenation.
[No Patentable Weight is given to non-functional descriptive claim language of “when the physiological system of the patient comprises the respiratory system of the patient, at least one of the first trend of change and the second trend of change comprises an indicator which is related to oxygenation” as there is no functional interaction with the display.]
See Display below.
Display
John et al. teaches patient state and trends. They also teach plot and graph. They do not teach display of patient stated with first and second trends.
Abrol et al. also in the business of patient state and trends teaches:
Patient monitoring including circulation, oxygenation, and neurology…
“Additional patient monitoring parameters that are displayable via single-patient GUI 200 may be organized into categories, and each patient monitoring category may be collapsed or expanded. When collapsed, no patient monitoring parameters for that category are displayed. When expanded, the patient monitoring parameters for that category are displayed. FIG. 2 shows each category in a collapsed configuration. The patient monitoring categories shown in FIG. 2 include a circulation category 310, an oxygenation category 312, a ventilation category 314, and a neurology category 316, although other categories are possible without departing from the scope of this disclosure. The displayed patient monitoring categories may be customized by the user, such that the user may select which categories will be displayed on that user's device. Each patient monitoring category includes a forward arrow, such as forward arrow 318, which when selected by the user causes the category to expand so that the patient monitoring parameters in that category may be viewed.” [0095]
Settings and supervisory application as examples of controlling the display…
“The patient monitoring parameter trends that are displayed along with the selected patient monitoring trend may be predetermined by the user, e.g., via a settings menu. In other examples, the patient monitoring parameter trends that are displayed along with the selected patient monitoring trend may be automatically determined by the supervisory application 44. For example, the supervisory application may include default sets of related patient monitoring parameters, and when one patient monitoring parameter in a set is selected, all other patient monitoring parameters in that set may also be displayed. In some examples, the supervisory application 44 may learn or otherwise adjust over time which patient monitoring parameter trends should be displayed together.” [0101]
Insights indicating patient state…
“As mentioned above, the supervisory application 44 is configured to apply insights to the received medical device data in order to provide user-selected notifications, predictions, etc., of patient status. The insights may include the rule-based streaming analytics algorithms performed by the stream processing module 106 and/or inference engine 110 described above (e.g., waveform analysis and event detection, thereby triggering alerts, detection of surgical phases, flow analysis, triaging algorithms, continuously predictive scoring, patient deterioration scoring, calculate risk indexes, identify early signs of trouble, sepsis prediction, onset of respiratory distress, end-of-case prediction, and clinical decision support). The insights may include artificial intelligence based models, such as machine learning or deep learning models. In general, any algorithm, model, or set of rules that may be applied to the medical device data in order to monitor patient state may be considered an insight. In some examples, particularly where the insight requires a high amount of processing power, the insight may be stored/executed on a cloud based device such as the MDD processing system 12.” [0077]
Set of trends…
“FIG. 4B shows a second view 420 of single-patient GUI 200 displayed on display device 202. Second view 420 includes a set of trends 422 that may be displayed in response to user selection of the SpO2 tile 404. The set of trends 422 may be displayed as an overlay on top of the first view 400, or the set of trends 422 may be displayed as a separate window, taking the place of or fully obscuring the first view 400. The set of trends 422 includes an SpO2 trend line 424 and a plurality of related trend lines, herein the fraction of inspired air comprised of oxygen (FiO2), end-tidal CO2 (EtCO2), blood pressure (NIBP, including diastolic and systolic measurements), and heart rate (HR). Each trend line is plotted on its own y-axis, such that the values of each patient monitoring parameter may be plotted on different scales and with different units where applicable. Each trend line is plotted on a common x-axis, so that the trend lines are time-aligned. The trend lines may be stacked vertically. In this way, relationships or correspondence of changes among the displayed patient monitoring parameters may be easily identified by a viewer.” [0099]
Fig. 4D teaches example of “No insights” (patient state) and trends (therefore, first and second trends)…
PNG
media_image1.png
530
490
media_image1.png
Greyscale
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of John et al. the ability to display trend information as taught by Abrol et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Abrol et al. who teaches the advantages of multivariate analysis and showing trends on charts with relevant information as this aids in analyzing patients health.
Regarding claim 2
The system for processing vital information according to claim 1, wherein the preset condition comprises at least one of:
the first trend of change is an uptrend, a downtrend or a fluctuating trend;
John et al. teaches:
Excessive (uptrend) shift…
“In the present invention excessive ST shift may also be identified using only two occurrences of 6 out of 8 beats (or 5 out of 8 beats) provided that the ST shift was more than a second higher threshold. This higher second threshold could be pre-set or based on some percentage (e.g. 120%) of the standard detection threshold. Such a sliding scale function that inversely weights two heart signal parameters, e.g. magnitude and duration, provides an advantage of more rapid and comprehensive abnormal event detection (while maintaining a similar level of specificity), since an acute change in amplitude of ST level can require less beats in order to trigger an alert, as long as these events are relatively more abnormal. Additionally, the values of the thresholds or weighting factors may be altered based upon the rate of onset of a cardiac feature. For example, if the amount of S-T deviation has grown rapidly in the recent past (as may be assessed using the trend data 560) then the values of the thresholds or weighting factors can be adjusted to address this rapid onset in a desired manner.” [0131]
the second trend of change is an uptrend, a downtrend or a fluctuating trend; and
Less beats (downtrend)…
“In the present invention excessive ST shift may also be identified using only two occurrences of 6 out of 8 beats (or 5 out of 8 beats) provided that the ST shift was more than a second higher threshold. This higher second threshold could be pre-set or based on some percentage (e.g. 120%) of the standard detection threshold. Such a sliding scale function that inversely weights two heart signal parameters, e.g. magnitude and duration, provides an advantage of more rapid and comprehensive abnormal event detection (while maintaining a similar level of specificity), since an acute change in amplitude of ST level can require less beats in order to trigger an alert, as long as these events are relatively more abnormal. Additionally, the values of the thresholds or weighting factors may be altered based upon the rate of onset of a cardiac feature. For example, if the amount of S-T deviation has grown rapidly in the recent past (as may be assessed using the trend data 560) then the values of the thresholds or weighting factors can be adjusted to address this rapid onset in a desired manner.” [0131]
a preset time correlation that exists between the first trend of change and the second trend of change.
Example of features based on time (preset time) and correlation between two sensors…
“The feature module 652 stores measurements of features of the sensed data, including both measurement of intra-beat and inter-beat features, such as amplitudes, widths, slopes, curvature, ratios, and times related to components of the beats of the cardiac waveform. The feature module 652 can also store statistical values related to features measurements based in the time, time-frequency, and frequency domains. The feature module 652 can also calculate, store, and access values and statistics related to transforms of the data (e.g., results of principal component analysis such as factor scores), indices which reflect a relation between two measures (e.g., ratios of Q-R/R-S amplitudes) or between two sensors (e.g., a correlation between data sensed from different sensors). The storage module's feature detector 652 can also store a history of multivariate measures (e.g., outputs of regression or discriminant formulas).” [0093]
Regarding claim 3
The system for processing vital information according to claim 2, wherein, when the preset condition further comprises:
a preset time correlation that exists between the first trend of change and the second trend of change;
John et al. teaches:
Example of features based on time (preset time) and correlation between two sensors…
“The feature module 652 stores measurements of features of the sensed data, including both measurement of intra-beat and inter-beat features, such as amplitudes, widths, slopes, curvature, ratios, and times related to components of the beats of the cardiac waveform. The feature module 652 can also store statistical values related to features measurements based in the time, time-frequency, and frequency domains. The feature module 652 can also calculate, store, and access values and statistics related to transforms of the data (e.g., results of principal component analysis such as factor scores), indices which reflect a relation between two measures (e.g., ratios of Q-R/R-S amplitudes) or between two sensors (e.g., a correlation between data sensed from different sensors). The storage module's feature detector 652 can also store a history of multivariate measures (e.g., outputs of regression or discriminant formulas).” [0093]
the processor is further configured to perform a following operation:
controlling the display to display the preset time correlation.
[No Patentable Weight is given to non-functional descriptive claim language of “display the preset time correlation” as there is no functional interaction with the display. Also, “to display” is indented use language given no patentable weight.]
The combined references teach display. They do not display with teach time correlation.
Abrol et al. also in the business of display teaches:
Settings and supervisory application as examples of controlling the display…
“The patient monitoring parameter trends that are displayed along with the selected patient monitoring trend may be predetermined by the user, e.g., via a settings menu. In other examples, the patient monitoring parameter trends that are displayed along with the selected patient monitoring trend may be automatically determined by the supervisory application 44. For example, the supervisory application may include default sets of related patient monitoring parameters, and when one patient monitoring parameter in a set is selected, all other patient monitoring parameters in that set may also be displayed. In some examples, the supervisory application 44 may learn or otherwise adjust over time which patient monitoring parameter trends should be displayed together.” [0101]
Predetermined period of time (preset time) and trend lines…
“In one embodiment, a system includes a display and a computing device operably coupled to the display and storing instructions executable to output, to the display, a graphical user interface (GUI) that includes real-time medical device data determined from output of one or more medical devices each monitoring a patient, and where at least some of the real-time medical device data displayed via the GUI is displayed as a plurality of patient monitoring parameter tiles, the GUI further including a predictive tile including a risk score indicative of a relative likelihood that the patient will exhibit a specified condition within a predetermined period of time, and responsive to a user input, display, on the GUI, a set of trend lines each showing values for a respective patient monitoring parameter over a time range, each trend line of the set of trend lines selected based on a contribution of each respective patient monitoring parameter to the risk score.” [0004]
Trend lines are time-aligned (correlated)…
“FIG. 4B shows a second view 420 of single-patient GUI 200 displayed on display device 202. Second view 420 includes a set of trends 422 that may be displayed in response to user selection of the SpO2 tile 404. The set of trends 422 may be displayed as an overlay on top of the first view 400, or the set of trends 422 may be displayed as a separate window, taking the place of or fully obscuring the first view 400. The set of trends 422 includes an SpO2 trend line 424 and a plurality of related trend lines, herein the fraction of inspired air comprised of oxygen (FiO2), end-tidal CO2 (EtCO2), blood pressure (NIBP, including diastolic and systolic measurements), and heart rate (HR). Each trend line is plotted on its own y-axis, such that the values of each patient monitoring parameter may be plotted on different scales and with different units where applicable. Each trend line is plotted on a common x-axis, so that the trend lines are time-aligned. The trend lines may be stacked vertically. In this way, relationships or correspondence of changes among the displayed patient monitoring parameters may be easily identified by a viewer.” [0099]
“…The trends GUI may include trends in selected patient monitoring parameters over time. FIG. 6 shows an example trends GUI, where each trend is visualized as a trend line over the same time duration (e.g., 10 minutes, 30 minutes, or the entire case). The trends GUI may be displayed in response to user selection of a trends button of a single-patient GUI context menu, such as trends button 502 of menu 500. In other examples, the trends GUI may be displayed in response to user selection of a trends icon displayed on the single-patient GUI, such as trends icon 474 of FIG. 4D. The time range of the patient monitoring parameter values displayed in the trends GUI may be adjusted when requested, as indicated at 2618. For example, the trends GUI may include a plurality of buttons each corresponding to a different time range, and user selection of one of the buttons may cause the time range of the displayed trends to change. Further, as also indicated at 2618, the trends GUI may show a quantified change in patient monitoring/medical device data over a specified time period when requested…” [0191]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to control display and display trend information with time correlation as taught by Abrol et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Abrol et al. who teaches the advantages of multivariate analysis and showing trends on charts with relevant information as this aids in analyzing patients health.
Regarding claim 4
The system for processing vital information according to claim 1, wherein the memory is further configured to store one or more preset rules, and corresponding relationships between said preset rules and preset patient states, wherein at least one of said preset rules comprises the preset condition;
John et al. teaches:
Patient state index defined (preset patient states) and patient state rules with threshold (preset rules)…
“Once the patient state index parameter is defined, cardiotrend operations are modified according to patient state rules (PS-rules) 906 and then the method returns to step 900. PS-rules determine how the patient state index parameter alters the operation of the cardiotrend system. For example, a PS-rule may dictate the adjustment of the detection algorithm 906a implemented by the diagnostic module 516 using a `threshold PS-rule`. The threshold PS-rulecan dictate that if patient state index is defined as exercising ("E") then the detection algorithm may be changed, so that criterion level required to detect abnormal ST deviation may be increased compared to that used when the patient is resting ("R"). By adjusting the level of ST deviation according to patient state (e.g., activity level), the threshold levels for detection of normal and abnormal cardiac activity may be sensibly adjusted (i.e., made physiologically appropriate and patient specific).” [0141]
Storage memory for threshold and monitoring criteria (rules)…
“… For example, the module 552 can store scores produced by the multivariate analysis module 562 so that a record of these scores is available for later analysis and downloading. Further, any information related to power consumption, error codes and errors which were reported by the control module 518 can be stored in the reference value module 552 as well as in the storage memory of the control module 518. In addition to sensed features and raw data, the monitoring results of diagnostic module 516 can also be stored. The reference data module 552, can also contain threshold and monitoring criteria. The reference data module can also hold summary statistics and measurements based upon raw features of the reference data and transformations such as spectral and histogram data, or trend data.
the operation of determining a patient state of the patient, which state is related to the preset condition, comprises:
determining whether the analysis results satisfy said one or more preset rules, and taking those satisfied preset rules as target rules; and
Program alarm (preset rule) related to drug/patient specific change (target rules)…
“The feature analysis module 554 and trend analysis module 560 of the diagnostic module 516 of the cardiotrend system 500 can monitor trends which may occur over time. For example, if over the period of a month there is a slow but steady increase in the number of pacing treatments provided by an implanted device, the trend analysis module 560 can issue an alert signal. The alert may be triggered, for instance, if a feature of the trend, such as the most current value, or the slope or variability of at least a portion of the trend, exceeds a threshold value. Additionally, just as different cardiac disorders can produce different changes in a subset of features within an individual, or the same disorder can produce different changes in different individuals, the results which occur in response to the type of drug provided to a patient will depend upon the particular drug and amount. Treatment benefit can only be assessed by understanding what type of therapeutic change would occur for a given type of abnormal pattern and according to the type of drug which is given. The physician can therefore program the diagnostic module protocol so that the trend graphs or alarms which are provided are related to drug/patient specific change (e.g., an improvement from an abnormal to normal state). Unlike the prior art, it is a feature of the present invention to provide the diagnostic module with a medication evaluation module 574B to allow programming of drug response protocols which are oriented towards monitoring the normalization rather than, or in addition to, monitoring for unwanted medical events. In one embodiment, alerts can be triggered when a plateau or worsening, rather than an improvement, is detected in the sensed data.” [0070]
determining the patient state of the patient according to said target rules and said corresponding relationships.
Improvement from abnormal to normal state (determining patient state) according to alarms/alerts (target rules)…
“… Treatment benefit can only be assessed by understanding what type of therapeutic change would occur for a given type of abnormal pattern and according to the type of drug which is given. The physician can therefore program the diagnostic module protocol so that the trend graphs or alarms which are provided are related to drug/patient specific change (e.g., an improvement from an abnormal to normal state). Unlike the prior art, it is a feature of the present invention to provide the diagnostic module with a medication evaluation module 574B to allow programming of drug response protocols which are oriented towards monitoring the normalization rather than, or in addition to, monitoring for unwanted medical events. In one embodiment, alerts can be triggered when a plateau or worsening, rather than an improvement, is detected in the sensed data.” [0070]
Regarding claim 5
The system for processing vital information according to claim 4, wherein the processor is further configured to perform the following operation:
controlling the display to display at least a portion of said target rules.
No Patentable Weight is given to non-functional descriptive claim language of “to display at least a portion of said target rules” as there is no functional interaction with the display. Also, “to display” is indented use language given no patentable weight.]
John et al. teaches:
Fig .13C gives example of rules…
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The combined references teach display of rules. They do not teach controlling the display.
Abrol et al. also in the business of display teaches:
Settings and supervisory application as examples of controlling the display…
“The patient monitoring parameter trends that are displayed along with the selected patient monitoring trend may be predetermined by the user, e.g., via a settings menu. In other examples, the patient monitoring parameter trends that are displayed along with the selected patient monitoring trend may be automatically determined by the supervisory application 44. For example, the supervisory application may include default sets of related patient monitoring parameters, and when one patient monitoring parameter in a set is selected, all other patient monitoring parameters in that set may also be displayed. In some examples, the supervisory application 44 may learn or otherwise adjust over time which patient monitoring parameter trends should be displayed together.” [0101]
Fig. 17, ref. 1704 teaches displaying rules…
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“The first view 1700 includes a first section of insights, referred to as the “quick picks” section, where insights that have been developed by other users may be browsed. For example, a first insight tile 1704 is displayed in the quick picks section. The first insight tile 1704 may include an indication of the insight rules (e.g., trigger an insight if total flow is greater than 6 pounds a minute for 10 minutes) for a first insight. The first insight tile 1704 may also include an indication of how many users have applied the first insight. The first view 1700 may include four insight tiles displayed as part of the quick picks section, but other numbers of insight tiles are possible. Further, additional insight tiles created by other users may be viewed by selecting the “view all” button within the quick picks section.” [0148]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to control display and display target rules as taught by Abrol et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Abrol et al. who teaches the advantages of displaying rules and the combined references benefit as they also teach displaying rules.
Regarding claim 6
The system for processing vital information according to claim 1, wherein, the operation of determining a patient state of the patient, which state is related to the preset condition, comprises:
inputting the multiple analysis results into a first machine learning model for determining patient state, so as to obtain the patient state of the patient which is outputted by the first machine learning model.
John et al. teaches:
Multivariate analysis….
“The diagnostic module 516 and its related methods provide monitoring results that can cause the cardiotrend system 500 to provide a number of CTO's. The monitoring results can be based upon the joint evaluation of 2 or more measures computed by modules embodied within the diagnostic module 516. In other words, rather than examining two or more measures independently, and comparing each of these to some selected criteria measures may be combined, for example, using the multivariate analysis module 562. Abnormal cardiac activity can be computed from the monitored signals that are being monitored. These can be related to temperature, electrical activity, a reflected optical signal, pressure or other measure and two or more measures, used to detect an anomalous cardiac event, can be obtained which are within or across these sensed signals…” [0082]
The combined references teach multivariate analysis and patient state. They do not teach machine learning.
Abrol et al. also in the business of multivariate analysis and patient state teaches:
Patient status (state) and machine learning…
“As mentioned above, the supervisory application 44 is configured to apply insights to the received medical device data in order to provide user-selected notifications, predictions, etc., of patient status. The insights may include the rule-based streaming analytics algorithms performed by the stream processing module 106 and/or inference engine 110 described above (e.g., waveform analysis and event detection, thereby triggering alerts, detection of surgical phases, flow analysis, triaging algorithms, continuously predictive scoring, patient deterioration scoring, calculate risk indexes, identify early signs of trouble, sepsis prediction, onset of respiratory distress, end-of-case prediction, and clinical decision support). The insights may include artificial intelligence based models, such as machine learning or deep learning models. In general, any algorithm, model, or set of rules that may be applied to the medical device data in order to monitor patient state may be considered an insight. In some examples, particularly where the insight requires a high amount of processing power, the insight may be stored/executed on a cloud based device such as the MDD processing system 12.” [0077]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to use machine learning as taught by Abrol et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Abrol et al. who teaches the advantages of machine learning with multivariate analysis and this facilitates such complex data analysis.
Regarding claim 7
The system for processing vital information according to claim 1, wherein the patient state of the patient further comprises at least one of:
an overall state of the patient, a state of an organ of the patient, a state of a physiological part of the patient, a state of a tissue of the patient.
John et al. teaches:
Example of physiological state and heart (organ) state…
“A "patient state value" pertains to the physiological, emotional, mental, periodic (e.g., circadian), or environmental state of a patient. "Patient state values" include, without limitation, a patient's mental state (e.g. angry, confused, etc.), physical state (e.g. walking, supine, sleeping etc.), time of day, patient input, evaluation of sensed data related to body temperature, blood pressure, or other available measures. "Patient state values" may also include heart related features such as heart rate, the presence of arrhythmias, as well as acoustic or chemical measures related to cardiac function, which may be obtained.” [0036]
Regarding claim 8
The system for processing vital information according to claim 1, wherein the processor is further configured to perform following operations:
determining a physiological reason which results in the patient state of the patient according to the multiple analysis results; and
John et al. teaches:
Multivariate analysis and worsening cardiac status (patient state)…
“The present invention cardiotrend 500 provides improved methods of choosing appropriate normative data records and utilizing multivariate and/or classification schemes. In one embodiment, the method entails: computing a clinical state vector as a combination of two or more features related to the heart status of a patient, where each feature is associated with a particular weighting factor; computing a difference score, which may be represented as a vector, between the clinical state vector and a reference state vector; plotting this difference score as trend data, wherein if the magnitude of the vector increases over time then this indicates a worsening of cardiac status; and modifying CTOs according to evaluation of this trend data, for example, modifying CTOs when the trend, or slope of the trend, exceeds a threshold criterion…” [0078]
controlling the display to display information which is related to the physiological reason.
[No Patentable Weight is given to non-functional descriptive claim language of “to display information which is related to the physiological reason” as there is no functional interaction with the display. Also, “to display” is indented use language given no patentable weight.]
The combined references teach display. They do not teach controlling the display and reason.
Abrol et al. also in the business of display teaches:
Settings and supervisory application as examples of controlling the display…
“The patient monitoring parameter trends that are displayed along with the selected patient monitoring trend may be predetermined by the user, e.g., via a settings menu. In other examples, the patient monitoring parameter trends that are displayed along with the selected patient monitoring trend may be automatically determined by the supervisory application 44. For example, the supervisory application may include default sets of related patient monitoring parameters, and when one patient monitoring parameter in a set is selected, all other patient monitoring parameters in that set may also be displayed. In some examples, the supervisory application 44 may learn or otherwise adjust over time which patient monitoring parameter trends should be displayed together.” [0101]
Information banner and risk score (reason)…
“FIG. 23B shows a second example 2320 of the second insight view of the single-patient GUI 200 displayed on display 202. In the second example, a second set of trends 2322 is shown. The second set of trends includes fewer trend lines than the first set of trends, as only two patient monitoring parameters (SpO2 and EtCO2) were determined to contribute positively to the hypoxia score. The second example 2320 also includes labeled time points across the bottom of the second set of trends 2322, indicating that the values for SpO2 and EtCO2 shown in the second set of trends 2322 are plotted over the prior 60 minutes of time. As can be appreciated by the second set of trends 2322, both SpO2 and EtCO2 started decreasing around 7:45-8:00, and continued decreasing at steady rates until about 8:15. Thus, the selected 60 minutes of time over which the patient monitoring parameters are plotted allows the contributing features of the selecting patient monitoring parameters (e.g., the decrease in the values of the SpO2 and EtCO2) to be visualized at the same time. The second example 2320 also includes a second information banner 2324, showing the time, the risk score, and the duration of the current risk score.” [0174]
Fig. 23B, ref. 2324…
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It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to control display and display a reason as taught by Abrol et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Abrol et al. who teaches the advantages of displaying rules with reason and the combined references benefit as they also teach rules.
Regarding claim 9
The system for processing vital information according to claim 8, wherein the memory is further configured to store one or more preset rules, and corresponding relationships between said preset rules and preset physiological reasons;
John et al. teaches:
Threshold and monitoring criteria (preset rules) contained (stored) in reference data module…
“… For example, the module 552 can store scores produced by the multivariate analysis module 562 so that a record of these scores is available for later analysis and downloading. Further, any information related to power consumption, error codes and errors which were reported by the control module 518 can be stored in the reference value module 552 as well as in the storage memory of the control module 518. In addition to sensed features and raw data, the monitoring results of diagnostic module 516 can also be stored. The reference data module 552, can also contain threshold and monitoring criteria. The reference data module can also hold summary statistics and measurements based upon raw features of the reference data and transformations such as spectral and histogram data, or trend data.” [0081]
Example of ST depression (physiological reason) and specified number of times (preset rules)…
“Table 3 shown in FIG. 13C illustrates how a consideration of both cardiac features and heart rate can be used to guide the strategy implemented by the diagnostic score rules. The table shows that ST-elevation may be evaluated as a function of heart rate, such that when ST-elevation occurs at a lower heart-rate, very few cardiac segments must be scored as abnormal prior to an alarm being sent. In contrast, when ST-elevation occurs at a higher heart-rate, the abnormal segment criterion may be increased slightly. In this example, ST-elevation is defined with respect to a single lead that has a "can to tip" polarity. In the case of detection of ST-depression, the diagnostic rule may require that abnormal segment scores occur a specified number of times during a normal heart-rate. When this occurs at a higher heart-rate, activity from an EMG or other sensor is also be used to ensure that the patient state is not exercising. Since this type of change may be normally induced by exercising, it should not lead to the detection of a medical event when the patient state data indicate that this is true. The use of patient state for adjusting the detection of medically relevant events improves performance since adjusting detection based upon heart rate level, patient state, and the type of features which have been observed, should increase sensitivity and specificity of the device.” [0180]
determining a physiological reason which results in the patient state of the patient according to the multiple analysis results, comprises:
determining whether the analysis results satisfy said one or more preset rules, and taking those satisfied preset rules as target rules; and
Example of unlocked (satisfy preset target rules) containment sections…
“…The output module 504 of the CTE 500B of FIG. 1A could include a set of pill dispensing containers, one or more of which can be automatically unlocked due to the type of alert warning which occurs. For example, one containment section can be unlocked for an alert which indicates a mild cardiac event, while two can be unlocked if the alarm signal indicates an event is more severe. Different drugs can also be provided for different alert indications as may have been prescribed previously by a physician. In other words, the dispensing of drug by the system in response to alert signals occur as dictated by a doctors prescription, wherein the physician's programming of the device is equivalent to a prescription. It is also envisioned that the EXD 502A or DTD 502B would contain at least one pill containment section.” [0069]
determining the physiological reason of the patient according to said target rules and said corresponding relationships.
Mild and sever cardiac event (physiological reason) according to target…
“…The output module 504 of the CTE 500B of FIG. 1A could include a set of pill dispensing containers, one or more of which can be automatically unlocked due to the type of alert warning which occurs. For example, one containment section can be unlocked for an alert which indicates a mild cardiac event, while two can be unlocked if the alarm signal indicates an event is more severe. Different drugs can also be provided for different alert indications as may have been prescribed previously by a physician. In other words, the dispensing of drug by the system in response to alert signals occur as dictated by a doctors prescription, wherein the physician's programming of the device is equivalent to a prescription. It is also envisioned that the EXD 502A or DTD 502B would contain at least one pill containment section.” [0069]
Regarding claim 10
The system for processing vital information according to claim 9, wherein the processor is further configured to perform a following operation:
controlling the display to associatively display at least a portion of said target rules, and the physiological reason.
[No Patentable Weight is given to non-functional descriptive claim language of “to associatively display at least a portion of said target rules, and the physiological reason” as there is no functional interaction with the display. Also, “to display” is indented use language given no patentable weight.]
The combined references teach display. They do not teach controlling the display and target rules and reason.
Abrol et al. also in the business of display teaches:
Settings and supervisory application as examples of controlling the display…
“The patient monitoring parameter trends that are displayed along with the selected patient monitoring trend may be predetermined by the user, e.g., via a settings menu. In other examples, the patient monitoring parameter trends that are displayed along with the selected patient monitoring trend may be automatically determined by the supervisory application 44. For example, the supervisory application may include default sets of related patient monitoring parameters, and when one patient monitoring parameter in a set is selected, all other patient monitoring parameters in that set may also be displayed. In some examples, the supervisory application 44 may learn or otherwise adjust over time which patient monitoring parameter trends should be displayed together.” [0101]
Information banner and risk score (reason and above hypoxia severity score (target rule)…
“FIG. 23B shows a second example 2320 of the second insight view of the single-patient GUI 200 displayed on display 202. In the second example, a second set of trends 2322 is shown. The second set of trends includes fewer trend lines than the first set of trends, as only two patient monitoring parameters (SpO2 and EtCO2) were determined to contribute positively to the hypoxia score. The second example 2320 also includes labeled time points across the bottom of the second set of trends 2322, indicating that the values for SpO2 and EtCO2 shown in the second set of trends 2322 are plotted over the prior 60 minutes of time. As can be appreciated by the second set of trends 2322, both SpO2 and EtCO2 started decreasing around 7:45-8:00, and continued decreasing at steady rates until about 8:15. Thus, the selected 60 minutes of time over which the patient monitoring parameters are plotted allows the contributing features of the selecting patient monitoring parameters (e.g., the decrease in the values of the SpO2 and EtCO2) to be visualized at the same time. The second example 2320 also includes a second information banner 2324, showing the time, the risk score, and the duration of the current risk score.” [0174]
Fig. 23B, ref. 2324…
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It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to control display and display rules and a reason as taught by Abrol et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Abrol et al. who teaches the advantages of displaying rules with reason and the combined references benefit as they also teach rules.
Regarding claim 11
The system for processing vital information according to claim 1, wherein the analysis result comprises at least one of:
a parameter value, an event, a result from secondary processing of parameter value, a result from secondary processing of event.
John et al. teaches:
Heart signal parameters…
“The feature extraction module 514 typically includes time, time-frequency, and frequency measurement algorithms which may be used to measure specific features of the sensed signals from the human patient. When these sensed signals are related to cardiac activity, the features which are measured may include heart signal parameters from an electrogram (e.g., using implanted electrodes), the electrocardiogram (ECG, obtained, for example, using subdermally or skin surface mounted electrodes), or a sonogram. Specifically, the feature extraction module 514 can provide temporal analysis, frequency analysis (e.g., FFT, filtering, or adaptive filtering), and time-frequency analysis (e.g., spectrogram, wavelet analysis) methods for analyzing cardiac activity and providing detection of cardiac abnormalities. Temporal analysis can include automatic peak-picking and template matching algorithms which detect and measure components of the monitored activity such as the p-wave or ST segment.” [0050]
Events…
“The present invention preferably includes a diagnostic module with methods for detection, control, and classification of cardiac events. These methods can use two or more measures of cardiac activity which are computed by modules embodied within the diagnostic module in the detection of cardiac events. The diagnostic module preferably employs methods that determine if, which, how, and when to provide one or more types of alarm warning, intervention (e.g. therapy), and/or storage of data.” [0009]
Regarding claim 12
A method for processing vital information, comprising
obtaining multiple types of patient data of a patient;
John et al. teaches:
Medical devices for sensing and recording (obtaining) biological activity (patient data)…
“The invention described herein relates to medical devices for monitoring biological activity. More particularly it describes a system incorporating a device capable of sensing and recording long segments of biological activity, providing early detection and/or prediction of medical conditions and possibly alarming the patient in response thereto. When the invention is preferably applied to the monitoring of cardiac status it is referred to as the "CardioTrend system".” [0007]
Example of patient state values (patient data)…
“A "patient state value" pertains to the physiological, emotional, mental, periodic (e.g., circadian), or environmental state of a patient. "Patient state values" include, without limitation, a patient's mental state (e.g. angry, confused, etc.), physical state (e.g. walking, supine, sleeping etc.), time of day, patient input, evaluation of sensed data related to body temperature, blood pressure, or other available measures. "Patient state values" may also include heart related features such as heart rate, the presence of arrhythmias, as well as acoustic or chemical measures related to cardiac function, which may be obtained.” [0036]
analyzing the patient data to obtain multiple analysis results, wherein the multiple analysis results at least comprise a first trend of change for first information and a second trend of change for second information, which respective information is extracted from the patient data;
Analyzing patient data and acquiring values based on awake or asleep…
“The above described operations of acquiring and analyzing patient data, and performing an ameliorative response (e.g. issuing and alarm or providing therapy) are executed in accordance with patient state information. For example, an alarm threshold value (detection operation) may be set according to whether a patient is awake or asleep.” [0010]
One or more (multiple) cardiotrend (trends) operations and adjust the detection of abnormal cardiac activity…
“FIG. 9A shows a method by which a patient's state is used to adjust one or more of the cardiotrend operations by modifying the operation of the detection algorithm, or the operation of the modules of an alarm module, in order to adjust the detection of abnormal cardiac activity or the generation of alarms, respectively;” [0028]
Two or more measures (first and second trends) and joint evaluation (multiple analysis or multivariate results), related to (respective information) such as temperature, electrical activity, etc. …
“The diagnostic module 516 and its related methods provide monitoring results that can cause the cardiotrend system 500 to provide a number of CTO's. The monitoring results can be based upon the joint evaluation of 2 or more measures computed by modules embodied within the diagnostic module 516. In other words, rather than examining two or more measures independently, and comparing each of these to some selected criteria measures may be combined, for example, using the multivariate analysis module 562. Abnormal cardiac activity can be computed from the monitored signals that are being monitored. These can be related to temperature, electrical activity, a reflected optical signal, pressure or other measure and two or more measures, used to detect an anomalous cardiac event, can be obtained which are within or across these sensed signals. It is an important and novel feature of the cardiotrend system 500 that feature, trend and histogram data can all be used to increase the accuracy of the monitoring result provided by the diagnostic module 516. In other words trend data are not merely used for storage, but are also used to provide the monitoring result. This is a valuable feature since histogram data may have a number of characteristics that do not lend themselves to providing certain types of diagnostic information. For example, histogram data do not weight current data more heavily than temporally more remote data as long as these are both within the period covered by the histogram. Additionally, as the amount of data which is incorporated into the histogram increases, the effect each new data value on the histogram's distribution decreases.” [0082]
Example of excessive ST shift and acute change in amplitude of ST level (changes in trends)…
“In the present invention excessive ST shift may also be identified using only two occurrences of 6 out of 8 beats (or 5 out of 8 beats) provided that the ST shift was more than a second higher threshold. This higher second threshold could be pre-set or based on some percentage (e.g. 120%) of the standard detection threshold. Such a sliding scale function that inversely weights two heart signal parameters, e.g. magnitude and duration, provides an advantage of more rapid and comprehensive abnormal event detection (while maintaining a similar level of specificity), since an acute change in amplitude of ST level can require less beats in order to trigger an alert, as long as these events are relatively more abnormal. Additionally, the values of the thresholds or weighting factors may be altered based upon the rate of onset of a cardiac feature. For example, if the amount of S-T deviation has grown rapidly in the recent past (as may be assessed using the trend data 560) then the values of the thresholds or weighting factors can be adjusted to address this rapid onset in a desired manner.” [0131]
wherein the first information and the second information are related to a same patient state of the patient;
Example of cardiac disease state vectors (same patient state)…
“…Accordingly, instead of using a single disease state vector to detect or quantify cardiac abnormalities, two or more vectors and reference data vector sets can be used so that evaluation of sensed data are adjusted according to the patient state. This can provide great advantage because decreases in the sensitivity/specificity performance of the vector does not result due to an attempt to use a single formula to assess different abnormal event signatures which may be inherent within different patient states. Rather each disease state vectors can serve to assess abnormality within, rather than across, each of several different patient states. For example, cardiac disease state vectors that are specifically designed to detect abnormalities when the patient is awake, asleep, or engaged in exercise can be invoked based upon a detection of a patient state by the state module 568.” [0078]
determining whether the first trend of change and the second trend of change satisfy a preset condition; and
Pre-set threshold value…
“The comparison module 564 can be used to compare (e.g., using a statistical comparison) current data to a specific reference value or to any reference data in the reference data module 552. Comparisons can be made with respect to a monitoring criterion such as a pre-set threshold value which may be a statistical criterion. The outcome of this comparison provides a monitoring result. If the comparison is statistical, then this can occur using the statistics module 559, which is located in the summary statistics module 558 which is designed to provide statistical computational functionality to any of the other modules of the system 500. For example, the comparison module 564 can compare the current average ST interval voltage of a current segment of data to values computed upon data measured earlier during a baseline data collection period (e.g., a self norm) in order to determine if an ST elevation event has occurred.” [0059]
Example of exceeds a threshold value (preset condition)…
“The feature analysis module 554 and trend analysis module 560 of the diagnostic module 516 of the cardiotrend system 500 can monitor trends which may occur over time. For example, if over the period of a month there is a slow but steady increase in the number of pacing treatments provided by an implanted device, the trend analysis module 560 can issue an alert signal. The alert may be triggered, for instance, if a feature of the trend, such as the most current value, or the slope or variability of at least a portion of the trend, exceeds a threshold value.” [0070]
“In the present invention excessive ST shift may also be identified using only two occurrences of 6 out of 8 beats (or 5 out of 8 beats) provided that the ST shift was more than a second higher threshold. This higher second threshold could be pre-set or based on some percentage (e.g. 120%) of the standard detection threshold. Such a sliding scale function that inversely weights two heart signal parameters, e.g. magnitude and duration, provides an advantage of more rapid and comprehensive abnormal event detection (while maintaining a similar level of specificity), since an acute change in amplitude of ST level can require less beats in order to trigger an alert, as long as these events are relatively more abnormal. Additionally, the values of the thresholds or weighting factors may be altered based upon the rate of onset of a cardiac feature. For example, if the amount of S-T deviation has grown rapidly in the recent past (as may be assessed using the trend data 560) then the values of the thresholds or weighting factors can be adjusted to address this rapid onset in a desired manner.” [0131]
displaying the first trend of change and the second trend of change, when the first trend of change and the second trend of change are determined to satisfy the preset condition.
[No Patentable Weight is given to non-functional descriptive claim language of “displaying the first trend of change and the second trend of change, when the first trend of change and the second trend of change are determined to satisfy the preset condition” as there is no functional interaction with the display.]
See Display below.
Display
The combined references teach display. They do not teach first and second trend and condition.
Abrol et al. also in the business of display teaches:
Fig. 23B and two trends that are down, with hypoxia severity score (preset condtion)…
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Information banner and risk score (reason and above hypoxia severity score (target rule)…
“FIG. 23B shows a second example 2320 of the second insight view of the single-patient GUI 200 displayed on display 202. In the second example, a second set of trends 2322 is shown. The second set of trends includes fewer trend lines than the first set of trends, as only two patient monitoring parameters (SpO2 and EtCO2) were determined to contribute positively to the hypoxia score. The second example 2320 also includes labeled time points across the bottom of the second set of trends 2322, indicating that the values for SpO2 and EtCO2 shown in the second set of trends 2322 are plotted over the prior 60 minutes of time. As can be appreciated by the second set of trends 2322, both SpO2 and EtCO2 started decreasing around 7:45-8:00, and continued decreasing at steady rates until about 8:15. Thus, the selected 60 minutes of time over which the patient monitoring parameters are plotted allows the contributing features of the selecting patient monitoring parameters (e.g., the decrease in the values of the SpO2 and EtCO2) to be visualized at the same time. The second example 2320 also includes a second information banner 2324, showing the time, the risk score, and the duration of the current risk score.” [0174]
Fig. 23B, ref. 2324…
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It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of John et al. the ability to display trend information as taught by Abrol et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Abrol et al. who teaches the advantages of multivariate analysis and showing trends on charts with relevant information as this aids in analyzing patients health.
Regarding claim 13
The method for processing vital information according to claim 12, wherein the preset condition comprises at least one of:
the first trend of change is an uptrend, a downtrend or a fluctuating trend;
John et al. teaches:
Excessive (uptrend) shift…
“In the present invention excessive ST shift may also be identified using only two occurrences of 6 out of 8 beats (or 5 out of 8 beats) provided that the ST shift was more than a second higher threshold. This higher second threshold could be pre-set or based on some percentage (e.g. 120%) of the standard detection threshold. Such a sliding scale function that inversely weights two heart signal parameters, e.g. magnitude and duration, provides an advantage of more rapid and comprehensive abnormal event detection (while maintaining a similar level of specificity), since an acute change in amplitude of ST level can require less beats in order to trigger an alert, as long as these events are relatively more abnormal. Additionally, the values of the thresholds or weighting factors may be altered based upon the rate of onset of a cardiac feature. For example, if the amount of S-T deviation has grown rapidly in the recent past (as may be assessed using the trend data 560) then the values of the thresholds or weighting factors can be adjusted to address this rapid onset in a desired manner.” [0131]
the second trend of change is an uptrend, a downtrend or a fluctuating trend; and
Less beats (downtrend)…
“In the present invention excessive ST shift may also be identified using only two occurrences of 6 out of 8 beats (or 5 out of 8 beats) provided that the ST shift was more than a second higher threshold. This higher second threshold could be pre-set or based on some percentage (e.g. 120%) of the standard detection threshold. Such a sliding scale function that inversely weights two heart signal parameters, e.g. magnitude and duration, provides an advantage of more rapid and comprehensive abnormal event detection (while maintaining a similar level of specificity), since an acute change in amplitude of ST level can require less beats in order to trigger an alert, as long as these events are relatively more abnormal. Additionally, the values of the thresholds or weighting factors may be altered based upon the rate of onset of a cardiac feature. For example, if the amount of S-T deviation has grown rapidly in the recent past (as may be assessed using the trend data 560) then the values of the thresholds or weighting factors can be adjusted to address this rapid onset in a desired manner.” [0131]
a preset time correlation that exists between the first trend of change and the second trend of change.
Example of features based on time (preset time) and correlation between two sensors…
“The feature module 652 stores measurements of features of the sensed data, including both measurement of intra-beat and inter-beat features, such as amplitudes, widths, slopes, curvature, ratios, and times related to components of the beats of the cardiac waveform. The feature module 652 can also store statistical values related to features measurements based in the time, time-frequency, and frequency domains. The feature module 652 can also calculate, store, and access values and statistics related to transforms of the data (e.g., results of principal component analysis such as factor scores), indices which reflect a relation between two measures (e.g., ratios of Q-R/R-S amplitudes) or between two sensors (e.g., a correlation between data sensed from different sensors). The storage module's feature detector 652 can also store a history of multivariate measures (e.g., outputs of regression or discriminant formulas).” [0093]
Regarding claim 14
The method for processing vital information according to claim 12, further comprising:
determining a patient state of the patient, which state is related to the preset condition;
John et al. teaches:
Patient state index defined (preset patient states) and patient state rules with threshold (preset condition)…
“Once the patient state index parameter is defined, cardiotrend operations are modified according to patient state rules (PS-rules) 906 and then the method returns to step 900. PS-rules determine how the patient state index parameter alters the operation of the cardiotrend system. For example, a PS-rule may dictate the adjustment of the detection algorithm 906a implemented by the diagnostic module 516 using a `threshold PS-rule`. The threshold PS-rulecan dictate that if patient state index is defined as exercising ("E") then the detection algorithm may be changed, so that criterion level required to detect abnormal ST deviation may be increased compared to that used when the patient is resting ("R"). By adjusting the level of ST deviation according to patient state (e.g., activity level), the threshold levels for detection of normal and abnormal cardiac activity may be sensibly adjusted (i.e., made physiologically appropriate and patient specific).” [0141]
Storage memory for threshold and monitoring criteria (rules)…
“… For example, the module 552 can store scores produced by the multivariate analysis module 562 so that a record of these scores is available for later analysis and downloading. Further, any information related to power consumption, error codes and errors which were reported by the control module 518 can be stored in the reference value module 552 as well as in the storage memory of the control module 518. In addition to sensed features and raw data, the monitoring results of diagnostic module 516 can also be stored. The reference data module 552, can also contain threshold and monitoring criteria. The reference data module can also hold summary statistics and measurements based upon raw features of the reference data and transformations such as spectral and histogram data, or trend data.
wherein determining a patient state of the patient, which state is related to the preset condition, comprises:
comparing the analysis result(s) with one or more preset rules, and determining one or more target rules which are satisfied by the analysis result(s); and
Program alarm (preset rule) related to drug/patient specific change (target rules)…
“The feature analysis module 554 and trend analysis module 560 of the diagnostic module 516 of the cardiotrend system 500 can monitor trends which may occur over time. For example, if over the period of a month there is a slow but steady increase in the number of pacing treatments provided by an implanted device, the trend analysis module 560 can issue an alert signal. The alert may be triggered, for instance, if a feature of the trend, such as the most current value, or the slope or variability of at least a portion of the trend, exceeds a threshold value. Additionally, just as different cardiac disorders can produce different changes in a subset of features within an individual, or the same disorder can produce different changes in different individuals, the results which occur in response to the type of drug provided to a patient will depend upon the particular drug and amount. Treatment benefit can only be assessed by understanding what type of therapeutic change would occur for a given type of abnormal pattern and according to the type of drug which is given. The physician can therefore program the diagnostic module protocol so that the trend graphs or alarms which are provided are related to drug/patient specific change (e.g., an improvement from an abnormal to normal state). Unlike the prior art, it is a feature of the present invention to provide the diagnostic module with a medication evaluation module 574B to allow programming of drug response protocols which are oriented towards monitoring the normalization rather than, or in addition to, monitoring for unwanted medical events. In one embodiment, alerts can be triggered when a plateau or worsening, rather than an improvement, is detected in the sensed data.” [0070]
determining the patient state of the patient, according to said one or more target rules and corresponding relationship(s) between said preset rule(s) and preset patient state(s).
Improvement from abnormal to normal state (determining patient state) according to alarms/alerts (target rules)…
“… Treatment benefit can only be assessed by understanding what type of therapeutic change would occur for a given type of abnormal pattern and according to the type of drug which is given. The physician can therefore program the diagnostic module protocol so that the trend graphs or alarms which are provided are related to drug/patient specific change (e.g., an improvement from an abnormal to normal state). Unlike the prior art, it is a feature of the present invention to provide the diagnostic module with a medication evaluation module 574B to allow programming of drug response protocols which are oriented towards monitoring the normalization rather than, or in addition to, monitoring for unwanted medical events. In one embodiment, alerts can be triggered when a plateau or worsening, rather than an improvement, is detected in the sensed data.” [0070]
Regarding claim 15
The method for processing vital information according to claim 12, wherein the patient state of the patient comprises at least one of:
an overall state of the patient, a state of a physiological system of the patient, a state of an organ of the patient, a state of a physiological part of the patient, a state of a tissue of the patient.
John et al. teaches:
Example of physiological state and heart (organ) state…
“A "patient state value" pertains to the physiological, emotional, mental, periodic (e.g., circadian), or environmental state of a patient. "Patient state values" include, without limitation, a patient's mental state (e.g. angry, confused, etc.), physical state (e.g. walking, supine, sleeping etc.), time of day, patient input, evaluation of sensed data related to body temperature, blood pressure, or other available measures. "Patient state values" may also include heart related features such as heart rate, the presence of arrhythmias, as well as acoustic or chemical measures related to cardiac function, which may be obtained.” [0036]
Regarding claim 16
The method for processing vital information according to claim15, wherein, the physiological system of the patient comprises at least one of:
a nervous system, a circulatory system, a respiratory system, a motor system, an endocrine system, a digestive system, a urinary system and a reproductive system, of the patient;
John et al. teaches:
Example of vagal/cranial nerve stimulators (nervous system)…
“…The improved treatment benefit realized by the methods and systems described here could be applied to therapy directed towards treatment with other types of implanted devices such as neurostimulators, vagal/cranial nerve stimulators, and drug pumps.” [0088]
Example of blood being supplied by arteries (circulatory system)…
“The cardiotrend system 500 as described herein may be primarily oriented towards monitoring of cardiac electrical activity. Other embodiments, however, are no less useful and may be preferred, instead of, or in addition to, sensed electrical activity in the monitoring of different disorders. For example, optical data can be obtained which is related to SAO2 levels, in order to detect ischemic status of different vessels supplying the heart. Abnormalities can be localized by computing difference between measures from different sensors, especially with respect to SA02 levels or flow velocity. While cardiac electrical activity may reflect the functional perfusion of the heart, this measure is an indirect measure of the actual flow rates and oxygen saturation of the blood being supplied by arteries to the heart. When SAO2 levels are measured optically, for both arterial and venous passages, functional oxygen availability and usage (the difference in input and output oxygen levels) can be computed…” [0202]
Breaths (respiratory system)…
“FIG. 12 is a flow chart that shows a detection scheme that may be implemented by the control module 518, sensing module 510 and diagnostic module 516, all of which are shown in FIG. 1B. The scheme shown in FIG. 12 provides a framework for interaction amongst these different modules. The scheme shown in FIG. 12 involves partitioning data into segments that may be time based or based on physiological events (heart beats, breaths etc.) Any type of data may be partitioned. For example, the data to be partitioned may be an electrocardiogram or may consist of mechanical data such as measures of left ventricular pressure, volume or strain. In the following discussion, for purposes of explanation, it will be assumed that that the data has the same periodicity as the cardiac cycle.” [0159]
Example of blood being supplied by arteries (circulatory system)…
“The cardiotrend system 500 as described herein may be primarily oriented towards monitoring of cardiac electrical activity. Other embodiments, however, are no less useful and may be preferred, instead of, or in addition to, sensed electrical activity in the monitoring of different disorders. For example, optical data can be obtained which is related to SAO2 levels, in order to detect ischemic status of different vessels supplying the heart. Abnormalities can be localized by computing difference between measures from different sensors, especially with respect to SA02 levels or flow velocity. While cardiac electrical activity may reflect the functional perfusion of the heart, this measure is an indirect measure of the actual flow rates and oxygen saturation of the blood being supplied by arteries to the heart. When SAO2 levels are measured optically, for both arterial and venous passages, functional oxygen availability and usage (the difference in input and output oxygen levels) can be computed…” [0202]
when the physiological system of the patient comprises the nervous system of the patient, at least one of the first trend of change and the second trend of change comprises an indicator which is related to a cranial nerve;
[No Patentable Weight is given to alternative claim language where selection of only one is required.]
when the physiological system of the patient comprises the circulatory system of the patient, at least one of the first trend of change and the second trend of change comprises an indicator which is related to hemodynamics or perfusion; and
One or more (multiple) cardiotrend (trends) operations and adjust the detection of abnormal cardiac activity…
“FIG. 9A shows a method by which a patient's state is used to adjust one or more of the cardiotrend operations by modifying the operation of the detection algorithm, or the operation of the modules of an alarm module, in order to adjust the detection of abnormal cardiac activity or the generation of alarms, respectively;” [0028]
Two or more measures (first and second trends) and joint evaluation (multiple analysis or multivariate results), related to (respective information) such as temperature, electrical activity, etc. …
“The diagnostic module 516 and its related methods provide monitoring results that can cause the cardiotrend system 500 to provide a number of CTO's. The monitoring results can be based upon the joint evaluation of 2 or more measures computed by modules embodied within the diagnostic module 516. In other words, rather than examining two or more measures independently, and comparing each of these to some selected criteria measures may be combined, for example, using the multivariate analysis module 562. Abnormal cardiac activity can be computed from the monitored signals that are being monitored. These can be related to temperature, electrical activity, a reflected optical signal, pressure or other measure and two or more measures, used to detect an anomalous cardiac event, can be obtained which are within or across these sensed signals. It is an important and novel feature of the cardiotrend system 500 that feature, trend and histogram data can all be used to increase the accuracy of the monitoring result provided by the diagnostic module 516. In other words trend data are not merely used for storage, but are also used to provide the monitoring result. This is a valuable feature since histogram data may have a number of characteristics that do not lend themselves to providing certain types of diagnostic information. For example, histogram data do not weight current data more heavily than temporally more remote data as long as these are both within the period covered by the histogram. Additionally, as the amount of data which is incorporated into the histogram increases, the effect each new data value on the histogram's distribution decreases.” [0082]
Cardiac electrical activity (indicator) reflect (related to) profusion…
“The cardiotrend system 500 as described herein may be primarily oriented towards monitoring of cardiac electrical activity. Other embodiments, however, are no less useful and may be preferred, instead of, or in addition to, sensed electrical activity in the monitoring of different disorders. For example, optical data can be obtained which is related to SAO2 levels, in order to detect ischemic status of different vessels supplying the heart. Abnormalities can be localized by computing difference between measures from different sensors, especially with respect to SA02 levels or flow velocity. While cardiac electrical activity may reflect the functional perfusion of the heart, this measure is an indirect measure of the actual flow rates and oxygen saturation of the blood being supplied by arteries to the heart. When SAO2 levels are measured optically, for both arterial and venous passages, functional oxygen availability and usage (the difference in input and output oxygen levels) can be computed.” [0202]
when the physiological system of the patient comprises the respiratory system of the patient, at least one of the first trend of change and the second trend of change comprises an indicator which is related to oxygenation.
[No Patentable Weight is given to alternative claim language where selection of only one is required.]
Regarding claim 17
A method for processing vital information, comprising
obtaining patient data of a patient;
John et al. teaches:
Medical devices for sensing and recording (obtaining) biological activity (patient data)…
“The invention described herein relates to medical devices for monitoring biological activity. More particularly it describes a system incorporating a device capable of sensing and recording long segments of biological activity, providing early detection and/or prediction of medical conditions and possibly alarming the patient in response thereto. When the invention is preferably applied to the monitoring of cardiac status it is referred to as the "CardioTrend system".” [0007]
Example of patient state values (patient data)…
“A "patient state value" pertains to the physiological, emotional, mental, periodic (e.g., circadian), or environmental state of a patient. "Patient state values" include, without limitation, a patient's mental state (e.g. angry, confused, etc.), physical state (e.g. walking, supine, sleeping etc.), time of day, patient input, evaluation of sensed data related to body temperature, blood pressure, or other available measures. "Patient state values" may also include heart related features such as heart rate, the presence of arrhythmias, as well as acoustic or chemical measures related to cardiac function, which may be obtained.” [0036]
analyzing the patient data to obtain multiple analysis results, wherein the multiple analysis results at least comprise a first trend of change for first information and a second trend of change for second information, which respective information is extracted from the patient data;
Analyzing patient data and acquiring values based on awake or asleep…
“The above described operations of acquiring and analyzing patient data, and performing an ameliorative response (e.g. issuing and alarm or providing therapy) are executed in accordance with patient state information. For example, an alarm threshold value (detection operation) may be set according to whether a patient is awake or asleep.” [0010]
One or more (multiple) cardiotrend (trends) operations and adjust the detection of abnormal cardiac activity…
“FIG. 9A shows a method by which a patient's state is used to adjust one or more of the cardiotrend operations by modifying the operation of the detection algorithm, or the operation of the modules of an alarm module, in order to adjust the detection of abnormal cardiac activity or the generation of alarms, respectively;” [0028]
Two or more measures (first and second trends) and joint evaluation (multiple analysis or multivariate results), related to (respective information) such as temperature, electrical activity, etc. …
“The diagnostic module 516 and its related methods provide monitoring results that can cause the cardiotrend system 500 to provide a number of CTO's. The monitoring results can be based upon the joint evaluation of 2 or more measures computed by modules embodied within the diagnostic module 516. In other words, rather than examining two or more measures independently, and comparing each of these to some selected criteria measures may be combined, for example, using the multivariate analysis module 562. Abnormal cardiac activity can be computed from the monitored signals that are being monitored. These can be related to temperature, electrical activity, a reflected optical signal, pressure or other measure and two or more measures, used to detect an anomalous cardiac event, can be obtained which are within or across these sensed signals. It is an important and novel feature of the cardiotrend system 500 that feature, trend and histogram data can all be used to increase the accuracy of the monitoring result provided by the diagnostic module 516. In other words trend data are not merely used for storage, but are also used to provide the monitoring result. This is a valuable feature since histogram data may have a number of characteristics that do not lend themselves to providing certain types of diagnostic information. For example, histogram data do not weight current data more heavily than temporally more remote data as long as these are both within the period covered by the histogram. Additionally, as the amount of data which is incorporated into the histogram increases, the effect each new data value on the histogram's distribution decreases.” [0082]
Example of excessive ST shift and acute change in amplitude of ST level (changes in trends)…
“In the present invention excessive ST shift may also be identified using only two occurrences of 6 out of 8 beats (or 5 out of 8 beats) provided that the ST shift was more than a second higher threshold. This higher second threshold could be pre-set or based on some percentage (e.g. 120%) of the standard detection threshold. Such a sliding scale function that inversely weights two heart signal parameters, e.g. magnitude and duration, provides an advantage of more rapid and comprehensive abnormal event detection (while maintaining a similar level of specificity), since an acute change in amplitude of ST level can require less beats in order to trigger an alert, as long as these events are relatively more abnormal. Additionally, the values of the thresholds or weighting factors may be altered based upon the rate of onset of a cardiac feature. For example, if the amount of S-T deviation has grown rapidly in the recent past (as may be assessed using the trend data 560) then the values of the thresholds or weighting factors can be adjusted to address this rapid onset in a desired manner.” [0131]
determining whether the first trend of change and the second trend of change satisfy a preset condition;
Pre-set threshold value…
“The comparison module 564 can be used to compare (e.g., using a statistical comparison) current data to a specific reference value or to any reference data in the reference data module 552. Comparisons can be made with respect to a monitoring criterion such as a pre-set threshold value which may be a statistical criterion. The outcome of this comparison provides a monitoring result. If the comparison is statistical, then this can occur using the statistics module 559, which is located in the summary statistics module 558 which is designed to provide statistical computational functionality to any of the other modules of the system 500. For example, the comparison module 564 can compare the current average ST interval voltage of a current segment of data to values computed upon data measured earlier during a baseline data collection period (e.g., a self norm) in order to determine if an ST elevation event has occurred.” [0059]
Example of exceeds a threshold value (preset condition)…
“The feature analysis module 554 and trend analysis module 560 of the diagnostic module 516 of the cardiotrend system 500 can monitor trends which may occur over time. For example, if over the period of a month there is a slow but steady increase in the number of pacing treatments provided by an implanted device, the trend analysis module 560 can issue an alert signal. The alert may be triggered, for instance, if a feature of the trend, such as the most current value, or the slope or variability of at least a portion of the trend, exceeds a threshold value.” [0070]
“In the present invention excessive ST shift may also be identified using only two occurrences of 6 out of 8 beats (or 5 out of 8 beats) provided that the ST shift was more than a second higher threshold. This higher second threshold could be pre-set or based on some percentage (e.g. 120%) of the standard detection threshold. Such a sliding scale function that inversely weights two heart signal parameters, e.g. magnitude and duration, provides an advantage of more rapid and comprehensive abnormal event detection (while maintaining a similar level of specificity), since an acute change in amplitude of ST level can require less beats in order to trigger an alert, as long as these events are relatively more abnormal. Additionally, the values of the thresholds or weighting factors may be altered based upon the rate of onset of a cardiac feature. For example, if the amount of S-T deviation has grown rapidly in the recent past (as may be assessed using the trend data 560) then the values of the thresholds or weighting factors can be adjusted to address this rapid onset in a desired manner.” [0131]
determining a patient state of the patient, which state is related to the preset condition, when the first trend of change and the second trend of change are determined to satisfy the preset condition; and
Example of first and second thresholds (preset conditions) being exceeded (satisfied)…
“The patient state index parameter may also be used to adjust the alarm protocol 906b implemented by the alarm module 522 using a `repeating-alarm PS-rule`. For example, if an alert signal has been triggered and the patient has turned the warning signal off then a subsequent alarm may not be issued, even if a subsequent abnormal event is detected, as long as the patient state index has remained constant. The repeating-alarm PS-Rule may dictate, in this example, that if the patient state index hasn't changed, then another alarm is not provided in response to subsequent detected abnormal events that fall within a specified time period (e.g., 2 minutes) unless these subsequent abnormal events exceed a secondary threshold…” [0142]
displaying indication information which indicates the patient state, and displaying the first trend of change and the second trend of change.
[No Patentable Weight is given to non-functional descriptive claim language of “displaying indication information which indicates the patient state, and displaying the first trend of change and the second trend of change” as there is no functional interaction with the display.]
See Display below.
Display
The combined references teach display. They do not teach first and second trend and condition.
Abrol et al. also in the business of display teaches:
Insights indicating patient state…
“As mentioned above, the supervisory application 44 is configured to apply insights to the received medical device data in order to provide user-selected notifications, predictions, etc., of patient status. The insights may include the rule-based streaming analytics algorithms performed by the stream processing module 106 and/or inference engine 110 described above (e.g., waveform analysis and event detection, thereby triggering alerts, detection of surgical phases, flow analysis, triaging algorithms, continuously predictive scoring, patient deterioration scoring, calculate risk indexes, identify early signs of trouble, sepsis prediction, onset of respiratory distress, end-of-case prediction, and clinical decision support). The insights may include artificial intelligence based models, such as machine learning or deep learning models. In general, any algorithm, model, or set of rules that may be applied to the medical device data in order to monitor patient state may be considered an insight. In some examples, particularly where the insight requires a high amount of processing power, the insight may be stored/executed on a cloud based device such as the MDD processing system 12.” [0077]
Set of trends…
“FIG. 4B shows a second view 420 of single-patient GUI 200 displayed on display device 202. Second view 420 includes a set of trends 422 that may be displayed in response to user selection of the SpO2 tile 404. The set of trends 422 may be displayed as an overlay on top of the first view 400, or the set of trends 422 may be displayed as a separate window, taking the place of or fully obscuring the first view 400. The set of trends 422 includes an SpO2 trend line 424 and a plurality of related trend lines, herein the fraction of inspired air comprised of oxygen (FiO2), end-tidal CO2 (EtCO2), blood pressure (NIBP, including diastolic and systolic measurements), and heart rate (HR). Each trend line is plotted on its own y-axis, such that the values of each patient monitoring parameter may be plotted on different scales and with different units where applicable. Each trend line is plotted on a common x-axis, so that the trend lines are time-aligned. The trend lines may be stacked vertically. In this way, relationships or correspondence of changes among the displayed patient monitoring parameters may be easily identified by a viewer.” [0099]
Fig. 4D teaches example of “No insights” (patient state) and trends…
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It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of John et al. the ability to display trend information as taught by Abrol et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Abrol et al. who teaches the advantages of multivariate analysis and showing trends on charts with relevant information as this aids in analyzing patients health.
Regarding claim 18
The method for processing vital information according to claim 17, wherein the preset condition comprises at least one of:
the first trend of change is an uptrend, a downtrend or a fluctuating trend;
John et al. teaches:
Excessive (uptrend) shift…
“In the present invention excessive ST shift may also be identified using only two occurrences of 6 out of 8 beats (or 5 out of 8 beats) provided that the ST shift was more than a second higher threshold. This higher second threshold could be pre-set or based on some percentage (e.g. 120%) of the standard detection threshold. Such a sliding scale function that inversely weights two heart signal parameters, e.g. magnitude and duration, provides an advantage of more rapid and comprehensive abnormal event detection (while maintaining a similar level of specificity), since an acute change in amplitude of ST level can require less beats in order to trigger an alert, as long as these events are relatively more abnormal. Additionally, the values of the thresholds or weighting factors may be altered based upon the rate of onset of a cardiac feature. For example, if the amount of S-T deviation has grown rapidly in the recent past (as may be assessed using the trend data 560) then the values of the thresholds or weighting factors can be adjusted to address this rapid onset in a desired manner.” [0131]
the second trend of change is an uptrend, a downtrend or a fluctuating trend; and
Less beats (downtrend)…
“In the present invention excessive ST shift may also be identified using only two occurrences of 6 out of 8 beats (or 5 out of 8 beats) provided that the ST shift was more than a second higher threshold. This higher second threshold could be pre-set or based on some percentage (e.g. 120%) of the standard detection threshold. Such a sliding scale function that inversely weights two heart signal parameters, e.g. magnitude and duration, provides an advantage of more rapid and comprehensive abnormal event detection (while maintaining a similar level of specificity), since an acute change in amplitude of ST level can require less beats in order to trigger an alert, as long as these events are relatively more abnormal. Additionally, the values of the thresholds or weighting factors may be altered based upon the rate of onset of a cardiac feature. For example, if the amount of S-T deviation has grown rapidly in the recent past (as may be assessed using the trend data 560) then the values of the thresholds or weighting factors can be adjusted to address this rapid onset in a desired manner.” [0131]
a preset time correlation that exists between the first trend of change and the second trend of change.
Example of features based on time (preset time) and correlation between two sensors…
“The feature module 652 stores measurements of features of the sensed data, including both measurement of intra-beat and inter-beat features, such as amplitudes, widths, slopes, curvature, ratios, and times related to components of the beats of the cardiac waveform. The feature module 652 can also store statistical values related to features measurements based in the time, time-frequency, and frequency domains. The feature module 652 can also calculate, store, and access values and statistics related to transforms of the data (e.g., results of principal component analysis such as factor scores), indices which reflect a relation between two measures (e.g., ratios of Q-R/R-S amplitudes) or between two sensors (e.g., a correlation between data sensed from different sensors). The storage module's feature detector 652 can also store a history of multivariate measures (e.g., outputs of regression or discriminant formulas).” [0093]
Regarding claim 19
The method for processing vital information according to claim 17, wherein the patient state of the patient comprises at least one of:
an overall state of the patient, a state of a physiological system of the patient, a state of an organ of the patient, a state of a physiological part of the patient, a state of a tissue of the patient.
John et al. teaches:
Example of physiological state and heart (organ) state…
“A "patient state value" pertains to the physiological, emotional, mental, periodic (e.g., circadian), or environmental state of a patient. "Patient state values" include, without limitation, a patient's mental state (e.g. angry, confused, etc.), physical state (e.g. walking, supine, sleeping etc.), time of day, patient input, evaluation of sensed data related to body temperature, blood pressure, or other available measures. "Patient state values" may also include heart related features such as heart rate, the presence of arrhythmias, as well as acoustic or chemical measures related to cardiac function, which may be obtained.” [0036]
Regarding claim 20
The method for processing vital information according to claim 19, wherein the physiological system of the patient comprises at least one of:
a nervous system, a circulatory system, and a respiratory system, of the patient;
John et al. teaches:
Example of vagal/cranial nerve stimulators (nervous system)…
“…The improved treatment benefit realized by the methods and systems described here could be applied to therapy directed towards treatment with other types of implanted devices such as neurostimulators, vagal/cranial nerve stimulators, and drug pumps.” [0088]
Example of blood being supplied by arteries (circulatory system)…
“The cardiotrend system 500 as described herein may be primarily oriented towards monitoring of cardiac electrical activity. Other embodiments, however, are no less useful and may be preferred, instead of, or in addition to, sensed electrical activity in the monitoring of different disorders. For example, optical data can be obtained which is related to SAO2 levels, in order to detect ischemic status of different vessels supplying the heart. Abnormalities can be localized by computing difference between measures from different sensors, especially with respect to SA02 levels or flow velocity. While cardiac electrical activity may reflect the functional perfusion of the heart, this measure is an indirect measure of the actual flow rates and oxygen saturation of the blood being supplied by arteries to the heart. When SAO2 levels are measured optically, for both arterial and venous passages, functional oxygen availability and usage (the difference in input and output oxygen levels) can be computed…” [0202]
Breaths (respiratory system)…
“FIG. 12 is a flow chart that shows a detection scheme that may be implemented by the control module 518, sensing module 510 and diagnostic module 516, all of which are shown in FIG. 1B. The scheme shown in FIG. 12 provides a framework for interaction amongst these different modules. The scheme shown in FIG. 12 involves partitioning data into segments that may be time based or based on physiological events (heart beats, breaths etc.) Any type of data may be partitioned. For example, the data to be partitioned may be an electrocardiogram or may consist of mechanical data such as measures of left ventricular pressure, volume or strain. In the following discussion, for purposes of explanation, it will be assumed that that the data has the same periodicity as the cardiac cycle.” [0159]
Example of blood being supplied by arteries (circulatory system)…
“The cardiotrend system 500 as described herein may be primarily oriented towards monitoring of cardiac electrical activity. Other embodiments, however, are no less useful and may be preferred, instead of, or in addition to, sensed electrical activity in the monitoring of different disorders. For example, optical data can be obtained which is related to SAO2 levels, in order to detect ischemic status of different vessels supplying the heart. Abnormalities can be localized by computing difference between measures from different sensors, especially with respect to SA02 levels or flow velocity. While cardiac electrical activity may reflect the functional perfusion of the heart, this measure is an indirect measure of the actual flow rates and oxygen saturation of the blood being supplied by arteries to the heart. When SAO2 levels are measured optically, for both arterial and venous passages, functional oxygen availability and usage (the difference in input and output oxygen levels) can be computed…” [0202]
when the physiological system of the patient comprises the nervous system of the patient, at least one of the first trend of change and the second trend of change comprises an indicator which is related to a cranial nerve;
See Trends below.
when the physiological system of the patient comprises the circulatory system of the patient, at least one of the first trend of change and the second trend of change comprises an indicator which is related to hemodynamics or perfusion; and
One or more (multiple) cardiotrend (trends) operations and adjust the detection of abnormal cardiac activity…
“FIG. 9A shows a method by which a patient's state is used to adjust one or more of the cardiotrend operations by modifying the operation of the detection algorithm, or the operation of the modules of an alarm module, in order to adjust the detection of abnormal cardiac activity or the generation of alarms, respectively;” [0028]
Two or more measures (first and second trends) and joint evaluation (multiple analysis or multivariate results), related to (respective information) such as temperature, electrical activity, etc. …
“The diagnostic module 516 and its related methods provide monitoring results that can cause the cardiotrend system 500 to provide a number of CTO's. The monitoring results can be based upon the joint evaluation of 2 or more measures computed by modules embodied within the diagnostic module 516. In other words, rather than examining two or more measures independently, and comparing each of these to some selected criteria measures may be combined, for example, using the multivariate analysis module 562. Abnormal cardiac activity can be computed from the monitored signals that are being monitored. These can be related to temperature, electrical activity, a reflected optical signal, pressure or other measure and two or more measures, used to detect an anomalous cardiac event, can be obtained which are within or across these sensed signals. It is an important and novel feature of the cardiotrend system 500 that feature, trend and histogram data can all be used to increase the accuracy of the monitoring result provided by the diagnostic module 516. In other words trend data are not merely used for storage, but are also used to provide the monitoring result. This is a valuable feature since histogram data may have a number of characteristics that do not lend themselves to providing certain types of diagnostic information. For example, histogram data do not weight current data more heavily than temporally more remote data as long as these are both within the period covered by the histogram. Additionally, as the amount of data which is incorporated into the histogram increases, the effect each new data value on the histogram's distribution decreases.” [0082]
Cardiac electrical activity (indicator) reflect (related to) profusion…
“The cardiotrend system 500 as described herein may be primarily oriented towards monitoring of cardiac electrical activity. Other embodiments, however, are no less useful and may be preferred, instead of, or in addition to, sensed electrical activity in the monitoring of different disorders. For example, optical data can be obtained which is related to SAO2 levels, in order to detect ischemic status of different vessels supplying the heart. Abnormalities can be localized by computing difference between measures from different sensors, especially with respect to SA02 levels or flow velocity. While cardiac electrical activity may reflect the functional perfusion of the heart, this measure is an indirect measure of the actual flow rates and oxygen saturation of the blood being supplied by arteries to the heart. When SAO2 levels are measured optically, for both arterial and venous passages, functional oxygen availability and usage (the difference in input and output oxygen levels) can be computed.” [0202]
See Trends below.
when the physiological system of the patient comprises the respiratory system of the patient, at least one of the first trend of change and the second trend of change comprises an indicator which is related to oxygenation.
See Trends below.
Trends
John et al. teaches patient state and trends. They also teach plot and graph. They do not teach details of trends of nervous or respiratory system.
Abrol et al. also in the business of trends teaches:
Patient monitoring including circulation, oxygenation, and neurology…
“Additional patient monitoring parameters that are displayable via single-patient GUI 200 may be organized into categories, and each patient monitoring category may be collapsed or expanded. When collapsed, no patient monitoring parameters for that category are displayed. When expanded, the patient monitoring parameters for that category are displayed. FIG. 2 shows each category in a collapsed configuration. The patient monitoring categories shown in FIG. 2 include a circulation category 310, an oxygenation category 312, a ventilation category 314, and a neurology category 316, although other categories are possible without departing from the scope of this disclosure. The displayed patient monitoring categories may be customized by the user, such that the user may select which categories will be displayed on that user's device. Each patient monitoring category includes a forward arrow, such as forward arrow 318, which when selected by the user causes the category to expand so that the patient monitoring parameters in that category may be viewed.” [0095]
Settings and supervisory application as examples of controlling the display…
“The patient monitoring parameter trends that are displayed along with the selected patient monitoring trend may be predetermined by the user, e.g., via a settings menu. In other examples, the patient monitoring parameter trends that are displayed along with the selected patient monitoring trend may be automatically determined by the supervisory application 44. For example, the supervisory application may include default sets of related patient monitoring parameters, and when one patient monitoring parameter in a set is selected, all other patient monitoring parameters in that set may also be displayed. In some examples, the supervisory application 44 may learn or otherwise adjust over time which patient monitoring parameter trends should be displayed together.” [0101]
Insights indicating patient state…
“As mentioned above, the supervisory application 44 is configured to apply insights to the received medical device data in order to provide user-selected notifications, predictions, etc., of patient status. The insights may include the rule-based streaming analytics algorithms performed by the stream processing module 106 and/or inference engine 110 described above (e.g., waveform analysis and event detection, thereby triggering alerts, detection of surgical phases, flow analysis, triaging algorithms, continuously predictive scoring, patient deterioration scoring, calculate risk indexes, identify early signs of trouble, sepsis prediction, onset of respiratory distress, end-of-case prediction, and clinical decision support). The insights may include artificial intelligence based models, such as machine learning or deep learning models. In general, any algorithm, model, or set of rules that may be applied to the medical device data in order to monitor patient state may be considered an insight. In some examples, particularly where the insight requires a high amount of processing power, the insight may be stored/executed on a cloud based device such as the MDD processing system 12.” [0077]
Set of trends…
“FIG. 4B shows a second view 420 of single-patient GUI 200 displayed on display device 202. Second view 420 includes a set of trends 422 that may be displayed in response to user selection of the SpO2 tile 404. The set of trends 422 may be displayed as an overlay on top of the first view 400, or the set of trends 422 may be displayed as a separate window, taking the place of or fully obscuring the first view 400. The set of trends 422 includes an SpO2 trend line 424 and a plurality of related trend lines, herein the fraction of inspired air comprised of oxygen (FiO2), end-tidal CO2 (EtCO2), blood pressure (NIBP, including diastolic and systolic measurements), and heart rate (HR). Each trend line is plotted on its own y-axis, such that the values of each patient monitoring parameter may be plotted on different scales and with different units where applicable. Each trend line is plotted on a common x-axis, so that the trend lines are time-aligned. The trend lines may be stacked vertically. In this way, relationships or correspondence of changes among the displayed patient monitoring parameters may be easily identified by a viewer.” [0099]
Fig. 4D teaches example of “No insights” (patient state) and trends…
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It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to have trends of various health parameters such as nervous system and respiratory system as taught by Abrol et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Abrol et al. who teaches the advantages of multivariate analysis and showing trends on charts with relevant information as this aids in analyzing patients health.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The following prior art teaches at least trends:
US-11666288-B2; US-11309079-B2; US-7801591-B1; US-9211096-B2; US-20210064224-A1; US-20170235910-A1; US-20120239420-A1; US-20150327776-A1; US-20150250428-A1; US-20090217194-A1; US-20100057646-A1; US-20200342966-A1; US-20210369394-A1; US-20080091471-A1; US-20120310050-A1; US-20200066415-A1; US-20250318787-A1;WO-2013171620-A1
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/KENNETH BARTLEY/Primary Examiner, Art Unit 3684