DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/18/26 has been entered.
Status of Claims
Claims 1, 5, 7-11, 13-14, 18, 20-24, and 26-27 are rejected. Claims 2-4, 6, 12, 15-17, 19, 25, and 28-30 are canceled.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 2/23/26, 2/27/26, 3/2/26, 3/30/26, 4/9/26, and 4/16/26 are being considered by the examiner.
Response to Arguments
Claim Objections
The previous claim objection of claims 11 and 24 has been withdrawn in view of the amendment.
Claim Rejections - 35 USC § 112
The previous 112(d) rejection of claims 6 and 19 has been withdrawn in view of the amendment.
Claim Rejections - 35 USC § 101
Applicant’s arguments, see Remarks, filed 2/18/26, with respect to claims 1, 5-11, 13-14, 18-24, and 26-27 have been fully considered and are persuasive. Specifically, the 101 rejection of claims 1, 5-11, 13-14, 18-24 has been withdrawn in view of the amendment of previous dependent claims 2-4 into independent claims 1, 14, and 27, where the structure was not well-understood, routine, or conventional.
Claim Rejections - 35 USC § 103
Applicant's arguments filed 2/18/26 have been fully considered but they are not persuasive.
Applicant asserts that the amendment to claim 1 including "detecting one or more alarms generated by one or more of the plurality of bedside monitoring devices;" and "processing the one or more detected alarms to determine an authenticity of the one or more detected alarms, wherein processing the one or more detected alarms utilizes one or more of: volume information; volatility information; bias information; and persistence information; and stationary information" is not taught by the combination of cited references. However, the Examiner disagrees. Sampath teaches detecting one or more alarms generated by one or more of the plurality of bedside monitoring devices (Sampath, ¶77-the physiological monitoring system 100 includes a plurality of bedside devices, e.g., patient monitoring devices 110; ¶76-a physiological monitoring system 100 (e.g., alarm notification system); ¶81-the sensor processing module 104 in certain embodiments generates alarms in response to physiological parameters exceeding certain safe thresholds; ¶86-receive real-time viewing of physiological patient parameters and waveforms on demand or in the event of an alarm or alert).
While Sampath does not explicitly teach processing the one or more detected alarms to determine an authenticity of the one or more detected alarms, wherein processing the one or more detected alarms utilizes one or more of: volume information; volatility information; bias information; and persistence information; and stationary information, Treacy teaches processing the one or more detected alarms to determine an authenticity of the one or more detected alarms (¶6-the processor categorizes each of the plurality of alarms in the alarm data and aggregates the alarm data; ¶24-the processing unit 12 is used to facilitate clinician proposal, evaluation, and selection of updates to alarm value limits of automated alarms; ¶45-provide an indication that the alarm event was a false positive or did not carry a strong correlation to current patient condition; ¶18-if too many clinically irrelevant alarms are initiated too frequently, this can distract clinicians and bother patients. Therefore, interventions are often needed to tailor the practices and alarms to the conditions in which the medical devices are used), wherein processing the one or more detected alarms utilizes one or more of: volume information (¶32-a reviewing clinician may use the historical dashboard GUI 100 and exemplarily the historical number of alarm events at 116 to evaluate changes implemented to reduce alarm burden; ¶48-the system may report a total number of alarm events which would fall into each category rather than a percentage falling above, below, and within the alarm threshold limits; Figs. 4-8, the Examiner notes that only one factor for processing is required by the claim language); volatility information; bias information; and persistence information; and stationary information.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Sampath to include processing the one or more detected alarms to determine an authenticity of the one or more detected alarms, wherein processing the one or more detected alarms utilizes one or more of: volume information; volatility information; bias information; and persistence information; and stationary information of Treacy in order to assist clinicians and/or clinical managers in making and implementing decisions related to alarm burden (Treacy, ¶19), because if too many clinically irrelevant alarms are initiated too frequently, this can distract clinicians and bother patients (Treacy, ¶18). Interventions are often needed to tailor the practices and alarms to the conditions in which the medical devices are used (Treacy, ¶18).
Specification
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required:
Regarding claims 1, 14, and 27, the limitations of “wherein the subsequent data signals concern a first physiological characteristic of a patient” and “wherein the other data signals concern one or more additional physiological characteristics of the patient having a correlation to the first physiological characteristic” appear to lack proper antecedent basis in the specification. The specification discloses the following:
The machine-defined monitoring criteria may be compartmentalized (e.g., gender, race, age, location, device type, device class, seasonality, time of day, etc.). The device may be monitored to receive subsequent data signals indicative of the device (¶6);
The analysis of massive datasets aims to extract meaningful insights, patterns, correlations, or trends from the vast amount of available data (¶122); and
So in the event that the outlier for patient 232 is a reduced heart rate, information process 10 may examine 308 other data signals (e.g., respiratory rate and/or blood gas saturation) from the device (e.g., one or more of first vendor devices 202 and/or one or more of second vendor devices 206 ) to determine if an issue exists. Therefore, if the other data signals (e.g., respiratory rate and/or blood gas saturation) from the device are normal, information process 10 may determine that an issue does not exist (e.g., patient 322 is not having a medical issue) (¶132).
However, the specification does not explicitly disclose “a first physiological characteristic” and “one or more additional physiological characteristics of the patient having a correlation to the first physiological characteristic” as claimed.
Claim Objections
Claims 1, 14, and 27 are objected to because of the following informalities: the limitation of “including one or more of: enabling the remote adjustment of the one or more monitoring criteria on a single bedside monitoring device; enabling the remote adjustment of the one or more monitoring criteria on a plurality of bedside monitoring devices; enabling the remote adjustment of the one or more monitoring criteria on a plurality of bedside monitoring devices based upon device vendor and/or device type” in claim 1, lines 11-19 for example, is missing an “and” before the last option and should recite --including one or more of: enabling the remote adjustment of the one or more monitoring criteria on a single bedside monitoring device; enabling the remote adjustment of the one or more monitoring criteria on a plurality of bedside monitoring devices; and enabling the remote adjustment of the one or more monitoring criteria on a plurality of bedside monitoring devices based upon device vendor and/or device type--. Appropriate correction is required.
Claims 1, 14, and 27 are objected to because of the following informalities: the limitation of “wherein processing the one or more detected alarms utilizes one or more of: volume information; volatility information; bias information; and persistence information; and stationary information” includes multiple “and” in the list. Applicant is encouraged to remove the “and” after “bias information” for clarity purposes. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 5, 7-11, 13-14, 18, 20-24, and 26-27 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In claim 1, the limitation of “a computing device” in lines 10-11 seems unclear. It remains unclear whether “a computing device” in line 1 is the same or different than “a computing device” in lines 10-11. For the purpose of examination, the Examiner is interpreting “a computing device” in lines 10-11 to be the same as “a computing device” in line 1, because the specification does not describe multiple computing devices being used at once.
In claims 1, 14, and 27, the limitation of “the device” (see claim 1, line 21 for example) seems unclear. Claims 1, 14, and 27 recite a computing device (see claim 1, line 1 for example), a first vendor device (see claim 1, line 5 for example), a second vendor device (see claim 1, line 5 for example), and a single bedside monitoring device (see claim 1, lines 13-14 for example). Therefore, it remains unclear which of these “the device” is referring back to. For the purpose of examination, the Examiner is interpreting “the device” to be referring to a device of one or more of the plurality of bedside monitoring devices. Dependent claims 5, 7-11, 13, 18, 20-24, and 26 are rejected for the same deficiency in independent claims 1, 14, and 27.
In claims 1, 14, and 27, the limitation of “the plurality of bedside monitoring devices” (see claim 1, lines 20, 23-24, 26, and 35-36 for example) seems unclear. It remains unclear whether this is referring back to “a plurality of bedside monitoring devices” (see claim 1, line 3 for example), “a plurality of bedside monitoring devices” (see claim 1, lines 15-16 for example), or “a plurality of bedside monitoring devices (see claim 1, lines 17-18 for example). For the purpose of examination, the Examiner is interpreting “the plurality of bedside monitoring devices” (see claim 1, lines 20, 23-24, 26, and 35-36 for example) to be referring back to “a plurality of bedside monitoring devices” (see claim 1, line 3 for example). If “a plurality of bedside monitoring devices” (see claim 1, lines 15-16 for example) and “a plurality of bedside monitoring devices (see claim 1, lines 17-18 for example) are the same as “a plurality of bedside monitoring devices” (see claim 1, line 3 for example), Applicant is encouraged to change the occurrences of “a plurality of bedside monitoring devices” after line 3 to –the plurality of bedside monitoring devices--. Dependent claims 5, 7-11, 13, 18, 20-24, and 26 are rejected for the same deficiency in independent claims 1, 14, and 27.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 5, 7-8, 14, 18, 20-21, and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Sampath (US 20180174680 filed on 2/12/18) in view of Treacy (US 20180096110 filed on 10/31/17 as cited in the IDS) and D’Angelo (US 11257587 filed on 9/10/19).
Regarding claims 1, 14, and 27, Sampath teaches a computer-implemented method, a computer program product, and a computing system executed on a computing device, including a processor (¶77-one or more sensor processing modules 104) and memory (¶90-storage device 114) configured to perform operations comprising: interfacing with a plurality of bedside monitoring devices to receive data signals (network interface module in Fig. 1; ¶79-the physiological monitoring system 100 includes a plurality of bedside devices, e.g., patient monitoring devices 110. The patient monitoring devices 110 of various embodiments include sensors 102, one or more sensor processing modules 104, and a communications module, e.g., network interface module 106. In the depicted embodiment, two patient monitoring devices 110 are shown), wherein the data signals have monitoring criteria (¶379-the analysis of the physiological parameter data is performed in substantially real-time by, for example, the bedside patient monitoring devices in order to detect alarm conditions as they occur), enabling remote adjustment of one or more of the monitoring criteria using a computing device separate from the plurality of bedside monitoring devices (¶170-the user interface module 734 may also allow clinicians to admit and discharge patients, remotely modify device alarm limits, and the like), including one or more of: enabling the remote adjustment of the one or more monitoring criteria on a single bedside monitoring device (¶170-the user interface module 734 may include, for example, software for displaying physiological information, patient information, and medical event information for a plurality of patient monitors 740 . The user interface module 734 may also allow clinicians to admit and discharge patients, remotely modify device alarm limits); enabling the remote adjustment of the one or more monitoring criteria on a plurality of bedside monitoring devices (¶381-adjusting alarm criteria to be specifically adapted for that particular hospital or patient care facility; ¶170-the user interface module 734 may also allow clinicians to admit and discharge patients, remotely modify device alarm limits; ¶373-number of bedside patient monitors; ¶379); enabling the remote adjustment of the one or more monitoring criteria on a plurality of bedside monitoring devices based upon device vendor and/or device type (¶237-variety of factors can be included in the detection logic…the type of medical equipment involved (e.g., patient monitoring equipment or some other type of medical device); ¶381-specially adapted alarm criteria are advantageous because alarm criteria that work well at one hospital, or for one type of patient, are not necessarily guaranteed to work well at another hospital, or for another type of patient…can be due to differences in the type of monitoring equipment that is used; ¶381-adjusting alarm criteria to be specifically adapted for that particular hospital or patient care facility; ¶170-the user interface module 734 may also allow clinicians to admit and discharge patients, remotely modify device alarm limits; ¶373-number of bedside patient monitors; ¶379); monitoring one or more of the plurality of bedside monitoring devices to receive subsequent data signals indicative of the device (¶167-if an additional (or different) parameter is subsequently measured by the patient monitor 740 , the RRDB module 740 may dynamically update the parameter descriptors that are sent to the MMS 720); comparing the subsequent data signals to defined signal norms to identify outliers (¶369-each bar in the graph 3000 may be representative of, for example, a combination of false positive alarm events and correctly detected alarm events (e.g., detection of an alarm event when the patient was actually in need of medical assistance); ¶397- the reporting module can determine how many of the true alarm conditions that were correctly identified at the actual time of monitoring using the first alarm criteria; ¶367; ¶381); investigating the outliers to determine if an issue exists with one or more of the plurality of bedside monitoring devices (¶397-information regarding the number of true alarm conditions that would go undetected using a given simulated alarm criteria can be provided to hospital administrators to aid in determining whether a proposed change to the alarm criteria should be adopted; ¶367; ¶381), wherein the subsequent data signals concern a first physiological characteristic of a patient (¶9-receive physiological information from at least one patient; ¶10-multiple medical patient monitoring devices that are capable of collecting physiological information from multiple patients), and investigating the outliers includes examining other data signals from one or more of the plurality of bedside monitoring devices (¶94-certain transactions of the physiological monitoring system 100 are journaled such that a timeline of recorded events may later be re-constructed to evaluate the quality of healthcare given. These transactions include state changes relating to physiological information from the patient monitoring devices 100 , to the patient monitoring devices 110 , to the hospital WLAN 126 connection, to user operation, and to system behavior; ¶101-by connecting to one or more other devices 260 in some embodiments, the network interface module 270 is able to associate patient context information and other context information with one or more other devices 260; ¶107-a communications protocol based on XML technologies allows bedside devices to interface), wherein the other data signals concern one or more additional physiological characteristics of the patient having a correlation to the first physiological characteristic (¶182-the process 800 B enables physiological information from the RRDB and medical events to be correlated in time; ¶184-this correlation may include reconstructing a timeline of medical events, with values of physiological parameters (optionally including waveforms) provided in the correct time sequence on the timeline; ¶396-a particular medical intervention for a patient can be correlated with a detected alarm event for that patient if the medical intervention and the alarm event occurred within a certain amount of time of one another); adjusting outlier definition criteria to eliminate the outliers if an issue does not exist, including: defining one or more adjusted alarm thresholds (¶367-devices and methods for providing data that would aid in the selection of an alarm threshold that would reduce false positives while still maintaining false negatives at or below a satisfactory level would be very useful. Such devices and methods could be used for establishing alarm criteria for a wide variety of physiological parameters; ¶371-a hospital or other patient care facility could make relatively small changes to the alarm criteria used in monitoring a physiological parameter while disparately impacting the number of detected alarms and false positives. In some cases, the number of detected alarms could be significantly reduced, for example, by reducing the number of false positives, a relatively small adjustment to alarm criteria (e.g., an alarm threshold value), the techniques described herein may still be useful in some circumstances for incrementally reducing the number of false positives in a safe manner; ¶381); and detecting one or more alarms generated by one or more of the plurality of bedside monitoring devices (¶77-the physiological monitoring system 100 includes a plurality of bedside devices, e.g., patient monitoring devices 110; ¶76-a physiological monitoring system 100 (e.g., alarm notification system); ¶81-the sensor processing module 104 in certain embodiments generates alarms in response to physiological parameters exceeding certain safe thresholds; ¶86-receive real-time viewing of physiological patient parameters and waveforms on demand or in the event of an alarm or alert).
However, Sampath does not teach wherein the plurality of bedside monitoring devices include at least a first vendor device and a second vendor device, and wherein interfacing with the plurality of beside monitoring devices further includes normalizing at least a data signal from the first vendor device and a data signal from the second vendor device so that the first vendor device and the second vender device can work together; rendering a graphical representation providing a comparison between a level at which a current alarm threshold is being exceeded and an expected level at which the one or more adjusted alarm thresholds would be expected to be exceeded; and processing the one or more detected alarms to determine an authenticity of the one or more detected alarms, wherein processing the one or more detected alarms utilizes one or more of: volume information; volatility information; bias information; and persistence information; and stationary information.
Treacy relates to the field of medical devices. More specifically, the present disclosure relates to systems and methods for management of medical device alarms (¶1). Treacy further teaches the invention using the following steps:
rendering a graphical representation providing a comparison between a level at which a current alarm threshold is being exceeded and an expected level at which the one or more adjusted alarm thresholds would be expected to be exceeded (¶48-two alarms are associated with the heart rate physiological data. Specifically, a low heart rate limit 186 and a high heart rate limit 188. The current heart rate alarm limit values associated with these alarms are visually presented on the histogram 182 in relation to the histogram relating the associated physiological data of the patient for the particular parameter alarm (heart rate) being considered. The user is able to use this GUI 180 to test prospective new limits for the parameter alarms by entering new threshold values in the user interface 190. By selecting the update histogram button the user interface 190, the histogram 182 can update to graphically depict the prospective low heart rate alarm value 192 which is exemplarily 40 beats per minute and to reflect the proposed high heart rate alarm value 194 which exemplarily is proposed to be adjusted to 155 beats per minute. By making such an adjustment, the prospective analysis portion 184 is updated in a prospective reporting section 196 to indicate the proposed new alarm limit values; see Fig. 8); and processing the one or more detected alarms to determine an authenticity of the one or more detected alarms (¶6-the processor categorizes each of the plurality of alarms in the alarm data and aggregates the alarm data; ¶24-the processing unit 12 is used to facilitate clinician proposal, evaluation, and selection of updates to alarm value limits of automated alarms; ¶45-provide an indication that the alarm event was a false positive or did not carry a strong correlation to current patient condition; ¶18-if too many clinically irrelevant alarms are initiated too frequently, this can distract clinicians and bother patients. Therefore, interventions are often needed to tailor the practices and alarms to the conditions in which the medical devices are used), wherein processing the one or more detected alarms utilizes one or more of: volume information (¶32-a reviewing clinician may use the historical dashboard GUI 100 and exemplarily the historical number of alarm events at 116 to evaluate changes implemented to reduce alarm burden; ¶48-the system may report a total number of alarm events which would fall into each category rather than a percentage falling above, below, and within the alarm threshold limits; Figs. 4-8, the Examiner notes that only one factor for processing is required by the claim language); volatility information; bias information; and persistence information; and stationary information.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Sampath to include rendering a graphical representation providing a comparison between a level at which a current alarm threshold is being exceeded and an expected level at which the one or more adjusted alarm thresholds would be expected to be exceeded; and processing the one or more detected alarms to determine an authenticity of the one or more detected alarms, wherein processing the one or more detected alarms utilizes one or more of: volume information; volatility information; bias information; and persistence information; and stationary information of Treacy in order to assist clinicians and/or clinical managers in making and implementing decisions related to alarm burden (Treacy, ¶19), because if too many clinically irrelevant alarms are initiated too frequently, this can distract clinicians and bother patients (Treacy, ¶18). Interventions are often needed to tailor the practices and alarms to the conditions in which the medical devices are used (Treacy, ¶18).
While the combination of Sampath and Treacy teaches that many different bedside devices for monitoring various physiological parameters are available from different vendors or providers (Sampath, ¶280), the combination fails to teach wherein the plurality of bedside monitoring devices include at least a first vendor device and a second vendor device, and wherein interfacing with the plurality of beside monitoring device further includes normalizing at least a data signal from the first vendor device and a data signal from the second vendor device so that the first vendor device and the second vender device can work together.
D'Angelo relates to improved computer-based platforms or systems, improved computing devices and components and/or improved computing objects configured for one or more novel technological applications of source-agnostic real-time analysis and prioritization of data for direct decision making through the collection, aggregation, normalization and processing of raw data sourced from diverse vendor specific databases (col. 1 and lines 40-47). D’Angelo further teaches the invention using the following step:
wherein the plurality of bedside monitoring devices include at least a first vendor device and a second vendor device, and wherein interfacing with the plurality of beside monitoring devices further includes normalizing at least a data signal from the first vendor device and a data signal from the second vendor device so that the first vendor device and the second vender device can work together (col. 1 and lines 46-47 normalization and processing of raw data sourced from diverse vendor specific databases; col. 16 and lines 63-65-restructuring the raw data, including the patient-related data from various vendors into a normalized, common format to produce common format normalized patient-related data).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Sampath to include wherein the plurality of bedside monitoring devices include at least a first vendor device and a second vendor device, and wherein interfacing with the plurality of beside monitoring device further includes normalizing at least a data signal from the first vendor device and a data signal from the second vendor device so that the first vendor device and the second vender device can work together of D’Angelo in order to enable the collection, management, analysis and visualization of real-time actionable data for workforce and service management with compatibility across distinct database vendors (D’Angelo, col. 5 and lines 11-14).
Regarding claims 5 and 18, the combination of Sampath, Treacy, and D’Angelo teaches the method and program of claims 1 and 14, wherein the one or more monitoring criteria includes one or more thresholds (Sampath, ¶200-a clinician may also use an input device to alter patient monitoring settings such as…physiological parameter alarm limits (e.g., alarm thresholds); ¶365-alarm criteria can include a threshold value).
Regarding claims 7 and 20, the combination of Sampath, Treacy, and D’Angelo teaches the method and program of claims 1 and 14, wherein the data signals concern one or more details of the plurality of bedside monitoring devices and/or uses of the plurality of bedside monitoring devices (Sampath, ¶77-physiological monitoring system 100 includes a plurality of bedside devices, e.g., patient monitoring devices 110, the patient monitoring devices 110 of various embodiments include sensors 102; ¶79-sensors 102 obtain physiological information from a medical patient, the physiological information includes one or more physiological parameters or values and waveforms corresponding to the physiological parameters; ¶81-generates waveforms from signals received from the sensors 102).
Regarding claims 8 and 21, the combination of Sampath, Treacy, and D’Angelo teaches the computer-implemented method and the computer program product of claims 1 and 14, wherein the one or more monitoring criteria includes user-defined monitoring criteria (Treacy, ¶63-upon a user input at 326, the alarm limit settings of at least one medical device are adjusted exemplarily by sending the post alarm limit settings to one or more of the medical devices from the medical device alarm management system).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Sampath to include wherein the one or more monitoring criteria includes user-defined monitoring criteria of Treacy in order to assist clinicians and/or clinical managers in making and implementing decisions related to alarm burden (Treacy, ¶19).
Claims 9-11 and 22-24 are rejected under 35 U.S.C. 103 as being unpatentable over Sampath in view of Treacy and D’Angelo as applied to claims 1 and 14 above, and further in view of Saripalli (US 20200337648 filed on 11/27/19).
Regarding claims 9 and 22, the combination of Sampath, Treacy, and D’Angelo teaches the computer-implemented method and the computer program product of claims 1 and 14. However, the combination of Sampath, Treacy, and D’Angelo does not teach wherein the one or more monitoring criteria includes machine-defined monitoring criteria.
Saripalli teaches wherein the one or more monitoring criteria includes machine-defined monitoring criteria (¶56-a model, such as a DL model, etc., can determine or predict when to react to an alarm versus turn the alarm off, etc).
Saripalli relates generally to medical data processing and, more particularly, to a medical machine time-series event data processor and associated methods (¶2). Saripalli additionally relates to alarm fatigue (¶32).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective
filing date of the claimed invention to modify the invention of Sampath to include the one or more monitoring criteria including machine-defined monitoring criteria of Saripalli in order for false alarm prediction to predict anomalies during surgeries such as false alarms, etc., to avoid alarm fatigue (Saripalli, ¶111).
Regarding claims 10 and 23, the combination of Sampath, Treacy, D’Angelo, and Saripalli teaches the computer-implemented method and the computer program product of claims 9 and 22, wherein the machine-defined monitoring criteria is defined via massive data sets processed by machine learning (“ML”) (Saripalli, ¶50-a larger dataset results in a more accurate, more robust deployed deep neural network model that can be applied to transform disparate medical data into actionable results (e.g., system configuration/settings, computer-aided diagnosis results, image enhancement, etc.); ¶80; ¶85; ¶123).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective
filing date of the claimed invention to modify the invention of Sampath to include wherein the machine-defined monitoring criteria is defined via massive data sets processed by machine learning (“ML”)of Saripalli in order for false alarm prediction to predict anomalies during surgeries such as false alarms, etc., to avoid alarm fatigue (Saripalli, ¶111).
Regarding claims 11 and 24, the combination of Sampath, Treacy, D’Angelo, and Saripalli teaches the computer-implemented method and the computer program product of claims 9 and 22, wherein the machine-defined monitoring criteria is compartmentalized (Saripalli, ¶86-data can be aggregated based on demographic (e.g., age, sex, income level, marital status, occupation, race, etc.), aggregated data can be analyzed and used to classify/categorize a patient to determine a relevant data set for training and/or testing of an associated neural network model; ¶100).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective
filing date of the claimed invention to modify the invention of Sampath to include wherein the machine-defined monitoring criteria is compartmentalized of Saripalli in order for false alarm prediction to predict anomalies during surgeries such as false alarms, etc., to avoid alarm fatigue (Saripalli, ¶111).
Claims 13 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Sampath in view of Treacy and D’Angelo as applied to claims 1 and 14 above, and further in view of Dyell (US 20150364022 filed on 6/12/15).
Regarding claims 13 and 26, the combination of Sampath, Treacy, and D’Angelo teaches the computer-implemented method and the computer program product of claims 1 and 14, and one or more of the plurality of bedside monitoring devices (Sampath, ¶77-the physiological monitoring system 100 includes a plurality of bedside devices, e.g., patient monitoring devices 110, communications module 106; ¶80-the sensor processing module 104 receives physiological information from the sensors 102). However, the combination of Sampath, Treacy, and D’Angelo does not teach wherein adjusting the outlier definition criteria includes: defining bespoke outlier definition criteria.
Dyell teaches wherein adjusting the outlier definition criteria includes: defining bespoke outlier definition criteria (¶78-alarm condition 44 may be tailored to specific users; ¶35-outlier).
Dyell relates in general to the field of healthcare systems and, more particularly, to systems and methods related to alarm fatigue management (¶2).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective
filing date of the claimed invention to modify the invention of Sampath to include wherein adjusting the outlier definition criteria includes: defining bespoke outlier definition criteria of Dyell in order for alarm fatigue management (Dyell, ¶2).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20160093205: Alarms that do not correspond to a clinically significant event may be referred to as nuisance alarms or false alarms (¶20).
US 20150186608: Once the time span between alarm events has been determined in steps 58 and 60 , the system proceeds to step 62 in which the processor determines whether the alarm condition is a continuing alarm. For example, if the patient's blood pressure has exceeded the upper alarm threshold for a continuous period of time, the system determines that the alarm is a continuing alarm and proceeds to step 64 . If the alarm was just triggered and is not a continuing alarm, the system returns to step 50 and continues to acquire data related to the monitored physiological parameters for the patient (¶32).
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