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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 29 October 2025 has been entered.
Response to Amendment
Claims 1 & 3-13 were previously pending in this application. The amendment filed 29 October 2025 has been entered and the following has occurred: Claim 1 has been amended. No claims have been cancelled or added.
Claims 1 & 3-13 remain pending in the application.
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 & 3-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The claims recite subject matter within a statutory category as a machine (claims 1 & 3-13) which recite associated steps of:
a data collection system comprising a first processor coupled to a database and coupled to a multiplicity of patient monitoring devices, wherein
each patient monitoring device of said multiplicity of patient monitoring devices is configured to obtain a time series of patient data samples, said patient data samples comprising vital sign values of one or more associated patient parameters;
receive one or more threshold values;
when said patient data samples are outside said one or more threshold values, generate an alarm; and
transmit alarm data to said first processor, wherein
said alarm data comprises said patient data samples while said alarm is active, wherein
said one or more threshold values comprise an upper threshold and a lower threshold;
said first processor is configured to collect said alarm data from said each patient monitoring data;
generate an alarm summary record associated with said alarm data and store said alarm summary record in said database, wherein
said alarm summary record comprises:
said alarm;
an alarm start time;
an alarm duration;
a minimum value of said patient data samples of said alarm data; and
a maximum value of said patient data samples of said alarm data; and,
an alarm threshold analysis system comprising a second processor coupled to said database, wherein
said second processor is configured to retrieve alarm summary records over a time period from said database; and,
process said alarm summary records and calculate an expected change in a number of alarms over said time period as a function of one or more modified threshold values based on said alarm summary records, wherein
said one or more modified threshold values comprise a modified upper threshold value as a modification to the upper threshold and a modified lower threshold value as a modification to the lower threshold; wherein
the one or more modified threshold values are selected to reduce the number of alarms to a desired level;
determine said one or more modified threshold values based on analysis and extrapolation of alarm-summary records and corresponding patient-data samples;
evaluate a range of modified threshold values of said one or more modified threshold values for a desired time period to generate a functional relationship between potential modifications to the one or more modified threshold values and a resulting number of alarms;
determine and select a new threshold value of said one or more modified threshold values based on said functional relationship; and
transmit said new threshold value to one or more of said multiplicity of patient monitoring devices to replace one or more of said one or more threshold values that exist in said one or more said multiplicity of patient monitoring devices to reduce the number of alarms to the desired level;
said collect said alarm data, said generate said alarm summary record, said retrieve alarm summary records, said process said alarm summary records, said determine said one or more modified threshold values, and said replace one or more of said one or more threshold values that exist in said one or more of said multiplicity of patient monitoring devices are fully automated to form a closed-loop system that iteratively adjusts device behavior to achieve a desired rate of alarms.
These steps of monitoring one or more patients on one or more patient monitoring devices, receiving one or more threshold values, transmitting an alarm when said patient data samples are outside said one or more threshold values, generating a record of one or more of the transmitted alarms and retrieving said alarm summary record and calculating an expected change in a number of alarms over a future time period based on said received alarm summary record, as drafted, under the broadest reasonable interpretation, includes methods of organizing human activity. That is, the performance of the steps by the system effectively manages the typical behavior of medical staff regarding one or more patients that are being monitored under their care. For instance, said performance of the steps recited relates to human activity at least by monitoring one or more actions and/or physiological data of the patient and potentially generating an alarm and an associated alarm summary record for future management of said patient by the hospital staff. That is, the typical behavior of the hospital staff regarding the issuing of alarms is effectively managed by the system performing the steps recited. Further, while aspects of the threshold values and automated updating thereof via a closed-loop system are specified in the limitations found at the end of the claim, i.e. potentially representing additional limitations, these aspects still fall in line with managing the typical behavior of medical staff regarding one or more patients that are being monitored under their care, but recited for specifically tailoring alarms to certain conditions. This is further supported by the dependent claims reciting actions relating to issuance of one or more alarms, such as editing one or more thresholds for alarm issuance in order to effect the change in a number of alarms over said future period of time. Accordingly, the claim recites an abstract idea.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 3-13, reciting particular aspects of how determining a new threshold value, classifying an alarm summary record, performing various forms of analysis on said alarm summary record in view of patient physiological data, e.g. clustering analysis, and generating a frequency distribution may be performed but for recitation of generic computer components).
This judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception (such as recitation of a first processor, a database, a multiplicity of patient monitoring devices, and a second processor, amounts to invoking computers as a tool to perform the abstract idea, see Applicant’s Specification [0036] & [0038] for a first processor; [0038] for a database; [0031]-[0032] for a multiplicity of patient monitoring devices; and [0036] & [0038] for a second processor; see MPEP 2106.05(f));
add insignificant extra-solution activity to the abstract idea (such as recitation of obtaining a time series of patient data samples said patient data samples comprising vital sign values of one or more associated patient parameters, receiving one or more threshold values said one or more threshold values comprising an upper threshold and a lower threshold, transmitting alarm data to a processor, retrieving alarm summary records over a time period from a database, transmitting new threshold values to one or more patient monitoring devices amounts to mere data gathering; recitation of generating an alarm when data samples are outside one or more threshold values, generating an alarm summary record associated with said alarm data, calculating an expected change in a number of alarms over said period of time as a function of one or more modified threshold values based on said alarm summary records, specifying threshold values of the modified threshold values, process said alarm summary records, evaluating/determining a range of modified/new threshold values of said one or more modified threshold values by extrapolating alarm-summary records and patient-data samples amounts to selecting a particular data source or type of data to be manipulated, recitation of storing alarm summary record in a database, forming a closed loop that iteratively adjusts device behavior to achieve a desired rate of alarms and transmitting parameters determined by said closed loop, amounts to insignificant application, see MPEP 2106.05(g));
generally link the abstract idea to a particular technological environment or field of use (such as recitation of the steps recited for patient monitoring and/or outputting alarms to one or more medical providers, such as in a clinical setting, see MPEP 2106.05(h)).
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 3-13, which recite limitations relating to a second processor, patient monitoring devices, additional limitations which amount to invoking computers as a tool to perform the abstract idea, see Applicant’s Specification [0036] & [0038] for second processor; [0031]-[0032] for patient monitoring devices; see MPEP 2106.05(f); claims 4 & 10 which recite limitations relating to transmitting a new threshold value to one or more patient monitoring devices, obtaining a classification of each alarm summary record of said alarm summary records, obtaining a distribution of said patient data samples, additional limitations which add insignificant extra-solution activity to the abstract idea which amounts to mere data gathering; claims 3-13 which recite limitations relating to performing determinations, classifications, and/or calculations based on received alarm data, alarm threshold data, alarm summary records, and/or patient data, modifying one or more threshold values based on said determinations, classifications, and/or calculations, additional limitations which add insignificant extra-solution activity to the abstract idea by selecting a particular data source or type of data to be manipulated, not storing an alarm summary record when said alarm duration is below a duration threshold value amounts to insignificant application for simply amounting to an elected design feature versus a technical step, see MPEP 2106.05(g); claims 3-13, which generally recite the application of the steps recited to the field of patient monitoring. additional limitations which generally link the abstract idea to a particular technological environment or field of use). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such as obtaining a time series of patient data samples, receiving one or more threshold values, transmitting alarm data to a processor, retrieving alarm summary records over a time period from a database, transmitting new threshold values to one or more patient monitoring devices, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); generating an alarm when data samples are outside one or more threshold values, generating an alarm summary record associated with said alarm data, calculating an expected change in a number of alarms over said period of time as a function of one or more modified threshold values based on said alarm summary records, process said alarm summary records, evaluating/determining a range of modified/new threshold values of said one or more modified threshold values, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii); maintaining one or more alarm summary records associated with said alarm and patient data, e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii); storing alarm summary record in a database, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv); analyzing alarm summary records for calculation of expected change in a number of alarms, which under BRI includes electronic scanning or parsing, e.g., electronic scanning or extracting data from a physical document, Content Extraction, MPEP 2106.05(d)(II)(v); implementing the determinations from the closed loop to iteratively calculates/adjusts device behavior to achieve a desired rate of alarms, see Dyell Par [0097]-[0099] which discloses employing a closed-loop control system, such that the system can be configured to account for any hysteresis of its feedback loop, where hysteresis indicates a dependence of the output of system on its current input and its history of past inputs, i.e. outputs/behavior of the system forms a closed-loop system to iteratively adjust the system to prevent alarm fatigue, as specifically mentioned in Dyell Par [0095] & [0097], such that based on monitored trends, etc. in available data may trigger shifts in how alarms are to be outputted for corresponding devices; utilizing closed loop algorithms in a medical monitoring environment, see Beck Par [0332] which discloses a closed loop control algorithm being employed to control one or more user-set or user-changed parameter settings, such as corresponding to presented alerts or alarms for ventilation systems).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 3-13, additional limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, i.e. claims 4 & 10 which recite limitations relating to transmitting a new threshold value to one or more patient monitoring devices, obtaining a classification of each alarm summary record of said alarm summary records, obtaining a distribution of said patient data samples, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); claims 3-13 which recite limitations relating to performing determinations, classifications, and/or calculations based on received alarm data, alarm threshold data, alarm summary records, and/or patient data, modifying one or more threshold values based on said determinations, classifications, and/or calculations, forming a closed loop that iteratively determine device behavior to achieve a desired rate of alarms e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii); claims 3-5, which recite limitations relating to determining and updating a record of one or more threshold alarm values, eliminating one or more alarm summary records,, e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii); claims 3-13, which generally recite using data that is either stored or retrieved via a database/memory, storing computerized instructions for performance of the steps recited by the processor/processing devices, storing and maintaining one or more alarm summary records, storing and updating one or more of threshold alarm values, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv); claims 3-13, which generally recite using data that is either stored or retrieved via a database/memory and, under BRI, includes extraction of said data via parsing and/or electronic scanning of one or more documents, such as the alarm summary records, e.g., electronic scanning or extracting data from a physical document, Content Extraction, MPEP 2106.05(d)(II)(v)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 & 3-13 are rejected under 35 U.S.C. 103 as being unpatentable over De Waele et al. (U.S. Patent Publication No. 2016/0051206), hereinafter “De Waele”, in view of Dyell et al. (U.S. Patent Publication No. 2023/0363678), hereinafter “Dyell”.
Claim 1 –
Regarding Claim 1, De Waele discloses a system that efficiently calculates and sets alarm thresholds for patient monitoring devices, comprising:
a data collection system comprising a first processor coupled to a database and coupled to a multiplicity of patient monitoring devices (See De Waele Par [0020] which discloses one or more medical monitors which receive vital sign signal values from monitored patients, such as a non-invasive or invasive blood pressure (BP) monitor, SpO2 or blood oximetry device, respiratory rate (RR) monitor, electrocardiogram (ECG) monitor, Heart Rate (HR) monitor and the like; See De Waele Par [0028]-[0029] & Fig .1 which disclose the system containing one or more processing devices and/or connected databases, data structures, non-transitory computer readable media, etc., for storing alarm settings or alarm profiles, the suggested profiles, the monitor log, the central log, the normative settings, the normative vital sign signals, and the patient data) wherein
each patient monitoring device of said multiplicity of patient monitoring devices is configured to obtain a time series of patient data samples, said patient data samples comprising vital sign values of one or more associated patient parameters (See De Waele Par [0020] which discloses one or more medical monitors which receive vital sign signal values from monitored patients, such as a non-invasive or invasive blood pressure (BP) monitor, SpO2 or blood oximetry device, respiratory rate (RR) monitor, electrocardiogram (ECG) monitor, Heart Rate (HR) monitor and the like; See De Waele Par [0007] which discloses alarm settings including at least one of an upper limit and a lower limit, i.e. value, for at least one monitored vital sign);
receive one or more threshold values (See De Waele Par [0002] which discloses one or more minimum and/or maximum threshold limit values; See De Waele Par [0021] which discloses receiving one or more alarm setting such as one or more upper and/or lower limit, i.e. threshold, values for a vital signal);
when said patient data samples are outside said one or more threshold values, generate an alarm (See De Waele Par [0002] which discloses one or more minimum and/or maximum threshold limit values, and upon exceeding one or more of the threshold limit values, sending an alert/generating an alarm; See De Waele Par [0021] which discloses receiving one or more alarm setting such as one or more upper and/or lower limit, i.e. threshold, values for a vital signal); and
transmit alarm data to said first processor (See De Waele Par [0023] which discloses a monitor log including alarm settings at an alarm event, alarm setting change history, etc., constituting alarm data), wherein
said alarm data comprises said patient data samples while said alarm is active (See De Waele Par [0023] which discloses including a vital sign signal history and/or vital sign signals according to alarm events or other time intervals), wherein
said one or more threshold values comprise an upper threshold and a lower threshold (See De Waele Par [0007] which discloses alarm settings including at least one of an upper limit and a lower limit, i.e. value, for at least one monitored vital sign);
said first processor is configured to collect said alarm data from said each patient monitoring device (See De Waele Par [0023] which discloses a monitor log, i.e. alarm summary record, including alarm settings at an alarm event, alarm setting change history, etc., constituting alarm data and is stored by the medical monitoring unit which can then possibly be received by a central log);
generate an alarm summary record associated with said alarm data and store said alarm summary record in said database (See De Waele Par [0023] which discloses a monitor log, i.e. alarm summary record, including alarm settings at an alarm event, alarm setting change history, etc., constituting alarm data and is stored by the medical monitoring unit which can then possibly be received by a central log), wherein
said alarm summary record comprises:
said alarm (See De Waele Par [0023] which discloses a monitor log, i.e. alarm summary record, including alarm settings at an alarm event, alarm setting change history, etc., constituting alarm data);
an alarm start time (See De Waele Par [0038] which discloses the system identifying a time interval, either a minimum or a maximum after which a different alarm setting value is recommended, and would thereby constitute an “alarm start time” under BRI because this represents a time segment, i.e. start time to end time, that a certain alarm parameter is used);
an alarm duration (See De Waele Par [0038] which discloses the system identifying a time interval, either a minimum or a maximum after which a different alarm setting value is recommended, and would thereby constitute an “alarm duration” under BRI because this represents a duration of time that a certain alarm parameter is used);
a minimum value of said vital sign values of said patient data samples of said alarm data during an alarm (See De Waele Par [0007] which discloses alarm settings including at least one of an upper limit and a lower limit, i.e. value, for at least one monitored vital sign); and
a maximum value of said vital sign values of said patient data samples of said alarm data during an alarm (See De Waele Par [0007] which discloses alarm settings including at least one of an upper limit and a lower limit, i.e. value, for at least one monitored vital sign); and,
an alarm threshold analysis system comprising a second processor coupled to said database (See De Waele Par [0028]-[0029] & Fig .1 which disclose the system containing one or more processing devices and/or connected databases, data structures, non-transitory computer readable media, etc., for storing alarm settings or alarm profiles, the suggested profiles, the monitor log, the central log, the normative settings, the normative vital sign signals, and the patient data), wherein
said second processor is configured to retrieve alarm summary records over a time period from said database (See De Waele Par [0023] which discloses a monitor log, i.e. alarm summary record, including alarm settings at an alarm event, alarm setting change history, etc., constituting alarm data and is stored by the medical monitoring unit which can then possibly be received by a central log);
process said alarm summary records and calculate an expected change in a number of alarms over said time period as a function of one or more modified threshold values based on said alarm summary records (See De Waele Par [0038] which discloses the system identifying a time interval, either a minimum or a maximum after which a different alarm setting value is recommended, such that after a predetermined time interval or function thereof is satisfied, a second alarm setting is recommended based on certain variables, including alarm counts, i.e. number of alarms over said time period), wherein
said one or more modified threshold values comprise a modified upper threshold value as a modification to the upper threshold and a modified lower threshold value as a modification to the lower threshold (See De Waele Par [0022] which discloses a setting manager configured to receive changes, i.e. new or updated values, to alarm settings by electronic transmission from a suggested profile data store; See De Waele Par [0027] which discloses the observational analyzer recommending changes to alarms settings for the patient based on current vital sign data and/or alarm data, and sends the recommended changes to the alerting device or other computing device), wherein
the one or more modified threshold values are selected manually or automatically, to reduce the number of alarms to a desired level (See De Waele Par [0022] which discloses a setting manager configured to receive changes, i.e. new or updated values, to alarm settings by electronic transmission from a suggested profile data store; See De Waele Par [0027] which discloses the observational analyzer recommending changes to alarms settings for the patient based on current vital sign data and/or alarm data, and sends the recommended changes to the alerting device or other computing device);
determine said one or more modified threshold values based on analysis and extrapolation of alarm-summary records and corresponding patient-data samples (See De Waele Par [0027] which discloses the observational analyzer recommending changes to alarms settings for the patient based on current vital sign data and/or alarm data, and sends the recommended changes to the alerting device or other computing device; See De Waele Par [0037]-[0038] which discloses collected patient vital signs, i.e. patient-data samples, being averaged over a time interval, such that if the average vital sign data stream exceeds a threshold limit for time sufficiently long to infer that exceeding the limit is not a short term aberration, the data associated during that time (i.e. the data described in De Waele Par [0027] including vital sign data and/or alarm data) is collected over said time span to define a new alarm limit threshold, i.e. the new/modified threshold is extrapolated based on historical alarm/threshold values and patient-data samples from the time span associated with the exceeding of the threshold limit for a prolonged timeframe, and specifically states that a time interval, ether a minimum or maximum after which a different alarm setting value is recommended, i.e. future time period and therefore constitutes extrapolation of said data);
determine and select a new threshold value of said one or more modified threshold values (See De Waele Par [0027] which discloses the observational analyzer recommending changes to alarms settings for the patient based on current vital sign data and/or alarm data, and sends the recommended changes to the alerting device or other computing device; See De Waele Par [0032] which specifies said changes including changes in a single alarm setting value, or changes in combinations of one or more alarm setting values of one or more vital signs such as a change in upper limit value, i.e. upper threshold, of RR is recommended, changes in upper and lower limit values, i.e. upper and lower thresholds, of RR are recommended, or changes in upper and lower limit values of RR and SpO2, i.e. upper and lower thresholds of one or more vital signs, are recommended, etc.); and
transmit said new threshold value to one or more of said multiplicity of patient monitoring devices to replace one or more of said one or more threshold values that exist in said one or more of said multiplicity of patient monitoring devices to reduce the number of alarms to the desired level (See De Waele Par [0022] which discloses a setting manager configured to receive changes, i.e. new or updated values, to alarm settings by electronic transmission from a suggested profile data store; See De Waele Par [0027] which discloses the observational analyzer recommending changes to alarms settings for the patient based on current vital sign data and/or alarm data, and sends the recommended changes to the alerting device or other computing device; See De Waele Par [0038] which discloses a notice sent with the recommendation, i.e. change, to alerting devices, i.e. multiplicity of monitoring devices and as in disclosed in De Waele Par [0003], these recommendations/changes are made to reduce overall alarm fatigue);
While De Waele generally discloses updating or modifying alarm settings by electronic transmission, such as to reduce the number of alarms generated, De Waele is generally silent regarding determining a functional relationship between potential modifications to the threshold values and a resulting number of alarms for said purposes of reducing the number of alarms and/or the various steps recited in a closed-loop system for iteratively adjusting device behavior as given by the following limitation:
evaluate a range of modified threshold values of said one or more modified threshold values for a desired time period to generate a functional relationship between potential modifications to the one or more modified threshold values and a resulting number of alarms;
determine and select a new threshold value of said one or more modified threshold values based on said functional relationship;
wherein
said collect said alarm data, said generate said alarm summary record, said retrieve alarm summary records, said process said alarm summary records, said determine said one or more modified threshold values, and said replace one or more of said one or more threshold values that exist in said one or more of said multiplicity of patient monitoring devices are fully automated to form a closed-loop system that iteratively adjusts device behavior to achieve a desired rate of alarms.
However, Dyell discloses evaluate a range of modified threshold values of said one or more modified threshold values for a desired time period to generate a functional relationship between potential modifications to the one or more modified threshold values and a resulting number of alarms (See Dyell Par [0096]-[0099] which discloses the system monitoring trends in available data in order to trigger shifts in how alarms are presented, such that a fatigue model can be generated using a feedback loop, for example, as a user experiences more and more alarms throughout a shift (i.e. a desired time period), that user's alarm fatigue rises, and therefore, the systems sensing and quantifying a rise in alarm fatigue level automatically modulates, i.e. modifies, alarm outputs/conditions to mitigate effects of the user’s alarm fatigue; Dyell Par [0099] further specifies that frames of reference, e.g. desired time period, can span hours, days, weeks, months, and/or years) determine and select a new threshold value of said one or more modified threshold values based on said functional relationship (See Dyell Par [0096]-[0099] which discloses the system monitoring trends in available data in order to trigger shifts in how alarms are presented, such that a fatigue model can be generated using a feedback loop, for example, as a user experiences more and more alarms throughout a shift (i.e. a desired time period), that user's alarm fatigue rises, and therefore, the systems sensing and quantifying a rise in alarm fatigue level automatically modulates, i.e. modifies, alarm outputs/conditions to mitigate effects of the user’s alarm fatigue; Dyell Par [0099] further specifies that frames of reference, e.g. desired time period, can span hours, days, weeks, months, and/or years; See Dyell Par [0089]-[0093] specifically mentions considerations of particular medical devices that may be more prone to issuing excessive alarms because a threshold condition is too low, or has varying measurement sensitivities, such that the system can determine potential alarm conditions and consider a measurement sensitivity to determine a minimum amount of change a physiological measurement undergoes before the medical device detects the change for triggering alarms to reduce unnecessary alarm generation, such as by determining when fewer or greater alarms would be issued, and specifically recites if a medical device is set to detect body temperature changes only when the temperature change is 0.2 degrees or greater, fewer alarms would be generated based on small fluctuations around the threshold value, and thereby also establishes a functional relationship between potential modifications of said devices and a resulting number of alarms from said modifications based, for instance, measurement sensitivity) wherein, collect said alarm data, said generate said alarm summary record, said retrieve alarm summary records, said process said alarm summary records, said determine said one or more modified threshold values, and said replace one or more of said one or more threshold values that exist in said one or more of said multiplicity of patient monitoring devices are fully automated to form a closed-loop system that iteratively adjusts device behavior to achieve a desired rate of alarms (see Dyell Par [0097]-[0099] which discloses employing a closed-loop control system, such that the system can be configured to account for any hysteresis of its feedback loop, where hysteresis indicates a dependence of the output of system on its current input and its history of past inputs, i.e. outputs/behavior of the system forms a closed-loop system to iteratively adjust the system to prevent alarm fatigue, as specifically mentioned in Dyell Par [0095] & [0097], such that based on monitored trends, etc. in available data may trigger shifts in how alarms are to be outputted for corresponding devices; See Dyell Par [0089]-[0093] specifically mentions considerations of particular medical devices that may be more prone to issuing excessive alarms because a threshold condition is too low, or has varying measurement sensitivities, such that the system can determine potential alarm conditions and consider a measurement sensitivity to determine a minimum amount of change a physiological measurement undergoes before the medical device detects the change for triggering alarms to reduce unnecessary alarm generation, such as by determining when fewer or greater alarms would be issued, and specifically recites if a medical device is set to detect body temperature changes only when the temperature change is 0.2 degrees or greater, fewer alarms would be generated based on small fluctuations around the threshold value, and thereby also establishes a functional relationship between potential modifications of said devices and a resulting number of alarms from said modifications based, for instance, measurement sensitivity; See Dyell Par [0078] & [0092]-[0093] which discloses implementing or changing alarms and/or types of alarms based on alarm condition and this having a direct effect on how frequently the medical device generates alarms, such as potentially reducing the number of alarms). The disclosure of Dyell is directly applicable to the disclosure of De Waele because both disclosures share limitations and capabilities, such as being directed towards modifying and monitoring one or more alarm/alert parameters.
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 disclosure of De Waele which discloses updating or modifying alarm settings by electronic transmission, such as to reduce the number of alarms generated to further include evaluate a range of modified threshold values of said one or more modified threshold values for a desired time period to generate a functional relationship between potential modifications to the one or more modified threshold values and a resulting number of alarms and using a closed-loop system to iteratively adjust , as disclosed by Dyell, because this allows for an automatic fatigue model to be generated using a feedback loop, such that a system can automatically modulate, i.e. modify, the alarm outputs to that user to mitigate effects of the user’s alarm fatigue (See Dyell Par [0096]-[0099]).
Claim 3 –
Regarding Claim 3, De Waele and Dyell disclose the system of claim 1 in its entirety. De Waele further discloses a system, wherein:
said second processor is further configured to
determine said new threshold value as a modified threshold value that results in a target value of said expected change in said number of alarms over said time period (See De Waele Par [0032] which specifies said changes including changes in a single alarm setting value, or changes in combinations of one or more alarm setting values of one or more vital signs such as a change in upper limit value, i.e. upper threshold, of RR is recommended, changes in upper and lower limit values, i.e. upper and lower thresholds, of RR are recommended, or changes in upper and lower limit values of RR and SpO2, i.e. upper and lower thresholds of one or more vital signs, are recommended, etc.; See De Waele Par [0038] which discloses the system identifying a time interval, either a minimum or a maximum after which a different alarm setting value is recommended, such that after a predetermined time interval or function thereof is satisfied, a second alarm setting is recommended based on certain variables, including alarm counts, i.e. number of alarms over said time period).
Claim 4 –
Regarding Claim 4, De Waele and Dyell disclose the system of claim 1 in its entirety. De Waele further discloses a system, wherein:
said second processor is further configured to obtain or calculate a classification of each alarm summary record of said alarm summary records as
a high alarm corresponding to said patient data samples above said upper threshold (See De Waele Par [0036] which discloses deriving high limit and low limit setting values being derived from cluster distribution such that separation values such as quantiles or standard deviations can be used to identify distinct high and low clusters, e.g. 1% and 99% quantiles, and 99% quantile representing a “high” value above the separation value), or
a low alarm corresponding to said patient data samples below said lower threshold (See De Waele Par [0036] which discloses deriving high limit and low limit setting values being derived from cluster distribution such that separation values such as quantiles or standard deviations can be used to identify distinct high and low clusters, e.g. 1% and 99% quantiles, and 1% quantile representing a “low” value above the separation value).
Claim 5 –
Regarding Claim 5, De Waele and Dyell disclose the system of claim 4 in its entirety. De Waele further discloses a system, wherein:
said calculate said expected change in said number of alarms over said time period comprises
when one of said one or more modified threshold values corresponds to said modified upper threshold value, eliminate said alarm summary records having said maximum value of said patient data samples below said modified upper threshold value (Under broadest reasonable interpretation, contingent limitations require only those steps that must be performed and does not have to include steps that are not required to be performed because conditions precedent are not met; however, the BRI of a system claim having structure that performs a function that is conditional, at least requires the structure for performing the function should the condition occur, but still does not have to teach said function, see MPEP 2111.04(II); therefore, De Waele Par [0028]-[0029] & Fig. 1 disclosing the system containing one or more processing devices and/or connected databases, data structures, non-transitory computer readable media, etc., for storing and managing storage of alarm settings or alarm profiles, the suggested profiles, the monitor log, the central log, the normative settings, the normative vital sign signals, and the patient data and for editing or modifying threshold values effectively reads on the limitation herein because the conditions of “when one of said one or more modified threshold values corresponds to a modified upper threshold” does not have to ever necessarily be met under BRI, because all of the modified threshold values could correspond to a lower threshold); and
when one of said one or more modified threshold values corresponds to said modified lower threshold value, eliminate said alarm summary records having said minimum value of said patient data samples above said modified lower threshold value (Under broadest reasonable interpretation, contingent limitations require only those steps that must be performed and does not have to include steps that are not required to be performed because conditions precedent are not met; however, the BRI of a system claim having structure that performs a function that is conditional, at least requires the structure for performing the function should the condition occur, but still does not have to teach said function, see MPEP 2111.04(II); therefore, De Waele Par [0028]-[0029] & Fig. 1 disclosing the system containing one or more processing devices and/or connected databases, data structures, non-transitory computer readable media, etc., for storing alarm settings or alarm profiles, the suggested profiles, the monitor log, the central log, the normative settings, the normative vital sign signals, and the patient data and for editing or modifying threshold values effectively reads on the limitation herein because the conditions of “when one of said one or more modified threshold values corresponds to a modified upper threshold” does not have to ever necessarily be met under BRI, because all of the modified threshold values could correspond to an upper threshold value; therefore while this claim is considered met because De Waele effectively discloses the entirety of the limitation under 35 U.S.C. 103).
Claim 6 –
Regarding Claim 6, De Waele and Dyell disclose discloses the system of claim 4 in its entirety. De Waele further discloses a system, wherein:
said alarm summary record further comprises
a first value of said patient data samples of said alarm data (See De Waele Par [0007] which discloses alarm settings including at least one of an upper and a lower limit, i.e. value, for at least one monitored vital sign; See De Waele Par [0023] which discloses a monitor log, i.e. alarm summary record, including alarm settings at an alarm event, alarm setting change history, vital sign signals according to alarm events or other time intervals, etc.); and
said calculate the classification of said each alarm summary record comprises
apply a k-means clustering algorithm with two clusters to a dataset comprising said first value of said patient data samples of said alarm summary records (See De Waele Par [0034] which discloses applying a k-means algorithm for automated clustering, such that a representative or center value of each cluster is computed as an alarm setting value in one or more suggested profiles);
calculate a separation value as a mean of centroids of said two clusters (See De Waele Par [0034] which discloses applying a k-means algorithm for automated clustering, such that a representative or center value, i.e. centroid, of each cluster is computed as an alarm setting value in one or more suggested profiles; See De Waele Par [0036] which discloses deriving high limit and low limit setting values being derived from cluster distribution such that separation values such as quantiles or standard deviations can be used to identify distinct high and low clusters, e.g. 1% and 99% quantiles, ±2 standard deviations, etc.);
classify said each alarm summary record as said high alarm when said first value of said each alarm summary record is above said separation value (See De Waele Par [0036] which discloses deriving high limit and low limit setting values being derived from cluster distribution such that separation values such as quantiles or standard deviations can be used to identify distinct high and low clusters, e.g. 1% and 99% quantiles, and 99% quantile representing a “high” value above the separation value); and
classify said each alarm summary record as said low alarm when said first value of said each alarm summary record is below said separation value (See De Waele Par [0036] which discloses deriving high limit and low limit setting values being derived from cluster distribution such that separation values such as quantiles or standard deviations can be used to identify distinct high and low clusters, e.g. 1% and 99% quantiles, and 1% quantile representing a “low” value above the separation value).
Claim 7 –
Regarding Claim 7, De Waele and Dyell disclose the system of claim 6 in its entirety. De Waele further discloses a system, wherein:
said second processor is further configured to calculate one or both of said upper threshold and said lower threshold (See De Waele Par [0034] which discloses applying a k-means algorithm for automated clustering, such that a representative or center value, i.e. centroid, of each cluster is computed as an alarm setting value in one or more suggested profiles; See De Waele Par [0036] which discloses deriving high limit and low limit setting values, i.e. thresholds, being derived from cluster distribution such that separation values such as quantiles or standard deviations can be used to identify distinct high and low clusters, e.g. 1% and 99% quantiles, ±2 standard deviations, etc.).
Claim 8 –
Regarding Claim 8, De Waele discloses the system of claim 7 in its entirety. De Waele further discloses a system, wherein:
said calculate one or both of said upper threshold and said lower threshold comprises:
calculate a first frequency distribution of first values of said alarm summary records that are classified as high alarms (See De Waele Par [0033]-[0034] & Fig. 3 which discloses a correlation plot and/or cluster analysis of two alarm settings or limits, such that a first alarm limit and second alarm limit are represented and dots are sized to represent the frequency of occurrence in the analyzed monitor logs or extracted normative settings and/or normative vital sign signals, i.e. first and second frequency distributions for each alarm limit, such that a representative or center value of each cluster is computed as an alarm setting value in one or more suggested profiles, the higher center cluster value representing high alarm, and the lower center cluster value representing a low alarm);
calculate a first modal frequency as a frequency of a mode of all or a portion of said first frequency distribution (See De Waele Par [0033]-[0034] & Fig. 3 which discloses a correlation plot and/or cluster analysis of two alarm settings or limits, such that a first alarm limit and second alarm limit are represented and dots are sized to represent the frequency of occurrence in the analyzed monitor logs or extracted normative settings and/or normative vital sign signals, i.e. frequency distribution, such that a representative or center value of each cluster is computed as an alarm setting value in one or more suggested profiles);
calculate a second frequency distribution of first values of said alarm summary records that are classified as low alarms (See De Waele Par [0033]-[0034] & Fig. 3 which discloses a correlation plot and/or cluster analysis of two alarm settings or limits, such that a first alarm limit and second alarm limit are represented and dots are sized to represent the frequency of occurrence in the analyzed monitor logs or extracted normative settings and/or normative vital sign signals, i.e. first and second frequency distributions for each alarm limit, such that a representative or center value of each cluster is computed as an alarm setting value in one or more suggested profiles, the higher center cluster value representing high alarm, and the lower center cluster value representing a low alarm);
calculate a second modal frequency as a frequency of a mode of all or a portion of said second frequency distribution (See De Waele Par [0033]-[0034] & Fig. 3 which discloses a correlation plot and/or cluster analysis of two alarm settings or limits, such that a first alarm limit and second alarm limit are represented and dots are sized to represent the frequency of occurrence in the analyzed monitor logs or extracted normative settings and/or normative vital sign signals, i.e. frequency distribution, such that a representative or center value of each cluster is computed as an alarm setting value in one or more suggested profiles);
calculate said upper threshold as a smallest value above said separation value having a frequency in said first frequency distribution greater than a first fraction times said first modal frequency (See De Waele Par [0033]-[0034] & Fig. 3 which discloses a correlation plot and/or cluster analysis of two alarm settings or limits, such that a first alarm limit and second alarm limit are represented and dots are sized to represent the frequency of occurrence in the analyzed monitor logs or extracted normative settings and/or normative vital sign signals, i.e. frequency distribution, such that a representative or center value of each cluster is computed as an alarm setting value in one or more suggested profiles; See De Waele Par [0034] which discloses applying a k-means algorithm for automated clustering, such that a representative or center value, i.e. centroid, of each cluster is computed as an alarm setting value in one or more suggested profiles; See De Waele Par [0036] which discloses deriving high limit and low limit setting values, i.e. thresholds, being derived from cluster distribution such that separation values such as quantiles or standard deviations can be used to identify distinct high and low clusters, e.g. 1% and 99% quantiles, ±2 standard deviations, etc.; furthermore, this portion of the claim seems to read as routine optimization within prior art conditions or through routine experimentation, i.e. when the general conditions of a claim are disclosed in the prior art, it is not inventive to discover optimum or workable ranges or values by routine experimentation, such as in this case a smallest value above said separation value or a largest value below said separation value, and therefore De Waele generally disclosing the separation of both clusters and identifying distinct high and low clusters are different quantiles or standard deviations, choosing the value for the degree of separation such as a smallest value above said separation value or a largest value below said separation value does not add inventiveness or patentability of subject matter, and thereby seems to be entirely met by De Waele); and
calculate said lower threshold as a largest value below said separation value having a frequency in said second frequency distribution greater than a second fraction times said second modal frequency (See De Waele Par [0033]-[0034] & Fig. 3 which discloses a correlation plot and/or cluster analysis of two alarm settings or limits, such that a first alarm limit and second alarm limit are represented and dots are sized to represent the frequency of occurrence in the analyzed monitor logs or extracted normative settings and/or normative vital sign signals, i.e. frequency distribution, such that a representative or center value of each cluster is computed as an alarm setting value in one or more suggested profiles; See De Waele Par [0034] which discloses applying a k-means algorithm for automated clustering, such that a representative or center value, i.e. centroid, of each cluster is computed as an alarm setting value in one or more suggested profiles; See De Waele Par [0036] which discloses deriving high limit and low limit setting values, i.e. thresholds, being derived from cluster distribution such that separation values such as quantiles or standard deviations can be used to identify distinct high and low clusters, e.g. 1% and 99% quantiles, ±2 standard deviations, etc.; furthermore, this portion of the claim seems to read as routine optimization within prior art conditions or through routine experimentation, i.e. when the general conditions of a claim are disclosed in the prior art, it is not inventive to discover optimum or workable ranges or values by routine experimentation, see MPEP 2144.05(II), such as in this case, specifying the degree of separation for identifying lower and higher thresholds as a smallest value above said separation value or a largest value below said separation value, and therefore because De Waele generally discloses the separation of both clusters and identifying distinct high and low clusters at different quantiles or standard deviations, Applicant choosing the value for the degree of separation such as a smallest value above said separation value or a largest value below said separation value does not add inventiveness or patentability of subject matter, and therefore this claim seems to be rendered obvious by the disclosure of De Waele, unless these degrees of separation for identifying lower and higher thresholds are shown to be critical, are shown to be taught away by De Waele or other prior art, are not recognized as a result-effective variable in the prior art, and/or are only disclosed in a very broad range in the prior art so as to not invite optimization by one of ordinary skill in the art).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the disclosure of De Waele and Dyell by performing routine optimization within prior art conditions already described by De Waele, i.e. deriving high limit and low limit setting values/thresholds, from cluster distribution such that separation values can be used to identify distinct high and low clusters, in order to discover optimized separation values for determining higher and lower alarm thresholds, i.e. smallest value above said separation value or a largest value below said separation value, as described in the limitations found above.
Claim 9 –
Regarding Claim 9, De Waele and Dyell disclose the system of claim 4 in its entirety. De Waele further discloses a system, wherein:
said second processor is further configured to calculate an expected increase in said number of alarms over said time period when said upper threshold is decreased to a smaller upper threshold or said lower threshold is increased to a larger lower threshold (See De Waele Par [0032] which specifies said changes including changes in a single alarm setting value, or changes in combinations of one or more alarm setting values of one or more vital signs such as a change in upper limit value, i.e. upper threshold, of RR is recommended, changes in upper and lower limit values, i.e. upper and lower thresholds, of RR are recommended, or changes in upper and lower limit values of RR and SpO2, i.e. upper and lower thresholds of one or more vital signs, are recommended, etc.; See De Waele Par [0038] which discloses the system identifying a time interval, either a minimum or a maximum after which a different alarm setting value is recommended, such that after a predetermined time interval or function thereof is satisfied, a second alarm setting is recommended based on certain variables, including alarm counts, i.e. number of alarms over said time period).
Claim 10 –
Regarding Claim 10, De Waele and Dyell disclose the system of claim 9 in its entirety. De Waele further discloses a system, wherein:
said calculate said expected increase in said number of alarms over said time period comprises
obtain or estimate a distribution of said patient data samples (See De Waele Par [0030]-[0031] and Fig. 2 which discloses utilizing linear regression and extrapolation on a graph with dots representing a frequency of usage or a frequency of occurrence in a population of data, i.e. frequency distribution);
when said upper threshold is decreased to said smaller upper threshold, calculate said expected increase in said number of alarms as a frequency of said distribution between said smaller upper threshold and said upper threshold divided by a frequency of said distribution above said upper threshold multiplied by said number of alarms (See De Waele Par [0030]-[0031] and Fig. 2 which discloses utilizing linear regression and extrapolation on a graph with dots representing a frequency of usage or a frequency of occurrence in a population of data, i.e. frequency distribution, for determining maximum and minimum value alarm settings and can utilize multi-parameter correlation of multiple alarm setting or limit values and corresponding multiple vital sign signal values, i.e. one or more distributions; See De Waele Par [0032] which specifies said changes including changes in a single alarm setting value, or changes in combinations of one or more alarm setting values of one or more vital signs such as a change in upper limit value, i.e. upper threshold, of RR is recommended, changes in upper and lower limit values, i.e. upper and lower thresholds, of RR are recommended, or changes in upper and lower limit values of RR and SpO2, i.e. upper and lower thresholds of one or more vital signs, are recommended, etc.; See De Waele Par [0038] which discloses the system identifying a time interval, either a minimum or a maximum after which a different alarm setting value is recommended, such that after a predetermined time interval or function thereof is satisfied, a second alarm setting is recommended based on certain variables, including alarm counts, i.e. number of alarms over said time period); and
when said lower threshold is increased to said larger lower threshold, calculate said expected increase in said number of alarms as a frequency of said distribution between said lower threshold and said larger lower threshold divided by a frequency of said distribution below said lower threshold multiplied by said number of alarms (See De Waele Par [0030]-[0031] and Fig. 2 which discloses utilizing linear regression and extrapolation on a graph with dots representing a frequency of usage or a frequency of occurrence in a population of data, i.e. frequency distribution, for determining maximum and minimum value alarm settings and can utilize multi-parameter correlation of multiple alarm setting or limit values and corresponding multiple vital sign signal values, i.e. one or more distributions; See De Waele Par [0032] which specifies said changes including changes in a single alarm setting value, or changes in combinations of one or more alarm setting values of one or more vital signs such as a change in upper limit value, i.e. upper threshold, of RR is recommended, changes in upper and lower limit values, i.e. upper and lower thresholds, of RR are recommended, or changes in upper and lower limit values of RR and SpO2, i.e. upper and lower thresholds of one or more vital signs, are recommended, etc.; See De Waele Par [0038] which discloses the system identifying a time interval, either a minimum or a maximum after which a different alarm setting value is recommended, such that after a predetermined time interval or function thereof is satisfied, a second alarm setting is recommended based on certain variables, including alarm counts, i.e. number of alarms over said time period).
Claim 11 –
Regarding Claim 11, De Waele and Dyell disclose the system of claim 9 in its entirety. De Waele further discloses a system, wherein:
said calculate said calculate said expected increase in said number of alarms over said time period comprises:
calculate one or both of:
a first distribution of expected alarm maximum values based on extrapolation of a frequency distribution of said maximum value of said alarm summary records (See De Waele Par [0030]-[0031] and Fig. 2 which discloses utilizing linear regression and extrapolation on a graph with dots representing a frequency of usage or a frequency of occurrence in a population of data, i.e. frequency distribution, for determining maximum and minimum value alarm settings and can utilize multi-parameter correlation of multiple alarm setting or limit values and corresponding multiple vital sign signal values, i.e. one or more distributions);
a second distribution of expected alarm minimum values based on extrapolation of a frequency distribution of said minimum value of said alarm summary records (See De Waele Par [0030]-[0031] and Fig. 2 which discloses utilizing linear regression and extrapolation on a graph with dots representing a frequency of usage or a frequency of occurrence in a population of data, i.e. frequency distribution, for determining maximum and minimum value alarm settings and can utilize multi-parameter correlation of multiple alarm setting or limit values and corresponding multiple vital sign signal values, i.e. one or more distributions); and
when said upper threshold is decreased to said smaller upper threshold, calculate said expected increase in said number of alarms as a total frequency of said first distribution between said smaller upper threshold and said upper threshold (See De Waele Par [0030]-[0031] and Fig. 2 which discloses utilizing linear regression and extrapolation on a graph with dots representing a frequency of usage or a frequency of occurrence in a population of data, i.e. frequency distribution, for determining maximum and minimum value alarm settings and can utilize multi-parameter correlation of multiple alarm setting or limit values and corresponding multiple vital sign signal values, i.e. one or more distributions; See De Waele Par [0032] which specifies said changes including changes in a single alarm setting value, or changes in combinations of one or more alarm setting values of one or more vital signs such as a change in upper limit value, i.e. upper threshold, of RR is recommended, changes in upper and lower limit values, i.e. upper and lower thresholds, of RR are recommended, or changes in upper and lower limit values of RR and SpO2, i.e. upper and lower thresholds of one or more vital signs, are recommended, etc.; See De Waele Par [0038] which discloses the system identifying a time interval, either a minimum or a maximum after which a different alarm setting value is recommended, such that after a predetermined time interval or function thereof is satisfied, a second alarm setting is recommended based on certain variables, including alarm counts, i.e. number of alarms over said time period); and
when said lower threshold is increased to said larger lower threshold, calculate said expected increase in said number of alarms as a total frequency of said second distribution between said lower threshold and said larger lower threshold (See De Waele Par [0030]-[0031] and Fig. 2 which discloses utilizing linear regression and extrapolation on a graph with dots representing a frequency of usage or a frequency of occurrence in a population of data, i.e. frequency distribution, for determining maximum and minimum value alarm settings and can utilize multi-parameter correlation of multiple alarm setting or limit values and corresponding multiple vital sign signal values, i.e. one or more distributions; See De Waele Par [0032] which specifies said changes including changes in a single alarm setting value, or changes in combinations of one or more alarm setting values of one or more vital signs such as a change in upper limit value, i.e. upper threshold, of RR is recommended, changes in upper and lower limit values, i.e. upper and lower thresholds, of RR are recommended, or changes in upper and lower limit values of RR and SpO2, i.e. upper and lower thresholds of one or more vital signs, are recommended, etc.; See De Waele Par [0038] which discloses the system identifying a time interval, either a minimum or a maximum after which a different alarm setting value is recommended, such that after a predetermined time interval or function thereof is satisfied, a second alarm setting is recommended based on certain variables, including alarm counts, i.e. number of alarms over said time period).
Claim 12 –
Regarding Claim 12, De Waele and Dyell disclose the system of claim 11 in its entirety. De Waele further discloses a system, wherein:
said extrapolation of said frequency distribution of said maximum value and said extrapolation of said frequency distribution of said minimum value comprises linear regression (See De Waele Par [0030]-[0031] and Fig. 2 which discloses utilizing linear regression and extrapolation on a graph with dots representing a frequency of usage or a frequency of occurrence in a population of data, i.e. frequency distribution, for determining maximum and minimum value alarm settings and can utilize multi-parameter correlation of multiple alarm setting or limit values and corresponding multiple vital sign signal values).
Claim 13 –
Regarding Claim 13, De Waele and Dyell disclose the system of claim 1 in its entirety. De Waele further discloses a system, wherein:
said first processor is further configured to not store said alarm summary record when said alarm duration is below a duration threshold value (Under broadest reasonable interpretation, contingent limitations require only those steps that must be performed and does not have to include steps that are not required to be performed because conditions precedent are not met; however, the BRI of a system claim having structure that performs a function that is conditional, at least requires the structure for performing the function should the condition occur, but still does not have to teach said function, see MPEP 2111.04(II); therefore, De Waele Par [0028]-[0029] & Fig. 1 disclosing the system containing one or more processing devices and/or connected databases, data structures, non-transitory computer readable media, etc., for storing alarm settings or alarm profiles, the suggested profiles, the monitor log, the central log, the normative settings, the normative vital sign signals, and the patient data and for editing or modifying threshold values effectively reads on the limitation herein because the conditions of “when said alarm duration is below a duration threshold value” does not have to ever necessarily be met under BRI, because alarm duration can potentially always be above a duration threshold value; therefore this claim limitation is effectively met by De Waele under 35 U.S.C. 103).
Response to Arguments
Applicant's arguments filed 29 October 2025 have been fully considered but they are not persuasive:
Regarding Claim Objections to claim 1 & 3-13, Applicant argues on p. 10 of Arguments/Remarks that claim 1 has been amended to overcome previous claim objections and should be withdrawn for claim 1 and claims dependent from claim 1. Examiner agrees with Applicant’s arguments. Therefore, the claim objections for claims 1 & 3-13 have been withdrawn.
Regarding 35 U.S.C. 101 rejections of claims 1 & 3-13, Applicant argues on p. 10-14 of Arguments/Remarks that claim 1 as amended includes sufficient structural and transformational limitations that integrate into a practical application and improve the technology in the art. Applicant further argues that the limitations cannot be done with the human mind alone and is not merely a method of organizing human activity. Examiner respectfully disagrees with Applicant’s arguments. As presented above in the ‘Claim Rejections - 35 USC § 101’ section of this Office Action, each of the limitations found in independent claim 1 do not integrate into the characterized abstract idea into practical application and/or improve the technology in the art for the reasons provided in the mentioned section. Furthermore, the typical behavior of the hospital staff regarding the issuing of alarms is effectively managed by the system performing the steps recited, as is relates to issuance of one or more alarms, such as editing one or more thresholds for alarm issuance in order to effect the change in a number of alarms over said future period of time. As such, these aspects do effectively amount to a method of organizing human activity. As such, claims 1 & 3-13 remain rejected under 35 U.S.C. 101.
Regarding 35 U.S.C. 101 rejections of claims 1 & 3-13, Applicant argues on p. 14-16 of Arguments/Remarks in view of Alice/Mayo Step 2A, Prongs 1 & 2 that the amended claims provide technological improvement in device operation and a specific improvement in computer and device functionality. Applicant further argues that the claims yield a specific technological improvement via automatic control of alarm thresholds across multiple patient devices/monitors, and further notes Enfish and CardioNet in substantiating said arguments of a technological improvement. Applicant further specifies aspects that purportedly integrate any such abstract idea into a specific technological solution, such as a field-configured data structure, a two-processor split, and a closed-loop actuation. Examiner respectfully disagrees with Applicant’s arguments. While Examiner is not necessarily arguing against Applicant’ system being able to implement or automatically control alarm threshold across multiple patient devices/monitors, Examiner does contend that this reads as an improvement to the already-characterized abstraction rather than an improvement to the devices or field of art itself. That is, merely transmitting alarm threshold parameters to multiple patient devices based on analysis (e.g. via a closed-loop system) that is automatically performed simply entails aspects of data gathering, data analysis/repetitive calculation, and transmission of data over a network to one or more devices, which are all abstract steps. An improvement to an abstraction still typically constitutes the abstraction itself. Regarding Enfish, the claims in Enfish effectively recited steps for improvements in computer capabilities/technology and the specification of Enfish discussed the prior art and how the invention improved the way the computer stores and retrieves data in memory based on utilizing a self-referential database, which substantially differs from the instant set of claims. That is, the instant set of claims does not necessarily improve on shortcomings found in prior art systems or the technological components implementing the judicial exception at hand. Rather, the steps recited merely apply a judicial exception using one or more computer components to achieve a desired result or effect, i.e. a desired rate of alarms. It should be noted that a claim that merely generically recites an effect or result from any method by which it is accomplished is not directed to patent-eligible subject matter, because it merely states that the abstract idea should be applied to achieve a desired result, Internet Patents Corporation v. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015), and in the instant set of claims “achieving a desired rate of alarms” without further specifying how the conditions of “a desired rate of alarms” can be met or achieved reads as generically reciting an effect or result from any method by which it is accomplished. That is, while the claims generally recite the system being automated to adjust device behavior, Applicant does not define the conditions of “a desired rate of alarms” so that one of ordinary skill in the art could effectively determine what the desired rate of alarms actually constitutes beyond stating that a “desired rate of alarms” is in-fact being achieved. Furthermore, Applicant does not necessarily delineate said desired rate of alarms from the standpoint of organizing human activity and/or managing personal behavior, such as the desired rate of alarms relating to a technological standpoint or some kind of back-end implementation of said operational parameters being changed. Regarding CardioNet, the claims of CardioNet recited implementation of specific algorithms into a particular machine, i.e. a cardiac monitoring device. That is, steps of data gathering, data analysis/repetitive calculation, and transmission of data over a network to one or more devices are recited in the instantly filed claims and therefore vastly differ from the particular machine and specific algorithm that addressed a particular problem of variability in the beat-to-beat timing caused by premature ventricular beats identified by the device’s ventricular beat detector of CardioNet, LLC v. InfoBionic, Inc. Regarding arguments of a field-configured data structure constituting a specific technological solution by enabling downstream predictive model performance, Examiner argues similar stances to those found above regarding merely improved data gathering/data manipulation efforts still constituting the abstraction itself because an improvement to an abstraction still constitutes the abstraction. Regarding arguments of a two-processor split collecting and structuring alarm data in a first processor and deriving a functional relationship and selecting a new threshold to be transmitted to one or more devices, Examiner contends that improved methods of collecting and structuring alarm data and/or performing repetitive calculations to derive a functional relationship and select a new threshold to be transmitted to one or more devices amount to merely improved data gathering/data manipulation and repetitive calculation, therefore still constituting the abstraction itself. Regarding arguments of a closed-loop actuation writing back new thresholds and doing so iteratively, Examiner contends that closed-loop analysis is well-understood, routine, and/or conventional in prior art systems and while said analysis may be recited for or applied to the field of optimizing alarm thresholds, this does not necessarily entail a meaningful or practical application. While Applicant further argues that no cited case treating “data gathering/analysis” as abstract involved autonomous actuation of field devices to converge a measured system output to a target, Examiner contends that while “actuation” verbiage is continually used by Applicant in the claims/arguments at hand, the “actuation” being discussed is merely transmitting a data parameter, that has been derived by an automated analysis, to one or more devices over a network. That is, the desired rate of alarms does not necessarily relate to an improvement of a technological standpoint or some kind of back-end implementation of said operational parameters being changed, but rather, merely changing one or more patient data/device thresholds for emitting alarms when exceeded/surpassed. As such, the limitations read as merely applying a judicial exception using one or more computer components to achieve a desired result or effect rather than a resulting practical application or technological improvement, as argued by Applicant. Therefore, claims 1 & 3-13 remain rejected under 35 U.S.C. 101.
Regarding 35 U.S.C. 101 rejections of claims 1 & 3-13, Applicant argues on p. 16-17 of Arguments/Remarks that under Alice/Mayo Step 2B, the claims amount to significantly more because De Waele and Dyell do not describe predictive, quantitative modeling of alarm counts as a function of threshold edits followed by automatic, iterative actuation of medical devices to drive system behavior to a target alarm rate. Applicant further argues in view of Thales Visionix v. U.S., McRO v. Bandai, and CardioNet v. Infobionic, stating that each of these cases were upheld as patent-eligible when the claimed method improved a medical monitoring technology rather than merely applying math. Examiner respectfully disagrees with Applicant’s arguments. More specifically, Examiner agrees that Thales Visionix v. U.S., McRO v. Bandai, and CardioNet v. Infobionic, were upheld as patent-eligible when the claimed method improved a medical monitoring technology rather than merely applying math, however this does not seem to be the case/fact-pattern found in the instant set of claims. That is, the instant set of claims does not necessarily improve on shortcomings found in prior art systems or the technological components implementing the judicial exception at hand. Rather, the steps recited merely apply a judicial exception using one or more computer components to achieve a desired result or effect, i.e. a desired rate of alarms. While Applicant again argues in view of the automation and technical feedback control and “actuation” of field devices to converge a measured system output to a target constituting an improvement to the operation of the patient monitoring systems, the “actuation” and/or automation being discussed is merely transmitting a data parameter, that has been derived by an automated analysis, to one or more devices over a network. That is, the desired rate of alarms does not necessarily relate to an improvement of a technological standpoint or some kind of back-end implementation of said operational parameters being changed, but rather, merely changing one or more patient data/device thresholds for emitting alarms when exceeded/surpassed. Improved methods of collecting and structuring alarm data and/or performing repetitive calculations to derive a functional relationship and select a new threshold to be transmitted to one or more devices, even in an automated fashion, amount to merely improved data gathering/data manipulation and repetitive calculation, therefore still constituting the abstraction itself. Furthermore, while Applicant argues against De Waele and Dyell disclosing predictive, quantitative modeling, e.g. closed-loop iterative analysis of medical data to derive a target alarm rate to be implemented by editing one or more thresholds in one or more devices, Examiner contends that the combination of De Waele and Dyell do disclose said aspects. That is, Dyell effectively discloses employing a closed-loop control system, such that the system can be configured to account for any hysteresis of its feedback loop, where hysteresis indicates a dependence of the output of system on its current input and its history of past inputs, i.e. outputs/behavior of the system forms a closed-loop system to iteratively adjust the system to prevent alarm fatigue, as specifically mentioned in Dyell Par [0095] & [0097], such that based on monitored trends, etc. in available data may trigger shifts in how alarms are to be outputted for corresponding devices. Furthermore, a plurality of prior art references effectively discloses the use of a closed-loop algorithm for optimizing parameters in a more generalized, medical monitoring environment. Therefore, merely applying said closed-loop algorithm to optimize alarm rates to prevent alarm fatigue, which is already effectively rendered well-understood, routine, and/or conventional by Dyell, would also not constitute significantly more at least by the prior art, e.g. Beck, rendering closed-loop algorithms in general as well-understood, routine, and/or conventional activity found in the prior art. As such, the limitations do not amount to significantly more than the recited abstract idea itself. Therefore, claims 1 & 3-13 remain rejected under 35 U.S.C. 101.
Regarding 35 U.S.C. 103 rejections of claims 1 & 3-13, Applicant argues on p. 17-21 of Arguments/Remarks that De Waele and Dyell does not disclose the entirety of the claims in view of the newly amended limitations found in independent claim 1. More specifically, Applicant argues that De Waele is silent regarding the claim’s “functional relationship” requirement, i.e. predicting the future number of alarms as a function of hypothetical threshold modifications to existing devices, and Dyell does not cure said deficiencies, because Dyell modulates how alarms are presented or distributed in response to a user’s fatigue level, it does not model alarm counts as a function of, nor computer expected alarm-count deltas from modified thresholds, nor does it re-write device thresholds on that basis. Examiner agrees with Applicant’s arguments. Therefore, the 35 U.S.C. 103 rejections for claims 1 & 3-13 have been withdrawn. However, upon further consideration, a new ground of rejection is made under 35 U.S.C. 103 over newly cited portions of De Waele in view of newly cited portions of Dyell. Newly cited portions of De Waele rely on De Waele Par [0027] & [0037]-[0038] which generally discloses recommending changes to alarms settings for the patient based on current vital sign data and/or alarm data, and sends the recommended changes to the alerting device or other computing device such that the collected patient vital signs, i.e. patient-data samples, are averaged over a time interval, such that if the average vital sign data stream exceeds a threshold limit for time sufficiently long to infer that exceeding the limit is not a short term aberration, the data associated during that time (i.e. the data described in De Waele Par [0027] including vital sign data and/or alarm data) is collected over said time span to define a new alarm limit threshold, i.e. the new/modified threshold is extrapolated based on historical alarm/threshold values and patient-data samples from the time span associated with the exceeding of the threshold limit for a prolonged timeframe, and specifically states in Par [0038] that a time interval, ether a minimum or maximum after which a different alarm setting value is recommended, i.e. future time period and therefore constitutes extrapolation of said data. Therefore, De Waele discloses efforts of determining said one or more modified threshold values based on analysis and extrapolation of alarm-summary records and corresponding patient-data samples. Newly cited Dyell is relied upon to read on the newly amended limitation “said collect said alarm data, said generate said alarm summary record, said retrieve alarm summary records, said process said alarm summary records, said determine said one or more modified threshold values, and said replace one or more of said one or more threshold values that exist in said one or more of said multiplicity of patient monitoring devices are fully automated to form a closed-loop system that iteratively adjusts device behavior to achieve a desired rate of alarms” by Dyell Par [0097]-[0099] effectively disclosing employing a closed-loop control system, such that the system can be configured to account for any hysteresis of its feedback loop, where hysteresis indicates a dependence of the output of system on its current input and its history of past inputs, i.e. outputs/behavior of the system forms a closed-loop system to iteratively adjust the system to prevent alarm fatigue, as specifically mentioned in Dyell Par [0095] & [0097], such that based on monitored trends, etc. in available data may trigger shifts in how alarms are to be outputted for corresponding devices. While Applicant argues that De Waele and Dyell do not disclose said aspects of predicting a future number of alarms as a function of hypothetical threshold modifications, De Waele clearly discloses efforts of determining one or more modified threshold values based on analysis and extrapolation of received patient and/or threshold data and recommending said modified threshold value for a time interval, ether a minimum or maximum after which a different alarm setting value is recommended, i.e. future time period and therefore constitutes “extrapolation” of said data. Furthermore, Dyell discloses a closed-loop control system, such that the system can be configured to account for any hysteresis of its feedback loop, where hysteresis indicates a dependence of the output of system on its current input and its history of past inputs, i.e. outputs/behavior of the system forms a closed-loop system to iteratively adjust the system to prevent alarm fatigue, as specifically mentioned in Dyell Par [0095] & [0097], such that based on monitored trends, etc. in available data may trigger shifts in how alarms are to be outputted for corresponding devices. Therefore under a new ground of rejection with newly cited portions of De Waele and Dyell, the references effectively read on the newly amended/specified limitations found in independent claim 1. As such, claims 1 & 3-13 remain rejected under 35 U.S.C. 103.
Regarding 35 U.S.C. 103 rejections of claims 1 & 3-13, Applicant argues on p. 21 of Arguments/Remarks that De Waele and Dyell do not effectively disclose an “evaluating a range of modified threshold values to generate a functional relationship between potential modifications and a resulting number of alarms”, but rather De Waele clusters existing settings and vital trends and Dyell updates fatigue models and presentation, not device thresholds or any threshold-count function relationships. Examiner respectfully disagrees with Applicant’s arguments. That is, Dyell Par [0097]-[0099] discloses employing a closed-loop control system, such that the system can be configured to account for any hysteresis of its feedback loop, where hysteresis indicates a dependence of the output of system on its current input and its history of past inputs, i.e. outputs/behavior of the system forms a closed-loop system to iteratively adjust the system to prevent alarm fatigue, as specifically mentioned in Dyell Par [0095] & [0097], such that based on monitored trends, etc. in available data may trigger shifts in how alarms are to be outputted for corresponding devices. Furthermore, Dyell Par [0089]-[0093] specifically mentions considerations of particular medical devices that may be more prone to issuing excessive alarms because a threshold condition is too low, or has varying measurement sensitivities, such that the system can determine potential alarm conditions and consider a measurement sensitivity to determine a minimum amount of change a physiological measurement undergoes before the medical device detects the change for triggering alarms to reduce unnecessary alarm generation, such as by determining when fewer or greater alarms would be issued, and specifically recites if a medical device is set to detect body temperature changes only when the temperature change is 0.2 degrees or greater, fewer alarms would be generated based on small fluctuations around the threshold value, and thereby establishes a functional relationship between potential modifications of said devices and a resulting number of alarms from said modifications based, for instance, measurement sensitivity. Therefore, Dyell effectively discloses “evaluating a range of modified threshold values to generate a functional relationship between potential modifications and a resulting number of alarms”. While Applicant argues that the specific feedback loop of the independent claims (predict -> select -> actuate threshold -> measure -> iterative to target alarm rate), Examiner contends that Dyell does perform said feedback loop. That is, Dyell Par [0089]-[0093] determines a functional relationship between potential modifications and a resulting number of alarms, i.e. “predicting”. Dyell Par [0078] & [0092]-[0093] discloses implementing or changing alarms and/or types of alarms based on alarm condition and this having a direct effect on how frequently the medical device generates alarms, such as potentially reducing the number of alarms, i.e. constituting transmitting and/or effectuating change in alarm conditions of the one or more alerting devices, i.e. “selecting” and “actuating” alarm conditions, e.g. thresholds. Then, Dyell Par [0097] specifically mentions a closed-loop control system that manages changes in alarm outputs, such that the system senses and quantifies, i.e. “measures”, a rise in alarm fatigue level, modulates its alarm outputs to that user to mitigate the effects of that user's alarm fatigue on a closed-loop basis until a desired alarm fatigue level is reached, i.e. the system iteratively performs said steps of “predicting”, “selecting”, “actuating”, and “measuring”. As such, claims 1 & 3-13 remain rejected under 35 U.S.C. 103.
Regarding 35 U.S.C. 103 rejections of claims 1 & 3-13, Applicant argues on p. 21-24 of Arguments/Remarks that De Waele and Dyell do not disclose “determining and selecting a new threshold… based on said functional relationship and transmitting said new threshold value to patient monitoring devices… [in a] closed-loop system that iteratively adjusts device behavior to achieve a desired rate of alarms.” Examiner respectfully disagrees with Applicant’s arguments. As explained above, Dyell effectively discloses “evaluating a range of modified threshold values to generate a functional relationship between potential modifications and a resulting number of alarms” and further discloses at Par [0097]-[0099] employing a closed-loop control system, such that the system can be configured to account for any hysteresis of its feedback loop, where hysteresis indicates a dependence of the output of system on its current input and its history of past inputs, i.e. outputs/behavior of the system forms a closed-loop system to iteratively adjust the system to prevent alarm fatigue such that based on monitored trends, etc. in available data may trigger shifts in how alarms are to be outputted for corresponding devices, such as based on the considerations found in Dyell Par [0089]-[0093] wherein the system determines certain medical devices that may be more prone to issuing excessive alarms because a threshold condition is too low, or has varying measurement sensitivities, such that the system can determine potential alarm conditions and consider a measurement sensitivity to determine a minimum amount of change a physiological measurement undergoes before the medical device detects the change for triggering alarms to reduce unnecessary alarm generation, such as by determining when fewer or greater alarms would be issued. Regarding De Waele/Dyell not disclosing the limitation “transmits said new threshold value to patient monitoring devices”, Examiner points to De Waele Par [0027] which discloses the observational analyzer recommending changes to alarms settings for the patient based on current vital sign data and/or alarm data, and sends the recommended changes to the alerting device or other computing device, thereby constituting determination of a new alarm setting threshold, and transmission of said alarm setting threshold as required by the argued limitation and further points to Dyell Par [0078] & [0092]-[0093] which discloses implementing or changing alarms and/or types of alarms based on alarm condition and this having a direct effect on how frequently the medical device generates alarms, such as potentially reducing the number of alarms, i.e. constituting transmitting and/or effectuating change in alarm conditions of the one or more alerting devices. While Applicant argues, e.g. on p. 23 of Arguments/Remarks, that Dyell concerns fatigue levels and alarm presentation and not threshold edits or alarm-count predictions, clearly Dyell sets forth relationships therebetween via the cited portions explained above. Therefore, these limitations are effectively met by the combination of De Waele and Dyell under 35 U.S.C. 103. As such, claims 1 & 3-13 remain rejected under 35 U.S.C. 103.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Wilson et al. (U.S. Patent Publication No. 2018/0102046) discloses a system for recognizing and reducing alarm fatigue such as by changing certain alarm/monitoring parameters in one or more devices;
Freeman et al. (U.S. Patent Publication No. 2020/0383647) discloses a system or identifying incessant false alarm events and monitors contributing to alarm fatigue in real-time based on retrospective analysis;
Woodward et al. (U.S. Patent Publication No. 2019/0180592) discloses a system for developing a closed loop alarm management method, such that alarm fatigue may be reduced by optimizing alarm conditions and/or thresholds;
Schlesinger et al. (U.S. Patent Publication No. 2017/0039822) discloses a system for reducing alarm fatigue by dynamically managing volume of alarms based on ambient noise conditions and/or other factors.
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/H.R./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684