DETAILED ACTION
Status of Claims
This action is in reply to the communication filed on 17 April, 2026.
Claims 1, 11 and 21 have been amended.
Claims 1 – 30 are currently pending and have been examined.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 17 April, 2026 has been entered.
Claim Interpretation
The claims recite terms that require interpretation in order to clearly understand their meaning. For example, Claims 1, 11 and 21 recite “interfacing with a bedside monitor to receive data signals.” The specification discloses that devices are coupled to the information processor (10) (i.e. a server) using wired or wireless techniques that are well-known. The devices – i.e. vendor devices – may include a medical device or other types of devices. Medical devices include monitoring devices such as a bedside monitor. The bedside monitor itself is disclosed as a vital sign monitor.
The claims further recite that the data signals “have monitoring criteria”. The specification discloses monitoring criteria as an “alarm threshold”; and data signals “having” such a threshold means that the threshold is associated with the data, and applied to the data in a comparison to determine if an alarm condition (or outlier) exists. “Having” a threshold does not convey that the threshold is embedded in, attached to, or incorporated in the data signal itself.
Claims 1, 11 and 21 also recite “normalizing” data signals from different vendor devices to generate homogenized signals that can “work together”. Here, the specification describes normalizing as “transforming data into a standardized format or range”. The raw data can be normalized by formatting the data in a particular representations. Alternately, the raw data can be statistically normalized to produce a common scale using conventional techniques such as Min-Max normalization or Z-score normalization. Signals that can work together means that the data has a common range for comparisons.
Claims 1, 11 and 21 also recite “enabling” adjustment of the monitoring criteria. Here, the specification describes enabling as providing instructions to enable the user to make the adjustment. Examiner adopts these meanings.
Further, the claims recite “detecting” one or more alarms; processing the alarms to determine an “authenticity”, including “defining volume, volatility, bias, persistence and stationary information” for the detected alarms; and defining a “bespoke” monitoring criteria for non-authentic alarms. The specification discloses that detecting alarms is part of the bedside monitor’s ordinary function. Alarms may occur for changes in a patient’s condition or potential issues with the monitoring device.
Determining authenticity includes defining volume, volatility, bias, persistence and stationary information, which are described as characteristics of the data, that are calculated and compared to a threshold or baseline. The broadest reasonable interpretation of determining authenticity includes manually setting a data characteristic threshold and comparing actual data characteristics to the threshold.
The claims use the term “bespoke” monitoring criteria. Bespoke is defined as “made for a particular customer or user” (Oxford Dictionary). As such, bespoke monitoring criteria are construed as alarm thresholds for a particular bedside monitoring device. Such criteria may be user-defined or machine defined. The broadest reasonable interpretation of this limitation includes user defined criteria
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.
The following rejection is formatted in accordance with MPEP 2106.
Claim 1 is representative. Claim 1 recites:
A computer-implemented method, executed on a computing device, comprising:
interfacing with a bedside monitoring device to receive data signals, wherein the data signals have monitoring criteria; and
wherein the data signals include first data signals from one or more first vendor devices and second data signals from one or more second vendor devices;
normalizing the first data signals and the second data signals to generate a plurality of homogenized signals so that the first data signals and the second data signals can work together;
enabling adjustment of one or more of the monitoring criteria;
suggesting a proposed change to a user concerning the one or more monitoring criteria, including
detecting one or more alarms associated with the bedside monitoring device;
processing the one or more detected alarms to determine an authenticity of the one or more detected alarms, including defining one or more of:
volume information for the one or more detected alarms;
volatility information for the one or more detected alarms;
bias information for the one or more detected alarms;
persistence information for the one or more detected alarms; and
stationary information for the one or more detected alarms; and
defining a bespoke monitoring criteria for the bedside monitoring device if one or more of the one or more detected alarms is determined to be non-authentic;
providing a representation of a current level at which alarm thresholds are being exceeded at the monitoring criteria and a simulation of a level at which the alarm thresholds would be exceeded at the proposed change concerning the one or more monitoring criteria; and
providing instructions to the user concerning the proposed change;
acoustically monitoring a medical environment including the beside monitoring device to generate an acoustic signal indicative of audio within the medical environment;
processing the acoustic signal to identify one or more audible alarms;
categorizing the one or more audible alarms based upon one or more of an alarm type, and alarm severity, and alarm duration, an alarm magnitude, and an alarm frequency;
training an AI model based upon, at least in part, the categorized audible alarms; and
using the trained AI model to extract patterns concerning a relationship between one or more of alarm counts and alarm types and one or more of patient demographics, hospital locations, staffing levels, staff attrition levels, and staff satisfaction levels.
Claim 11 recites medium with instructions executed by a processor, and Claim 21 recites a system that executes the steps of the method recited in Claim 1.
Claims 1 - 30 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), and does not include additional elements that either: 1) integrate the abstract idea into a practical application, or 2) that provide an inventive concept – i.e. element that amount to significantly more than the abstract idea. The Claims are directed to an abstract idea because, when considered as a whole, the plain focus of the claims is on an abstract idea.
STEP 1
The claims are directed to a system, a method and non-transitory computer readable medium which are included in the statutory categories of invention.
STEP 2A PRONG ONE
The claims, as illustrated by Claim 1, recite limitations that encompass an abstract idea including:
enabling adjustment of one or more of the monitoring criteria;
suggesting a proposed change to a user concerning the one or more monitoring criteria, including
detecting one or more alarms associated with the bedside monitoring device;
processing the one or more detected alarms to determine an authenticity of the one or more detected alarms, including defining one or more of:
volume information for the one or more detected alarms;
volatility information for the one or more detected alarms;
bias information for the one or more detected alarms;
persistence information for the one or more detected alarms; and
stationary information for the one or more detected alarms; and
defining a bespoke monitoring criteria for the bedside monitoring device if one or more of the one or more detected alarms is determined to be non-authentic; and
providing instructions to the user concerning the proposed change;
processing the acoustic signal to identify one or more audible alarms;
categorizing the one or more audible alarms based upon one or more of an alarm type, and alarm severity, and alarm duration, an alarm magnitude, and an alarm frequency;
extract patterns concerning a relationship between one or more of alarm counts and alarm types and one or more of patient demographics, hospital locations, staffing levels, staff attrition levels, and staff satisfaction levels.
The claims, as illustrated by Claim 1, recite limitations that encompass an abstract idea within the “mental processes” grouping – concepts performed in the human mind including observation, evaluation, judgment and opinion. The claims recite a computer “interfacing” with a bedside monitoring device to receive data signals associated with a threshold (i.e. a monitoring criteria), suggesting a change to the threshold, and providing instructions concerning the change to a user to enable adjustment of the threshold. The claims require “enabling” adjustment of a threshold by providing instructions to a user concerning a suggested or proposed change. The specification discloses instructions to a user may be graphical and/or text based [00179], rendered on a generic computing device – a tablet, laptop, desktop computer, etc. [0408]. Providing the instructions “enables” the adjustment. Similarly, under the broadest reasonable interpretation, suggesting a proposed change to a monitoring threshold is a process that, except for generic computer implementation steps, can be performed mentally. The specification expressly discloses that changes – i.e. bespoke monitoring criteria - are “user-defined” or “proposed by a clinician.” [00137,00376, 00383]
Similarly, detecting and processing alarms to determine authenticity includes comparing calculated characteristics of the data to thresholds or baseline characteristics. Such comparisons can be performed mentally. In addition defining volume, volatility, bias, persistence and stationary information may be user-defined – i.e. performed mentally.
The claims further recite monitoring audio in a medical environment, identifying alarms by type, severity, duration, magnitude and frequency, and extracting relationships between counts (or types) and patient demographics, hospital locations, or staff characteristics. Monitoring and identifying alarms by type, etc. is an ordinary mental process. Nurses and other healthcare workers routinely listen for (i.e. monitoring) and categorize alarms based on their sound characteristics. Similarly, extracting relationships is construed as “counting” alarms sorted or filtered by patient demographics, hospital locations, or staff characteristics. Counting and sorting alarms can be performed mentally. As such, the claims recite an abstract idea within the mental process grouping.
The claims, as illustrated by Claim 1, recite limitations that encompass an abstract idea within the “certain methods of organizing human activity” grouping –
managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions.
Enabling adjustment of a monitoring threshold by suggesting proposed changes and providing instructions is process that merely organizes this human activity. This type of activity, i.e. managing alarms, includes conduct that would normally occur when monitoring patients. For example, it is routine in medicine establish data signal norms for a particular patient using generalized norms [00156], and to monitor and categorize alarms. As such, the claims recite an abstract idea within the certain methods of organizing human activity grouping.
The claims, as illustrated by Claim 1, also recite limitations that encompass a mathematical formula or relationship:
normalizing the first data signals and the second data signals to generate a plurality of homogenized signals so that the first data signals and the second data signals can work together.
The claims normalize data from a first and second vendor device. The specification discloses that normalizing may be performed using known mathematical techniques such as Min-Max Normalization or Z-Score Normalization. (00103). As such, the claims recite a mathematical formula or relationship.
STEP 2A PRONG TWO
The claims recite limitations that include additional elements beyond those that encompass the abstract idea above including:
a computing device;
interfacing with a bedside monitoring device to receive data signals;
wherein the data signals include first data signals from one or more first vendor devices and second data signals from one or more second vendor devices;
detecting one or more alarms associated with the bedside monitoring device;
providing a representation of a current level at which alarm thresholds are being exceeded at the monitoring criteria and a simulation of a level at which the alarm thresholds would be exceeded at the proposed change concerning the one or more monitoring criteria;
acoustically monitoring a medical environment including the beside monitoring device to generate an acoustic signal indicative of audio within the medical environment;
training an AI model based upon, at least in part, the categorized audible alarms; and using the trained AI model to extract patterns
However, these additional elements do not integrate the abstract idea into a practical application of that idea in accordance with the MPEP. (see MPEP 2106.05)
The computing device and AI model are recited at a high level of generality such that they amount to no more than instructions to apply the abstract idea using generic computer components. These elements merely adds instructions to implement the abstract idea on a computer, and generally link the abstract idea to a particular technological environment. Interfacing with a bedside monitor – i.e. coupling a computer to a well-known and purely conventional bedside monitor using known network communication techniques, and detecting alarms - acoustically monitoring a medical environment including the beside monitoring device to generate an acoustic signal are insignificant extra-solution activities – i.e. a data gathering steps.
Similarly, displaying the results of the abstract process – i.e. providing a representation of a current level, and a simulated level with an adjusted threshold, on a generic computer display, for example – is ancillary to the abstract idea itself; does not improve the computer itself, or any other technology; nor does the display of results provide a meaningful limitation beyond generally linking the abstract idea to a particular technological environment.
In particular, the claims applying established methods of machine learning to an abstract process in a new data environment – i.e. applying a trained model to the categorized alarm information. The specification teaches that the model may be trained to recognize patterns in the data using known techniques (@ 0145). Machine learning limitations reciting broad, functionally described, well-known techniques executed by generic and conventional computing devices does not provide a practical application of the abstract diagnostic process. “Today we hold only that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under §101.” (Recentive Analytics, Inc. v. Fox Corp. (Fed. Cir. 2025)).
The specification discloses providing a device user interface (UI) (006) on a client device such as a personal computer (0054), by executing a web browser, for example. (0057 – 0058). The specification further discloses that post-processing data, including visualizations on display systems, are commonly used (00113 – 00122). In particular, the specification discloses a “UX – User Experience” for a variety of disclosed functions, including a Threshold Manager UX that allows for visually monitoring operations, gathering threshold data, selecting a “viewing lens” - (i.e. a content filtering mechanism), and “rendering” the gathered information. (00305 – 00314).
The disclosed “UX” “optimizes a user experience, and enhances their overall satisfaction.” (00281) The “UX” “provide meaningful and satisfying experiences, enhance customer satisfaction, increase engagement, and build long-term user loyalty” (00283). These improvements are relevant to the user, and are not technological improvements.
Nothing in the claim recites specific limitations directed to an improved technology or technological process. Similarly, the specification is silent with respect to these kinds of improvements. A general purpose computer that applies a judicial exception by use of conventional computer functions, as is the case here, does not qualify as a particular machine, nor does the recitation of a generic computer impose meaningful limits in the claimed process. (see Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17 (Fed. Cir. 2014)). As such, the additional elements recited in the claim do not integrate the abstract threshold management process into a practical application of that process.
STEP 2B
The additional elements identified above do not amount to significantly more than the abstract threshold management process. Interfacing with a bedside monitor – i.e. coupling a computer to a well-known and purely conventional bedside monitor using known network communication techniques, disclosed at a high level of generality, is a well-understood, routine and conventional computer function – i.e. receiving or transmitting data over a network as in Symantec, TLI, OIP and buySAFE. Displaying the results of the abstract process (i.e. providing representations of data) is an ancillary part of the abstract process itself as in Electric Power Group.
The additional structural elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generic computer structure (i.e. a computing device/processor and memory, computer-readable medium). Each of the above components are disclosed in the specification as being purely conventional and/or known in the industry. Because the specification describes these additional elements in general terms, without describing particulars, Examiner concludes that the claim limitations may be broadly, but reasonably construed, as reciting well-understood, routine and conventional computer components and techniques. The specification describes the elements in a manner that indicates that they are sufficiently well-known that the specification does not need to describe the particulars in order to satisfy U.S.C. 112. Considered as an ordered combination the limitations recited in the claims add nothing that is not already present when the steps are considered individually. As such, the additional elements recited in the claim do not provide significantly more than the abstract threshold management process, or an inventive concept.
The dependent claims add additional features including:
those that merely serve to further narrow the abstract idea above such as:
providing step-by-step instructions for local or remote implementation (Claim 2, 3, 12, 13, 22, 23);
further limiting the type of monitoring criteria (Claim 5, 7, 8, 15, 17, 18, 25, 27, 28);
further limiting the type of data signal (Claims 6, 16, 26);
further limiting the type of medical device (Claim 10, 20, 30);
those that recite additional abstract ideas such as:
enabling user to effectuate proposed change (Claim 4, 14, 24);
processing massive data sets by ML (Claim 9, 19, 29);
those that recite well-understood, routine and conventional activity or computer functions; those that recite insignificant extra-solution activities; or those that are an ancillary part of the abstract idea.
The limitations recited in the dependent claims, in combination with those recited in the independent claims add nothing that integrates the abstract idea into a practical application, or that amounts to significantly more. As such, the additional element do not integrate the abstract idea into a practical application, or provide an inventive concept that transforms the claims into a patent eligible invention.
The apparatus claims are no different from the method claims in substance. “The equivalence of the method, system and media claims is readily apparent.” “The only difference between the claims is the form in which they were drafted.” (Bancorp). The method claims recite the abstract idea implemented on a generic computer, while the apparatus claims recite generic computer components configured to implement the same idea. Specifically, Claims 11 – 30 merely add the generic hardware noted above that nearly every computer will include. The apparatus claim’s requirement that the same method be performed with a programmed computer does not alter the method’s patentability under U.S.C. 101 (In re Grams). Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 – 30 are rejected under 35 U.S.C. 103 as being unpatentable over Hubert et al.: (US PGPUB 2017/0095217 A1) in view of D’Angelo et al.: (US 11,257,587 B1) in view of Albert et al.: (US PGPUB 2006/0117558 A1) and in view of Treacy et al.: (US PGPUB 2018/0096110 A1).
CLAIMS 1, 11 and 21
Hubert discloses a patient monitor advisor that includes the following limitations:
A computer-implemented method, executed on a computing device, (Hubert 0008, 0038);
comprising:
interfacing with a monitoring device to receive data signals, wherein the data signals have monitoring criteria; (Hubert 0019);
enabling adjustment of one or more of the monitoring criteria; (Hubert 0018, 0019, 0022);
suggesting a proposed change to a user concerning the one or more monitoring criteria, (Hubert 0004 – 0006, 0019); including
detecting one or more alarms associated with the bedside monitoring device; (Hubert 0002, 0007, 0019);
processing the one or more detected alarms to determine an authenticity of the one or more detected alarms, including defining one or more of: volume information for the one or more detected alarms; volatility information for the one or more detected alarms; bias information for the one or more detected alarms; persistence information for the one or more detected alarms; and stationary information for the one or more detected alarms; (Hubert 0005, 0029, 0030, 0032, 0040, 0043); and
defining a bespoke monitoring criteria for the bedside monitoring device if one or more of the one or more detected alarms is determined to be non-authentic; (Hubert 0003 – 0006, 0019); and
providing instructions to the user concerning the proposed change; (Hubert 0006 – 0008, 0019 – 0022, 0037).
Hubert discloses a system and method for determining (i.e. suggesting) and displaying (i.e. providing) actionable advice to a user including specific actionable advice in the form of instructional text that enable the user to set-up, tailor or adjust one or more alarm limits (i.e. monitoring criteria) for a patient monitoring system. Adjustments to the alarm limits may be made using up/down arrows on a touch screen display of the monitor such that they are “specific to a current patient, the specific needs in a present situation, or a present mode or setting of the patient monitoring system.” – i.e. bespoke. A processor interfaces with one or more sensors which convey the sensed parameters in the form of electrical signals to one or more processors associated with the monitor using wired or wireless techniques. The processor analyzes the sensor signals and presents alarms when the monitored parameters are outside of “prescribed limits”. Alarm limits may be defined by a user including a “frequency” or “repetition” of alarm occurrences, stability of the patient sensor readings, percentage of time an alarm must be present, alarm count per time period, etc. This fairly teaches the recited defined information types.
With respect to the following limitations:
wherein the data signals include first data signals from one or more first vendor devices and second data signals from one or more second vendor devices; normalizing the first data signals and the second data signals to generate a plurality of homogenized signals so that the first data signals and the second data signals can work together; (D’Angelo col. 1 line 40 – 47, col. 2 line 52 – 61, col. 6 line 18 – 32, col. 16 line 23 – 66, col. 22 line 7 – 22 and 50 – 60, col. 23 line 1 – 36).
Hubert does not disclose normalizing data from different vendor devices. D’Angelo discloses a healthcare administration system that includes receiving data signals from patient monitoring equipment such as medical measurement devices (e.g., pulse oximeter, sphygmomanometers, blood glucose, cholesterol or other biomarker measuring devices). The data collected is sourced from diverse vendor specific devices, and is normalized to facilitate comparison of data fields involving the use of interoperability. Normalizing can render the data in a common format or using a common statistical representation to facilitate more accurate and reliable analysis of data. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing data of the claimed invention, to have modified the patient monitor advisor system of Hubert so as to have included normalizing data from disparate vendor devices, in accordance with the teaching of D’Angelo, in order to facilitate comparison of data fields and to facilitate more accurate and reliable analysis of data.
With respect to the following limitations:
a bedside monitoring device; (Albert 0017, 0021);
acoustically monitoring a medical environment including the beside monitoring device to generate an acoustic signal indicative of audio within the medical environment; (Albert 0017, 0056, 0059, 0060, 0073, 0105 – 0109, 0116);
processing the acoustic signal to identify one or more audible alarms; categorizing the one or more audible alarms based upon one or more of an alarm type, and alarm severity, and alarm duration, an alarm magnitude, and an alarm frequency; (Albert Abstract, 0017, 0018, 0034, 0055, 0060 – 0064, 0073, 0076, 0110, 0116, 0123);
training an AI model based upon, at least in part, the categorized audible alarms; (Albert 0024, 0042, 0062, 0064, 0110, 0138).
Hubert discloses patient monitoring devices in general, but does not expressly disclose a “bedside” monitoring device. Hubert does not disclose acoustic monitoring to identify and categorize audible alarms. Albert expressly discloses a bedside monitoring device in a network configuration that includes monitoring audible alarms from various types of monitor, including bedside health monitors. A microphone receives ambient sounds and a computer detects alarm signals from the sounds. The system categorizes the alarm signal by type, frequency and severity using a trained neural network. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing data of the claimed invention, to have modified the patient monitor advisor system of Hubert/D’Angelo so as to have included acoustical monitoring to detect and classify alarm sounds for bedside monitors, in accordance with the teaching of Albert, in order to allow for monitoring of home alarms (Albert 0008).
With respect to the following limitations:
providing a representation of a current level at which alarm thresholds are being exceeded at the monitoring criteria and a simulation of a level at which the alarm thresholds would be exceeded at the proposed change concerning the one or more monitoring criteria; (Treacy Abstract, 0006, 0024, 0032, 0035, 0038 – 0045, 0048 - 0054);
extract patterns concerning a relationship between one or more of alarm counts and alarm types and one or more of patient demographics, hospital locations, staffing levels, staff attrition levels, and staff satisfaction levels; (Treacy 0006, 0021 - 0023, 0026, 0028 – 0031, 0033 - 0036.
Hubert discloses providing a representation of a current level at which alarm thresholds are being exceeded at the monitoring criteria, advice for adjusting the threshold, and a graphical means for adjusting the threshold, but does not expressly disclose “simulating” a level at the proposed change (Hubert Fig. 1, 0018 – 0022). For example, Hubert discloses a patient monitor that displays the current level at which the alarm thresholds are being exceeded. Fig. 1 shows the area above the upper limit line (40); in this case – time in an alarm state, and arrows for adjusting the limit up or down. Nonetheless, while Hubert may reasonably teach adjusting thresholds, this does not expressly encompass “simulating” threshold changes.
Treacy discloses a medical device alarm management system (@ 0001 – 0003) that includes a processor for receiving and analyzing medical device alarm data comprising a plurality of alarms from a plurality of medical devices located at one or more hospitals, hospital care units, or other medical facilities (i.e. locations). Treacy categorizes each alarm, aggregates the plurality of alarms, (i.e. extract patterns) and displays a summary of the alarms in a dashboard displayed on a graphical user interface, including total alarms, each medical device’s contribution to the total (i.e. alarm count), and by category (i.e. type). A graphical depiction of the alarm burden includes visual graphs and numeric values for total number of alarms by location, total alarms by type, alarm priority, etc. Treacy incorporates staffing or personnel information in the analysis.
Treacy also facilitates the proposal, evaluation and selection of updates to alarm value limits (i.e. alarm thresholds) including displaying a current level of alarms – the alarm burden, for example in a heat map. A user may propose a change to one or more alarm setting parameters, and test the effect by entering new threshold values in the user interface. The graphical depiction is updated to indicate the proposed new alarm limit value and the effect on the number of alarms produced.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing data of the claimed invention, to have modified the patient monitor advisor system of Hubert/D’Angelo/Albert so as to have included “simulating” changes in alarm thresholds for bedside monitors, and displaying statistical relations, including by using a trained model as in Albert, in accordance with the teaching of Treacy, in order to reduce alarm burden on clinicians.
Albert further discloses the following limitations:
With respect to Claims 11 and 21, Hubert discloses:
A computer program product residing on a non-transitory computer readable medium having a plurality of instructions stored thereon which, when executed by a processor, cause the processor to perform operations; (Hubert 0038);
A computing system including a processor and memory configured to perform operations; (Hubert 0038).
CLAIMS 4 - 6, 14 - 16 and 24 - 26
The combination of Hubert/D’Angelo/Albert/Treacy discloses the limitations above relative to Claims 1, 11 and 21. Additionally, Hubert discloses the following limitations:
enabling the user to effectuate the proposed change of the one or more monitoring criteria; (Hubert 0004, 0018, 0019, 0022);
wherein the one or more of monitoring criteria includes one or more thresholds; (Hubert 0018, 0019, 0022);
wherein the data signals concern one or more details of the bedside monitoring device and/or uses of the bedside monitoring device; (Hubert 0017, 0019).
Hubert teaches enabling alarm limit (i.e. thresholds) changes based on data from the monitor.
CLAIMS 7 - 10, 17 - 20 and 27 - 30
The combination of Hubert/D’Angelo/Albert/Treacy discloses the limitations above relative to Claims 1, 11 and 21. Additionally, Hubert discloses the following limitations:
wherein the monitoring criteria includes user-defined monitoring criteria; (Hubert 0004, 0019, 0040);
wherein the monitoring criteria includes machine-defined monitoring criteria; (Hubert 0004, 0019, 0029);
wherein the machine-defined monitoring criteria is defined via massive data sets processed by ML; (Hubert 0033);
wherein the bedside monitoring device includes a medical device, and wherein the medical device includes one or more sub-medical devices; (Hubert 0019, Figure 1).
Hubert discloses that medical personnel know how to set-up an alarm limit for a specific patient (i.e. user defined alarm limits). Further, advice is delayed allowing the user to initiate the change on their own. Hubert discloses computer determined alarm limits based on an algorithm that learns them. Learning inherently includes “massive data sets”. Hubert discloses a monitor (i.e. a medical device) with multiple sensors (i.e. sub-medical devices).
CLAIMS 2, 12 and 22
The combination of Hubert/D’Angelo/Albert/Treacy discloses the limitations above relative to Claims 1, 11 and 21. Additionally, Hubert discloses the following limitations:
wherein providing instructions to the user concerning the proposed change includes: providing step-by-step instructions to the user concerning the proposed change; (Hubert 0027 – 0029).
Hubert discloses specific instructional advice and actions, including illustrations and text regarding recommended procedures, and concise explanations.
CLAIMS 3, 13 and 23
The combination of Hubert/D’Angelo/Albert/Treacy discloses the limitations above relative to Claims 2, 12 and 22. Additionally, Hubert discloses the following limitations:
wherein providing step-by-step instructions to the user concerning the proposed change includes one or more of: providing step-by-step, locally-implemented, device-UI instructions to the user concerning the proposed change; providing step-by-step, remotely-implemented, device-UI instructions to the user concerning the proposed change; and providing step-by-step, remotely-implemented, system-UI instructions to the user concerning the proposed change; (Hubert 0007, 0019 - 0023, 0032, 0034).
Hubert discloses displaying instruction on the display of the patient monitoring system (i.e. locally-implemented, device-UI instructions).
Response to Arguments
Applicant's arguments filed 14 April, 2026 have been fully considered but they are not persuasive.
The U.S.C. §101 Rejection
Applicant asserts that the claims are not directed to an abstract idea, and that any such abstract idea is integrated into a practical application, because of newly amended additional features. Applicant declines to explain why these features integrate the abstract idea into a practical application. Examiner asserts that they do not integrate the identified abstract ideas; rather these additional limitations present additional abstract ideas.
The new limitations require the computing device to normalize data from a first and second vendor device. Normalizing involves known mathematical techniques, which are additional abstract processes. The specification teaches that normalizing allows comparisons.
Examiner notes here that the specification further asserts that normalizing allows devices from disparate vendors to communicate with each other. Nonetheless, this feature is not recited in the claims. The disparate vendor devices only communicate with the computing device that performs the normalization, not with each other.
The U.S.C. §103 Rejection
Applicant asserts that the prior art of record does not disclose the amended limitations. Examiner agrees. However, on further search and consideration, a new grounds of rejection in view of D’Angelo is presented herein.
CONCLUSION
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
“Adaptive threshold-based alarm strategies for continuous vital signs monitoring”; van Rossum et al.; Journal of Clinical Monitoring and Computing; 11 February, 2021 discloses strategies to reduce alarm overload.
US 8,401,606 B2 to Mannheimer discloses a nuisance alarm reduction method in a physiological monitor, including setting dynamic alarm thresholds.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to John A. Pauls whose telephone number is (571) 270-5557. The Examiner can normally be reached on Mon. - Fri. 8:00 - 5:00 Eastern. If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Robert Morgan can be reached at (571) 272-6773.
Official replies to this Office action may now be submitted electronically by registered users of the EFS-Web system. Information on EFS-Web tools is available on the Internet at: http://www.uspto.gov/patents/process/file/efs/guidance/index.jsp. An EFS-Web Quick-Start Guide is available at: http://www.uspto.gov/ebc/portal/efs/quick-start.pdf.
Alternatively, official replies to this Office action may still be submitted by any one of fax, mail, or hand delivery. Faxed replies should be directed to the central fax at (571) 273-8300. Mailed replies should be addressed to “Commissioner for Patents, PO Box 1450, Alexandria, VA 22313-1450.” Hand delivered replies should be delivered to the “Customer Service Window, Randolph Building, 401 Dulany Street, Alexandria, VA 22314.”
/JOHN A PAULS/Primary Examiner, Art Unit 3683
Date: 28 July, 2026
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