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 .
This communication is in response to amendments filed on 04/15/2026.
In the application claims 1-39 are pending.
Applicant’s arguments with respect to the 35 USC 101 rejections and non-statutory double patenting rejections are persuasive and the rejections are withdrawn.
Applicant’s arguments with respect to the 35 USC 103 rejections were fully considered; however, the arguments are moot in view of the new grounds of rejections.
Specification
Amendments to the Specification filed on 04/15/2026 is accepted and entered.
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.
Claim(s) 1-39 are rejected under 35 U.S.C. 103 as being unpatentable over Boyer (US 2016/0093205 A1), in view of Shi et al (US 2023/0098165 A1), in view of Campbell (US 2022/0241502 A1), and further in view of Picardi (US 2022-0068097 A1).
Consider claim 1, Boyer teaches, a computer-implemented method, executed on a computing device (12, Boyer teaches, “processor configured to execute code… e.g., stored in a memory of the monitor 12” See ¶ 0022), Boyer teaches, “generating an alarm in response to a determination that the physiological signal or physiological parameter value meets an alarm condition” See ¶ 0006, comprising:
defining an incident as the occurrence of a plurality of required alarms, Boyer teaches, “[f]or example, the physiological signal or physiologic data may be received from a sensor (e.g., the sensor 14) or from one or more local or remote medical devices.” See ¶ 0043; Boyer teaches, “[a]n alarm is generated by a medical device (such as the monitor 12 or a therapeutic device, such as a ventilator) when an alarm condition or protocol is met. Alarm conditions include several types, such as physiologic alarm conditions, patient event alarm conditions, and device alarm conditions…. Further, alarm conditions may be based on a combination of different alarm conditions, such as two physiologic parameters each violating a respective limit, a combined alarm index violating a limit, or specified combinations of monitor and sensor status events.” See ¶ 0025; Boyer teaches, “[i]n another embodiment, analysis of the collected relevance event data may reveal a relationship between two or more physiologic parameters, and modifying the alarm condition may include combining alarm conditions from two or more physiologic parameters. The new combined alarm is not triggered unless both (or all) conditions are met” See ¶ 0055;
monitoring a plurality of devices to detect the occurrence of one or more alarms, thus defining a plurality of detected alarms, Boyer teaches, “collecting relevance data for triggered alarms in accordance with an embodiment. The method 250 includes receiving a physiological signal or physiologic data (block 252). For example, the physiological signal or physiologic data may be received from a sensor (e.g., the sensor 14) or from one or more local or remote medical devices… The method 250 also includes determining whether the physiological signal or physiologic data meets an alarm condition (block 254)… In response to determining that the physiological signal or physiologic data meets the alarm condition, the method 250 includes generating an alarm (block 256). Additionally, the method 250 includes receiving a relevance indicator (e.g., the relevance indicator 220) indicating the relevance of the generated alarm (block 258) and storing the relevance indicator and the alarm condition (block 260).” See ¶ 0043; Boyer teaches, “[i]n another embodiment, analysis of the collected relevance event data may reveal a relationship between two or more physiologic parameters, and modifying the alarm condition may include combining alarm conditions from two or more physiologic parameters. The new combined alarm is not triggered unless both (or all) conditions are met” See ¶ 0055; and
including:
[[acoustically]] monitoring a medical environment to generate an acoustic signal indicative of audio within the medical environment, Boyer teaches, “alarm conditions may be based on a combination of different alarm conditions, such as two physiologic parameters each violating a respective limit, a combined alarm index violating a limit, or specified combinations of monitor and sensor status events. Referring to FIG. 1, when an alarm is generated by the monitor 12, textual or graphical alarm information may be displayed on the display 16, visible warning lights such as indicator lights 22 may be illuminated, and an audible warning may be sounded via speaker 24.” See ¶ 0025, “modifying alarm conditions may include tagging an alarm with a nuisance or low-value identifier, to identify it as a potential nuisance alarm. This identifier can be used to create a display on the screen to advise the caregiver that the alarm is potentially of low relevance. In another embodiment, modifying alarm conditions may include reducing the severity of the alarm when the alarm condition shows consistently low relevance values.” See ¶ 0058;
detecting one or more of a quantity and quality of alarms within the medical environment, Boyer teaches, “the stored relevance event data 222 includes information about the generated alarm 208. This data may include the type of alarm that triggered … the date and time that it was triggered, the duration of time that the alarm sounded before it was silenced or canceled, the severity of the alarm, the frequency and types of other alarms over a specified time period before or after the generated alarm or the relevance input, and other data. The medical device may store two relevant timestamps. In particular, the medical device may store the time that the alarm was triggered and the time that the user provided the relevance feedback. This may be useful in assessing the time delay between the two events and determining the weight given to the user's feedback. The relevance event data 222 may also include data regarding alarms that were not rated… a total number of alarms generated may be stored, including those that were not rated, so that a frequency of rated alarms can be determined.” See ¶ 0033, Boyer teaches, “histogram may be generated using binary relevance feedback (e.g., relevant or irrelevant, or 0 or 1) and/or rating scale feedback (e.g., a rating between 1 and 5).” See ¶ 0046.
categorizing the one or more alarms, thus defining categorized alarms, Boyer teaches, “the stored relevance event data 222 includes information about the generated alarm 208. This data may include the type of alarm that triggered (such as a high pulse rate alarm, or low respiration rate alarm, or sensor disconnected alarm, or others),” See ¶ 0033, “Additionally, in some embodiments, the plurality of histograms may include different levels of alarm conditions. For example, a first histogram may include the distribution of relevance responses for all types of pulse oximetry alarms (e.g., for a predetermined number of alarm events with one or more medical devices and one or more patients), second histogram may include the distribution of relevance responses for pulse oximetry alarms with a particular alarm condition (e.g., a predetermined threshold for minimum oxygen saturation), a third histogram may include the distribution of relevance responses for pulse oximetry alarms when the patient's heart rate is in a normal range, and so forth.” See ¶ 0046.
[[predicting]] determining the occurrence of the incident if a defined portion of the plurality of required alarms has occurred, Boyer teaches, “[a]n alarm is generated by a medical device (such as the monitor 12 or a therapeutic device, such as a ventilator) when an alarm condition or protocol is met. Alarm conditions include several types, such as physiologic alarm conditions, patient event alarm conditions, and device alarm conditions…. Further, alarm conditions may be based on a combination of different alarm conditions, such as two physiologic parameters each violating a respective limit, a combined alarm index violating a limit, or specified combinations of monitor and sensor status events” See ¶ 0025; Boyer teaches, “[i]n another embodiment, analysis of the collected relevance event data may reveal a relationship between two or more physiologic parameters, and modifying the alarm condition may include combining alarm conditions from two or more physiologic parameters. The new combined alarm is not triggered unless both (or all) conditions are met” See ¶ 0055.
Boyer does not explicitly state, predicting the occurrence of the incident; nonetheless, in an analogous art, Shi teaches, “systems… for correlating an event with an existing event record based on machine-learning correlation models. The system may comprise one or more processors, event memory storing information related to a plurality of existing event records, and one or more memory devices storing program code to be executed by the one or more processors.” See ¶ 0003, Shi teaches, “[t]he trained supervised machine-learning model(s) 114 may be configured to predict whether a newly received alert (or received alert information) is correlated with one or more of the plurality of existing incident records 142. The one or more correlated existing incident records may be referred to as correlation candidates.” See ¶ 0039. Shi teaches, “retrieve a frequent pattern model prediction for the event, determine first patterns for the event based on the frequent pattern model prediction, perform a first search of the event memory for matching frequent patterns in the plurality of existing event records, and return a first list of possible event records correlated to the event from the plurality of existing event records in response the first search, (2) retrieve a sequential pattern model prediction for the event,” See ¶ 0126.
It would have been obvious to one of ordinary skilled in the art at the time of invention (effective filing date for AIA application) to modify the invention of Boyer and have a “trained supervised machine-learning model(s) 114 may be configured to predict whether a newly received alert (or received alert information) is correlated with one or more of the plurality of existing incident” as suggested by Shi in an effort to determine, “whether a newly received alert is related to an existing issue (i.e., existing incident record) that's already being worked on, or a whether the new alert pertains to a new issue that should be opened in their incident” See ¶ 0001.
With respect to,
acoustically monitoring a medical environment to generate an acoustic signal indicative of audio within the medical environment, in an analogous art, Campbell teaches, “A medical device may determine an occurrence of an alert condition. The medical device may transmit, to a separate device, data indicative of the occurrence of the alert condition. After transmitting the data indicative of the occurrence of the alert condition to the separate device, the medical device may determine, based on an audio signal generated by a microphone of the medical device, that the separate device has outputted an audible alert. The audio signal may correspond to audio captured by the microphone.” See Abstract. Campbell teaches, “Based on the audio signal generated by microphone 17, medical device 14 may determine whether at least a portion of the audio captured by microphone 17 includes an audible alert corresponding to the occurrence of the alert condition. For example, medical device 14 may process the audio signal to determine whether the audio captured by microphone 17 includes the audible alert that was requested by medical device 14 to be outputted by patient device 24.” See ¶ 0028;
It would have been obvious to one of ordinary skilled in the art at the time of invention (effective filing date for AIA application) to modify the combination of Boyer-Shi and have a “medical device 14 may use microphone 17 or any other suitable audio input device included in or coupled to medical device 14 to monitor for the audible alert” as suggested by Campbell ¶ 0027, “after transmitting the data indicative of the occurrence of the alert condition to the separate device, determining, based on an audio signal generated by a microphone, that the separate device has outputted an audible alert.” Providing a system that make sure that audible alert has been delivered to the user.
With respect to, training an Al model based, at least in part, upon the categorized alarms, in an analogous art, Picardi teaches, “identifying patterns in activity or account status at a residential facility that seem to precede alarm events.” See ¶ 0004. Picardi teaches, “the base station 144 may determine whether the alarm risk score 326 is a verified alarm or a false alarm. If a false alarm determination 332 is made, then the alarm risk score 326, the current account status 302, the current user behavior 312, and the account identification 322 is provided to the training model 336. The false alarm data is provided to the training model 336 so that the model 232 can be tuned to detect future false alarms of this type. The base station 144 determines a false alarm type or a verified alarm type from the alarm risk score 326 by communicating with the central station 102 corresponding to the particular residential facility 102.” See ¶ 0067, Picardi teaches, “determining a category of the alarm the first monitoring property is at risk for based on a data integration with a central station that corresponds to the first monitoring property; determining whether the category of the alarm for the first monitoring property is a false alarm or a verified alarm; and, based on a determination of whether the category of the alarm for the first monitoring property is a false alarm or a verified alarm, providing the false alarm to a training model of the neural network model or providing the verified alarm to the user corresponding to the first monitoring property.” See ¶ 0078.
It would have been obvious to one of ordinary skilled in the art at the time of invention (effective filing date for AIA application) to modify the combination of Boyer-Shi- Campbell and have a system that can determining a category of the alarm and based on a determination of whether the category of the alarm for the first monitoring property is a false alarm or a verified alarm, providing the false alarm to a training model of the neural network model or providing the verified alarm to the user corresponding to the first monitoring property as suggested by Picardi ¶ 0078 in an effort to tarin the AI based learning models to prevent future false alarms and correct identification of future false alarms.
Consider claim 2, the computer-implemented method of claim 1 wherein defining an incident as the occurrence of a plurality of required alarms includes:
defining an incident as the occurrence of a plurality of required alarms within a defined period of time, Boyer teaches, “analysis of the collected relevance event data may reveal a relationship between two or more physiologic parameters, and modifying the alarm condition may include combining alarm conditions from two or more physiologic parameters. The new combined alarm is not triggered unless both (or all) conditions are met… Accordingly, a new alarm protocol may be created that triggers an alarm when these identified conditions are all met at the same time.” See ¶ 0055.
Consider claim 3, the computer-implemented method of claim 1 wherein monitoring a plurality of devices to detect the occurrence of alarms includes:
monitoring the plurality of devices to receive data signals indicative of the plurality of devices, Boyer teaches, “collecting relevance data for triggered alarms in accordance with an embodiment. The method 250 includes receiving a physiological signal or physiologic data (block 252). For example, the physiological signal or physiologic data may be received from a sensor (e.g., the sensor 14) or from one or more local or remote medical devices… The method 250 also includes determining whether the physiological signal or physiologic data meets an alarm condition (block 254)… In response to determining that the physiological signal or physiologic data meets the alarm condition, the method 250 includes generating an alarm (block 256). Additionally, the method 250 includes receiving a relevance indicator (e.g., the relevance indicator 220) indicating the relevance of the generated alarm (block 258) and storing the relevance indicator and the alarm condition (block 260)” See ¶ 0043; and
comparing the data signals to defined signal norms to identify one or more of the plurality of detected alarms, Boyer teaches, “An alarm is generated by a medical device (such as the monitor 12 or a therapeutic device, such as a ventilator) when an alarm condition or protocol is met. Alarm conditions include several types, such as physiologic alarm conditions, patient event alarm conditions, and device alarm conditions. Physiologic alarm conditions trigger an alarm when a measured or calculated physiologic parameter satisfies an alarm condition, such as when the parameter value crosses a threshold, deviates from a specified range, matches a stored pattern, deviates from a threshold for a specified time and/or extent (e.g., exceeding a limit on a value of an integral taken between the parameter value and a threshold), or meets other conditions that indicate a clinically significant event.).” See ¶ 0025.
Consider claim 4, the computer-implemented method of claim 3 wherein the data signals concern one or more details of the plurality of devices and/or one or more uses of the plurality of devices, Boyer teaches, “An alarm is generated by a medical device (such as the monitor 12 or a therapeutic device, such as a ventilator) when an alarm condition or protocol is met. Alarm conditions include several types, such as physiologic alarm conditions, patient event alarm conditions, and device alarm conditions.” See ¶ 0025; Boyer teaches, “collecting relevance data for triggered alarms in accordance with an embodiment. The method 250 includes receiving a physiological signal or physiologic data (block 252). For example, the physiological signal or physiologic data may be received from a sensor (e.g., the sensor 14) or from one or more local or remote medical devices.” See ¶ 0043.
Consider claim 5, the computer-implemented method of claim 3 wherein the defined signal norms include user-defined signal norms, Boyer teaches, “the stored relevance event data 222 may include information about the patient such as patient characteristics (e.g., age, weight, height, gender, race, condition, diagnosis, or others) or the patient's overall health index. In some embodiments, the patient health index is a numerical value provided by the user. For example, a caregiver may assess the physiological parameter data of the patient and determine a patient health index…In other embodiments, the patient health index 330 may be a numeric value between −5 and 5 or between −3 and 3, where a patient health index of 0 is indicative of an acceptable or normal physiological status and a higher patient health index (positive or negative) is indicative of a worsening physiological status.” See ¶ 0035.
Consider claim 6, the computer-implemented method of claim 3 wherein the defined signal norms include machine-defined signal norms, Boyer teaches, “The physiological input 204 may include an incoming raw or processed physiologic signal, or measured or calculated physiologic data. The physiological input 204 may be received from a sensor coupled to the patient (e.g., the sensor 14) or from other medical devices” see ¶ 0030; Boyer teaches, “collecting relevance data for triggered alarms in accordance with an embodiment. The method 250 includes receiving a physiological signal or physiologic data (block 252). For example, the physiological signal or physiologic data may be received from a sensor (e.g., the sensor 14) or from one or more local or remote medical devices…Further, in some embodiments, the plurality of physiological signals may include at least two different types of physiological signals (e.g., a photoplethysmograph signal, an electrocardiography signal, a blood pressure signal, etc.) and the plurality of physiological parameter values may include at least two different types of physiological parameter values (e.g., oxygen saturation, heart rate, respiration rate, blood pressure, BISPECTRAL™ index, etc.).” See ¶ 0043
Consider claim 7, the computer-implemented method of claim 6 wherein the machine-defined signal norms are defined via massive data sets that are processed by machine learning, Boyer teaches, “the processor 206 may include a statistical analysis engine or machine learning engine 224. The statistical analysis engine or machine learning engine 224 may analyze the collected relevance event data 222 to identify and modify nuisance alarm conditions.” See ¶ 0039; Boyer teaches, “The data associated with the plurality of generated alarms may include the alarm conditions and other relevance event data, as described in detail above.” See ¶ 0049; Boyer teaches, “Analyzing the data and the relevance indicators may include performing statistical analysis on the collected data…or any other classification or learning-based algorithms” See ¶ 0052.
Consider claim 8, the computer-implemented method of claim 6 wherein the machine-defined signal norms are compartmentalized, wherein the machine-defined signal norms include one or more of: gender, race, age, location, device type, device class, seasonality, time of day, Boyer teaches, “Thresholds or other alarm conditions may also vary with patient characteristics such as age, weight, gender, or others. Alarm conditions that rely on multiple parameters may be enabled or disabled based on the available parameters in a particular situation with a particular patient” See ¶ 0057.
Consider claim 9, the computer-implemented method of claim 1, wherein the plurality of devices includes one or more of: a medical device, a process control device, a networking device, a manufacturing device, an agricultural device, an energy / refining device, an aerospace device, a forestry device, and a defense device,
Consider claim 10, the computer-implemented method of claim 1 wherein the plurality of devices are geographically dispersed, Boyer teaches, “the physiological signal or physiologic data may be received from a sensor (e.g., the sensor 14) or from one or more local or remote medical devices.” See ¶ 0043.
Consider claim 11, the computer-implemented method of claim 1 wherein the defined portion of the plurality of required alarms is defined via massive data sets that are processed by machine learning, Boyer teaches, “the processor 206 may include a statistical analysis engine or machine learning engine 224. The statistical analysis engine or machine learning engine 224 may analyze the collected relevance event data 222 to identify and modify nuisance alarm conditions.” See ¶ 0039; Boyer teaches, “The data associated with the plurality of generated alarms may include the alarm conditions and other relevance event data, as described in detail above.” See ¶ 0049; Boyer teaches, “Analyzing the data and the relevance indicators may include performing statistical analysis on the collected data…or any other classification or learning-based algorithms” See ¶ 0052.
Consider claim 12, the computer-implemented method of claim 1 wherein predicting the occurrence of the incident if a defined portion of the plurality of required alarms has occurred includes:
requiring that the defined portion of the plurality of required alarms have occurred in a defined sequence, Boyer teaches, “data may include the type of alarm that triggered (such as a high pulse rate alarm, or low respiration rate alarm, or sensor disconnected alarm, or others), the date and time that it was triggered, the duration of time that the alarm sounded before it was silenced or canceled, the severity of the alarm, the frequency and types of other alarms over a specified time period before” See ¶ 0033, Boyer teaches, “analysis of the collected relevance event data may reveal a relationship between two or more physiologic parameters, and modifying the alarm condition may include combining alarm conditions from two or more physiologic parameters. The new combined alarm is not triggered unless both (or all) conditions are met… Accordingly, a new alarm protocol may be created that triggers an alarm when these identified conditions are all met at the same time.” See ¶ 0055. Therefore there is a suggestion that alarm events occurred in a defined sequence, nonetheless, in an analogous art, Shi teaches, “frequent-pattern and sequential pattern mining may be performed using historical alert data to learn co-occurrence of alerts and dependency information.” See ¶ 0021, Shi teaches, “the sequential pattern algorithm may output sequential pattern model 120. Sequential pattern model 120 may be trained based on historical alert data and may output a sequence of co-occurring alert dimensions…. Machine-learning-based correlation engine 112 may be configured to store these sequential patterns in a sequential pattern lookup table for faster searching. This sequential pattern lookup table may include confidence scores for each of the sequential patterns. At prediction time, machine-learning-based correlation engine 112 may be configured to perform a look up to this sequential pattern table to find any sequential patterns that apply to alert information 110 of the received alert. If a matching sequential pattern is found in the sequential pattern lookup table, machine-learning-based correlation engine 112 may be configured to perform a search of the plurality of existing incident records 142 to find one or more correlated existing incidents with matching sequential patterns.” See ¶ 0041.
Consider claim 13, the computer-implemented method of claim 1 wherein predicting the occurrence of the incident if a defined portion of the plurality of required alarms has occurred includes:
requiring that the defined portion of the plurality of required alarms have occurred within a defined period of time, Boyer teaches, “analysis of the collected relevance event data may reveal a relationship between two or more physiologic parameters, and modifying the alarm condition may include combining alarm conditions from two or more physiologic parameters. The new combined alarm is not triggered unless both (or all) conditions are met… Accordingly, a new alarm protocol may be created that triggers an alarm when these identified conditions are all met at the same time.” See ¶ 0055.
Consider claim 14, a computer program product residing on a non-transitory computer readable medium having a plurality of instructions stored thereon which, Boyer teaches, “non-transitory machine-readable medium or media having instructions recorded thereon for execution by a processor” See ¶ 0041, when executed by a processor, cause the processor to perform operations, comprising:
defining an incident as the occurrence of a plurality of required alarms, See rejection of claim 1;
monitoring a plurality of devices to detect the occurrence of one or more alarms, thus defining a plurality of detected alarms, See rejection of claim 1;
including:
acoustically monitoring a medical environment to generate an acoustic signal indicative of audio within the medical environment, See rejection of claim 1;
detecting one or more of a quantity and quality of alarms within the medical environment, See rejection of claim 1; and
categorizing the one or more alarms, thus defining categorized alarms, See rejection of claim 1;
training an AI model based, at least in part, upon the categorized alarms, See rejection of claim 1; and
predicting the occurrence of the incident if a defined portion of the plurality of required alarms has occurred, See rejection of claim 1.
Consider claim 15, the computer program product of claim 14 wherein defining an incident as the occurrence of a plurality of required alarms includes:
defining an incident as the occurrence of a plurality of required alarms within a defined period of time, See rejection of claim 2.
Consider claim 16, the computer program product of claim 14 wherein monitoring a plurality of devices to detect the occurrence of alarms includes:
monitoring the plurality of devices to receive data signals indicative of the plurality of devices; and
comparing the data signals to defined signal norms to identify one or more of the plurality of detected alarms, See rejection of claim 3.
Consider claim 17, the computer program product of claim 16 wherein the data signals concern one or more details of the plurality of devices and/or one or more uses of the plurality of devices, See rejection of claim 4.
Consider claim 18, the computer program product of claim 16 wherein the defined signal norms include user-defined signal norms, See rejection of claim 5.
Consider claim 19, the computer program product of claim 16 wherein the defined signal norms include machine-defined signal norms, See rejection of claim 6.
Consider claim 20, the computer program product of claim 19 wherein the machine-defined signal norms are defined via massive data sets that are processed by machine learning, See rejection of claim 7.
Consider claim 21, the computer program product of claim 19 wherein the machine-defined signal norms are compartmentalized, wherein the machine-defined signal norms include one or more of: gender, race, age, location, device type, device class, seasonality, time of day, See rejection of claim 8.
Consider claim 22, the computer program product of claim 14 wherein the plurality of devices includes one or more of: a medical device, a process control device, a networking device, a manufacturing device, an agricultural device, an energy / refining device, an aerospace device, a forestry device, and a defense device, See rejection of claim 9.
Consider claim 23, the computer program product of claim 14 wherein the plurality of devices are geographically dispersed, See rejection of claim 10.
Consider claim 24, the computer program product of claim 14 wherein the defined portion of the plurality of required alarms is defined via massive data sets that are processed by machine learning, See rejection of claim 11.
Consider claim 25, the computer program product of claim 14 wherein predicting the occurrence of the incident if a defined portion of the plurality of required alarms has occurred includes:
requiring that the defined portion of the plurality of required alarms have occurred in a defined sequence, See rejection of claim 12.
Consider claim 26, the computer program product of claim 14 wherein predicting the occurrence of the incident if a defined portion of the plurality of required alarms has occurred includes:
requiring that the defined portion of the plurality of required alarms have occurred within a defined period of time, See rejection of claim 13.
Consider claim 27, Boyer teaches, a computing system including a processor 206 and memory 210, Boyer teaches, “processor configured to execute code (e.g., stored in a memory of the monitor 12” See ¶ 0022, configured to perform operations comprising:
defining an incident as the occurrence of a plurality of required alarms, See rejection of claim 1;
monitoring a plurality of devices to detect the occurrence of one or more alarms, thus defining a plurality of detected alarms See rejection of claim 1;
including:
acoustically monitoring a medical environment to generate an acoustic signal indicative of audio within the medical environment, See rejection of claim 1;
detecting one or more of a quantity and quality of alarms within the medical environment, See rejection of claim 1; and
categorizing the one or more alarms, thus defining categorized alarms, See rejection of claim 1;
training an AI model based, at least in part, upon the categorized alarms, See rejection of claim 1; and
predicting the occurrence of the incident if a defined portion of the plurality of required alarms has occurred, See rejection of claim 1.
Consider claim 28, the computing system of claim 27 wherein defining an incident as the occurrence of a plurality of required alarms includes:
defining an incident as the occurrence of a plurality of required alarms within a defined period of time, See rejection of claim 2.
Consider claim 29, the computing system of claim 27 wherein monitoring a plurality of devices to detect the occurrence of alarms includes:
monitoring the plurality of devices to receive data signals indicative of the plurality of devices; and
comparing the data signals to defined signal norms to identify one or more of the plurality of detected alarms, See rejection of claim 3.
Consider claim 30, the computing system of claim 29 wherein the data signals concern one or more details of the plurality of devices and/or one or more uses of the plurality of devices, See rejection of claim 4.
Consider claim 31, the computing system of claim 29 wherein the defined signal norms include user-defined signal norms, See rejection of claim 5.
Consider claim 32, the computing system of claim 29 wherein the defined signal norms include machine-defined signal norms, See rejection of claim 6.
Consider claim 33, the computing system of claim 32 wherein the machine-defined signal norms are defined via massive data sets that are processed by machine learning, See rejection of claim 7.
Consider claim 34, the computing system of claim 32 wherein the machine-defined signal norms are compartmentalized, wherein the machine-defined signal norms include one or more of: gender, race, age, location, device type, device class, seasonality, time of day, See rejection of claim 8
Consider claim 35, the computing system of claim 27 wherein the plurality of devices includes one or more of: a medical device, a process control device, a networking device, a manufacturing device, an agricultural device, an energy / refining device, an aerospace device, a forestry device, and a defense device, See rejection of claim 9.
Consider claim 36, the computing system of claim 27 wherein the plurality of devices are geographically dispersed, See rejection of claim 10.
Consider claim 37, the computing system of claim 27 wherein the defined portion of the plurality of required alarms is defined via massive data sets that are processed by machine learning, See rejection of claim 11.
Consider claim 38, the computing system of claim 27 wherein predicting the occurrence of the incident if a defined portion of the plurality of required alarms has occurred includes:
requiring that the defined portion of the plurality of required alarms have occurred in a defined sequence, See rejection of claim 12.
Consider claim 39, the computing system of claim 27 wherein predicting the occurrence of the incident if a defined portion of the plurality of required alarms has occurred includes:
requiring that the defined portion of the plurality of required alarms have occurred within a defined period of time, See rejection of claim 13.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hu, Xiao et al. (US 2017/0046499 A1) teaches, “Methods for predicting patient deterioration or clinical events by detecting patterns in heterogeneous temporal clinical data streams that are predictive of certain clinical end points and matching the patient state with those patterns are described. The detected patterns, referred to as SuperAlarm triggers, are a predictive combination of frequently co-occurring monitor alarms, conditions and laboratory test results that can predict patient deterioration for imminent life-threatening events. SuperAlarm triggers may also exhibit patterns in the sequence of SuperAlarms that are triggered over the monitoring time of a patient. Sequential patterns of SuperAlarm triggers may also indicate a temporal process of change in patient status.” See abstract.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Omer S. Khan whose telephone number is (571)270-5146. The examiner can normally be reached 10:00 am to 8:00 pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian A. Zimmerman can be reached at 571-272-3059. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Omer S Khan/Primary Examiner, Art Unit 2686
Prosecution Insights