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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 ----09/25/2025 has been entered.
Status of Claims
Claims 1-7, 9-14, 16-18 and 21-24 are currently pending and have been examined.
Claims 1, 9-14, 16-18, and 21-22 have been amended.
Claims 8, 15, 19-20 have been canceled with claims 8 and 15 being newly canceled in the claims filed 09/25/2025.
Claims 23-24 are new.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 7 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 discloses, “a healthcare facility network including a neural network…” and claim 7 discloses, “determine whether the alert suppression criteria is satisfied using a neural network having weights…” The newly added claim 23 discloses, “wherein the neural network…” which clarifies that the neural networks of claims 1 and 23 are the same. Thus, in regards to the neural networks of claims 1 and 7, is unclear if these are the same neural network or different neural networks. The Examiner is interpreting it to be the same neural network.
Appropriate correction is required.
Thus, claim 7 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
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 1-7, 9-14, 16-18 and 21-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed invention is directed to an abstract idea without significantly more. Claims 1-7, 9-14, 16-18 and 21-24 are directed to a system, method, or product which are one of the statutory categories of invention. (Step 1: YES).
Independent Claim 1 discloses an alert management system for use in a healthcare facility, the alert management system comprising a healthcare facility network including a neural network; a plurality of patient monitor devices communicatively coupled to the network and operable to monitor one or more statuses associated with a corresponding patient and to send alert data to the network if a respective status is indicative of an alert condition, wherein the plurality of patient monitor devices include a patient bed including a patient position detector and a guardrail position detector, and wherein the plurality of patient monitor devices also include any one or more of the following: a pulse oximeter, a heart rate monitor, a breathing monitor, an incontinence event detector, a patient fall detector, an anesthesia machine, a feeding pump, a wound vacuum, or a compression pump; a location tracking system coupled to the network, the location tracking system including tags worn by caregivers and tag readers located throughout the healthcare facility to communicate with the tags; a body sensor transported by at least one caregiver and configured to sense an activity level of the at least one caregiver; a plurality of caregiver notification devices some of which are installed in patient rooms of the healthcare facility and some of which are carried by the caregivers, the plurality of caregiver notification devices also being communicatively coupled to the network, and a compute device coupled to the network and comprising: circuitry configured to: receive the alert data transmitted from the patient bed and from any other patient monitor device of the plurality of patient monitor devices and, in response, generate an alert to be provided to the at least one caregiver of the one or more caregivers for display on any caregiver notification device of the plurality of caregiver notification devices being carried by the at least one caregiver and for display on any caregiver notification device installed in any patient room in which the at least one caregiver is located as determined by the location tracking system unless alert suppression criteria is satisfied; determine whether the alert suppression criteria indicative of one or more factors associated with alert-related fatigue is satisfied for the at least one caregiver; wherein the alert suppression criteria is a function of activity level data from the body sensor; and suppress, in response to a determination that the alert suppression criteria is satisfied, the alert from being displayed on any caregiver notification device of the plurality of notification devices being carried by the at least one caregiver and from being displayed on any caregiver notification device installed in any patient room in which the at least one caregiver is located as determined by the location tracking system; wherein the circuitry is configured such that, if the alert suppression criteria is not satisfied, the alert is provided to the at least one caregiver; wherein the circuitry is further configured to determine a result of providing the alert to the caregiver and to provide the determined result to the neural network as training data; wherein the neural network is trained using the training data such that the alert suppression criteria is updated.
The examiner is interpreting the above bolded limitations as additional elements as further discussed below. The remaining un-bolded limitations are merely directed to determining if an alert should be suppressed based on patient alert data and caregiver fatigue. The series of steps recited above describe managing personal behavior or relationships or interactions between people and thus are grouped as certain methods of organizing human activity which is an abstract idea. (Step 2A- Prong 1: YES. The claims are abstract).
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05.f), (2) Adding insignificant extra- solution activity to the judicial exception (MPEP 2106.05.g), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05.h).
Independent Claim 1 recites the following additional elements:
A healthcare facility network including a neural network
A plurality of patient monitor devices include a patient bed including a patient position detector and a guardrail position detector, and wherein the plurality of patient monitor devices also include any one or more of the following: a pulse oximeter, a heart rate monitor, a breathing monitor, an incontinence event detector, a patient fall detector, an anesthesia machine, a feeding pump, a wound vacuum, or a compression pump
A location tracking system coupled to the network including tags worn by caregivers and tag readers located throughout the healthcare facility to communicate with the tags
A body sensor transported by at least one caregiver and configured to sense an activity level of the at least one caregiver
A plurality of caregiver notification devices some of which are installed in patient rooms of the healthcare facility and some of which are carried by the caregivers, the plurality of caregiver notification devices also being communicatively coupled to the network
A compute device coupled to the network and comprising of circuitry
Data transmitted from the patient bed and from any other patient monitor device of the plurality of patient monitor devices
Alert for display on any caregiver notification device of the plurality of notification devices being carried by the at least caregiver and for display on any caregiver notification device installed in any patient room in which the at least one caregiver is located as determined by the location tracking system
Activity level data from the body sensor
The neural network is trained using the training data such that the alert suppression criteria is updated
In particular, the healthcare facility network including a neural network, location tracking system coupled to the network including tags worn by caregivers and tag readers located throughout the healthcare facility to communicate with the tags, body sensor transported by the caregiver, the plurality of caregiver notification devices some of which are installed in patient rooms of the healthcare facility and some of which are carried by the caregivers, the plurality of caregiver notification devices also being communicatively coupled to the network the compute device coupled to the network comprising of circuitry, and the neural network is trained using the training data such that the alert suppression criteria is updated are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea by adding the words ‘apply it' (or an equivalent) with the judicial exception.
Applicant’s specification states, “The disclosed embodiments may be implemented, in some cases, in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried by or stored on a transitory or non- transitory machine-readable (e.g., computer-readable) storage medium, which may be read and executed by one or more processors. A machine-readable storage medium may be embodied as any storage device, mechanism, or other physical structure for storing or transmitting information in a form readable by a machine (e.g., a volatile or non-volatile memory, a media disc, or other media device),” (Para 27). The applicant’s specification further states, “the illustrative alert management compute device 150 includes a compute engine 200, an input/output (I/O) subsystem 206, communication circuitry 208, and a data storage subsystem 212. Of course, in other embodiments, the alert management compute device 150 may include other or additional components, such as those commonly found in a computer (e.g., a display, peripheral devices, etc.). Additionally, in some embodiments, one or more of the illustrative components may be incorporated in, or otherwise form a portion of, another component,” (Para 32).
Accordingly, these additional elements are broadly disclosed, with no particularity, and performing a routine function in an expected manner. Further, when considered separately and as an ordered combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 1 further recites the additional element of a plurality of patient monitor devices include a patient bed including a patient position detector and a guardrail position detector, and wherein the plurality of patient monitor devices also include any one or more of the following: a pulse oximeter, a heart rate monitor, a breathing monitor, an incontinence event detector, a patient fall detector, an anesthesia machine, a feeding pump, a wound vacuum, or a compression pump. The plurality of patient monitor devices merely generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(1) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
Accordingly, claim 1 is directed to an abstract idea(s) without a practical application. (Step 2A-Prong 2: NO: the additional claimed elements are not integrated into a practical application).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the healthcare facility network including a neural network, location tracking system coupled to the network including tags worn by caregivers and tag readers located throughout the healthcare facility to communicate with the tags, body sensor transported by the caregiver, the plurality of caregiver notification devices some of which are installed in patient rooms of the healthcare facility and some of which are carried by the caregivers, the plurality of caregiver notification devices also being communicatively coupled to the network the compute device coupled to the network comprising of circuitry, and the neural network is trained using the training data such that the alert suppression criteria is updated amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more' ). MPEP2106.05(I)(A) indicates that merely saying "apply it” or equivalent to the abstract idea cannot provide an inventive concept ("significantly more").
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the plurality of patient monitor devices include a patient bed including a patient position detector and a guardrail position detector, and wherein the plurality of patient monitor devices also include any one or more of the following: a pulse oximeter, a heart rate monitor, a breathing monitor, an incontinence event detector, a patient fall detector, an anesthesia machine, a feeding pump, a wound vacuum, or a compression pump were considered to generally link the abstract idea to a particular technological environment or field of use. This has been re-evaluated under the ‘significantly more' analysis and has been found insufficient to provide significantly more. MPEP2106.05 (A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide an inventive concept (‘significantly more"). Accordingly, even in combination, this additional element does not provide significantly more. As such the independent claim 1 is not patent eligible. (Step 2B: NO. The claims do not provide significantly more).
Dependent claim(s) 2-7, 9-14, 16-18 and 21-24 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Dependent Claims 7, 11-12, and 18 further recite the additional elements of:
A neural network (Claims 7 and 18)
A mobile communication device (Claim 11)
A caregiver notification device installed in a hospital (Claim 12)
A caregiver communication device (Claim 21)
In particular, the neural network of claims 7 and 18, the mobile communication device of claim 11, caregiver notification device installed in a hospital of claim 12, and the caregiver communication device of claim 21 are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea by adding the words ‘apply it' (or an equivalent) with the judicial exception. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Accordingly, even in combination, these additional elements do not 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. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the neural network of claims 7 and 18, the mobile communication device of claim 11, caregiver notification device installed in a hospital of claim 12, and the caregiver communication device of claim 21 amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more' ). MPEP2106.05(I)(A) indicates that merely saying "apply it” or equivalent to the abstract idea cannot provide an inventive concept ("significantly more"). Accordingly, even in combination, these additional elements do not provide significantly more.
Therefore, dependent claims 2-7, 9-14, 16-18 and 21-24 are also directed to an abstract idea.
Thus, Claims 1-7, 9-14, 16-18 and 21-24 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 a 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.
Claims 1-7, 9-14, 21, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Mahalingam (US PG Pub 2017/0181645 A1), in view of Smith (US Patent 7,570,152 B2) further in view of Dyell (US PG Pub 2019/0038199 A1), Kee Yuan Ngiam (Big data and machine learning algorithms for health-care delivery), and Kiani (US PG Pub 2014/0333440 A1).
Regarding Claim 1, Mahalingam discloses:
An alert management system for use in a healthcare facility
a plurality of patient monitor devices communicatively coupled to the network and operable to monitor one or more statuses associated with a corresponding patient and to send alert data to the network if a respective status is indicative of an alert condition, wherein… the plurality of patient monitor devices also include any one or more of the following: a pulse oximeter, a heart rate monitor, a breathing monitor, an incontinence event detector, (Para 68-69 disclose more information than just an analyte measurement may be desirable. For example, in the case of measuring glucose levels, many current technologies do not put such glucose measurements into a contextual framework that allows for greater analysis. Advantageously, having such an analysis can reveal more about a Host's conditions, treatments, and responses that can allow for improved response predictions and enhanced therapies… Contextual data can be provided with alarms or notifications to Hosts and/or Remote Monitors. Para 80 discloses Sensor Data can include data based on data collected by sensors associated with a Host or Remote Monitor (s). This Sensor Data can include, data from health rate monitors… pulse oximeters, wearables (e.g., smart watches… breathing monitors, sleep monitors, posture monitors…), accelerometers, gyroscopes, speedometers, pedometers, blood pressure readers, pump data for administration of other drugs, drug sensors ( e.g., breathalyzers and sensors configured to measure intoxication or presence of drugs), medical devices, and/or other devices that measure a characteristic of the Host and/or Remote Monitor(s). In some cases, such sensor data can be indicative of a Host's and/or Remote Monitor(s)' health… alerting of Hosts and/or Remote Monitors can be modified based on such data. Para 155 discloses such pattern recognition can be performed on any data described in this disclosure (e.g., analyte measurements, communications, Processed Data, Contextual Data, Health Data, System Data, Treatment Data, User Data, Sensor Data, Summary Data, and/or other data mentioned in this disclosure). Advantageously, more data types can provide context and further information on the health condition of the Host, as well as the Host's glucose level. Further, a pattern may be based on a combination of previous pattern data and a currently detected situation, whereby the combined information generates a predictive alert.)
a location tracking system coupled to the network, (Para 124 discloses Locator 306 can identify the location of Remote Monitoring Device 300 and/or a Remote Monitor who is associated with Remote Monitoring Device 300. For example, Locator 306 can include GPS, RFID, GLONASS, and/or any system that can identify location. In some implementations, Locator 306 can be positioned within a chassis of Remote Monitoring Device 300. In other implementations, Locator 306 can be positioned on a Remote Monitor, not within a chassis of Remote Monitoring Device 300. See Further: Paras 295-296)
a body sensor transported by the caregiver and configured to sense an activity level of the caregiver; (Para 80 discloses Sensor Data can include data based on data collected by sensors associated with a Host or Remote Monitor(s). This Sensor Data can include data from health rate monitors, activity trackers, pulse oximeters, wearables (e.g., smart watches, smart rings, workout monitors, electrocardiographs, bioimpedence sensors, breathing monitors, sleep monitors, posture monitors, habit detectors, temperature trackers, fabrics embedded with sensors, moisture detectors, etc. [body sensors])… In some cases, such sensor data can be indicative of a Host's and/or Remote Monitor(s)′ health. As a non-limiting example, data from an accelerometer can indicate whether a Host and/or Remote Monitor is sleeping, exercising, and/or any other activity. In some cases alerting of Hosts and/or Remote Monitors can be modified based on such data. Para 85 discloses it can be desirable to know a Remote Monitor's condition. For example, contextual data about a Remote Monitor may be received at a server. The contextual data about the Remote Monitor operating a remote monitoring device can include, for example, time, amount, and/or type of (i) an activity undertaken by the remote monitor user, (ii) a level of stress experienced by the remote monitor user, or (iii) an environmental condition experienced by the remote monitor user, or a combination of (i)-(iii). The emotions, activities, and stresses that the Remote Monitor experiences may be informative, especially when it can be perceived by a Host and/or can affect the Host's health. In such a case, the Remote Monitor's condition can be a factor that affects an analyte measurement such as the Host's glucose levels. The condition of the Remote Monitor can also roll back to the health of the Host. The Remote Monitor's condition can be used to gauge the engagement of the Remote Monitor as well as predict whether the Remote Monitor is suffering from alarm fatigue, is likely to assist the Host effectively and timely, etc. As a result, it can be desirable to collect data on the condition of the Remote Monitor, process it, and provide it as Summary Data to the Host, Remote Monitor, or other Remote Monitors. Accordingly, if processing a Remote Monitor's contextual data indicates an inability of the Remote Monitor to react to alarms or notifications, Host and/or other Remote Monitors can be provided with Summary Data including at least some of Remote Monitor's contextual data to alert them.)
a plurality of caregiver notification devices some of which are carried by the caregivers, the plurality of caregiver notification devices also being communicatively coupled to the network, and (Para 57 discloses Remote Monitors can have a particular role with respect to a Host. For example, Remote Monitors can include Caretakers of the Host, such as parents, spouses, relatives, guardians, significant others, teachers, health practitioners, etc. Para 92 discloses either directly or through a network, Block 108 can transmit data to other device(s ), such as other sensor(s ), host monitoring device(s ), mobile devices (e.g., tablets, cellphones, smartphones, e-readers, phablets, and the like) [caregiver notification devices carried by the caregivers], any device with access to the internet and/or any network protocol, computers (e.g., laptops, desktops, personal computers, etc.), and/or any desirable device. In some cases, any of the aforementioned other device(s) may also be remote monitoring devices, servers, and/or host monitoring devices. Para 159 discloses, in some implementations, such as when Remote Monitoring Device is a mobile device that runs a mobile application to monitor a Host, when the mobile application is open, it can request Data.. from the server… See Further: Para 386.)
a compute device coupled to the network and comprising: circuitry configured to: (Para 16 discloses providing, by the server [compute device], an alert informative of an event associated with the analyte state of the host to selected remote monitoring devices based on notification rules that define circumstances to send the alert to a respective remote monitoring device. Para 92 discloses either directly or through a network, Block 108 can transmit data to other device(s ), such as other sensor(s ), host monitoring device(s ), mobile devices (e.g., tablets, cellphones, smartphones, e-readers, phablets, and the like), any device with access to the internet and/or any network protocol, computers (e.g., laptops, desktops, personal computers, etc.), and/or any desirable device. In some cases, any of the aforementioned other device(s) may also be remote monitoring devices, servers, and/or host monitoring devices. Para 104 discloses such data can be transmitted to one or more remote monitoring device(s) either directly or through a network.)
receive the alert data transmitted from any patient monitor device of the plurality of patient monitors devices and, in response generate an alert to be provided to a designated caregiver of the one or more caregivers for display on any caregiver notification device of the plurality of caregiver notification devices being carried by the designated caregiver unless alert suppression criteria is satisfied; (Para 21 discloses FIGS. 1A and 1B illustrate flow charts where a host monitoring device and a remote monitoring device receive, process, and transmit data, respectively. Para 92 discloses either directly or through a network, Block 108 can transmit data to other device(s ), such as other sensor(s ), host monitoring device(s ), mobile devices (e.g., tablets, cellphones, smartphones, e-readers, phablets, [notification devices being carried by the designated caregiver] and the like), any device with access to the internet and/or any network protocol, computers (e.g., laptops, desktops, personal computers, etc.), and/or any desirable device. In some cases, any of the aforementioned other device(s) may also be remote monitoring devices. Para 99-101 discloses In Block 154, the received data from Block 152 can be processed. Block 154 can also receive other data from Blocks 156, 158. By way of illustrative example, a processor (e.g., a processor included in a remote monitoring device and/or server) can receive the data… Such sent data can include any of the data described in this disclosure (e.g., analyte measurements, communications, Processed Data, Contextual Data, Health Data, System Data, Treatment Data, User Data, Sensor Data, Summary Data, etc.). Signals can also be sent, received, and/or transmitted between such processor and the aforementioned host monitoring device(s), remote monitoring device(s), server(s), and/or other devices(s), including any device described with respect to Blocks 152, 156, 158. In some implementations, Block 154 can perform signal processing, pattern recognition, and/or any other analysis on the data received from Blocks 152, 156, 158. For example, such signal processing, pattern recognition, and/or any other analysis can be performed by a remote monitoring device and/or a server. In some cases, data can be generated based on the received data… in some implementations, a server can receive a communication regarding a Host based on the Host's glucose level. Block 154 can then process the communication and determine whether and how often to alert and/or notify one or more Remote Monitors ( e.g., using their remote monitoring devices) [generate an alert unless alert suppression criteria is satisfied] based on predefined settings, such as predefined frequency of alerts, priority of alerts, availability of Remote Monitor, availability of other Remote Monitor( s ), severity of notification…
determine whether the alert suppression criteria indicative of one or more factors associated with alert-related fatigue is satisfied for the designated caregiver, wherein the alert suppression criteria is a function of activity level data from the body sensor; and suppress, in response to a determination that the alert suppression criteria is satisfied, the alert from being displayed on any caregiver notification device of the plurality of notification devices being carried by the designated caregiver and (Para 18 further discloses processing, by the server, an alert informative of a dangerous event associated with the analyte state of the host, in which the processed alert is within a set of notification rules associated with at least one of the authorized remote monitoring devices [alert suppression criteria.. of designated caregiver]; receiving, by the server, an instruction to suppress sending the message associated with the alert to the at least one of the authorized remote monitoring devices; and suppressing, by the server, the sending of the message to the at least one of the authorized remote monitoring devices. Para 92 discloses either directly or through a network, Block 108 can transmit data to other device(s ), such as other sensor(s ), host monitoring device(s ), mobile devices (e.g., tablets, cellphones, smartphones, e-readers, phablets, [notification devices being carried by the designated caregiver] and the like), any device with access to the internet and/or any network protocol, computers (e.g., laptops, desktops, personal computers, etc.), and/or any desirable device. In some cases, any of the aforementioned other device(s) may also be remote monitoring devices, servers, and/or host monitoring devices. Para 80 discloses sensor data can include data based on data collected by sensors associated with a… remote monitor(s). This sensor data can include data from health rate monitors, activity trackers… wearables [body sensor]… and/or other devices that measure a characteristic of the… Remote Monitor(s). In some cases, such sensor data can be indicative of a… Remote Monitor(s)’ health… In some cases, alerting of… Remote Monitors can be modified based on such data. Para 85 discloses it can be desirable to know a Remote Monitor's condition. For example, contextual data about a Remote Monitor may be received at a server. The contextual data about the Remote Monitor operating a remote monitoring device can include, for example, time, amount, and/or type of (i) an activity [amount of an activity thus reads on activity level] undertaken by the remote monitor user… The Remote Monitor's condition can be used to gauge the engagement of the Remote Monitor as well as predict whether the Remote Monitor is suffering from alarm fatigue, is likely to assist the Host effectively and timely, etc. As a result, it can be desirable to collect data on the condition of the Remote Monitor, process it, and provide it as Summary Data to the Host, Remote Monitor, or other Remote Monitors. Accordingly, if processing a Remote Monitor's contextual data indicates an inability of the Remote Monitor to react to alarms or notifications, Host and/or other Remote Monitors can be provided with Summary Data including at least some of Remote Monitor's contextual data to alert them. Para 99-101 discloses Block 154 can then process the communication and determine whether and how often to alert and/or notify one or more Remote Monitors ( e.g., using their remote monitoring devices) [generate an alert unless alert suppression criteria is satisfied] based on predefined settings. Para 192 further discloses the server can provide the notifications to the remote monitoring device(s) based on the notification rules associated with the respective remote monitoring device(s). See Further: Para 386.)
wherein the circuitry is further configured to determine a result of providing the alert to the caregiver and (Para 200 discloses based on the reception of communications, Host Monitoring Device 902, Remote Monitoring Device 906, and/or other devices can send responses to communications, such as messages, acknowledgements, notifications, alerts, dismissals, feedback, error messages, etc. For example, Host Monitoring Device 902 can send Signal 930 to Network(s) 904, where Signal 930 is a response to a communication received by Host Monitoring Device 902. Para 290 discloses detection can be based on location, pattern of communications, and/or responses by Remote Monitors and/or Hosts (e.g., repeated ignoring of communications, any feedback indicative of over/under communication, and/or user inputted changes to communication settings).)
Mahalingam discloses, “proximity can be used in conjunction with patten recognition, such as any pattern recognition described in this disclosure, to determine whether more or fewer communications are desirable,” (Mahalingam Paras 295-296), “In some implementations, Locator 306 can identify the location of Remote Monitoring Device 300 and/or a Remote Monitor… For example, Locator 306 can include GPS, RFID, GLONASS, and/or any system that can identify location,” (Mahalingam Para 124). Further, Mahalingam Para 80 discloses… such sensor data can be indicative of a… Remote Monitor(s)’ health… In some cases, alerting of… Remote Monitors can be modified based on such data.” Thus, it would have been obvious to utilize the sensor data of the remote monitor to determine if the alert suppression criteria is satisfied wherein the alert suppression criteria is based on an activity level of a caregiver (remote monitor). Therefore, when Smith discloses suppressing an alarm when a caregiver enters the room, it would have been obvious to utilize the activity tracker or wearable as disclosed by Mahalingam in order to determine when a caregiver has reached the location of the alarm (caregiver has stopped walking). Thus, it would have been obvious to combine the above limitations of Mahalingam with the following limitations that Smith discloses:
generate an alert for display on any caregiver notification device installed in any patient room in which the caregiver is located as determined by the location tracking system unless alert suppression criteria is satisfied… some of which [of the plurality of caregiver notification devices] are installed in patient rooms of the healthcare facility and suppress, in response to a determination that the alert suppression criteria is satisfied, the alert from being displayed on any notification device installed in any patient room in which the caregiver located as determined by the location tracking system; (Para Column 4, lines 21-40 discloses the caregiver will carry, preferably in the form of a badge… that, upon activation, transmits an ultrasonic signal to a receiver that is preferably located within each electronic patient monitor in the room [caregiver notification device installed in any patient room]… Within each patient monitor will preferably be an ultrasonic receiver that, upon receipt, recognition, and verification (e.g., receipt of one or more consecutive pulses at the proper frequency and time spacing) of the appropriate signal, will preferably suspend broadcast of its audio alarm [where the alarm is generated until the alert suspension criteria is satisfied (the caregiver that stops walking when they reach the patient room)], suspend transmission of the alarm to a remote site such as a nurses' station, and/or suspend the monitoring function of the unit. Note that the monitor's visual alarm cues (such as flashing lights, etc.) might… be suspended according to the preferences of the programmer/or caregiver [thus disclosing suppressing both audio and visual alarms from being displayed where Applicant’s Specification at paragraph 30 further discloses that a notification device may be a device capable of producing audible notifications]).
the location tracking system including tags worn by caregivers and tag readers located throughout the healthcare facility to communicate with the tags, (Column 17, lines 5-18 further discloses a system for automatically suspending the operation of a patient monitor, wherein is preferably provided a passive RFID-enabled (or similar technology) badge [tag] that is worn by the caregiver. In the preferred arrangement, RFID readers [tag readers] will be placed, for example, in patient room doorways within the institution. Then, each time a caregiver enters or exits a room that fact will be noted, and the patient monitors therein will respond accordingly. Further, and as has been discussed previously, preferably each caregiver's badge will be assigned a serial number which is readable by the RFID system. This configuration makes it possible to silence different ones of the patient's alarms based on the capabilities of the individual caregiver.)
Mahalingam Para 92 discloses a remote monitoring device may be a number of other devices including for example host monitoring device(s), mobile devices, wearables, any device with access to the internet and/or any network protocol, computers (e.g., laptops, desktops, personal computers, etc.) and/or any desirable device. Thus, it would have been obvious one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the systems and methods for remote and host monitoring communications as taught by Mahalingam in the patient monitor in the room of the method and apparatus for temporarily disabling a patient monitor as taught by Smith in order to prevent a detriment to patient care… as alarms may make communication with the patient or another caregiver difficult and may frighten or unnecessarily disturb the patient (Smith Column 2, lines 41-46).
While the combination of Mahalingam and Smith disclose the above limitations, it does not fully disclose the following limitation that Dyell discloses:
A healthcare facility network including a neural network; (Para 18 discloses system 10 comprises a network 11 (generally indicated by an arrow) connecting an alarm management engine 12 with one or more user devices 14 and medical devices 16. Alarm management engine 12 interfaces with a user fatigue model database 18, a medical device model database 20, and a patient conditions database 22. Para 35 discloses alarm management engine 12 can also (e.g., alternatively, or additionally) implement unsupervised learning techniques, such as artificial neural network… deep convolutional neural networks, and deep recurrent neural networks. Para 54 discloses alarms may be generated at 8:16 AM and communicated to nurse Alice's work station, as nurse Alice has a lower inferred fatigue level than nurse Bob, but not displayed visibly or audibly at nurse Bob's work station, whereas alarms generated at 8:24 AM may be communicated to both nurse Alice and nurse Bob. Medical device 16 and/or adapter 46 may generate alarm indicator 34 indicating that alarms have been generated and/or communicated (e.g., based on alarm condition 44). Such alarm indicator 34 can facilitate feedback and/or machine learning at alarm management engine 12.)
[to] provide the determined result to the neural network as training data (Para 54 discloses medical device 16 and/or adapter 46 may generate alarm indicator 34 indicating that alarms have been generated and/or communicated (e.g., based on alarm condition 44). Such alarm indicator 34 can facilitate feedback and/or machine learning at alarm management engine 12.)
wherein the circuitry is configured such that, if the alert suppression criteria is not satisfied, the alert is provided to the at least one caregiver (Para 35 discloses alarm management engine 12 can also (e.g., alternatively, or additionally) implement unsupervised learning techniques, such as artificial neural network… deep convolutional neural networks, and deep recurrent neural networks. Para 54 discloses alarm condition calculator 42 generates alarm condition 44 based on information from medical device database 20, patient conditions database 22 and user fatigue calculated by alarm fatigue calculator 40, and communicates alarm condition 44 to an adapter 46 located at medical device 16. Adapter 46 monitors and filters alarms from medical device 16. In the example, alarms may not be generated at medical device 16 for blood pressure readings at 8:16 AM and 8:20 AM; however, alarms may be generated at 8:24 AM in view of the rate of change of blood pressure. In some embodiments, alarms may be generated at 8:16 AM and communicated to nurse Alice's work station, as nurse Alice has a lower inferred fatigue level than nurse Bob [alert is provided when alert suppression criteria is not satisfied], but not displayed visibly or audibly at nurse Bob's work station, whereas alarms generated at 8:24 AM may be communicated to both nurse Alice and nurse Bob. Medical device 16 and/or adapter 46 may generate alarm indicator 34 indicating that alarms have been generated and/or communicated (e.g., based on alarm condition 44). Such alarm indicator 34 can facilitate feedback and/or machine learning at alarm management engine 12.)
It would have been obvious one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of the systems and methods for remote and host monitoring communications as taught by Mahalingam and the patient monitor in the room of the method and apparatus for temporarily disabling a patient monitor as taught by Smith with the neural network of the alarm fatigue management system and methods as taught by Dyell in order to more accurately modify alarms at a medical device for alarm fatigue management.
While the combination of Mahalingam, Smith, and Dyell disclose the above limitations, it does not fully disclose the following limitation that Kee Yuan Ngiam discloses:
wherein the neural network is trained using the training data such that the suppression criteria is updated (Training machine learning for clinical applications on page e266, paragraph 2 discloses unlike medical devices, a unique feature of AI tools is their ability to be continuously improved with new data. This process is called incremental learning, in which outcomes data from a trained AI system are incorporated into a closed data feedback loop and used to refine the predictive accuracies of the system through iterative retraining of the model. 34,35 This feature distinguishes trainable neural networks from immutable scoring systems or standardised software. Figure 1 shows the training process, clinical trial evaluation, and its implementation in clinical settings for machine learning algorithms in health-care delivery.)
It would have been obvious one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of the systems and methods for remote and host monitoring communications as taught by Mahalingam and the patient monitor in the room of the method and apparatus for temporarily disabling a patient monitor as taught by Smith, and the neural network of the alarm fatigue management system and methods as taught by Dyell with the feedback loop as taught by Kee Yuan Ngiam in order to refine the predictive accuracies of the system of the system through iterative retraining of the model (Training machine learning for clinical applications on page e266, paragraph 2).
While the combination of Mahalingam, Smith, Dyell, and Kee Yuan Ngiam disclose the above limitations, it does not fully disclose the following limitation that Kiani discloses
a plurality of patient monitor devices communicatively coupled to the network and operable to monitor one or more statuses associated with a corresponding patient and to send alert data to the network if a respective status is indicative of an alert condition, wherein the plurality of patient monitor devices include a patient bed including a patient position detector and a guardrail position detectors (Para 6 discloses a patient safety system can comprise an adjustable bed adjustable between at least a lowered position and a raised position. A first sensor can be configured to detect the presence of a care provider and a second sensor can be configured to detect when a patient is trying to leave the bed. Para 31 discloses In an embodiment, when the care provider leaves the room, the safety bed does not return to the default safety position immediately, but waits until patient motion is sensed which indicate the patient may be attempting to get off the bed [thus disclosing a patient position detector]. Para 38 discloses when the bed sensor 32 detects the patient's movements and determines that the patient is trying to leave the bed 10, the computer 34 can respond accordingly. In some embodiments, a signal can be sent to a light and/or alarm which can be positioned on the bed or in the patient's room to alert the care provider. A signal can be transmitted to the monitoring station 36 to alert the care provider. Para 42 discloses the care provider can adjust the safety bed 10 to a raised position and move the guard rails 26 to a lowered configuration for improved access to the patient. In some embodiments, an alarm that sounds when the guard rails 26 are lowered [thus detecting the position of the guard rails] or when the bed 10 is raised can be deactivated or overridden by the care provider. In some embodiments, the alarm can be automatically deactivated when the care provider's presence is detected by the sensor 16. When the sensor 16 no longer detects the care provider's presence in the room, the safety bed 10 can automatically return to its default safe configuration; e.g. the bed 10 is changed to a flat, lowered position and the guard rails 26 are moved to a raised configuration.)
It would have been obvious one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of the systems and methods for remote and host monitoring communications as taught by Mahalingam and the patient monitor in the room of the method and apparatus for temporarily disabling a patient monitor as taught by Smith, the neural network of the alarm fatigue management system and methods as taught by Dyell and the feedback loop as taught by Kee Yuan Ngiam with the patient safety system with automatically adjusting bed as taught by Kiani in order to improve the safety of a patient by sending an alert to the care provider and / or sound an alarm if the patient tries to leave the bed and a care provider is not present with the patient (Kiani Abstract) and reduce the risk of accidental patient injury (Kiani Para 5).
Regarding Claim 2, this claim recites the limitations of Claim 1 and as to those limitations is rejected for the same basis and reasons as disclosed above. The combination of Mahalingam, Smith, Dyell, Kee Yuan Ngiam, and Kiani disclose the following limitation that Mahalingam further discloses:
The alert management system of claim 1, wherein to determine whether the alert suppression criteria is satisfied comprises to determine whether the alert suppression criteria is satisfied based on a model of caregiver fatigue. (Para 85-86 discloses the Remote Monitor's condition can be used to gauge the engagement of the Remote Monitor as well as predict whether the Remote Monitor is suffering from alarm fatigue, is likely to assist the Host effectively and timely, etc. As a result, it can be desirable to collect data on the condition of the Remote Monitor, process it, and provide it as Summary Data to the Host, Remote Monitor, or other Remote Monitors. Accordingly, if processing a Remote Monitor's contextual data indicates an inability of the Remote Monitor to react to alarms or notifications, Host and/or other Remote Monitors can be provided with Summary Data including at least some of Remote Monitor's contextual data to alert them. For example, such Summary Data can include a worry scale of a Remote Monitor, which can be based on processing Remote Monitor data ( e.g., analyte measurements, communications, Processed Data, Contextual Data, Health Data, System Data, Treatment Data, User Data, Sensor Data, Summary Data, and/or any data described in this disclosure). In some implementations, the worry scale can account for data relating to how the Remote Monitor's physiological condition may affect the health of the Host. In some cases an upper threshold can be used with the worry scale where if the Remote Monitor has a rating on the scale greater than or equal to a predetermined value, communication settings (e.g., communication frequency, privileges, notifications, alerts, etc.) can be changed.).
Regarding Claim 3, this claim recites the limitations of Claim 2 and as to those limitations is rejected for the same basis and reasons as disclosed above. While Mahalingam discloses schedules and a Remote Monitor’s calendar (Mahalingam para 77), the combination of Mahalingam, Smith, Dyell, Kee Yuan Ngiam, and Kiani disclose the following limitation that Dyell further discloses:
The alert management system of claim 2, wherein to determine whether the alert suppression criteria is satisfied based on a model of caregiver fatigue comprises to determine whether the alert suppression criteria is satisfied based on a work schedule of each caregiver. (Para 19 discloses user fatigue model attribute 26 comprises a shift information for users on a particular floor of a hospital; status data 27 can indicate that the shift is a night shift. See Para 21 clarifying the various details of user shift data. Para 40 discloses a user is alert with a reduced fatigue relative to the baseline in the morning, but after a 12 hour shift the user's fatigue level is heightened relative to that user's baseline. See Paras 51-53 describing the two nurses, Alice and Bob. Wherein Alice starts her shift at 8 am and Bob is set to end is shift at 9 am after 8 hours of continuous work at the ICU. Alarms may be generated at 8:16 AM and communicated to nurse Alice's work station, as nurse Alice has a lower inferred fatigue level than nurse Bob, but not displayed visibly or audibly at nurse Bob's work station [alert suppression criteria is satisfied based on a work schedule of each caregiver].)
It would have been obvious one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of the systems and methods for remote and host monitoring communications as taught by Mahalingam and the patient monitor in the room of the method and apparatus for temporarily disabling a patient monitor as taught by Smith, the feedback loop as taught by Kee Yuan Ngiam and the patient safety system with automatically adjusting bed as taught by Kiani with the neural network of the alarm fatigue management system and methods as taught by Dyell in order to more accurately determine alarm fatigue based on various parameters of a caregiver’s shift including how long a caregiver has been working the current shift (in regards to general fatigue and the amount of alarms throughout the shift).
Regarding Claim 4, this claim recites the limitations of Claim 1 and as to those limitations is rejected for the same basis and reasons as disclosed above. The combination of Mahalingam, Smith, Dyell, Kee Yuan Ngiam, and Kiani disclose the following limitation that Mahalingam further discloses:
The alert management system of claim 1, wherein the circuitry is further configured to determine whether to suppress the alert as a function of medical record data associated with a patient to whom the alert pertains. (Para 152 discloses the pattern detector can detect the pattern and/or generate an output, which can be provided to a report generator at Secure Server 504 for generating a communication and/or display page to Host Monitoring Devices 502A-N, Remote Monitoring Devices 508A-N, Workstation 506, any other device described in this disclosure, and/or any system. Para 154 discloses patterns can be recognized based on one or more predefined triggers (also referred to as criteria, rules, and filters). Furthermore, the one or more predefined triggers may be variable and adjustable based user input and/or programmatically based on one or more rules at Secure Server 504. Para 155 discloses the pattern recognition can be performed on any data described in this disclosure (e.g., analyte measurements, communications, Processed Data, Contextual Data, Health Data, System Data, Treatment Data, User Data, Sensor Data, Summary Data, and/or other data mentioned in this disclosure). Advantageously, more data types can provide context and further information on the health condition of the Host, as well as the Host's glucose level. Further, a pattern may be based on a combination of previous pattern data and a currently detected situation, whereby the combined information generates a predictive alert.)
Regarding Claim 5, this claim recites the limitations of Claim 1 and as to those limitations is rejected for the same basis and reasons as disclosed above. The combination of Mahalingam, Smith, Dyell, Kee Yuan Ngiam, and Kiani disclose the following limitation that Mahalingam further discloses:
The alert management system of claim 1, wherein to determine whether alert suppression criteria is satisfied comprises to determine whether a defined alert suppression time period associated with the patient monitor device is satisfied. (Para 239 discloses a remote monitoring device (e.g., Remote Monitoring Device 300), host monitoring device ( e.g., Host Monitoring Device 200), server (e.g., Secure Server 504), and/or other devices described in this disclosure can have a delay before transmitting alerts or notifications (e.g., 30 minutes or other desirable delay as described herein). Para 101 discloses a server can receive a communication regarding a Host based on the Host's glucose level. Block 154 can then process the communication and determine whether and how often to alert and/or notify one or more Remote Monitors ( e.g., using their remote monitoring devices) based on predefined settings, such as predefined frequency of alerts, priority of alerts, availability of Remote Monitor, availability of other Remote Monitor( s ), severity of notification, classification of Remote Monitor, position in hierarchy of classification of Remote Monitor, and/or other factors.)
Regarding Claim 6, this claim recites the limitations of Claim 1 and as to those limitations is rejected for the same basis and reasons as disclosed above. The combination of Mahalingam, Smith, Dyell, Kee Yuan Ngiam, and Kiani disclose the following limitation that Mahalingam further discloses:
The alert management system of claim 1, wherein to determine whether the alert suppression criteria is satisfied comprises to determine whether a priority associated with the alert data satisfies a reference priority in the alert suppression criteria. (Para 101 discloses Block 154 can then process the communication and determine whether and how often to alert and/or notify one or more Remote Monitors ( e.g., using their remote monitoring devices) based on predefined settings, such as predefined frequency of alerts, priority of alerts, availability of Remote Monitor, availability of other Remote Monitor( s ), severity of notification, classification of Remote Monitor, position in hierarchy of classification of Remote Monitor, and/or other factors.)
Regarding Claim 7, this claim recites the limitations of Claim 1 and as to those limitations is rejected for the same basis and reasons as disclosed above. While Mahalingam discloses “a processor can learn this pattern and/or make recommendations to a Host to change the timing of insulin, give an alert, or modify, ignore, or elevate any alerts regarding such insulin drops when such happens (Mahalingam para 155) and the suppression of alerts based on notification rules (Mahalingam para 18), the combination of Mahalingam, Smith, Dyell, Kee Yuan Ngiam, and Kiani disclose the following limitation that Dyell further discloses:
The alert management system of claim 1, wherein to determine whether the alert criteria is satisfied comprises to determine whether the alert criteria is satisfied using a neural network having weights based on the alert criteria. (Para 19 discloses For example, user fatigue model attribute 26 comprises a number of hours worked by the user; status data 27 can indicate that the user worked for 8 hours. In another example, user fatigue model attribute 26 comprises a shift information for users on a particular floor of a hospital; status data 27 can indicate that the shift is a night shift. Para 43 discloses When alarm fatigue is classified along a continuum, alarms generated may be modified based on user fatigue model attributes 26 seen as contributing more to alarm fatigue levels… user fatigue attributes 26 can be weighted according to importance or strength of correlations in some embodiments [wherein the user fatigue attributes are the alert criteria]. Para 35 discloses alarm management engine 12 can also (e.g., alternatively, or additionally) implement unsupervised learning techniques, such as artificial neural network… deep convolutional neural networks, and deep recurrent neural networks. See Further: Paras 51-54)
It would have been obvious one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of the systems and methods for remote and host monitoring communications as taught by Mahalingam and the patient monitor in the room of the method and apparatus for temporarily disabling a patient monitor as taught by Smith, the feedback loop as taught by Kee Yuan Ngiam and the patient safety system with automatically adjusting bed as taught by Kiani with the neural network of the alarm fatigue management system and methods as taught by Dyell in order to more accurately modify alarms at a medical device for alarm fatigue management by weighting user fatigue attributes by importance or strength of correlation. (Dyell Para 43).
Regarding Claim 9, this claim recites the limitations of Claim 1 and as to those limitations is
rejected for the same basis and reasons as disclosed above. The combination of Mahalingam, Smith, Dyell, Kee Yuan Ngiam, and Kiani disclose the following limitation that Mahalingam further discloses:
The alert management system of claim [[8]] 1, wherein the circuitry is further configured to select a subset of the caregivers to receive the alert based on a model of caregiver fatigue for each caregiver. (Para 85-86 discloses the Remote Monitor's condition can be used to gauge the engagement of the Remote Monitor as well as predict whether the Remote Monitor is suffering from alarm fatigue, is likely to assist the Host effectively and timely, etc. As a result, it can be desirable to collect data on the condition of the Remote Monitor, process it, and provide it as Summary Data to the Host, Remote Monitor, or other Remote Monitors. Accordingly, if processing a Remote Monitor's contextual data indicates an inability of the Remote Monitor to react to alarms or notifications, Host and/or other Remote Monitors can be provided with Summary Data including at least some of Remote Monitor's contextual data to alert them. For example, such Summary Data can include a worry scale of a Remote Monitor, which can be based on processing Remote Monitor data ( e.g., analyte measurements, communications, Processed Data, Contextual Data, Health Data, System Data, Treatment Data, User Data, Sensor Data, Summary Data, and/or any data described in this disclosure). In some implementations, the worry scale can account for data relating to how the Remote Monitor's physiological condition may affect the health of the Host. In some cases an upper threshold can be used with the worry scale where if the Remote Monitor has a rating on the scale greater than or equal to a predetermined value, communication settings (e.g., communication frequency, privileges, notifications, alerts, etc.) can be changed.)
Regarding Claim 10, this claim recites the limitations of Claim 1 and as to those limitations is
rejected for the same basis and reasons as disclosed above. The combination of Mahalingam, Smith, Dyell, Kee Yuan Ngiam, and Kiani disclose the following limitation that Mahalingam further discloses:
The alert management system of claim [[8]] 1, wherein the circuitry is further configured to select a subset of the caregivers to receive the alert as a function of a proximity of each caregiver to a patient associated with the monitor device that produced the alert data. (Para 17 discloses determining, by the server, the ability or inability of each of the host-designated selected remote monitoring devices to react to an alert informative of a dangerous event associated with the analyte state of the host based on a proximity of a host-designated selected remote monitoring device to the host device within a predetermined distance.)
Regarding Claim 11, this claim recites the limitations of Claim 1 and as to those limitations is rejected for the same basis and reasons as disclosed above. The combination of Mahalingam, Smith, Dyell, Kee Yuan Ngiam, and Kiani disclose the following limitation that Mahalingam further discloses:
The alert management system of claim [[8]] 1, wherein to provide the alert comprises to provide the alert to a mobile communication device carried by one of the caregivers. (Para 63 discloses remote monitoring devices can run a software application, such as a mobile application, also referred to as an "app," (e.g., a mobile application downloaded from an entity that created and/or owns and/or licenses the app, and/or an app store such as from APPLE, INC. or GOOGLE INC., or other), that performs the functionality and/or has the structure described.)
Regarding Claim 12, this claim recites the limitations of Claim 1 and as to those limitations is rejected for the same basis and reasons as disclosed above. The combination of Mahalingam, Smith, Dyell, Kee Yuan Ngiam, and Kiani disclose the following limitation that Mahalingam further discloses:
The alert management system of claim [[8]] 1, wherein to provide the alert comprises to provide the alert to a caregiver notification device installed in a hospital. (Para 56 discloses Remote Monitors can include any entity that can remotely monitor on its devices characteristics of a Host measured by Host device(s). Remote Monitors can include any of the aforementioned example entities described with respect to Hosts. Remote Monitors can also include electronic systems configured to monitor a person's health, such as hospital computer systems [caregiver notification device installed in a hospital], medical databases, hospital beds, medical robots, personal health monitors, automated health systems, and the like.)
Regarding Claim 13, this claim recites the limitations of Claim 1 and as to those limitations is rejected for the same basis and reasons as disclosed above. The combination of Mahalingam, Smith, Dyell, Kee Yuan Ngiam, and Kiani disclose the following limitation that Mahalingam further discloses:
The alert management system of claim [[8]] 1, wherein to provide the alert further comprises to provide data indicative of a reason why the alert was provided. (Para 16 discloses providing, by the server, an alert informative of an event associated with the analyte state of the host to selected remote monitoring devices based on notification rules that define circumstances to send the alert to a respective remote monitoring device. Para 239 discloses if the reason for which Communication 1402 is to be sent has been addressed, the alert may not be sent to remote monitoring devices of one or more of Remote Monitors 1412, 1414, 1416. For example, no alert may be sent at all, or a lower level alert and/or notification stating the issue and/or that the issue has been addressed can be sent.)
Regarding Claim 14, this claim recites the limitations of Claim 8 and as to those limitations is rejected for the same basis and reasons as disclosed above. The combination of Mahalingam, Smith, Dyell, Kee Yuan Ngiam, and Kiani disclose the following limitation that Mahalingam further discloses:
The alert management system of claim [[8]] 1, wherein to provide the alert further comprises to provide data indicative of one or more steps to be performed to address the alert. (Para 74 discloses Health Data can include data generated by a bolus calculator that makes suggestions on bolus administration. The bolus calculator can estimate the bolus desired to cover any carbohydrates eaten or drank in order to correct high glucose [address the alert]. Such data can be estimated for a Host and/or Remote Monitor to determine bolus administration, if any, which could be therapeutically beneficial. Para 88 discloses data can be generated based on received data. For example, in the case of a continuous glucose monitor, Block 104 can analyze measurements taken by the continuous glucose monitor to determine glucose concentration, generate communications ( e.g., messages, notifications, alerts, interrogative signals, status signals, synchronization signals, timer signals, data, information, etc.) based on the measurements and/or received data. Similarly, in the cases of continuous glucose monitor, Block 104 can detect upward or downward trends in glucose levels, make recommendations, and/or any other desired information that may be relevant about the Host.)
Regarding Claim 21, this claim recites the limitations of Claim 1 and as to those limitations is rejected for the same basis and reasons as disclosed above. The combination of Mahalingam, Smith, Dyell, Kee Yuan Ngiam, and Kiani disclose the following limitation that Mahalingam further discloses:
The alert management system of claim 1, wherein the circuitry of the compute device is configured to route the alert for display on one or more caregiver communication devices carried by caregivers other than the at least one caregiver. (Para 86 discloses communications. In some implementations, the opposite may be true where an over-worried Remote Monitor is seen as not being fit to take care of a Host. In such a case, the Remote Monitor's communication settings may be changed so that he/she receives fewer communications (e.g., only important notifications and/or none at all), and/or the Remote Monitor can be moved in classification so that he/she has fewer responsibilities and/or communications. Para 92 discloses either directly or through a network, Block 108 can transmit data to other device(s ), such as… mobile devices (e.g., tablets, cellphones, smartphones, e-readers, phablets, [notification devices being carried by the designated caregiver] and the like), any device with access to the internet and/or any network protocol, computers (e.g., laptops, desktops, personal computers, etc.), and/or any desirable device. In some cases, any of the aforementioned other device(s) may also be remote monitoring devices, servers, and/or host monitoring devices. Para 97 discloses if that particular Primary Caretaker is under high stress as evidenced by blood pressure, pulse, temperature, and/or other measurements and/or data… that Primary Caretaker may not be in a position to adequately take care of a Host when the Host is in need. In some cases, that Primary Caretaker's remote monitoring device and/or a server can receive data indicative of high stress and/or determine ( e.g., using a processor of the remote monitoring device and/or server [compute device]) that other Caretakers and/ or Remote Monitors should receive information first instead of that Primary Caretaker [at least one caregiver].
Regarding Claim 23, this claim recites the limitations of Claim 1 and as to those limitations is rejected for the same basis and reasons as disclosed above. The combination of Mahalingam, Smith, Dyell, Kee Yuan Ngiam, and Kiani disclose the following limitation that Mahalingam further discloses:
The alert management system of claim 1, wherein the neural network is further configured to update the suppression criteria based on an age of a patient associated with the monitor device that produced the alert data. (Para 62 discloses the normal concentration of glucose for various ages (adult, children up to 6, between age 6 and 12) thus disclosing how the suppression criteria would vary based on an age (as health glucose levels vary and as such the need for an alarm varies by age as one value may be considered healthy for one age group (and not in need of an alarm) but would be a cause for alarm in another age group). Para 149 discloses the processing at Secure Server 504 can also include associating metadata with the data received from the devices (e.g., Host Monitoring Devices 502A-N, Remote Monitoring Devices 508A-N, other devices, and/or any device disclosed in this disclosure), and/or sensors (e.g., Sensors 205, 305). Examples of metadata include patient information… [which] can include the patient's age, weight, sex, home address and/or any past health-related information, such as whether the patient has been diagnosed as a Type I or Type II diabetic, high-blood pressure, and/or as having any health condition. See Further: Para 53.)
Claims 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Mahalingam (US PG Pub 2017/0181645 A1), in view of Smith (US Patent 7,570,152 B2) further in view of Dyell (US PG Pub 2019/0038199 A1), Kee Yuan Ngiam (Big data and machine learning algorithms for health-care delivery), Kiani (US PG Pub 2014/0333440 A1) and King (US PG Pub 2016/0078747).
Regarding Claim 16, this claim recites the limitations of Claim 15 and as to those limitations is
rejected for the same basis and reasons as disclosed above. While Mahalingam in para 200 discloses, “based on the reception of communications, Host Monitoring Device 902, Remote Monitoring Device 906, and/or other devices can send responses to communications, such as messages, acknowledgements [accept], notifications, alerts, dismissals [dismissed alert], feedback, error messages, etc,” the combination of Mahalingam, Smith, Dyell, Kee Yuan Ngiam, and Kiani does not fully disclose the following limitation that King discloses:
The alert management system of claim [[15]] 1, wherein to determine [[a]] the result comprises to determine whether the alert was accepted or dismissed by [[a]] at least one caregiver who received the alert. (Para 77 discloses the rules 250 related to user response to alarms can determine the options for responding to an alarm that are presented to the user 160 along with the alarm. A simple example would allow the user 160 to hit a button (or make some other input) to acknowledge that they have received the alarm. A slightly more complex example might allow the user 160 to accept or reject the alarm. Other examples may allow the user 160 to forward the alarm to another specific user 160, to speak with the patient 110, to view the patient 110, to request more information about the patient 110 or the nature of the alarm, and so forth.).
It would have been obvious one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of the systems and methods for remote and host monitoring communications as taught by Mahalingam and the patient monitor in the room of the method and apparatus for temporarily disabling a patient monitor as taught by Smith, the neural network of the alarm fatigue management system and methods as taught by Dyell, the feedback loop as taught by Kee Yuan Ngiam and the patient safety system with automatically adjusting bed as taught by Kiani with the intelligent presentation of alarms and messages in mobile health systems as taught by King in order to reduce alarm fatigue while improving the delivery and routing of alarms (King para 15).
Regarding Claim 17, this claim recites the limitations of Claim 1 and as to those limitations is rejected for the same basis and reasons as disclosed above. The combination of Mahalingam, Smith, Dyell, Kee Yuan Ngiam, and Kiani does not fully disclose the following limitation that King discloses:
The alert management system of claim [[15]] 1, wherein to determine [[a]] the result comprises to determine whether the alert was forwarded by a recipient caregiver to another caregiver. (Para 77 discloses the rules 250 related to user response to alarms can determine the options for responding to an alarm that are presented to the user 160 along with the alarm. A simple example would allow the user 160 to hit a button (or make some other input) to acknowledge that they have received the alarm. A slightly more complex example might allow the user 160 to accept or reject the alarm. Other examples may allow the user 160 to forward the alarm to another specific user 160, to speak with the patient 110, to view the patient 110, to request more information about the patient 110 or the nature of the alarm, and so forth.)
It would have been obvious one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of the systems and methods for remote and host monitoring communications as taught by Mahalingam and the patient monitor in the room of the method and apparatus for temporarily disabling a patient monitor as taught by Smith, the neural network of the alarm fatigue management system and methods as taught by Dyell, the feedback loop as taught by Kee Yuan Ngiam and the patient safety system with automatically adjusting bed as taught by Kiani with the intelligent presentation of alarms and messages in mobile health systems as taught by King in order to reduce alarm fatigue while improving the delivery and routing of alarms (King para 15).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Mahalingam (US PG Pub 2017/0181645 A1), in view of Smith (US Patent 7,570,152 B2) further in view of Dyell (US PG Pub 2019/0038199 A1), Kee Yuan Ngiam (Big data and machine learning algorithms for health-care delivery), Kiani (US PG Pub 2014/0333440 A1) and Seemakurty (US Patent 11,195,616 B1).
Regarding Claim 18, this claim recites the limitations of Claim 1 and as to those limitations is
rejected for the same basis and reasons as disclosed above. The combination of Mahalingam, Smith, Dyell, Kee Yuan Ngiam, and Kiani does not fully disclose the following limitation that Seemakurty discloses:
The alert management system of claim [[15]] 1, wherein to update the alert suppression criteria comprises to at least one of (Column 7, lines 59-61 disclose the plurality of predictive models comprises a convolution neural network (CNN) and a tree-based model. Column 8, lines 17-22 disclose the method further comprises using an alert generation module operatively coupled to the health status prediction module, wherein the alert generation module is configured to generate one or more predictive alerts for the one or more patients based on prediction of the health status of the one or more patients. Column 26, lines 52-55 discloses one or more ML predictive models used to detect health status changes can be further trained or re-trained using updated data sets incorporating feedback (e.g., new labelled data). Column 32, lines 49-56 disclose if an average value of a measured health parameter exceeds the new reactive threshold, the ensemble model 910 generates a level 2 alert 970, as described herein. The generated alert level 2 may accept feedback from a stakeholder, as described herein. Based on the feedback, a second proactive intervention 970 may be applied to the system. The second proactive intervention 970 may change a reactive alarm threshold [changing the assigned suppression value based on the result.)
It would have been obvious one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of the systems and methods for remote and host monitoring communications as taught by Mahalingam and the patient monitor in the room of the method and apparatus for temporarily disabling a patient monitor as taught by Smith, the neural network of the alarm fatigue management system and methods as taught by Dyell, the feedback loop as taught by Kee Yuan Ngiam and the patient safety system with automatically adjusting bed as taught by Kiani with the systems and methods using ensemble machine learning techniques for future event detection as taught by Seemakurty in order to improve timeliness and accuracy to effectively respond to changing health status. (Column 1, lines 5-27).
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Mahalingam (US PG Pub 2017/0181645 A1), in view of Smith (US Patent 7,570,152 B2) further in view of Dyell (US PG Pub 2019/0038199 A1), Kee Yuan Ngiam (Big data and machine learning algorithms for health-care delivery), Kiani (US PG Pub 2014/0333440 A1) and Baker (US PG Pub 2020/0211360 A1).
Regarding Claim 22, this claim recites the limitations of Claim 1 and as to those limitations is rejected for the same basis and reasons as disclosed above. While Mahalingam discloses transmitting an alert to other Caretakers and/or Remote Monitors due to high stress of the Primary Caretaker (Mahalingam Para 97), and both Mahalingam and Smith disclose tracking the location of a Remote Monitor (Mahalingam Para 124) and a caregiver (Smith Column 17, lines 7-8), the combination of Mahalingam, Smith, Dyell, Kee Yuan Ngiam, and Kiani does not fully disclose the following limitation that Baker discloses:
The alert management system of claim 1, wherein the circuitry of the compute device is configured to route the alert for display on any caregiver notification device installed in any corresponding patient room in which one or more caregivers other than the at least one caregiver are located as determined by the location tracking system. (Para 124 discloses after receiving the nurse call 20 at block 114, algorithm 100 proceeds to block 116 to determine whether a primary caregiver is available. For each patient in the patient rooms 1-1 through 3-L of Units 1-3, a primary caregiver [designated caregiver] and a secondary caregiver is typically assigned within the nurse call system 14 using master nurse station computer 28 or using some other administration computer of system 10…. if at block 116 of algorithm 100 the primary caregiver is designated as unavailable, algorithm 100 proceeds to block 124 as indicated by the circles containing the letter “B” in FIGS. 2A and 2B. At block 124, algorithm 100 determines whether the secondary caregiver is available. If at block 124 algorithm 100 determines that the secondary caregiver is available (e.g., the secondary caregiver has not been determined to be unavailable at block 104), algorithm 100 proceeds to send a message to notify the secondary caregiver of the nurse call 20, as indicated at block 126 of FIG. 2B. To reiterate, such notifications of nurse calls 20 may appear on the screens of one or more of room stations 32, master station 28, status board 50, and/or wireless communication devices 52, 54, 56, 58 carried by the secondary caregiver. In this regard, the notification of nurse call 20 may appear on the room station 32 where the secondary caregiver [caregiver other than designated caregiver] is determined to be located by the real time locating systems (RTLS) 12.)
It would have been obvious one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of the systems and methods for remote and host monitoring communications as taught by Mahalingam and the patient monitor in the room of the method and apparatus for temporarily disabling a patient monitor as taught by Smith, the neural network of the alarm fatigue management system and methods as taught by Dyell, the feedback loop as taught by Kee Yuan Ngiam and the patient safety system with automatically adjusting bed as taught by Kiani with the system and method for caregiver availability determination as taught by Baker in order to improve systems and methods for routing alert calls to available caregivers and refraining from routing such alert calls to unavailable caregivers (Baker Para 3).
Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over Mahalingam (US PG Pub 2017/0181645 A1), in view of Smith (US Patent 7,570,152 B2) further in view of Dyell (US PG Pub 2019/0038199 A1), Kee Yuan Ngiam (Big data and machine learning algorithms for health-care delivery), Kiani (US PG Pub 2014/0333440 A1) and the NPL reference “Saving a Deep Learning model in Keras.”
Regarding Claim 24, this claim recites the limitations of Claim 1 and as to those limitations is
rejected for the same basis and reasons as disclosed above. While Mahalingam and Dyell disclose suppressing an alert or alarm based on caregiver fatigue (see claim 1) and thus reads on the alert suppression criteria, the combination of Mahalingam, Smith, Dyell, Kee Yuan Ngiam, and Kiani does not fully disclose the following limitation that the reference “Saving a Deep Learning model in Keras” discloses:
The alert management system of claim 1, wherein the computer device is further configured to export the [data] for use in other… systems in other clinical environments. (Paras 1-2 disclose training a neural network/deep learning model usually takes a lot of time, particularly if the hardware capacity of the system doesn't match up to the requirement. Once the training is done, we save the model to a file. To reuse the model at a later point of time to make predictions, we load the saved model. Through Keras, models can be saved in three formats: YAML format, JSON format, HDF5 format… YAML and JSON files store only model structure, whereas, HDF5 file stores complete neural network model along with structure and weights. Therefore, if the model structure is saved using YAML or JSON format, weights should be stored in an HDF5 file to store the entire model.)
It would have been obvious one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of the systems and methods for remote and host monitoring communications as taught by Mahalingam and the patient monitor in the room of the method and apparatus for temporarily disabling a patient monitor as taught by Smith, the neural network of the alarm fatigue management system and methods as taught by Dyell, the feedback loop as taught by Kee Yuan Ngiam and the patient safety system with automatically adjusting bed as taught by Kiani with the exporting of a neural network as taught by the NPL reference “Saving a Deep Learning model in Keras” in order to save time by saving a neural network to a file once it is already trained for later use in other environments.
Related Prior Art
US PG Pub 20170061777 A1 discloses proximity based alarm suppression as taught by Kelly. Further, Kelly discloses, to paraphrase, suppressing an alarm when a clinician is determined to be in proximity to the infusion device that has an alarm within the patient room.
US PG Pub 2017/0000427 A1 discloses a system for convergence of alarms from medical equipment as taught by Meredith. Further, Meredith discloses, to paraphrase, detecting the presence of a staff member via a near-field communication (NFC) protocol and suppressing notifications for staff members with a presence indicated within the monitored room.
US PG Pub 2010/0127866 A1 discloses controlling an alarm state based on the presence or absence of a caregiver in a patients room as taught by Klein.
Response to Arguments
Applicant’s arguments filed 09/25/2025 with respect to 35 U.S.C. § 101 have been fully considered but are not persuasive. Applicant argues that “because training a neural network does not recite a judicial exception according to the Deputy Commissioner[’s August 4, 2025 memorandum], this same language logically cannot then be considered merely a technological environment or field of use.” The Examiner respectfully disagrees. The Memorandum was looking at the differences between example 39, which illustrates claim limitations that merely involve an abstract idea, and example 47, which shows limitations that recite an abstract idea. Example 39 claimed training the neural network wherein the specification of example 39 discloses, “The neural networks are then trained with this expanded training set using stochastic learning with backpropagation which is a type of machine learning algorithm that uses the gradient of a mathematical loss function to adjust the weights of the network.” The claim itself does not claim the mathematical concept, it merely involves an abstract idea by claiming the training of the neural network (wherein the specification clarifies that this training is accomplished with a mathematical concept). On the other hand, example 47, claim 2, clearly claims, “training, by the computer, the ANN based on the input data and a selected training algorithm to generate a trained ANN, wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm.” Here, the mathematical concept is clearly claimed in regards to the training of the ANN. As such, example 47 shows limitations that recite an abstract idea, and are not merely involving an abstract idea like example 39.
The memorandum does not state that ‘training a neural network’ is not a judicial exception, further, it does not disclose that if a claim recites ‘training a neural network’ then it automatically does not recite a judicial exception which is what the Examiner believes the Applicant is arguing as further discussed below. Example 39 is eligible because no judicial exception is recited (e.g., it becomes eligible at Step 2A Prong 1), not due to ‘training a neural network’. ‘Training a neural network’ was not part of the judicial exception in example 39 because there was no judicial exception; similarly, in the instant application, training the neural network is not considered to be part of the judicial exception as set forth in the analysis under Step 2A Prong 1, but it is an additional element that fails to integrate the judicial exception into a practical application or provide significantly more (see the analysis in Step 2A Prong 2 and Step 2B).
As such, this argument is not persuasive, and the previous 35 U.S.C. § 101 is held.
Further, the Applicant argues that “because claims ‘as a whole’ are evaluated in the abstract idea analysis, if any portion of a claim does not recite a judicial exception, then the claim as a whole cannot recite judicial exception.” This is an incorrect interpretation of what is eligible under 35 U.S.C. § 101. MPEP 2106(I) discloses, “first, the claimed invention must be to one of the four statutory categories. 35 U.S.C. 101 defines the four categories of invention that Congress deemed to be the appropriate subject matter of a patent: processes, machines, manufactures and compositions of matter… second, the claimed invention also must qualify as patent-eligible subject matter, i.e., the claim must not be directed to a judicial exception unless the claim as a whole includes additional limitations amounting to significantly more than the exception.” Emphasis added. Analyzing the claims as a whole does not mean that “if any portion of a claim does not recite a judicial exception, then the claim as a whole cannot recite judicial exception.” If this were the case, then any claim disclosing a computer or a processor would be eligible as the hardware of a computer or a processor does not recite a judicial exception. The additional elements of a claim are not analyzed as a part of the abstract idea. Instead, the Examiner analyzes the additional elements to see if they integrate the judicial exception into a practical application or if they amount to significantly more than the exception.
In regards to the neural network, as presented above, the amendment of “provid[ing] the determined result to the neural network as training data: wherein the neural network is trained using the training data such that the alert suppression criteria is updated,” was found to be recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea by adding the words ‘apply it' (or an equivalent) with the judicial exception. Thus, merely training the neural network based on a result of the abstract idea does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The Examiner notes that the claim never uses the neural network, it merely claims training the neural network to update alert suppression criteria. The independent claim does not disclose using the alert suppression criteria from the neural network to determine if the alert suppression criteria is satisfied or not. It isn’t until dependent claim 7 where the neural network is used to determine whether the alert suppression criteria is satisfied. As previously presented, the neural network in claim 7 used to determine whether the alert suppression criteria is satisfied was found to be recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea by adding the words ‘apply it' (or an equivalent) with the judicial exception.
MPEP 2106.05(f) discloses, “Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’". Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965).” As such, this argument is not persuasive, and the previous 35 U.S.C. § 101 is held.
Applicant’s arguments filed 09/25/2025 with respect to 35 U.S.C. § 103 have been fully considered and are persuasive regarding the newly added limitations (a patient bed with a patient position detector and a guardrail position detector). Therefore, the previous 35 U.S.C.
§ 103 rejection has been withdrawn. However, upon further consideration, a new grounds of rejection under 35 U.S.C. § 103 necessitated by Applicant’s amendments as disclosed above.
Conclusion
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/SARA JESSICA MORICE DE VARGAS/Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681