DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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, 5-16, 30, 59, and 61-62 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1
Claims 1, 5-16, 30, 59, and 61-62 are within the four statutory categories. However, as will be shown below, claims 1, 5-16, 30, 59, and 61-62 are nonetheless unpatentable under 35 U.S.C. 101.
Claims 1, 16, and 30 are representative of the inventive concept and recite:
Claim 1
A computer-implemented method for use when a patient is experiencing or about to experience an episode of nausea, which when executed by data processing hardware of a computing device causes the data processing hardware to perform operations comprising:
receiving, from one or more monitoring devices, patient parameter data or one or more datasets of a patient, the patient parameter data or one or more datasets collected by the one or more monitoring devices;
determining whether the patient is experiencing or about to experience an episode of nausea, based on:
(a) receiving the patient parameter data or one or more datasets of the patient;
(b) performing one or more initial processing steps on the received patient parameter data or one or more datasets, including: calculating a trend and/or variability in the patient parameter data or one or more datasets by employing one or more of;
(1) a moving average function for averaging data collected during a time window or one or more datasets;
(2) a digital filter for the data during a time window or one or more datasets;
(3) at least one technique calculating the variability of the data over various time windows or one or more datasets;
(c) generating an indicator score based on the processed patient parameter data or one or more datasets and/or a large patient population training dataset;
(d) comparing the indicator score to a threshold indicative or predictive of nausea whether the patient is experiencing or about to experience an episode of nausea; and when the patient is experiencing or about to experience an episode of nausea as determined by the indicator score indicating that the patient is likely experiencing or likely to experience an episode of nausea,
communicating one or more therapy signals, including a nausea therapy delivering device activation signal, to a receiver device in communication with the computing device, the receiver device configured to contribute to providing nausea therapy to the patient via a nausea therapy delivery device associated with the patient; wherein the nausea therapy delivering device activation signal when received by the receiver device activates the nausea therapy delivery device to deliver a nausea therapeutic to the patient.
*Claim 16 recites similar limitations as claim 1, but for a system.
Claim 30
A method for treating a disease or preventing a disease progression in a subject comprising: (a) tracking one or more symptoms, clinical manifestations, or therapy signals of the disease via a monitoring device;
(b) analyzing the symptoms, clinical manifestations, or therapy signals of the disease and determining, quantitively assessing, or qualitatively assessing whether the subject is experiencing or about to experience an episode of the disease, via a computing device, based on:
(i)receiving patient parameter data or one or more datasets of the patient;
(ii) performing one or more initial processing steps on the received patient parameter data or one or more datasets, including: calculating a trend and/or variability in the patient parameter data or one or more datasets by employing one or more of;
(1) a moving average function for averaging data collected during a time window or one or more datasets;
(2) a digital filter for the data during a time window or one or more datasets;
(3) at least one technique calculating the variability of the data over various time windows or one or more datasets;
(iii) generating an indicator score based on the processed patient parameter data or one or more datasets and/or a large patient population training dataset;
(iv) comparing the indicator score to a threshold indicative or predictive of nausea;
(c) communicating the symptoms, clinical manifestations, or therapy signals of the disease to a receiver device in communication with the computing device;
(d) enabling administration of a therapeutic agent to the subject in need thereof for treating or preventing the disease; wherein the disease is nausea, retch, and/or vomiting;
and (e) administering the therapeutic agent to the subject in need thereof via a nausea therapy delivery device associated with the patient via a nausea therapy delivery device associated with the patient, wherein the method comprises monitoring the patient's status either continuously, on- demand, prodromal, or at the onset of symptoms of nausea, and/or vomiting.
Step 2A Prong One
The broadest reasonable interpretation of these steps includes mental processes because the
highlighted components can practically be performed by the human mind (in this case, the process of
“determining”, “processing”, “calculating”, “comparing”, “tracking”, “analyzing”, “monitoring”) or using pen and paper. Other than reciting generic computer components/functions such as “monitoring device”, “computing device”, “delivery device”, “receiver device”, “data processing hardware”, and “memory hardware storing instructions”, nothing in the claims precludes the highlighted portions from practically being performed in the mind. For example, in claim 1, but for the system language, the claim encompasses the user collecting patient data and using it to understand if a patient is experiencing or will experience an episode of nausea. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components/functions, then it falls within “Mental Processes” grouping of abstract ideas. Additionally, the mere nominal recitation of a generic computer does not take the claim limitation out of the mental process grouping. Thus, the claim recites a mental process. The recitation of generic computer components/functions such as “generating”, “receiving”, “enabling administration” and “administering” also covers behavioral or interactions between people (i.e. the computer and user interface), and/or managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions — in this case a person is able to physically follow the steps to gather and process data to provide actionable health status information), hence the claim falls under “Certain Methods of Organizing Human Activity”. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Dependent claims 5-15, 59, and 61-62 recite additional subject matter which further narrows or
defines the abstract idea embodied in the claims.
Step 2A Prong Two
This judicial exception is not integrated into a practical application. In particular, the claims recite the
following additional limitations:
Claim 1 recites: “computer”, “hardware”, “monitoring device”, “nausea therapy delivery device”, “computing device”, “receiving, from one or more monitoring devices, patient parameter data or one or more datasets of a patient, the patient parameter data or one or more datasets collected by the one or more monitoring devices”, and “communicating one or more therapy signals, including a nausea therapy delivering device activation signal, to a receiver device in communication with the computing device, the receiver device configured to contribute to providing nausea therapy to the patient via a nausea therapy delivery device associated with the patient; wherein the nausea therapy delivering device activation signal when received by the receiver device activates the nausea therapy delivery device to deliver a nausea therapeutic to the patient”
Claim 30 recites: “monitoring device”, “computing device”, “receiver device in communication with the computing device”, “nausea therapy delivery device”, “communicating the symptoms, clinical manifestations, or therapy signals of the disease to a receiver device in communication with the computing device”
In particular, the additional elements do no integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more limitations which:
Amount to mere instructions to apply an exception (MPEP 2106.05(f)). The limitations are recited as being performed by a computer-implemented method and computing device (or computer), hardware (components of a computer), monitoring device, memory storing hardware instructions, data processing hardware, memory processing hardware, memory hardware storing instructions, receiver device, and nausea therapy delivery device. These limitations are recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer.
Add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea such as the recitation of “receiving, from one or more monitoring devices, patient parameter data or one or more datasets of a patient, the patient parameter data or one or more datasets collected by the one or more monitoring devices”, “communicating one or more therapy signals, including a nausea therapy delivering device activation signal, to a receiver device in communication with the computing device, the receiver device configured to contribute to providing nausea therapy to the patient via a nausea therapy delivery device associated with the patient; wherein the nausea therapy delivering device activation signal when received by the receiver device activates the nausea therapy delivery device to deliver a nausea therapeutic to the patient” , and ““communicating the symptoms, clinical manifestations, or therapy signals of the disease to a receiver device in communication with the computing device”
Dependent claim 7 recites user interface
Dependent claims 14-15 recite predictive model
Dependent claim 62 recites network and transmission
In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more limitations which:
Amount to mere instructions to apply an exception (MPEP 2106.05(f)). Network, determining data and generating outputs “executing” and “training” a predictive model , but providing nothing more than mere instructions to implement an abstract idea on a generic computer. The predictive model is used to generally apply the abstract idea without limiting how it functions.
Add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea such as: Recitation of user interface and transmission.
Dependent claims 5-6, 8-13, 59, and 61 do not include any additional elements beyond those already recited in independent claims 1, 16, and 30 and dependent claims 7, 14, 15, and 62, hence do not integrate the aforementioned abstract idea into a practical application. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or machine learning model or improves any other technology. Their collective function merely provides conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B
Claims 1, 16, and 30 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements: A computer in claim 1; amount to no more than mere instructions to apply an exception to the abstract idea. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields as demonstrated by:
Recitation of an additional element such as user interface is expressly configured to perform generic functions (Para 0017, Morra et al (W02018072035A1) discloses: “Client devices may also comprise a graphical user interface (GUI) or a browser application provided on a display (e.g., monitor screen, LCD or LED display, projector, etc.).”) in a manner that would be well understood, routine, and conventional.
Recitation of predictive model in claims 14 and 15 is not deemed to add significantly more than the abstract idea because of its common use to those in the art (Figure 2, Para 0021, Morra(W02018072035A1) discloses: “Fig. 2 presents a system that includes an analysis server 216 that can use historic data 218 to generate one or more predictive models 220.”) in a manner that would be well-understood, routine, and conventional.
Recitation of an additional element such as “receiving patient parameter data” is expressly configured to perform collection of data from a device where data is stored (Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362) in a manner that would be well-understood, routine, and conventional
Recitation of an additional element such as storage configured to hold/store data (Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701) in a manner that would be well-understood, routine, and conventional
Recitation of an additional element such as communication of a signal or with a device (Para 0016, Morra(W02018072035A1) discloses: “The health tracking device 102 may communicate with analysis server 108 via network 106 for transmitting sensor data at any given timepoint.” ) in a manner that would be well-understood, routine, and conventional
Recitation of transmission, which in this case refers to sending/receiving data ((TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614,118 USPQ2d 1744, 1748 (Fed. Cir. 2016)) in a manner that would be well-understood, routine, and conventional
Dependent claims 5-6, 8-13, 59, and 61 do not include any additional elements beyond those already recited in independent claims 1, 16, and 30 and dependent claims 7, 14, 15, and 62. Therefore, they are not deemed to be significantly more than the abstract idea because, as stated above, the limitations of the aforementioned dependent claims amount to no more than generally linking the abstract idea to a particular technological environment or field of use, and/or do not recite and additional elements not already recited in independent claims 1, 16, and 30 hence do not amount to “significantly more” than the abstract idea.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 5-16, 30, 59, and 61-62 are rejected under 35 U.S.C. 103 is being unpatentable over Morra et al (W02018072035A1) in view of Hayter (US10493202B2), and in further view of Hoekman(US20200078544A1).
Claim 1
Morra discloses:
A computer-implemented method for when a patient is experiencing or about to experience an episode of nausea(Para 0015, Morra discloses a sickness prediction system), which when executed by data processing hardware(Para 0041, Morra discloses data processing hardware) of a computing device causes the data processing hardware to perform operations comprising: receiving (Para 0005, Morra discloses: “The system comprises a memory device having executable instructions stored therein, and a processing device, in response to the executable instructions, operative to: receive [RECEIVING] user symptoms associated with a user of the health tracking device at a sickness timepoint…”), from one or more monitoring devices (Para 0015, Morra discloses: “The system may collect data from wearable or non-wearable monitoring devices [MONITORING DEVICE], such as fitness trackers, wrist bands, bracelets, watches, rings, pendants, jewelry, implants, radio-frequency identification (RFID) device, or other devices optionally connected by Internet, Bluetooth, NFC or WiFi,...”) , patient parameter data (Para 0006, Morra discloses “The health sensor data may include a measurement selected from the group consisting of: heart rate, pulse rate, galvanic skin response, sleep, blood oxygen level, body temperature, pulse…”[THESE MEASUREMENTS ARE PATIENT PARAMETER DATA AS DESCRIBED IN SPECIFICATIONS, PARA 0012]) or one or more datasets of a patient (Para 0015, Morra discloses: “the application may be operative to compare the new sensor data with the calibrated sickness models to predict the probability of the user having a recurring sickness. [USER CAN BE A PATIENT]), the patient parameter data (Para 0006, Morra discloses “The health sensor data may include a measurement selected from the group consisting of: heart rate, pulse rate, galvanic skin response, sleep, blood oxygen level, body temperature, pulse…”[THESE MEASUREMENTS ARE PATIENT PARAMETER DATA AS DESCRIBED IN SPECIFICATIONS, PARA 0012]) or one or more datasets collected by the one or more monitoring devices (Para 0015, Morra discloses: “The system may collect data from wearable or non-wearable monitoring devices [MONITORING DEVICE], such as fitness trackers, wrist bands, bracelets, watches, rings, pendants, jewelry, implants, radio-frequency identification (RFID) device, or other devices optionally connected by Internet, Bluetooth, NFC or WiFi,...”); determining whether the patient is experiencing or about to experience an episode of nausea(Para 0006, Morra discloses: “In one embodiment, the user symptoms may include an indication of sickness. The user symptoms may include at least one of headaches, cough, fever, chest congestion, runny nose, nasal congestion, fatigue, fever, aches, pains, sore throat, sneezing, watery eyes, malaise, chills, nausea [NAUSEA], vomiting…”), based on:
(a) receiving(Para 0005, Morra discloses receiving user symptom data) the patient parameter data (Para 0006, Morra discloses “The health sensor data may include a measurement selected from the group consisting of: heart rate, pulse rate, galvanic skin response, sleep, blood oxygen level, body temperature, pulse…”[THESE MEASUREMENTS ARE PATIENT PARAMETER DATA AS DESCRIBED IN SPECIFICATIONS, PARA 0012]) or one or more datasets of the patient (Para 0015, Morra discloses: “the application may be operative to compare the new sensor data with the calibrated sickness models to predict the probability of the user having a recurring sickness. [USER CAN BE A PATIENT]) ,
(b) performing one or more initial processing steps on the received patient parameter data or one or more datasets, including:
calculating a trend and/or variability in the patient parameter data(Para 0033, Morra discloses using trends and patterns of patient data) or one or more datasets by employing one or more of;
(1) a moving average function for averaging data collected during a time window or one or more datasets;
(2) a digital filter for the data during a time window or one or more datasets;
(3)
(c) generating an indicator score based on the processed patient parameter data(Para 0018, Morra discloses a sick score based on patient data) or one or more datasets and/or a large patient population training dataset;
(d) comparing the indicator score to a threshold indicative or predictive of nausea(Para 0024, Morra discloses a health risk score satisfying a threshold value which determines patient sickness risk);
and when the patient (Para 0015, Morra discloses: “the application may be operative to compare the new sensor data with the calibrated sickness models to predict the probability of the user having a recurring sickness. [USER CAN BE A PATIENT]) is experiencing or about to experience an episode of nausea(Para 0006, Morra discloses: “In one embodiment, the user symptoms may include an indication of sickness. The user symptoms may include at least one of headaches, cough, fever, chest congestion, runny nose, nasal congestion, fatigue, fever, aches, pains, sore throat, sneezing, watery eyes, malaise, chills, nausea [NAUSEA], vomiting…”) as determined by the indicator score indicating that the patient is likely experiencing or likely to experience an episode of nausea(Para 0018, Morra discloses a sick score, which is a sickness prediction based on patient data),
Morra does not explicitly disclose: at least one technique calculating the variability of the data over various time windows or one or more datasets, nausea therapy delivering device, therapy signal, receiver device, therapy delivery device activation
Hayter discloses:
at least one technique calculating the variability of the data over various time windows or one or more datasets(Figure 8, Hayter discloses the determination of analyte variability at various time frequencies)
therapy signals (Figure 1, Hayter discloses “Control Unit” and a pathway (arrow) to “Insulin Delivery Unit”, which acts as a [THERAPY SIGNAL])
receiver device (Figure 1, Hayter discloses “Control Unit” in figure 1, which acts as [RECEIVER DEVICE])
the nausea therapy delivering device activation signal when received by the receiver device activates the nausea therapy delivery device to deliver a nausea therapeutic to the patient(Figure 7, Column 2, Line 18 Hayter discloses: “ method and device for monitoring a closed loop control operation including signal levels received from an analyte sensor and automatic delivery of medication at least in part in response to [THERAPY DELIVERY DEVICE ACTIVATION VIA SIGNAL] the received analyte sensor signals, determining whether the signal level received from the analyte sensor…”)
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the sickness prediction system of Morra to add therapy signals,
receiver device, and therapy delivery device activation, as taught by Hayter. One of ordinary skill would have been so motivated to provide a means to manage and utilize therapy administration to improve the condition of the patient, in this case, the condition being diabetes, by managing glucose (Column 1, Line 57, Hayter discloses: “With the continued rise in the number of diagnosed diabetic conditions, there is on-going research to develop closed loop control systems to automate the insulin delivery based on the real time monitoring of the fluctuation in the glucose levels.”).
Hayter does not explicitly disclose: nausea therapy delivering device
Hoekman discloses:
nausea therapy(Para 0081, Hoekman discloses Lorazepam delivery which is an anti-nausea therapy) delivering device(Para 0127, Hoekman discloses a device that delivers nasal medications)
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the sickness prediction system of Morra to nausea therapy delivery device, as taught by Hoekman. One of ordinary skill would have been so motivated to provide a means to improve patient well-being with use of a device that relieves nausea, but in this case, for a nasal drug delivery device (Para 0008, Hoekman discloses “There is a need for devices that can deliver compounds to the upper nasal cavity for direct nose-to-brain delivery. Certain existing nasal drug delivery devices do not adequately propel the drug from the device.”).
Claim 5
Morra does not explicitly disclose:
The method of claim 1, wherein the nausea therapy delivering device comprises: an automatic injector; an infusion pump; an inhaler; a transcutaneous patch; a sub-dermal implantable pump; or a small molecule, biologic, or cell therapy administrator.
Hayter discloses:
The method of claim 1, wherein the nausea therapy delivering device comprises: an automatic injector (Column 4, Line 16 Hayter discloses: “The medication delivery devices may include one or more reservoirs or containers to hold the medication for delivery in fluid connection with an infusion set, for example, including an infusion tubing and/or cannula [INJECTOR].”); an infusion pump (Column 4, Line 8, Hayter discloses: “Embodiments include medication delivery devices such as external infusion pumps [INFUSION PUMP], implantable infusion pumps, on-body patch pump, or any other processor controlled medication delivery devices that are in communication with one or more control units…”); an inhaler; a transcutaneous patch(Column 4, Line 8, Hayter discloses: “Embodiments include medication delivery devices such as external infusion pumps, implantable infusion pumps, on-body patch pump [PATCH], or any other processor controlled medication delivery devices that are in communication with one or more control units…”); a sub-dermal implantable pump (Column 4, Line 8, Hayter discloses: “Embodiments include medication delivery devices such as external infusion pumps, implantable infusion pumps [IMPLANTABLE PUMP], on-body patch pump, or any other processor controlled medication delivery devices that are in communication with one or more control units…”); or a small molecule, biologic, cell therapy administrator(Column 4, Line 12, Hayter discloses: “The medication delivery devices may include one or more reservoirs or containers to hold the medication for delivery in fluid connection with an infusion set [THERAPY ADMINISTRATOR]…”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary
skill in the art to have modified the sickness prediction system of Morra to various types of therapy
delivering modalities, as taught by Hayter. One of ordinary skill would have been so motivated to
provide various means to deliver therapy in order to improve the condition of a sick patient, in this case,
the condition being diabetes (Column 1, Line 57, Hayter discloses: “With the continued rise in the
number of diagnosed diabetic conditions, there is on-going research to develop closed loop control
systems to automate the insulin delivery based on the real time monitoring of the fluctuation in
the glucose levels.”).
Claim 6
Morra discloses:The method of claim 1, wherein: the one or more therapy signals comprise a notification signal; the receiver device comprises a user computing device(Figure 1, Para 0008, Morra discloses: “Fig. 1 illustrates a computing system according to an embodiment of the present invention.” [COMPUTING DEVICE]); and communicating the one or more therapy signals comprises communicating the notification signal to the user computing device (Figure 1, Para 0008, Morra discloses: ” Fig. 1 illustrates a computing system according to an embodiment of the present invention.”[COMPUTING DEVICE]).
Morra does not disclose: therapy signal, notification signal, and receiver device
Hayter discloses:
therapy signals (Figure 1, Hayter discloses “Control Unit” and a pathway
(arrow) to “Insulin Delivery Unit”, which acts as a [THERAPY SIGNAL])
notification signal (Column 10, Line 67 Hayter discloses: “...a user interface such as a display unit
or audible/vibratory notification in the insulin delivery unit 120 and/or
the analyte monitoring unit 130 may indicate a notification [NOTIFICATION SIGNAL] for the user
to confirm the presence of the detected deviation of the monitored closed loop operation
parameter.”)
receiver device (Figure 1, Hayter discloses “Control Unit” in figure 1, which acts as
[RECEIVER DEVICE])
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary
skill in the art to have modified the sickness prediction system of Morra to include: therapy signal,
notification signal, and receiver device , as taught by Hayter. One of ordinary skill would have been so
motivated to provide various means to inform the patient of therapy administration or the like, in order
to improve the condition of a sick patient, in this case, the condition being diabetes (Column 1, Line 57,
Hayter discloses: “With the continued rise in the number of diagnosed diabetic conditions, there is ongoing research to develop closed loop control systems to automate the insulin delivery based on the real time monitoring of the fluctuation in the glucose levels.”).
Claim 7
Morra discloses:The method of claim 6, wherein the notification signal when received by the user computing device is configured to display a message on a user interface, the message prompting the patient (Para 0015, Morra discloses: “the application may be operative to compare the new sensor data with the calibrated sickness models to predict the probability of the user having a recurring sickness. [USER CAN BE A PATIENT]) to self-administer a nausea (Para 0006, Morra discloses: “In one embodiment, the user symptoms may include an indication of sickness. The user symptoms may include at least one of headaches, cough, fever, chest congestion, runny nose, nasal congestion, fatigue, fever, aches, pains, sore throat, sneezing, watery eyes, malaise, chills, nausea [NAUSEA], vomiting...”) therapeutic via a nausea (Para 0006, Morra discloses: “In one embodiment, the user symptoms may include an indication of sickness. The user symptoms may include at least one of headaches, cough, fever, chest congestion, runny nose, nasal congestion, fatigue, fever, aches, pains, sore throat, sneezing, watery eyes, malaise, chills, nausea [NAUSEA], vomiting...”) therapy delivery device.
Morra does not disclose: user interface, prompting, self-administer, therapeutic, and therapy delivery
Device
Hayter discloses:
user interface(Column 10, Line 67, Hayter discloses: “ ...in the closed loop control
system 100 (FIG. 1), a user interface [USER INTERFACE] such as a display unit or
audible/vibratory notification in the insulin delivery unit 120...”)
prompting(Column 10, Line 67, Hayter discloses: “ ... analyte monitoring unit 130 may indicate a
notification for the user to confirm [PROMPTING] the presence of the detected deviation of the
monitored closed loop operation parameter.”)
self-administer (Column 11, Line 23, Hayter discloses: “... certain adverse conditions detected
may prompt the control unit 140 (FIG. 1) to request confirmation prior to automatically
responding to such occurrence of adverse condition, and further when no confirmation is
received, the control unit 140 (FIG. 1) may temporarily revert to a semi-closed loop or nonclosed
loop manual delivery mode [SELF-ADMINISTERED] ...”)
therapeutic (Column 19, Line 25, Hayter discloses: “ The medication [MEDICATION IS A
THERAPEUTIC] may include insulin, or glucagon, or one or more combinations thereof.”)
therapy delivery device (Figure 1, Hayter displays signal flow from “Body” (sensors) to “Insulin
delivery unit” [THERAPY DELIVERY DEVICE])
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary
skill in the art to have modified the sickness prediction system of Morra to include: user interface,
prompting, self-administer, therapeutic, and therapy delivery device, as taught by Hayter. One of
ordinary skill would have been so motivated to provide a means to provide updates and error messages
to a patient through prompts and a user interface. In case of a system error, the patient should be notified of the error so the patient is able to self-administer therapy. These elements are necessary for
the patient to manage and treat the illness, in this case, the condition being diabetes treated with
insulin (Column 1, Line 57, Hayter discloses: “With the continued rise in the number of diagnosed
diabetic conditions, there is on-going research to develop closed loop control systems to automate the
insulin delivery based on the real time monitoring of the fluctuation in the glucose levels.”).
Claim 8
Morra discloses:The method of claim 7, wherein the operations further comprise, after communicating
the notification signal to the user computing device(Figure 1, Para 0008, Morra discloses: ” Fig. 1
illustrates a computing system according to an embodiment of the present invention.” [COMPUTING
DEVICE]), receiving a confirmation signal from the user computing device (Figure 1, Para 0008,
Morra discloses: ” Fig. 1 illustrates a computing system according to an embodiment of the present
invention.”[COMPUTING DEVICE]) indicating that the patient(Para 0015, Morra discloses: “the
application may be operative to compare the new sensor data with the calibrated sickness models to
predict the probability of the user having a recurring sickness. [USER CAN BE A PATIENT]) (10) self-administered the nausea therapeutic via the nausea (Para 0006, Morra discloses: “In one embodiment,
the user symptoms may include an indication of sickness. The user symptoms may include at least one
of headaches, cough, fever, chest congestion, runny nose, nasal congestion, fatigue, fever, aches, pains,
sore throat, sneezing, watery eyes, malaise, chills, nausea [NAUSEA], vomiting...”) therapy delivery
device.
Morra does not disclose: notification signal, confirmation signal, self-administered, therapeutic, and
delivery device
Hayter discloses:
notification signal (Column 10, Line 67 Hayter discloses: “...a user interface such as a display unit
or audible/vibratory notification in the insulin delivery unit 120 and/or
the analyte monitoring unit 130 may indicate a notification [NOTIFICATION SIGNAL] for the user
to confirm the presence of the detected deviation of the monitored closed loop operation
parameter.”)
confirmation signal (Column 11, Line 23, Hayter discloses: “... certain adverse conditions
detected may prompt the control unit 140 (FIG. 1) to request confirmation [CONFIRMATION
SIGNAL] prior to automatically responding to such occurrence of adverse condition, and further
when no confirmation is received, the control unit 140 (FIG. 1) may temporarily revert to a semiclosed loop or non-closed loop manual delivery mode ..."”)
self-administer (Column 11, Line 23, Hayter discloses: “... certain adverse conditions detected
may prompt the control unit 140 (FIG. 1) to request confirmation prior to automatically
responding to such occurrence of adverse condition, and further when no confirmation is
received, the control unit 140 (FIG. 1) may temporarily revert to a semi-closed loop or nonclosed
loop manual delivery mode [SELF-ADMINISTERED] ...”)
therapeutic (Column 19, Line 25, Hayter discloses: “ The medication [MEDICATION IS A
THERAPEUTIC] may include insulin, or glucagon, or one or more combinations thereof.”)
therapeutic delivery device (Figure 1, Hayter displays signal flow from “Body” (sensors) to
“Insulin delivery unit” [THERAPEUTIC DELIVERY DEVICE])
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary
skill in the art to have modified the sickness prediction system of Morra to include: notification signal,
confirmation signal, self-administered, therapeutic, and delivery device, as taught by Hayter. One of
ordinary skill would have been so motivated to provide various means to inform the patient of therapy
administration and provide a means to actually deliver the therapy itself, in order to improve the
condition of a sick patient, in this case, the condition being diabetes (Column 1, Line 57, Hayter
discloses: “With the continued rise in the number of diagnosed diabetic conditions, there is on-going
research to develop closed loop control systems to automate the insulin delivery based on the real
time monitoring of the fluctuation in the glucose levels.”).
Claim 9
Morra discloses:The method of claim 1, wherein the patient parameter data (Para 0006, Morra discloses
“The health sensor data may include a measurement selected from the group consisting of: heart rate,
pulse rate, galvanic skin response, sleep, blood oxygen level, body temperature, pulse...”[THESE
MEASUREMENTS ARE PATIENT PARAMETER DATA AS DESCRIBED IN SPECIFICATIONS, PARA 0012])
comprises one or more physiologic parameters of the patient (Para 0015, Morra discloses: “the
application may be operative to compare the new sensor data with the calibrated sickness models to
predict the probability of the user having a recurring sickness. [USER CAN BE A PATIENT]).
Morra does not disclose: physiologic parameter
Hayter discloses:
physiologic parameters (Column 18, Line 16, Hayter discloses: “ may include monitoring a
plurality of parameters associated with a closed loop control operation including
continuously monitoring a physiological condition [PHYSIOLOGICAL CONDITION IS A
PHYSIOLOGICAL PARAMETER] and automatic administration of a medication, detecting an
adverse condition associated with the monitored physiological condition or the medication
administration deviating from a predetermined safety level.”)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary
skill in the art to have modified the sickness prediction system of Morra to include a physiological
parameter, as taught by Hayter. One of ordinary skill would have been so motivated to utilize various
data types to be able to predict the onset of illness, in order to improve the potential condition of a sick
patient, in this case, the condition being diabetes (Column 1, Line 57, Hayter discloses: “With the
continued rise in the number of diagnosed diabetic conditions, there is on-going research to develop
closed loop control systems to automate the insulin delivery based on the real time monitoring of the
fluctuation in the glucose levels.”).
Claim 10
Morra discloses:The method of claim 9, wherein one or more physiologic parameters of the patient
comprise at least one of: electrodermal activity [Art is optional due to “at least one of” indication in
claim], skin temperature [Art is optional due to “at least one of” indication in claim], core
temperature(Para 0021, Morra discloses: “may include a collection of readings from sensors 204 such as, electrocardiography (ECG) for sensors for heart rate (e.g., minute, average, resting, active), sleep
sensors to detect, e.g., rapid eye movement (REM sleep), sleep/awake duration, disturbance,
movement), and pulse oximeter/photoplethysmogram (PPG) sensors for blood oxygen readings, as well
as sensors for detecting body temperature [CORE TEMPERATURE], blood pressure, blood sugar
levels...”), electrocardiography(Para 0021, Morra discloses: “may include a collection of readings from
sensors 204 such as, electrocardiography (ECG)[ELECTROCARDIOGRAPHY] for sensors for heart rate
(e.g., minute, average, resting, active), sleep sensors to detect, e.g., rapid eye movement (REM sleep),
sleep/awake duration, disturbance, movement), and pulse oximeter/photoplethysmogram (PPG)
sensors for blood oxygen readings, as well as sensors for detecting body temperature, blood pressure,
blood sugar levels...”), blood pressure(Para 0021, Morra discloses: “may include a collection of readings
from sensors 204 such as, electrocardiography (ECG)[ELECTROCARDIOGRAPHY] for sensors for heart
rate (e.g., minute, average, resting, active), sleep sensors to detect, e.g., rapid eye movement (REM
sleep), sleep/awake duration, disturbance, movement), and pulse oximeter/photoplethysmogram (PPG)
sensors for blood oxygen readings, as well as sensors for detecting body temperature, blood pressure
[BLOOD PRESSURE], blood sugar levels...”), heart rate(Para 0021, Morra discloses: “may include a
collection of readings from sensors 204 such as, electrocardiography (ECG)[ELECTROCARDIOGRAPHY] for
sensors for heart rate (e.g., minute, average, resting, active) [HEART RATE], sleep sensors to detect, e.g.,
rapid eye movement (REM sleep), sleep/awake duration, disturbance, movement), and pulse
oximeter/photoplethysmogram (PPG) sensors for blood oxygen readings, as well as sensors for
detecting body temperature, blood pressure, blood sugar levels...”), heartrate variability [Art is optional
due to “at least one of” indication in claim], voice or speech patterns [Art is optional due to “at least one
of” indication in claim], or actigraphy [Art is optional due to “at least one of” indication in claim].
Claim 11
Morra discloses:The method of claim 1, wherein the patient parameter data comprises at least one
of demographic parameters or anthropometric parameters. (Para 0022, Morra discloses: “... data 222
may include personal data, such as a user's sex [DEMOGRAPHIC/ANTHROPOMETRIC PARAMETER]. The
user profile data 222 may include biometric data, such as a user's
age[DEMOGRAPHIC/ANTHROPOMETRIC PARAMETER], weight DEMOGRAPHIC/ANTHROPOMETRIC
PARAMETER], height DEMOGRAPHIC/ANTHROPOMETRIC PARAMETER], blood pressure, total
cholesterol, FIDL cholesterol, or other appropriate biometric data. The user profile data 222 may include
health behavior data] DEMOGRAPHIC/ANTHROPOMETRIC PARAMETER], such as a total number of
minutes of exercise per week, an intensity level for exercise, e.g., overall or for particular exercise
activities, whether and how much a user smokes[DEMOGRAPHIC/ANTHROPOMETRIC PARAMETER], or
other appropriate health behavior datal]DEMOGRAPHIC/ANTHROPOMETRIC PARAMETER]. The analysis
server 216 may create predictive models 220 that receive, as input, user attributes such as sex, age,
whether the user smokes, whether and how much the user exercises, body mass
index DEMOGRAPHIC/ANTHROPOMETRIC PARAMETER], blood pressure, total cholesterol, high-density
lipoprotein (HDL) level, chronic disease diagnosis DEMOGRAPHIC/ANTHROPOMETRIC PARAMETER], or a
combination thereof. The user profile data 222 may be generated from survey data, data measured by
sensors 204, or a combination. In some examples, the user profile data 222 may be data from a
biometric screening, a health risk assessment, a questionnaire, or a combination thereof.”)
Claim 12
Morra discloses:
The method of claim 11, wherein the at least one of demographic parameters or
anthropometric parameters comprises at least one of: age (Para 0022, Morra discloses: “... data 222 may include personal data, such as a user's sex. The user profile data 222 may include biometric data, such as a user's age [AGE], weight...”), gender(Para 0022, Morra discloses: “... data 222 may include personal data, such as a user's sex [GENDER].”), height(Para 0022, Morra discloses: “... user profile data 222 may include biometric data, such as a user's age, weight, height [HEIGHT], blood pressure...”), weight(Para 0022, Morra discloses: “...user profile data 222 may include biometric data, such as a user's age, weight[WEIGHT], height...”), body mass index(Para 0022, Morra discloses: “... create predictive models 220 that receive, as input, user attributes such as sex, age, whether the user smokes, whether and how much the user exercises, body mass index [BODY MASS INDEX], blood pressure...”), or disease
state. (Para 0022, Morra discloses: “... predictive models 220 that receive, as input, user attributes such
as sex, age, whether the user smokes, whether and how much the user exercises, body mass index,
blood pressure, total cholesterol, high-density lipoprotein (HDL) level, chronic disease diagnosis
[DISEASE STATE], or a combination thereof.”)
Claim 13
Morra discloses:
The method of claim 1, wherein the patient parameter data comprises at least one of: electrodermal activity [Art is optional due to “at least one of” indication in claim], skin temperature [Art is optional due to “at least one of” indication in claim], core temperature(Para 0021, Morra discloses: “may include a collection of readings from sensors 204 such as, electrocardiography (ECG) for sensors for heart rate (e.g., minute, average, resting, active), sleep sensors to detect, e.g., rapid eye movement (REM sleep), sleep/awake duration, disturbance, movement), and pulse oximeter/photoplethysmogram (PPG) sensors for blood oxygen readings, as well as sensors for detecting body temperature [CORE TEMPERATURE], blood pressure, blood sugar levels...”), electrocardiography(Para 0021, Morra discloses: “may include a collection of readings from sensors 204 such as, electrocardiography (ECG)[ELECTROCARDIOGRAPHY] for sensors for heart rate (e.g., minute, average, resting, active), sleep sensors to detect, e.g., rapid eye movement (REM sleep),sleep/awake duration, disturbance, movement), and pulse oximeter/photoplethysmogram (PPG) sensors for blood oxygen readings, as well as sensors for detecting body temperature, blood pressure, blood sugar levels...”), blood pressure(Para 0021, Morra discloses: “may include a collection of readings from sensors 204 such as, electrocardiography (ECG)[ELECTROCARDIOGRAPHY] for sensors for heart rate (e.g., minute, average, resting, active), sleep sensors to detect, e.g., rapid eye movement (REM sleep), sleep/awake duration, disturbance, movement), and pulse oximeter/photoplethysmogram (PPG) sensors for blood oxygen readings, as well as sensors for detecting body temperature, blood pressure [BLOOD PRESSURE], blood sugar levels...”), heart rate(Para 0021, Morra discloses: “may include a collection of readings from sensors 204 such as, electrocardiography (ECG)[ELECTROCARDIOGRAPHY] for sensors for heart rate (e.g., minute, average, resting, active) [HEART RATE], sleep sensors to detect, e.g., rapid eye movement (REM sleep), sleep/awake duration, disturbance, movement), and pulse oximeter/photoplethysmogram (PPG) sensors for blood oxygen readings, as well as sensors for detecting body temperature, blood pressure, blood sugar levels...”), heartrate variability [Art is optional due to “at least one of” indication in claim], actigraphy [Art is optional due to “at least one of” indication in claim], voice or speech patterns[Art is optional due to “at least one of” indication in claim], age(Para 0022, Morra discloses: “... data 222 may include personal data, such as a user's sex. The user profile data 222 may include biometric data, such as a user's age [AGE], weight...”), gender(Para 0022, Morra discloses: “... data 222 may include personal data, such as a user's sex [GENDER].”), height(Para 0022, Morra discloses: “... user profile data 222 may include biometric data, such as a user's age, weight, height [HEIGHT], blood pressure...”), weight(Para 0022, Morra discloses: “...user profile data 222 may include biometric data, such as a user's age, weight WEIGHT], height...”), body mass index(Para 0022, Morra discloses: “... create predictive models 220 that receive, as input, user attributes such as sex, age, whether the user smokes, whether and how much the user exercises, body mass index [BODY MASS INDEX], blood pressure...”), disease state(Para 0022, Morra discloses: “... predictive models 220 that receive, as input, user attributes such as sex, age, whether the user smokes, whether and how much the user exercises, body mass index, blood pressure, total cholesterol, high-density lipoprotein (HDL) level, chronic disease diagnosis [DISEASE STATE], or a combination thereof.”), or patient reported outcome. (Para 0021 and 0022, Morra discloses: “...the user profile data 222 may be data from a biometric screening, a health risk assessment, a questionnaire [PATIENT REPORTED OUTCOME], or a combination thereof.”)
Claim 14
Morra discloses:
The method of claim 1, wherein determining, quantitively assessing, or qualitatively
assessing whether the patient is experiencing or about to experience an episode of nausea
comprises executing a predictive model (Para 0022, Morra discloses: “ The analysis server 216 may
create predictive models 220 that receive, as input, user attributes such as sex, age, whether the user
smokes, whether and how much the user exercises, body mass index, blood pressure...”) configured to
receive the patient parameter data (Para 0006, Morra discloses “The health sensor data may include a
measurement selected from the group consisting of: heart rate, pulse rate, galvanic skin response,
sleep, blood oxygen level, body temperature, pulse...”[THESE MEASUREMENTS ARE PATIENT
PARAMETER DATA AS DESCRIBED IN SPECIFICATIONS, PARA 0012]) and generate an indicator score indicating whether the patient is experiencing or about to experience the episode of
nausea. (Para 0018, Morra discloses: “the analysis server 108 may utilize the user's data - including, for
example, sensor and input data - and the calibrated sicknesses models over the course of time to implement changes to the sickness prediction application's ability to better predict future sicknesses in
the user with greater accuracy. A sick score [SICK SCORE IS AN INDICATOR SCORE] may be generated
from the probability determined by the analysis server 108 and present the score to the user through
the sickness prediction application on client device 104, or on health tracking device 102...”)
Claim 15
Morra discloses:
The method of claim 14, wherein the predictive model(Para 0022, Morra discloses: “ The
analysis server 216 may create predictive models 220 that receive, as input, user attributes such as sex,
age, whether the user smokes, whether and how much the user exercises, body mass index, blood
pressure...”) is trained on a corpus of training parameters (Para 0023, Morra discloses: “the
analysis server 216 may train predictive models (supervised machine learning) using particular data sets
[TRAINING PARAMETERS] from historic data 218 that correspond to a time point when the user reports
sickness or ailments (labeling sick data) and learn a relationship of a reported sickness on the health
metrics of the user.”) associated with training parameter data collected from a first group of
subjects who suffer from episodes of nausea (Para 0023, Morra discloses: “the analysis server 216 may
train predictive models (supervised machine learning) using particular data sets from historic data 218
that correspond to a time point when the user reports sickness or ailments (labeling sick data)[SUBJECTS
WHO SUFFER FROM EPISODES OF NAUSEA] and learn a relationship of a reported sickness on the health
metrics of the user.”) and a second group of subjects who do not suffer from episodes of nausea(Para
0023, Morra discloses: “the analysis server 216 may train predictive models (supervised machine
learning) using particular data sets from historic data 218 that correspond to a time point when the user
reports sickness or ailments (labeling sick data)[SUBJECTS WHO SUFFER FROM EPISODES OF NAUSEA]
and learn a relationship of a reported sickness on the health metrics of the user.”)
Claim 16
Claim 16 contains similar elements to claim 1. See claim 1 analysis
Claim 30
Morra discloses:
A method for treating a disease (Para 0024, Morra discloses: “For instance, the data classifier 224 may use a predictive model and user attribute data to determine a score that is indicative of the user developing a particular disease. The data classifier 224 may use the score to determine task recommendations to improve the score [TREATING A DISEASE], e.g., to reduce the likelihood that the user will develop the particular disease.”) or preventing a disease progression(Para 0024, Morra discloses: “The data classifier 224 may use the score to determine task recommendations to improve the score, e.g., to reduce the likelihood that the user will develop the particular disease.”[PREVENT DISEASE PROGRESSION]) in a subject comprising: (a) tracking one or more symptoms (Para 0006, Morra discloses: “The user symptoms [SYMPTOMS] may include at least one of headaches, cough, fever, chest congestion, runny nose, nasal congestion, fatigue, fever, aches, pains, sore throat, sneezing, watery eyes, malaise, chills, nausea, vomiting, and diarrhea…”) clinical manifestations(Para 0006, Morra discloses: “The user symptoms [SYMPTOMS CAN BE CLINICAL MANIFESTATIONS] may include at least one of headaches, cough, fever, chest congestion, runny nose, nasal congestion, fatigue, fever, aches, pains, sore throat, sneezing, watery eyes, malaise, chills, nausea, vomiting, and diarrhea…”) , or therapy signals of the disease (Para 0023, Morra discloses: “…on the types of user profile data 222 for the user, e.g., specific numerical values, numerical ranges, no numerical data, whether disease [DISEASE] or family medical history data is available, etc., or based on particular user attribute data for the user, e.g., age, sex, or both.”) via a monitoring device(Para 0015, Morra discloses: “The system may collect data from wearable or non-wearable monitoring devices [MONITORING DEVICE], such as fitness trackers, wrist bands, bracelets, watches, rings, pendants, jewelry, implants, radio-frequency identification (RFID) device, or other devices optionally connected by Internet, Bluetooth, NFC or WiFi,...”);(b) analyzing(Figure 2, Morra displays ANALYSIS SERVER 216) the symptoms(Para 0006, Morra discloses: “The user symptoms [SYMPTOMS] may include at least one of headaches, cough, fever, chest congestion, runny nose, nasal congestion, fatigue, fever, aches, pains, sore throat, sneezing, watery eyes, malaise, chills, nausea, vomiting, and diarrhea…”), clinical manifestations(Para 0006, Morra discloses: “The user symptoms [SYMPTOMS CAN BE CLINICAL MANIFESTATIONS] may include at least one of headaches, cough, fever, chest congestion, runny nose, nasal congestion, fatigue, fever, aches, pains, sore throat, sneezing, watery eyes, malaise, chills, nausea, vomiting, and diarrhea…”), or therapy signals of the disease (Para 0023, Morra discloses: “…on the types of user profile data 222 for the user, e.g., specific numerical values, numerical ranges, no numerical data, whether disease [DISEASE] or family medical history data is available, etc., or based on particular user attribute data for the user, e.g., age, sex, or both.”) and [[to]] determining, quantitively assessing (Para 0024, Morra discloses: “The analysis server 216 may combine an output of one or more predictive models to determine an overall health risk score [QUANTITATIVE ASSESSMENT] for the user.”) , or qualitatively assessing whether the subject is experiencing or about to experience (Para 0003, Morra discloses: “ prediction system, and in particular, collecting data from a health tracking device and utilizing the collected data to identify and predict [EXPERIENCE OR ABOUT TO EXPERIENCE] new or recurring illnesses.”) an episode of the disease(Para 0023, Morra discloses: “…on the types of user profile data 222 for the user, e.g., specific numerical values, numerical ranges, no numerical data, whether disease [DISEASE] or family medical history data is available, etc., or based on particular user attribute data for the user, e.g., age, sex, or both.”), via a computing device (Figure 1, Para 0008, Morra discloses: ” Fig. 1 illustrates a computing system according to an embodiment of the present invention.”[COMPUTING DEVICE]), based on:(i) receiving(Para 0005, Morra discloses receiving user symptom data) patient parameter data(Para 0006, Morra discloses “The health sensor data may include a measurement selected from the group consisting of: heart rate, pulse rate, galvanic skin response, sleep, blood oxygen level, body temperature, pulse…”[THESE MEASUREMENTS ARE PATIENT PARAMETER DATA AS DESCRIBED IN SPECIFICATIONS, PARA 0012]) or one or more datasets of the patient(Para 0015, Morra discloses: “the application may be operative to compare the new sensor data with the calibrated sickness models to predict the probability of the user having a recurring sickness. [USER CAN BE A PATIENT]);(ii) performing one or more initial processing steps on the received patient parameter data or one or more datasets, including: calculating a trend and/or variability in the patient parameter data(Para 0033, Morra discloses using trends and patterns of patient data) or one or more datasets by employing one or more of; (1) a moving average function for averaging data collected during a time window or one or more datasets;(2) a digital filter for the data during a time window or one or more datasets;(3) ; (c) communicating the symptoms(Para 0006, Morra discloses: “The user symptoms [SYMPTOMS] may include at least one of headaches, cough, fever, chest congestion, runny nose, nasal congestion, fatigue, fever, aches, pains, sore throat, sneezing, watery eyes, malaise, chills, nausea, vomiting, and diarrhea…”), clinical manifestations(Para 0006, Morra discloses: “The user symptoms [SYMPTOMS CAN BE CLINICAL MANIFESTATIONS] may include at least one of headaches, cough, fever, chest congestion, runny nose, nasal congestion, fatigue, fever, aches, pains, sore throat, sneezing, watery eyes, malaise, chills, nausea, vomiting, and diarrhea…”), or therapy signals of the disease (Para 0023, Morra discloses: “…on the types of user profile data 222 for the user, e.g., specific numerical values, numerical ranges, no numerical data, whether disease [DISEASE] or family medical history data is available, etc., or based on particular user attribute data for the user, e.g., age, sex, or both.”) to a receiver device (Figure 2, Morra discloses an analysis server [RECEVIER DEVICE] which receives data) in communication (Figure 2, Morra discloses Network, 214, which communicates [COMMUNICATION] between analysis server (receiver device) and computing system) with the computing device(Figure 1, Para 0008, Morra discloses: ” Fig. 1 illustrates a computing system according to an embodiment of the present invention.”[COMPUTING DEVICE]); (d) enabling administration(Figure 1, Hayter displays signal flow from “Insulin delivery unit” to “Body” to [ENABLES ADMINISTRATION OF THERAPEUTIC AGENT]) of a therapeutic agent (Column 19, Line 25, Hayter discloses: “ The medication [MEDICATION IS A THERAPEUTIC AGENT] may include insulin, or glucagon, or one or more combinations thereof.”)to the subject in need thereof for treating or preventing (Para 0033, Morra discloses: “The sickness prediction application may also provide the user with daily recommendations on how to prevent [PREVENTING] any future sickness based on the sick score.”) the disease(Para 0023, Morra discloses: “…on the types of user profile data 222 for the user, e.g., specific numerical values, numerical ranges, no numerical data, whether disease [DISEASE] or family medical history data is available, etc., or based on particular user attribute data for the user, e.g., age, sex, or both.”); wherein the disease(Para 0023, Morra discloses: “…on the types of user profile data 222 for the user, e.g., specific numerical values, numerical ranges, no numerical data, whether disease [DISEASE] or family medical history data is available, etc., or based on particular user attribute data for the user, e.g., age, sex, or both.”) is nausea, retch, and/or vomiting (Para 0006, Morra discloses: “The user symptoms may include at least one of headaches, cough, fever, chest congestion, runny nose, nasal congestion, fatigue, fever, aches, pains, sore throat, sneezing, watery eyes, malaise, chills, nausea [NAUSEA], vomiting [VOMITING], and diarrhea…”); and (e) administering the therapeutic agent to the subject in need thereof via a nausea therapy(Para 0081, Hoekman discloses Lorazepam delivery which is an anti-nausea therapy) delivery device(Para 0127, Hoekman discloses a device that delivers/administers nasal medications) associated with the patient via a nausea therapy delivery device associated with the patient, wherein the method comprises monitoring the patient's status either continuously(Column 18, Line 16, Hayter discloses: “ may include monitoring a plurality of parameters associated with a closed loop control operation including continuously monitoring a physiological condition [PHYSIOLOGICAL CONDITION IS A PHYSIOLOGICAL PARAMETER]...”), on- demand, prodromal, or at the onset of symptoms of nausea, and/or vomiting
Morra does not explicitly disclose: at least one technique calculating the variability of the data over various time windows, enabling administration, therapeutic agent, nausea therapy delivery device, monitoring the patient's status either continuously
Hayter discloses:
at least one technique calculating the variability of the data over various time windows(Figure 8, Hayter discloses the determination of analyte variability at various time frequencies)
enabling administration (Figure 1, Hayter displays signal flow from “Insulin delivery unit” to
“Body” to [ENABLES ADMINISTRATION OF THERAPEUTIC AGENT])
therapeutic agent (Column 19, Line 25, Hayter discloses: “ The medication [MEDICATION IS A
THERAPEUTIC AGENT] may include insulin, or glucagon, or one or more combinations thereof.”)
monitoring the patient's status either continuously(Column 18, Line 16, Hayter discloses: “ may include monitoring a plurality of parameters associated with a closed loop control operation including continuously monitoring a physiological condition [PHYSIOLOGICAL CONDITION IS A PHYSIOLOGICAL PARAMETER]...”)
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the sickness prediction system of Morra to add enabling
administration and therapeutic agent, as taught by Hayter. One of ordinary skill would have been so
motivated to provide a means to manage the illness and improve the condition of the patient, in this
case, the condition being diabetes, by managing glucose levels through use of insulin as a therapeutic
agent (Column 1, Line 57, Hayter discloses: “With the continued rise in the number of diagnosed
diabetic conditions, there is on-going research to develop closed loop control systems to automate the
insulin delivery based on the real time monitoring of the fluctuation in the glucose levels.”).
Hayter does not explicitly disclose: nausea therapy delivery device
Hoekman discloses: nausea therapy delivery device
nausea therapy(Para 0081, Hoekman discloses Lorazepam delivery which is an anti-nausea therapy) delivery device(Para 0127, Hoekman discloses a device that delivers/administers nasal medications)
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the sickness prediction system of Morra to nausea therapy delivery device, as taught by Hoekman. One of ordinary skill would have been so motivated to provide a means to improve patient well-being with use of a device that relieves nausea, but in this case, for a nasal drug delivery device (Para 0008, Hoekman discloses “There is a need for devices that can deliver compounds to the upper nasal cavity for direct nose-to-brain delivery. Certain existing nasal drug delivery devices do not adequately propel the drug from the device.”).
Claim 59
Morra discloses:
The method of claim 30, wherein the method includes one or more of the following
optional features:(1) the one or more therapy signals includes a therapy delivering device activation
signal; (2) the receiver device (Figure 2, Morra discloses an analysis server [RECEVIER DEVICE] which
receives data) includes a therapy delivery device associated with the patient(Para 0015, Morra discloses:
“the application may be operative to compare the new sensor data with the calibrated sickness models
to predict the probability of the user having a recurring sickness. [USER CAN BE A PATIENT]) who is
experiencing or about to experience an episode of nausea and/or vomiting(Para 0006, Morra discloses:
“In one embodiment, the user symptoms may include an indication of sickness. The user symptoms may
include at least one of headaches, cough, fever, chest congestion, runny nose, nasal congestion, fatigue,
fever, aches, pains, sore throat, sneezing, watery eyes, malaise, chills, nausea [NAUSEA], vomiting
[VOMITIING]...”); (3) the operation of communicating the therapy signals includes communicating the
therapy delivering device activation signal to the therapy delivery device; and (4) the therapy
delivering device activation signal when received by the therapy delivery device is configured to activate
the therapy delivery device and cause the therapy delivery device (400) to deliver a therapeutic agent to
the patient (Para 0015, Morra discloses: “the application may be operative to compare the new sensor
data with the calibrated sickness models to predict the probability of the user having a recurring
sickness. [USER CAN BE A PATIENT]); wherein the therapy delivery device includes an automatic injector,
an infusion pump, an inhaler, a transcutaneous patch, a sub-dermal implantable pump, or a small
molecule, biologic, or cell therapy administrator.
Morra does not disclose: therapy delivering device activation signal, therapy signal, activation signal,
therapy delivery device/delivery device, therapeutic agent, wherein the therapy delivery device includes an automatic injector, an infusion pump, an inhaler, a transcutaneous patch, a sub-dermal implantable
pump, or a small molecule, biologic, cell therapy administrator
Hayter discloses:
therapy delivering device activation signal/activation signal (Figure 7, Column 2, Line 18 Hayter
discloses: “ method and device for monitoring a closed loop control operation including signal
levels received from an analyte sensor and automatic delivery of medication at least in part in
response to [THERAPY DELIVERY DEVICE ACTIVATION VIA SIGNAL] the received analyte sensor
signals, determining whether the signal level received from the analyte sensor...”)
therapy signal (this is optional by “one or more” and need not be taught)
therapy delivery device/delivery device (Figure 1, Hayter displays signal flow from
“Body” (sensors) to “Insulin delivery unit” [THERAPY DELIVERY DEVICE])
therapeutic agent (Column 19, Line 25, Hayter discloses: “ The medication [MEDICATION IS A
THERAPEUTIC AGENT] may include insulin, or glucagon, or one or more combinations thereof.”)
automatic injector (Column 4, Line 16 Hayter discloses: “The medication delivery devices may
include one or more reservoirs or containers to hold the medication for delivery in fluid
connection with an infusion set, for example, including an infusion tubing and/or cannula
[INJECTOR].”);
an infusion pump (Column 4, Line 8, Hayter discloses: “Embodiments include medication
delivery devices such as external infusion pumps [INFUSION PUMP], implantable infusion
pumps, on-body patch pump, or any other processor controlled medication delivery devices that
are in communication with one or more control units...”)
a transcutaneous patch(Column 4, Line 8, Hayter discloses: “Embodiments include medication
delivery devices such as external infusion pumps, implantable infusion pumps, on-body patch
pump [PATCH], or any other processor controlled medication delivery devices that are in
communication with one or more control units...”)
a sub-dermal implantable pump (Column 4, Line 8, Hayter discloses: “Embodiments include
medication delivery devices such as external infusion pumps, implantable infusion pumps
[IMPLANTABLE PUMP], on-body patch pump, or any other processor controlled medication
delivery devices that are in communication with one or more control units...)
a small molecule, biologic, cell therapy (Limitation is optional because “one or more”)
administrator (Column 4, Line 12, Hayter discloses: “The medication delivery devices may
include one or more reservoirs or containers to hold the medication for delivery in fluid
connection with an infusion set [THERAPY ADMINISTRATOR]...”)
inhaler (this is optional by “one or more” and need not be taught)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the sickness prediction system of Morra to add: therapy
delivering device activation signal, therapy signal, activation signal, therapy delivery device/delivery
device, therapeutic agent, wherein the therapy delivery device includes an automatic injector, an
infusion pump, an inhaler, a transcutaneous patch, a sub-dermal implantable pump, or a small molecule,
biologic, and cell therapy administrator, as taught by Hayter. One of ordinary skill would have been so
motivated to provide a means to manage the illness and improve the condition of the patient, in this
case, the condition being diabetes, by managing glucose levels through use of insulin as a therapeutic
agent (Column 1, Line 57, Hayter discloses: “With the continued rise in the number of diagnosed
diabetic conditions, there is on-going research to develop closed loop control systems to automate the
insulin delivery based on the real time monitoring of the fluctuation in the glucose levels.”).
Claim 61
Morra discloses:
The method of claim 1, wherein a trained predictive model configured to receive the processed patient parameter data or one or more datasets and/or a large patient population training dataset is executed to generate the indicator score and compare the indicator score to a threshold indicative or predictive of nausea(Para 0024, Morra discloses a health risk score satisfying a threshold value which determines patient sickness risk).
Claim 62
Morra discloses:
The method of claim 1, wherein the transmission of patient parameter data or one or more datasets and one or more therapy signals is performed wired or over a wireless network(Para 0019, Morra discloses wireless network).
Response to Arguments
35 U.S.C. 101
(Page 9-10) Regarding the assertion that the amended claims have limitations that cannot practically be performed by the human mind.
Applicant's arguments filed have been fully considered but they are not persuasive. The limitations not considered abstract(unhighlighted) would be considered additional elements, which amount to mere instructions to apply an exception (MPEP 2106.05(f)) and add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea. Please refer to the above 101 analysis.
(Page 11) Regarding the assertion that the claims are eligible at Step 2A because they are not directed to the recited judicial exception.
Applicant's arguments filed have been fully considered but they are not persuasive. The claim are directed to the recited judicial exception and the identified additional elements amount to mere instructions to apply an exception (MPEP 2106.05(f)) and add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea. Please refer to the above 101 analysis.
(Pages 11-12) Regarding the assertion that the amended claims include additional elements that are sufficient to amount to significantly more than the judicial exception and the limitations do amount to more than elements recognized as well-understood, routine, and conventional activity.
Applicant's arguments filed have been fully considered but they are not persuasive. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements: A computer in claim 1; amount to no more than mere instructions to apply an exception to the abstract idea. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity.
35 U.S.C. 103
Applicant’s arguments with respect to claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Halperin(US8403865B2): Halperin discloses a system which monitors and predicts clinical episodes or monitors the severity and progression of the episode as it occurs. Some disclosures of this invention are similar to that of this pending instant application. (Specifications, page 5)
Sanchez(US20180218123): Sanchez discloses a system to predict a health affliction and adjusting devices of the environment to reduce the effects of those devices on the health affliction. Some disclosures of this invention are similar to that of this pending instant application. (Specifications, pages 3-4)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/S.G.P./Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685
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