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 .
Application Status
This is the first non-final action on the merits. Claims 1-20 as originally filed on June 6, 2025 are currently pending and considered below.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-3, 5, 8-10, 12 and 15-18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 5, 7, 10-12 and 16-18 of U.S. Patent No 10,490,309 B1 in view of Rosenfeld (US 2006/0271407 A1) and Hidalgo Perez (ES 2540159 B1).
Claims 1-6, 8-13 and 15-19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 6, 8 and 11-17 of U.S. Patent No 10,910,110 B1 in view of Rosenfeld (US 2006/0271407 A1) and Hidalgo Perez (ES 2540159 B1).
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-8, 10-12, 14-16, 19 and 20 of U.S. Patent No 12,057,231 B1 in view of Rosenfeld (US 2006/0271407 A1) and Hidalgo Perez (ES 2540159 B1).
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4, 6, 8, 10-12, 14-16 and 19-21 of Application No. 18/671,843 in view of Rosenfeld (US 2006/0271407 A1) and Hidalgo Perez (ES 2540159 B1).
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of Application No. 19/028,327 in view of Rosenfeld (US 2006/0271407 A1) and Hidalgo Perez (ES 2540159 B1).
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 1-20 of Application No. 19/028,386 in view of Rosenfeld (US 2006/0271407 A1) and Hidalgo Perez (ES 2540159 B1).
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of Application No. 19/028,886 in view of Rosenfeld (US 2006/0271407 A1) and Hidalgo Perez (ES 2540159 B1).
Instant Application
US Patent 10,490,309 B1
1. (Currently Amended) One or more non-transitory media having instructions that, when executed by one or more hardware processors, cause the one or more hardware processors to facilitate a plurality of operations for preventing or managing clinical deterioration associated with at least one patient, the operations comprising:
1. Computer-readable media having computer-executable instructions embodied thereon that when executed, facilitate a method for periodically monitoring at least one patient, the method comprising:
collecting, by a measurement device comprising a sensor, a first sequence of measurements corresponding to a first set of physiologic data for a patient;
collecting physiological data of a patient, wherein the physiological data comprises a set of data points collected at a plurality of data measurement times by one or more physiologic sensors, and wherein each data point of the set of data points includes a corresponding date-time stamp;
detecting constants in one or both of the first sequence of measurements and the first set of physiologic data;
in response to the detecting of the constants, delaying generation of a patient forecast;
collecting, after the delaying and via the measurement device, a second sequence of measurements corresponding to a second set of physiologic data for the patient;
generating the patient forecast, the patient forecast generated using one or more evolutionary algorithms and based on processing patient data comprising the first set of physiologic data and the second set of physiologic data, wherein: the patient forecast comprises a predicted physiologic parameter for the at least one patient at a future time based on a time series that corresponds to the patient data;
generating a patient forecast using particle swarm optimization, the patient forecast comprising predicted physiologic parameters for the patient at a future time based on the time series;
comparing the patient forecast with control limit information associated with the physiologic data;
determining, in response to the comparing, whether the patient forecast meets a condition associated with the control limit information; and
determining that the patient forecast is within a control limit range associated with the collected physiological data; and
based on the predicted physiologic parameter and based further on the patient forecast meeting the condition, generating instructions to present via an electronic user interface indicating notification content selected from a group comprising modifying a treatment program associated with the at least one patient and preventing a future occurrence corresponding to the patient forecast.
based on the predicted physiologic parameters, generating instructions to modify a treatment program associated with the patient to prevent the future occurrence of the patient forecast.
2. (New) The one or more non-transitory media of claim 1, wherein the patient data corresponds to measurements (a) selected from a group comprising heart rate data, blood pressure data, and oxygen saturation data and (b) entered or automatically determined using one or both of an electronic health record computer system and a biometric hemostasis sensor positioned in proximity to the at least one patient.
2. The media of claim 1, wherein the physiologic parameters are collected from an electronic health record (EHR) computer system.
3. The media of claim 1, wherein the physiologic parameters comprise numerical heart rate and systolic blood pressure data acquired for computing a shock index.
3. (New) The one or more non-transitory media of claim 1, wherein the operations further comprise generating the time series using a plurality of serial physiologic measurements over time, wherein the plurality of serial physiologic measurements comprise numerical heart rate data and systolic blood pressure data represented as a Hankel matrix, and wherein generating the patient forecast corresponds to using an optimization fitness objective function.
10. The media of claim 1, wherein the serial measurements are represented as a Hankel matrix.
11. The media of claim 4, wherein the serial measurements are represented as a Hankel matrix and further comprises minimizing the determinant of the Hankel matrix.
5. (New) The one or more non-transitory media of claim 1, wherein the operations further comprise combining at least two values of a physiologic variable or at least two values of a composite variable for generating the patient forecast, and wherein the combining comprises an exponentially weighted moving average of the at least two values of the physiologic variable or the at least two values of the composite variable.
5. The media of claim 4, further comprising combining a plurality of the predicted physiologic parameters.
7. The media of claim 5, wherein the combining means is the exponentially weighted moving average or other linear combination of the values.
Instant Application
US Patent 10,910,110 B1
U.S. Patent 12,057,231 B1
1. (Currently Amended) One or more non-transitory media having instructions that, when executed by one or more hardware processors, cause the one or more hardware processors to facilitate a plurality of operations for preventing or managing clinical deterioration associated with at least one patient, the operations comprising:
1. Computer-readable media having computer-executable instructions embodied thereon that when executed by at least one computer processor, facilitate a method for periodically monitoring at least one patient, the method comprising:
1. One or more non-transitory media having instructions that, when executed by one or more processors, cause the one or more processors to facilitate a plurality of operations for preventing or managing hemodynamic deterioration, the operations comprising:
collecting, by a measurement device comprising a sensor, a first sequence of measurements corresponding to a first set of physiologic data for a patient;
collecting hemostasis data for the at least one patient, wherein the hemostasis data comprises a set of data points collected at a plurality of data measurement times by one or more hemostasis sensors, and wherein each data point of the set of data points includes a corresponding date-time stamp;
generating, based on received patient data comprising hemostasis data, a patient forecast…
detecting constants in one or both of the first sequence of measurements and the first set of physiologic data;
in response to the detecting of the constants, delaying generation of a patient forecast;
collecting, after the delaying and via the measurement device, a second sequence of measurements corresponding to a second set of physiologic data for the patient;
generating the patient forecast, the patient forecast generated using one or more evolutionary algorithms and based on processing patient data comprising the first set of physiologic data and the second set of physiologic data, wherein: the patient forecast comprises a predicted physiologic parameter for the at least one patient at a future time based on a time series that corresponds to the patient data;
generating a patient forecast using particle swarm optimization, the patient forecast comprising predicted hemostasis parameters for the at least one patient at a future time based on the time series;
generating, based on received patient data comprising hemostasis data, a patient forecast using an evolutionary timeseries analytical algorithm selected from a group comprising particle swarm optimization (PSO) and differential evolution (DE), the patient forecast comprising a predicted hemostasis parameter for at least one patient at a future time based on a time series that corresponds to the received patient data;
comparing the patient forecast with control limit information associated with the physiologic data;
determining, in response to the comparing, whether the patient forecast meets a condition associated with the control limit information; and
determining that the patient forecast is within a control limit range associated with the collected hemostasis data; and
determining that the patient forecast is within a control limit range associated with the hemostasis data; and
based on the predicted physiologic parameter and based further on the patient forecast meeting the condition, generating instructions to present via an electronic user interface indicating notification content selected from a group comprising modifying a treatment program associated with the at least one patient and preventing a future occurrence corresponding to the patient forecast.
based on the predicted hemostasis parameters, generating instructions to modify a treatment program associated with the at least one patient to prevent a future occurrence of the patient forecast.
based on the predicted hemostasis parameter, generating instructions to modify a treatment program associated with the at least one patient to prevent a future occurrence corresponding to the patient forecast.
Also see claim 11
2. (New) The one or more non-transitory media of claim 1, wherein the patient data corresponds to measurements (a) selected from a group comprising heart rate data, blood pressure data, and oxygen saturation data and (b) entered or automatically determined using one or both of an electronic health record computer system and a biometric hemostasis sensor positioned in proximity to the at least one patient.
3. The media of claim 1, wherein the hemostasis data is collected from an electronic health record (EHR) computer system.
2. The one or more non-transitory media of claim 1, wherein the received patient data corresponds to measurements that were entered or automatically determined using one or both of an electronic health record computer system and sensors in proximity to the at least one patient.
3. The one or more non-transitory media of claim 1, wherein the received patient data corresponds to measurements obtained via one or more hemostasis sensors.
4. The one or more non-transitory media of claim 1, wherein the operations further comprise generating the time series using a plurality of serial measurements over time, the plurality of serial measurements comprising numerical heart rate data and systolic blood pressure data.
3. (New) The one or more non-transitory media of claim 1, wherein the operations further comprise generating the time series using a plurality of serial physiologic measurements over time, wherein the plurality of serial physiologic measurements comprise numerical heart rate data and systolic blood pressure data represented as a Hankel matrix, and wherein generating the patient forecast corresponds to using an optimization fitness objective function.
11. The media of claim 1, wherein the time series comprise serial measurements that are represented as a Hankel matrix.
12. The media of claim 5, wherein the time series comprise serial measurements that are represented as a Hankel matrix, and wherein the particle swarm optimization uses an objective function that minimizes a determinant of the Hankel matrix, either alone or with one or more additional terms of the objective function.
5. The one or more non-transitory media of claim 4, wherein the plurality of serial measurements are represented as a Hankel matrix.
20. The patient monitoring system of claim 16, wherein generating the patient forecast comprises using an optimization fitness objective function.
4. (New) The one or more non-transitory media of claim 1, wherein the operations further comprise removing noise from the patient data prior to using two or more evolutionary algorithms.
2. The media of claim 1, wherein the particle swarm optimization comprises algorithms that perform population-based stochastic searches and that remove noise.
6. The one or more non-transitory media of claim 1, wherein the operations further comprise removing noise from the received patient data prior to using the PSO.
5. (New) The one or more non-transitory media of claim 1, wherein the operations further comprise combining at least two values of a physiologic variable or at least two values of a composite variable for generating the patient forecast, and wherein the combining comprises an exponentially weighted moving average of the at least two values of the physiologic variable or the at least two values of the composite variable.
6. The media of claim 5, further comprising combining values of the physiologic variable or the composite variable for generating the patient forecast.
8. The media of claim 6, wherein the combining comprises an exponentially weighted moving average or other linear combination of the values.
7. The one or more non-transitory media of claim 1, wherein the operations further comprise combining at least two values of a physiologic variable or at least two values of a composite variable for generating the patient forecast.
8. The one or more non-transitory media of claim 7, wherein the combining comprises an exponentially weighted moving average of the at least two values of the physiologic variable or the at least two values of the composite variable.
6. (New) The one or more non-transitory media of claim 1, wherein the time series corresponds to a period of time in which the at least one patient was taking anticoagulants*, and wherein the operations further comprise providing an alert for the at least one patient to contact a health- care provider based on the patient forecast.
(* for US Patent 10,910,110 B1 only)
15. The computer-implemented method of claim 13, wherein the method further comprises providing an alert for the patient to contact a health-care provider based on the predicted hemostasis parameters.
19. The system of claim 16, wherein the collected hemostasis data comprises an INR time series.
10. The one or more non-transitory media of claim 1, wherein the operations further comprise providing an alert for the at least one patient to contact a health-care provider based on the patient forecast.
12. The computer-implemented method of claim 11, wherein the time series corresponds to a period of time in which the at least one patient was taking anticoagulants.
7. (New) The one or more non-transitory media of claim 1, wherein the operations further comprise determining a length of the time series based on a composite variable corresponding at least partially to the predicted physiologic parameter and a frequency at which measurements are acquired for the composite variable, wherein generating the patient
forecast comprises using a determined value of the composite variable at a plurality of future time points.
N/A
14. The computer-implemented method of claim 11, wherein generating the patient forecast comprises using a determined value of a composite variable at a plurality of future time points.
15. The computer-implemented method of claim 11, further comprising determining a length of the time series based on a variable of the predicted hemostasis parameter and a frequency at which the variable is measured.
Instant Application
Application No. 18/671,843
Application No. 19/028,327
Application No. 19/028,386
Application No. 19/028,886
1. (Currently Amended) One or more non-transitory media having instructions that, when executed by one or more hardware processors, cause the one or more hardware processors to facilitate a plurality of operations for preventing or managing clinical deterioration associated with at least one patient, the operations comprising:
1. (Currently Amended) One or more non-transitory media having computer-readable instructions that, when executed by one or more hardware processors (OMHPs), cause the OMHPs to perform a plurality of operations for preventing or managing clinical deterioration associated with at least one patient, the operations comprising:
generating a patient forecast via the OMHPs, wherein generating
1. (Currently Amended) One or more non-transitory media having instructions that, when executed by one or more hardware processors, cause the one or more hardware processors to facilitate a plurality of operations for preventing or managing clinical deterioration associated with at least one patient, the operations comprising:
1. (Currently Amended) One or more non-transitory media having instructions that, when executed by one or more hardware processors, cause the one or more hardware processors to facilitate a plurality of operations for preventing or managing clinical deterioration associated with at least one patient, the operations comprising:
1. (Currently Amended) One or more non-transitory media having instructions that, when executed by one or more processors, cause the one or more processors to facilitate a plurality of operations, the operations comprising:
collecting, by a measurement device comprising a sensor, a first sequence of measurements corresponding to a first set of physiologic data for a patient;
collecting physiologic data from a set of sensors that are in proximity to the at least one patient and that are configured to generate time-stamped physiological measurements relating to the at least one patient;
generating, based on received patient data comprising at least a portion of physiologic data, a patient forecast…
Also see claim 2
collecting time-series patient data corresponding to at least one of International Normalized Ratio (INR) type data, central venous pressure (CVP) type data, or a combination of the INR type data and the CVP type data;
Also see claim 2
generating, based on received patient data comprising physiologic data, a patient forecast…
Also see claim 2
detecting constants in one or both of the first sequence of measurements and the first set of physiologic data;
in response to the detecting of the constants, delaying generation of a patient forecast;
collecting, after the delaying and via the measurement device, a second sequence of measurements corresponding to a second set of physiologic data for the patient;
generating the patient forecast, the patient forecast generated using one or more evolutionary algorithms and based on processing patient data comprising the first set of physiologic data and the second set of physiologic data, wherein: the patient forecast comprises a predicted physiologic parameter for the at least one patient at a future time based on a time series that corresponds to the patient data;
producing a patient forecast using a set of evolutionary algorithms selected from a group comprising at least particle swarm optimization and differential evolution, wherein:
the patient forecast comprises a predicted physiologic parameter for the at least one patient at a future time based on a time series that corresponds to the received set of patient data;
generating, based on received patient data comprising at least a portion of physiologic data, a patient forecast using an ensemble of evolutionary algorithms, the ensemble of evolutionary algorithms comprising at least a first evolutionary algorithm and a second evolutionary algorithm, and the first evolutionary algorithm differing from the second evolutionary algorithm,
wherein:
the patient forecast comprises a predicted physiologic parameter for the at least one patient at a future time based on a time series that corresponds to the received patient data;
generating, based on the time-series patient data comprising physiologic data relating to (a) a composite variable formed using the INR type data and/or CVP type data or (b) a composite function using the INR type data and/or the CVP type data, a patient forecast,
wherein: the patient forecast comprises a predicted physiologic parameter for the at least one patient at a future time based on a time series that corresponds to the time-series patient data;
Also see claim 4
generating, based on received patient data comprising physiologic data, a patient forecast using one or more evolutionary algorithms,
wherein:
the patient forecast comprises a predicted physiologic parameter for a particular patient at a future time based on a time series that corresponds to the received patient data;
comparing the patient forecast with control limit information associated with the physiologic data;
determining, in response to the comparing, whether the patient forecast meets a condition associated with the control limit information; and
comparing the patient forecast with control limit information associated with the physiologic data; determining, in response to the comparing, whether the patient forecast meets a condition associated with the control limit information;
comparing the patient forecast with control limit information associated with the physiologic data;
determining, in response to the comparing, whether the patient forecast meets a condition associated with the control limit information;
comparing the patient forecast with control limit information associated with the physiologic data; determining, in response to the comparing, whether the patient forecast meets a condition associated with the control limit information;
comparing the patient forecast with control limit information associated with the physiologic data; and
determining, in response to the comparing, that the predicted physiologic parameter is not within a range associated with the control limit information…
based on the predicted physiologic parameter and based further on the patient forecast meeting the condition, generating instructions to present via an electronic user interface indicating notification content selected from a group comprising modifying a treatment program associated with the at least one patient and preventing a future occurrence corresponding to the patient forecast.
based on the predicted physiologic parameter and based further on the patient forecast meeting the condition, generating a set of instructions to present via an electronic user interface indicating notification content selected from a group comprising modifying a treatment program associated with the at least one patient and preventing a future occurrence corresponding to the patient forecast
based on the predicted physiologic parameter and based further on the patient forecast meeting the condition, generating instructions to present via an electronic user interface indicating notification content selected from a group comprising modifying a treatment program associated with the at least one patient and preventing a future occurrence corresponding to the patient forecast.
based on the predicted physiologic parameter and based further on the patient forecast meeting the condition, generating instructions to present via an electronic user interface indicating notification content selected from a group comprising modifying a treatment program associated with the at least one patient and preventing a future occurrence corresponding to the patient forecast.
based at least partially on the predicted physiologic parameter and based further on the comparing, an intervening action comprising a particular hemodynamic or hemodynamic deterioration medicament is administered to the particular patient to treat the particular patient by preventing or managing a future occurrence corresponding to the patient forecast.
2. (New) The one or more non-transitory media of claim 1, wherein the patient data corresponds to measurements (a) selected from a group comprising heart rate data, blood pressure data, and oxygen saturation data and (b) entered or automatically determined using one or both of an electronic health record computer system and a biometric hemostasis sensor positioned in proximity to the at least one patient.
2. (Currently Amended) The one or more non-transitory media of claim 1, wherein the received set of patient data corresponds to measurements that were automatically determined using the set of sensors in proximity to the at least one patient.
3. (Currently Amended) The one or more non-transitory media of claim 1, wherein the received set of patient data corresponds to measurements obtained via one or more hemostasis sensors.
19. (Currently Amended) The patient monitoring system of claim 16, wherein the received set of patient data comprises measurement information selected from a group comprising heart rate data, blood pressure data, and oxygen saturation data.
2. (New) The one or more non-transitory media of claim 1, wherein the received patient data corresponds to measurements (a) selected from a group comprising heart rate data, blood pressure data, and oxygen saturation data and (b) entered or automatically determined using one or both of an electronic health record computer system and a biometric hemostasis sensor positioned in proximity to the at least one patient.
2. (New) The one or more non-transitory media of claim 1, wherein the time-series patient data corresponds to measurements (a) selected from a group comprising heart rate data, blood pressure data, and oxygen saturation data and (b) entered or automatically determined using one or both of an electronic health record computer system and a biometric hemostasis sensor positioned in proximity to the at least one patient.
2. (New) The one or more non-transitory media of claim 1, wherein the received patient data corresponds to measurements (a) selected from a group comprising heart rate data, blood pressure data, and oxygen saturation data and (b) entered or automatically determined using one or both of an electronic health record computer system and a biometric hemostasis sensor positioned in proximity to the particular patient.
3. (New) The one or more non-transitory media of claim 1, wherein the operations further comprise generating the time series using a plurality of serial physiologic measurements over time, wherein the plurality of serial physiologic measurements comprise numerical heart rate data and systolic blood pressure data represented as a Hankel matrix, and wherein generating the patient forecast corresponds to using an optimization fitness objective function.
4. (Currently Amended) The one or more non-transitory media of claim 1, wherein the operations further comprise generating the time series using a plurality of serial measurements over time, the plurality of serial measurements represented via the OMHPs in a form of a Hankel matrix and comprising numerical heart rate data and systolic blood pressure data.
20. (Original) The patient monitoring system of claim 16, wherein generating the patient forecast comprises using an optimization fitness objective function.
3. (New) The one or more non-transitory media of claim 1, wherein the operations further comprise generating the time series using a plurality of serial physiologic measurements over time, wherein the plurality of serial physiologic measurements comprise numerical heart rate data and systolic blood pressure data represented as a Hankel matrix, and wherein generating the patient forecast corresponds to using an optimization fitness objective function.
3. (New) The one or more non-transitory media of claim 1, wherein the operations further comprise generating the time series using a plurality of serial physiologic measurements over time, wherein the plurality of serial physiologic measurements comprise numerical heart rate data and systolic blood pressure data represented as a Hankel matrix, and wherein generating the patient forecast corresponds to using an optimization fitness objective function.
3. (New) The one or more non-transitory media of claim 1, wherein the operations further comprise generating the time series using a plurality of serial physiologic measurements over time, wherein the plurality of serial physiologic measurements comprise numerical heart rate data and systolic blood pressure data represented as a Hankel matrix, and wherein generating the patient forecast corresponds to using an optimization fitness objective function.
4. (New) The one or more non-transitory media of claim 1, wherein the operations further comprise removing noise from the patient data prior to using two or more evolutionary algorithms.
6. (Currently Amended) The one or more non-transitory media of claim 1, wherein the operations further comprise removing noise from the received set of patient data prior to using the set of evolutionary algorithms.
21. (Currently Amended) The patient monitoring system of claim 16, wherein the set of evolutionary algorithms comprises a first differential evolutionary algorithm and a second differential evolutionary algorithm, and wherein the first differential evolutionary algorithm differs from the second differential evolutionary algorithm.
4. (New) The one or more non-transitory media of claim 1, wherein the operations further comprise removing noise from the received patient data prior to using two or more evolutionary algorithms.
4. (New) The one or more non-transitory media of claim 1, wherein a relation of the INR type data to the CVP type data is determined to facilitate generating the patient forecast, and wherein the operations further comprise removing noise from the time-series patient data prior to using two or more evolutionary algorithms.
4. (New) The one or more non-transitory media of claim 1, wherein the operations further comprise removing noise from the received patient data prior to using two or more evolutionary algorithms.
5. (New) The one or more non-transitory media of claim 1, wherein the operations further comprise combining at least two values of a physiologic variable or at least two values of a composite variable for generating the patient forecast, and wherein the combining comprises an exponentially weighted moving average of the at least two values of the physiologic variable or the at least two values of the composite variable.
8. (Currently Amended) The one or more non-transitory media of claim 1,wherein the operations further comprise combining at least two values of a physiologic variable or at least two values of a composite variable for generating the patient forecast, and wherein the combining comprises an exponentially weighted moving average of the at least two values of the physiologic variable or the at least two values of the composite variable.
5. (New) The one or more non-transitory media of claim 1, wherein the operations further comprise combining at least two values of a physiologic variable or at least two values of a composite variable for generating the patient forecast, and wherein the combining comprises an exponentially weighted moving average of the at least two values of the physiologic variable or the at least two values of the composite variable.
5. (New) The one or more non-transitory media of claim 1, wherein the operations further comprise combining at least two values of a physiologic variable or at least two values of a composite variable for generating the patient forecast, and wherein the combining comprises an exponentially weighted moving average of the at least two values of the physiologic variable or the at least two values of the composite variable.
5. (New) The one or more non-transitory media of claim 1, wherein the operations further comprise combining at least two values of a physiologic variable or at least two values of a composite variable for generating the patient forecast, and wherein the combining comprises an exponentially weighted moving average of the at least two values of the physiologic variable or the at least two values of the composite variable.
6. (New) The one or more non-transitory media of claim 1, wherein the time series corresponds to a period of time in which the at least one patient was taking anticoagulants, and wherein the operations further comprise providing an alert for the at least one patient to contact a health- care provider based on the patient forecast.
10. (Original) The one or more non-transitory media of claim 1, wherein the operations further comprise providing an alert for the at least one patient to contact a health-care provider based on the patient forecast.
12. (Original) The computer-implemented method of claim 11, wherein the time series corresponds to a period of time in which the at least one patient was taking anticoagulants.
6. (New) The one or more non-transitory media of claim 1, wherein the time series corresponds to a period of time in which the at least one patient was taking anticoagulants, and wherein the operations further comprise providing an alert for the at least one patient to contact a health- care provider based on the patient forecast.
6. (New) The one or more non-transitory media of claim 1, wherein the time series corresponds to a period of time in which the at least one patient was taking anticoagulants, and wherein the operations further comprise providing an alert for the at least one patient to contact a health- care provider based on the patient forecast.
6. (New) The one or more non-transitory media of claim 1, wherein the time series corresponds to a period of time in which the particular patient was taking anticoagulants, and wherein the operations further comprise providing an alert for the particular patient to contact a health- care provider based on the patient forecast.
7. (New) The one or more non-transitory media of claim 1, wherein the operations further comprise determining a length of the time series based on a composite variable corresponding at least partially to the predicted physiologic parameter and a frequency at which measurements are acquired for the composite variable, wherein generating the patient
forecast comprises using a determined value of the composite variable at a plurality of future time points.
14. (Original) The computer-implemented method of claim 11, wherein generating the patient forecast comprises using a determined value of a composite variable at a plurality of future time points.
15. (Original) The computer-implemented method of claim 11, further comprising determining a length of the time series based on a variable of the predicted physiologic parameter and a frequency at which the variable is measured.
7. (New) The one or more non-transitory media of claim 1, wherein the operations further comprise determining a length of the time series based on a composite variable corresponding at least partially to the predicted physiologic parameter and a frequency at which measurements are acquired for the composite variable, wherein generating the patient forecast comprises using a determined value of the composite variable at a plurality of future time points.
7. (New) The one or more non-transitory media of claim 1, further comprising determining a length of the time series based on a composite variable corresponding at least partially to the predicted physiologic parameter and a frequency at which measurements are acquired for the composite variable, wherein generating the patient forecast comprises using a determined value of the composite variable at a plurality of future time points.
7. (New) The one or more non-transitory media of claim 1, further comprising determining a length of the time series based on a composite variable corresponding at least partially to the predicted physiologic parameter and a frequency at which measurements are acquired for the
The following limitations of the instant application are the difference between the instant claims and each of these patents and/or applications: claims 1-3, 5, 7, 10-12 and 16-18 of U.S. Patent No 10,490,309 B1, claims 1-3, 6, 8 and 11-17 of U.S. Patent No 10,910,110 B1, claims 1-8, 10-12, 14-16, 19 and 20 of U.S. Patent No 12,057,231 B1, claims 1-4, 6, 8, 10-12, 14-16 and 19-21 of Application No. 18/671,843, claims 1-20 of Application No. 19/028,327, Claims 1-20 of Application No. 19/028,386 and claims 1-20 of Application No. 19/028,886. The limitations of the instant application are:
detecting constants in one or both of the first sequence of measurements and the first set of physiologic data;
in response to the detecting of the constants, delaying generation of a patient forecast;
collecting, after the delaying and via the measurement device, a second sequence of measurements corresponding to a second set of physiologic data for the patient;
generating the patient forecast based on processing patient data comprising the first set of physiologic data and the second set of physiologic data
Rosenfeld teaches detecting constants in one or both of the first sequence of measurements and the first set of physiologic data, in response to the detecting of the constants, delaying generation of a patient forecast and collecting, after the delaying and via the measurement device, a second sequence of measurements corresponding to a second set of physiologic data for the patient (e.g. see [0094]-[0095], [0038], [0081]).
It would have been obvious to one of ordinary skill in the art to detect constants in one or both of the first sequence of measurements and the first set of physiologic data, in response to the detecting of the constants, delaying generation of a patient forecast and collecting, after the delaying and via the measurement device, a second sequence of measurements corresponding to a second set of physiologic data for the patient as taught by Rosenfeld for the purposes of constantly evaluating patient data and expediting processing of the patient data “if an event has occurred” (Rosenfeld [0095]).
Rosenfeld does not teach generating the patient forecast based on processing patient data comprising the first set of physiologic data and the second set of physiologic data. However, Hidalgo Perez teaches generating the patient forecast based on processing patient data comprising the first set of physiologic data and the second set of physiologic data (e.g. see pgs. 8-9, “Description of…Various Forms”; pgs. 3-4, “Description of the Invention”).
It would have been obvious to one of ordinary skill in the art to generating the patient forecast based on processing patient data comprising the first set of physiologic data and the second set of physiologic data as taught by Hidalgo Perez for the purposes of having predictive ability to know and avoid severe clinical events (Hidalgo Perez, pgs. 2-3, “Background of the Invention”).
In addition, the following limitation of the instant application is the difference between the instant claims and claims 1-3, 6, 8 and 11-17 of U.S. Patent No 10,910,110 B1:
the time series corresponds to a period of time in which the at least one patient was taking anticoagulants
Rosenfeld teaches the time series corresponds to a period of time in which the at least one patient was taking anticoagulants (e.g. see Table 1, [0122]).
It would have been obvious to one of ordinary skill in the art to the time series corresponds to a period of time in which the at least one patient was taking anticoagulants as taught by Rosenfeld for the purposes of considering “changes in multiple variables” such as medications given to the patient (Rosenfeld [0089]).
Further, it would have been obvious to one of ordinary skill in the art to modify the computer-readable media of claim 1 of U.S. Patent No 10,910,110 B1 to apply the functional operations set forth by the method of claim 13 of U.S. Patent No 10,910,110 B1 and the structural configurations of the system of claim 16 of U.S. Patent No 10,910,110 B1, because doing so represents a predictable application of the same underlying invention.
It would have been obvious to one of ordinary skill in the art to modify the computer-readable media of claim 1 of U.S. Patent No 12,057,231 B1 to apply the functional operations set forth by the method of claim 11 of U.S. Patent No 12,057,231 B1 and the structural configurations of the system of claim 16 of U.S. Patent No 12,057,231 B1, because doing so represents a predictable application of the same underlying invention.
It would have been obvious to one of ordinary skill in the art to modify the computer-readable media of claim 1 of Application No. 18/671,843 to apply the functional operations set forth by the method of claim 11 of Application No. 18/671,843 and the structural configurations of the system of claim 16 of Application No. 18/671,843, because doing so represents a predictable application of the same underlying invention.
Claims 8-10, 12 and 15-18 of the instant application recite substantially similar limitations as claims 1-7 of the instant application, and, as such are rejected for similar reasons as given above. See also claims 12 and 16-18 of U.S. Patent No 10,490,309 B1.
Claims 8-13 and 15-19 of the instant application recite substantially similar limitations as claims 1-7 of the instant application, and, as such are rejected for similar reasons as given above. See also claims 13, 14, 16 and 17 of U.S. Patent No 10,910,110 B1.
Claims 8-20 of the instant application recite substantially similar limitations as claims 1-7 of the instant application, and, as such are rejected for similar reasons as given above. See also claims 11, 16 and 19 of U.S. Patent No 12,057,231 B1.
Claims 8-20 of the instant application recite substantially similar limitations as claims 1-7 of the instant application, and, as such are rejected for similar reasons as given above. See also claims 11 and 16 of Application No. 18/671,843.
Claims 8-20 of the instant application recite substantially similar limitations as claims 1-7 of the instant application, and, as such are rejected for similar reasons as given above. See also claims 8-20 of Application No. 19/028,327.
Claims 8-20 of the instant application recite substantially similar limitations as claims 1-7 of the instant application, and, as such are rejected for similar reasons as given above. See also claims 8-20 of Application No. 19/028,386.
Claims 8-20 of the instant application recite substantially similar limitations as claims 1-7 of the instant application, and, as such are rejected for similar reasons as given above. See also claims 8-20 of Application No. 19/028,886.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-7 recite one or more non-transitory media having instructions that, when executed by one or more hardware processors, cause the one or more hardware processors to facilitate a plurality of operations for preventing or managing clinical deterioration associated with at least one patient, which is within the statutory category of an article of manufacture. Claims 8-14 recite a system having one or more hardware processors configured to facilitate a plurality of operations for preventing or managing clinical deterioration associated with at least one patient, which is within the statutory category of a machine. Claims 15-20 recite a computer-implemented method for preventing or managing clinical deterioration associated with at least one patient, which is within the statutory category of a process.
Step 2A - Prong One:
Regarding Prong One of Step 2A , the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they "recite" a judicial exception or in other words whether a judicial exception is "set forth" or "described" in the claims. An "abstract idea" judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. Representative independent claim 1 includes limitations that recite at least one abstract idea.
Specifically, independent claim 1 recites: One or more non-transitory media having instructions that, when executed by one or more hardware processors, cause the one or more hardware processors to facilitate a plurality of operations for preventing or managing clinical deterioration associated with at least one patient, the operations comprising:
collecting, by a measurement device comprising a sensor, a first sequence of measurements corresponding to a first set of physiologic data for a patient;
detecting constants in one or both of the first sequence of measurements and the first set of physiologic data;
in response to the detecting of the constants, delaying generation of a patient forecast;
collecting, after the delaying and via the measurement device, a second sequence of measurements corresponding to a second set of physiologic data for the patient;
generating the patient forecast, the patient forecast generated using one or more evolutionary algorithms and based on processing patient data comprising the first set of physiologic data and the second set of physiologic data, wherein:
the patient forecast comprises a predicted physiologic parameter for the at least one patient at a future time based on a time series that corresponds to the patient data;
comparing the patient forecast with control limit information associated with the physiologic data;
determining, in response to the comparing, whether the patient forecast meets a condition associated with the control limit information; and
based on the predicted physiologic parameter and based further on the patient forecast meeting the condition, generating instructions to present via an electronic user interface indicating notification content selected from a group comprising modifying a treatment program associated with the at least one patient and preventing a future occurrence corresponding to the patient forecast.
The underlined limitations constitute concepts performed in the human mind, methods of organizing human activity and mathematical concepts. The claim encompasses a mental process of detecting constants in the first sequence or physiological data, comparing the patient forecast with control limit information and determining the patient forecast meets a condition associated with the control limit information. The identified abstract idea, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind except for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind except for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Additionally, the claim recites the steps of collecting measurements and generating instructions to present notification content selected from a group comprising modifying a treatment program associated with the at least one patient and preventing a future occurrence corresponding to the patient forecast, which encompasses an abstract idea that falls under the methods of organizing human activity, specifically associated with managing personal behavior or relationships or interactions between people (e.g. presenting content to a patient to modify a treatment program or prevent a future occurrence of clinical deterioration). If the claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. See MPEP § 2106.04(a). Furthermore, the claim encompasses an abstract idea that falls under the mathematical concepts grouping because generating a patient forecast using one or more evolutionary algorithms, under its broadest reasonable interpretation, represents mathematical calculations and relationships (see MPEP 2106.04(a)(2)). The abstract idea for Claims 8 and 15 are identical as the abstract idea for Claim 1, because the only difference between Claim 1 and 8 is that Claim 1 recites an apparatus, whereas Claim 8 recites a system, and because the only difference between Claims 1 and 11 is that Claim 1 recites an apparatus, whereas Claim 11 recites a method. Any limitations not identified above as part of the limitation in the mind, methods of organizing human activity or mathematical concepts, are deemed “additional elements” and will be discussed further in detail below. Accordingly, independent claims 1, 8 and 15 recite at least one abstract idea.
Additionally, dependent claims 2-7, 9-14 and 16-20 further narrow the abstract idea described in the independent claims. Claims 2, 9 and 16 describe the patient data. Claims 3, 10 and 17 further describe generating the time series. Claims 4 and 11 describe removing noise from the patient data. Claims 5, 12 and 18 describe combining values for generating forecast. Claims 6, 13 and 19 describe the time series and providing an alert. Claims 7, 14 and 20 describe generating the forecast using a plurality of future time points. Claims 2, 9 and 16 partially narrow the abstract idea as described above, and also introduce additional element(s) which will be discussed in Step 2A Prong 2 and Step 2B. These limitations only serve to further limit the abstract idea and hence, are directed toward fundamentally the same abstract ideas as independent claims 1, 8 and 15.
Step 2A - Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As such, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a "practical application."
In the present case, claims 1-20 as a whole do not integrate the abstract idea into a practical application because they do not impose meaningful limits on practicing the abstract idea. The additional elements or combination of additional elements, beyond the above-noted at least one abstract idea will be described as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the “abstract idea(s)”).
Specifically, independent claim 1 recites: One or more non-transitory media having instructions that, when executed by one or more hardware processors, cause the one or more hardware processors to facilitate a plurality of operations for preventing or managing clinical deterioration associated with at least one patient, the operations comprising:
collecting, by a measurement device comprising a sensor, a first sequence of measurements corresponding to a first set of physiologic data for a patient;
detecting constants in one or both of the first sequence of measurements and the first set of physiologic data;
in response to the detecting of the constants, delaying generation of a patient forecast;
collecting, after the delaying and via the measurement device, a second sequence of measurements corresponding to a second set of physiologic data for the patient;
generating the patient forecast, the patient forecast generated using one or more evolutionary algorithms and based on processing patient data comprising the first set of physiologic data and the second set of physiologic data, wherein:
the patient forecast comprises a predicted physiologic parameter for the at least one patient at a future time based on a time series that corresponds to the patient data;
comparing the patient forecast with control limit information associated with the physiologic data;
determining, in response to the comparing, whether the patient forecast meets a condition associated with the control limit information; and
based on the predicted physiologic parameter and based further on the patient forecast meeting the condition, generating instructions to present via an electronic user interface indicating notification content selected from a group comprising modifying a treatment program associated with the at least one patient and preventing a future occurrence corresponding to the patient forecast.
The independent claims recite the additional elements of non-transitory media, processors, user interface, measurement device and sensor that implement the identified abstract idea. The non-transitory media, processor and user interface are not described by the applicant and are recited at a high-level of generality such that they amounts to no more than mere instructions to apply the exception using a generic computer component.
The dependent claims 2 and 3 recite additional element(s) that implement the identified abstract idea. Claims 2, 9 and 16 describe an electronic health record computer system and biometric hemostasis sensor. However, these functions do not integrate a practical application more than the abstract idea because:
the electronic health record computer system represents mere instructions to apply the abstract idea on a computer (i.e., merely invoking the computer structure as a tool used to execute the limitations); and,
the biometric hemostasis sensor generally links the use of a judicial exception to a particular technological environment or field of use.
Accordingly, the claims as a whole do not integrate the abstract idea into a practical application as they do not impose any meaningful limits on practicing the abstract idea.
Step 2B
Regarding Step 2B, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
When viewed as a whole, claims 1-20 do not include additional limitations that are sufficient to amount to significantly more than the judicial exception because the claims recite processes that are routine and well-known in the art and simply implements the process on a computer(s) is not enough to qualify as "significantly more."
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using non-transitory media, processors, user interface, measurement device and sensor to perform the noted steps amount 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”).
The dependent claims 2 and 3 recite additional element(s) that implement the identified abstract idea. Claims 2, 9 and 16 describe an electronic health record computer system and biometric hemostasis sensor. However, these functions are not deemed significantly more than the abstract idea because:
the electronic health record computer system represents mere instructions to apply the abstract idea on a computer (i.e., merely invoking the computer structure as a tool used to execute the limitations); and,
the biometric hemostasis sensor generally links the use of a judicial exception to a particular technological environment or field of use.
Therefore, claims 1-20 are rejected under 35 USC §101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 5-9, 12-16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hidalgo Perez (ES 2540159 B1) in further view of Rosenfeld (US 2006/0271407 A1) and Eder (US 2015/0317449 A1).
Regarding claim 1, Hidalgo Perez teaches: One or more non-transitory media having instructions that, when executed by one or more hardware processors, cause the one or more hardware processors to facilitate a plurality of operations for preventing or managing clinical deterioration associated with at least one patient (pg. 2, “Background of the Invention” and pg. 9, “Description of…Various Forms”), the operations comprising:
collecting, by a measurement device comprising a sensor, a first sequence of measurements corresponding to a first set of physiologic data for a patient; (collecting patient data using a glucose meter (i.e. measurement device) of time-series of glucose or carbohydrate intake at specific time intervals “k”; “data collection 1 is carried out during a period of given time k, for example seven days” (i.e. a first sequence), e.g. see pgs. 8-9, “Description of…Various Forms” and pgs. 3-4, “Description of the Invention”)
collecting, […] via the measurement device, a second sequence of measurements corresponding to a second set of physiologic data for the patient; (“data collection 1 is carried out during a period of given time k…Subsequently, 2 is evaluated if the model (= GL function) is correct…If yes, it continues to collect data”, e.g. see pgs. 8-9, “Description of…Various Forms”; “In each iteration, the estimated glucose is obtained using the previous estimated values, and the values of carbohydrates and insulin at that instant…GL (k + 1) = f (GL; CH; IS; IL)” (i.e. second sequence), e.g. see pg. 15, Section 3.1; sequential collection at “15-minute intervals”, e.g. see pg. 12, Section 3.1)
generating the patient forecast, the patient forecast generated using one or more evolutionary algorithms and based on processing patient data comprising the first set of physiologic data and the second set of physiologic data, wherein: the patient forecast comprises a predicted physiologic parameter for the at least one patient at a future time based on a time series that corresponds to the patient data; (generating a forecast “GL (k + 1)” of glucose levels at a future time based on the time series by applying the “evolutionary algorithm” utilizing BNF grammar, mapping functions, fitness functions, crossover and mutation (which includes processing the first set and second set) to generate the predictive model, e.g. see pgs. 8-9, “Description of…Various Forms”; pgs. 3-4, “Description of the Invention”)
Hidalgo Perez does not teach:
detecting constants in one or both of the first sequence of measurements and the first set of physiologic data;
in response to the detecting of the constants, delaying generation of a patient [prediction];
collecting, after the delaying and via the measurement device, a second sequence of measurements corresponding to a second set of physiologic data for the patient;
However, Rosenfeld in the analogous art of patient monitoring and predictive models (e.g. see [0016]) teaches:
detecting constants in one or both of the first sequence of measurements and the first set of physiologic data; (evaluating the incoming stream of monitored patient data locally using an “urgent consultation warning system (herein, the UCWS)” to detect if an “event” has occurred such as “changes in hemodynamic and respiratory measures over time” (if the UCWS determines no event has occurred, the data is stable/unchanging which constitutes detecting a constant), e.g. see [0094]-[0095])
in response to the detecting of the constants, delaying generation of a patient [prediction]; (when the UCWS detects stable/constant data, it stores the data locally and sends it only at a “pre-established time such as hour”; because the transmission to the remote predictive command center is “delayed”, the decision support system is subsequently delayed in generating an updated patient prediction, e.g. see [0094], [0038])
collecting, after the delaying and via the measurement device, a second sequence of measurements corresponding to a second set of physiologic data for the patient; (“Patient monitoring device 115 acquires physiological data from a patient in real-time.”, e.g. see [0081]; the UCWS constantly evaluates the delayed data; once it detects a change/event, it triggers “immediate reporting of all stored monitored and patient data” (i.e. collecting a second sequence of measurements), e.g. see [0095])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hidalgo Perez to include detecting constants in one or both of the first sequence of measurements and the first set of physiologic data, in response to the detecting of the constants, delaying generation of a patient prediction and collecting, after the delaying and via the measurement device, a second sequence of measurements corresponding to a second set of physiologic data for the patient as taught by Rosenfeld, for the purposes of constantly evaluating patient data and expediting processing of the patient data “if an event has occurred” (Rosenfeld [0095]).
Hidalgo Perez and Rosenfeld do not teach:
comparing the patient forecast with control limit information associated with the physiologic data;
determining, in response to the comparing, whether the patient forecast meets a condition associated with the control limit information; and
based on the predicted physiologic parameter and based further on the patient forecast meeting the condition, generating instructions to present via an electronic user interface indicating notification content selected from a group comprising modifying a treatment program associated with the at least one patient and preventing a future occurrence corresponding to the patient forecast.
However, Eder in the analogous art of modeling physiological time-series data to forecast patient health ([0075], [0202], [0355]) teaches:
comparing the patient forecast with control limit information associated with the physiologic data; (establishing “requirements” that are “absolute and relative” for physiological parameters and checking if the predicted forecast drops below or exceeds these limits, e.g. see [0247], Table 13, [0323])
determining, in response to the comparing, whether the patient forecast meets a condition associated with the control limit information; and (determining if the forecasted measured values meet the condition of crossing the required level (i.e. control limit) indicating that patient survival or health is at risk, e.g. see [0323])
based on the predicted physiologic parameter and based further on the patient forecast meeting the condition, generating instructions to present via an electronic user interface indicating notification content selected from a group comprising modifying a treatment program associated with the at least one patient and preventing a future occurrence corresponding to the patient forecast. (if the forecast meets the risk condition, the system automatically transmits protocol updates via its interface to configure medical delivery equipment to modify a treatment program to improve health and prevent negative future occurrences, e.g. see [0355]-[0359], [0195])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hidalgo Perez and Rosenfeld to include comparing the patient forecast with control limit information associated with the physiologic data, determining, in response to the comparing, whether the patient forecast meets a condition associated with the control limit information and based on the predicted physiologic parameter and based further on the patient forecast meeting the condition, generating instructions to present via an electronic user interface indicating notification content selected from a group comprising modifying a treatment program associated with the at least one patient and preventing a future occurrence corresponding to the patient forecast as taught by Eder, for the purposes of automating protocol operations to “improve the health of the subject entity” (Eder [0355]).
Regarding claim 2, Hidalgo Perez, Rosenfeld and Eder teach the one or more non-transitory media of claim 1 as described above.
Hidalgo Perez does not teach:
wherein the patient data corresponds to measurements (a) selected from a group comprising heart rate data, blood pressure data, and oxygen saturation data and (b) entered or automatically determined using one or both of an electronic health record computer system and a biometric hemostasis sensor positioned in proximity to the at least one patient
However, Rosenfeld in the analogous art teaches:
wherein the patient data corresponds to measurements (a) selected from a group comprising heart rate data, blood pressure data, and oxygen saturation data and (b) entered or automatically determined using one or both of an electronic health record computer system and a biometric hemostasis sensor positioned in proximity to the at least one patient (acquiring “physiological data from a patient in real-time” including heart rate and blood pressure, e.g. see [0081], [0087]; taking measurements at a “patient monitoring station” and exporting patient data to a “hospital data system”, e.g. see [0071], [0107])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hidalgo Perez to include wherein the patient data corresponds to measurements selected from a group comprising heart rate data, blood pressure data, and oxygen saturation data and entered or automatically determined using one or both of an electronic health record computer system and a biometric hemostasis sensor positioned in proximity to the at least one patient as taught by Rosenfeld, for the purposes of capturing multiple variable dependencies (Rosenfeld [0089]).
Regarding claim 5, Hidalgo Perez, Rosenfeld and Eder teach the one or more non-transitory media of claim 1 as described above.
Hidalgo Perez does not teach:
wherein the operations further comprise combining at least two values of a physiologic variable or at least two values of a composite variable for generating the patient forecast, and wherein the combining comprises an exponentially weighted moving average of the at least two values of the physiologic variable or the at least two values of the composite variable
However, Eder in the analogous art teaches:
wherein the operations further comprise combining at least two values of a physiologic variable or at least two values of a composite variable for generating the patient forecast, and wherein the combining comprises an exponentially weighted moving average of the at least two values of the physiologic variable or the at least two values of the composite variable (“Composite variables…are mathematical combinations of item variables”, e.g. see [0098]; evaluating these variables using forecasting algorithms that combine past and present values via exponential smoothing, “single exponential smoothing, double exponential smoothing…Winters exponential smoothing-reduced time period and Holt-Winters exponential smoothing” (i.e. exponentially weighted moving average), e.g. see [0197])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hidalgo Perez to include combining at least two values of a physiologic variable or at least two values of a composite variable for generating the patient forecast, and wherein the combining comprises an exponentially weighted moving average of the at least two values of the physiologic variable or the at least two values of the composite variable as taught by Eder, for the purposes of forecasting the “range of values that can be expected” (Eder [0323]).
Regarding claim 6, Hidalgo Perez, Rosenfeld and Eder teach the one or more non-transitory media of claim 1 as described above.
Hidalgo Perez does not teach:
wherein the time series corresponds to a period of time in which the at least one patient was taking anticoagulants, wherein the operations further comprise providing an alert […] to contact a health-care provider based on the patient forecast
However, Rosenfeld in the analogous art teaches:
wherein the time series corresponds to a period of time in which the at least one patient was taking anticoagulants, wherein the operations further comprise providing an alert […] to contact a health-care provider based on the patient forecast (“Heparin treatment” and “Warfarin treatment” (i.e. anticoagulants) in algorithms for care for monitored patients, e.g. see Table 1, [0122]; “determine whether the rule for a monitored patient has been contravened. In the event the rule has been contravened, the remote command center issues an alert”, e.g. see [0085]; “The alert may take the form of suggested diagnoses that are vetted by a series of questions posed by the continued care software 420 to a caregiver”, e.g. see [0098])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hidalgo Perez to include the time series corresponds to a period of time in which the at least one patient was taking anticoagulants and providing an alert to contact a health-care provider based on the patient forecast as taught by Rosenfeld, for the purposes of considering “changes in multiple variables” such as medications given to the patient and alerting to prevent the patient’s condition from deteriorating (Rosenfeld [0089], [0086]).
Hidalgo Perez and Rosenfeld do not teach:
providing an alert for the at least one patient based on the patient forecast
However, Eder in the analogous art teaches:
providing an alert for the at least one patient based on the patient forecast (“providing medical advice, medical diagnoses and/or medical treatments that are appropriate to the resilient context of an individual patient”, e.g. see [0002]; “Proposals are prepared for transmission to the subject entity for each procedure, each treatment and each medication that was identified” which are offered by “medical service providers”, e.g. see [0355], [0351])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hidalgo Perez to include providing an alert for the at least one patient based on the patient forecast as taught by Eder, for the purposes of “improv[ing] the health of the subject entity” (Eder [0355]).
Regarding claim 7, Hidalgo Perez, Rosenfeld and Eder teach the one or more non-transitory media of claim 1 as described above.
Hidalgo Perez does not teach:
wherein the operations further comprise determining a length of the time series based on a composite variable corresponding at least partially to the predicted physiologic parameter and a frequency at which measurements are acquired for the composite variable,
wherein generating the patient forecast comprises using a determined value of the composite variable at a plurality of future time points
However, Eder in the analogous art teaches:
wherein the operations further comprise determining a length of the time series based on a composite variable corresponding at least partially to the predicted physiologic parameter and a frequency at which measurements are acquired for the composite variable, (defining the base time period and calculating length/frequency dynamically based on the system settings and the variable being tracked, configuring the “calculation frequency? (by minute, hour, day, week, etc.)” and the “Base number of periods (optional, for both history and forecast data)”, e.g. see Table 12)
wherein generating the patient forecast comprises using a determined value of the composite variable at a plurality of future time points (running multi-period simulations that forecast values across a plurality of future time horizons, “The simulation bots run probabilistic multi-period simulations of measure performance”; “In doing so, the bots will forecast the range of values that can be expected for the specified measure by subject entity (22) for each scenario.” (at future time points), e.g. see [0316]-[0317])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hidalgo Perez to include determining a length of the time series based on a composite variable corresponding at least partially to the predicted physiologic parameter and a frequency at which measurements are acquired for the composite variable and generating the patient forecast comprises using a determined value of the composite variable at a plurality of future time points as taught by Eder, for the purposes of providing simulations of parameters for a “number of time periods in the future” (Eder [0317], [0263]).
Claims 8 and 15 recite substantially similar limitations as those already addressed in claim 1, and, as such are rejected for similar reasons as given above.
Claims 9 and 16 recite substantially similar limitations as those already addressed in claim 2, and, as such are rejected for similar reasons as given above.
Claims 12 and 18 recite substantially similar limitations as those already addressed in claim 5, and, as such are rejected for similar reasons as given above.
Claims 13 and 19 recite substantially similar limitations as those already addressed in claim 6, and, as such are rejected for similar reasons as given above.
Claims 14 and 20 recite substantially similar limitations as those already addressed in claim 7, and, as such are rejected for similar reasons as given above.
Claims 3, 4, 10, 11 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Hidalgo Perez, Rosenfeld and Eder in further view of Palivonaite (“Short-term time series algebraic forecasting with internal smoothing”, Neurocomputing, 2014).
Regarding claim 3, Hidalgo Perez, Rosenfeld and Eder teach the one or more non-transitory media of claim 1 as described above.
Hidalgo Perez further teaches:
wherein the operations further comprise generating the time series using a plurality of serial physiologic measurements over time, […] wherein generating the patient forecast corresponds to using an optimization fitness objective function (collecting patient data of time-series of glucose or carbohydrate intake at specific time intervals “k”, e.g. see pgs. 8-9, “Description of…Various Forms” and pgs. 3-4, “Description of the Invention”; evaluating errors using a fitness function, “c) calculate the error Ek that entails: calculate ek as the difference between the data obtained from the patient and the N-expressions GLk; and, apply a fitness function to each of the previously calculated errors ek”, e.g. see pg. 4 “Description of the Invention”; “If the grammar limits the search space of the Evolutionary Grammar GE, the mission of the "fitness" functions (also known as fitness) is to guide the evolution towards a good solution. To calculate the "fitness", first you get the Time series of the complete glucose, GL, from the phenotype generated by the genotype” (optimizing the predictive algorithm), e.g. see pg. 16 “3.3 Evaluation fitness or aptitude”; Table 2 lists optimization objective fitness functions)
Hidalgo Perez does not teach:
wherein the plurality of serial physiologic measurements comprise numerical heart rate data and systolic blood pressure data […]
However, Rosenfeld in the analogous art teaches:
wherein the plurality of serial physiologic measurements comprise numerical heart rate data and systolic blood pressure data […] (acquiring “physiological data from a patient in real-time” including heart rate and blood pressure, e.g. see [0081], [0087]; determining changes in vital signs over time, e.g. see [0086])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hidalgo Perez to include wherein the plurality of serial physiologic measurements comprise numerical heart rate data and systolic blood pressure data as taught by Rosenfeld, for the purposes of capturing multiple variable dependencies (Rosenfeld [0089]).
Hidalgo Perez, Rosenfeld and Eder do not teach:
time series data represented as a Hankel matrix
However, Palivonaite in the analogous art of applying evolutionary algorithms and optimizing fitness objective functions to perform time-series forecasting (e.g. see Abstract) teaches:
time series data represented as a Hankel matrix (“An algebraic prediction technique based on the Hankel rank for the identification of the skeleton algebraic sequences in short-term time series”, e.g. see pg. 161, Section 1; structuring a sequence of serial measurements directly into a Hankel matrix, “The Hankel matrix (the catelecticant matrix with constant skew diagonals) H(n) constructed from the elements of this sequence”, e.g. see pg. 162, Section 2; “Let us now construct the fitness function for the set of corrections…In general, the goal is to maximize the fitness function by making small corrections to the sequence of observations”, e.g. see pg. 163, Section 3.2; “Evolutionary algorithms are exploited for the identification of the set of corrections”; “We will also use PSO for the selection of a near-optimal set of corrections.”, e.g. see Abstract and Section 4)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hidalgo Perez, Rosenfeld and Eder to include time series data represented as a Hankel matrix as taught by Palivonaite, for the purposes of formatting the time-series data to extract the underlying “algebraic sequence” while separating it from noise (Palivonaite, pg. 162, Section 3.1).
Regarding claim 4, Hidalgo Perez, Rosenfeld and Eder teach the one or more non-transitory media of claim 1 as described above.
Hidalgo Perez does not teach:
[…] using two or more evolutionary algorithms
However, Eder in the analogous art teaches:
[…] using two or more evolutionary algorithms (“The session would proceed to a pre-processing block (722) for pre-processing tasks such as discretization, transformation and/or filtering.”, e.g. see [0234]; initiating multiple distinct simulation modules, e.g. “simulation bots”, “extended entity simulation bots” and “mission simulation bots”; each of these distinct modules/bots “run an unconstrained genetic algorithm simulation that evolves to the most negative value possible” (the system utilizes a plurality of evolutionary algorithms to process the data and generate the forecast), e.g. see [0316], [0319], [0323])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hidalgo Perez to include using two or more evolutionary algorithms as taught by Eder, for the purposes of using separate algorithms to model “function measures”, “extended entity” components and “mission measure” (Eder [0313], [0320], [0323]).
Hidalgo Perez, Rosenfeld and Eder do not teach:
wherein the operations further comprise removing noise from the patient data prior to using evolutionary algorithms
However, Palivonaite in the analogous art teaches:
wherein the operations further comprise removing noise from the patient data prior to using evolutionary algorithms (“Unfortunately, real world series are usually contaminated with more or less noise.”, e.g. see pg. 162 Section 3; utilizing smoothing methods which are an “industrial technique to remove inherent random variation in a collection of data”, e.g. see pg. 163 Section 3.2; the system first calculates the smoothed, noise-filtered moving average, “Compute the smoothed moving average”; then executes the evolutionary algorithm, “Repeat 100 times…Compute a single set of corrections…using the PSO fitness function” (an evolutionary algorithm is executed 100 times to produce the forecast), e.g. see pg. 166, Section 4B)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hidalgo Perez, Rosenfeld and Eder to include removing noise from the patient data prior to using evolutionary algorithms as taught by Palivonaite, for the purposes of formatting the time-series data to extract the underlying “algebraic sequence” while separating it from noise (Palivonaite, pg. 162, Section 3.1).
Claims 10 and 17 recite substantially similar limitations as those already addressed in claim 3, and, as such are rejected for similar reasons as given above.
Claim 11 recite substantially similar limitations as those already addressed in claim 4, and, as such is rejected for similar reasons as given above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Reference Michelson (US 2009/0318775 A1) discloses characterizing clinical outcomes in a subject. Reference Gillam (US 2012/0173468 A1) discloses medical data prediction using genetic algorithms.
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/A.A./
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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