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 .
Election/Restrictions
Applicant’s election without traverse of Group I (Claims 1-9 and 19-20) in the reply filed on 03/11/2026 is acknowledged.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 7-9, 19, and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Haddad (US 20200353250 A1).
In regards to claim 1 Haddad teaches a system comprising:
one or more input/output devices; and one or more processors configured to:
receive, from an implantable medical device via the one or more input/output devices, a plurality of diagnostic parameters of a patient associated with heart failure, wherein at least one of the plurality of diagnostic parameters is measured by the implantable medical device ([0048] The sensor system 150 may include any suitable device for acquiring patient data. In some embodiments, the sensor system 150 may include a patient implantable device 202, such as an implantable medical device (IMD));
obtain environmental factor information associated with heart failure for the patient via the one or more input/output devices ([0032] The sensor system 150 may include one or more sensors to detect various parameters related to a patient and an environment related to the patient);
determine a heart failure (HF) risk score indicating probability of occurrence of a heart failure event of the patient, wherein the HF risk score is derived using a model that uses the plurality of diagnostic parameters monitored over time and the environmental factor information ([0034] “In particular, the historical patient data may be used to determine a covariance with current patient data or to determine various risk factors based on patient background or history, for example, using artificial intelligence (AI)” [0074] In some examples, various devices of the treatment management system 100 may generate data to perform any of the various functions or operations described herein, e.g., generate a heart failure risk status based on the patient metric comparisons or create patient metrics from the raw metric data. [0077] The evaluation period serves as an evaluation window that encompasses data, acquired from each patient, that are within the boundaries (e.g., start and end times));
and in response to the HF risk score exceeding a threshold value, provide an alert or a suggestion to modify a therapy delivered to the patient by the implantable medical device via the one or more input/output devices ([0141] In some embodiments, the patient may not be stable 372 and the patient score may indicate that physician input is needed instead of management by the treatment management system. For example, an overall HF risk score may exceed a higher threshold level that indicates an HF event, which may suggest the patient should be seen in a hospital, emergency department, ambulance, observation unit, urgent care, or HF/cardiology clinic, by a nurse or physician. In such cases, the treatment management system may automatically notify the nurse or physician, as well as the patient, and may enter into an override mode to stop administration of treatment).
In regards to claim 2 Haddad teaches the system of claim 1, wherein the model is a Bayesian belief network ([0075] “[0075] Heart failure (HF) risk status can be calculated in a number of ways known to a person of skilled art having the benefit of this disclosure. One example of calculating a risk score, or risk status, is described in U.S. Patent Publication No. 2019/0069851, filed Aug. 31, 2018, and U.S. Patent Publication No. 2019/0125273, filed Apr. 26, 2018, which are incorporated by reference in this disclosure”; Sharma (US 20190125273 A1) [0047] “For example, a Bayesian Belief Network may be applied to the values of the patient metrics to determine the risk level, e.g., the probability, that patient 14 will be admitted to the hospital for heart failure”).
In regards to claim 7 Haddad teaches the system of claim 1, wherein the at least one of the diagnostic parameters comprises intrathoracic impedance ([0080] “The patient implantable device 202 may provide patient data (e.g., diagnostic information, real-time data related to absolute intrathoracic impedance that may be indicative of hypervolemia or hypovolemia, etc”).
In regards to claim 8 Haddad teaches the system of claim 1, wherein the at least one of the diagnostic parameters is derived at a server using transmitted data from the implantable medical device ([0048-0049] “The sensor system 150 may include any suitable device for acquiring patient data. In some embodiments, the sensor system 150 may include a patient implantable device 202, such as an implantable medical device (IMD), having a patient implantable sensor”).
In regards to claim 9 Haddad teaches the system of claim 1, wherein the therapy comprises at least one of a substance delivered by an implantable pump, cardiac resynchronization therapy, refractory period stimulation, or cardiac potentiation therapy ([0179] “In embodiment A16, a system comprises the system according to any A embodiment, wherein the treatment delivery system comprises at least one of a drug dispenser to contain one or more drugs, an automated treatment pump, or a graphical user interface to provide treatment information to the patient”).
In regards to claim 19 Haddad teaches a system comprising:
an implantable medical device comprising one or more sensors configured to monitor over time at least one primary diagnostic parameter associated with heart failure ([0048-0049] “The sensor system 150 may include any suitable device for acquiring patient data. In some embodiments, the sensor system 150 may include a patient implantable device 202, such as an implantable medical device (IMD), having a patient implantable sensor”);
and an external device interacting with the implantable medical device to obtain the at least one primary diagnostic parameter associated with heart failure ([0086] “The patient data may be provided by a sensor system, which may include an implantable device, a wearable device, or an external device. Some patient data may be automatically provided, and other patient data may be provided to confirm a risk score” )
and a server comprising ([0085] treatment optimization system 160 is the risk score generator [0106] risk score generator is stored on cloud server):
a processor configured receive the at least one primary diagnostic parameter from the implantable medical device ([0085] Fig.2 Processor 222 in treatment optimization system 160);
obtain environmental factor information associated with heart failure for the patient ([0032] The sensor system 150 may include one or more sensors to detect various parameters related to a patient and an environment related to the patient);
determine a heart failure (HF) risk score indicating probability of occurrence of a heart failure event of the patient, wherein the HF risk score is derived using a model that uses the plurality of diagnostic parameters monitored over time and the environmental factor information ([0034] “In particular, the historical patient data may be used to determine a covariance with current patient data or to determine various risk factors based on patient background or history, for example, using artificial intelligence (AI)” [0074] In some examples, various devices of the treatment management system 100 may generate data to perform any of the various functions or operations described herein, e.g., generate a heart failure risk status based on the patient metric comparisons or create patient metrics from the raw metric data. [0077] The evaluation period serves as an evaluation window that encompasses data, acquired from each patient, that are within the boundaries (e.g., start and end times));
and in response to the HF risk score exceeding a threshold value, provide an alert or a suggestion to modify a therapy delivered to the patient by the implantable medical device via the one or more input/output devices ([0141] In some embodiments, the patient may not be stable 372 and the patient score may indicate that physician input is needed instead of management by the treatment management system. For example, an overall HF risk score may exceed a higher threshold level that indicates an HF event, which may suggest the patient should be seen in a hospital, emergency department, ambulance, observation unit, urgent care, or HF/cardiology clinic, by a nurse or physician. In such cases, the treatment management system may automatically notify the nurse or physician, as well as the patient, and may enter into an override mode to stop administration of treatment).
In regards to claim 20 Haddad teaches system of claim 19, wherein server comprises a cloud based server ([0106] risk score generator is stored on cloud server).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 3-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Haddad (US 20200353250 A1) as applied to claim 1, in view of Shah (Global association of air pollution and heart failure: a systematic review and meta-analysis).
In regards to claim 3 Haddad teaches the system of claim 1. Haddad fails to teach a system wherein the environmental factor information comprises particulate matter exposure level. Shah teaches that particulate matter exposure has an effect on heart failure (Abstract Findings “Increases in particulate matter concentration were associated with heart failure hospitalization or death”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the system of Haddad so that particulate matter concentration where the patient lives is one of the parameters. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of accounting for a user’s environment when determining heart failure risk.
In regards to claim 4 Haddad teaches the system of claim 1. Haddad fails to teach a system wherein the one or more processors are configured to obtain the environmental factor information based on location information for the patient. Shah teaches that particulate matter exposure which varies based on location has an effect on heart failure (Abstract Findings “Increases in particulate matter concentration were associated with heart failure hospitalization or death”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the system of Haddad so that particulate matter concentration at the location that the patient lives is one of the parameters. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of accounting for a user’s environment when determining heart failure risk.
In regards to claim 5 modified Haddad teaches the system of claim 4, wherein the one or more processors are configured to receive the location information for the patient from a computing device of the patient ([0032] “a graphical or audible user interface to accept user input”, User input would be the location’s particulate matter of Shah).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Haddad (US 20200353250 A1) as applied to claim 1, in view of Alahmad (Associations Between Extreme Temperatures and Cardiovascular Cause-Specific Mortality: Results From 27 Countries)
In regards to claim 6 Haddad teaches the system of claim 1. Haddad fails to teach a system wherein the environmental factor information comprises environmental temperature. Alahmad teaches that extreme temperature is associated with a higher risk of heart disease deaths (Abstract Results “At extreme temperature percentiles, heat (99th percentile) and cold (1st percentile) were associated with higher risk of dying from any cardiovascular cause, ischemic heart disease, stroke, and heart failure as compared to the minimum mortality temperature, which is the temperature associated with least mortality”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the system of Haddad so that temperature where the patient lives is one of the parameters. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of accounting for a user’s geographic zone temperature when determining heart failure risk.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUCY EPPERT whose telephone number is (571)270-0818. The examiner can normally be reached M-F 7:30-5:00 EST.
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/LUCY EPPERT/ Examiner, Art Unit 3791
/ETSUB D BERHANU/ Primary Examiner, Art Unit 3791
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