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 .
Response to Amendment
The amendment filed 10/24/2025 has been entered. Amendments to claims 1, 5, 7, 9-11, and 16-19, cancellation of claims 3-4 and 12-15, and new claims 20-24 are acknowledged. Claims 1, 5-11, and 16-24 remain pending in the application. Applicant’s amendments to the claims have overcome the objections and 112(b) rejections previously set forth in the Non-Final Office Action mailed 07/24/2025.
Claim Interpretation
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “sensor electronics module” in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
“sensor electronic module” will be interpreted as hardware or software in communication with a sensor consistent applicant’s specification para [0085]: “include hardware, firmware, and/or software that enables measurement of levels of analyte(s) via a continuous analyte sensor(s) 202”
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-2, 5-11, and 17-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen (US 20250049360 A1) in view of Shah et al. (US 20170325749 A1), hereinafter Shah.
Regarding claim 1, Chen discloses a monitoring system (abstract), comprising: a continuous analyte sensor ([0040]: "analyte concentration data may be communicated automatically and periodically,", [0025]: “lactate-responsive analyte sensors”) configured to generate analyte measurements associated with analyte levels of a patient ([0025]: "lactate-responsive analyte sensors"); a sensor electronics module coupled to the continuous analyte sensor and configured to receive and process the analyte measurements ([0037]: " As shown, sensing system 100 includes sensor control device 102 and reader device 120 that are configured to communicate with one another over a local communication path or link, which may be wired or wireless, uni- or bi-directional, and encrypted or non-encrypted.").
a memory comprising executable instructions ([0040]: " with the data being stored in a memory", [0037]); and one or more processors in data communication with the sensor electronics module and configured by the executable instructions to ([0038]: “A processor (not shown) may be communicatively coupled to sensor 104, with the processor being physically located within sensor housing 103 or reader device 120”) generate a disease prediction using analyte data associated with one for more analytes ([0087]: " a diagnosis or analysis of organ failure or dysregulation or the possibility of organ failure or dysregulation."), where the one or more processors being configured to generate the disease prediction comprises the one or more processors being configured to: receive analyte data from the sensor electronics module ([0038]: “may be communicatively coupled to sensor 104”), the analyte data comprising the lactate measurements associated with at least a first time period ([0083]: "following lactate level trends over time and/or determining an instantaneous lactate level at a particular time."); process two or more analyte values of the analyte data from the first time period to determine at least one lactate derived metric indicative of a function of a liver of the patient ([0088]: “Lactate levels obtained according to the present disclosure may be diagnostically combined with other analyte levels and/or physiological markers for determining organ function and/or failure, particularly of the liver.”);
Chen fails to disclose receiving the analyte data from the sensor electronics module, the analyte data comprising lactate measurements associated with a second time period; process two or more analyte values of the analyte data from the second time period to determine a second lactate metric indicative of the function of the liver of the patient; compare the first lactate metric to the second lactate metric; and generate the disease prediction based on a difference between the first lactate metric and the second lactate metric.
Shah discloses a continuous analyte monitoring system ([0009]: “a sensor array for insertion within subcutaneous tissue for continuous monitoring of at least two analytes”) wherein the processor is further configured to: receive the analyte data from the sensor electronics module, the analyte data comprising lactate measurements associated with a second time period ([0086]: “looking at measurements over windows of time… rates of change of lactate”); process two or more analyte values of the analyte data from the second time period to determine a second lactate metric indicative of the function of the liver of the patient ([0034]: “triage environments the system is intended to rapidly determine a risk score for sepsis based on risk metrics that are indicative of an existing sepsis condition… patient is found to be septic, the system is capable of transitioning from a diagnostic tool to a monitoring device capable of providing data to assist in evaluating the efficacy of ongoing therapy. Assuming a triage patient is deemed to not be septic, the system can transition to a patient monitoring mode where the objective is to output a risk score that provides notification that a patient is trending toward conditions indicative of sepsis”, [0086]: “is also capable of looking at measurements over windows of time, or the rates of change of primary inputs”, wherein the system evaluates a metric to determine sepsis then transitions to monitoring a second time post determination); compare the first lactate metric to the second lactate metric ([0090]: “trending data for lactate concentrations may similarly be calculated to support acute prediction/diagnosis of sepsis”, wherein trending data is a comparison of lactate concentration obtained at a first and second time) and generate the disease prediction based on a difference between the first lactate metric and the second lactate metric ([0090]: “support acute prediction/diagnosis of sepsis”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the system disclosed by Chen with the second metric as disclosed by Shah in order to allow for determination of when a patient is trending toward disease (Shah [0034]).
Regarding claim 2, Chen discloses the continuous analyte sensor comprises: an electroactive working electrode of conductor material configured to be inserted into a skin of the patient ([0064]: "The working electrode is disposed upon the sensor tail and may be inserted in the tissue to facilitate lactate analyses therein."), wherein the electroactive working electrode is surrounded by a sensing membrane for sensing the analyte levels ([0046]: "membrane 220 overcoats at least active area 218 and may optionally overcoat some or all of working electrode 214… composition of membrane 220 may vary to promote a desired flux of lactate to active area 218, thereby providing a desired signal intensity and stability").
Regarding claim 5, Chen as modified by Shah discloses the monitoring system of claim 1 but fails to disclose wherein the one or more processors is further configured to generate one or more recommendations for treatment based, at least in part, on the disease prediction.
Shah further discloses wherein the one or more processors are is further configured to generate one or more recommendations for treatment based, at least in part, on the disease prediction ([0039]: “any treatment protocol and provide real-time, actionable information to external control algorithms whose aim is to optimize the management of therapy delivery to subjects with sepsis or a high risk of developing sepsis. In many embodiments, this is accomplished by modifying therapy based on system outputs achieving targets and/or set points for specific risk metrics based on a specific clinical protocol.”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the monitoring system disclosed by Chen to include the recommendations for treatment as disclosed by Shah in order to improve patient outcomes by allowing for real-time management of a disease state (Shah [0039]).
Regarding claim 6, Chen as modified by Shah discloses the monitoring system of claim 5, and Shah further discloses wherein the one or more recommendations for treatment comprise at least one of: lifestyle modification recommendations; drug prescription recommendations ([0097]: “fluid resuscitation or infusion of dobutamine”); surgical procedure recommendations; or medical device recommendations for use by the patient.
Regarding claim 7, Chen discloses wherein the disease prediction comprises at least one of: an indication of a presence of liver disease in the patient; an indication of a severity of the liver disease in the patient; a score associated with the liver disease of the patient; an indication of a level of risk of the patient being diagnosed with the liver disease; or an indication of a level of improvement or deterioration of the liver disease in the patient ([0087]: "may monitor organ function, particularly liver function, by assessing the activity of lactate dehydrogenase or another suitable lactate-responsive enzyme (e.g., lactate oxidase)... a diagnosis or analysis of organ failure or dysregulation or the possibility of organ failure or dysregulation. ").
Regarding claim 8, Chen discloses the indication of the level of improvement or the deterioration of the liver disease in the patient is based, at least in part, on at least one of: a procedure previously performed on the patient; a drug previously ingested by the patient ([0085]: "response to anti-infective agents and/or treatments administered to the subject to treat the sepsis or infection or symptoms thereof.") or a metric that is determined based on a combination of two or more of a lactate clearance rate, a lactate area under a curve, a lactate baseline, a lactate rate of change, or a postprandial lactate level.
Regarding claim 9, Chen discloses the first and second lactate metric comprises at least one of a lactate clearance rate, a lactate area under a curve, a lactate baseline, a lactate rate of change, or a postprandial lactate level (Figs 3 and 4, as modified by Shah), or a metric that is determined based on a combination of two or more of a lactate clearance rate, a lactate area under a curve, a lactate baseline, a lactate rate of change, or a postprandial lactate level.
Regarding claim 10, Chen discloses one or more non-analyte sensors configured to generate non-analyte sensor data during the first time period ([0088]: “Additional markers that may be assayed in combination with lactate for assaying organ function include, for example, body temperature, heart rate, respiratory rate, blood pressure, decreased urine output, abrupt changes in mental status, decreased platelet count, and other markers (e.g., C reactive proteins (CRP), procalcitonin, pancreatic stone protein (PSP), circulating complement (C3 and C4), ferritin, cholesterol, albumin, cortisol, and neutrophil gelatinase associated lipocalin)”).
Regarding claim 11, Shah disclose the first and second lactate metric comprises a first and second lactate clearance rate, respectively ([0097]: “increasing lactate clearance rate above a specified threshold,”, wherein an increasing lactate clearance rate requires at least two lactate clearance rate measurements).
Regarding claim 15, Chen as modified by Shah discloses the monitoring system of claim 11. Shah further discloses the processor being configured to correct the first lactate clearance rate of the patient comprises the processor being configured to: identify the at least one period of increased lactate is not due to physical exertion by the patient, using the non-analyte sensor data ([0092]: “when a physical sensor measuring movement detects continuous motion associated with exercise. Other modifications to the lactate risk value chart can be made based on other inputs such as, but not limited to chronic disease states and certain types of medication.”, wherein the sensor detects when exercise occurs); compare the data generated by the non-analyte sensor data with other non-analyte sensor data for one or more other periods of increased lactate not due to physical exertion ([0072]: “when a physical sensor measuring movement detects continuous motion associated with exercise. Other modifications to the lactate risk value chart can be made based on other inputs such as, but not limited to chronic disease states and certain types of medication.”) and having pre-determined lactate clearance rate breakdowns, wherein the pre-determined lactate clearance rate breakdowns represent a breakdown of lactate clearance by at least one of the liver, kidneys, muscles, and a heart of the patient ([0097]: “elevated lactate levels coincide with an increasing lactate clearance rate above a specified threshold, the risk score can be determined without taking into account the elevated lactate levels.”); determine a second lactate clearance rate indicative of lactate clearance by only the liver of the patient based, at least in part, on the comparison ([0092]: “acute increases (followed by gradual decreases) in lactate concentration can occur as a result of physical exertion such as exercise, for remote monitoring embodiments the maximum relative weighting factor can be limited to 0.5 rather than 1 for lactate concentrations between four and six milliequivalents per liter.”); and wherein the disease prediction is generated using at least the analyte data for one or more analytes and the second lactate clearance rate ([0092]: “the change of lactate risk value based on exercise occurs automatically when a physical sensor measuring movement detects continuous motion associated with exercise”).
Regarding claim 17, Chen discloses the monitoring system of claim 1 but fails to disclose the one or more processors are further configured to: obtain at least one of demographic information, food consumption information, activity level information, or medication information related to the patient, and wherein the disease prediction is generated further using at least one of the demographic information, the food consumption information, the activity level information, or the medication information.
Shah discloses wherein one or more processors are further configured to: obtain at least one of demographic information, food consumption information, activity level information, or medication information related to the patient ([0092]: “certain types of medication”), and wherein the disease prediction is generated further using at least one of the demographic information, the food consumption information, the activity level information, or the medication information ([0092]: “Other modifications to the lactate risk value chart can be made based on other inputs such as, but not limited to chronic disease states and certain types of medication.”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the system disclosed by Chen to include the factoring of additional inputs within the disease prediction as disclosed by Shah in order to account for elements that may cause in lactate measurements (Shah [0077]: “ketoacidosis and metformin consumption may lead to increased lactate in a subject already predisposed to generally higher lactate levels”).
Regarding claim 18, Chen discloses the monitoring system of claim 1 but fails to disclose the one or more analytes further include at least one of glucose or ketones.
Shah discloses a continuous analyte monitoring system ([0009]: “a sensor array for insertion within subcutaneous tissue for continuous monitoring of at least two analytes”) wherein the measurement of one or more analytes further include at least one of glucose or ketones ([0041]: “to sensors configured to continuously measure glucose”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the monitoring system disclosed by Chen to include the measurement of glucose as disclosed by Shah in order to allow for the system to factor in diabetes as a confounding variable in the disease prediction model (Shah [0077]).
Regarding claim 19, Chen discloses the one or more analytes of the patient are monitored continuously, semi-continuously, or periodically ([0040]: "analyte concentration data may be communicated automatically and periodically,")
Regarding claim 20, Shah discloses wherein the one or more processors being configured to process the analyte data from each of the first time period and second time period to determine the first and second lactate clearance rate comprises the one or more processors being configured to: identify at least one period of increased lactate of the patient during the at least the first time period ([0097]: “elevated lactate levels”); calculate a first lactate clearance rate of the patient after the at least one period of increased lactate ([0097]: “elevated lactate levels coincide with an increasing lactate clearance rate above a specified threshold.”); and correct the first lactate clearance rate of the patient to isolate lactate clearance by a liver of the patient based, at least in part, on the non-analyte sensor data ([0092]: “However, because acute increases (followed by gradual decreases) in lactate concentration can occur as a result of physical exertion such as exercise, for remote monitoring embodiments the maximum relative weighting factor can be limited to 0.5 rather than 1 for lactate concentrations between four and six milliequivalents per lite… a physical sensor measuring movement detects continuous motion associated with exercise”, wherein the exercise level can be considered non-analyte sensor data and the weight of clearance is corrected based on exercise).
Regarding claim 21, Shah discloses wherein the one or more processors are configured to calculate each of the first and second lactate clearance rates of the patient after the at least one period of increased lactate ([0097]: “elevated lactate levels coincide with an increasing lactate clearance rate above a specified threshold.”).
Regarding claim 22, Shah further discloses the processor being configured to calculate the first lactate clearance rate of the patient after the at least one period of increased lactate comprises the processor being configured to: determine a maximum lactate level of the patient during the at least one period of increased lactate ([0092]: “the lactate risk value increases sharply upon exceeding 2.0 milliequivalents per liter and reaching a maximum of 1 after the lactate concentration approaches limits outside of lactic acidosis.”, Fig 4B); determine an amount of time the maximum lactate level takes to decrease to a percentage of a baseline lactate level or a percentage of the maximum lactate level of the patient after the at least one period of increased lactate ([0035]: “can easily see if lactate levels are increasing or decreasing and additionally the rate at which lactate levels are changing”, [0061]: “real time data and trends derived from the data”, wherein the lactate trends are calculated and updated in real time); and calculate the first lactate clearance rate of the patient using the determined maximum lactate level of the patient, the baseline lactate level of the patient, and the determined amount of time the maximum lactate level takes to decrease to the percentage of the baseline lactate level of the patient ([0090]: “trending data for lactate concentrations may similarly be calculated to support acute prediction/diagnosis of sepsis…elevated lactate concentrations coinciding with regular or faster than regular lactate clearance may not be considered cause for alarm because the subject continues to clear lactate from the body at a regular or above regular rate.”, wherein the lactate clearance baseline is calculated and the measurements are in real time).
Regarding claim 23, Shah further discloses the processor being configured to correct the first lactate clearance rate of the patient comprises the processor being configured to: identify the at least one period of increased lactate is not due to physical exertion by the patient, using the non-analyte sensor data ([0092]: “when a physical sensor measuring movement detects continuous motion associated with exercise. Other modifications to the lactate risk value chart can be made based on other inputs such as, but not limited to chronic disease states and certain types of medication.”, wherein the sensor detects when exercise occurs); compare the data generated by the non-analyte sensor data with other non-analyte sensor data for one or more other periods of increased lactate not due to physical exertion ([0072]: “when a physical sensor measuring movement detects continuous motion associated with exercise. Other modifications to the lactate risk value chart can be made based on other inputs such as, but not limited to chronic disease states and certain types of medication.”) and having pre-determined lactate clearance rate breakdowns, wherein the pre-determined lactate clearance rate breakdowns represent a breakdown of lactate clearance by at least one of the liver, kidneys, muscles, and a heart of the patient ([0097]: “elevated lactate levels coincide with an increasing lactate clearance rate above a specified threshold, the risk score can be determined without taking into account the elevated lactate levels.”); determine a second lactate clearance rate indicative of lactate clearance by only the liver of the patient based, at least in part, on the comparison ([0092]: “acute increases (followed by gradual decreases) in lactate concentration can occur as a result of physical exertion such as exercise, for remote monitoring embodiments the maximum relative weighting factor can be limited to 0.5 rather than 1 for lactate concentrations between four and six milliequivalents per liter.”); and wherein the disease prediction is generated using at least the analyte data for one or more analytes and the second lactate clearance rate ([0092]: “the change of lactate risk value based on exercise occurs automatically when a physical sensor measuring movement detects continuous motion associated with exercise”).
Regarding claim 24, Shah further discloses wherein the processor being configured to correct the first lactate clearance rate of the patient comprises the processor being configured to: identify the at least one period of increased lactate is due to physical exertion by the patient using the non-analyte sensor data ([0092]: “a physical sensor measuring movement detects continuous motion associated with exercise.”); compare the non-analyte sensor data with other non-analyte sensor data for one or more other periods of increased lactate due to physical exertion ([0072]: “when a physical sensor measuring movement detects continuous motion associated with exercise. Other modifications to the lactate risk value chart can be made based on other inputs such as, but not limited to chronic disease states and certain types of medication.”) and having pre-determined lactate clearance rate breakdowns, wherein the pre-determined lactate clearance rate breakdowns represent a breakdown of lactate clearance by at least one of the liver, kidneys, muscles, and a heart of the patient ([0097]: “elevated lactate levels coincide with an increasing lactate clearance rate above a specified threshold, the risk score can be determined without taking into account the elevated lactate levels.”); and determine a second lactate clearance rate indicative of lactate clearance by only the liver of the patient based, at least in part, on the comparison ([0092]: “acute increases (followed by gradual decreases) in lactate concentration can occur as a result of physical exertion such as exercise, for remote monitoring embodiments the maximum relative weighting factor can be limited to 0.5 rather than 1 for lactate concentrations between four and six milliequivalents per liter.”), and wherein the disease prediction is generated using at least the analyte data for the one or more analytes and the second lactate clearance rate ([0092]: “the change of lactate risk value based on exercise occurs automatically when a physical sensor measuring movement detects continuous motion associated with exercise”).
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Shah in view of Su et al. (US 20120177260 A1), hereinafter Su.
Chen as modified by Shah discloses the monitoring device of claim 1 but fails to disclose the disease prediction is generated using a model trained using training data, wherein the training data comprises records of historical patients with varying stages of liver disease.
Su discloses a model for generating a liver disease prediction ([0017]: “Predictive statistical models were developed to evaluate the various measures of the liver and quantitatively detect an undesirable medical condition of the liver, such as cirrhosis.”) wherein the disease prediction is generated using a model trained using training data ([0030]: “Each model derived from the training set was then applied to the livers in the validation set to test the model for accuracy by measuring the AUROC”), wherein the training data comprises records of historical patients with varying stages of liver disease ([0021]: “Beginning with the selection of the patient population, the cohort of patients studied was identified through two mechanisms… All patients who had liver biopsies reviewed… 1172 cases were found where a CT scan was performed within 6 months of a liver biopsy. Of these patients, 54 consecutive patients with cirrhosis and normal liver biopsy were identified as specified”, [0030]: “Cirrhotic and normal patients were assigned randomly to the training or validation set in a 3:1 ratio.”, wherein the patient data comprises the training data).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the disease prediction as disclosed by Chen to include the model trained with training data as disclosed by Su in order to more accurately detect liver disease.
Response to Arguments
Applicant’s arguments, see Remarks pages 9-11, filed 10/24/2025, with respect to the rejection(s) of claim(s) 1-19 under 35 U.S.C. § 102/103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of 35 U.S.C. § 103 (see rejection above).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAVYA SHOBANA BALAJI whose telephone number is (703)756-5368. The examiner can normally be reached Monday - Friday 8:30 - 5:30 ET.
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/KAVYA SHOBANA BALAJI/ Examiner, Art Unit 3791
/DANIEL L CERIONI/ Primary Examiner, Art Unit 3791