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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on April 16, 2026 has been entered.
Response to Amendment
This office action is responsive to the amendment filed on April 16, 2026. As directed by the amendment: claims 1 and 36 have been amended, claim 22 has been cancelled, and claims 42-47 have been added. Thus, claims 1, 2, 4, 5, 9, 20, 24, 26, 27, 29, 31, 32, 36, and 41-47 are presently pending in this application.
Applicant’s amendments to the Specification, Drawings, and Claims have overcome each and every 112(d) rejections previously set forth in the Final Office Action mailed December 16, 2025.
Response to Arguments
Applicant’s arguments, see pg. 9, filed April 16, 2026, with respect to the rejection(s) of claim(s) 1 under U.S.C. 103 have been fully considered and are persuasive, specifically to Charlton not teaching or disclosing the amended claim limitation. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Applicant’s amendments.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 2, 4, 5, 9, 20, 24, 26, 27, 29, 31, 36, and 42-45 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wariar (US 20070179389), hereinafter “Wariar ‘389”, in view of Verbitskiy (US 20090312621) and in view of Wariar (US 20130178786), hereinafter “Wariar ‘786”.
Regarding claim 1, Wariar ‘389 discloses a healthcare monitoring system, the healthcare monitoring system arranged to receive a flow of biofluid from a biofluid source in or on the patient (paragraph 25 discloses the system receives urine), the healthcare monitoring system comprising:
a biofluid reservoir configured to couple to the biofluid source for receiving the flow of biofluid from the patient (“bag” in paragraph 44 receives urine); and
at least one biofluid sensing arrangement (strips 102, pads 104, detector 110, emitter 108, scale 120, remote computer 122 in fig. 1, and clearance data calculation module 206 form the “biofluid sensing arrangement”, see [0041]-[0044]) configured to determine a volume of the biofluid in the biofluid reservoir produced over a period of time (paragraph 44 discloses determining volume of the urine in the container by determining the weight, this volume would be produced over a period of time since the patient last urinated) to provide at least one output biofluid measurement value indicative of the volume of the biofluid in the biofluid reservoir (paragraphs 44 and 45 discloses using the volume to provide a clearance rate);
wherein the healthcare monitoring system is configured to infer a change in the patient's condition based, at least in part, on a trained model applied to the at least one output biofluid measurement value (paragraphs 46 and 50 discloses using a boundary generation module 214 and CHF decompensation detection module 214 to analyze, in part, the clearance rate to detect decompensation of the heart), wherein the healthcare monitoring system is further configured to determine the trained model from historical data to associate a rule with a biofluid output (paragraph 46 discloses using historical data to develop boundaries for a normal range for the patient parameters, including clearance rate), wherein, responsive to the healthcare monitoring system inferring a change in the patient's condition, by comparison of the at least one output biofluid measurement value with the associated rule, the healthcare monitoring system is configured to initiate a response action designated by the rule (paragraph 54 discloses issuing an alert).
However, Wariar ‘389 fails to disclose the system is configured to determine the trained model from an application of machine learning techniques and the response action is a request to a fluid bolus mechanism to increase or decrease a measured amount of fluid delivered to the patient specified by the rule.
Verbitskiy teaches a system which is configured to apply machine learning techniques in order to set and adapt threshold values for a physiological parameter (paragraph 38). Since Wariar ‘389 already discloses the use of a machine learning module (learning module 900 in fig. 7), it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to have modified the system of Wariar ‘389 so that the trained module (i.e. boundary generation module 214) is determined from the application of machine learning techniques, as taught by Verbitskiy. This modification would enable the trained module to adapt the boundary data of the patient as the patient parameters change (paragraph 38).
Wariar ‘786 teaches a similar system configured to detect heart failure in a patient (fig. 8) and teaches that administration of drugs can improve heart contractility (paragraph 76). Wariar ‘786 discloses using a processor to determine if a physiological parameter satisfies a rule (paragraph 83 discloses comparing measured parameters to parameter targets) and, upon determining that the measured parameter is outside of the target, submitting a request to a fluid bolus mechanism to increase or decrease a measured amount of fluid delivered to the patient specified by the rule (paragraphs 84 and 88 discloses controlling a “therapy circuit” to change drug infusion rate or drug infusion dose based on the comparison between measured parameters and the targets).
Since Wariar ‘389 is directed towards detection of heart failure similar to Wariar ‘786 (paragraph 2) and Wariar ‘389 discloses using measured parameters values in comparison to parameter targets to determine the likelihood of heart failure (paragraph 46), it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to have modified the response action of Wariar ‘389 to further include a request to a fluid bolus mechanism to increase or decrease a measured amount of fluid delivered to the patient specified by the rule, as taught by Wariar ‘786, since Wariar ‘786 teaches that this response can improve contractility of the heart and can manage symptoms of heart failure (paragraph 76).
Regarding claim 2, in the modified system of Wariar ‘389, Wariar ‘389 discloses the system is configured to generate an alert based on an inference of the change in the patient's condition (see Wariar ‘389, [0070], 709 can receive alerts regarding the patient’s health and well-being).
Regarding claim 4, in the modified system of Wariar ‘389, Wariar ‘389 discloses the biofluid sensing arrangement (see Wariar ‘389, Fig. 1, strips 102, pads 104, detector 110, emitter 108, scale 120, remote computer 122, and clearance data calculation module 206, see [0041]-[0044]) is further configured to determine values indicative of at least one of composition (see Wariar ‘389, [0025], 104 changes state (color shade and/or intensity) in proportion to the concentration of a given chemical constituent in the urine, each pad may sense a different constituent), temperature or other biometric of the biofluid in the biofluid reservoir.
Regarding claim 5, in the modified system of Wariar ‘389, Wariar ‘389 discloses the system is further configured to communicate the alert to a device (see Wariar ‘389, Fig. 7, peripheral devices 709) to enable a user to be notified of the inference of the change in the patient's condition (see Wariar ‘389, [0070], 709 can receive alerts regarding the patient’s health and well-being).
Regarding claim 9, in the modified system of Wariar ‘389, Wariar ‘389 discloses the biofluid sensing arrangement (see Wariar ‘389, Fig. 1, strips 102, pads 104, detector 110, emitter 108, scale 120, remote computer 122, and clearance data calculation module 206, see [0041]-[0044]) is configured to determine the volume of biofluid in the biofluid reservoir by weighing the biofluid reservoir (see Wariar ‘389, the weight of the container is taken by scale 120, [0044]).
Regarding claim 20, in the modified system of Wariar ‘389, Wariar ‘389 discloses the system is configured to infer the change in the patient's condition using a knowledge base (see Wariar ‘389, Fig. 7, analysis module 716, see [0095]-[0096]) and rules (see Wariar ‘389, [0099], information from patients with similar disease states) determined using the knowledge base (see Wariar ‘389, Fig. 7, analysis module 716, [0099]).
Regarding claim 24, in the modified system of Wariar ‘389, Wariar ‘389 discloses the historical data (see Wariar ‘389, [0107]-[0108], 906 uses various algorithms and mathematical modeling) comprises a field (see Wariar ‘389, [0103], data related to a given population) pertaining to a demographic group (see Wariar ‘389, Fig. 7, population analysis module 904, [0103)).
Regarding claim 26, in the modified system of Wariar ‘389, Wariar ‘389 discloses the system is configured to determine at least one rule (see Wariar ‘389, paragraph 46 discloses generating a boundary for each parameter) from the trained model (boundary generation module 214 in fig. 2).
Regarding claim 27, in the modified system of Wariar ‘389, Wariar ‘389 discloses the rule defines a danger zone on at least one parameter (paragraph 46 discloses a “normal range” for each parameter with the zone outside of this range being the “danger zone”), wherein the danger zone defines a threshold on the at least one parameter to indicate when a measurement for that parameter indicates deterioration in the patient (paragraphs 46 and 50).
Regarding claim 29, in the modified system of Wariar ‘389, Wariar ‘389 discloses the system is configured to infer the change in the patient's condition responsive to a determination that the at least one parameter is indicating deterioration in the condition of the patient (paragraph 53 discloses decompensation of CHF being detected).
Regarding claim 31, in the modified system of Wariar ‘389, Wariar ‘389 discloses the response action is an instruction to a connected device (paragraph 54).
Regarding claim 36, in the modified system of Wariar ‘389, Wariar ‘786 discloses the fluid bolus mechanism, responsive to receiving the request, implements the request and delivers the requested measured amount or a rate to the fluid to a patient (paragraph 88).
Regarding claim 42, Wariar ‘389 discloses a healthcare monitoring system (fig. 1), comprising:
a biofluid reservoir couplable to a biofluid source in or on a patient for receiving a flow of biofluid from the patient (“bag” in paragraph 44);
at least one biofluid sensing arrangement configured to determine a volume of the biofluid in the biofluid reservoir produced over a period of time (strips 102, pads 104, detector 110, emitter 108, scale 120, remote computer 122 in fig. 1, and clearance data calculation module 206 form the "biofluid sensing arrangement"; paragraph 44 discloses determining volume of the urine in the container by determining the weight, this volume would be produced over a period of time since the patient last urinated) and provide at least one output biofluid measurement value indicative of the volume of the biofluid in the biofluid reservoir (paragraphs 44 and 45 discloses using the volume to provide a clearance rate);
a processor communicatively coupled to the at least one biofluid sensing arrangement (computer 122 in fig. 1), the processor configured to:
receive the at least one output biofluid measurement value (paragraph 37).
determine a trained model from historical data to associate a rule with a biofluid output (boundary generation module 214 in fig. 2; paragraph 46),
infer a change in the patient's condition based, at least in part, on the trained model applied to the at least one output biofluid measurement value, by comparison of the at least one output biofluid measurement value with the associated rule (CHF decompensation detection module 216 in fig. 2; paragraph 50), and
initiate a response action designated by the rule (paragraph 54 discloses issuing an alert).
However, Wariar ‘389 does not teach or disclose the processor is configured to determine the trained model from an application of machine learning techniques and a fluid bolus mechanism fluidly couplable to the patient to deliver fluid to the patient; the response action comprising an instruction to the fluid bolus mechanism to increase or decrease a measured amount of the fluid delivered to the patient.
Verbitskiy teaches a system which is configured to apply machine learning techniques in order to set and adapt threshold values for a physiological parameter (paragraph 38). Since Wariar ‘389 already discloses the use of a machine learning module (learning module 900 in fig. 7), it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to have modified the system of Wariar ‘389 so that the trained module (i.e. boundary generation module 214) is determined from the application of machine learning techniques, as taught by Verbitskiy. This modification would enable the trained module to adapt the boundary data of the patient as the patient parameters change (paragraph 38).
Wariar ‘786 teaches a similar system configured to detect heart failure in a patient (fig. 8) and teaches that administration of drugs can improve heart contractility (paragraph 76). Wariar ‘786 discloses a fluid bolus mechanism fluidly couplable to the patient to deliver fluid to the patient (“IV drug delivery subsystem” in paragraph 86) and using a processor to determine if a physiological parameter satisfies a rule (paragraph 83 discloses comparing measured parameters to parameter targets) and, upon determining that the measured parameter is outside of the target, submitting a request to a fluid bolus mechanism to increase or decrease a measured amount of fluid delivered to the patient specified by the rule (paragraphs 84 and 88 discloses controlling a “therapy circuit” to change drug infusion rate or drug infusion dose based on the comparison between measured parameters and the targets).
Since Wariar ‘389 is directed towards detection of heart failure similar to Wariar ‘786 (paragraph 2) and Wariar ‘389 discloses using measured parameters values in comparison to parameter targets to determine the likelihood of heart failure (paragraph 46), it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to have modified the system of Wariar ‘389 to include the fluid bolus mechanism of Wariar ‘786 and to modify the response action of Wariar ‘389 to further include a request to a fluid bolus mechanism to increase or decrease a measured amount of fluid delivered to the patient specified by the rule, as taught by Wariar ‘786, since Wariar ‘786 teaches that this response can improve contractility of the heart and can manage symptoms of heart failure (paragraph 76).
Regarding claim 43, in the modified system of Wariar ‘389, Wariar ‘389 discloses the biofluid output is urine output (paragraph 44 discloses urine), the processor is further configured to receive an input relating to a respiratory rate of the patient (fig. 1 and 2 shows the computer 124 receives data from pacemaker 126; paragraph 63 discloses the pacemaker to measure thoracic impedance which can be used to approximate breathing rate), and inferring the change in the patient's condition is further based on the input received by the processor (fig. 2 shows the prediction of decompensation of CHF is also based on baseline transthoracic impedance measurements 210).
Regarding claim 44, in the modified system of Wariar ‘389, Wariar ‘389 discloses the trained model is trained on historical data related to heart failure (paragraph 54 discloses detecting heart failure so that the data would be related to heart failure).
Regarding claim 45, in the modified system of Wariar ‘389, Wariar ‘786 discloses the response action comprises an instruction to the fluid bolus mechanism to adjust a rate of fluid delivered to the patient (paragraph 88).
Claim(s) 32 is rejected under 35 U.S.C. 103 as being unpatentable over Wariar ‘389 in view of Verbitskiy and in view of Wariar ‘786, as applied to claim 1 above, and in further view of Wong (US 2010/0081951).
Regarding claim 32, modified Wariar ‘389 teaches all of the claimed limitations set forth in claim 1, as discussed above. Wariar ‘389 discloses the response action (see [0070], alert to the caregiver) is a message to a remote device (see [0070], remote peripheral device 709). However, modified Wariar ‘389 fails to teach wherein the message comprises content relating to the frequency of monitoring of the patient.
Wong teaches a similar device in the same field of endeavor wherein the message (see Fig. 1, electronic display 7 with message, see [0073]) comprises content relating to the frequency of monitoring of the patient (see [0073], message displayed indicates that an increased frequency of monitoring is required).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the response action as taught by modified Wariar ‘389 to indicate the need for an increased frequency as of monitoring as taught by Wong as this can detect and increased likelihood of discovering the issue at an earlier stage (see [0073]).
Claim(s) 41 is rejected under 35 U.S.C. 103 as being unpatentable over Wariar ‘389 in view of Verbitskiy and in view of Wariar ‘786, as applied to claim 1 above, and in further view of Joshua (US 2016/0058286).
Regarding claim 41, modified Wariar ‘389 teaches all of the claimed limitations set forth in claim 1, as discussed above. Wariar ‘389 further discloses the response action (see Wariar ‘389, [0070], alert to the caregiver) is a message to a remote device (see Wariar ‘389, [0070], remote peripheral device 709). However, modified Wariar ‘389 fail to disclose to indicate a need to decrease a currency frequency of monitoring of the patient.
Joshua teaches a similar device in the same field of endeavor to indicate a need to decrease a currency frequency of monitoring of the patient (see [0115]).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the message as taught by modified Wariar ‘389 to indicate the need to decrease a frequency of monitoring the patient as taught by Joshua as frequency at which data is acquired may be decreased based on the condition of the patient (see [0115]).
Claim(s) 46 is rejected under 35 U.S.C. 103 as being unpatentable over Wariar ‘389 in view of Verbitskiy and in view of Wariar ‘786, as applied to claim 42 above, and in further view of Lichte (US 5586085).
Regarding claim 46, modified Wariar ‘389 teaches all of the claimed limitations set forth in claim 42, as discussed above, but does not teach or disclose the at least one biofluid sensing arrangement comprises at least one ultrasonic sensor configured to determine an amount of the biofluid in the biofluid reservoir by reflecting ultrasonic waves off a surface of the biofluid in the biofluid reservoir.
Lichte teaches a sensing arrangement configured to detect an amount of fluid in a reservoir (fig. 1) comprising at least one ultrasonic sensor configured to determine an amount of the fluid in the fluid reservoir by reflecting ultrasonic waves off a surface of the fluid in the fluid reservoir (transducer 110 in fig. 1; 8:6-15).
Accordingly, the prior art references teach that it is known that a scale and an ultrasonic sensor attached to the reservoir are elements that are functional equivalents for determining a volume of fluid in a reservoir. Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to have substituted the scale of Wariar ‘389 for at least one ultrasonic sensor configured to determine an amount of the fluid in the fluid reservoir by reflecting ultrasonic waves off a surface of the fluid in the fluid reservoir. The substitution would have resulted in an equivalent means for determining the amount of biofluid in the reservoir.
Claim(s) 47 is rejected under 35 U.S.C. 103 as being unpatentable over Wariar ‘389 in view of Verbitskiy and in view of Wariar ‘786, as applied to claim 42 above, and in further view of Siposs (US 4435173).
Regarding claim 47, modified Wariar ‘389 teaches all of the claimed limitations set forth in claim 42, as discussed above. Wariar ‘786 further discloses that the fluid bolus mechanism is an IV drug delivery system (paragraph 114) configured to deliver the amount of fluid to the patient (paragraph 88). However, modified Wariar ‘389 does not explicitly teach or disclose the fluid bolus mechanism comprises at least one container containing the fluid, at least one syringe, and at least one cannula, the at least one syringe configured to provide the amount of fluid to the patient from the at least one container via the at least one cannula.
Siposs is directed towards an IV drug delivery system (fig. 1; 3:17-27 discloses delivering a drug into a vein) which comprises least one container containing a fluid (medication reservoir 24 in fig. 1), at least one syringe (syringe 14 in fig. 1), and at least one cannula (needle 22 in fig. 1), the at least one syringe configured to provide an amount of fluid to the patient from the at least one container via the at least one cannula (6:13-15). Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to have modified the generic intravenous drug delivery device of modified Wariar ‘389 to include at least one container containing the fluid, at least one syringe, and at least one cannula, the at least one syringe configured to provide the amount of fluid to the patient from the at least one container via the at least one cannula, as taught by Siposs, to enable repeated filling and delivery of the fluid.
Conclusion
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/COURTNEY FREDRICKSON/Primary Examiner, Art Unit 3783
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