DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-20 are currently pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08-13-2025 has been considered by the examiner.
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 1-3, 6, 8-11, 14 and 16-20 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over TOTH (U.S. Publication 2014/0081154) in view of TAKAGI (U.S. Publication 2018/0160948) and further in view of ROVATTI (U.S. Publication 2024/0350040).
Regarding claims 1, 9, 17 and 20,
TOTH teaches a method for characterizing fluidic content flowing through a conduit, the method comprising:
accessing sensor data from a sensor arrangement (910, 950 in figs 9a and 9b, respectively) included in a housing (915 in fig 9a and 955 in fig. 9b) configured to position the sensor arrangement proximate to and covering at least a portion of the conduit([0148] in combination with FIG. 7 shows lumen 705, which accommodates blood flow there through (as indicated by arrow 710)) through which the fluidic content including a patient fluid is flowing (as shown in fig. 7), the sensor arrangement including a first sensor having a first measuring modality and a second sensor having a second measuring modality (see at least sensors 920a, 920b in fig. 9A and 1320a, 1320b per fig. 13 and [0171]);
classifying flow of the fluidic content as corresponding to at least one of a first flow type and a second flow type based on an output from the machine learning algorithm ([0152] teaches FIG. 8 demonstrates temporal waveforms of the received signal on a photodiode as polled by an external reader over time. In the case of normal or acceptable blood flow through the vascular graft, as indicated by waveform 810, the temporal waveform generally undulates with the blood flow velocity around a first DC offset. In the case of partially or greatly obstructed blood flow though the venous graft, as indicated by waveform 820, the temporal waveform will change, thus fig. 8 teaches at least 2 classifications via waveform);
quantifying flow of the fluidic content based on at least one of (i) an output from the first sensor if the flow was classified as the first flow type and (ii) an output from the second sensor if the flow was classified as the second flow type ([0194] teaches that one or more of the sensory modules may monitor fluid flow);
estimating a concentration of a fluid component of the patient fluid in the fluidic content flowing through the conduit ([0090] teaches that one or more sensory modules 130 may also be configured to monitor the concentration of a chemical species such as for example, glucose levels, pH, sugar, blood oxygen, glucose, moisture, radiation levels, chemical activity, ionic species, enzymatic species, oxygen, carbon dioxide, and the like); and
characterizing passage of the patient fluid through the conduit based on the quantified flow of the fluidic content ([0154] teaches that the monitored flow rate information may be used to define normal waveforms and abnormal waveforms, interpreted as corresponding to “characterizing passage of patient fluid…” as recited).
Toth fails to expressly teach that the quantified flow and the estimated concentration of fluid component are used to characterize the passage of the patient fluid through the conduit.
TAKAGI teaches in [0031]-[0033] detecting, transmitting and receiving information for specifying a change corresponding to an increase or a decrease in the concentration or the amount of hemoglobin (i.e., estimation a concentration of a fluid component) and determining whether or not a rate of the change in a blood flow exceeds a predetermined reference value (i.e., quantifying a flow of the fluidic content).
Before the effective filing date of the invention, it would have been obvious to modify the system of Toth per the teachings of Takagi, considering both the quantified flow and the estimated concentration of fluid component when to characterizing the passage of the patient fluid through the conduit, since Takagi teaches that this biological information may be utilized to determining when blood flow exceeds a predetermined threshold reference value, thereby enabling a medical practitioner to better perform smooth medical practices and inform the ideal posture of a patient.
Toth and Takagi fail to expressly teach providing at least a portion of the sensor data as an input to a machine learning algorithm trained to classify flow of fluidic content flowing through a conduit based on training data.
ROVATTI teaches in [0247] determining a number of blood parameters of interest; [0250] teaches using neural networks, and furthermore that a neural network may be trained so that it receives as input the signals from the various detectors plus other input variables linked to blood volume variation and plasma sodium concentration. The neural network provides the value of the desired blood parameters as an output interpreted as corresponding to "classify flow" as broadly recited because determining parameters is a form of flow characterization.
Before the effective filing date of the invention it would have been obvious to further modify Toth and employ machine learning for the purpose of improving accuracy in determining physiological blood parameters associated with flowing blood, fluid-flow classification being generally derived from such parameters.
Regarding claims 2, 10 and 18,
Takagi teaches that the patient fluid is blood, the fluid component is hemoglobin, and the step of characterizing passage of the patient fluid includes quantifying a volume of blood flowing through the conduit([0031] teaches detecting an increase or a decrease in the concentration of hemoglobin in blood; [0031] teaches detecting the amount of hemoglobin in blood.)
Regarding claims 3 and 11,
Toth teaches that the step of quantifying flow of the fluidic content includes estimating a volumetric flow rate of the fluidic content([0194] teaches that one or more of the sensory modules may monitor fluid flow).
Regarding claims 6 and 14,
Toth teaches that the first and second flow types each include one of laminar flow, turbulent flow, varying velocity or flow rate, and intermittent flow ([0152] teaches detecting normal (laminar), detecting greatly obstructed blood flow (intermittent); [0203] teaches detecting blood turbulence).
Regarding claims 8, 16 and 19,
Rovatti teaches that the step of estimating the concentration of the fluid component of the patient fluid in the fluidic content flowing through the conduit is based on the output from the machine learning algorithm or an output from another machine learning algorithm ([0250] teaches a neural network may be trained so that it receives as input the signals from the various detectors plus other input variables linked to blood volume variation and plasma sodium concentration.)
Claims 4 and 12 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over TOTH (U.S. Publication 2014/0081154) in view of TAKAGI (U.S. Publication 2018/0160948) and ROVATTI (U.S. Publication 2024/0350040) and further in view of KOSTNER (U.S. Publication 2017/0184433).
Regarding claims 4 and 12,
Toth (modified) fails to further teach the features recited in claim 4.
KOSTNER teaches quantifying of the flow of the fluidic content includes estimating a mass flow rate of the fluidic content ([0039] teaches that a flow sensor can be used for measuring flow velocities, mass flow rates, and/or volumetric flow rates of fluids such as body fluids).
Before the effective filing date of the invention, it would have been obvious to further modify the system of Toth per the teachings of Kostner, so as to estimate a mass flow rate of the fluidic content for the purpose of determining the mass of the fluid in question.
Claims 5 and 13 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over TOTH (U.S. Publication 2014/0081154) in view of TAKAGI (U.S. Publication 2018/0160948) and ROVATTI (U.S. Publication 2024/0350040) and further in view of Bennett (U.S. Publication 2011/0009817).
Regarding claims 5 and 13,
Toth teaches in [0152], FIG. 8 waveforms 810, 820 obtained from an electro-optical sensory module related to blood flow and in [0203] teaches thermal mass flow sensors. Toth fails to expressly teach that the first and second measuring modalities each include one of optical, ultrasonic, and thermal.
BENNETT teaches that measuring modalities may be derived from incorporating multiple types of sensors ([0009]).
Before the effective filing date of the invention, it would have been obvious to further modify the system of Toth per the teachings of Bennett, such that the first and second measuring modalities each include one of optical, ultrasonic, and thermal, because different sensor types measure different fluid properties. Furthermore, correlating those measurements improve fluid characterization.
Allowable Subject Matter
Claims 7 and 15 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DIONNE PENDLETON whose telephone number is (571)272-7497. The examiner can normally be reached M-F 9a-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Davetta Goins can be reached at 571-272-2957. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DIONNE PENDLETON/Primary Examiner, Art Unit 2689
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