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 on 02/20/2026 has been entered. Claims 1 - 15 are pending. Applicant has addressed all objections raised in the non-final mailed on 01/13/2026. As such, the objection to claim 11 is withdrawn.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3, 4, 5, & 12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In regard to claim 3, lines 1 - 3 recite, “the mathematical model further comprises or further is a function, polynomial function, or a numerical model”. However, it is unclear how a mathematical model “is, a regression model, a lumped parameter model, a decision tree, a random forest, or a neural network” as set forth in claim 1 from which claim 3 depends, and “further is a function, polynomial function, or a numerical model”. Examiner recommends amending claim 3 to exclude the phrase “or further is” because as written, claim 3 conflicts with the limitations of claim 1 from which claim 3 depends. Claim 4 is rejected by virtue of dependence on claim 3.
In regard to claim 5, claim 5 recites, “a quality measurement unit… configured to measure a quality of the sensed blood flow velocity” and “the sensed blood flow velocity is configured to be evaluated by means of at least one of a regression coefficient, a correlation coefficient, or a fitting coefficient”, but does not specify what structure is being used to “evaluate” the blood flow velocity or if measuring “a quality of the sensed blood flow velocity” is the same step as or a separate step from evaluating “the sensed blood flow velocity”. Additionally, as written, the claim language includes “the sensed blood flow velocity is configured to be evaluated…” which does not positively claim the implied step of evaluating the blood flow velocity. As such, examiner is interpreting that the claim requires a quality measurement unit that is configured to measure a quality or parameter related to the sensed blood flow velocity.
In regard to claim 12, lines 1 - 2 recite, “the mathematical model is a numerical model,” which directly conflicts with the limitations of claim 1 which specify that the mathematical model “comprises, or is, a regression model, a lumped parameter model, a decision tree, a random forest, or a neural network,” making the metes and bounds of claim 12 unclear.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 3 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
In regard to claim 3, lines 1 - 3 recite, “the mathematical model further comprises or further is a function, a polynomial function, or a numerical model”. However, claim 1 specifies that the mathematical model comprises or is a “regression model” or “a lumped parameter model”, both of which can encompass a “function”, “polynomial function”, or “numerical model” such that claim 3 is not further limiting. Examiner notes that while claim 3 includes the limitation “the mathematical model is configured to output a velocity vector of the flow velocity,” the step of determining a velocity vector of the flow velocity and outputting the velocity vector are not positively claimed due to the use of the phrase “configured to” such that the mathematical model merely needs to be capable of outputting a result such as a velocity vector and claim 3 is not further limiting.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 102
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 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.
Claims 1, 2, 5, 7, 9, & 15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Qi (US 20190357875 A1 - Cited by Applicant).
In regard to claims 1 & 15, Qi discloses an arrangement and method for measuring a flow velocity in a blood vessel comprising:
A catheter configured to be inserted into a blood vessel; Qi discloses an endovascular device (FIG. 6, component 150) within a catheter (FIG. 6, component 90) that is configured to be inserted into a blood vessel (paragraph [0135] - [0136]).
A plurality of flow velocity sensors coupled to the catheter; Qi further discloses a plurality of sensors, specifically two or more ultrasound sensors (paragraph [0117]), which are used to measure blood flow velocity based on the correlation of blood flow and time (paragraph [0230]).
A sensor network coupled to the plurality of flow velocity sensors and a processor coupled to the sensor network; Qi discloses that the plurality of flow velocity sensors are connected to a pre-processor unit that receives the acoustic signals from the two or more ultrasound sensors (paragraph [0117]) and further communicates with a processing unit to transmit information (paragraph [0043]). One of obvious skill in the art would recognize that the arrangement disclosed by Qi includes a sensor network coupled to the plurality of sensors which allows for data relating to flow velocity to be transmitted to both the pre-processor unit and processor unit, as described on page 5 of the Applicant’s specification.
Wherein each of the plurality of flow velocity sensors is configured to sense a velocity of a blood flow. Qi discloses that the ultrasound sensors measure signals that are used to calculate blood flow velocity (paragraph [0276]).
Wherein an output of the sensor network is configured to be input into a mathematical model stored in the processor, wherein the mathematical model comprises a neural network. Qi discloses that the output of the sensor network, or more specifically the measurements of the ultrasound sensors (paragraph [0117]), are input to a pre-processor and processor that store a collection of mathematical models that include logic models, probabilistic reasoning models, neural networks, interference engines, classifiers, or combinations of the same (paragraph [0110]).
Wherein the processor is configured to execute the mathematical model (paragraph [0110]).
Wherein the mathematical model is configured to calculate the flow velocity in the blood vessel where the catheter is located based on the output of the sensor network. Qi discloses that the mathematical model stored in the pre-processor and processor process the ultrasound signals or other optional signal data to calculate information such as the highest average blood flow velocity of a velocity profile (paragraph [0263]).
And wherein the method comprises:
sensing a velocity of a blood flow of the blood vessel by each of the plurality of flow velocity sensors. Qi discloses that the ultrasound sensors measure signals that are used to calculate blood flow velocity (paragraph [0276]).
transmitting the sensed velocity by each of the flow velocity sensors to the sensor network; Qi discloses that the plurality of flow velocity sensors are connected to a pre-processor unit that receives the acoustic signals from the two or more ultrasound sensors (paragraph [0117]) and further communicates with a processing unit to transmit information (paragraph [0043]).
inputting an output of the sensor network into a mathematical model stored in the processor, wherein the mathematical model comprises a neural network. Qi discloses that the output of the sensor network, or more specifically the measurements of the ultrasound sensors (paragraph [0117]) are input to a pre-processor and processor that store a collection of mathematical models (paragraph [0110]), where the mathematical model includes a neural network (paragraph [0110]).
and calculating, by the mathematical model, the flow velocity in a blood vessel where the catheter is located based on the output of the sensor network, wherein the processor executes the mathematical model. Qi discloses that the mathematical model stored in the pre-processor and processor is used to process the ultrasound signals or other optional signal data to calculate information such as the highest average blood flow velocity of a velocity profile (paragraph [0263]) using a mathematical model, such as a neural network (paragraph [0110]).
In regard to claim 2, Qi discloses the invention as set forth for claim 1, wherein the sensor network further comprises a pressure sensor, wherein the pressure sensor is configured to sense a pressure within the blood vessel. Qi discloses that their catheter assembly can include multiple other types of sensors (paragraph [0115]), including a pressure sensor (FIG. 2, see “pressure sensor”).
In regard to claim 5, Qi discloses the invention as set forth for claim 1, further comprising a quality measurement unit coupled to the processor, wherein the quality measurement unit is configured to measure a quality of the sensed blood flow velocity and wherein the blood flow velocity is configured to be evaluated by means of at least one of a regression coefficient, a correlation coefficient, or a fitting coefficient. In lines with the 112(b) rejection issued on claim 5 above, Examiner notes that claim 5 recites “a quality of the sensed blood flow parameter”, which under broadest reasonable interpretation would include any descriptive parameter about the sensed blood flow velocity and additionally that the phrase “the blood flow velocity is configured to be evaluated” merely includes the limitation that the blood flow velocity can be evaluated by means of a regression, correlation, or fitting coefficient, not that the blood flow velocity is being evaluated by those means. Qi discloses a quality measurement unit or pre-processor (paragraph [0033]) that extracts information related to one or more desired parameters including qualities or information about blood flow velocity such as the highest, lowest, mean, or average velocity (paragraph [0041]) and that the pre-processor is connected to the processor (paragraph [0033]). Qi discloses that information such as velocity profile is based on the correlation of blood flow and time (paragraph [0230]) and additionally that processing methods and algorithms may identify important or unique signatures in data to be used in correlating ultrasound data and/or other optional signal information to determine the highest average velocity of a velocity profile (paragraph [0263]) including determining if the velocity reading is useful (paragraph [0282]). Qi further discloses that parameters associated with different features and algorithms include constants, coefficients, and weighing factors that can be adjusted to improve the algorithms related to velocity of blood flow such as those used to determine a location of a catheter tip using velocity of blood flow as an input parameter (paragraph [0124]).
In regard to claim 7, Qi discloses the invention as set forth for claim 1, further comprising a display screen, wherein an output of the mathematical model is signaled to a user by the display screen. Qi discloses that information on antegrade and retrograde blood flow are displayed on a display screen or user interface (FIG. 7, see “Antegrade Flow” and “Retrograde Flow”; paragraphs [0122] - [0123]).
In regard to claim 9, Qi discloses the claimed invention as set forth for claim 1. Qi discloses that their system is used to monitor blood flow patterns (paragraph [0145]) and is further concerned with identifying turbulence within the blood vessel by identifying and filtering frequency components indicative of high degrees of turbulence (paragraph [0223]).
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.
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.
Claims 3, 4, 10, & 11 are rejected under 35 U.S.C. 103 as being unpatentable over Qi (US 20190357875 A1 - Cited by Applicant) as applied to claim 1 above, and further in view of Chevalier (US 20120316419 A1 - Cited by Applicant).
In regard to claim 3, Qi discloses the invention as set forth for claim 1, wherein the processing of data includes mathematical analysis of the data collected from the sensor network including the use of mathematical functions in combination with other mathematical models including neural networks (paragraph [0159]). Qi additionally discloses that both blood flow velocity and direction are determined and output from the data collected from the sensor network (paragraph [0276]) and that the endovascular device (FIG. 2, component 150) can comprise any number of different sensors including a conductive wire, but does not specify that the mathematical model outputs a velocity vector.
However, Chevalier teaches a catheter assembly for with a plurality of conductive wire or anemometric sensors measuring blood flow velocity where the blood flow characteristics, including blood flow velocity vectors (paragraph [0086]) are calculated using a predetermined relationship or function between the resistance of the anemometric sensors and the flow velocity of the blood (paragraph [0090]).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date to have modified the catheter assembly disclosed by Qi with the teaching of Chevalier that a plurality of probes positioned on a catheter assembly can be used to mathematically determine blood velocity movements using a function (Chevalier, paragraphs [0086] & [0090] because Qi discloses that that the endovascular device (FIG. 2, component 150) can comprise any number of different sensors including a conductive wire for measuring physiological information for processing using any suitable form of analysis (paragraph [0135]). Thus the modification of the plurality of blood flow velocity sensors and processing method disclosed by Qi with the teaching that a plurality of sensors can positioned on a catheter assembly that can be used to mathematically determine both blood flow velocity using a predetermined relationship or function between the sensor and blood flow velocity would be considered combining prior art elements, in this case the sensor assemblies and processing methods, to yield the predictable results of determining functional flow measurements inside a blood vessel.
In regard to claim 4, Qi as modified discloses the invention as set forth for claim 3, wherein the velocity vector is independent of the catheter orientation. Chevalier teaches that sensors can be arranged on the catheter in such a way that the sensors align with the axes of an orthogonal system of co-ordinates to allow for the simultaneous determination of three blood flow velocity components (Vx, Vy, and Vz) in a Cartesian reference (paragraph [0092]) in order to address major drawbacks of known ultrasound blood flow velocity measurement systems (paragraph [0029]) such as eliminating the dependence of the blood flow velocity measurement on the angle of the probe as described in page 2 of Applicant’s specification.
In regard to claim 10, Qi discloses the claimed invention as set forth for claim 1. While Qi discloses that the endovascular device (FIG. 2, component 150) can comprise any number of different sensors including a conductive wire (paragraph [0135]), they do not specify that the plurality of flow velocity sensors are hot-wire anemometer sensors.
However, Chevalier teaches a catheter assembly for with a plurality of sensors measuring blood flow velocity where the sensors are anemometric sensors (paragraph [0026]), including hot thin-film probes, hot-wire probes, or anemometric probes of other configurations (paragraph [0024]).
It would have been obvious to one of ordinary skill in the art to have modified the blood flow velocity sensors of the catheter assembly disclosed by Qi to include the hot-wire anemometer sensors taught by Chevalier because it would be considered simple substitution of one known element, in this case the blood flow velocity sensors disclosed by Qi, for another, the hot-wire anemometer sensors taught by Chevalier, to obtain the predictable results of determining functional flow measurements inside a blood vessel.
In regard to claim 11, Qi as modified discloses the claimed invention as set forth for claim 10, wherein each of the plurality of flow velocity sensors are configured to thermally influence at least one other flow velocity sensor in the plurality of flow velocity sensors. Chevalier discloses that their sensor arrangement deduces flow velocity by heating a short pulse of current of one anemometer sensor to reach a second anemometer sensor operating as a resistance thermometer (paragraph [0083]).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Qi (US 20190357875 A1 - Cited by Applicant) as applied to claim 1 above, and further in view of Torii (Torii R., et al. A computational study on the influence of catheter-delivered intravascular probes on blood flow in a coronary artery model. J Biomech. 2007;40(11):2501-2509. doi:10.1016/j.jbiomech.2006.11.004 - Previously Cited)
In regard to claim 6, Qi discloses the invention as set forth for claim 1. While Qi discloses the use of various mathematical models (paragraph [0110]) including mathematical analysis of sensor inputs that include the use of a combination of mathematical functions and neural networks (paragraph [0159]) and additionally that the endovascular device (FIG. 2, component 150) can comprise any number of different sensors including ultrasound sensors and pressure sensors (paragraph [0135]), they do not disclose that the mathematical model comprises information about a geometry of the catheter and/or an impact of the catheter on the flow velocity, wherein the information is configured to allow for the mathematical model to compensate for the geometry of the catheter and/or the impact of the catheter on the flow velocity.
However, Torii teaches that the impact of a catheter assembly with intravascular sensors on blood flow velocity can be modeled mathematically based on ultrasound data (Section 3.2: “The influence of catheter on velocity”, FIG. 7).
It would have been obvious to have modified the catheter disclosed by Qi with the teaching of Torii that the impact of a catheter assembly with intravascular sensors on blood flow velocity can be modeled mathematically based on ultrasound data because not accounting for the impact of the presence of a catheter in a blood vessel leads to an underestimation in velocity measurement attributed to catheter blockage (Torri, Section: “Abstract”).
Claims 8 & 12 are rejected under 35 U.S.C. 103 as being unpatentable over Qi (US 20190357875 A1 - Cited by Applicant) as applied to claim 1 above, and further in view of Liu (Liu B., et al., Influence of model boundary conditions on blood flow patterns in a patient specific stenotic right coronary artery. Biomed Eng Online. 2015;14 Suppl 1(Suppl 1):S6. doi:10.1186/1475-925X-14-S1-S6 - Previously Cited).
In regard to claim 8, Qi discloses the invention as set forth for claim 1. While Qi discloses that the processing of ultrasound data can be done through a variety of modeling techniques, including logic models, probabilistic reasoning models, neural networks, interference engines, classifiers, or combinations of the same (paragraph [0110]), process the ultrasound signals to determine information about blood flow velocity profiles (paragraph [0263]), they do not specify that the processor is configured to tailor the mathematical model to be specific for different blood vessel geometries and flow conditions.
However, Liu teaches that ultrasound measurements taken using intravascular ultrasound (IVUS) can be processed and utilized to generate an image-based model of an artery (Section: “Patient-specific plaque geometry and flow data,” paragraph 1) where the 3D geometry of the artery is utilized to simulate and calculate a blood flow velocity profile at different sections of the artery (FIG. 4). Additionally, Liu teaches that their model utilizes multiple boundary conditions to examine flow distribution or flow conditions in the model (Section: “The flow model and numerical method,” paragraphs 1 - 3).
It would have been obvious to one of ordinary skill in the art to have modified the arrangement disclosed by Qi with the teaching of Liu that ultrasound data can be utilized to generate a model of an artery where the 3D geometry of the vessel and different flow conditions are utilized to determine blood flow velocity (Liu, FIGs. 4 - 6) because Qi already discusses the use of multiple models to determine information about blood flow velocity profiles (Qi, paragraph [0263]) and modifying the arrangement disclosed by Qi with the models taught by Liu would be considered combining prior art elements according to known methods to yield the predictable result of using mathematical models with intravascular ultrasound data inputs to determine information about blood flow velocity profiles.
In regard to claim 12, Qi discloses the claimed invention as set forth for claim 1. While Qi discloses the use of various mathematical models (paragraph [0110]) including mathematical analysis of sensor inputs that include the use of a combination of mathematical functions and neural networks (paragraph [0159]), they do not specify that the mathematical model is a mathematical model that comprises a Navier-Stokes equation, wherein an output of the Navier-Stokes equation is compared with the sensed blood flow velocity, and wherein an index of merit is configured to be calculated based on the comparison.
However, Liu teaches that Navier-Stokes equations can be used as the governing equations of a numerical model to calculate velocity profiles of blood flow (Section: “The flow model and numerical method”, see paragraph 2, “V-NSO model,” and “V-P model”). Liu additionally compares the simulated velocity magnitude from the numerical solutions to flow velocity data from a patient (Section: “Validation of numerical solutions,” paragraph 1; FIG. 4a). Examiner notes that the calculation of an index of merit based on the comparison is not positively claimed and merely intended use and that the assembly disclosed by Qi as modified by Liu meets the claim limitations as best understood in light of the U.S.C. 35 112(b) rejection of claim 12.
It would have been obvious to one of ordinary skill in the art to have modified the catheter assembly disclosed by Qi with the teaching of Liu that a mathematical model comprises the Navier-Stokes equation because Liu already discloses that multiple types of mathematical models can be utilized to determine blood flow velocity and modifying the mathematical model with the numerical model based on the Navier-Stokes equation taught by Liu would be considered simple substitution of one known element for another, in this case the numerical models used, to obtain the predictable results of determining functional flow measurements inside a blood vessel.
Claim 13 is are rejected under 35 U.S.C. 103 as being unpatentable over Qi (US 20190357875 A1 - Cited by Applicant) as applied to claim 1 above, and further in view of Fan (US 20200233006 A1 - Previously Cited)
In regard to claim 13, Qi discloses the claimed invention as set forth for claim 1. While Qi discloses the use of various mathematical models (paragraph [0110]) including mathematical analysis of sensor inputs that include the use of a combination of mathematical functions and neural networks (paragraph [0159]) and additionally that the endovascular device (FIG. 2, component 150) can comprise any number of different sensors including a conductive wire (paragraph [0135]), they do not specify that the mathematical model is a lumped parameter model, wherein the lumped parameter model comprises or consists of discrete entities configured to approximate the behavior of the output of the plurality of flow velocity sensors, and wherein the lumped parameter model is defined by:
d
Q
d
t
=
-
h
*
A
(
T
t
-
T
e
n
v
=
-
h
*
A
∆
T
(
t
)
wherein Q is thermal energy in Joules, h is a heat transfer coefficient between the catheter and the blood flow, A is a surface area of the heat transfer, T is a temperature of a surface of the catheter, Tenv is a temperature of the environment and A
∆
t(t) is a time-dependent thermal gradient between the environment and the catheter.
However, Fan teaches that fluid flow velocity can be determined using an anemometer by approximating using the equation
h
*
A
∆
T
(
t
)
where h is the convective heat transfer coefficient, A is the surface area of the wire that is undergoing heat transfer, and ΔT=T.sub.w−T.sub.f is the temperature difference between the wire and fluid (Equation (6), paragraphs [0055] - [0056]).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date to have modified the catheter assembly disclosed by Qi with the teaching of Fan that flow velocity can be determined using an anemometer and approximated using a mathematical model based on the heat conductance equation because Qi discloses that the catheter device can comprise any number of different sensors including a conductive wire for processing in an algorithm or other suitable form of analysis (paragraph [0135]) and further that mathematical analysis of sensor inputs that include the use of mathematical functions and neural networks (paragraph [0159]) such that using the sensor assembly and method of calculating fluid flow velocity taught by Fan would be considered combining prior art elements according to known methods to yield predictable results of assessing blood flow velocity.
Claim 14 is are rejected under 35 U.S.C. 103 as being unpatentable over Qi (US 20190357875 A1 - Cited by Applicant) as applied to claim 1 above, and further in view of Torp (US 20210251599 A1 - Previously Cited).
In regard to claim 14, Qi discloses the claimed invention as set forth for claim 1. While Qi discloses that extracted information by the pre-processing and processing methods represents information on the real-time sensor environment (paragraph [0325]) and further that the process information includes information on blood flow direction and velocity (paragraph [0041]), they do not disclose that their assembly comprises an alarm, wherein the alarm indicates to a user if the blood flow velocity falls outside of a predetermined range.
However, Torp teaches a system for monitoring blood-flow in real time that includes monitoring velocity values of blood flow (paragraph [0075]) from ultrasound measurements where the system issues an audible or visual alert if the spatial-maximum velocity value satisfies a predetermined alert criterion (paragraph [0080]), such as falling below a preset threshold (paragraph [0548]).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the catheter assembly disclosed by Qi with the teaching of Torp that a blood flow monitoring system can include an alert to indicate that measured blood flow velocity falls outside of a threshold because it would be considered combining prior art elements according to known methods to yield the predictable result of monitoring blood flow dynamics.
Response to Arguments
Applicant’s arguments, see Remarks, filed 02/20/2026, with respect to the rejection of claims 1 - 15 under 35 U.S.C. 112 have been fully considered and are persuasive. The rejections of claims 1 - 15 under 35 U.S.C. 112 have been withdrawn. However, based on the amendment filed on 02/20/2026, a new grounds of rejection of claims 3, 4, 5, & 12 under 35 U.S.C. 112 have been issued above.
Applicant’s arguments, see Applicant’s arguments, see Remarks, filed 02/20/2026, with respect to the rejection of claims 1 - 15 under 35 U.S.C. 102 and 103 have been fully considered and are partially persuasive. Applicant argues that the amendment includes further claim limitations in independent claims 1 and 15 that specify that the mathematical model comprises a regression model, lumped parameter model, decision tree, random forest, or neural network. While it is correct that the specified mathematical models of amended claim 1 are not suggested by Van Der Horst as modified by Chevalier (Examiner notes that some, but not all, of the forms of mathematical model recited in original claim 3 have been incorporated into claim 1 by the amendment and that Applicant does not argue relative to the previously presented scope of claim 3), Qi does explicitly disclose that the preprocessing and processing of sensor data performed by their system includes the use of logic models, probabilistic reasoning models, neural networks, interference engines, classifiers, or combinations of the same (paragraph [0110]). Examiner notes that these details were discussed in the prior 102 rejection based on Qi, but were not explicitly asserted against claim 3 since the combination of Van Der Horst and Chevalier was deemed to meet the claim scope. As such, given the amended claim scope, the rejections of claims 1 and 15 under 35 U.S.C. 102 in view of Van Der Horst and the rejections of claims 3, 4, 6, 9, 10, 11, 13, and 14 under 35 U.S.C. 103 in view of Van Der Horst as modified have been withdrawn. The rejection of claims 1, 2, 5, 7, & 15 under 35 U.S.C. 102 in view of Qi and claims 8 & 12 under 35 U.S.C. 103 in view of Qi as modified by Liu have been maintained and updated to account for the amended scope. Upon further consideration in light of the amendments, a new grounds of rejection under 35 U.S.C. 102 of claim 9 has been made in view of Qi and a new grounds of rejection under 35 U.S.C. 103 of claims 3, 4, 10, & 11, have been made in view of Qi as modified by Chevalier, claim 6 in view of Qi as modified by Torii, claim 13 in view of Qi as modified by Fan, and claim 14 in view of Qi as modified by Torp above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/ERIC F WINAKUR/Primary Examiner, Art Unit 3791
/S.C.P./Examiner, Art Unit 3791
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