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
This office action is responsive to the preliminary amendment filed on March 1, 2024. As directed by the amendment: claim(s) 3, 5, 7, 9, 11, 13-18 and 20 have been amended, no claim(s) have been cancelled, and claim(s) have been added. Thus, claims 1-20 are currently pending in the application.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention details a system and method (Step 1) directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
In accordance with MPEP 2106.04, each of Claims 1-20 has been analyzed to determine whether it is directed to any judicial exceptions.
Step 2A, Prong 1 per MPEP 2106.04(a)
Each of Claims 1-20 recites at least one step or instruction for determining ventilatory threshold for a subject, which is grouped as a mental process in MPEP 2106.04(a)(2)(III) or a certain method of organizing human activity in MPEP 2106.04(a)(2)(II) or mathematical concept in MPEP 2106.04(a)(2)(I). Accordingly, each of Claims 1-20 recites an abstract idea.
Specifically, Claim 1 recites
A monitoring system configured for determining risk of postpartum haemorrhage to a patient, the monitoring system comprising:
an electrical potential sensor for collecting patient data; (additional element)
at least one electrode for attaching the electrical potential sensor to a body of the patient; and (additional element)
a communications module for transmitting the patient data to a detection controller, (additional element)
wherein the detection controller (additional element) is configured to determine the risk of the postpartum haemorrhage based on the patient data. (observation, judgment or evaluation, which is grouped as a mental process in MPEP 2106.04(a)(2)(III))
Additionally, Claim 19 recites
A method of detecting a high risk of postpartum haemorrhage in a patient, the method comprising:
collecting patient data from a monitor having a plurality of medical electrode members attached to a body of a patient, the patient data being collected from at least one sensor type; (additional element)
estimating a likelihood of the postpartum haemorrhage by processing the patient data from the at least one sensor type to form a plurality of descriptors for data points in the patient data, the plurality of descriptors being processed by a machine learning model to estimate the likelihood; and (observation, judgment or evaluation, which is grouped as a mental process in MPEP 2106.04(a)(2)(III))
displaying the determined postpartum haemorrhage risk to an operator. (additional element)
Step 2A, Prong 2 per MPEP 2106.04(d)
The above-identified abstract idea in each of independent Claims 1 and 19 (and their respective dependent Claims 2-18 and 20) is not integrated into a practical application under MPEP 2106.04(d) because the additional elements (identified above in independent Claims 1 and 19), either alone or in combination, generally link the use of the above-identified abstract idea to a particular technological environment or field of use according to MPEP 2106.05(h) or represent insignificant extra-solution activity according to MPEP 2106.05(g). More specifically, the additional elements of: electrical potential sensor, electrode, detection controller, communications module and display are generic and used for data gathering adding insignificant extra-solution activity to the judicial exception in independent Claims 1 and 19 (and their respective dependent claims) which do not improve the functioning of a computer, or any other technology or technical field according to MPEP 2106.04(d)(1) and 2106.05(a). Nor do these above-identified additional elements serve to apply the above-identified abstract idea with, or by use of, a particular machine according to MPEP 2106.05(b), effect a transformation according to MPEP 2106.05(c), provide a particular treatment or prophylaxis according to MPEP 2106.04(d)(2) or apply or use the above-identified abstract idea in some other meaningful way beyond generally linking the use thereof to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception according to MPEP 2106.04(d)(2) and 2106.05(e). Furthermore, the above-identified additional elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer in accordance with MPEP 2106.05(f). For at least these reasons, the abstract idea identified above in independent Claims 1 and 19 (and their respective dependent claims) is not integrated into a practical application in accordance with MPEP 2106.04(d).
Moreover, the above-identified abstract idea is not integrated into a practical application in accordance with MPEP 2106.04(d) because the claimed method and system merely implements the above-identified abstract idea (e.g., mental process) using rules (e.g., computer instructions) executed by a computer (e.g., external programming device or computer as claimed). In other words, these claims are merely directed to an abstract idea with additional generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer according to MPEP 2106.05(f). Additionally, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims according to MPEP 2106.05(a). That is, like Affinity Labs of Tex. v. DirecTV, LLC, the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution. Thus, for these additional reasons, the abstract idea identified above in independent Claims 1 and 19 (and their respective dependent claims) is not integrated into a practical application under MPEP 2106.04(d)(I).
Accordingly, independent Claims 1 and 19 (and their respective dependent claims) are each directed to an abstract idea according to MPEP 2106.04(d).
Step 2B per MPEP 2106.05
None of Claims 1-20 include additional elements that are sufficient to amount to significantly more than the abstract idea in accordance with MPEP 2106.05 for at least the following reasons.
These claims require the additional elements of: electrical potential sensor, electrode, detection controller, communications module and display. The above-identified additional elements are generically claimed computer components which enable the above-identified abstract idea(s) to be conducted by performing the basic functions of automating mental tasks. The courts have recognized such computer functions as well understood, routine, and conventional functions when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See, MPEP 2106.05(d)(II) along with Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Per Applicant’s specification, [0007]-[0014] details that the electrical potential sensor could consist of an electromyography (EMG) sensor, an electrohepatogram (EHG) sensor and electrocardiogram (ECG) sensor which are generic and commercially available items. Per Applicant’s specification, [0055]-[0063] details the use of electrodes (electrode assembly) wherein the electrodes could be a flexible sheet made of an insulating material (i.e. cloth plastic, closed cell foam and etc.) that may include various shapes and attachments that c. Further, in applicant’s specification [0049]-[0050] and [0068] cites the detection controller as being generic and commercially available items such as a notebook computer, tablet, server or PDA. Per Applicant’s specification, [0050]-[0052] details a communications module that can utilize a variety of communication networks such as LAN, WAN, ethernet, mobile telephone networks wired and wireless which details the generality of what can be utilized. Per applicant’s specification [0047], the display can be a visual display such as a monitor or a printer or could also be a tablet, smartphone or other computing device which are generic and commercially available.
Accordingly, in light of Applicant’s specification, the claimed term computer is reasonably construed as a generic computing device. Like SAP America vs Investpic, LLC (Federal Circuit 2018), it is clear, from the claims themselves and the specification, that these limitations require no improved computer resources, just already available technology, with their already available basic functions, to use as tools in executing the claimed process. See MPEP 2106.05(f).
Furthermore, Applicant’s specification does not describe any special programming or algorithms required for computers. This lack of disclosure is acceptable under 35 U.S.C. §112(a) since this hardware performs non-specialized functions known by those of ordinary skill in the computer arts. By omitting any specialized programming or algorithms, Applicant's specification essentially admits that this hardware is conventional and performs well understood, routine and conventional activities in the computer industry or arts. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of the above-identified additional elements because it describes these additional elements in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a) (see MPEP 2106.05(d)(I)(2) and 2106.07(a)(III)). Adding hardware that performs “‘well understood, routine, conventional activit[ies]’ previously known to the industry” will not make claims patent-eligible (TLI Communications along with MPEP 2106.05(d)(I)).
The recitation of the above-identified additional limitations in Claims 1 and 19 amounts to mere instructions to implement the abstract idea on a computer. Simply using a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See MPEP 2106.05(f) along with Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer, does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
A claim that purports to improve computer capabilities or to improve an existing technology may provide significantly more. See MPEP 2106.05(a) along with McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); and Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). However, a technical explanation as to how to implement the invention should be present in the specification for any assertion that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes. That is, per MPEP 2106.05(a), the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Here, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. Instead, as in Affinity Labs of Tex. v. DirecTV, LLC 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016), the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution.
For at least the above reasons, the method and system of Claims 1-20 are directed to applying an abstract idea as identified above on a general purpose computer without (i) improving the performance of the computer itself or providing a technical solution to a problem in a technical field according to MPEP 2106.05(a), or (ii) providing meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that these claims amount to significantly more than the abstract idea itself according to MPEP 2106.04(d)(2) and 2106.05(e).
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements in independent Claims 1 and 19 (and their dependent claims) do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment according to MPEP 2106.05(h). When viewed as a combination, these above-identified additional elements simply instruct the practitioner to implement the claimed functions with well-understood, routine and conventional activity specified at a high level of generality in a particular technological environment according to MPEP 2106.05(h). When viewed as whole, the above-identified additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself according to MPEP 2106.04(d)(2) and 2106.05(e). Moreover, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity according to MPEP 2106.05(g). As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application as required by MPEP 2106.05.
Therefore, for at least the above reasons, none of the Claims 1-20 amounts to significantly more than the abstract idea itself. Accordingly, Claims 1-20 are not patent eligible and rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-2, 7-13 and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Marossero (US 2005/0267377 A1) in view of Kumar (“A novel solution for finding postpartum haemorrhage using fuzzy neural techniques”) (as listed on the IDS submitted on 4/22/2024)
Regarding claim 1, Marossero discloses a monitoring system configured for determining risk of postpartum haemorrhage to a patient (e.g. abstract [0017]), the monitoring system comprising: an electrical potential sensor for collecting patient data (e.g. [0026]; [0052]); at least one electrode for attaching the electrical potential sensor to a body of the patient (e.g. [0061]-[0062] Fig 1:1a-d); and a communications module for transmitting the patient data to a detection controller (e.g. [0075]; [0087] Fig 3:30),
Marossero is silent regarding wherein the detection controller is configured to determine the risk of the postpartum haemorrhage based on the patient data. Marossero does disclose the need for monitoring the uterine tone to provide an early warning of atony and potential hemorrhage during postpartum (e.g. [0017]; [0023]).
However, Kumar discloses a solution for finding postpartum haemorrhage using fuzzy neural techniques (e.g. 1. Introduction “An efficient fuzzy neural technique-based rule algorithm is developed for input parameters of temperature, pulse rate, blood pressure, and sweat rate of pregnant women to predict the risk in developing PPH” and 3.5 Self Perspective neural network and Algorithm-V the fuzzy neural technique is utilized to have the parameters as inputs for determining the PPH affected patients) utilizing sensor data (e.g. 3. Proposed System “The proposed method is to analyse the parameters (temperature, blood pressure, pulse rate and sweat rate) of women in labour ward. It has high accuracy to predict the PPH (Postpartum Haemorrage) patients and has a fast training processor for predicting whether the patients are affected by PPH or not”) wherein the detection controller is configured to determine the risk of the postpartum haemorrhage based on the patient data (e.g. abstract; 3. Proposed System “The parameter value in the obtained data indicates the biological condition of pregnant women. If parameter value exceeds from the normal level, then the pregnant women is affected by PPH” 4.1 Indicator definition of PPH performance “The membership function of each patient is used to evaluate the performance.” “Membership function value of 1 indicates patient state with NPPH, 0 indicates patient state with HPPH, and values in between 0 and 1 indicated MPPH” Table 1 The output later compares the level of parameters with the normal ranges. If it exceeds, then the patients are affected by PPH; Fig 12).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify the system of Marossero to incorporate the teachings of Kumar to have additional sensors (temperature, blood pressure, pulse rate and sweat rate) and wherein the detection controller is configured to determine the risk of the postpartum haemorrhage based on the patient data for the purpose of being able to provide an early warning of atony and potential hemorrhage during postpartum (e.g. Marossero [0017]; [0023]).
Regarding claim 2, modified Marossero discloses wherein the electrical potential sensor is a sensor selected from the set of sensors consisting of an electromyography (EMG) sensor, an electrohepatogram (EHG) sensor and electrocardiogram (ECG) sensor (e.g. [0026]; [0052]).
Regarding claim 7, modified Marossero discloses further comprising a patient response sensor (e.g. Kumar 3. Proposed System “The proposed method is to analyse the parameters (temperature, blood pressure, pulse rate and sweat rate) of women in labour ward. It has high accuracy to predict the PPH (Postpartum Haemorrage) patients and has a fast training processor for predicting whether the patients are affected by PPH or not”).
Regarding claim 8, modified Marossero discloses wherein the patient response sensor is a sensor selected from the set of sensors consisting of a temperature sensor and a sweat sensor (e.g. Kumar 3. Proposed System “The proposed method is to analyse the parameters (temperature, blood pressure, pulse rate and sweat rate) of women in labour ward. It has high accuracy to predict the PPH (Postpartum Haemorrage) patients and has a fast training processor for predicting whether the patients are affected by PPH or not”).
Regarding claim 9, modified Marossero discloses wherein the detection controller is configured to apply a machine learning model to the patient data to determine the risk of postpartum haemorrhage (e.g. Kumar 1. Introduction “An efficient fuzzy neural technique-based rule algorithm is developed for input parameters of temperature, pulse rate, blood pressure, and sweat rate of pregnant women to predict the risk in developing PPH”).
Regarding claim 10, modified Marossero discloses wherein the machine learning model is a nonlinear model (e.g. Kumar 1. Introduction “An efficient fuzzy neural technique-based rule algorithm is developed for input parameters of temperature, pulse rate, blood pressure, and sweat rate of pregnant women to predict the risk in developing PPH”).
Regarding claim 11, modified Marossero discloses wherein the detection controller is configured to apply the machine learning model to the patient data to determine the risk of the postpartum haemorrhage by estimating a likelihood of the postpartum haemorrhage (e.g. Kumar 3.5 Self Perspective neural network and Algorithm-V the fuzzy neural technique is utilized to have the parameters as inputs for determining the PPH affected patients).
Regarding claim 12, modified Marossero discloses wherein the estimated likelihood is compared to a predetermined threshold to determine if postpartum haemorrhage is likely Kumar 3.5 Self Perspective neural network and Algorithm-V the fuzzy neural technique is utilized to have the parameters as inputs for determining the PPH affected patients the predetermined threshold is the normal level as detailed in 3 Proposed system)
Regarding claim 13, modified Marossero discloses wherein the detection controller includes a trained classifier configured to determine the risk of the postpartum haemorrhage (e.g. Kumar 3.5 Self Perspective neural network and Algorithm-V the fuzzy neural technique is utilized to have the parameters as inputs for determining the PPH affected patients)
Regarding claims 16 and 17, modified Marossero is silent regarding wherein the monitoring system has a positive predictive value of at least 70% and a negative predictive value of at least 70%.
However, Kumar does disclose in Table 2 and EQS. 13-15 the accuracy of determining a positive predictive value (HPPH and MPPH) being 25% respectively which would mean that it could accurately determine some level of PPH that requires medical attention at about 50%. Furthermore, Kumar does disclose in Table 2 the accuracy of determining a negative predictive value (NPPH) being 50%. Additionally, the system is utilizing a fuzzy neural technique that is a machine learning device that is able to improve as it is fed more data points and information. Therefore, it would have been obvious to try to keep feeding the machine learning model and utilizing routine optimization of the system to continually feed it data in order to achieve high levels of accuracy as the equations for determining accuracy would improve with more data points.
Regarding claim 18, modified Marossero discloses wherein the detection controller is configured to automatically perform a method comprising :receiving the patient data from the monitoring device; estimating a likelihood of the postpartum haemorrhage by processing the patient data from to form a plurality of descriptors for data points in the patient data, the plurality of descriptors being processed by a machine learning model to estimate the likelihood; determining a postpartum haemorrhage risk for the patient by comparing the estimated likelihood to a predetermined threshold; and displaying the determined postpartum haemorrhage risk to an operator (e.g. ).
Regarding claim 19, Marossero discloses a method of detecting a high risk of postpartum haemorrhage in a patient (e.g. abstract [0017]), the method comprising: collecting patient data from a monitor having a plurality of medical electrode members attached to a body of a patient, the patient data being collected from at least one sensor type (e.g. [0061]-[0062] Fig 1:1a-d).
Marossero is silent regarding estimating a likelihood of the postpartum haemorrhage by processing the patient data from the at least one sensor type to form a plurality of descriptors for data points in the patient data, the plurality of descriptors being processed by a machine learning model to estimate the likelihood; and displaying the determined postpartum haemorrhage risk to an operator.
Marossero does disclose the need for monitoring the uterine tone to provide an early warning of atony and potential hemorrhage during postpartum (e.g. [0017]; [0023]) and displaying the results of algorithm operations of the data to the user (e.g. [0088] Figs. 4A-F).
However, Kumar discloses a solution for finding postpartum haemorrhage using fuzzy neural techniques (e.g. 1. Introduction “An efficient fuzzy neural technique-based rule algorithm is developed for input parameters of temperature, pulse rate, blood pressure, and sweat rate of pregnant women to predict the risk in developing PPH” and 3.5 Self Perspective neural network and Algorithm-V the fuzzy neural technique is utilized to have the parameters as inputs for determining the PPH affected patients) utilizing sensor data (e.g. 3. Proposed System “The proposed method is to analyse the parameters (temperature, blood pressure, pulse rate and sweat rate) of women in labour ward. It has high accuracy to predict the PPH (Postpartum Haemorrage) patients and has a fast training processor for predicting whether the patients are affected by PPH or not”) estimating a likelihood of the postpartum haemorrhage by processing the patient data from the at least one sensor type to form a plurality of descriptors for data points in the patient data, the plurality of descriptors being processed by a machine learning model to estimate the likelihood (e.g. abstract; 3. Proposed System “The parameter value in the obtained data indicates the biological condition of pregnant women. If parameter value exceeds from the normal level, then the pregnant women is affected by PPH” 4.1 Indicator definition of PPH performance “The membership function of each patient is used to evaluate the performance.” “Membership function value of 1 indicates patient state with NPPH, 0 indicates patient state with HPPH, and values in between 0 and 1 indicated MPPH” Table 1 The output later compares the level of parameters with the normal ranges. If it exceeds, then the patients are affected by PPH; Fig 12).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify the method of Marossero to incorporate the teachings of Kumar of estimating a likelihood of the postpartum haemorrhage by processing the patient data from the at least one sensor type to form a plurality of descriptors for data points in the patient data, the plurality of descriptors being processed by a machine learning model to estimate the likelihood for the purpose of being able to provide an early warning of atony and potential hemorrhage during postpartum (e.g. Marossero [0017]; [0023]) and then displaying the determined postpartum haemorrhage risk to an operator as already deemed necessary in Marossero (e.g. Marossero [0088] Figs. 4A-F).
Regarding claim 20, modified Marossero discloses further comprising: Determining a postpartum haemorrhage risk for the patient by comparing the estimated likelihood to a predetermined threshold (e.g. Kumar 3.5 Self Perspective neural network and Algorithm-V the fuzzy neural technique is utilized to have the parameters as inputs for determining the PPH affected patients).
Claim(s) 3-6 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Marossero in view of Kumar as applied to claim 1 above, and further in view of Penders (US 2020/0196958 A1)
Regarding claim 3, modified Marossero is silent regarding further comprises a movement sensor.
However, Penders discloses a system and method for monitoring uterine activity wherein the system further comprises a movement sensor (e.g. [0184]; [0201] accelerometer and gyroscope) as the system already utilizes electrodes to capture both EHG and EMG signals.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify the modified system of Marossero to incorporate the teachings of Penders to further comprises a movement sensor as it is well known to have these systems together to monitor different maternal aspects of uterine activity in combination with electrodes measuring EHG or EMG signals (e.g. Penders [0184]).
Regarding claim 4, newly Modified Marossero discloses wherein the movement sensor is a sensor selected from the set of sensors consisting of an accelerometer and a gyroscope (e.g. Penders [0184]; [0201]).
Regarding claim 5, modified Marossero is silent regarding further comprises a deformation sensor.
However, Penders discloses a system and method for monitoring uterine activity wherein the system further comprises a deformation sensor (e.g. [0184]; [0201] stretch sensor) as the system already utilizes electrodes to capture both EHG and EMG signals.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify the modified system of Marossero to incorporate the teachings of Penders to further comprise a deformation sensor as it is well known to have these systems together to monitor different maternal aspects of uterine activity in combination with electrodes measuring EHG or EMG signals (e.g. Penders [0184]).
Regarding claim 6, wherein the deformation sensor is a sensor selected from the set of sensors consisting of a flex sensor and a stretch sensor (e.g. Penders [0184]; [0201]).
Regarding claim 14, modified Marossero is silent regarding wherein the electrical potential sensor, the at least one electrode, the communications module and the detection controller are located within a housing.
However, Penders discloses a system and method for monitoring uterine activity wherein the electrical potential sensor, the at least one electrode, the communications module and the detection controller are located within a housing (e.g. Fig 2 [0188] ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify the modified system of Marossero to incorporate the teachings of Penders wherein the electrical potential sensor, the at least one electrode, the communications module and the detection controller are located within a housing as it discloses both the detection controller within a housing and separated from the housing which details that both embodiments are possible with this type of monitoring (e.g. Penders Figs 2 and 3 [0188]-[0190]).
Regarding claim 15, modified Marossero is silent regarding wherein the detection controller is located separately from the electrical potential sensor.
However, Penders discloses a system and method for monitoring uterine activity wherein the detection controller is located separately from the electrical potential sensor (e.g. Fig 3 [0188]-[0190] ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify the modified system of Marossero to incorporate the teachings of Penders wherein the detection controller is located separately from the electrical potential sensor as it discloses both the detection controller within a housing and separated from the housing which details that both embodiments are possible with this type of monitoring (e.g. Penders Figs 2 and 3 [0188]-[0190]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSANDRA F HOUGH whose telephone number is (571)270-7902. The examiner can normally be reached Monday-Thursday 7 am - 4 pm.
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Jessandra Hough December 9, 2025
/J.F.H./Examiner, Art Unit 3796
/William J Levicky/Primary Examiner, Art Unit 3796