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 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 is directed to an abstract idea without significantly more. Claims 1-20 is/are drawn a method and an apparatus which is/are a statutory category of invention (Step 1: YES).
The claim limitations within independent claims 1 and 13 that set forth or describe the abstract idea is/are: “receive/receiving PPH factor information for the patient, wherein the PPH factor information includes at least two of the following: ultrasound data generated based on one or more abdominal ultrasound images of the patient obtained in a measurement period; electromyography (EMG) data obtained during the measurement period from abdominal electrodes on the patient; cardiac measurement data obtained from the patient during the measurement period; a uterine health indicator for the patient”, “process/processing the PPH factor information to determine a PPH probability index indicating a probability that the patient will develop PPH and a PPH severity index predicting a severity of PPH”, “generate/generating a PPH risk index for the measurement period based on the PPH probability index and the PPH severity index.”. The reasons that the limitations is/are considered an abstract idea is/are the following: The limitations of “receive/receiving PPH factor information for the patient, wherein the PPH factor information includes at least two of the following: ultrasound data generated based on one or more abdominal ultrasound images of the patient obtained in a measurement period; electromyography (EMG) data obtained during the measurement period from abdominal electrodes on the patient; cardiac measurement data obtained from the patient during the measurement period; a uterine health indicator for the patient”, “process/processing the PPH factor information to determine a PPH probability index indicating a probability that the patient will develop PPH and a PPH severity index predicting a severity of PPH”, “generate/generating a PPH risk index for the measurement period based on the PPH probability index and the PPH severity index.” is a process directed to a concept relating to organizing or analyzing information in a way that can be performed in human mental work, i.e. under its broadest reasonable interpretation covers performance of the limitation in the mind with the aid of pen and paper but for the recitation of generic computer components. That is, other than reciting “processor configured to” (claim 1) nothing in the claim element precludes the steps from practically being performed in the mind with the aid of pen and paper. For example but for the “processor configured to” (claim 1), “receive/receiving PPH factor information for the patient, wherein the PPH factor information includes at least two of the following: ultrasound data generated based on one or more abdominal ultrasound images of the patient obtained in a measurement period; electromyography (EMG) data obtained during the measurement period from abdominal electrodes on the patient; cardiac measurement data obtained from the patient during the measurement period; a uterine health indicator for the patient”, “process/processing the PPH factor information to determine a PPH probability index indicating a probability that the patient will develop PPH and a PPH severity index predicting a severity of PPH”, and “generate/generating a PPH risk index for the measurement period based on the PPH probability index and the PPH severity index.” in the context of this claim encompasses the user, with the aid of pen and paper, being given PPH factor information, determining a PPH probability index and PPH severity index and determining a PPH risk index based on the determined PPH probability index and PPH severity index. There is nothing to suggest an undue level of complexity in the receiving, processing and generating steps. A human in their mind can determine a PPH risk index based on a determined PPH probability index and PPH severity index when provided with PPH factor information. If a claim limitation, under its broadest reasonable interpretation covers a metal process, i.e. performance of the limitation in the mind, but for the recitation of generic computer components, then it falls with the “Mental Processes” grouping of abstract ideas. Accordingly the claims recite an abstract idea. Although not drawn to the same subject matter, the claimed limitation(s) is/are similar to concepts that have been identified as abstract by the courts, such as: collecting information, analyzing it, and reporting certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom, 830 F.3d 1350, 119 U.S.P.Q.2d 1739 (Fed. Cir. 2016), selecting certain information, analyzing it using mathematical techniques, and reporting or displaying the results of the analysis in SAP America Inc. v. Investpic, LLC, 890 F.3d 1016, 126 USPQ2d 1638 (Fed Cir. 2018). Thus, the claim(s) are directed to a judicial exception and fall squarely within the realm of "abstract ideas," which is a patent-ineligible concept. (Step 2A: Prong One YES).
Analyzing the claim as whole for a practical application, the claim does not include additional elements/steps that are sufficient to amount to significantly more than the judicial exception. The additionally recited element(s) appended to the abstract idea in claim 1 “a processor configured to”. As discussed above with respect to integration of abstract idea into a practical, the additional element of “a processor configured to” perform the receiving, processing and generating steps amount to no more than mere instruction to apply the exception using generic computer components. The “processor” is purely general-purpose computer components recited as carrying out the general-purpose computer functions of storing data, processing data and displaying to enable the abstract process. As such, this/these recitation(s) is/are nothing more than nominal recitation(s) of a computer covering an abstract concept. See Bancorp Servs. v. Sun Life Assurance Co., 687 F.3d 1266, 103 USPQ2d 1425 (Fed. Circ. 2012). See also Mayo Collaborative Services v. Prometheus Laboratories Inc., 101 USPQ2d 1961 (U.S. 2012), which establishes that a claim cannot simply state the abstract idea and add the words "apply it”. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (Step 2A, Prong Two, NO).
Claims 1 and 13 do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception (i.e., an inventive concept) for the same reasons as described above. e.g., all elements are directed to purely general-purpose computer components recited as carrying out the general-purpose computer functions of storing data, processing data and displaying to enable the abstract process, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Further, regarding the processor, applicant discloses “It should be appreciated that such block components may be realized by any number of hardware, software, and/or firmware components configured to perform the specified functions” (e.g. para. [0056]-[0057], [0080] of published application US 2026/0000344), therefore the recited “processor” is nothing more than purely general-purpose computer components recited as carrying out the general-purpose computer functions of processing data and displaying to enable the abstract process, Similarly, when considered as an ordered combination, the additional components/steps of the claim(s) add nothing that is not already present when the steps are considered separately (Step 2B: NO). The claims are not patent eligible.
Claim(s) 2-12 and 14-20 depend directly or indirectly from claim(s) 1 and 13. Therefore, the dependent claims rely upon the same abstract idea as the independent claim(s), as set forth above. Additionally, the dependent claims do nothing more than further limiting the abstract idea while failing to qualify as "significantly more", and the specificity of an abstract idea does not make it any "less abstract” as it is still directed to concepts relating to organizing or analyzing information in a way that can be performed mentally or is analogous to human mental work subject matter. Therefore, the dependent claim(s) are also not patent eligible for the reasons discussed above. Claim(s) 2-4, 9-10,14-15 and 19-20 fail(s) to provide significantly more, when considered as an ordered combination, as it/they merely provide further limitation regarding the abstract idea, which can still nonetheless be considered mental processes, i.e. performed in the mind with the aid of pen and paper and/or mathematical concepts. Claim(s) 5-8, 11-12,16-18 fail(s) to provide significantly more, when considered as an ordered combination, as it/they merely provide further limitations regarding data that is received, which is merely nominally, insignificantly or tangentially related to the performance of the steps, i.e. amounts to mere data gathering, which is a form of insignificant extra-solution activity (pre-solution activity. All uses of the recited judicial exception require the pre-solution activity of data gathering.
The instantly rejected claim(s) are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more. In the interest of advancing prosecution, the examiner suggests: providing evidence, for example, delineating how the abstract idea and/or additional elements appended to the abstract idea results in an improvement to the technology/technical field, which can show eligibility and/or adding a practical application of the claimed method outside of the computer (e.g. treating a patient). See MPEP § 716.01(c) for examples of providing evidence supported by an appropriate affidavit or declaration. For additional guidance, applicant is directed generally to MPEP §2106.
Claim Rejections - 35 USC § 102
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.
Claim(s) 1-5, 7-9, 11, 13-16 and 18-19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2024/0038402 to Peri et al. (Peri) (cited by applicant).
In reference to at least claim 1
Peri discloses a patient monitoring system for monitoring a maternal patient for postpartum hemorrhage (PPH) (e.g. “severe bleeding after birth”, para. [0176], “postpartum hemorrhage”, para. [0168], [0273]-[0274]), the system comprising: a processor (e.g. “A system comprising; a processor executing..”, claims 1 and 8) configured to: receive PPH factor information for the patient (e.g. “both structured data (including, but not limited to, i.e., bio-chemistry or bio-markers, socio-economic and demographic) and unstructured data (including, but not limited to, i.e. images, genetics, Clinician notes/images/videos, social media)”, para. [0032], [0173]; “MIHIC system acquires data from multiple sources “, para. [0050]), wherein the PPH factor information includes at least two of the following: ultrasound data generated based on one or more abdominal ultrasound images of the patient obtained in a measurement period (“imaging studies (esp. ultrasound”)”, para. [0173]); electromyography (EMG) data obtained during the measurement period from abdominal electrodes on the patient; cardiac measurement data obtained from the patient during the measurement period (e.g. “Blood pressure (mmHg)”, para. [0050], [0172]); a uterine health indicator for the patient (e.g. “placental abruption”, para. [0168], [0275], “overdistended uterus”, para. [0276]); process the PPH factor information to determine a PPH probability index indicating a probability that the patient will develop PPH and a PPH severity index predicting a severity of PPH (e.g. “The MIHIC system helps Clinicians detect the risk of maternal mortality in women by understanding and quantifying risk as a single score. MIHIC scores are statistically computed at a patient (a pregnant mother) level by calculating specific scores of the mother, fetus and infant for a defined set of risks and then statistically deriving the overall MIHIC score”, para. [0032], “The MIHIC System consumes the input data and utilizes advanced machine learning and statistical techniques to output a MIHIC score (a value between 0 and 1) which represents the risk of the mother having a postpartum hemorrhage during the pregnancy.” para. [0273]-0274]); generate a PPH risk index for the measurement period based on the PPH probability index and the PPH severity index (e.g. “The MIHIC system helps Clinicians detect the risk of maternal mortality in women by understanding and quantifying risk as a single score. MIHIC scores are statistically computed at a patient (a pregnant mother) level by calculating specific scores of the mother, fetus and infant for a defined set of risks and then statistically deriving the overall MIHIC score”, para. [0032], “The MIHIC system will enable the Clinicians to identify the women with high risk of severe bleeding post-delivery”, para. [0176], “The MIHIC System consumes the input data and utilizes advanced machine learning and statistical techniques to output a MIHIC score (a value between 0 and 1) which represents the risk of the mother having a postpartum hemorrhage during the pregnancy.” para. [0273]-0274]).
In reference to at least claim 2
Peri discloses wherein the processor is configured to utilize a machine learning model trained to determine the PPH probability index and the PPH severity index based on the PPH factor information (e.g. “The suite of AI algorithms comprise a set of machine learning models/techniques trained to learn to extract information/data from the structured and unstructured data, assemble knowledge from the extracted data and map the assembled data to the characteristics of associated maternal, fetal and infant risks to perform a risk prediction”, para. [0037], “Artificial Intelligence for Prediction: In various embodiments of the claimed invention, the MIHIC system as disclosed herein strives to predict the likelihood of maternal, fetal and infant risks. For predicting each risk, the system employs a suite of Artificial intelligence techniques to determine a risk score with values between 0 and 1. These techniques can be trained on millions of medical records having medical, clinical and biological characteristics of the mothers to attain the ability to generalize maternal and infant risks.”, para. [0079]).
In reference to at least claim 3
Peri discloses wherein the PPH risk index comprises a first value being the PPH probability index and a second value being the PPH severity index (e.g. “The MIHIC system comprises an algorithm and a method to determine a risk score between 0 and 1 (derived by applying an algorithm to process probabilities of risk). The MIHIC system uses self-learning models including, but not limited to, reinforcement learning to continually improve the prediction of score and stratification of risk level as Low, Medium and High.”, abstract, para. [0041]).
In reference to at least claim 4
Peri discloses wherein the first value and the second value are each one of high, medium, or low (e.g. “The MIHIC system comprises an algorithm and a method to determine a risk score between 0 and 1 (derived by applying an algorithm to process probabilities of risk). The MIHIC system uses self-learning models including, but not limited to, reinforcement learning to continually improve the prediction of score and stratification of risk level as Low, Medium and High.”, abstract, para. [0041]).
In reference to at least claim 5
Peri discloses wherein the ultrasound data includes ultrasound result data generated based on the abdominal ultrasound images and indicating at least one of a retained placental tissue (e.g. “Retained Placentas”, para. [0131]), a uterine tear, endometritis, and uterine atony.
In reference to at least claim 7
Peri discloses wherein the cardiac measurements include at least one of a pulse transmit time (PTT), a heart rate, blood pressure measurement for the patient (e.g. “Blood pressure (mmHg)”, para. [0050], [0172]).
In reference to at least claim 8
Peri discloses wherein the uterine health indicator is a score generated based on at least one of a uterus type (e.g. “Overdistended uterus”, para. [0274]; “Uterus Size”, para. [0143]), a menstrual history (e.g. “Gestational Weeks”, para. [0050]), a previous birth history (e.g. “Delivery Outcomes”, para. [0118]), and a hormone health history.
In reference to at least claim 9
Peri discloses wherein the processor is further configured to utilize a trained machine learning model to generate the uterine health score (e.g. “” (iv) forwarding the pre-processed/cleaned data to the AI suite for exploration of factors associated with risks;”, para. [0037], [0040], “The MIHIC system leverages a suite of Machine/Deep Learning algorithms for exploration of factors associated with risk and subsequently computes the scores for various types of risks., para. [0055]-[0056]) based on at least one of the uterus type (e.g. “Overdistended uterus”, para. [0274]; “Uterus Size”, para. [0143]), the menstrual history (e.g. “Gestational Weeks”, para. [0050]), the previous birth history (e.g. “Delivery Outcomes”, para. [0118]), and the hormone health history.
In reference to at least claim 11
Peri discloses wherein the PPH factor information includes at least three of the ultrasound data (“imaging studies (esp. ultrasound”)”, para. [0173]); the EMG data, the EMG data, the cardiac measurement data (e.g. “Blood pressure (mmHg)”, para. [0050], [0172]), and the uterine health indicator for the patient (e.g. “placental abruption”, para. [0168], [0275], “overdistended uterus”, para. [0276]).
In reference to at least claim 13
Peri discloses a method for monitoring a patient for postpartum hemorrhage (PPH) (e.g. “severe bleeding after birth”, para. [0176], “postpartum hemorrhage”, para. [0168], [0273]-[0274]), the method comprising: receiving PPH factor information for the patient (e.g. “both structured data (including, but not limited to, i.e., bio-chemistry or bio-markers, socio-economic and demographic) and unstructured data (including, but not limited to, i.e. images, genetics, Clinician notes/images/videos, social media)”, para. [0032], [0173]; “MIHIC system acquires data from multiple sources “, para. [0050]), wherein the PPH factor information includes at least two of the following: ultrasound data generated based on one or more abdominal ultrasound images of the patient obtained in a measurement period (“imaging studies (esp. ultrasound”)”, para. [0173]); electromyography (EMG) data obtained during the measurement period from abdominal electrodes on the patient; cardiac measurement data obtained from the patient during the measurement period (e.g. “Blood pressure (mmHg)”, para. [0050], [0172]); a uterine health indicator for the patient (e.g. “placental abruption”, para. [0168], [0275], “overdistended uterus”, para. [0276]); processing the PPH factor information to determine a PPH probability index indicating a probability that the patient will develop PPH and a PPH severity index predicting a severity of PPH (e.g. “The MIHIC system helps Clinicians detect the risk of maternal mortality in women by understanding and quantifying risk as a single score. MIHIC scores are statistically computed at a patient (a pregnant mother) level by calculating specific scores of the mother, fetus and infant for a defined set of risks and then statistically deriving the overall MIHIC score”, para. [0032], “The MIHIC System consumes the input data and utilizes advanced machine learning and statistical techniques to output a MIHIC score (a value between 0 and 1) which represents the risk of the mother having a postpartum hemorrhage during the pregnancy.” para. [0273]-0274]); generating a PPH risk index for the measurement period based on the PPH probability index and the PPH severity index (e.g. “The MIHIC system helps Clinicians detect the risk of maternal mortality in women by understanding and quantifying risk as a single score. MIHIC scores are statistically computed at a patient (a pregnant mother) level by calculating specific scores of the mother, fetus and infant for a defined set of risks and then statistically deriving the overall MIHIC score”, para. [0032], “The MIHIC system will enable the Clinicians to identify the women with high risk of severe bleeding post-delivery”, para. [0176], “The MIHIC System consumes the input data and utilizes advanced machine learning and statistical techniques to output a MIHIC score (a value between 0 and 1) which represents the risk of the mother having a postpartum hemorrhage during the pregnancy.” para. [0273]-0274]).
In reference to at least claim 14
Peri discloses wherein determining the PPH probability index and the PPH severity index includes utilizing a machine learning model that has been trained to determine the PPH probability index and the PPH severity index based on the PPH factor information (e.g. “The suite of AI algorithms comprise a set of machine learning models/techniques trained to learn to extract information/data from the structured and unstructured data, assemble knowledge from the extracted data and map the assembled data to the characteristics of associated maternal, fetal and infant risks to perform a risk prediction”, para. [0037], “Artificial Intelligence for Prediction: In various embodiments of the claimed invention, the MIHIC system as disclosed herein strives to predict the likelihood of maternal, fetal and infant risks. For predicting each risk, the system employs a suite of Artificial intelligence techniques to determine a risk score with values between 0 and 1. These techniques can be trained on millions of medical records having medical, clinical and biological characteristics of the mothers to attain the ability to generalize maternal and infant risks.”, para. [0079]).
In reference to at least claim 15
Peri discloses wherein the PPH risk index comprises a first value being the PPH probability index and a second value being the PPH severity index (e.g. “The MIHIC system comprises an algorithm and a method to determine a risk score between 0 and 1 (derived by applying an algorithm to process probabilities of risk). The MIHIC system uses self-learning models including, but not limited to, reinforcement learning to continually improve the prediction of score and stratification of risk level as Low, Medium and High.”, abstract, para. [0041]).
In reference to at least claim 16
Peri discloses wherein the ultrasound data includes ultrasound result data generated based on the abdominal ultrasound images and indicating at least one of a retained placental tissue (e.g. “Retained Placentas”, para. [0131]), a uterine tear, endometritis, and uterine atony.
In reference to at least claim 18
Peri discloses wherein the cardiac measurements include at least one of a pulse transmit time (PTT), a heart rate, blood pressure measurement for the patient (e.g. “Blood pressure (mmHg)”, para. [0050], [0172]).
In reference to at least claim 19
Peri discloses utilizing a machine learning model trained to generate the uterine health indicator (e.g. “” (iv) forwarding the pre-processed/cleaned data to the AI suite for exploration of factors associated with risks;”, para. [0037], [0040], “The MIHIC system leverages a suite of Machine/Deep Learning algorithms for exploration of factors associated with risk and subsequently computes the scores for various types of risks., para. [0055]-[0056]) based on at least one of the uterus type (e.g. “Overdistended uterus”, para. [0274]; “Uterus Size”, para. [0143]), the menstrual history (e.g. “Gestational Weeks”, para. [0050]), the previous birth history (e.g. “Delivery Outcomes”, para. [0118]), and the hormone health history.
Claim(s) 1-2,6,10,13-14,17 and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by WO 2023/028662 to McDonald et al. (McDonald) (cited by applicant).
In reference to at least claim 1
McDonald discloses a patient monitoring system for monitoring a maternal patient for postpartum hemorrhage (PPH) (e.g. “determining risk of postpartum haemorrhage to a patient”, para. [007]), the system comprising: a processor (e.g. “processing system 100 generally includes at least one processor 102”, para. [046]) configured to: receive PPH factor information for the patient (e.g. “The one or more sensors 1210 collect patient data”, para. [042]), wherein the PPH factor information includes at least two of the following: ultrasound data generated based on one or more abdominal ultrasound images of the patient obtained in a measurement period ; electromyography (EMG) data obtained during the measurement period from abdominal electrodes on the patient (e.g. “The PPH monitor may collect patient data using as an electrical potential sensor, such as an EMG sensors”, para. [065], [099]); cardiac measurement data obtained from the patient during the measurement period (e.g. “The sensors may be one or more of the same sensor types or a mixture of sensor types where each sensor type may be present one or more times. One sensor that may be used in the PPH monitors is an electrical potential sensor, such as electromyography (EMG), electrohepatogram (EHG) or electrocardiogram (ECG) sensors where an ECG also provides cardiac and uterine activity information.”, para. [008], [099]); a uterine health indicator for the patient (e.g. “The sensors may be one or more of the same sensor types or a mixture of sensor types where each sensor type may be present one or more times. One sensor that may be used in the PPH monitors is an electrical potential sensor, such as electromyography (EMG), electrohepatogram (EHG) or electrocardiogram (ECG) sensors where an ECG also provides cardiac and uterine activity information.”, para. [099]); process the PPH factor information to determine a PPH probability index indicating a probability that the patient will develop PPH and a PPH severity index predicting a severity of PPH (e.g. “The descriptors are passed into a trained nonlinear model to generate an output such as probabilities, pseudo-probabilities or scores where the output indicates the likelihood of a PPH.”, para. [085]-[090]); generate a PPH risk index for the measurement period based on the PPH probability index and the PPH severity index (e.g. “The descriptors are passed into a trained nonlinear model to generate an output such as probabilities, pseudo-probabilities or scores where the output indicates the likelihood of a PPH.”, para. [085]-[090]).
In reference to at least claim 2
McDonald discloses wherein the processor is configured to utilize a machine learning model trained to determine the PPH probability index and the PPH severity index based on the PPH factor information (e.g. “the patient data 825 is analysed by a machine learning model or a trained classifier and a likelihood value produced.”, para. [071], [076]).
In reference to at least claim 6
McDonald discloses wherein the EMG data includes at least one of EMG signal data recorded from the abdominal electrodes and EMG result data generated based on the EMG signal data indicating at least one of abdominal muscle contraction activity, uterine activity, and uterine atony (e.g. “One sensor that may be used in the PPH monitors is an electrical potential sensor, such as electromyography (EMG), electrohepatogram (EHG) or electrocardiogram (ECG) sensors where an ECG also provides cardiac and uterine activity information.,” para. [065], [099]).
In reference to at least claim 10
McDonald discloses wherein the processor is further configured to: compare the PPH risk index to a threshold (e.g. “The estimated likelihood may be compared to a predetermined threshold”, para. [018], [045]); and generate an alert based on the comparison and/or adjust a frequency of receiving the PPH factor information and a frequency of generating the PPH risk index based on the comparison (e.g. “ In one example, the output process 860 may provide a visible and/or audible alarm on the display 850 when PPH is likely.”, para. [071]-[072]).
In reference to at least claim 13
McDonald discloses a method for monitoring a patient for postpartum hemorrhage (PPH) (e.g. “determining risk of postpartum haemorrhage to a patient”, para. [007]), the method comprising: receiving PPH factor information for the patient (e.g. “The one or more sensors 1210 collect patient data”, para. [042]), wherein the PPH factor information includes at least two of the following: ultrasound data generated based on one or more abdominal ultrasound images of the patient obtained in a measurement period ; electromyography (EMG) data obtained during the measurement period from abdominal electrodes on the patient (e.g. “The PPH monitor may collect patient data using as an electrical potential sensor, such as an EMG sensors”, para. [065], [099]); cardiac measurement data obtained from the patient during the measurement period (e.g. “The sensors may be one or more of the same sensor types or a mixture of sensor types where each sensor type may be present one or more times. One sensor that may be used in the PPH monitors is an electrical potential sensor, such as electromyography (EMG), electrohepatogram (EHG) or electrocardiogram (ECG) sensors where an ECG also provides cardiac and uterine activity information.”, para. [008], [099]); a uterine health indicator for the patient (e.g. “The sensors may be one or more of the same sensor types or a mixture of sensor types where each sensor type may be present one or more times. One sensor that may be used in the PPH monitors is an electrical potential sensor, such as electromyography (EMG), electrohepatogram (EHG) or electrocardiogram (ECG) sensors where an ECG also provides cardiac and uterine activity information.”, para. [099]); processing the PPH factor information to determine a PPH probability index indicating a probability that the patient will develop PPH and a PPH severity index predicting a severity of PPH (e.g. “The descriptors are passed into a trained nonlinear model to generate an output such as probabilities, pseudo-probabilities or scores where the output indicates the likelihood of a PPH.”, para. [085]-[090]); generating a PPH risk index for the measurement period based on the PPH probability index and the PPH severity index (e.g. “The descriptors are passed into a trained nonlinear model to generate an output such as probabilities, pseudo-probabilities or scores where the output indicates the likelihood of a PPH.”, para. [085]-[090]).
In reference to at least claim 14
McDonald discloses wherein determining the PPH probability index and the PPH severity index includes utilizing a machine learning model that has been trained to determine the PPH probability index and the PPH severity index based on the PPH factor information (e.g. “the patient data 825 is analysed by a machine learning model or a trained classifier and a likelihood value produced.”, para. [071], [076]).
In reference to at least claim 17
McDonald discloses wherein the EMG data includes at least one of EMG signal data recorded from the abdominal electrodes and EMG result data generated based on the EMG signal data indicating at least one of abdominal muscle contraction activity, uterine activity, and/or uterine atony (e.g. “One sensor that may be used in the PPH monitors is an electrical potential sensor, such as electromyography (EMG), electrohepatogram (EHG) or electrocardiogram (ECG) sensors where an ECG also provides cardiac and uterine activity information.,” para. [065], [099]).
In reference to at least claim 20
McDonald discloses comparing the PPH risk index to a threshold (e.g. “The estimated likelihood may be compared to a predetermined threshold”, para. [018], [045]); and generating an alert based on the comparison and/or adjust a frequency of receiving the PPH factor information and a frequency of generating the PPH risk index based on the comparison (e.g. “ In one example, the output process 860 may provide a visible and/or audible alarm on the display 850 when PPH is likely.”, para. [071]-[072]).
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) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2024/0038402 to Peri et al. (Peri) in view of WO 2023/028662 to McDonald et al. (McDonald).
In reference to at least claim 12
Peri teaches a system according to claim 1, see claim 1 rejection above. Peri further discloses wherein the PPH factor information includes at least ultrasound data (“imaging studies (esp. ultrasound”)”, para. [0173]); the cardiac measurement data (e.g. “Blood pressure (mmHg)”, para. [0050], [0172]), and the uterine health indicator for the patient (e.g. “placental abruption”, para. [0168], [0275], “overdistended uterus”, para. [0276]).
However, Peri does not explicitly disclose the PPH factor information includes EMG data.
McDonald discloses a patient monitoring system for monitoring a maternal patient for postpartum hemorrhage (PPH) (e.g. “determining risk of postpartum haemorrhage to a patient”, para. [007]), the system comprising: a processor (e.g. “processing system 100 generally includes at least one processor 102”, para. [046]) configured to: receive PPH factor information for the patient (e.g. “The one or more sensors 1210 collect patient data”, para. [042]), wherein the PPH factor information includes at least two of the following: ultrasound data generated based on one or more abdominal ultrasound images of the patient obtained in a measurement period ; electromyography (EMG) data obtained during the measurement period from abdominal electrodes on the patient (e.g. “The PPH monitor may collect patient data using as an electrical potential sensor, such as an EMG sensors”, para. [065], [099]); cardiac measurement data obtained from the patient during the measurement period (e.g. “The sensors may be one or more of the same sensor types or a mixture of sensor types where each sensor type may be present one or more times. One sensor that may be used in the PPH monitors is an electrical potential sensor, such as electromyography (EMG), electrohepatogram (EHG) or electrocardiogram (ECG) sensors where an ECG also provides cardiac and uterine activity information.”, para. [008], [099]); a uterine health indicator for the patient (e.g. “The sensors may be one or more of the same sensor types or a mixture of sensor types where each sensor type may be present one or more times. One sensor that may be used in the PPH monitors is an electrical potential sensor, such as electromyography (EMG), electrohepatogram (EHG) or electrocardiogram (ECG) sensors where an ECG also provides cardiac and uterine activity information.”, para. [099]); process the PPH factor information to determine a PPH probability index indicating a probability that the patient will develop PPH and a PPH severity index predicting a severity of PPH (e.g. “The descriptors are passed into a trained nonlinear model to generate an output such as probabilities, pseudo-probabilities or scores where the output indicates the likelihood of a PPH.”, para. [085]-[090]); generate a PPH risk index for the measurement period based on the PPH probability index and the PPH severity index (e.g. “The descriptors are passed into a trained nonlinear model to generate an output such as probabilities, pseudo-probabilities or scores where the output indicates the likelihood of a PPH.”, para. [085]-[090]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Peri to have the PPH factor information include EMG data to provide information regarding electrical potential, as taught by McDonald, in order to provide additional patient information that further aids in determining postpartum hemorrhage in advance (‘662, para. [003]).
Conclusion
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/JENNIFER L GHAND/Examiner, Art Unit 3796