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 .
Election/Restrictions
Applicant’s election without traverse of group 1, claims 1-28 in the reply filed on 11/26/2025 is acknowledged.
Claim Objections
Claims 4-28 are objected to under 37 CFR 1.75(c) as being in improper form because a multiple dependent claim cannot depend on any other multiple dependent claim. See MPEP § 608.01(n). Accordingly, the claims have not been further treated on the merits.
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.
Claim(s) 1-3 is/are rejected under 35 U.S.C. 101 because the claimed invention, considering all claim elements both individually and in combination as a whole, do not amount to significantly more than a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea).
Claim 1 is a claim to a process, machine, manufacture, or composition of matter and therefore meets one of the categorical limitations of 35 U.S.C. 101. However, claim 1 meets the first prong of the step 2A analysis because it is directed to a/an abstract idea, as evidenced by the claim language of “applying a predictive model to the plurality of physiological measurements from the patient to generate a classification corresponding to a vascular condition, the predictive model having been trained, using a machine learning system”, “extracting a set of features from the physiological readings to generate a training dataset based on the physiological readings from the subjects in the study cohort;”, “applying machine learning techniques to the training dataset to train the predictive model such that the predictive model is configured to accept the plurality of physiological measurements and generate a classification corresponding to the vascular condition, wherein applying the machine learning techniques comprises performing automated feature selection to identify a subset of the set of features and refitting the predictive model based on the subset of features, wherein the subset of features corresponds to the plurality of physiological measurements”, and “and outputting or storing the classification in association with the patient.”. This claim language, under the broadest, reasonable interpretation, encompasses subject matter that may be performed by a human using mental steps or with pen and paper that can involve basic critical thinking, which are types of activities that have been found by the courts to represents abstract ideas (i.e., the mental comparison in Ambry Genetics, or the diagnosing an abnormal condition by performing clinical tests and thinking about the results in Grams). The claim language also meets prong 2 of the step 2A analysis because the above-recited claim language does not integrate the abstract idea into a practical application. The disclosed technologies do not improve a technical field (see MPEP 2106.05(a)), affect a particular treatment for a disease or medical condition (see MPEP 2106.04(d)(2)), effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.04(d)(2)), apply the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), or apply the judicial exception in some meaningful way beyond generally linking the use of the abstract idea to a particular technological environment (MPEP 2106.04(d)(2) and 2106.05(e)). As a result, step 2A is satisfied and the second step, step 2B, must be considered.
With regard to the second step, the claim does not appear to recite additional elements that amount to significantly more. The additional elements are “obtaining, via a first oximeter probe secured to an upper extremity of a patient and/or a second oximeter probe secured to a lower extremity of the patient, a plurality of physiological measurements from the patient” and “acquiring, using one or more pulse oximeters, physiological readings from subjects in a study cohort;”. However, these elements are not “significantly more” because they are well-known, routine, and/or conventional as evidenced by paras [0003]: “ Pulse oximeter devices based on photoplethysmography techniques are well known in the art “ and [0077]: “traditional PPG/oximeter locations such as the ear lobe or fingertip as well as non-traditional PPG/oximeter locations such as the wrist, forearm, upper arm, leg, chest or any other location” of Eisen et al. (US 20110082355 A1). Therefore, these elements do not add significantly more and thus the claim as a whole does not amount to significantly more than a judicial exception.
Additionally, the ordered combination of elements do not add anything significantly more to the claimed subject matter. Specifically, the ordered combination of elements do not have any function that is not already supplied by each element individually. That is, the whole is not greater than the sum of its parts.
In view of the above, independent claim 1 fails to recite patent-eligible subject matter under 35 U.S.C. 101. Dependent claim(s) 2-3 fail to cure the deficiencies of independent claim 1 by merely reciting additional abstract ideas, further limitations on abstract ideas already recited, and/or additional elements that are not significantly more. Thus, claim(s) 1-3 is/are rejected under 35 U.S.C. 101.
Section 33(a) of the America Invents Act reads as follows:
Notwithstanding any other provision of law, no patent may issue on a claim directed to or encompassing a human organism.
Claim 1 is rejected under 35 U.S.C. 101 and section 33(a) of the America Invents Act as being directed to or encompassing a human organism. See also Animals - Patentability, 1077 Off. Gaz. Pat. Office 24 (April 21, 1987) (indicating that human organisms are excluded from the scope of patentable subject matter under 35 U.S.C. 101).
Claim 1 recites the limitation “via a first oximeter probe secured to an upper extremity of a patient and/or a second oximeter probe secured to a lower extremity of the patient.”. As the claim is applying a limitation to the patient/subject, it encompasses a human organism within the scope of the claim.
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) 1-3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khosousi (US 20200205745 A1) in view of Phattraprayoon et al. (“Accuracy of pulse oximeter readings from probe placement on newborn wrist and ankle”).
Regarding claim 1, Khosousi discloses a computer-implemented method (abstract) comprising: obtaining, via a first oximeter probe and/or a second oximeter probe, a plurality of physiological measurements from the patient ([0070]: "that acquires a plurality of biophysical signals 104 (e.g., phase-gradient biophysical signals) via any number of measurement probes 114 (shown as probes 114 a, 114 b, 114 c, 114 d, 114 e, and 1140 from a subject 106 to produce a biophysical data set 108.", [0010]: "include raw signal(s) acquired via a pulse oximeter "); applying a predictive model to the plurality of physiological measurements from the patient to generate a classification corresponding to a vascular condition ([0075]: " Assessment system 110, in some embodiments, and as shown in FIG. 1, includes a second set of one or more neural networks 132 b (e.g., a second set of one or more deep neural networks, a second set of one or more convolutional neural networks, etc.), or ensemble(s) thereof, each trained in this embodiment with a set of training cardiac signal data set acquired from patients diagnosed with the cardiac disease or condition and labeled with a presence/location and/or non-presence/non-location of the cardiac disease or condition in a region of the myocardium or a particular coronary artery (e.g., from a set of coronary arteries)"), the predictive model having been trained, using a machine learning system ([0075]: "each trained in this embodiment with a set of training cardiac signal data set acquired from patients "), by: acquiring, using one or more pulse oximeters ([0208]: “a pulse oximetry signal data set”), physiological readings from subjects in a study cohort ([0023]: “receiving, by a processor, a biophysical signal data set of a subject acquired from one or more channels of one or more sensors;”); extracting a set of features from the physiological readings to generate a training dataset based on the physiological readings from the subjects in the study cohort ([0109]: " to extract the beats from one or more of the other channels (e.g., ORTH2 and ORTH3). As shown, assessment system 110 generates a first beat-to-beat segment from channel “1” (406 a) (also referred to as channel “ORTH1”) that are phase aligned with both a beat-to-beat segment from channel “2” (406 b) (also referred to as channel “ORTH2”) and a beat-to-beat segment from channel “3” (406 c) (also referred to as channel “ORTH3”), which can be used collectively as one input to the convolutional neural network… as input to the neural network(s)… for training or analysis."); and applying machine learning techniques to the training dataset to train the predictive model such that the predictive model is configured to accept the plurality of physiological measurements and generate a classification corresponding to the vascular condition ([0075]: "The one or more neural networks 132 a, in some embodiments, receive(s) the pre-processed data sets 118 to train a classifier and/or to perform a classification on the received input."), wherein applying the machine learning techniques comprises performing automated feature selection to identify a subset of the set of features and refitting the predictive model based on the subset of features ([0082]: " the modeling algorithm is configured to iteratively and recursively select candidate basis functions to add to the model until a stopping condition is reached (e.g., an assessed accuracy value reaches a pre-defined accuracy value (e.g., X %), the model reaches a maximum allowable number of candidates, and/or the model has included all available candidates)."), wherein the subset of features corresponds to the plurality of physiological measurements ([0082]: “within the pre-processed biophysical signal data set 118 (or the acquired biophysical signal data set 108)”); and outputting or storing the classification in association with the patient ([0075]: "The output of the second set of one or more neural networks 132 b (e.g., one or more deep neural network(s), one or more convolutional neural network(s), etc.), or ensemble(s) thereof, in some embodiments, is a value (134 b), e.g., a binary value or a risk/likelihood score, that indicates presence/location of cardiac disease or condition at a region of the myocardium and/or a location in the coronary artery”).
While Khosousi discloses obtaining a plurality of physiological measurements from the patient via oximeter probe ([0010], [0075]), they fail to specify obtaining it via a first oximeter probe secured to an upper extremity of a patient and/or a second oximeter probe secured to a lower extremity of the patient.
Phattraprayoon discloses obtaining, via a first oximeter probe secured to an upper extremity of a patient and/or a second oximeter probe secured to a lower extremity of the patient (Page 2 Methods para 2: “We measured the initial SpO2 detected at the palm and ipsilateral wrist first, then at 30 s, and at 1 min, and we repeated the same procedure over the sole and ipsilateral ankle.”) , a plurality of physiological measurements from the patient (Table 2).
It would have been obvious to a person of ordinary skill in the art to modify the method disclosed by Khosousi to include the first oximeter probe secured to an upper extremity of a patient and second oximeter probe secured to a lower extremity of the patient as disclosed by Phattraprayoon in order to improve the accuracy of recorded SpO2 measurements (Phattraprayoon Page 4 Discussion para 5).
Regarding claim 2, Khosousi further discloses wherein the vascular condition is a congenital heart disease ([0013]: “congenital heart disease”).
Regarding claim 3, Phattraprayoon discloses wherein the patient and the subjects in the cohort are newborns and/or infants (title).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Weston et al. (US20080233576A1) – discloses a machine learning algorithm
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/KAVYA SHOBANA BALAJI/Examiner, Art Unit 3791
/DANIEL L CERIONI/Primary Examiner, Art Unit 3791