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 .
Status of Claims
This action is in reply to the application filed on 09/19/2022.
Claims 25-59 are currently pending and have been examined.
Claims 1-24 have been canceled in a preliminary amendment.
Claims 25-59 have been rejected.
Drawings
New corrected drawings in compliance with 37 CFR 1.121(d) are required in this application because FIG. 4-7 of the drawings are illegible and do not comply with line uniformity, density definition requirement and the use of shading standards put forth in 37 CFR 1.84 (l) and (m):
l) All drawings must be made by a process which will give them satisfactory reproduction characteristics. Every line, number, and letter must be durable, clean, black (except for color drawings), sufficiently dense and dark, and uniformly thick and well-defined. The weight of all lines and letters must be heavy enough to permit adequate reproduction. This requirement applies to all lines however fine, to shading, and to lines representing cut surfaces in sectional views. Lines and strokes of different thicknesses may be used in the same drawing where different thicknesses have a different meaning.
(m) Shading. The use of shading in views is encouraged if it aids in understanding the invention and if it does not reduce legibility. Shading is used to indicate the surface or shape of spherical, cylindrical, and conical elements of an object. Flat parts may also be lightly shaded. Such shading is preferred in the case of parts shown in perspective, but not for cross sections. See paragraph (h)(3) of this section. Spaced lines for shading are preferred. These lines must be thin, as few in number as practicable, and they must contrast with the rest of the drawings. As a substitute for shading, heavy lines on the shade side of objects can be used except where they superimpose on each other or obscure reference characters. Light should come from the upper left corner at an angle of 45°. Surface delineations should preferably be shown by proper shading. Solid black shading areas are not permitted, except when used to represent bar graphs or color.
The Examiner further notes that due the grayscale conversion process inherent in the Office's electronic application filing system, elements of FIG. 4-7 are rendered partially illegible which interferes with providing a clear disclosure of the invention to the public.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as "amended." If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either "Replacement Sheet" or "New Sheet" pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Applicant is advised to employ the services of a competent patent draftsperson outside the Office, as the U.S. Patent and Trademark Office no longer prepares new drawings.
Thus, new corrected drawings in compliance with 37 CFR 1.121(d) are required in this application for FIG. 4-7. Examiner notes that the Fig. 4 filed 03/02/2023 is still illegible and does not comply with line uniformity, density definition requirement and the use of shading standards put forth in 37 CFR 1.84 (l) and (m).
Claim Objections
Claims 45 and 54 are objected to because of the following informalities:
Claim 45 currently discloses in step (b), “product of step (b)” and thus step (b) references the product of itself. The Examiner is interpreting the limitation to read, “the product of step (a)…” Appropriate correction is required.
Claim 54 discloses in step (d), “obtain an assessment of the patient. Wherein the assessment…” Thus, disclosing a period in the middle of the claim. See MPEP 608.01(m), “Each claim begins with a capital letter and ends with a period. Periods may not be used elsewhere in the claims except for abbreviations. See Fressola v. Manbeck, 36 USPQ2d 1211 (D.D.C. 1995).” Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
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 33-34, 37 42, 51, 53, and 55-58 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.
Claim 33 discloses, “wherein the feature engineering is comprised of feature extraction to result in features and wherein the feature extraction involves a technique or method selected from the group consisting of …Gaussian mixture models, and Naive Bayes classifiers which group together similar feature sets by plurality of features extracted or plurality of statistically summarized inputs and assign group labels to each instance of a set of features.” It is unclear if the ”which group together similar feature sets by plurality of features extracted or plurality of statistically summarized inputs and assign group labels to each instance of a set of features” applies to each of the techniques or methods disclosed or only for the Naïve Bayes classifiers due to the way the claim is formatted. The Examiner is interpreting it to only apply to the Naïve Bayes classifiers. Dependent claim 34 is rejected as dependent on a rejected claim.
Claim 37 discloses step (d) and skips to step (f) instead of step (e). Further, the limitation discloses, “methods; processes for the signal and model assessment…” It is unclear if the semi-colon is meant so that “processes for the signal and model assessment…” would be the missing step (e) or if the claim is supposed to mirror a similar limitation from claim 45. The Examiner is interpreting step (d) to mirror that of claim 45 to read as, “… methods or processes for the signal and model assessment,” and step (f) to read as step (e). Appropriate correction is required.
Claim 42 discloses, “wherein during Step b) the input data is conditioned using an engineering system selected from the group consisting of filtering in time, frequency, wavelet, or other domains defined by a span of output of a convolutional neural network prior to a final layer which is a connected layer, so that the transformation does not remove any information from the data that is being transformed.” It is unclear if the “defined by a span of output of a convolutional neural network prior to a final layer which is a connected layer, so that the transformation does not remove any information from the data that is being transformed” applies to each of the engineering systems or only for the “other domains.” The Examiner is interpreting it to only apply to the “other domains.”
Claim 51 discloses, “wherein the method predicts a degree of certainty… wherein each of the degrees of confidence is based at least on…” There is a lack of antecedent basis for “the degrees of confidence.” The Examiner is interpreting the limitation as “the method predicts a degrees of confidence… wherein each of the degrees of confidence are based at least on…”
Claim 53 is currently dependent on Claim 37. However, claim 53 discloses, “the discriminator component…” The discriminator component is disclosed in claim 52, not in claim 37. Thus, the scope of this claim is unclear as to which claim (37 or 52) claim 53 is dependent on. The Examiner is interpreting claim 53 to be dependent on claim 52. Examiner notes that there would be a lack of antecedent basis for “the discriminator component” in claim 53 if the claims were dependent on claim 37. Appropriate correction is required.
Claims 55-58 are currently dependent on Claim 45. However, claims 55 and 57-58 disclose, “the assessment predictions...” The assessment predictions are disclosed in independent claim 54, not independent claim 45. Thus, the scope of these claims are unclear as to which claim (45 or 54) claims 55 and 57-58 are dependent on. The Examiner is interpreting claims 55-58 to be dependent on claim 54. Examiner notes that there would be a lack of antecedent basis for “the assessment predictions” in claims 55 and 57-58 if the claims were dependent on claim 45. Appropriate correction is required.
Claim 56 discloses generating a report, based on para 32 of the Applicants specification, where the report is disclosed in a paragraph that focuses on the assessment predictions. Thus, the scope of this claim is unclear as to which claim (45 or 54) claim 56 are dependent on. Thus, the Examiner is interpreting claim 56 to further be dependent on claim 54 and not claim 45. Appropriate correction is required.
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 25-59 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed invention is directed to an abstract idea without significantly more. Claims 25-59 are directed to a system, method, or product which are one of the statutory categories of invention. (Step 1: YES).
Independent Claim 25 discloses a method for assessment of a patient during perioperative care comprising the steps of: a) selectively obtaining a plurality of input data from one or more measurement devices, selection and collection methods and/or processes; b) subjecting the input data to a transformation selected from the group consisting of conditioning, feature engineering and combinations thereof; c) translating the transformed input data into metrics; and d) using the metrics obtained in step c to obtain an assessment of the patient.
Independent Claim 54 discloses a method for improving a patient's recovery using assessment predictions generated during perioperative care comprising a) selectively obtaining a plurality of input data from one or more measurement devices, selection and collection methods and/or processes; b) subjecting the input data to a transformation selected from the group consisting of conditioning, feature engineering and combinations thereof; c) translating the transformed input data into metrics; and d) using the metrics obtained in Step c to obtain an assessment of the patient wherein the assessment is further configured to predict an outcome of a set of possible further surgeries for the patient at a specific point in time after the surgery; wherein the further configured assessment predictions for possible further surgeries are configured to predict a degree of confidence for each of the assessment predictions, where the degree of confidence indicates the likelihood that the patient will achieve the assessment prediction.
The above limitations are merely directed to a mathematical concept which is an abstract idea. As further discussed below, dependent claims 30, 32-34, 36, 40-42 specifically disclose that the data conditioning may be selected from transformations of the input data from the time domain to other domains (claim 30), data transformation may be selected from fourier, wavelet, short-time fourier, cepstral analysis, empirical mode decomposition, or wavelet decomposition (claim 32), feature engineering may be selected from discrete fourier and short term fourier transform as well additional mathematical concepts (claim 33) and thus multiresolution analysis and signal decomposition using wavelet transforms (claim 34) and the translation of step c involves a mathematical model to transform the data from step b (claim 36) the transformation and/or decomposition of the feature engineering is selected from box cox transformation, eigen value, vector decomposition, principal component analysis (PCA), kernel PCA, truncated singular value decomposition, multidimensional scaling, isometric mapping, t-distributed stochastic neighbor embedding, wavelet denoising (claim 40), the feature selection of the feature engineering is selected from Kullback-Leibier convergence, mnianumni redundancy maximum relevance, impurity-based feature importance using random forest regression models, F-statistic or f-test, neighborhood component analysis, backward elimination, forward selection, permutation feature importance, factor analysis, and relief algorithm for regression (claim 41) transforming selected from dimensionality reduction techniques consisting of box cox transformation, eigenvalue, and vector decomposition, principal component analysis (PCA), backward elimination, forward selection, random forests impurity-based importance, permutation feature importance, factor analysis, linear discriminant analysis, truncated singular value decomposition, kernel PCA, t-distributed stochastic neighbor embedding, multidimensional scaling, isometric mapping (claim 42). Thus, identifying steps b and c as being directed to a mathematical concept (Step 2A- Prong 1: YES. The claims are abstract).
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05.f), (2) Adding insignificant extra- solution activity to the judicial exception (MPEP 2106.05.g), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05.h).
Independent Claims 1 and 54 disclose the following additional element:
selectively obtaining a plurality of input data from one or more measurement devices, selection and collection methods and/or processes;
In particular, selectively obtaining a plurality of input data from one or more measurement devices, selection and collection methods and/or processes is recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea by adding the words ‘apply it’ (or an equivalent) with the judicial exception.
Accordingly, claim(s) 1 and 54 are directed to an abstract idea(s) without a practical application. (Step 2A-Prong 2: NO: the additional claimed elements are not integrated into a practical application).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the selectively obtaining a plurality of input data from one or more measurement devices, selection and collection methods and/or processes amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more’). MPEP2106.05(I)(A) indicates that merely saying "apply it” or equivalent to the abstract idea cannot provide an inventive concept ("significantly more").
Accordingly, even in combination, this additional element does not provide significantly more. As such the independent claims 1 and 54 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more).
Dependent claim(s) 26-53 and 55-59 are similarly rejected because they either further define/narrow the abstract idea (wherein dependent claims 30, 32-34, 36, 40-42 specifically disclose that the data conditioning may be selected from transformations of the input data from the time domain to other domains (claim 30), data transformation may be selected from fourier, wavelet, short-time fourier, cepstral analysis, empirical mode decomposition, or wavelet decomposition (claim 32), feature engineering may be selected from discrete fourier and short term fourier transform as well additional mathematical concepts (claim 33) and thus multiresolution analysis and signal decomposition using wavelet transforms (claim 34) and the translation of step c involves a mathematical model to transform the data from step b (claim 36) the transformation and/or decomposition of the feature engineering is selected from box cox transformation, eigen value, vector decomposition, principal component analysis (PCA), kernel PCA, truncated singular value decomposition, multidimensional scaling, isometric mapping, t-distributed stochastic neighbor embedding, wavelet denoising (claim 40), the feature selection of the feature engineering is selected from Kullback-Leibier convergence, mnianumni redundancy maximum relevance, impurity-based feature importance using random forest regression models, F-statistic or f-test, neighborhood component analysis, backward elimination, forward selection, permutation feature importance, factor analysis, and relief algorithm for regression (claim 41) transforming selected from dimensionality reduction techniques consisting of box cox transformation, eigenvalue, and vector decomposition, principal component analysis (PCA), backward elimination, forward selection, random forests impurity-based importance, permutation feature importance, factor analysis, linear discriminant analysis, truncated singular value decomposition, kernel PCA, t-distributed stochastic neighbor embedding, multidimensional scaling, isometric mapping (claim 42)) and/or do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
Dependent claims 29, 40, 42, and 52-53 do further disclose the additional element(s) of a non-invasive sensor medical device (claim 29), a neural network (claim 40), a convolutional neural network (claim 42), a generative neural network comprising a generator component and a discriminator component (claim 52 and 53)
In particular, the non-invasive sensor medical device (claim 29), a neural network (claim 40), a convolutional neural network (claim 42), a generative neural network comprising a generator component and a discriminator component (claim 52 and 53) are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea by adding the words ‘apply it’ (or an equivalent) with the judicial exception. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the non-invasive sensor medical device (claim 29), a neural network (claim 40), a convolutional neural network (claim 42), a generative neural network comprising a generator component and a discriminator component (claim 52 and 53) amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more’). MPEP2106.05(I)(A) indicates that merely saying "apply it” or equivalent to the abstract idea cannot provide an inventive concept ("significantly more").Accordingly, even in combination, these additional element do not provide significantly more.
Therefore, the dependent claims are also directed to an abstract idea.
Thus, Claims 25-59 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 25-28, 30-31, 35-36, 38, 40, and 43-44 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Shelton (US PG Pub 2022/0241474 A1).
Regarding Claim 25,
A method for assessment of a patient during perioperative care comprising the steps of:
a) selectively obtaining a plurality of input data from one or more measurement devices, selection and collection methods and/or processes; (Para 41 discloses the biomarkers 20005 may relate to physiologic systems 20007, which may include, but are not limited to, behavior and psychology, cardiovascular system, renal system, skin system, nervous system, gastrointestinal system, respiratory system, endocrine system, immune system, tumor, musculoskeletal system, and/or reproductive system. Information from the biomarkers may be determined and/or used by the computer-implemented patient and surgeon monitoring system 20000, for example. Para 351 and FIG. 11D discloses an example of an electroencephalogram (EEG) sensing system 20315. As illustrated in FIG. 11D, the sensing system 20315 may include one or more EEG sensor units 20317. The EEG sensor units 20317 may include a plurality of conductive electrodes placed in contact with the scalp. The conductive electrodes may be used to measure small electrical potentials that may arise outside of the head due to neuronal action within the brain. The EEG sensing system 20315 may measure a biomarker, for example, delirium by identifying certain brain patterns. See further: 142, 156, 383, 388, 450)
b) subjecting the input data to a transformation selected from the group consisting of conditioning, feature engineering and combinations thereof; (Para 351 and Fig. 11D disclose one or more EEG sensor units 20317. The EEG sensor units 20317 may include a plurality of conductive electrodes placed in contact with the scalp. The conductive electrodes may be used to measure small electrical potentials that may arise outside of the head due to neuronal action within the brain. The EEG sensing system 20315 may measure a biomarker, for example, delirium by identifying certain brain patterns, for example, a slowing or dropout of the posterior dominant rhythm and loss of reactivity to eyes opening and closing. The ring sensing system 20315 may have a signal conditioning unit for filtering [data conditioning which is a transformation according to the Applicant’s definition based on claims 25 and 30) and amplifying the electrical potentials, a microcontroller to digitize the electrical signals, and a wireless (e.g., a low energy Bluetooth) module to transfer the data to a smart device, for example, as described in FIGS. 7B through 7D. Para 365 discloses a trained model may be any type of processing logic that performs an analysis and generates a prediction or determination derived from or generated based on empirical data, which may be referred to interchangeably as logic, an algorithm, a model, an ML algorithm or model, a neural network (NN), deep learning, artificial intelligence (AI), and so on. Para 383 discloses measured/measurement data may be processed and/or transformed, for example, into (e.g., to determine) one or more patient biomarkers. An analysis may be performed, for example, using one or more patient biomarkers and/or measured/measurement data. Measurement data/information and/or patient biomarkers (e.g., detected or determined before, during, and/or after a surgical procedure) may be used to establish one or more thresholds for one or more patient biomarkers (e.g., to perform one or more analyses, make one or more determinations and/or recommendations)… Patient biomarkers may include, for example, one or more of the following, a maximum amount of oxygen a person can utilize or consume (e.g., during exercise) (VO2 Max), respiration rate, heart rate variability, physical reaction (coughing and sneezing), and oxygen saturation, electroencephalogram (EEG), electrocardiogram (ECG or EKG), rapid eye movement sleep (REM sleep or REMS), gastrointestinal (GI) motility, etc.)
c) translating the transformed input data into metrics; and (Para 364 discloses a model trained on vectorized training data may process vectorized input data. For example, a model may receive vectorized patient-specific data as input and classify the data as being indicative of one or more complications and/or one or more recovery milestones (e.g., each associated with a probability, likelihood, or confidence level). Para 368 discloses hub 25258 may receive measurement data associated with patient biomarker(s) from first, second and third sensing systems 25253, 25254, 25255, generate predicted complications to monitor patient for, determine patient monitoring thresholds for recovery milestones and/or complications, analyze patient biomarker measurements relative to the thresholds for the predicted complications and/or recovery milestones, generate and send (e.g., wirelessly transmit) recommendations, such as instructions to display to a patient or HCP and/or a control program or adjustments thereto for (e.g., patient specific) operation of controllable device 25256, etc.)
d) using the metrics obtained in step c to obtain an assessment of the patient. (Para 4 discloses systems, methods, and instrumentalities are disclosed herein for a (e.g., pre-, in-, and/or post-operative) patient monitoring system. Patient biomarkers may be monitored before, during, and/or after thoracic surgery to predict complications, detect complications, track recovery, and/or make pre-, in- and/or post-surgery recommendations to avoid predicted complications and/or mitigate detected complications. Para 364 discloses predictions of complications and/or recovery milestones may be generated, for example, by one or more machine learning (ML) models, such as predictive models, trained to make predictions after being trained on training data. For example, a model may receive vectorized patient-specific data as input and classify the data as being indicative of one or more complications and/or one or more recovery milestones (e.g., each associated with a probability, likelihood, or confidence level). Para 399 discloses a notification (e.g., an actionable notification) may be reported, for example, in real-time (e.g., real-time reporting). A notification (e.g., an actionable notification) may indicate, for example, a thoracic post-surgery complication or the achievement of a thoracic post-surgery milestone [recovery tracking]. Reports may be generated, provided, and/or received (e.g., by an HCP) periodically (e.g., hourly, daily, weekly) and/or aperiodically (e.g., ad hoc, on demand by sender and/or recipient, based on emergency or threshold detection, and/or the like)…)
Regarding Claim 26, this claim recites the limitations of Claim 25 and as to those limitations is
rejected for the same basis and reasons as disclosed above. Further, Shelton discloses:
The method of claim 25, wherein the input data is selected from the group consisting of past diagnoses, test results for blood biomarkers, proteins, metabolites, and/or cholesterol, biomedical vital signs collected from non-invasive medical devices, patient reported responses to Quality of Recovery questionnaires, physiological and biological data, a height of the patient, a weight of the patient, a gender of the patient, an age of the patient, a medical history and/or physical examination records of the patient, a medical status of the patient, a body mass index (BMI) of the patient, an ethnicity of the patient, a medical prescription history of the patient, a medical prescription status of the patient, types of treatments and medications received by the patient, types of medical treatments for health issues and insurance or claims information previously received by the patient, diet information for the patient, psychological history of the patient, a genetic indicator of the patient, biomarkers of the patient, the Electronic Medical Record of the patient information and combinations thereof. (Para 41 discloses the biomarkers 20005 may relate to physiologic systems 20007, which may include, but are not limited to, behavior and psychology, cardiovascular system, renal system, skin system, nervous system, gastrointestinal system, respiratory system, endocrine system, immune system, tumor, musculoskeletal system, and/or reproductive system. Information from the biomarkers may be determined and/or used by the computer-implemented patient and surgeon monitoring system 20000, for example. Para 142 discloses behavior and psychology scores may include scores for social interaction, diet, sleep, activity, and/or psychological status. Para 156 discloses a computing system, as described herein, may select one or more biomarkers (e.g., data from biomarker sensing systems) from skin-related biomarkers, including skin conductance, skin perfusion pressure, sweat, autonomic tone, and/or pH for analysis. Para 351 and FIG. 11D discloses an example of an electroencephalogram (EEG) sensing system 20315. As illustrated in FIG. 11D, the sensing system 20315 may include one or more EEG sensor units 20317. The EEG sensor units 20317 may include a plurality of conductive electrodes placed in contact with the scalp. The conductive electrodes may be used to measure small electrical potentials that may arise outside of the head due to neuronal action within the brain. The EEG sensing system 20315 may measure a biomarker, for example, delirium by identifying certain brain patterns, for example, a slowing or dropout of the posterior dominant rhythm and loss of reactivity to eyes opening and closing. The ring sensing system 20315 may have a signal conditioning unit for filtering and amplifying the electrical potentials, a microcontroller to digitize the electrical signals, and a wireless (e.g., a low energy Bluetooth) module to transfer the data to a smart device, for example, as described in FIGS. 7B through 7D. Para 383 discloses patient biomarkers may include, for example, one or more of the following, a maximum amount of oxygen a person can utilize or consume (e.g., during exercise) (VO2 Max), respiration rate, heart rate variability, physical reaction (coughing and sneezing), and oxygen saturation, electroencephalogram (EEG), electrocardiogram (ECG or EKG), rapid eye movement sleep (REM sleep or REMS), gastrointestinal (GI) motility, etc. Para 388 discloses the post-surgical complication may be any type of complication, such as a PAL. The first patient biomarker or the second patient biomarker may be any biomarker, such as one of the following: a blood lactate, a sweat lactate, an indication of GI motility, a heart rate variability, a blood pH value, a sweat pH value, etc [where claim 27 discloses EEG, ECG or EKG, pH, and lactate as physiological and biological data]. Para 450 discloses nutritional status may be (e.g., additionally and/or alternatively) determined, for example, based on one or more of the following patient biomarkers: sweat, tears, urine, alcohol use, blood/serum ratio, saliva, etc. Para 449 discloses an occurrence of a complication, such as a PAL, may be (e.g., additionally and/or alternatively) predicted or determined (e.g., by a model used for predicting a complication, such as a PAL) based on a set of patient parameters, such as one or more of the following variables (e.g., predictive or determinative variables): a patient's age, a patient's body-mass index (BMI).)
Regarding Claim 27, this claim recites the limitations of Claim 26 and as to those limitations is rejected for the same basis and reasons as disclosed above. Further, Shelton discloses:
The method of claim 26, wherein the physiological and biological data is selected from the group consisting of electrocardiogram, electromyogram, electrooculogram, electroencephalogram, galvanic skin resistance, goniometric measurements of all joint angles, absolute inclination, and orientation of different parts of the body in multiple axes, photoplethysmographic measures at various sites on the body, heart sounds, lung sounds, gastrointestinal sounds, joint sounds, acoustic impedance, electromagnetic impedance, ultrasonic impedance, blood oxygen levels, temperatures measured at different locations of the body, sweat biomarkers measured at various areas of the body such as lactate, pH, alcohol, nicotine, sodium, glucose, urea, chloride, discrete blood, interstitial fluid, cerebrospinal fluid (CSF) biomarker panels and combinations thereof. (Para 43 discloses photoplethysmogram (PPG) in a sleep sensing system. Para 55 discloses the physical activity sensing system may measure physical activity data including accelerometer, magnetometer, gyroscope, global positioning system (GPS), PPG, and/or ECG. The physical activity sensing system may include a wearable device. The physical activity wearable device may include, but is not limited to, a watch, wrist band, vest, glove, belt, headband, shoe, and/or garment [various sites on the body]. Para 65 discloses based on the oxygen saturation data, the oxygen saturation sensing system may calculate oxygen saturation-related biomarkers including peripheral blood oxygen saturation (SpO2). Para 67 discloses photoplethysmography in a blood pressure sensing system. Para 77 discloses pH data including… sweat pH. Para 156 discloses a computing system, as described herein, may select one or more biomarkers (e.g., data from biomarker sensing systems) from skin-related biomarkers, including skin conductance, skin perfusion pressure, sweat, autonomic tone, and/or pH for analysis. Para 238 discloses measuring sweat lactate. Para 91 discloses based on peripheral temperature data, the peripheral temperature sensing system may determine peripheral temperature-related biomarkers including basal body temperature, extremity skin temperature, and/or patterns in peripheral temperature. Para 123 discloses a circadian rhythm sensing system may measure circadian rhythm data including light exposure, heart rate, core body temperature [thus core and peripheral read on different locations of the body]. Para 351 and FIG. 11D discloses an example of an electroencephalogram (EEG) sensing system 20315. As illustrated in FIG. 11D, the sensing system 20315 may include one or more EEG sensor units 20317. The EEG sensor units 20317 may include a plurality of conductive electrodes placed in contact with the scalp. The conductive electrodes may be used to measure small electrical potentials that may arise outside of the head due to neuronal action within the brain. The EEG sensing system 20315 may measure a biomarker, for example, delirium by identifying certain brain patterns, for example, a slowing or dropout of the posterior dominant rhythm and loss of reactivity to eyes opening and closing. The ring sensing system 20315 may have a signal conditioning unit for filtering and amplifying the electrical potentials, a microcontroller to digitize the electrical signals, and a wireless (e.g., a low energy Bluetooth) module to transfer the data to a smart device, for example, as described in FIGS. 7B through 7D. Para 388 discloses the post-surgical complication may be any type of complication, such as a PAL. The first patient biomarker or the second patient biomarker may be any biomarker, such as one of the following: a blood lactate, a sweat lactate, an indication of GI motility, a heart rate variability, a blood pH value, a sweat pH value, etc. Para 453 discloses Transdermal alcohol monitoring may provide continuous monitoring of alcohol consumption. A transdermal sensor (e.g., in a device such as a bracelet) may be worn next to the skin to sample (e.g., otherwise insensible) perspiration. A pump inside a device (e.g., bracelet) may take a sample (e.g., of sweat). The presence of alcohol in the sample (e.g., sweat) may be measured, for example, based on a reaction between ethanol in the sample and a fuel cell (e.g., similar to a fuel cell used in a breath sensor). A blood alcohol level may be calculated based on the measurement. See Further: Para 59.)
Regarding Claim 28, this claim recites the limitations of Claim 25 and as to those limitations is rejected for the same basis and reasons as disclosed above. Further, Shelton discloses:
The method of claim 25, wherein the input data comprises measurements made from patients reflective of physiological conditions selected from the group consisting of electrocardiogram, electromyogram, electroencephalogram, phonocardiogram, activity and posture, sweat, blood and urine analysis results, and historical information on diagnosed conditions, past surgical interventions, and history of medications. (Para 351 and FIG. 11D discloses an example of an electroencephalogram (EEG) sensing system 20315. As illustrated in FIG. 11D, the sensing system 20315 may include one or more EEG sensor units 20317. The EEG sensor units 20317 may include a plurality of conductive electrodes placed in contact with the scalp. The conductive electrodes may be used to measure small electrical potentials that may arise outside of the head due to neuronal action within the brain. The EEG sensing system 20315 may measure a biomarker, for example, delirium by identifying certain brain patterns, for example, a slowing or dropout of the posterior dominant rhythm and loss of reactivity to eyes opening and closing. The ring sensing system 20315 may have a signal conditioning unit for filtering and amplifying the electrical potentials, a microcontroller to digitize the electrical signals, and a wireless (e.g., a low energy Bluetooth) module to transfer the data to a smart device, for example, as described in FIGS. 7B through 7D.)
Regarding Claim 30, this claim recites the limitations of Claim 26 and as to those limitations is
rejected for the same basis and reasons as disclosed above. Further, Shelton discloses:
The method of claim 26, wherein the data conditioning is obtained by methods selected from the group consisting of filtering, trend removal when there are gradual drifts in the measurement values due to the instrumentation used to perform the measurement, signal processing methods that increase the proportion of physiologically relevant data to the noise, transformations of the input data from the time domain to other domains, application of filtering techniques to segment and extract quantitative or qualitative measures correlated with physiological factors in turn correlated to patient status assessments, neural networks and combinations thereof. (Para 351 and FIG. 11D discloses an example of an electroencephalogram (EEG) sensing system 20315. As illustrated in FIG. 11D, the sensing system 20315 may include one or more EEG sensor units 20317. The EEG sensor units 20317 may include a plurality of conductive electrodes placed in contact with the scalp. The conductive electrodes may be used to measure small electrical potentials that may arise outside of the head due to neuronal action within the brain. The EEG sensing system 20315 may measure a biomarker, for example, delirium by identifying certain brain patterns, for example, a slowing or dropout of the posterior dominant rhythm and loss of reactivity to eyes opening and closing. The ring sensing system 20315 may have a signal conditioning unit for filtering [data conditioning] and amplifying the electrical potentials, a microcontroller to digitize the electrical signals, and a wireless (e.g., a low energy Bluetooth) module to transfer the data to a smart device, for example, as described in FIGS. 7B through 7D.)
Regarding Claim 31, this claim recites the limitations of Claim 25 and as to those limitations is
rejected for the same basis and reasons as disclosed above. Further, Shelton discloses:
The method of claim 25, wherein the input data is conditioned and after conditioning, the input data is prepared by translating the input data into a format that is compatible with Step c. (Para 351 discloses a signal conditioning unit for filtering and amplifying the electrical potentials… Para 383 discloses measured/measurement data may be processed and/or transformed, for example, into (e.g., to determine) one or more patient biomarkers.)
Regarding Claim 35, this claim recites the limitations of Claim 25 and as to those limitations is
rejected for the same basis and reasons as disclosed above. Further, Shelton discloses:
The method of claim 25, wherein the assessment provides an overall metric that is reflective of the patient's state of recovery, a risk stratification score or number that is reflective of a probability or likelihood of a patient developing symptoms of a complication or risk of developing a condition that requires emergency treatment following a surgical procedure or combinations thereof. (Para 363 discloses post-surgical (e.g., recovery) monitoring may monitor patient biomarkers to track recovery metrics and/or complications for a surgery, such as lung surgery. Post-surgery monitoring of patient biomarkers may (e.g., be used to) track healing stage progress or recovery milestones and/or determine a probability of complications, which may, for example, result in PALs (e.g., after thoracic resection of a lung) [metrics reflective of the patient’s state of recovery].)
Regarding Claim 36, this claim recites the limitations of Claim 25 and as to those limitations is
rejected for the same basis and reasons as disclosed above.
The method of claim 25, wherein the transformation in Step b is obtained by clustering methods and the translation of the transformed input data into metrics of Step c involves a mathematical model to transform a cluster membership into a one-dimensional metric. (Para 4 discloses Systems, methods, and instrumentalities are disclosed herein for a (e.g., pre-, in-, and/or post-operative) patient monitoring system. Patient biomarkers may be monitored before, during, and/or after thoracic surgery to predict complications, detect complications, track recovery, and/or make pre-, in- and/or post-surgery recommendations to avoid predicted complications and/or mitigate detected complications. Para 41 discloses The biomarkers 20005 may relate to physiologic systems 20007, which may include, but are not limited to, behavior and psychology, cardiovascular system, renal system, skin system, nervous system, gastrointestinal system, respiratory system, endocrine system, immune system, tumor, musculoskeletal system, and/or reproductive system [thus disclosing clustering methods by clustering the variety of data based on the physiologic system]. Information from the biomarkers may be determined and/or used by the computer-implemented patient and surgeon monitoring system 20000, for example. Para 364 discloses a model trained on vectorized training data may process vectorized input data. For example, a model may receive vectorized patient-specific data as input and classify the data as being indicative of one or more complications and/or one or more recovery milestones (e.g., each associated with a probability, likelihood, or confidence level [one-dimensional metric]). Para 368 discloses hub 25258 may receive measurement data associated with patient biomarker(s) from first, second and third sensing systems 25253, 25254, 25255, generate predicted complications to monitor patient for, determine patient monitoring thresholds for recovery milestones and/or complications, analyze patient biomarker measurements relative to the thresholds for the predicted complications and/or recovery milestones, generate and send (e.g., wirelessly transmit) recommendations, such as instructions to display to a patient or HCP and/or a control program or adjustments thereto for (e.g., patient specific) operation of controllable device 25256, etc.)
Regarding Claim 38, this claim recites the limitations of Claim 25 and as to those limitations is
rejected for the same basis and reasons as disclosed above. Further, Shelton discloses:
The method of claim 25, wherein the feature engineering comprises the steps of feature transformation and/or decomposition. (Para 365 discloses a trained model may be any type of processing logic that performs an analysis and generates a prediction or determination derived from or generated based on empirical data, which may be referred to interchangeably as logic, an algorithm, a model, an ML algorithm or model, a neural network (NN), deep learning, artificial intelligence (AI), and so on.)
Regarding Claim 40, this claim recites the limitations of Claim 38 and as to those limitations is
rejected for the same basis and reasons as disclosed above. Further, Shelton discloses:
The method of claim 38, wherein the transformation and/or decomposition involves techniques selected from the group consisting of box cox transformation, eigen value, vector decomposition, principal component analysis (PCA), kernel PCA, truncated singular value decomposition, multidimensional scaling, isometric mapping, t-distributed stochastic neighbor embedding, wavelet denoising, neural networks and combinations thereof. (Para 365 discloses a trained model may be any type of processing logic that performs an analysis and generates a prediction or determination derived from or generated based on empirical data, which may be referred to interchangeably as logic, an algorithm, a model, an ML algorithm or model, a neural network (NN), deep learning, artificial intelligence (AI), and so on.)
Regarding Claim 43, this claim recites the limitations of Claim 25 and as to those limitations is
rejected for the same basis and reasons as disclosed above. Further, Shelton discloses:
The method of claim 25, wherein based on the assessment, the method provides further actions selected from the group consisting of planning, support, follow-up, patient compliance, recovery prediction and tracking, potential treatment modifications and combinations thereof. (Para 4 discloses systems, methods, and instrumentalities are disclosed herein for a (e.g., pre-, in-, and/or post-operative) patient monitoring system. Patient biomarkers may be monitored before, during, and/or after thoracic surgery to predict complications [recovery prediction], detect complications, track recovery, and/or make pre-, in- and/or post-surgery recommendations to avoid predicted complications [potential treatment modifications] and/or mitigate detected complications. Para 363 discloses post-surgical (e.g., recovery) monitoring may monitor patient biomarkers to track recovery metrics and/or complications for a surgery, such as lung surgery. Post-surgery monitoring of patient biomarkers may (e.g., be used to) track healing stage progress or recovery milestones [recovery tracking] and/or determine a probability of complications [recovery prediction], which may, for example, result in PALs (e.g., after thoracic resection of a lung) [metrics reflective of the patient’s state of recovery]. See further: Para 182.)
Regarding Claim 44, this claim recites the limitations of Claim 25 and as to those limitations is
rejected for the same basis and reasons as disclosed above. Further, Shelton discloses:
The method of claim 25, wherein the assessment is presented as a numeric, symbolic, image, or video. (Para 241 discloses a patient sensing system 20044 may receive a notification information from the surgical hub 20006 for displaying on a display unit or an HID of the patient sensing system 20044. The notification information may include a notification about a recovery milestone or a notification about a complication, for example, in case of post-surgical recovery… the patient sensing system 20044 may display the notification and the actionable severity level to the patient [an image of the assessment]. The patient sensing system may alert the patient using a haptic feedback. The visual notification and/or the haptic notification may be accompanied by an audible notification prompting the patient to pay attention to the visual notification provided on the display unit of the sensing system.)
Claim Rejections - 35 USC § 103
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 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 29 is rejected under 35 U.S.C. 103 as being unpatentable over Shelton (US PG Pub 2022/0241474) in view of Yao (Yao, S., Swetha, P., & Zhu, Y. (Nanomaterial‐enabled wearable sensors for Healthcare).
Regarding Claim 29, this claim recites the limitations of Claim 26 and as to those limitations is rejected for the same basis and reasons as disclosed above. While Shelton discloses a non-invasive medical device (see Shelton para 85) which discloses a heart rate sensing system (may include a watch, a wearable elastic band, a skin patch, a bracelet, garments, a wrist strap, earphone, headband, wearable chest patch) that uses ECG to measure heart rate data and Shelton Para 140 discloses, “the chem-phys hybrid sensor may measure biomarkers including lactate, ECG, and/or the like. For example, the patch may include nanomaterials,” it does not fully disclose the following limitation that Yao discloses:
The method of claim 26, wherein the biomedical vital signs are collected from non- invasive nanosensor medical devices. (Abstract discloses representative applications of nanomaterial-enabled wearable sensors for healthcare, including continuous health monitoring, daily and sports activity tracking, and multifunctional electronic skin are highlighted. Introduction discloses to achieve these features, nanomaterials that are compliant, possessing larger surface area and exceptional material properties, and compatible with low-cost fabrication processes are widely employed as building blocks for developing wearable sensors. Wearable applications discloses continuous monitoring of ECG signals plays an indispensable role in managing cardiovascular diseases. Nanomaterial-enabled dry electrodes have been demonstrated for wearable and long-term ECG sensing without irritating the skin.92, 95, 100 Apexcardiogram (ACG) represents a complementary tool to the ECG analysis, which accesses the hemodynamic state of the heart. Figure 9a depicts the application of an AuNP-based resistive strain sensor in detecting the temporal changes of volume and pressure in the heart under normal state and after exercise.115 The ACG waveform clearly indicated all the characteristic peaks, providing valuable information for cardiac diagnosis. Heart rate is closely related to the physical and mental state of a person 70 It can be calculated from the R–R interval of an ECG signal. [thus disclosing vital signs determined through a non-invasive (a wearable) nanosensor (nano-enabled wearable sensors)].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the thoracic post-surgical monitoring and complication prediction as taught by Shelton with the nanomaterial-enabled wearable sensors for healthcare as taught by Yao in order to enable “low-cost fabrication processes” and achieve “long-term ECG sensing without irritating the skin” (See Yao Abstract and Wearable application).
Claim(s) 32, 37 and 39 are rejected under 35 U.S.C. 103 as being unpatentable over Shelton (US PG Pub 2022/0241474) in view of Senanayke (A hybrid intelligent system for recovery and performance evaluation after).
Regarding Claim 32, this claim recites the limitations of Claim 25 and as to those limitations is
rejected for the same basis and reasons as disclosed above. Shelton does not fully disclose the following limitation that Senanayke discloses:
The method of claim 25, wherein the input data transformation is obtained by methods selected from the group consisting Fourier, wavelet, short-time Fourier, cepstral analysis, empirical mode decomposition, or wavelet decomposition. (Continuous Wavelet Transform (CWT) discloses CWT of an EMG signal emg (t) is defined in (6) where s represents the scale parameter, τ represents the translation diameter of time shifting and the basis function ψ∗ is obtained by scaling the mother wavelet at time τ and scale s.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the thoracic post-surgical monitoring and complication prediction as taught by Shelton with the hybrid intelligent system for recovery and performance evaluation after as taught by Senanayke in order to evaluate how the frequencies of the EMG signal changed over time.
Regarding Claim 37, this claim recites the limitations of Claim 25 and as to those limitations is
rejected for the same basis and reasons as disclosed above. Further, Shelton discloses:
The method of claim 25, further comprising a personalization method comprising: a. performing one or more improvement, conditioning, and/or correction methods or processes to account for data quality and confounders; b. performing data conditioning methods and processes for data conditioning and preparation of the data; (Para 351 and Fig. 11D disclose one or more EEG sensor units 20317. The EEG sensor units 20317 may include a plurality of conductive electrodes placed in contact with the scalp. The conductive electrodes may be used to measure small electrical potentials that may arise outside of the head due to neuronal action within the brain… The ring sensing system 20315 may have a signal conditioning unit for filtering and amplifying the electrical potentials, a microcontroller to digitize the electrical signals, and a wireless (e.g., a low energy Bluetooth) module to transfer the data to a smart device, for example, as described in FIGS. 7B through 7D. Para 365 discloses a trained model may be any type of processing logic that performs an analysis and generates a prediction or determination derived from or generated based on empirical data, which may be referred to interchangeably as logic, an algorithm, a model, an ML algorithm or model, a neural network (NN) [feature engineering according to claims 38 and 40], deep learning, artificial intelligence (AI), and so on. [wherein the broadest reasonable interpretation of the claim language discloses that conditioning data accounts for data quality and confounders].)
d. performing normalization, combination, and/or transformation methods or processes for the signal and model assessment to provide inputs for the assessment for improvements, conditioning, and correction; and (Para 351 and Fig. 11D disclose one or more EEG sensor units 20317. The EEG sensor units 20317 may include a plurality of conductive electrodes placed in contact with the scalp. The conductive electrodes may be used to measure small electrical potentials that may arise outside of the head due to neuronal action within the brain. The EEG sensing system 20315 may measure a biomarker, for example, delirium by identifying certain brain patterns, for example, a slowing or dropout of the posterior dominant rhythm and loss of reactivity to eyes opening and closing. The ring sensing system 20315 may have a signal conditioning unit for filtering [data conditioning which is a transformation according to the Applicant’s definition based on claims 25 and 30) and amplifying the electrical potentials, a microcontroller to digitize the electrical signals, and a wireless (e.g., a low energy Bluetooth) module to transfer the data to a smart device, for example, as described in FIGS. 7B through 7D. Para 365 discloses a trained model may be any type of processing logic that performs an analysis and generates a prediction or determination derived from or generated based on empirical data, which may be referred to interchangeably as logic, an algorithm, a model, an ML algorithm or model, a neural network (NN), deep learning, artificial intelligence (AI), and so on. Para 383 discloses measured/measurement data may be processed and/or transformed, for example, into (e.g., to determine) one or more patient biomarkers. An analysis may be performed, for example, using one or more patient biomarkers and/or measured/measurement data. Measurement data/information and/or patient biomarkers (e.g., detected or determined before, during, and/or after a surgical procedure) may be used to establish one or more thresholds for one or more patient biomarkers (e.g., to perform one or more analyses, make one or more determinations and/or recommendations)… Patient biomarkers may include, for example, one or more of the following, a maximum amount of oxygen a person can utilize or consume (e.g., during exercise) (VO2 Max), respiration rate, heart rate variability, physical reaction (coughing and sneezing), and oxygen saturation, electroencephalogram (EEG), electrocardiogram (ECG or EKG), rapid eye movement sleep (REM sleep or REMS), gastrointestinal (GI) motility, etc.)
e. using the output of step d to provide a personalized assessment of a perioperative patient. (Para 4 discloses systems, methods, and instrumentalities are disclosed herein for a (e.g., pre-, in-, and/or post-operative) patient monitoring system. Patient biomarkers may be monitored before, during, and/or after thoracic surgery to predict complications, detect complications, track recovery, and/or make pre-, in- and/or post-surgery recommendations to avoid predicted complications and/or mitigate detected complications. Para 364 discloses predictions of complications and/or recovery milestones may be generated, for example, by one or more machine learning (ML) models, such as predictive models, trained to make predictions after being trained on training data. For example, a model may receive vectorized patient-specific data as input and classify the data as being indicative of one or more complications and/or one or more recovery milestones (e.g., each associated with a probability, likelihood, or confidence level). Para 399 discloses a notification (e.g., an actionable notification) may be reported, for example, in real-time (e.g., real-time reporting). A notification (e.g., an actionable notification) may indicate, for example, a thoracic post-surgery complication or the achievement of a thoracic post-surgery milestone [recovery tracking]. Reports may be generated, provided, and/or received (e.g., by an HCP) periodically (e.g., hourly, daily, weekly) and/or aperiodically (e.g., ad hoc, on demand by sender and/or recipient, based on emergency or threshold detection, and/or the like). A report may include, for example, one or more values for one or more patient biomarkers, one or more results of one or more analyses performed based on one or more patient biomarker values, thresholds, baselines, etc., one or more threshold values, one or more baseline values, etc. A report may include, for example, data associated with an indication, such as data indicating that a thoracic post-surgery complication has been detected. A report may include, for example, data associated with therapy tracking, e.g., indicating patient therapy, whether a patient is performing therapy, the frequency of performance, whether the therapy is being performed correctly, etc. Para 440 discloses examples of notifications, recommendations, determinations, actions, and/or implementations (e.g., that may reduce or prevent one or more potential or predicted complications) may include, for example, one or more of the following: a selection or modification/change in a surgery plan, instrument choices, surgical approach, instrument configurations and/or schedule (e.g., of surgery and/or order of use of instruments during surgery).)
While Shelton discloses the above limitations, it does not fully disclose the following limitation that Senanayke discloses:
c. performing one or more feature extraction methods or processes to extract a plurality of features for signal and model assessment from one or more measurement devices and historic patient data; (Different time, frequency and time-frequency features are extracted from both kinematics and neuromuscular data [16]. Both signals are first synchronized (if the sampling rates are different) and then based on the monitored activity (walking/running, jumping or balance testing), the features extraction and selection step is performed. For this study, following features were extracted using disjoint segmentation. The length of the segment varies with the activity monitored.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the thoracic post-surgical monitoring and complication prediction as taught by Shelton with the hybrid intelligent system for recovery and performance evaluation after as taught by Senanayke in order to improve machine learning model performance by reducing the complexity of the data.
Regarding Claim 39, this claim recites the limitations of Claim 38 and as to those limitations is
rejected for the same basis and reasons as disclosed above. Shelton does not fully disclose the following limitation that Senanayke discloses:
The method of claim 38, wherein the feature engineering further comprises feature selection. (Feature extraction discloses different time, frequency and time-frequency features are extracted from both kinematics and neuromuscular data [16]. Both signals are first synchronized (if the sampling rates are different) and then based on the monitored activity (walking/running, jumping or balance testing), the features extraction and selection step is performed. For this study, following features were extracted using disjoint segmentation. The length of the segment varies with the activity monitored.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the thoracic post-surgical monitoring and complication prediction as taught by Shelton with the hybrid intelligent system for recovery and performance evaluation after as taught by Senanayke in order to improve machine learning model performance by reducing the complexity of the data.
Claim(s) 33-34 are rejected under 35 U.S.C. 103 as being unpatentable over Shelton (US PG Pub 2022/0241474) in view of Balili (Classification of heart sounds using discrete and continuous wavelet transform and random forests).
Regarding Claim 33, this claim recites the limitations of Claim 25 and as to those limitations is
rejected for the same basis and reasons as disclosed above. Shelton does not fully disclose the following limitation that Balili discloses:
The method of claim 25, wherein the feature engineering is comprised of feature extraction to result in features and wherein the feature extraction involves a technique or method selected from the group consisting of discrete Fourier and short-term fourier transforms, discrete cosine transform, autoregressive models, autoregressive moving average models, classes of linear predictive coding models, cepstral analysis derived mel-frequency cepstral coefficients, kernel-based modeling, multiresolution analysis using discrete and continuous wavelet transformations, wavelet packet transformations and decompositions, empirical mode decompositions, power spectrum estimation using techniques that measure spectral coupling across different signal modalities, non-negative matrix factorization, ambiguity kernel functions, a subset of the layers from a pre-trained multilayer neural networks used as a transformation from input data into feature vectors in the feature space, unsupervised or supervised clustering methods such as adaptive resonance-based neural networks, self-organizing maps, k-means clustering, k-nearest neighbors, Gaussian mixture models, and Naive Bayes classifiers which group together similar feature sets by plurality of features extracted or plurality of statistically summarized inputs and assign group labels to each instance of a set of features. (Introduction discloses wavelets-based approaches have been used in heart sound analysis because they are capable for providing both time and frequency information simultaneously in varying time and frequency resolutions (multiresolution analysis)… The current study aims to develop a novel approach in the classification of heart sounds by extracting features using both Discrete and Continuous Wavelet Transform and feeding them to a Random Forest classifier.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the thoracic post-surgical monitoring and complication prediction as taught by Shelton with the classification of heart sounds using discrete and continuous wavelet transform and random forests as taught by Balili in order to automate the classification of heart sounds which is traditionally detected by a physician (See Balili Introduction).
Regarding Claim 34, this claim recites the limitations of Claim 33 and as to those limitations is
rejected for the same basis and reasons as disclosed above. Shelton does not fully disclose the following limitation that Balili discloses:
The method of claim 33, wherein the feature extraction involves the use of multiresolution analysis and signal decomposition using wavelet transforms to condition heart sound data. (Introduction discloses wavelets-based approaches have been used in heart sound analysis because they are capable for providing both time and frequency information simultaneously in varying time and frequency resolutions (multiresolution analysis). Discrete wavelet transform has been used for heart sound segmentation. This approach involves decomposing the signal and reconstructing it using selected coefficients.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the thoracic post-surgical monitoring and complication prediction as taught by Shelton with the classification of heart sounds using discrete and continuous wavelet transform and random forests as taught by Balili in order to automate the classification of heart sounds which is traditionally detected by a physician (See Balili Introduction).
Claim(s) 41-42, 45-47, 49-50 and 59 are rejected under 35 U.S.C. 103 as being unpatentable over Shelton (US PG Pub 2022/0241474) in view of Senanayke (A hybrid intelligent system for recovery and performance evaluation after), further in view of Chowdhury (Variable selection strategies and its importance in clinical prediction modelling).
Regarding Claim 41, this claim recites the limitations of Claim 39 and as to those limitations is
rejected for the same basis and reasons as disclosed above. While Senanayke discloses in feature extraction, “different time, frequency and time-frequency features are extracted from both kinematics and neuromuscular data [16]… based on the monitored activity (walking/running, jumping or balance testing), the features extraction and selection step is performed. For this study, following features were extracted using disjoint segmentation,” the combination of Shelton and Senanayke does not fully disclose the following limitation that Chowdhury discloses:
The method of claim 39, wherein the method of feature selection is selected from the group consisting of measurement of mutual information using Kullback-Leibier convergence, minimum redundancy maximum relevance, impurity-based feature importance using random forest regression models, F-statistic or f-test, neighborhood component analysis, backward elimination, forward selection, permutation feature importance, factor analysis, and relief algorithm for regression, (Abstract discloses we will discuss the various variable selection techniques that can be applied during prediction model building (backward elimination, forward selection, stepwise selection and all possible subset selection), and the stopping rule/selection criteria in variable selection (p values, Akaike information criterion, Bayesian information criterion and Mallows’ Cp statistic. Basic principles of variable selection in clinical prediction modelling and the concept of variable selection discloses variable selection means choosing among many variables which to include in a particular model, that is, to select appropriate variables from a complete list of variables by removing those that are irrelevant or redundant. Importance of variable selection discloses variable selection has become the focus of much research in different areas including health. Variable selection offers many benefits such as improving the performance of models in terms of prediction, delivering variables more quickly and cost-effectively by reducing training and utilisation time, facilitating data visualisation and offering an overall better understanding of the underlying process that generated the data. Backward elimination discloses backward elimination is the simplest of all variable selection methods. This method starts with a full model that considers all of the variables to be included in the model. Variables then are deleted one by one from the full model until all remaining variables are considered to have some significant contribution to the outcome. Forward selection discloses one advantage of forward selection is that it starts with smaller models. Also, this procedure is less susceptible to collinearity (very high intercorrelations or interassociations among independent variables).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of the thoracic post-surgical monitoring and complication prediction as taught by Shelton and the hybrid intelligent system for recovery and performance evaluation after as taught by Senanayke with the variable selection strategies and its importance in clinical prediction modelling as taught by Chowdhury in order to select appropriate variables from a complete list of variables by removing those that are irrelevant or redundant (See Chowdhury basic principles of variable selection in clinical prediction modelling and the concept of variable selection).
Regarding Claim 42, this claim recites the limitations of Claim 25 and as to those limitations is
rejected for the same basis and reasons as disclosed above. Further, Shelton discloses:
The method of claim 25, the assessment of the patient using the metrics of Step d is a representation of the time varying status of a patient and indicates whether there has been a change in the overall status of the patient as a cumulative effect of changes that are manifesting among the metrics that were computed and chosen as relevant to tracking recovery after a surgery. (Para 4 discloses recovery milestones may be tracked based on patient biomarker measurements compared to predicted patient biomarker measurements for recovery stages. Para 47 discloses the core body temperature sensing system may calculate body temperature using the body temperature data. The core body temperature sensing system may transmit the calculated body temperature to a monitoring device. The monitoring device may track the core body temperature data over time and display it to a user. Para 389 discloses a threshold (e.g., the first threshold or the second threshold) may be adjusted based on a context. For example, the sensing system (e.g., via the one or more processors executing instructions) may be configured to determine a context based at least on: surgery recovery timeline… Para 473-474 discloses post-surgical patient monitoring (e.g., including analyses of patient biomarker measurements) may (e.g., be used to) generate one or more pain management plans, which may include administration of analgesics/narcotics … Patient logging of analgesics/narcotics taken over time may correspond to a timeline of changes in measured parameters, which may indicate or may be used to detect improvements in health.)
While Shelton discloses the above limitations, it does not fully disclose the following limitation that Senanayke further discloses:
wherein during Step b) the input data is conditioned using an engineering system selected from the group consisting of filtering in time, frequency, wavelet, or other domains defined by a span of output of a convolutional neural network prior to a final layer which is a connected layer, so that the transformation does not remove any information from the data that is being transformed (“Initial processing” section discloses the raw signals are filtered and transformed into the required format for visualization and feature extraction. Feature extraction discloses different time, frequency and time-frequency features are extracted from both kinematics and neuromuscular data)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the thoracic post-surgical monitoring and complication prediction as taught by Shelton with the hybrid intelligent system for recovery and performance evaluation after as taught by Senanayke in order to improve machine learning model performance by reducing the complexity of the data.
While the combination of Shelton and Senanayke discloses the above limitations, it does not fully disclose the following limitation that Chowdhury further discloses:
transforming the conditioned input data into qualitative or quantitative metrics using a method selected from the group consisting of dimensionality reduction techniques consisting of box cox transformation, eigenvalue, and vector decomposition, principal component analysis (PCA), backward elimination, forward selection, random forests impurity-based importance, permutation feature importance, factor analysis, linear discriminant analysis, truncated singular value decomposition, kernel PCA, t-distributed stochastic neighbor embedding, multidimensional scaling, isometric mapping and combinations thereof and wherein (Abstract discloses we will discuss the various variable selection techniques that can be applied during prediction model building (backward elimination, forward selection, stepwise selection and all possible subset selection), and the stopping rule/selection criteria in variable selection (p values, Akaike information criterion, Bayesian information criterion and Mallows’ Cp statistic. Importance of variable selection discloses variable selection has become the focus of much research in different areas including health. Variable selection offers many benefits such as improving the performance of models in terms of prediction, delivering variables more quickly and cost-effectively by reducing training and utilisation time, facilitating data visualisation and offering an overall better understanding of the underlying process that generated the data. Backward elimination discloses backward elimination is the simplest of all variable selection methods. This method starts with a full model that considers all of the variables to be included in the model. Variables then are deleted one by one from the full model until all remaining variables are considered to have some significant contribution to the outcome. Forward selection discloses one advantage of forward selection is that it starts with smaller models. Also, this procedure is less susceptible to collinearity (very high intercorrelations or interassociations among independent variables).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of the thoracic post-surgical monitoring and complication prediction as taught by Shelton and the hybrid intelligent system for recovery and performance evaluation after as taught by Senanayke with the variable selection strategies and its importance in clinical prediction modelling as taught by Chowdhury in order to select appropriate variables from a complete list of variables by removing those that are irrelevant or redundant (See Chowdhury basic principles of variable selection in clinical prediction modelling and the concept of variable selection).
Regarding Claim 45, this claim recites the limitations of Claim 25 and as to those limitations is
rejected for the same basis and reasons as disclosed above. Further, Shelton discloses:
The method of claim 25, wherein the method further comprises a continuous improvement method comprising: a. performing improvements, conditioning, and/or correction methods and processes to the input data to account for data quality and confounders; ((Para 351 and Fig. 11D disclose one or more EEG sensor units 20317. The EEG sensor units 20317 may include a plurality of conductive electrodes placed in contact with the scalp. The conductive electrodes may be used to measure small electrical potentials that may arise outside of the head due to neuronal action within the brain. The EEG sensing system 20315 may measure a biomarker, for example, delirium by identifying certain brain patterns, for example, a slowing or dropout of the posterior dominant rhythm and loss of reactivity to eyes opening and closing. The ring sensing system 20315 may have a signal conditioning unit for filtering [data conditioning which is a transformation according to the Applicant’s definition based on claims 25 and 30) and amplifying the electrical potentials, a microcontroller to digitize the electrical signals, and a wireless (e.g., a low energy Bluetooth) module to transfer the data to a smart device, for example, as described in FIGS. 7B through 7D. Para 365 discloses a trained model may be any type of processing logic that performs an analysis and generates a prediction or determination derived from or generated based on empirical data, which may be referred to interchangeably as logic, an algorithm, a model, an ML algorithm or model, a neural network (NN), deep learning, artificial intelligence (AI), and so on. Para 383 discloses measured/measurement data may be processed and/or transformed, for example, into (e.g., to determine) one or more patient biomarkers. An analysis may be performed, for example, using one or more patient biomarkers and/or measured/measurement data. Measurement data/information and/or patient biomarkers (e.g., detected or determined before, during, and/or after a surgical procedure) may be used to establish one or more thresholds for one or more patient biomarkers (e.g., to perform one or more analyses, make one or more determinations and/or recommendations)… Patient biomarkers may include, for example, one or more of the following, a maximum amount of oxygen a person can utilize or consume (e.g., during exercise) (VO2 Max), respiration rate, heart rate variability, physical reaction (coughing and sneezing), and oxygen saturation, electroencephalogram (EEG), electrocardiogram (ECG or EKG), rapid eye movement sleep (REM sleep or REMS), gastrointestinal (GI) motility, etc. [wherein the broadest reasonable interpretation of the claim language discloses that conditioning data accounts for data quality and confounders].)
d. performing one or more normalization, combination and/or transformation methods or processes for the signal and model assessment to provide inputs for the patient status assessment model for improvements, conditioning, and correction. (Para 351 and Fig. 11D disclose one or more EEG sensor units 20317. The EEG sensor units 20317 may include a plurality of conductive electrodes placed in contact with the scalp. The conductive electrodes may be used to measure small electrical potentials that may arise outside of the head due to neuronal action within the brain. The EEG sensing system 20315 may measure a biomarker, for example, delirium by identifying certain brain patterns, for example, a slowing or dropout of the posterior dominant rhythm and loss of reactivity to eyes opening and closing. The ring sensing system 20315 may have a signal conditioning unit for filtering [data conditioning which is a transformation according to the Applicant’s definition based on claims 25 and 30) and amplifying the electrical potentials, a microcontroller to digitize the electrical signals, and a wireless (e.g., a low energy Bluetooth) module to transfer the data to a smart device, for example, as described in FIGS. 7B through 7D. Para 365 discloses a trained model may be any type of processing logic that performs an analysis and generates a prediction or determination derived from or generated based on empirical data, which may be referred to interchangeably as logic, an algorithm, a model, an ML algorithm or model, a neural network (NN), deep learning, artificial intelligence (AI), and so on.)
While Shelton discloses the above limitations, it does not fully disclose the following limitation that Senanayke further discloses:
b. performing feature extraction methods and processes to the product of step b to extract a plurality of features for signal and model assessment from a plurality of measurement devices and historic patient data; (Different time, frequency and time-frequency features are extracted from both kinematics and neuromuscular data [16]. Both signals are first synchronized (if the sampling rates are different) and then based on the monitored activity (walking/running, jumping or balance testing), the features extraction and selection step is performed. For this study, following features were extracted using disjoint segmentation. The length of the segment varies with the activity monitored.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the thoracic post-surgical monitoring and complication prediction as taught by Shelton with the hybrid intelligent system for recovery and performance evaluation after as taught by Senanayke in order to improve machine learning model performance by reducing the complexity of the data.
While the combination of Shelton and Senanayke discloses the above limitations and Shelton further discloses in para 405, “Patient biomarkers may correlate to surgical procedures on one or more organs or tissues. For example, relevant patient biomarkers may be different in various portions of a surgical procedure,” (see further: para 410), the combination does not fully disclose the following limitation that Chowdhury further discloses:
c. performing a plurality of feature selection methods and processes for selecting features that are relevant to the assessment; (Abstract discloses we will discuss the various variable selection techniques that can be applied during prediction model building (backward elimination, forward selection, stepwise selection and all possible subset selection), and the stopping rule/selection criteria in variable selection (p values, Akaike information criterion, Bayesian information criterion and Mallows’ Cp statistic. Basic principles of variable selection in clinical prediction modelling and the concept of variable selection discloses variable selection means choosing among many variables which to include in a particular model, that is, to select appropriate variables from a complete list of variables by removing those that are irrelevant or redundant. Importance of variable selection discloses variable selection has become the focus of much research in different areas including health. Variable selection offers many benefits such as improving the performance of models in terms of prediction, delivering variables more quickly and cost-effectively by reducing training and utilisation time, facilitating data visualisation and offering an overall better understanding of the underlying process that generated the data. Backward elimination discloses backward elimination is the simplest of all variable selection methods. This method starts with a full model that considers all of the variables to be included in the model. Variables then are deleted one by one from the full model until all remaining variables are considered to have some significant contribution to the outcome. Forward selection discloses one advantage of forward selection is that it starts with smaller models. Also, this procedure is less susceptible to collinearity (very high intercorrelations or interassociations among independent variables).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of the thoracic post-surgical monitoring and complication prediction as taught by Shelton and the hybrid intelligent system for recovery and performance evaluation after as taught by Senanayke with the variable selection strategies and its importance in clinical prediction modelling as taught by Chowdhury in order to select appropriate variables from a complete list of variables by removing those that are irrelevant or redundant (See Chowdhury basic principles of variable selection in clinical prediction modelling and the concept of variable selection).
Regarding Claim 46, this claim recites the limitations of Claim 45 and as to those limitations is
rejected for the same basis and reasons as disclosed above. Further, Shelton discloses:
The method of claim 45, wherein the method further includes personalizing the assessment. (Para 366 discloses as discussed in various examples herein, a patient (e.g., patient 25252) may wear one or more devices with one or more sensors (e.g., first wearable sensing system 25253, second wearable sensing system 25254, and/or third wearable sensing system 25255) pre-, in-, and/or post-surgery to measure one or more patient biomarkers… Wearable sensing systems may include sensors that may be used to sense, monitor, or measure one or more patient biomarkers on and/or in any portion of a human body (e.g., wrist, arm, chest, waist, leg, foot, head, mouth) as discussed in FIGS. 11A-11D and FIGS. 7B-7D. For example, wearable sensor 25253 may be worn on the chest (e.g., near the clavicle), for example, to monitor one or more patient biomarkers (e.g., patient temperature, heart rate, HRV, coughing, sneezing, etc.). Second wearable sensor 25254 may be worn as a chest strap, for example, to monitor one or more of respiration information (e.g., respiratory rate, respiratory phase, such as inhale or exhale), diaphragmatic muscle tone (e.g., to provide a predictive value, such as ahead of a cough), temperature, heart rate, HRV, coughing, sweat, electrocardiogram (ECG), electromyography (EMG), mechanomyogram (MMG), hydration state, tissue perfusion, and/or other patient biomarkers. Third wearable sensing system 25255 is shown as a wrist strap (e.g., a watch or bracelet), which may be used to monitor, one or more patient biomarkers (e.g., heart rate, HRV, coughing, sweat, ECG, hydration state, tissue perfusion, and/or other patient biomarkers). See further: 59, 61, 85, 91, 123, 140 156, 160, 351, 388, 430, 450)
Regarding Claim 47, this claim recites the limitations of Claim 45 and as to those limitations is
rejected for the same basis and reasons as disclosed above. Further, Shelton discloses:
The method of claim 45, wherein the method further includes continuous improvement of the assessment through incorporation of patient specific data as part of the input data to improve the assessment. (Para 366 discloses as discussed in various examples herein, a patient (e.g., patient 25252) may wear one or more devices with one or more sensors (e.g., first wearable sensing system 25253, second wearable sensing system 25254, and/or third wearable sensing system 25255) pre-, in-, and/or post-surgery to measure one or more patient biomarkers… Wearable sensing systems may include sensors that may be used to sense, monitor, or measure one or more patient biomarkers on and/or in any portion of a human body (e.g., wrist, arm, chest, waist, leg, foot, head, mouth) as discussed in FIGS. 11A-11D and FIGS. 7B-7D. For example, wearable sensor 25253 may be worn on the chest (e.g., near the clavicle), for example, to monitor one or more patient biomarkers (e.g., patient temperature, heart rate, HRV, coughing, sneezing, etc.). Second wearable sensor 25254 may be worn as a chest strap, for example, to monitor one or more of respiration information (e.g., respiratory rate, respiratory phase, such as inhale or exhale), diaphragmatic muscle tone (e.g., to provide a predictive value, such as ahead of a cough), temperature, heart rate, HRV, coughing, sweat, electrocardiogram (ECG), electromyography (EMG), mechanomyogram (MMG), hydration state, tissue perfusion, and/or other patient biomarkers. Third wearable sensing system 25255 is shown as a wrist strap (e.g., a watch or bracelet), which may be used to monitor, one or more patient biomarkers (e.g., heart rate, HRV, coughing, sweat, ECG, hydration state, tissue perfusion, and/or other patient biomarkers).)
Regarding Claim 49, this claim recites the limitations of Claim 45 and as to those limitations is rejected for the same basis and reasons as disclosed above. Further, Shelton discloses:
The method of claim 45, wherein, the method is repeated and wherein each time the method is repeated, updated input data obtained from the patient is added to step (a) resulting in an assessment model that is unique for the patient. (Para 364 discloses the model may receive updated patient-specific data to update predicted complications and probabilities and/or recovery milestones and probabilities. Complication mitigation (e.g., recommended actions or recommendations) may be based, at least in part, on one or more complications (e.g., and probabilities) generated by one or more models.)
Regarding Claim 50, this claim recites the limitations of Claim 45 and as to those limitations is rejected for the same basis and reasons as disclosed above. Further, Shelton discloses:
The method of claim 45, wherein the further input data is selected from the group consisting of patient reported outcomes, physiological measures, and psychological measures and combinations thereof. (Para 366 discloses as discussed in various examples herein, a patient (e.g., patient 25252) may wear one or more devices with one or more sensors (e.g., first wearable sensing system 25253, second wearable sensing system 25254, and/or third wearable sensing system 25255) pre-, in-, and/or post-surgery to measure one or more patient biomarkers… Wearable sensing systems may include sensors that may be used to sense, monitor, or measure one or more patient biomarkers on and/or in any portion of a human body (e.g., wrist, arm, chest, waist, leg, foot, head, mouth) as discussed in FIGS. 11A-11D and FIGS. 7B-7D. For example, wearable sensor 25253 may be worn on the chest (e.g., near the clavicle), for example, to monitor one or more patient biomarkers (e.g., patient temperature, heart rate, HRV, coughing, sneezing, etc.). Second wearable sensor 25254 may be worn as a chest strap, for example, to monitor one or more of respiration information (e.g., respiratory rate, respiratory phase, such as inhale or exhale), diaphragmatic muscle tone (e.g., to provide a predictive value, such as ahead of a cough), temperature, heart rate, HRV, coughing, sweat, electrocardiogram (ECG), electromyography (EMG), mechanomyogram (MMG), hydration state, tissue perfusion, and/or other patient biomarkers. Third wearable sensing system 25255 is shown as a wrist strap (e.g., a watch or bracelet), which may be used to monitor, one or more patient biomarkers (e.g., heart rate, HRV, coughing, sweat, ECG, hydration state, tissue perfusion, and/or other patient biomarkers).)
Regarding Claim 59, this claim recites the limitations of Claim 45 and as to those limitations is
rejected for the same basis and reasons as disclosed above. Further, Shelton discloses:
The method of claim 45, wherein input data and/or derivatives are obtained from a method selected from the group consisting of electrical activity based metrics, bioimpedance based metrics, goniometric measurements of all joint angles, absolute inclination, and orientation of different parts of the body in multiple axes, photoplethysmographic measures at various sites on the body, heart sounds, lung sounds, gastrointestinal sounds, and joint sounds, blood oxygen levels, skin and/or body temperatures measured at different locations of the body, biological parameters, geographic location and altitude metrics, patient historic data, patient questionnaires, risk stratification metrics by means of a hazard ratio or index, or a recovery percentage score indicative of change and trajectory of change in a patient's status around an index or event which involves a surgical intervention or combinations thereof, wherein the biological parameters are selected from the group consisting of lactate, pH, alcohol, sodium, glucose, urea, chloride, discrete blood, interstitial fluid, cerebrospinal fluid biomarker panels, metabolic panels and combinations thereof. (Para 61 discloses the respiration sensing system may measure respiration data acoustically using a microphone to record airflow sounds. Para 70 discloses a blood sugar sensing system may measure blood sugar data including blood glucose level and/or tissue glucose level. Para 91 discloses based on peripheral temperature data, the peripheral temperature sensing system may determine peripheral temperature-related biomarkers including basal body temperature, extremity skin temperature, and/or patterns in peripheral temperature. Para 123 discloses a circadian rhythm sensing system may measure circadian rhythm data including light exposure, heart rate, core body temperature [thus core and peripheral read on different locations of the body]. Para 156 discloses a computing system, as described herein, may select one or more biomarkers (e.g., data from biomarker sensing systems) from skin-related biomarkers, including skin conductance, skin perfusion pressure, sweat, autonomic tone, and/or pH for analysis. Para 160 discloses cystic fibrosis and/or acidosis may be predicted based on electrolyte biomarkers, including chloride ions, pH, and other electrolytes. High lactate concentrations may be determined based on blood pH. Acidosis may be predicted based on high lactate concentrations. Sepsis, lung collapse, hemorrhage, and/or bleeding risk may be predicted based on predicted acidosis. Para 351 and FIG. 11D discloses an example of an electroencephalogram (EEG) sensing system 20315. As illustrated in FIG. 11D, the sensing system 20315 may include one or more EEG sensor units 20317. The EEG sensor units 20317 may include a plurality of conductive electrodes placed in contact with the scalp. The conductive electrodes may be used to measure small electrical potentials that may arise outside of the head due to neuronal action within the brain. The EEG sensing system 20315 may measure a biomarker, for example, delirium by identifying certain brain patterns, for example, a slowing or dropout of the posterior dominant rhythm and loss of reactivity to eyes opening and closing. The ring sensing system 20315 may have a signal conditioning unit for filtering and amplifying the electrical potentials, a microcontroller to digitize the electrical signals, and a wireless (e.g., a low energy Bluetooth) module to transfer the data to a smart device, for example, as described in FIGS. 7B through 7D. Para 388 discloses the post-surgical complication may be any type of complication, such as a PAL. The first patient biomarker or the second patient biomarker may be any biomarker, such as one of the following: a blood lactate, a sweat lactate, an indication of GI motility, a heart rate variability, a blood pH value, a sweat pH value, etc. Para 430 discloses Confusion may be inferred from the location or movement of a patient, which may be tracked, for example, by a location monitor (e.g., GPS tracker). An unexpected location change or disruption to normal routine may indicate that the user is suffering from confusion or delirium. Para 450 discloses nutritional status may be (e.g., additionally and/or alternatively) determined, for example, based on one or more of the following patient biomarkers: sweat, tears, urine, alcohol use, blood/serum ratio, saliva, etc. See further: Para 59, 85, 140, 366.)
Claim 48 is rejected under 35 U.S.C. 103 as being unpatentable over Shelton (US PG Pub 2022/0241474) in view of Senanayke (A hybrid intelligent system for recovery and performance evaluation after), in view of Chowdhury (Variable selection strategies and its importance in clinical prediction modelling) further in view of Sun (Building a patient-specific model using transfer learning for four-dimensional cone beam computer tomography augmentation) and Lewis (Sample size estimation for NPS confidence intervals).
Regarding Claim 48, this claim recites the limitations of Claim 45 and as to those limitations is rejected for the same basis and reasons as disclosed above. While Shelton Para 364 discloses the model may receive updated patient-specific data to update predicted complications and probabilities and/or recovery milestones and probabilities, it does not fully disclose the following limitation that Sun discloses:
The method of claim 45, wherein, a model is pre-trained on a population of patients wherein the method is repeated and continuously improved upon by adding further input data obtained from the patient each time the method is repeated to generate a prediction model that is unique for the patient. (Methods discloses the transfer learning method was used to further improve the performance of the deep learning model for individual patients. Specifically, a U-Net-based model was first trained to augment 4D-CBCT using group data [pre-training]. Next, transfer learning was used to fine tune the model based on a specific patient’s available data to improve its performance for that individual patient. Figure 2 discloses The U-Net model is first trained to augment undersampled CBCT to match with fully sampled CBCT or planning CT images using group data. The model is then retrained as a patient-specific model using an individual patient’s CT or prior CBCT data and transfer learning to optimize its performance for that individual. The network can be updated adaptively by adding the most recent day’s CBCT images to the training data [continuously improved upon].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of the thoracic post-surgical monitoring and complication prediction as taught by Shelton, the hybrid intelligent system for recovery and performance evaluation after as taught by Senanayke, and the variable selection strategies and its importance in clinical prediction modelling as taught by Chowdhury with the patient specific model using transfer learning as taught by Sun in order to personalize a pre-trained model “to fine tune the model based on a specific patient’s available data to improve its performance for that individual patient.”
While the combination of Shelton and Sun discloses the above limitations, it does not fully disclose the following limitation that Lewis discloses:
A population at least 50 patients (Simple approximate formula assuming maximum realistic variance discloses the sample size for 95% confidence and precision of ± .10 would be .75(1.962)/.102 − 3 = 286 (instead of 382). If relaxing confidence to 90% and precision to ± .15, the sample size would be 1.6452/.152 − 3 = 88 (instead of 118) thus disclosing the effect of confidence intervals desired on the sample size required. Thus, it would have been obvious to apply this to the methods as taught above to increase the sample size to output a higher confidence level of the data.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of the thoracic post-surgical monitoring and complication prediction as taught by Shelton, the hybrid intelligent system for recovery and performance evaluation after as taught by Senanayke, the variable selection strategies and its importance in clinical prediction modelling as taught by Chowdhury, and the patient specific model using transfer learning as taught by Sun with the sample size estimation for NPS confidence intervals as taught by Lewis in order to optimize sample size required to maximize confidence intervals.
Claim 51 is rejected under 35 U.S.C. 103 as being unpatentable over Shelton (US PG Pub 2022/0241474) in view of Senanayke (A hybrid intelligent system for recovery and performance evaluation after), further in view of Chowdhury (Variable selection strategies and its importance in clinical prediction modelling) and Lewis (Sample size estimation for NPS confidence intervals).
Regarding Claim 51, this claim recites the limitations of Claim 45 and as to those limitations is
rejected for the same basis and reasons as disclosed above. Further, Shelton discloses:
The method of claim 45, wherein the method predicts a degree of certainty… for each patient status assessment associated therewith, wherein each of the degrees of confidence is based at least on predicted data, historical data, and/or patient questionnaire data. (Para 364 discloses the model may receive vectorized patient-specific data as input and classify the data as being indicative of one or more complications and/or one or more recovery milestones (e.g., each associated with a probability, likelihood, or confidence level.)
While Shelton discloses the above limitation, the combination of Shelton, Senanayke and Chowdhury does not fully disclose the following limitation that Lewis discloses:
degree of certainty of from about 75% to about 95% (Simple approximate formula assuming maximum realistic variance discloses the sample size for 95% confidence and precision of ± .10 would be .75(1.962)/.102 − 3 = 286 (instead of 382). If relaxing confidence to 90% and precision to ± .15, the sample size would be 1.6452/.152 − 3 = 88 (instead of 118) thus disclosing 90 and 95% confidence intervals which reads on “about 75% to about 95%”.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of the thoracic post-surgical monitoring and complication prediction as taught by Shelton, the hybrid intelligent system for recovery and performance evaluation after as taught by Senanayke, and the variable selection strategies and its importance in clinical prediction modelling as taught by Chowdhury with the sample size estimation for NPS confidence intervals as taught by Lewis in order to maximize confidence intervals for reliability.
Claim(s) 52-53 are rejected under 35 U.S.C. 103 as being unpatentable over Shelton (US PG Pub 2022/0241474) in view of Senanayke (A hybrid intelligent system for recovery and performance evaluation after), further in view of Chowdhury (Variable selection strategies and its importance in clinical prediction modelling) and Dwivedi (Understanding GAN loss functions).
Regarding Claim 52, this claim recites the limitations of Claim 45 and as to those limitations is
rejected for the same basis and reasons as disclosed above. Shelton does not fully disclose the following limitation that Dwivedi discloses:
The method of claim 45, wherein a generative neural network is added, wherein the generative neural network comprises a generator component and a discriminator component. (In Understanding GAN Loss Functions the introductory paragraphs disclose Ian Goodfellow introduced Generative Adversarial Networks (GAN) in 2014… The idea behind GANs (a generative adversarial network) can be summarized like this: Two Neural Networks are involved. One of the networks, the Generator, starts off with a random data distribution and tries to replicate a particular type of distribution. The other network, the Discriminator, through subsequent training, gets better at classifying a forged distribution from a real one. Both of these networks play a min-max game where one is trying to outsmart the other.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of the thoracic post-surgical monitoring and complication prediction as taught by Shelton, the hybrid intelligent system for recovery and performance evaluation after as taught by Senanayke, and the variable selection strategies and its importance in clinical prediction modelling as taught by Chowdhury with the understanding of GAN Loss Functions as taught by Dwivedi in order to implement the minimization of a generator and maximization of a discriminator and for data augmentation.
Regarding Claim 53, this claim recites the limitations of Claim 52 and as to those limitations is
rejected for the same basis and reasons as disclosed above. Shelton does not fully disclose the following limitation that Dwivedi discloses:
The method of claim 52, wherein the input data applied to the discriminator component generates a set of features that are used to train another neural network or machine learning model. (Generator loss discloses while the generator is trained, it samples random noise and produces an output from that noise. The output then goes through the discriminator and gets classified as either “Real” or “Fake” based on the ability of the discriminator to tell one from the other. The generator loss is then calculated from the discriminator’s classification – it gets rewarded if it successfully fools the discriminator, and gets penalized otherwise. The following equation is minimized to training the generator…)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of the thoracic post-surgical monitoring and complication prediction as taught by Shelton, the hybrid intelligent system for recovery and performance evaluation after as taught by Senanayke, and the variable selection strategies and its importance in clinical prediction modelling as taught by Chowdhury with the understanding of GAN Loss Functions as taught by Dwivedi in order to implement the minimization of a generator and maximization of a discriminator and for data augmentation.
Claim(s) 54-58 are rejected under 35 U.S.C. 103 as being unpatentable over Shelton (US PG Pub 2022/0241474) in view of Harris (US PG Pub 2015/0248613 A1).
Regarding Claim 54, Shelton discloses:
A method for improving a patient's recovery using assessment predictions generated during perioperative care comprising
a) selectively obtaining a plurality of input data from one or more measurement devices, selection and collection methods and/or processes; (Para 41 discloses the biomarkers 20005 may relate to physiologic systems 20007, which may include, but are not limited to, behavior and psychology, cardiovascular system, renal system, skin system, nervous system, gastrointestinal system, respiratory system, endocrine system, immune system, tumor, musculoskeletal system, and/or reproductive system. Information from the biomarkers may be determined and/or used by the computer-implemented patient and surgeon monitoring system 20000, for example. Para 351 and FIG. 11D discloses an example of an electroencephalogram (EEG) sensing system 20315. As illustrated in FIG. 11D, the sensing system 20315 may include one or more EEG sensor units 20317. The EEG sensor units 20317 may include a plurality of conductive electrodes placed in contact with the scalp. The conductive electrodes may be used to measure small electrical potentials that may arise outside of the head due to neuronal action within the brain. The EEG sensing system 20315 may measure a biomarker, for example, delirium by identifying certain brain patterns. See further: 142, 156, 383, 388, 450.)
b) subjecting the input data to a transformation selected from the group consisting of conditioning, feature engineering and combinations thereof; (Para 351 and Fig. 11D disclose one or more EEG sensor units 20317. The EEG sensor units 20317 may include a plurality of conductive electrodes placed in contact with the scalp. The conductive electrodes may be used to measure small electrical potentials that may arise outside of the head due to neuronal action within the brain… The ring sensing system 20315 may have a signal conditioning unit for filtering and amplifying the electrical potentials, a microcontroller to digitize the electrical signals, and a wireless (e.g., a low energy Bluetooth) module to transfer the data to a smart device, for example, as described in FIGS. 7B through 7D. Para 365 discloses a trained model may be any type of processing logic that performs an analysis and generates a prediction or determination derived from or generated based on empirical data, which may be referred to interchangeably as logic, an algorithm, a model, an ML algorithm or model, a neural network (NN) [feature engineering according to claims 38 and 40], deep learning, artificial intelligence (AI), and so on.)
c) translating the transformed input data into metrics; and (Para 364 discloses a model trained on vectorized training data may process vectorized input data. For example, a model may receive vectorized patient-specific data as input and classify the data as being indicative of one or more complications and/or one or more recovery milestones (e.g., each associated with a probability, likelihood, or confidence level). Para 368 discloses hub 25258 may receive measurement data associated with patient biomarker(s) from first, second and third sensing systems 25253, 25254, 25255, generate predicted complications to monitor patient for, determine patient monitoring thresholds for recovery milestones and/or complications, analyze patient biomarker measurements relative to the thresholds for the predicted complications and/or recovery milestones, generate and send (e.g., wirelessly transmit) recommendations, such as instructions to display to a patient or HCP and/or a control program or adjustments thereto for (e.g., patient specific) operation of controllable device 25256, etc.)
the further configured assessment predictions… are configured to predict a degree of confidence for each of the assessment predictions, where the degree of confidence indicates the likelihood that the patient will achieve the assessment prediction. (Para 364 discloses a model trained on vectorized training data may process vectorized input data. For example, a model may receive vectorized patient-specific data as input and classify the data as being indicative of one or more complications and/or one or more recovery milestones (e.g., each associated with a probability, likelihood, or confidence level).)
While Shelton discloses the above limitations, it does not fully disclose the possible further surgeries that Harris discloses:
d) using the metrics obtained in Step c to obtain an assessment of the patient wherein
the assessment is further configured to predict an outcome of a set of possible further surgeries for the patient at a specific point in time after the surgery; wherein (Para 2 discloses the present disclosure relates generally to systems and methods for predicting metabolic and bariatric surgery outcomes. Para 7 discloses the outcome prediction module can be configured to predict an outcome of each of a plurality of different types of bariatric surgery for the patient based at least on the received data regarding the patient and on historical data regarding outcomes of a plurality of bariatric surgery procedures performed on a plurality of patients. Para 121 discloses the patient data input module 200 can be configured to receive a variety of different types of data regarding a patient. Non-limiting examples of patient data that can be received (automatically and/or manually) by the patient data input module 200 include identification data, clinical data, and genetic data… Non-limiting examples of clinical data include… factors associated with surgical risk (e.g., airway risks, smoking history, prior surgical procedures in the abdomen [thus the outcomes predicted for each of a plurality of different types of bariatric surgery reads on the possible further surgeries for the patient at a specific point in time after the surgery where in the surgery is the prior surgical procedures in the abdomen] , etc.), and psychological history of the patient. As will be appreciated by a person skilled in the art, patient clinical data can be collected in a variety of ways, such as being sampled, provoked, etc.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the thoracic post-surgical monitoring and complication prediction as taught by Shelton with the systems and methods for predicting metabolic and bariatric surgery outcomes in order to predict an outcome of each of a plurality of different types of surgeries for a patient to further personalize the outcome of the system.
Regarding Claim 55, this claim recites the limitations of Claim 54 and as to those limitations is rejected for the same basis and reasons as disclosed above. Further, Shelton discloses:
The method of claim 54, wherein the assessment predictions are based at least on measured data, derived data, extracted data, patient historic data, and/or patient questionnaire data. (Para 41 discloses the biomarkers 20005 may relate to physiologic systems 20007, which may include, but are not limited to, behavior and psychology, cardiovascular system, renal system, skin system, nervous system, gastrointestinal system, respiratory system, endocrine system, immune system, tumor, musculoskeletal system, and/or reproductive system. Information from the biomarkers may be determined and/or used by the computer-implemented patient and surgeon monitoring system 20000, for example. Para 351 and FIG. 11D discloses an example of an electroencephalogram (EEG) sensing system 20315. As illustrated in FIG. 11D, the sensing system 20315 may include one or more EEG sensor units 20317. The EEG sensor units 20317 may include a plurality of conductive electrodes placed in contact with the scalp. The conductive electrodes may be used to measure small electrical potentials that may arise outside of the head due to neuronal action within the brain. The EEG sensing system 20315 may measure a biomarker, for example, delirium by identifying certain brain patterns. See further: 142, 156, 383, 388, 450.)
Regarding Claim 56, this claim recites the limitations of Claim 54 and as to those limitations is rejected for the same basis and reasons as disclosed above. Further, Shelton discloses:
The method of claim 54, wherein a report is generated assessing the status of the patient. (Para 399 discloses reports may be generated, provided, and/or received (e.g., by an HCP) periodically (e.g., hourly, daily, weekly) and/or periodically (e.g., ad hoc, on demand by sender and/or recipient, based on emergency or threshold detection, and/or the like)… A report may include, for example, data associated with an indication, such as data indicating that a thoracic post-surgery complication has been detected. A report may include, for example, data associated with therapy tracking, e.g., indicating patient therapy, whether a patient is performing therapy, the frequency of performance, whether the therapy is being performed correctly, etc.)
Regarding Claim 57, this claim recites the limitations of Claim 54 and as to those limitations is rejected for the same basis and reasons as disclosed above. Further, Shelton discloses:
The method of claim 54, wherein the assessment prediction also provides treatment recommendations based on input data obtained from the patient post-surgery combined with the assessment predictions. (Para 398 discloses a patient biomarker monitoring/analysis system may be configured to communicably couple to a surgical hub. The surgical hub may be configured to communicably couple to one or more in-surgery and/or post-surgery (e.g., recovery) devices that may provide (e.g., display) one or more actionable notifications (e.g., to a patient and/or HCP). Para 400 discloses an actionable notification may be selected or changed, modified or updated from a default or existing action (e.g., based on monitoring and/or analyses of one or more patient biomarkers), for example, to enable a patient or an HCP to perform at least one recommended action. The type of actionable notification may depend on the patient biomarker measurement(s) and/or processing (e.g., based on baseline value(s), types of notifications available). An actionable notification may include, for example, patient exercises.)
Regarding Claim 58, this claim recites the limitations of Claim 54 and as to those limitations is
rejected for the same basis and reasons as disclosed above. Further, Shelton discloses:
The method of claim 54, wherein the assessment prediction also provides intervention planning, support, follow-up, patient compliance, recovery prediction and tracking, and potential treatment modification options. (Para 4 discloses systems, methods, and instrumentalities are disclosed herein for a (e.g., pre-, in-, and/or post-operative) patient monitoring system. Patient biomarkers may be monitored before, during, and/or after thoracic surgery to predict complications [recovery prediction], detect complications, track recovery, and/or make pre-, in- and/or post-surgery recommendations to avoid predicted complications [potential treatment modifications] and/or mitigate detected complications. Para 111 discloses based on detected hospital anxiety and/or depression, the mental aspect sensing system may generate a treatment plan [intervention planning], including pain relief therapy and/or pre-operative support. Para 145 discloses based on the selected biomarker sensing systems data, psychological status-related biomarkers, complications, and/or contextual information may be determined, including stress, anxiety, pain, positive emotions, abnormal states, and/or post-operative pain [follow up]. Based on the selected biomarker sensing systems data, psychological status-related conditions may be predicted, including physical symptoms of disease. Higher post-operative pain may be determined and/or predicted based on analyzed high levels of pre-operative stress, anxiety, and/or pain. Physical symptoms of disease may be predicted based on determined high optimism. Para 363 discloses post-surgical (e.g., recovery) monitoring may monitor patient biomarkers to track recovery metrics and/or complications for a surgery, such as lung surgery. Post-surgery monitoring of patient biomarkers may (e.g., be used to) track healing stage progress or recovery milestones [recovery tracking] and/or determine a probability of complications [recovery prediction], which may, for example, result in PALs (e.g., after thoracic resection of a lung) [metrics reflective of the patient’s state of recovery]. Para 403 discloses a patient may be prompted to perform an exercise (e.g., periodically, at set intervals, such as once per hour. Patient compliance may be tracked, for example, by monitoring patient biomarkers. Para 456 discloses a recommendation may be a selection and/or adaptation (e.g., change or modification of a default or existing selection) of one or more of the following: surgical preparation, in-surgery procedures, surgical instrument selection, surgical and/or post-surgical instrument settings, post-surgery procedures, in-surgery and/or post-surgery monitoring (e.g., recovery), etc. A recommendation may be selected or adapted to reduce or avoid one or more predictable complications, for example, based on patient biomarker monitoring alone or in combination with other information before, during and/or after surgery. See further: Para 182.)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SARA J MORICE DE VARGAS whose telephone number is (703)756-4608. The examiner can normally be reached M-F 8:30-5:30 pm.
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/SARA JESSICA MORICE DE VARGAS/Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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