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 disclose