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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114 ("RCE"), including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on February 6, 2026, has been entered.
Status of Claims
Claims 1, 2, 9, 13, 16, 21, 22, 24, 27, 31, 37, 47-49, 51, 56, 62-69, and 72-74 were previously pending and subject to a Final Office Action having a notification date of October 6, 2025 (“Final Office Action”). Following the Final Office Action, Applicant filed the RCE and an amendment on February 6, 2026 (“Amendment”), amending claims 1, 2, 9, 47, 49, 51, 62-67, and 72-74 and canceling claims 31, 48, 56, 68, and 69.
The present non-final Office Action addresses pending claims 1, 2, 9, 13, 16, 21, 22, 24, 27, 37, 47, 49, 51, 62-67, and 72-74 in the Amendment.
Response to Arguments
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §112
While these rejections are withdrawn in view of the Amendment, new rejections are presented herein as necessitated by the Amendment.
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §101
In relation to the claim rejections under 35 USC 101 set forth in the Final Office Action, these rejections are now withdrawn when currently pending claims 1, 2, 9, 13, 16, 21, 22, 24, 27, 37, 47, 49, 51, 62-67, and 72-74 are considered in view of the 2019 Revised Patent Subject Matter Eligibility Guidance (which collectively includes the guidance in the January 7, 2019 Federal Register notice and the October 2019 update issued by the USPTO as incorporated into the MPEP) and Applicant' s remarks in the Amendment.
For instance, while independent claim 1 recites certain limitations that include a “mental process," "certain methods of organizing human activity," and/or "mathematical concepts" abstract idea (e.g., measuring feature values for a patient, computing a surgical risk score indicative of a probability that the patient will develop a surgical site complication based on the feature values, determining each artificial feature via a mathematical operation (permutation, a sampling with replacement, a sampling without replacement, a combination, a knockoff and/or an inference) performed on the feature values of the non-artificial feature, etc.), the claims recite additional limitations that amount to a “practical application” of the abstract idea and/or are “significantly more” than the abstract idea as set forth below.
For instance, at least the following additional limitations amount to other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment (MPEP 2106.04(d)(I); MPEP 2106.05(e)):
-collecting one or more samples from the patient,
-obtaining an ex-vivo-stimulation sample, wherein the ex-vivo-stimulation sample is or is derived from the one or more samples from the patient, wherein the ex-vivo-stimulation sample comprises immune cells that are collected prior to a surgical procedure to be performed on the patient;
-measuring values of a plurality of omic biological features, wherein the plurality of omic biological features comprises a set of one or more immune-activation measurements of the patient, wherein each immune- activation measurement of the set is an amount of activation of an immune-related signaling protein yielded by ex vivo stimulation and is determined by:
-incubating the ex-vivo-stimulation sample with one or more immune activating agents selected from the group consisting of: a Toll-Like Receptor 4 (TLR4) agonist, interleukin-2 (IL-2), interleukin-4 (IL-4), interleukin-6 (IL-6), interleukin-113 (IL-113), Tumor Necrosis Factor-a (TNF-a), Interferon-a (IFN- a), and Phorbol 12-myristate 13-acetate (PMA)/ionomycin; and
-measuring an amount of activation of an immune-related signaling protein of the immune cells within the ex-vivo-stimulation sample by:
-contacting the immune cells with an affinity reagent that binds to the immune-related signaling protein in an activated state, and
-measuring an amount of binding of the affinity reagent to the immune-related signaling proteins in the activated state;
-wherein the detecting the activation state of immune-related signaling protein is performed subsequent to incubating the ex-vivo-stimulation sample with one or more immune activating agents;
-wherein at least one of a plurality of data layers of the ML model that is trained using a bootstrap procedure to calculate the surgical risk score represents a set of one or more immune-activation measurements of the patient of which each immune-activation measurement of the set is the amount of activation of the immune-related signaling protein yielded by the ex vivo stimulation; and
-wherein each data layer of the ML model includes omic biological feature values and at least one artificial feature for all individuals in a population of individuals, each artificial feature being a new variable obtained from a non-artificial feature among the plurality of omic biological features
Specifically, such limitations are meaningful "because they integrate the results of the analysis into a specific and tangible method that results in the method moving from abstract scientific principles to specific application." MPEP 2106.05(e).
Furthermore, many of the above additional limitations do not define well-understood, routine, conventional activity. MPEP 2106.05(d).
Also see Applicant's remarks at pages 6-11 of the Amendment.
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §103
On pages 12-13 of the Amendment, Applicant asserts that prior art does not disclose or suggest inputting immune-activation measurements generated by ex vivo stimulation of patient immune cells into an ML model that has at least one layer for such measurements. The Examiner disagrees.
Paragraph [0008] of Gaudilliere discusses how changes in signal intensity of proteins in in monocyte/immune populations correlates with a patient's time to recovery such as high or low probability of rapid recovery (surgical risk score) while [0165], [0170] discusses electronic generation of a report including a prognosis of the likelihood that the patient will have a surgery-attributable death/progression (surgical risk score). Furthermore, [0136]-[0137] discusses how the signature pattern generated from the sample can be compared with a baseline/reference profile to make a prognosis regarding recovery period (risk of surgical complications) such as through statistical analysis via machine learning, NNs, etc. ([0146]-[0148] and [0152]). Therefore, Gaudilliere discloses inputting the signature pattern (protein signal intensity/immune-activation measurements) into an ML model.
While Gaudilliere might not specifically disclose the ML model to include a "layer" for the immune-activation measurements, Wood discloses that it was known in the healthcare informatics and machine learning art for an ML model that computes surgical risk scores based on omic biological features (29:35-30:3 discloses applying a statistical model (ML model per 29:3-6) to patient attribute data/features (which includes proteomics/genomic data per 7:14-17) of a current patient to generate a SICK score which is a “surgical risk score” because it can be used to assess a probability of particular outcomes for a surgical procedure such as complication rates per 1:52-59) to be trained (4:25-38 and 16:9-14 discloses generating/training a statistical model (ML model per 18:13-19) using training data; also see training module 214 in Figure 2) using a bootstrap procedure (18:33-35 and 19:2 disclose how the training data can be analyzed using bootstrapped aggregation) on a plurality of individual data layers (each iteration of bootstrapped aggregation is considered an individual data layer), wherein each data layer represents one type of data from the plurality of omic biological features (13:65-67 and 14:55-58 disclose how the training data includes patient attributes of patients while 7:9-46 discloses various types of patient attributes including omic biological features per 7:14-17; therefore, each individual data layer represents one type of data or in other words, at least one of the data layers includes omic biological features).
Wood discloses (4:25-51 and 16:9-14) that performing training in this manner can generate ML models that are specifically tailored to the type of surgery being contemplated which therefore results in predictions with increased accuracy resulting in improved outcomes (2:5-22) while use of bootstrapped aggregation (including the above-noted iterations/layers on types of data from the omic features of the population) is known to decrease the variance of a model which prevents overfitting without necessarily increasing bias.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the ML model of Gaudilliere to be trained using a bootstrap procedure on a plurality of individual data, wherein each data layer represents one type of data from the plurality of omic biological features, wherein each data layer comprises data for a population of individuals, and wherein each omic biological feature includes feature values for all individuals in the population of individuals similar to as taught by Wood to advantageously generate ML models that are specifically tailored to the type of surgery being contemplated which therefore results in predictions with increased accuracy while decreasing the variance of the model which prevents overfitting without necessarily increasing bias. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
As Gaudilliere discloses a set of one or more immune-activation measurements of the patient of which each immune-activation measurement of the set is the amount of activation of the immune-related signaling protein yielded by the ex vivo stimulation ([0005], [0109] discloses how the omic biological features include measured single cell levels of activated signaling proteins in immune cells based on the ex vivo activation/stimulation) and Wood discloses how at least one data layer of the ML model (e.g., one of the iterations of the bootstrapping per 18:33-35 and 19:2) can includes omic biological features/attributes (13:65-67 and 14:55-58 disclose how the training data includes patient attributes of patients while 7:9-46 discloses various types of patient attributes including omic biological features per 7:14-17), then the Gaudilliere/Wood combination as combined discloses wherein at least one data layer represents a set of one or more immune-activation measurements of the patient of which each immune-activation measurement of the set is the amount of activation of the immune-related signaling protein yielded by the ex vivo stimulation as recited in the claims.
Claim Objections
Claims 49, 51, and 72 are objected to because of the following informalities:
-In claim 49, the fourth to last line, "regent" should be changed to --reagent--.
-In claim 51, line 9, "the the" should be changed to --the--.
-In claim 72, line 2, "individual" should be changed to --patient--.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
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 1, 2, 9, 13, 16, 21, 22, 24, 27, 37, 47, 49, 51, 62-67, and 72-74 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 1 recites the limitation "the detecting the activation state" in line 25. There is insufficient antecedent basis for this limitation in the claim. It appears that Applicant intended --the measuring the amount of activation-- consistent with line 19 of claim 1 and the Examiner will assume this is so for purposes of examination.
The remaining claims are rejected based on their dependency from claim 1.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, 47, 49, 51, 62-67, and 72-74 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2015/0241445 to Gaudilliere et al. (“Gaudilliere”) in view of U.S. Patent No. 11,848,106 to Wood (“Wood”) and NPL "SMOTE Bagging Algorithm for Imbalanced Dataset in Logistic Regression Analysis" to Hanifah et al. ("Hanifah"):
Regarding claim 1, Gaudilliere discloses a method for assessing immune activation of a patient for determining a surgical risk score indicative the patient's risk for a surgical complication following surgery ([0005], [0109] disclose measuring levels of activated signaling proteins in immune cells to provide an assessment of a patient's prognosis for time to recovery following surgery), comprising:
collecting one or more samples from the patient ([0005], [0109] discloses obtaining a biological sample from a patient contemplating a surgery),
obtaining an ex-vivo-stimulation sample, wherein the ex-vivo-stimulation sample is or is derived from the one or more samples from the patient ([0005], [0109] discloses ex vivo activating the sample), wherein the ex-vivo-stimulation sample comprises immune cells that are collected prior to a surgical procedure to be performed on the patient ([0005], [0109] discloses how the sample includes immune cells for a patient contemplating surgery (i.e., collected prior to a surgical procedure));
measuring values of a plurality of omic biological features, wherein the plurality of omic biological features comprises a set of one or more immune-activation measurements of the patient, wherein each immune-activation measurement of the set is an amount of activation of an immune-related signaling protein yielded by ex vivo stimulation ([0005], [0109] discloses measuring single cell levels of activated signaling proteins (proteomics, which are "omic biological features") in immune cell subsets in the ex vivo stimulated sample of the patient) and is determined by:
incubating the ex-vivo-stimulation sample with one or more immune activating agents selected from the group consisting of: a Toll-Like Receptor 4 (TLR4) agonist, interleukin-2 (IL-2), interleukin-4 (IL-4), interleukin-6 (IL-6), interleukin-113 (IL-113), Tumor Necrosis Factor-a (TNF-a), Interferon-a (IFN- a), and Phorbol 12-myristate 13-acetate (PMA)/ionomycin ([0060]-[0061], [0064]-[0066] disclose ex-vivo activating/incubating the cells (immune cells per [0005]) in the sample with TLR4 agonist, etc. while [0230] discloses doing so with IL6); and
measuring an amount of activation of an immune-related signaling protein of the immune cells within the ex-vivo-stimulation sample ([0005], [0109] discloses measuring single cell levels of activated signaling proteins (proteomics, which are "omic biological features") in immune cell subsets in the ex vivo stimulated sample of the patient) by:
contacting the immune cells with an affinity reagent that binds to the immune-related signaling protein in an activated state ([0086] discloses contacting the cells (immune cells per [0005]) with labeled activation state-specific affinity reagents that binds to signaling proteins while [0180] discloses staining the sample with antibodies (affinity reagent per [0013], [0072], [0073]) to recognize intracellular proteins (signaling proteins per [0109], [0115]), and
measuring an amount of binding of the affinity reagent to the immune-related signaling proteins in the activated state ([0067] discloses measuring antibody binding; [0087] discloses measuring the activation level of the signaling protein (measuring amount of binding); also see Figure 1a which discloses staining followed by measurement );
wherein the detecting the activation state of immune-related signaling protein is performed subsequent to incubating the ex-vivo-stimulation sample with one or more immune activating agents ([0229]-[0230] discloses how after the ex vivo incubation, the samples are processed for mass cytometry according to Example 1 and Figure 1 (which involves the above staining/contacting and measuring));
computing a surgical risk score for the patient by entering the values of the plurality of omic biological features into a machine learning model trained to generate surgical risk scores ([0008] discusses how changes in signal intensity of proteins in in monocyte/immune populations correlates with a patient's time to recovery such as high or low probability of rapid recovery (surgical risk score) while [0165], [0170] discusses electronic generation of a report including a prognosis of the likelihood that the patient will have a surgery-attributable death/progression (surgical risk score); furthermore, [0136]-[0137] discusses how the signature pattern generated from the sample can be compared with a baseline/reference profile to make a prognosis regarding recovery period (risk of surgical complications) such as through statistical analysis via machine learning, NNs, etc. ([0146]-[0148] and [0152]));
wherein the surgical risk score comprises a probability that the patient will develop a surgical site complication following the surgical procedure to be performed on the patient (again, [0008] and [0170] discuss prognosis of likelihood/probability of rapid recovery/surgery-attributable death/progression (which are indicative of surgical site complication probability following surgery);
…
…
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While Gaudilliere appears to be silent regarding the following bolded limitations, Wood discloses that it was known in the healthcare informatics and machine learning art for an ML model that computes surgical risk scores based on omic biological features (29:35-30:3 discloses applying a statistical model (ML model per 29:3-6) to patient attribute data/features (which includes proteomics/genomic data per 7:14-17) of a current patient to generate a SICK score which is a “surgical risk score” because it can be used to assess a probability of particular outcomes for a surgical procedure such as complication rates per 1:52-59) to be trained (4:25-38 and 16:9-14 discloses generating/training a statistical model (ML model per 18:13-19) using training data; also see training module 214 in Figure 2) using a bootstrap procedure (18:33-35 and 19:2 disclose how the training data can be analyzed using bootstrapped aggregation) on a plurality of individual data layers (each iteration of bootstrapped aggregation is considered an individual data layer), wherein each data layer represents one type of data from the plurality of omic biological features (13:65-67 and 14:55-58 disclose how the training data includes patient attributes of patients while 7:9-46 discloses various types of patient attributes including omic biological features per 7:14-17; therefore, each individual data layer represents one type of data)…, …;
wherein each data layer comprises data for a population of individuals (12:18-22 and 20:20-21 discuss how the model can be developed based on observations for a population of patients; accordingly, there is a population of patients considered for each iteration/data layer of the bootstrapped aggregation of 18:33-35 and 19:2); wherein each omic biological feature includes feature values for all individuals in the population of individuals (7:47-8:26 and 13:23-26 discusses how the historic patient training data (which from the population of individuals per 12:18-22 and 20:20-21) includes levels/degrees (values) of each attribute (feature) while 7:14-17 discloses omic biological features).
Wood discloses (4:25-51 and 16:9-14) that performing training in this manner can generate ML models that are specifically tailored to the type of surgery being contemplated which therefore results in predictions with increased accuracy resulting in improved outcomes (2:5-22) while use of bootstrapped aggregation (including the above-noted iterations/layers on types of data from the omic features of the population) is known to decrease the variance of a model which prevents overfitting without necessarily increasing bias.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the ML model of Gaudilliere to be trained using a bootstrap procedure on a plurality of individual data, wherein each data layer represents one type of data from the plurality of omic biological features, wherein each data layer comprises data for a population of individuals, and wherein each omic biological feature includes feature values for all individuals in the population of individuals similar to as taught by Wood to advantageously generate ML models that are specifically tailored to the type of surgery being contemplated which therefore results in predictions with increased accuracy while decreasing the variance of the model which prevents overfitting without necessarily increasing bias. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
As Gaudilliere discloses a set of one or more immune-activation measurements of the patient of which each immune-activation measurement of the set is the amount of activation of the immune-related signaling protein yielded by the ex vivo stimulation ([0005], [0109] discloses how the omic biological features include measured single cell levels of activated signaling proteins in immune cells based on the ex vivo activation/stimulation) and Wood discloses how at least one data layer of the ML model (e.g., one of the iterations of the bootstrapping per 18:33-35 and 19:2) can includes omic biological features/attributes (13:65-67 and 14:55-58 disclose how the training data includes patient attributes of patients while 7:9-46 discloses various types of patient attributes including omic biological features per 7:14-17), then the Gaudilliere/Wood combination as combined discloses wherein at least one data layer represents a set of one or more immune-activation measurements of the patient of which each immune-activation measurement of the set is the amount of activation of the immune-related signaling protein yielded by the ex vivo stimulation.
Furthermore, the Gaudilliere/Wood combination might be silent regarding each data layer further including at least one artificial feature that is a new variable obtained from a non-artificial feature among the plurality of omic biological features via a mathematical operation performed on the feature values of the non-artificial feature, the mathematical operation being chosen among the group consisting of: a permutation, a sampling with replacement, a sampling without replacement, a combination, a knockoff and an inference.
Nevertheless, Hanifah teaches (Abstract, section 3 on pages 6859-6860, and "The Model with SMOTEBagging" on page 6861) that it was known in the machine learning art to perform SMOTEBagging for imbalanced datasets in logistic regression analysis (which is a type of ML analysis), whereby synthetic data (artificial feature(s)) is generated for each subset of the bootstrap (for each "layer") via executing an algorithm that oversamples the minority class based on K-nearest neighbors (mathematical operation performed on feature values of non-artificial features)). This arrangement advantageously allows for generation of models that can handle imbalanced training data with increased accuracy and sensitivity (Abstract and pages 6863-6864).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for each data layer of the Gaudilliere/Wood combination to further include at least one artificial feature that is a new variable obtained from a non-artificial feature among the plurality of omic biological features via a mathematical operation performed on the feature values of the non-artificial feature, the mathematical operation including sampling (which is necessarily either with or without replacement) or a combination (the mathematical operation/algorithm considers number of bootstrap, k-nearest neighbors, and total number of oversampling which is a "combination") as taught by Hanifah to advantageously allow for generation of models that can handle imbalanced training data with increased accuracy and sensitivity. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Regarding claim 2, the Gaudilliere/Wood/Hanifah combination discloses the method of claim 1, further including generating values of a plurality of clinical features derived from clinical data obtained from the patient (6:44-7:54, 8:40-9:67 of Wood disclose determining/generation of various types of clinical features/attributes from patient clinical data); wherein computing a surgical risk score further comprises entering the values of the plurality of clinical features into the machine learning model trained to generated surgical risk scores (29:35-30:3 of Wood discloses applying a statistical model (ML model per 29:3-6) to patient attribute data/feature (which includes the above clinical features); generating a risk score based on such multi-factorial patient data (e.g. clinical data, genetic data, etc.) advantageously provides a more accurate indication of a patient's ability to withstand the impact of a clinical event thereby improving delivery of care per 3:39-4:7 of Wood; therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the Gaudilliere/Wood/Hanifah combination to have generated values of a plurality of clinical features derived from clinical data obtained from the patient, wherein computing a surgical risk score further comprises entering the values of the plurality of clinical features into the machine learning model trained to generated surgical risk scores similar to as taught by Wood to advantageously provide a more accurate indication of a patient's ability to withstand the impact of a clinical event thereby improving delivery of care. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id. ).
Regarding claim 47, the Gaudilliere/Wood/Hanifah combination discloses the method of claim 2, further including wherein the pre-surgery sample is or derived from a blood sample, a peripheral blood mononuclear cells (PBMC) fraction of a blood sample, a urine sample, a saliva sample, or dissociated cells from a tissue sample ([0230] of Gaudilliere discloses pre-operative blood samples and [0010] of Gaudilliere discloses blood and PBMC fractions).
Regarding claim 49, the Gaudilliere/Wood/Hanifah combination discloses the method of claim 1, further including obtaining a cytomic sample, wherein the cytomic sample is or is derived from the one or more samples from the patient, wherein the cytomic sample comprises immune cells derived from the patient that are not further stimulated ex vivo ([0123] discloses obtaining samples before surgery for ex vivo activation and samples after surgery (i.e., without further ex vivo stimulation); [0012] discloses obtaining samples in the absence of ex vivo stimulation; [0005]-[0006] and [0109] discuss some samples before surgery with ex vivo stimulation and other samples after surgery; the samples that are not further ex vivo stimulated are thus interpreted to be "cytomic" samples), wherein the plurality of omic biological features comprises a second set of immune-activation measurements of the patient ([0067]-[0068] and [0072]-[0074] discuss measuring/determining signaling protein response in response to binding by an affinity reagent of samples; the measurements obtained based on the above-noted cytomic samples are thus a "second set of immune-activation measurements"), wherein each immune-activation measurement of the second set is one or both of the following:
an amount of activation of an immune-related signaling protein within unstimulated immune cells of the cytomic sample ([0067]-[0068], [0072]-[0074], [0180] discuss measuring/determining signaling protein response in response to binding by an affinity reagent of samples; the measurements obtained based on the above-noted cytomic samples are thus an amount of immune-related signaling protein activation within unstimulated cells of the cytomic sample (e.g., [0012] discloses absence of ex vivo stimulation)), or
a frequency of an immune cell type within the cytomic sample ([0021], [0025], [0027], [0180] disclose frequencies of neutrophils, CD14+, etc.);
wherein the amount of activation of an immune-related signaling protein within unstimulated immune cells of the cytomic sample is determined by:
measuring an amount of activation of an immune-related signaling protein of the immune cells within the cytomic sample by:
contacting the immune cells with an affinity reagent that binds to the immune-related signaling protein in an activated state, and
measuring an amount of binding of the one or more affinity reagents to the immune-related signaling proteins in the activated state ([0067]-[0068], [0072]-[0074], [0180] discuss measuring/determining signaling protein response in response to binding by an affinity reagent/antibody of samples);
wherein the frequency of the immune cell type within the cytomic sample is determined by:
contacting the immune cells with an affinity [reagent] that binds to a surface or intracellular protein that is indicative of an immune cell type; and
measuring the frequency of cells with the affinity reagent bound to the surface or intracellular protein ([0180], [0194] discusses measuring/determining frequencies of different type of immune cells in response to staining by an antibody/affinity reagent of samples).
Regarding claim 51, the Gaudilliere/Wood/Hanifah combination discloses the method of claim 1, further including
obtaining a proteomic sample, wherein the proteomic sample is or is derived from the one or more samples from the patient, wherein proteomic sample comprises circulating extracellular proteins derived from blood of the patient ([0057], [0115] disclose extracellular protein and [0008], [0116] disclose circulating CD14+ monocytes); wherein the plurality of omic biological features comprises a second set of immune-activation measurements of the patient, wherein each immune-activation measurement of the second set is:
an amount of a circulating extracellular protein that is determined by:
contacting the proteomic sample with one or more affinity reagents that bind to the circulating extracellular proteins ([0011], [0013], [0117] disclose binding the CD14+ with affinity reagents); and
measuring an amount of binding of the one or more affinity reagents to the extracellular protein ([0011], [0068], [0087] disclose measuring the amount of binding).
Regarding claim 62, the Gaudilliere/Wood/Hanifah combination discloses the method of claim 1, further including wherein the immune-related signaling protein in the activated state is selected from phospho-Mitogen-Activated Protein Kinase-Activated Protein Kinase (pMAPKAPK2 (pMK2)) in neutrophils, phospho-ribosomal protein S6 (prpS6) in myeloid Dendritic Cells (mDCs), phospho-Nuclear Factor Kappa B (pNFKB) in CD7+CD56hiCD16lo Natural Killer (NK) cells, and Inhibitor of NFkB (IkB) in neutrophils, wherein the one or more immune activating agents comprises Lipopolysaccharide (LPS) ([0005]-[0007], [0229]-[0231] of Gaudilliere discloses that time to mild impairment of the hip (risk of developing a surgical site complication) correlates with pMAPKAPK2 activation (pMK2 in neutrophils) in response to pre-operative ex vivo LPS stimulation).
Regarding claim 63, the Gaudilliere/Wood/Hanifah combination discloses the method of claim 1, further including wherein the immune-related signaling protein in the activated state is selected from phospho-Signal Transducer and Activator of Transcription 3 (pSTAT3) in neutrophils, myeloid Dendritic Cells (mDCs), or regulatory T cells (Tregs), phospho-ribosomal protein S6 (prpS6) in CD56hiCD16lo Natural Killer (NK) cells or myeloid Dendritic Cells (mDCs), phospho-Signal Transducer and Activator of Transcription 5 (pSTAT5) in myeloid Dendritic Cells (mDCs), or plasmacytoid Dendritic Cells (pDCs), or Inhibitor of NFkB (IkB) in CD4+Tbet+ T helper 1 (Th1) cells, and phospho-Signal Transducer and Activator of Transcription 1 (pSTAT) in plasmacytoid Dendritic Cells (pDCs), wherein the one or more immune activating agents comprises interleukin-2 (IL-2), interleukin-4 (IL-4), and/or interleukin-4 (IL-6) ([0005]-[0007], [0174]-[0175], [0180], Table 3, and claim 13 of Gaudilliere discloses strong positive correlations between pSTAT3 signaling in neutrophils (pSTAT3) and surgical recovery/pain/impairment (e.g., in relation to hip surgery per [0020]) in response to ex vivo activation of a blood sample collected before surgery ([0020], [0109], [0180]) with IL-2/IL-6 ([0210])).
Regarding claim 64, the Gaudilliere/Wood/Hanifah combination discloses the method of claim 1, further including wherein the immune-related signaling protein in the activated state is selected from phospho-ribosomal protein S6 (prpS6) in neutrophils or myeloid Dendritic Cells (mDCs), phospho-Extracellular signal-Regulated Kinase (pERK) in Monocytic Myeloid Derived Suppressor Cells (M-MDSCs) or non-classical Monocytes (ncMCs), phopsho-cAMP Response Element-Binding Protein (pCREB) in yST Cells or Inhibitor of NFkB (IkB), phospho-P38 (pP38) or phospho-Extracellular signal-Regulated Kinase (pERK) in neutrophils or phopsho-cAMP Response Element-Binding Protein (pCREB) or phospho-Mitogen-Activated Protein Kinase-Activated Protein Kinase (pMAPKAPK2(pMK2)) in CD4+Tbet+ T helper 1 (Th1) cells or phospho-Extracellular signal-Regulated Kinase_pERK1 in CD4+CRTH2+ T helper 2(Th2) cells, wherein the one or more immune activating agents comprises Tumor Necrosis Factor-a (TNF-a) ([0005]-[0007], [0022]-[0025], [0174]-[0175], [0180], Table 3, and claim 13 of Gaudilliere discloses strong positive correlations between prpS6, pMPAKAP2, pP38, IkB, etc. and surgical recovery/pain/impairment (e.g., in relation to hip surgery per [0020]) in response to ex vivo activation of a blood sample collected before surgery ([0020], [0109], [0180]) with TNF-a ([0099])).
Regarding claim 65, the Gaudilliere/Wood/Hanifah combination discloses the method of claim 49, further including wherein the immune-related signaling protein in the activated state is selected from: phospho-Signal Transducer and Activator of Transcription 3 (pSTAT3) in neutrophils, Monocytic Myeloid Derived Suppressor Cells (M-MDSCs), classical Monocytes (cMCs), or non-classical Monocytes (ncMCs), phospho-Signal Transducer and Activator of Transcription 5 (pSTAT5) in regulatory T cells (Tregs) or CD45RA-memory CD4+T cells, phospho-Mitogen-Activated Protein Kinase-Activated Protein Kinase (pMAPKAPK2 (pMK2)) in myeloid Dendritic Cells (mDCs), phopsho-cAMP Response Element-Binding Protein (pCREB) or Inhibitor of NFKB (IKB) in CD4+Tbet* T helper 1 (Th1) cells, phospho-Signal Transducer and Activator of Transcription 6 (pSTAT6) in Natural Killer T (NKT) cells, and phospho- Extracellular signal-Regulated Kinase (pERK) in CD4+Tbet+ T helper 1 (Th1) cells ([0005]-[0007], [0174]-[0175], [0180], Table 3, and claim 13 of Gaudilliere discloses strong positive correlations between pSTAT3 signaling in neutrophils (pSTAT3) and surgical recovery/pain/impairment).
Regarding claim 66, the Gaudilliere/Wood/Hanifah combination discloses the method of claim 49, further including wherein the immune cell type is from: Monocytic Myeloid Derived Suppressor Cells (M-MDSCs), Granulocytic Myeloid Derived Suppressor Cells (G-MDSCs), non-classical Monocytes (ncMCs), T helper 17 (Th17) cells, CD4+-Chemoattractant Receptor-Homologous Molecule Expressed on T helper 2+ (CD4+CRTH2+), and T helper 2 (Th2) cell ([0184] of Gaudilliere discloses CD33+CD11b+CD14+HLA-DRlow monocytes (M-MDSCs)).
Regarding claim 67, the Gaudilliere/Wood/Hanifah combination discloses the method of claim 51, further including wherein the circulating extracellular protein is selected from: interleukin-1β (IL-1β), anaplastic lymphoma kinase (ALK), WW domain-containing oxidoreductase (WWOX), heat shock protein family H member 1 (HSPH1), interferon regulatory factor 6 (IRF6), catenin alpha 3 (CTNNA3), C-C Motif Chemokine Ligand 3 (CCL3), soluble Triggering Receptor Expressed On Myeloid Cells 1 (sTREM1), Integral Membrane Protein 2A (ITM2A), Transforming Growth Factor a (TGFa), LIF Interleukin 6 Family Cytokine (LIF), Adenosine Deaminase (ADA), Integrin Subunit Beta 3 (ITGB3), Eukaryotic Translation Initiation Factor 5A (EIF5A), Keratin 19 (KRT19U), and N-terminal prohormone of brain natriuretic peptide (NTproBNP) ([0099] of Gaudilliere discloses IL-1β).
Regarding claim 72, the Gaudilliere/Wood/Hanifah combination discloses the method of claim 1 further including
administering the surgical procedure on the individual ([0005]-[0010] discloses performing the surgery on the patient/individual);
wherein when the surgical risk score is above a threshold, the method further comprises:
administering a treatment regimen to the patient that is configured to reduce the probability that the patient develops a surgical site complication ([0008], [0016]-[0017], [0039], and claim 29 discuss selecting/administering/implementing various patient treatment/care based on the classification/analysis (i.e., based on the prognosis of rapid recovery following surgery as noted above) which would necessarily be configured to reduce the likelihood of surgical complications as that is the purpose of treatment/care; furthermore, there are necessarily different threshold/levels of the surgical complication prognosis likelihood above/below which various different treatments/care paths are selected (e.g., different treatments for very high vs. very low likelihoods of surgical complications)).
Regarding claim 73, the Gaudilliere/Wood/Hanifah combination discloses the method of claim 72, further including wherein the treatment regimen comprises:
administering an antibiotic or immune modulator to the patient ([0017] of Gaudilliere discloses administering a therapeutic agent that decreases the activation of CD14+ monocytes (administering an immune modulator)).
Regarding claim 74, the Gaudilliere/Wood/Hanifah combination discloses the method of claim 72 further including wherein administering the surgical procedure comprises reducing the probability that the patient develops a surgical site complication by:
delaying or adjusting the timing of surgery, or adjusting the surgical approach ([0008] of Gaudilliere discloses delaying surgery and [0017] of Gaudilliere discloses decision-making for proceeding with elective surgery, extended hospital stay, extended care at an intermediate facility, increased post-surgery follow-up, and the like)).
Claims 9, 13, 16, and 27 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2015/0241445 to Gaudilliere et al. (“Gaudilliere”) in view of U.S. Patent No. 11,848,106 to Wood (“Wood”) and NPL "SMOTE Bagging Algorithm for Imbalanced Dataset in Logistic Regression Analysis" to Hanifah et al. ("Hanifah"), and further in view of U.S. Patent No. 11,961,619 to LaBorde (“LaBorde”) and U.S. Patent No. 9,311,568 to Feller et al. (“Feller”):
Regarding claim 9, the Gaudilliere/Wood/Hanifah combination discloses the method of claim 1, further including wherein
the machine learning model includes weights for a set of biological omic features of the plurality of omic biological features (8:26-39 of Wood notes how weights are assigned to the patient attributes (which includes proteomic (biological omic) attributes/features per 3:51-55 and 7:34-37 of Wood); similar to as discussed previously, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the ML model of Gaudilliere to be trained using a bootstrap procedure and include weights for a set of biological omic features of the plurality of omic biological features similar to as taught by Wood to advantageously generate ML models that are specifically tailored to the type of surgery being contemplated which therefore results in predictions with increased accuracy while decreasing the variance of the model which prevents overfitting without necessarily increasing bias. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.).
However, the Gaudilliere/Wood/Hanifah combination appears to be silent regarding during training of the machine learning model and for each data layer, for every repetition of the bootstrap, initial weights are computed for the plurality of omic biological features and the at least one artificial feature associated with that data layer using an initial statistical learning technique, and at least one selected feature is determined for each data layer, based on a statistical criteria depending on the computed initial weights.
Nevertheless, LaBorde teaches that it was known in the healthcare informatics and machine learning art to analyze historical patient attributes such as lab findings, vitals, medical history, etc. and corresponding surgical outcomes to develop a model for predicting the need for surgical intervention (3:54-67), where developing the model includes assigning and updating synaptic weights of the model (which correspond to neurons/attributes per 5:18-21 and 21:1-14) after each of a plurality of training iterations based on whether a calculated global error is greater than a pre-established level (using a statistical learning technique)(24:7-25:37). This arrangement advantageously increases accuracy of the model by selecting and updating weights to result in outcomes that approximate training data outcomes.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have computed weights for the plurality of omic biological features (which also includes the artificial feature as noted above per the combination with Hanifah) during training of the machine learning model for each data layer and for every repetition of the bootstrap using a statistical learning technique in the system of the Gaudilliere/Wood/Hanifah combination as taught by LaBorde to advantageously increase accuracy of the model by selecting and updating weights to result in outcomes that approximate training data outcomes. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id. In relation to the statistical learning technique being an “initial” statistical learning technique and the weight being “initial” weights, during each iteration, each weight calculated before a subsequent weight is an “initial” weight calculated using an “initial” statistical learning technique.
Furthermore, Feller teaches (15:41-16:20) that it was known in the machine learning art before the effective filing date of the claimed invention to omit features having a weight of zero or below a threshold (statistical criteria) from an ML model to beneficially reduce computational time to subsequently select a representative image (an output) for a recipe (corresponding input feature). After omission of the features having a weight of zero or below a threshold, the remaining features either have a non-zero weight or a weight above a threshold and are thus “selected” for inclusion in the model.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have determined at least one selected feature for each data layer based on the computed initial weights being non-zero or greater than a threshold (a “statistical criteria”) in the system of the Gaudilliere/Wood/Hanifah/LaBorde combination as taught by Feller to advantageously reduce the high dimensionality associated with greater numbers of feature sets thereby improving the accuracy and efficiency of a classifier for predicting the particular class and thereby beneficially reduce computational time to subsequently select a representative image (an output) for a recipe (corresponding input feature). A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Regarding claim 13, the Gaudilliere/Wood/Hanifah/LaBorde/Feller combination discloses the method of claim 9, further including wherein the statistical criteria depends on significant weights among the computed initial weights (as discussed at 15:41-16:20 of Feller, the weights of the non-omitted features are “significant” because they beneficially reduce computational time to subsequently select a representative image (an output) for a recipe (corresponding input feature));
the significant weights being non-zero weights, when an initial statistical learning technique is a sparse regression technique (as discussed at 16:17-18 of Feller, the retained features can have non-zero weights while 15:46 discloses how the ML algorithm can include logistic regression; further, as evidenced by NPL “Overview of Sparse Modeling” at page 1, a sparse regression technique is selection of features having non-zero weights while less important features have zero weights; therefore, the retention of features having non-zero weights in Feller is a “sparse regression” technique)
the significant weights being weights above a predefined weight threshold, when the initial statistical learning technique is a non-sparse regression technique (as discussed at 16:17-18 of Feller, the retained features can have weights above a threshold which is a “non-sparse regression” technique (i.e., because it is not a “sparse regression” technique); again, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have determined at least one selected feature for each data layer based on the computed initial weights being non-zero or greater than a threshold (a “statistical criteria”) in the system of the Gaudilliere/Wood/Hanifah/LaBorde combination as taught by Feller to advantageously reduce the high dimensionality associated with greater numbers of feature sets thereby improving the accuracy and efficiency of a classifier for predicting the particular class and thereby beneficially reduce computational time to subsequently select a representative image (an output) for a recipe (corresponding input feature). A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.).
Regarding claim 16, the Gaudilliere/Wood/Hanifah/LaBorde/Feller combination discloses the method of claim 9, further including wherein the initial weights are further computed for a plurality of values of a hyperparameter, wherein the hyperparameter is a parameter whose value is used to control the learning process (33:38-40 of LaBorde discloses how hyperparameters can be tuned during training to give the best performance on the validation set, where the weights are calculated/adjusted during the training; accordingly, the weights are calculated/adjusted as the hyperparameters are tuned (various values of the hyperparameters) which “controls the learning process”; it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have computed the initial weights of the Gaudilliere/Wood/Hanifah/LaBorde/Feller combination for a plurality of values of a hyperparameter that is a parameter whose value is used to control the learning process as taught by LaBorde to advantageously give the best performance on the validation set thereby improving model accuracy and because a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). The courts have made clear that the teaching, suggestion, or motivation test is flexible and an explicit suggestion to combine the prior art is not necessary. The motivation to combine may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007)).
Regarding claim 27, the Gaudilliere/Wood/Hanifah/LaBorde/Feller combination discloses the method of claim 9, further including wherein during the machine learning, the weights of the model are further computed using a final statistical learning technique on the data associated to the set of selected features (as noted previously, LaBorde teaches that it was known in the healthcare informatics and machine learning art to analyze historical patient attributes such as lab findings, vitals, medical history, etc. and corresponding surgical outcomes to develop a model for predicting the need for surgical intervention (3:54-67), where developing the model includes assigning and updating synaptic weights of the model (which correspond to neurons/attributes per 5:18-21 and 21:1-14) after each of a plurality of training iterations based on whether a calculated global error is greater than a pre-established level (using a “final” statistical learning technique)(24:7-25:37); this arrangement advantageously increases accuracy of the model by selecting and updating weights to result in outcomes that approximate training data outcomes; similar to as discussed above, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have computed the model weights using a “final” statistical learning technique on the data associated to the set of selected features (the retained features) during the machine learning as taught by LaBorde to advantageously increase accuracy of the model by selecting and updating weights to result in outcomes that approximate training data outcomes and because a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). The courts have made clear that the teaching, suggestion, or motivation test is flexible and an explicit suggestion to combine the prior art is not necessary. The motivation to combine may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007)).
Claim 37 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2015/0241445 to Gaudilliere et al. (“Gaudilliere”) in view of U.S. Patent No. 11,848,106 to Wood (“Wood”) and NPL "SMOTE Bagging Algorithm for Imbalanced Dataset in Logistic Regression Analysis" to Hanifah et al. ("Hanifah"), and further in view of U.S. Patent App. Pub. No. 2021/0224599 to Tajima et al. (“Tajima”):
Regarding claim 37, the Gaudilliere/Wood/Hanifah combination discloses the method of claim 1, but appears to be silent regarding wherein during training the machine learning model, the method further comprises, before obtaining artificial features:
generating additional values of the plurality of non-artificial features based on the obtained values and using a data augmentation technique;
the artificial features being then obtained according to both the obtained values and the generated additional values.
Nevertheless, Tajima teaches ([0062]) that it was known in the healthcare informatics art to utilize synthetic minority over-sampling technique (SMOTE) (data augmentation technique) to generate interpolated values (additional values) of the training data (the non-artificial features) based on the values of the training data and perform sampling (obtaining artificial features as discussed in relation to claim 7) of the training data values and the interpolated values (the generated additional values) to advantageously improve detection performance.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention during the training of the machine learning model, for the method to further comprise, before obtaining artificial features: generating additional values of the plurality of non-artificial features based on the obtained values and using a data augmentation technique; and the artificial features being then obtained according to both the obtained values and the generated additional values in the system of the Gaudilliere/Wood/Hanifah combination as taught by Tajima to advantageously improve detection performance and because a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). The courts have made clear that the teaching, suggestion, or motivation test is flexible and an explicit suggestion to combine the prior art is not necessary. The motivation to combine may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007).
Allowable Subject Matter
Claims 21, 22, and 24 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claims 21 and 24, the prior art fails to further disclose the statistical criteria to be based on an occurrence frequency of the significant weights together with the limitations of claim 13.
Regarding claim 22, the prior art fails to further disclose the initial weights to be further computed for a plurality of values of a hyperparameter, wherein the hyperparameter is a parameter whose value is used to control the learning process; and wherein for each feature, a unitary occurrence frequency is calculated for each hyperparameter value and is equal to a number of the significant weights related to said feature for the successive bootstrap repetitions divided by the number bootstrap repetitions.
Conclusion
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/JONATHON A. SZUMNY/Primary Examiner, Art Unit 3686