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 .
DETAILED ACTION
In the Amendment dated 16 July, 2025, the following occurred:
Claims 14, 16, 17, 23, and 25 were canceled.
Claims 1-13, 15, 18-22, and 24 are pending.
Priority
This application claims priority to Patent Application No. GB2300798.2 dated 19 January 2023.
Information Disclosure Statement
The Information Disclosure Statements (IDS) submitted on 16 July 2025 and 22 May 2026 are in compliance with the provisions of 37 CFR 1.97 and have been fully considered by the Examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(I). The following figures are unsatisfactory for reproduction because they are blurry:
Fig. 5-9, 11-17
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. 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.
Claim Objections
Claims 6-8, 18, and 19 are objected to because of the following informalities:
In claim 6, line 1, “the health system data” should read “the health care system data.”
In claim 7, line 1, “values for a set of selected set of input features” should read “values for a set of selected input features.”
In claim 8, line 30-32, “the model” should read “the machine learning derived procedure.”
In claim 18, line 4-5, “health system data” should read “health care system data.”
In claim 19, line 7, “machine learning derive procedure” should read “machine learning derived procedure.”
Appropriate corrections are 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 7, 13, and 19 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 pre-AIA the applicant regards as the invention.
Claim 7 recites “b) the demographic data are representative of: maternal age at expected date of delivery, gestational age at eligibility.” The claim is indefinite because it is unclear whether both the maternal age and the gestational age are required. For the purposes of examination, the Examiner interprets the claim as “maternal age at expected date of delivery and gestational age at eligibility.”
Claim 13 recites “the one or more feature reduction processes.” There is a lack of antecedent basis for this. For examination purposes, claim 13 will be read as “the method of claim 4.”
Claim 19 recites “the method of claim 14” and thereafter refers to "the training data," etc. These features lack antecedent basis. For examination purposes, claim 19 will be read as “the method of claim 18.”
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 19 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 19 recites “the method of claim 14.” Claim 14 was cancelled by amendment and thus Claim 19 does not further limit a claim upon which it depends. For examination purposes, claim 19 will be read as “the method of claim 18.” Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13, 15, 18-22, and 24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 18, and 24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claims recite a method and system for obtaining adverse outcome information, and therefore meet step 1.
Step 2A1
The limitations of (Claim 1 being representative) obtaining input data comprising at least subject data for the subject, wherein the subject data comprises a combination of at least two of: demographic data; vital sign data representative of one or more vital signs of the subject; and sample data representative of one or more parameters obtainable from an analysis of a sample; providing the obtained input data to […] obtain at least one of the risk level or probability associated with one or more adverse maternal outcomes for the subject, wherein the input data further comprises health care system data representing a value for at least one statistic or indicator associated with at least one of the health care system, a country of the health care system or a region of the health care system, as drafted, is a process that, under the broadest reasonable interpretation, falls in the grouping of certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions).
The limitations of (Claim 18) obtaining training data associated with a plurality of subjects, wherein the training data comprises subject data, the subject data comprising a combination of at least two of: health system data; demographic data; symptom data representative of the presence of one or more symptoms; vital sign data representative of one or more vital signs of the subject and sample data representative of one or more parameters obtainable using an analysis of a sample; and performing a training process using at least some of the training data to obtain a trained machine learning derived procedure configured to receive further input data comprising at least further subject data for a further subject with suspected or confirmed preeclampsia and obtain at least one of a risk level or a probability associated with one or more adverse maternal outcomes for the subject, wherein the training data and further input data comprises health care system data representing a value for at least one statistic associated with at least one of the health care system, a country of the health care system or a region of the health care system, as drafted, is a process that, under the broadest reasonable interpretation, falls in the grouping of certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions).
That is, other than reciting a method implemented by a computer, the claimed invention amounts to managing personal behavior or interaction between people. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people (i.e., rules or instructions for a person or persons to follow) but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional element of one or more processors with one or more non-transitory memories that implements the identified abstract idea. The computing elements are not exclusively described by the applicant and are recited at a high-level of generality (i.e., generic computer components, see, e.g., Page 33) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Further, obtaining values is considered insignificant extra solution activity such as pre-solution activity e.g., data gathering (performed by receiving/transmitting/etc.) See MPEP 2106.05(g).
The claim further recites the additional element of using a machine learning derived procedure configured to obtain a risk level and/or probability associated with one or more adverse maternal outcomes for a subject. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible). The Examiner notes that the machine learning derived procedure is described in the Specification at Page 4, Line 1-4 as encompassing a combination of one or more of random forest and/or regression models with a generalized linear mixed model, optionally, wherein the machine learning derived procedure comprises a combination of a Bayesian generalized linear mixed model (GLMM) and a random forest model. Alternatively, or in addition, the implementation of the machine learning derived procedure to the clinical data merely confines the use of the abstract idea (i.e., the trained models) to a particular technological environment or field of use (the noted types of ML) and thus fails to add an inventive concept to the claims. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B
The claims do 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 element of using a processor to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component cannot provide an inventive concept (“significantly more”).
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of a machine learning derived procedure was determined to represent “apply it” on a generic computer. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP 2106.05(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 elements do not provide significantly more. As such the claim is not patent eligible.
Claims 2-13, 15, and 19-22 are similarly rejected because they either further define/narrow the abstract idea 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.
Claims 2 and 8 merely describe obtaining input data, which further defines the abstract idea.
Claim 3 merely describes receiving user input data or displaying at least one of the obtained risk level or probability, which further defines the abstract idea.
Claims 4, 7, 11, and 12 merely describe the obtained input data, which further defines the abstract idea.
Claims 5 and 6 merely describe the health care system data, which further defines the abstract idea.
Claims 9 and 10 merely describe the machine learning derived procedure, which further defines the abstract idea.
Claims 13 and 20 merely describe the one or more feature reduction processes, which further defines the abstract idea.
Claim 15 merely describes the risk level or probability, the adverse maternal outcome, or the machine learning derived procedure, which further defines the abstract idea.
Claims 19 and 22 merely describe the training process or the training data, which further defines the abstract idea.
Claim 21 merely describes the training method, which further defines the abstract idea.
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.
Claims 1-3, 5, 6, 8, 15, 18, 22, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Bellesia et al. (U.S. 2025/0226093) in view of Peri et al. (U.S. 2021/0118574), referred to hereinafter as Bellesia and Peri, respectively.
REGARDING CLAIM 1
Bellesia teaches the claimed method of obtaining at least one of a risk level or a probability associated with one or more adverse maternal outcomes for a subject with suspected or confirmed preeclampsia comprising: [Para. 0123 teaches a method for generating values indicative of a risk that subjects develop preterm preeclampsia.]
obtaining input data comprising at least subject data for the subject, wherein the subject data comprises a combination of at least two of: [Para. 0123 teaches identifying input data.]
demographic data; [Table 2 teaches demographic characteristics.]
vital sign data representative of one or more vital signs of the subject; and [Para. 0027 teaches detecting physiological readings such as heart activity/signals.]
sample data representative of one or more parameters obtainable from an analysis of a sample; [The Examiner notes the sample data is optional and the option was not taken.]
Bellesia may not explicitly teach
providing the obtained input data to a machine learning derived procedure configured to obtain at least one of the risk level or probability associated with one or more adverse maternal outcomes for the subject, wherein the input data further comprises health care system data representing a value for at least one statistic or indicator associated with at least one of the health care system, a country of the health care system or a region of the health care system.
However, Peri teaches the following:
providing the obtained input data to a machine learning derived procedure configured to obtain at least one of the risk level or probability associated with one or more adverse maternal outcomes for the subject, wherein the input data further comprises health care system data representing a value for at least one statistic or indicator associated with at least one of the health care system, a country of the health care system or a region of the health care system. [Para. 0194 teaches consuming input data and utilizing machine learning to output a MIHIC score which represents the risk of the mother having placental abruption. Para. 0066 teaches indicators such as maternal mortality rate and spread of maternal mortality by geographical area. Para. 0031 teaches Maternal and Infant Health Intelligence & Cognitive Insights (MIHIC) scores are generated by applying advanced Artificial Intelligence and Deep Learning methods. Table 1 teaches Maternal Mortality Rate is a characteristic considered for computation of the MIHIC score.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the computer-implemented method of Bellesia to provide input to the machine learning derived procedure as taught by Peri, with the motivation of improving healthcare delivery (see Peri at Para. 0002).
REGARDING CLAIM 2
Bellesia in view of Peri teaches the claimed method of claim 1.
Bellesia further teaches
wherein obtaining input data comprises at least one of a), b), c), or d): [Para. 0123 teaches identifying input data.]
b) performing a vital sign measurement to obtain the vital sign data; [Para. 0027 teaches detecting physiological readings such as heart activity/signals.]
d) obtaining a sample from the subject and performing a sample analysis on the sample to obtain the sample data, or, wherein the sample comprises a blood sample and wherein the sample analysis comprises a blood sample analysis to obtain at least one of: a) one or more haematological parameters including haematocrit, platelet count, total leukocyte count; or b) one or more renal parameters including lactate dehydrogenase, aspartate transaminase, alanine transaminase, serum albumin. [Para. 0035 teaches obtaining and analyzing health data of a patient. Test data corresponds to various laboratory tests performed on samples of the patient.]
Peri further teaches
a) receiving user input data representative of at least one of the country or region of the health care system and retrieving the health care system data representing a value for at least one statistic associated with at least one of the country or region of the health care system; [Table 1 teaches Site of hospital and Maternal Mortality Rate.]
c) receiving user input data representative of the demographic data; [Para. 0130 teaches receiving demographic data about the patient as input.]
REGARDING CLAIM 3
Bellesia in view of Peri teaches the claimed method of claim 1.
Peri further teaches
further comprising at least one of receiving user input data representing at least some input data or displaying at least one of the obtained risk level or probability. [Para. 0033 teaches displaying the statistical measures of risk factors. The MIHIC system provides the statistical measures of the risk factors in the form of a MIHIC Score.]
REGARDING CLAIM 5
Bellesia in view of Peri teaches the claimed method of claim 1.
Peri further teaches
wherein the health care system data comprises at least one non-clinical statistic or indicator. [Para. 0066 teaches indicators such as maternal mortality rate and spread of maternal mortality by geographical area.]
REGARDING CLAIM 6
Bellesia in view of Peri teaches the claimed method of claim 1.
Peri further teaches
wherein the health system data comprises at least one of a national or regional per capita gross domestic product or a national or regional maternal mortality ratio. [Para. 0066 teaches indicators such as maternal mortality rate and spread of maternal mortality by geographical area.]
REGARDING CLAIM 8
Bellesia in view of Peri teaches the claimed method of claim 1.
Bellesia further teaches
wherein obtaining the input data comprises: obtaining sample data for a first time and providing the sample data as part of first input data to the model to obtain at least one of the risk level or probability associated with the first time and obtaining sample data for a second, subsequent time and providing the sample data as part of the input data to the model to obtain at least one of the risk level or probability associated with the second time. [Para. 0036 teaches predicted prognosis is used in evaluation of a patient’s health and changes or trends therein, such as whether the patient is deteriorating or improving as indicated by changes in prognoses over time from the ML modeler as new tests are run or otherwise as new health data becomes available. The Examiner further notes that the sample data is optional and the option was not taken.]
REGARDING CLAIM 15
Bellesia in view of Peri teaches the claimed method of claim 1.
Peri further teaches
wherein at least one of a), b), or c): a) at least one of the risk level or probability represents at least one of the risk level or probability of an occurrence of the adverse maternal outcome within one or more predefined time periods, optionally or, wherein the predefined time period comprises at least one of two days or seven days, b) the adverse maternal outcome comprises at least one of: maternal death; an adverse central nervous system event; a cardiorespiratory event; a hematologic event; a hepatic event; a renal event; or one or more of: placental abruption, severe ascites, bell's palsy; or c) the machine learning derived procedure is configured to classify the subject into one of a plurality of risk levels based on a probability of the occurrence of one or more adverse maternal events in a predetermined time period. [Para. 0119 teaches placental abruption.]
REGARDING CLAIM 18
Bellesia teaches the claimed method of training a machine learning derived procedure comprising:
obtaining training data associated with a plurality of subjects, wherein the training data comprises subject data, the subject data comprising a combination of at least two of: [Para. 0006 teaches generating a training dataset.]
health system data; [The Examiner notes the health care system data is optional and the option was not taken.]
demographic data; [Table 2 teaches demographic characteristics.]
symptom data representative of the presence of one or more symptoms; [The Examiner notes the symptom data is optional and the option was not taken.]
vital sign data representative of one or more vital signs of the subject; and [Para. 0027 teaches detecting physiological readings such as heart activity/signals.]
sample data representative of one or more parameters obtainable using an analysis of a sample; [The Examiner notes the sample data is optional and the option was not taken.]
Bellesia may not explicitly teach
performing a training process using at least some of the training data to obtain a trained machine learning derived procedure configured to receive further input data comprising at least further subject data for a further subject with suspected or confirmed preeclampsia and obtain at least one of a risk level or a probability associated with one or more adverse maternal outcomes for the subject, wherein the training data and further input data further comprises health care system data representing a value for at least one statistic associated with at least one of the health care system, a country of the health care system or a region of the health care system.
However, Peri teaches the following:
performing a training process using at least some of the training data to obtain a trained machine learning derived procedure configured to receive further input data comprising at least further subject data for a further subject with suspected or confirmed preeclampsia and obtain at least one of a risk level or a probability associated with one or more adverse maternal outcomes for the subject, wherein the training data and further input data further comprises health care system data representing a value for at least one statistic associated with at least one of the health care system, a country of the health care system or a region of the health care system. [Para. 0194 teaches consuming input data and utilizing machine learning to output a MIHIC score which represents the risk of the mother having placental abruption. Para. 0066 teaches indicators such as maternal mortality rate and spread of maternal mortality by geographical area. Para. 0031 teaches Maternal and Infant Health Intelligence & Cognitive Insights (MIHIC) scores are generated by applying advanced Artificial Intelligence and Deep Learning methods. Table 1 teaches Maternal Mortality Rate is a characteristic considered for computation of the MIHIC score.]
Motivation to combine the teaching of Peri with the teaching of Bellesia is the same as that used with respect to claim 1 and is therefore reiterated here.
REGARDING CLAIM 22
Bellesia in view of Peri teaches the claimed method of claim 18.
Bellesia further teaches
wherein at least one of a) or b): a) the training data comprises at least one of: training data for subjects with pre-eclampsia prior to 34 weeks gestation; or training data for subjects in sub-Saharan Africa, North and South America, South Asia, Europe and Oceania; or b) the method further comprises identifying missing data and replacing missing data using a missing data replacement scheme. [Para. 0023 teaches generating and using artificial neural network models for the prediction of early onset preeclampsia (preeclampsia with birth <34 weeks gestation).]
REGARDING CLAIM 24
Claim 24 is analogous to Claim 18, thus Claim 24 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 18.
Claims 7 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Bellesia in view of Peri and Wild (U.S. 2021/012082).
REGARDING CLAIM 7
Bellesia in view of Peri teaches the claimed method of claim 1.
Peri further teaches
wherein the input data are representative of values for a set of selected set of input features, wherein:
a) the health care system data are representative of: […] national or regional maternal mortality ratio; [Para. 0066 teaches indicators such as maternal mortality rate and spread of maternal mortality by geographical area.]
c) the vital sign data are representative of: height, weight at time of assessment, systolic blood pressure, diastolic blood pressure, […]; and [Table 1 teaches height, weight, systolic blood pressure, and diastolic blood pressure.]
d) the sample data are representative of: haematology sample data comprising haematocrit, platelet count, total leukocyte count; renal sample data comprises: serum creatinine, uric acid; hepatic sample data comprising: lactate dehydrogenase, aspartate transaminase, alanine transaminase, serum albumin. [The Examiner notes the sample data is optional and the option was not taken.]
Bellesia further teaches
b) the demographic data are representative of: maternal age at expected date of delivery, gestational age at eligibility; [Para. 0115 teaches maternal age and gestational age.]
Bellesia in view of Peri may not explicitly teach
… national per capita gross domestic product…
…oxygen saturation…
However, Wild teaches the following:
… national per capita gross domestic product… [Para. 0114 teaches Gross Domestic Product of the country in which the subject resides.]
…oxygen saturation… [Para. 0041 teaches blood oxygen saturation levels.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the computer-implemented method of Bellesia in view of Peri to include national gross domestic product and oxygen saturation as taught by Wild, with the motivation of reducing mortality rates (see Wild at Para. 0200).
REGARDING CLAIM 12
Bellesia in view of Peri teaches the claimed method of claim 1.
Peri further teaches
wherein the input data comprises data representing values for at least the following input variables: […] maternal mortality ratio, systolic and diastolic blood pressure, uric acid. [Para. 0066 teaches indicators such as maternal mortality rate. Table 1 teaches systolic blood pressure and diastolic blood pressure. Para. 0049 teaches uric acid.]
Bellesia in view of Peri may not explicitly teach
…national per capita gross domestic product…
However, Wild teaches the following:
…national per capita gross domestic product… [Para. 0114 teaches Gross Domestic Product of the country in which the subject resides.]
Motivation to combine the teaching of Wild with the teachings of Bellesia and Peri is the same as that used with respect to claim 7 and is therefore reiterated here.
Claims 4, 13, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bellesia in view of Peri and Gujral et al. (U.S. 2025/0166723), referred to hereinafter as Gujral.
REGARDING CLAIM 4
Bellesia in view of Peri teaches the claimed method of claim 1.
Bellesia in view of Peri may not explicitly teach
wherein at least one of a), b), or c): a) the obtained input data represent values for a reduced set of input variables, wherein the selected set of input variables are selected from a larger set of input variables in accordance with a feature reduction process; b) wherein the input data further comprises input representing an elapsed time and wherein at least one of the risk level or probability is obtained in dependence on at least the elapsed time; or c) performing one or more feature reduction processes comprising a recursive feature elimination process based on importance scores for a larger set of parameters.
However, Gujral teaches the following:
wherein at least one of a), b), or c): a) the obtained input data represent values for a reduced set of input variables, wherein the selected set of input variables are selected from a larger set of input variables in accordance with a feature reduction process; b) wherein the input data further comprises input representing an elapsed time and wherein at least one of the risk level or probability is obtained in dependence on at least the elapsed time; or c) performing one or more feature reduction processes comprising a recursive feature elimination process based on importance scores for a larger set of parameters. [Para. 0057 teaches applying recursive feature elimination based on importance scores.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the computer-implemented method of Bellesia in view of Peri to perform a recursive feature elimination process based on importance scores as taught by Gujral, with the motivation of improving predictive accuracy (see Gujral at Para. 0057).
REGARDING CLAIM 13
Bellesia in view of Peri and Gujral teaches the claimed method of claim 4.
Gujral further teaches
wherein the one or more feature reduction processes comprises a recursive feature elimination process based on importance scores for the larger set of parameters. [Para. 0057 teaches applying recursive feature elimination based on importance scores.]
REGARDING CLAIM 19
Bellesia in view of Peri teaches the claimed method of claim 18.
Bellesia in view of Peri may not explicitly teach
wherein at least one of a) or b): a) the training process comprises performing a feature reduction process to select a reduced set of input variables from a larger set of input variables and training the machine learning derived procedure to receive input data representative of values for the reduced set of input variables; or b) wherein the training data is representative of data obtained over a time period, such that the trained machine learning derive procedure is configured to receive input representative of an elapsed time and obtain at least one of the risk level or probability is obtained in dependence on at least the elapsed time.
However, Gujral teaches the following:
wherein at least one of a) or b): a) the training process comprises performing a feature reduction process to select a reduced set of input variables from a larger set of input variables and training the machine learning derived procedure to receive input data representative of values for the reduced set of input variables; or b) wherein the training data is representative of data obtained over a time period, such that the trained machine learning derive procedure is configured to receive input representative of an elapsed time and obtain at least one of the risk level or probability is obtained in dependence on at least the elapsed time. [Para. 0057 teaches applying recursive feature elimination based on importance scores.]
Motivation to combine the teaching of Gujral with the teachings of Bellesia and Peri is the same as that used with respect to claim 4 and is therefore reiterated here.
REGARDING CLAIM 20
Bellesia in view of Peri and Gujral teaches the claimed method of claim 19.
Gujral further teaches
wherein the feature reduction process comprises:
training one or more models using first data representative of a first group of features; [Para. 0025 teaches the training set of inputs may include features of a group of kinase inhibitors.]
determining importance scores for each feature of the first group of features; and [Para. 0069 teaches ranking kinases by importance score.]
selecting the reduced set of features based on the determined importance scores. [Para. 0069 teaches removing the bottom 25% of kinases.]
Motivation to combine the teaching of Gujral with the teachings of Bellesia and Peri is the same as that used with respect to claim 4 and is therefore reiterated here.
Claims 9 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Bellesia in view of Peri and Stubbs et al. (U.S. 2021/0133607), referred to hereinafter as Stubbs.
REGARDING CLAIM 9
Bellesia in view of Peri teaches the claimed method of claim 1.
Bellesia in view of Peri may not explicitly teach
wherein the machine learning derived procedure comprises at least one part configured to take into account a magnitude and rate of change of the obtained input data.
However, Stubbs teaches the following:
wherein the machine learning derived procedure comprises at least one part configured to take into account a magnitude and rate of change of the obtained input data. [Para. 0075 teaches taking into account data thresholds such as rate of change and magnitude. This time series data from sensors is used as input to an inference engine (such as a deep learning model).]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the computer-implemented method of Bellesia in view of Peri to take into account a magnitude and rate of change as taught by Stubbs, with the motivation of improving monitoring, control, and intelligent diagnosis (see Stubbs at Para. 0007).
REGARDING CLAIM 11
Bellesia in view of Peri teaches the claimed method of claim 1.
Bellesia in view of Peri may not explicitly teach
wherein the input data does not include symptom data representative of one or more symptoms of the subject.
However, Stubbs teaches the following:
wherein the input data does not include symptom data representative of one or more symptoms of the subject. [Para. 0077 teaches data from various sensors is used as input to a machine learning inference engine. This data does not include symptoms.]
Motivation to combine the teaching of Stubbs with the teachings of Bellesia and Peri is the same as that used with respect to claim to claim 9 and is therefore reiterated here.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Bellesia in view of Peri and Malki et al. (U.S. 2024/0221959), referred to hereinafter as Malki.
REGARDING CLAIM 10
Bellesia in view of Peri teaches the claimed method of claim 1.
Bellesia in view of Peri may not explicitly teach
wherein at least one of a) the machine learning derived procedure comprises a combination of one or more of random forest or regression models with a generalised linear mixed model, optionally, wherein, or the machine learning derived procedure comprises a combination of a Bayesian generalized linear mixed model (GLMM) and a random forest model.
However, Malki teaches the following:
wherein at least one of a) the machine learning derived procedure comprises a combination of one or more of random forest or regression models with a generalised linear mixed model, optionally, wherein, or b) the machine learning derived procedure comprises a combination of a Bayesian generalized linear mixed model (GLMM) and a random forest model. [Claim 1 teaches using a random forest and Bayesian GLMM to obtain estimated predictive probabilities.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the computer-implemented method of Bellesia in view of Peri to use a Bayesian GLMM and a random forest as taught by Malki, with the motivation of improving predictive performance (see Malki at Para. 0018).
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Bellesia in view of Peri, Gujral, and Malki.
REGARDING CLAIM 21
Bellesia in view of Peri teaches the claimed method of claim 18.
Bellesia in view of Peri may not explicitly teach
wherein the training method comprises performing an iterative process comprising:
a) fitting a plurality of models of a first type using training data for a plurality of subjects over a plurality of time points using a target outcome;
b) fitting at least one model of a second type using at least predictions obtained from the plurality of models of the first type models and the target outcome; and
However, Gujral teaches the following:
wherein the training method comprises performing an iterative process comprising:
a) fitting a plurality of models of a first type using training data for a plurality of subjects over a plurality of time points using a target outcome; [Para. 0040 teaches multiple models. Para. 0022 teaches a group of subjects.]
b) fitting at least one model of a second type using at least predictions obtained from the plurality of models of the first type models and the target outcome; and [Para. 0040 teaches determining a second model based on the first model. The second model re-ranks the target compound.]
Motivation to combine the teaching of Gujral with the teachings of Bellesia and Peri is the same as that used with respect to claim 4 and is therefore reiterated here.
Bellesia in view of Peri and Gujral may not explicitly teach
c) using an output from the second model to update the target outcome for fitting the plurality of models of the first type, or wherein the first type comprises a random forest based model and the second type comprises a generalized linear mixed model.
However, Malki teaches the following:
c) using an output from the second model to update the target outcome for fitting the plurality of models of the first type, or wherein the first type comprises a random forest based model and the second type comprises a generalized linear mixed model. [Claim 1 teaches using a random forest and Bayesian GLMM to obtain estimated predictive probabilities.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the computer-implemented method of Bellesia in view of Peri and Gujral to use a Bayesian GLMM and a random forest as taught by Malki, with the motivation of improving predictive performance (see Malki at Para. 0018).
Conclusion
Prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Tuytten et al. (U.S. 2022/0005605) which discloses a system and method of generating a model to detect, or predict the risk of, an outcome.
Kwon (U.S. 2023/0148955) which discloses a method of providing diagnostic information on Alzheimer’s disease using a brain network.
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/CAMRYN B LEWIS/
Examiner, Art Unit 3683
/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683
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