DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-8 and 13-20 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Cha et al. (US Pub. 20200005900).
Referring to claim 1, Cha discloses A method comprising:
providing a dataset to a machine learning model, wherein the dataset comprises claims-based electronic data [fig. 4; pars. 36-45 and 159-162; a system comprises a server, a machine learning (ML) engine, and one or more data sources; the server receives input data from a data source, processes the input data into feature datasets, then transmits the feature datasets to the ML engine; the data source includes electronic health record (EHR) systems comprising patient information such as claims information];
receiving an output from the machine learning model in response to the machine learning model processing at least a portion of the dataset, wherein the output comprises a predicted value of a biomarker [pars. 12, 24, 63, 74-88, 162-164; the feature datasets, which include various predictive features (e.g., biomarkers), are processed by the ML engine to determine various risk information, such as a predictive value (i.e., a feature weight) of each feature and a risk score for each patient];
processing the portion of the dataset for identifying information associated with at least one individual in response to determining the predicted value of the biomarker satisfies one or more criteria [pars. 26, 39, 112, 129, and 140; the system determines whether the risk score for each patient exceeds a threshold score or is within a certain range or threshold (e.g., the system compares the patient’s eGFR biomarker to a given threshold value) to identify patients who satisfy criteria for executing a patient workflow]; and
transmitting, via a communication network to one or more communication devices, an electronic communication comprising information associated with the predicted value of the biomarker [pars. 26, 112, and 113; the patient workflow comprises transmitting notifications to the patient, the notifications including the various risk information].
Referring to claim 2, Cha discloses The method of claim 1, wherein: the claims-based electronic data is associated with a group of individuals; and the biomarker is associated with individuals in the group of individuals [pars. 36-45; note the patient information].
Referring to claim 3, Cha discloses The method of claim 1, wherein the dataset comprises prescription-based electronic data [pars. 38 and 43; the patient information includes medications information comprising information relating to one or more medications administered or prescribed to a patient].
Referring to claim 4, Cha discloses The method of claim 1, wherein the biomarker comprises hemoglobin A1C [par. 12; Table 14; the various predictive features include hemoglobin A1c].
Referring to claim 5, Cha discloses The method of claim 1, wherein the dataset does not include measured values of the biomarker [pars. 63-70; note the processing of the input data into the feature datasets; each feature is created by subjecting the patient information to any number of combinations, aggregations, transformations, normalizations, or imputations, and calculating summary statistics for the resulting data].
Referring to claim 6, Cha discloses The method of claim 1, wherein the output comprises: a predicted health status of the at least one individual corresponding to the predicted value of the biomarker [pars. 3, 24, 63, 67, 75, 80, 123, and 154; note the various risk information predicted based on the various predictive features; the various predictive features are grouped based on medical events or conditions].
Referring to claim 7, Cha discloses The method of claim 6, wherein the predicted health status comprises at least one of: a medical condition of the at least one individual [pars. 3, 24, 63, 75, 80, 123, and 154; note the medical events or conditions]; a predicted risk of the at least one individual with respect to developing the medical condition [pars. 3, 24, 63, 75, 80, 123, and 154; note the various risk information; and a predicted severity of the medical condition [pars. 121-123; the risk information represents different outcomes related to the medical events or conditions (e.g., different levels of a patient’s renal function decline)].
Referring to claim 8, Cha discloses The method of claim 1, further comprising: providing a training dataset to the machine learning model [pars. 36-45 and 92; the system generates training data relating to some or all of the above features (i.e., the various feature datasets)], wherein the training dataset comprises: claims-based electronic data of a set of reference individuals [pars. 36-45 and 92; note the claims information]; prescription-based electronic data of the set of reference individuals [pars. 36-45 and 92; the patient information includes medications information comprising information relating to one or more medications administered or prescribed to a patient]; measured biometric data of the set of reference individuals, wherein the measured biometric data comprises reference measured values of the biomarker [pars. 12, 24, 36-45, 63, 74-88, and 92; note the various predictive features (e.g., biomarkers); and diagnosed medical conditions associated with the set of reference individuals [pars. 35-45 and 92; the patient information includes diagnoses and procedures (DP) information].
Referring to claim 13, Cha discloses The method of claim 1, further comprising: providing a target medical condition to the machine learning model; receiving a second output from the machine learning model in response to providing the target medical condition, wherein the second output comprises: one or more biomarkers indicative of the target medical condition; and a predicted value of the one or more biomarkers; processing the portion of the dataset for the identifying information associated with the at least one individual in response to determining the predicted value of the one or more biomarkers satisfies one or more second criteria; and transmitting, via the communication network to the one or more communication devices, a second electronic communication comprising information associated with the predicted value of the one or more biomarkers, wherein the second output comprises at least one of: one or more claims-based identifiers indicative of the target medical condition; and one or more prescription-based identifiers indicative of the target medical condition [par. 202; note targeting of ML engine to risk of renal failure; see also the rejection for claim 1] .
Referring to claim 14, Cha discloses The method of claim 1, wherein the claims-based electronic data comprises Current Procedural Terminology (CPT) codes [par. 128; the patient information includes CPT codes] and National Drug Code (NDC) numbers [par. 57; the patient information includes NDC codes].
Referring to claim 15, Cha discloses The method of claim 1, wherein the output comprises a range of values of the biomarker [pars. 26, 32, 42, and 104; the risk scores may be based on range values of the various predictive features].
Referring to claim 16, Cha discloses The method of claim 1, wherein the output comprises a predicted value of one or more additional biomarkers associated with the at least one individual [pars. 12, 24, 63, 74-88, 162-164; note the various risk information based on the various predictive features].
Referring to claim 17, Cha discloses The method of claim 1, wherein the electronic communication comprises at least a portion of the output from the machine learning model [pars. 26, 112, and 113; note the notifications including the various risk information].
Referring to claim 18, Cha discloses The method of claim 1, wherein the one or more communication devices comprise a communication device of the at least one individual, a communication device of a care provider of the at least one individual, or both [pars. 26, 112, and 113; the notifications are transmitted to the patient and/or to any number of providers associated with the patient].
Referring to claim 19, see at least the rejection for claim 1. Cha further discloses A system comprising: a communications interface; a processor coupled with the communications interface; and a memory coupled with the processor, wherein the memory stores data that, when executed by the processor, enables the processor to perform the claimed steps [fig. 5, network interface 560, processor 510; and system memory 520].
Referring to claim 20, see at least the rejection for claim 1. Cha further discloses A non-transitory computer-readable medium comprising instructions stored therein that, when executed by a processor, cause the processor to perform the claimed steps [fig. 5, storage media 540, system memory 520, processor 510].
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 9-12 are rejected under 35 U.S.C. 103 as being unpatentable over Cha in view of Liao et al. (US Pub. 20240105336).
Referring to claim 9, Cha does not appear to explicitly disclose The method of claim 1, wherein: the input comprises one or more candidate intervention actions; and the output comprises a predicted effect of each of the one or more candidate interventions, wherein the predicted effect comprises: a second predicted value of the biomarker; and a temporal value associated with achieving the second predicted value.
However, Liao discloses The method of claim 1, wherein: the input comprises one or more candidate intervention actions; and the output comprises a predicted effect of each of the one or more candidate interventions, wherein the predicted effect comprises: a second predicted value of the biomarker; and a temporal value associated with achieving the second predicted value [figs. 4A, 6, 8, and 9; pars. 5-13; determining assessment of a subject’s medical condition (e.g., cardiovascular, metabolic, or renal syndrome, disease, or disorder) comprises determining a response of the subject to medical treatments, including determining a prediction, a progression, or a regression of a health marker of the subject over the future period of time; the medical treatments may include clinical recommendations, where the clinical recommendations may modify at least one actionable clinical variable].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the output of the ML engine to include medical treatments and clinical recommendations as taught by Liao, with a reasonable expectation of success. The motivation for doing so would have been to facilitate treating and managing chronic conditions, in addition to predicting, assessing, and diagnosing them [Liao, par. 3].
Referring to claim 10, Cha does not appear to explicitly disclose The method of claim 1, wherein the input comprises: one or more candidate treatment plans; and the output comprises a predicted effect of each of the one or more candidate treatment plans, wherein the predicted effect comprises: a second predicted value of the biomarker; and a temporal value associated with achieving the second predicted value.
However, Liao discloses The method of claim 1, wherein the input comprises: one or more candidate treatment plans; and the output comprises a predicted effect of each of the one or more candidate treatment plans, wherein the predicted effect comprises: a second predicted value of the biomarker; and a temporal value associated with achieving the second predicted value [figs. 4A, 6, 8, and 9; pars. 5-13; determining assessment of a subject’s medical condition (e.g., cardiovascular, metabolic, or renal syndrome, disease, or disorder) comprises determining a response of the subject to medical treatments, including determining a prediction, a progression, or a regression of a health marker of the subject over the future period of time; the medical treatments may include clinical recommendations, where the clinical recommendations may modify at least one actionable clinical variable].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the output of the ML engine to include medical treatments and clinical recommendations as taught by Liao, with a reasonable expectation of success. The motivation for doing so would have been to facilitate treating and managing chronic conditions, in addition to predicting, assessing, and diagnosing them [Liao, par. 3].
Referring to claim 11, Cha discloses The method of claim 10, wherein the one or more candidate treatment plans comprise at least one of: a medical procedure; an action of the at least one individual; and a drug regimen [figs. 4A, 6, 8, and 9; pars. 14, 17, and 20; the medical treatments include cardiac amyloidosis treatment, diuretic therapy, an alternative treatment based on the assessment].
Referring to claim 12, Liao discloses The method of claim 1, wherein the output comprises respective rankings corresponding to: implementing a candidate intervention action; and implementing a candidate treatment plan [fig. 9, pars. 90, 95, and 102; note implicit ranking of treatment plans (which include clinical recommendations) via visualization; also note determination of which clinical trials are most likely to be successful].
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Naidoo et al. (US Pub. 20220147865) discloses using HER data and machine learning to identify patients at risk of a specific disease or condition.
Contact Information
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/Grace Park/Primary Examiner, Art Unit 2144
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