Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the application filed on January 16, 2025.
2. Claim(s) 1-20 are currently pending and have been examined.
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 1-20 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. Based upon consideration of all of the relevant factors with respect to the claims as a whole, the claims are directed to non-statutory subject matter which do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the following analysis:
Independent Claim(s) 1, 8 and 15 are directed to an abstract idea consisting of a methods and systems comprising algorithmic analysis of medical time-series data to predict a disease risk and decide a treatment.
Independent Claim 1 recites “receiving, via at least one processor of the one or more hardware processors associated with an electronic digital memory at a medical records computer system, a plurality of measurements of one or more urinary parameters; determining a time series of measurements from the received plurality of measurements; based on the time series of measurements, determining a set of Holder exponents and an RQA recurrence rate; utilizing the RQA recurrence rate and at least a subset of the set of Holder exponents in an algorithmic predictor, the algorithmic predictor associated with the one or more hardware processors, the electronic digital memory, the medical records computer system, or any combination thereof; and utilizing the algorithmic predictor, generating a forecast of the a likelihood of urolithiasis for a target patient over a future time interval; wherein, based at least partially on the generated forecast, initiating an intervening action comprising a particular treatment procedure is administered to the target patient in connection with treating a particular condition associated with the generated forecast and associated with the likelihood of urolithiasis for the target patient..”
Independent Claim 8 recites “receiving, via at least one processor of the one or more hardware processors associated with an electronic digital memory at a medical records computer system, a plurality of measurements of one or more urinary parameters; determining a time series of measurements from the received plurality of measurements; based on the time series of measurements, determining a set of Holder exponents and an RQA recurrence rate; utilizing the RQA recurrence rate and at least a subset of the set of Holder exponents in an algorithmic predictor, the algorithmic predictor associated with the one or more hardware processors, the electronic digital memory, the medical records computer system, or any combination thereof; and utilizing the algorithmic predictor, generating a forecast of a likelihood of urolithiasis for a target patient over a future time interval, wherein, based at least partially on the generated forecast, an intervening action comprising a particular treatment procedure is administered to the target patient in connection with treating a particular condition associated with the generated forecast and associated with the likelihood of urolithiasis for the target patient.”
Independent Claim 15 recites “receiving, via at least one processor of a set of one or more hardware processors associated with an electronic digital memory at a medical records computer system, a plurality of measurements of one or more urinary parameters; determining a time series of measurements from the received plurality of measurements; based on the time series of measurements, determining a set of Holder exponents and an RQA recurrence rate; utilizing the RQA recurrence rate and at least a subset of the set of Holder exponents in an algorithmic predictor, the algorithmic predictor associated with the set of one or more hardware processors, the electronic digital memory, the medical records computer system, or any combination thereof; and utilizing the algorithmic predictor, generating a forecast of a likelihood of urolithiasis for a target patient over a future time interval, wherein, based at least partially on the generated forecast, an intervening action comprising a particular treatment procedure is administered to the target patient in connection with treating a particular condition associated with the generated forecast and associated with the likelihood of urolithiasis for the target patient.”
The limitations of Claims 1, 8 and 15, as drafted, under its broadest reasonable interpretation, covers the performance of a Mental Process which are concepts performed in the human mind (including an observation, evaluation, judgment, opinion), Certain Methods Of Organizing Human Activity which are concepts performed by managing personal behavior, relationships or interactions between people (including fundamental economic principles, commercial or legal interactions, social activities, teaching, and following rules or instructions) and Mathematical Concepts which are concepts performed that encompasses mathematical relationships, mathematical formulas or equations, and mathematical calculations, but for the recitation of generic computer components. That is, other than reciting, “one processor, one or more hardware processors, electronic digital memory, medical records computer system, an algorithmic predictor” nothing in the claim element precludes the step from practically being performed by evaluating medical data and choosing treatment using calculations. For example, but for the “processor” language, “receiving” in the context of this claim encompasses the user manually retrieving a plurality of measurements of one or more urinary parameters. Similarly, the determining, a time series of measurements from the received plurality of measurements, covers performance of evaluating medical data and choosing treatment using calculations, but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of evaluating medical data and choosing treatment using calculations, but for the recitation of generic computer components, then it falls within the “Mental Process, Certain Methods of Organizing Human Activity and Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of using a “one processor, one or more hardware processors, electronic digital memory, medical records computer system, an algorithmic predictor” to perform all of the “receiving, determining, utilizing and generating” steps. The “one processor, one or more hardware processors, electronic digital memory, medical records computer system, an algorithmic predictor” is/are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) of executing computer-executable instructions for implementing the specified logical function(s) such that it amounts no more than mere instructions to apply the exception using a generic computer component. 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. The claim is directed to an abstract idea.
Claim 1 has the following additional elements (i.e., one processor, one or more hardware processors, electronic digital memory, medical records computer system, an algorithmic predictor). Claim 8 has the following additional elements (i.e., one processor, one or more hardware processors, electronic digital memory, medical records computer system, an algorithmic predictor). Claim 15 has the following additional elements (i.e., one processor, one or more hardware processors, electronic digital memory, medical records computer system, an algorithmic predictor). Looking to the specification, these components are described at a high level of generality (see paragraph 68; a portion of computing system 120 may be embodied on user interface 142, application 140, and/or EHR system(s) 160.In one embodiment, system 120 comprises one or more computing devices, such as a server, desktop computer, laptop, or tablet, cloud-computing device or distributed computing architecture, a portable computing device such as a laptop, tablet, ultra-mobile P.C., or a mobile phone). The use of a general-purpose computer, taken alone, does not impose any meaningful limitation on the computer implementation of the abstract idea, so it does not amount to significantly more than the abstract idea. Also, although the claims add “[storage]” steps, it is only considered as insignificant extrasolution activity. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology and their collective functions merely provide a conventional computer implementation of the abstract idea. Furthermore, the additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generally linking the abstract idea to a particular technological environment or field of use, as the courts have found in Parker v. Flook. Therefore, there are no limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception.
It is worth noting that the above analysis already encompasses each of the current dependent claims (i.e., claims 2-7, 9-14 and 16-20). Particularly, each of the dependent claims also fails to amount to “significantly more’ than the abstract idea since each dependent claim is directed to a further abstract idea, and/or a further conventional computer element/function utilized to facilitate the abstract idea. Accordingly, none of the current claims implements an element—or a combination of elements—directed to an inventive concept (e.g., none of the current claims is reciting an element—or a combination of elements—that provides a technological improvement over the existing/conventional technology). These information characteristics do not change the fundamental analogy to the abstract idea grouping of “Mental Process, Certain Methods of Organizing Human Activity and Mathematical Concepts,” and, when viewed individually or as a whole, they do not add anything substantial beyond the abstract idea. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore, the claims when taken as a whole are ineligible for the same reasons as the independent claims.
Claims 1-20 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Pub. No.: US 2011/0313788 A1 to AMLAND et al. in view of Pub. No.: US 2015/0213227 A1 to Vairavan et al. further in view of Pub. No.: US 2016/0183886 A1 to KONNO.
Claim 1 : “A system having one or more hardware processors configured to facilitate a plurality of operations, the operations comprising: receiving, via at least one processor of the one or more hardware processors associated with an electronic digital memory at a medical records computer system, a plurality of measurements of one or more urinary parameters; determining a time series of measurements from the received plurality of measurements; based on the time series of measurements, determining a set of Hölder exponents and an RQA recurrence rate; utilizing the RQA recurrence rate and at least a subset of the set of Hölder exponents in an algorithmic predictor, the algorithmic predictor associated with the one or more hardware processors, the electronic digital memory, the medical records computer system, or any combination thereof; and utilizing the algorithmic predictor, generating a forecast of the a likelihood of urolithiasis for a target patient over a future time interval; wherein, based at least partially on the generated forecast, an intervening action comprising a particular treatment procedure is administered to the target patient in connection with treating a particular condition associated with the generated forecast and associated with the likelihood of urolithiasis for the target patient.”
Amland et al. teaches:
“A system having one or more hardware processors configured to facilitate a plurality of operations” Amland describes “an exemplary computing system environment 20” including a general-purpose computing device in the form of a server 22, where “[c]omponents of the server 22 may include, without limitation, a processing unit, internal system memory, and a suitable system bus for coupling various system components, including database cluster 24, with the server 22.” The system operates in a networked medical information environment and executes program modules. See paragraphs 18–23.
“receiving, via at least one processor…associated with an electronic digital memory at a medical records computer system, a plurality of measurements of one or more urinary parameters” Amland states that the method “includes receiving patient data for the patient,” and “wherein the patient data is received from an electronic medical record” and that clinically relevant data used as training data “may come from… hospital electronic medical records… [and] laboratory test results.” In FIG. 5, an orders panel includes “URINALYSIS MICROSCOPIC (UA MICROSCOPIC)” among the lab orders, showing urine-based lab data is part of the EMR environment. See paragraphs 4, 33, 38–39; claim 4; FIG. 5.
“determining a time series of measurements from the received plurality of measurements” Amland’s FIG. 4 shows “LAB RESULTS 02/23–02/25” graphing BUN, creatinine clearance (CRCL), and creatinine (CR) values at multiple timestamps (e.g., 02/23 07:00, 02/23 14:00, 02/24 07:00), indicating repeated measurements of lab values over time. Amland explains that readmission risk scores may be “recalculated over the length of hospitalization,” implying use of multiple time-spaced measurements. See paragraph 11 (description of FIG. 4) and paragraphs 30–31; see also FIG. 4.
“utilizing…in an algorithmic predictor, the algorithmic predictor associated with the one or more hardware processors, the electronic digital memory, the medical records computer system, or any combination thereof” Amland explains that “readmission risk prediction models may be generated using linear regression techniques over clinically relevant data” and that “a logistic regression model” is “built using the retrieved clinically relevant data” and then used to “develop[] a readmission risk prediction model… capable of calculating a readmission risk score for patients based on patient data.” These models are embedded in or in communication with electronic medical record systems. See paragraphs 3–4, 17–19, 34–36; claims 1 and 18.
“utilizing the algorithmic predictor, generating a forecast of the a likelihood of urolithiasis for a target patient over a future time interval” Amland describes “computing a readmission risk score… the readmission risk score representing a probability of readmission for the patient” and explains that “the readmission risk may be based on readmission within a predetermined period of time, such as within 7 days after discharge, within 30 days after discharge, within 60 days after discharge, within 90 days after discharge, etc.” See paragraphs 3–4 and 28–30; claims 1 and 18.
“wherein, based at least partially on the generated forecast, an intervening action comprising a particular treatment procedure is administered…” Amland discloses that “inpatient treatment interventions may be identified based on the patient’s readmission risk and provided as clinical decision support to a clinician,” and that readmission risk “may be used to modify a patient’s care plan including recommending alternate therapies, performing additional studies, and/or extending the patient’s length of stay.” It further states that readmission risk may be used in discharge planning and outpatient activities, including scheduling surveillance calls and in-person appointments. See paragraphs 30–32; claims 6, 9, 12–14; FIGS. 4–6.
Amland et al. fails to explicitly teach:
“a plurality of measurements of one or more urinary parameters” (urinalysis appears as an order, but Amland does not specifically define the core predictive variables as urinary parameters).
“determining a time series of measurements” by name (time-varying labs are shown, but the term “time series” and explicit time-series feature extraction are not used).
“based on the time series of measurements, determining a set of Hölder exponents and an RQA recurrence rate.”
“utilizing the RQA recurrence rate and at least a subset of the set of Hölder exponents in an algorithmic predictor.”
“generating a forecast of a likelihood of urolithiasis” (Amland’s probability is of readmission, not urolithiasis).
A future time interval corresponding to “about 3 years to about 5 years.”
Vairavan et al. teaches:
Vairavan describes a medical modeling system where at least one processor “is programmed to receive measurements of a plurality of predictive variables for the patient” and “calculate the risk of the physiological condition by applying the received measurements to at least one model modeling the risk… using the plurality of predictive variables,” where “the at least one model includes at least one of a hidden Markov model and a logistic regression model,” and “an indication of the risk of the physiological condition is output to a clinician.” The predictive variables include lab tests and physiological variables such as creatinine, blood urea nitrogen (BUN), bilirubin, vitals, and clearly “Daily Urine Output” as shown in FIG. 3. The record for each patient “includes one or more measurements over time, typically a plurality of measurements over time (i.e., a time series) for each variable… spanning… a period of 48 hours… with vital signs and lab tests measured every hour.” See Abstract; paragraphs 6–8, 11–12, 45–49; FIG. 3.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include systems/methods as taught by Vairavan et al. within the systems/methods as taught by Amland et al. with the motivation of improving Amland’s readmission-risk decision-support system by incorporating Vairavan’s explicit time-series modeling of physiological and lab measurements (including daily urine output and other renal markers) using logistic-regression-based risk models, thereby enabling more accurate and continuously updated predictions of future disease risk from time-series clinical data. See paragraphs 12–15 and 47–49 of Vairavan et al. for the discussion of time-series mortality-risk estimation and real-time risk computation.
Amland et al. and Vairavan et al. fail to explicitly teach:
“determining a set of Hölder exponents” from the urinary-parameter time series.
“determining an RQA recurrence rate” from the time series.
“utilizing the RQA recurrence rate and at least a subset of the set of Hölder exponents” as explicit predictive features.
“generating a forecast of a likelihood of urolithiasis” specifically.
A future horizon of “about 3 years to about 5 years.”
Konno teaches:
Konno describes a biological information predicting apparatus that includes a biological parameter acquiring section configured to acquire a first biological parameter and a second biological parameter, and a biological information predicting section “configured to predict a future trend of the second biological parameter based on a future prediction model and a history of values of the first biological parameter… the future prediction model defining a relationship between a change of the first biological parameter and a change of the second biological parameter,” and a notifying section that provides a notification related to the second parameter based on the prediction. It explains that the predicting section “refers to a history of values of the pulse rate (PR) to calculate the future value of the pulse rate (PR)… [and] the future value of the heart rate (HR)” and that a future risk level of HR is defined in accordance with the changing trend of PR. Konno further states that the biological parameters “are not limited to the pulse rate (PR) and the heart rate (HR)… [and] may be other various parameters,” and that the future prediction model “may be defined in a manner other than that described above.” See Abstract; paragraphs 10, 18, 76–79, 80–83; FIGS. 1–2 and 6–7.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include systems/methods as taught by Konno with the systems/methods as taught by Amland et al. and Vairavan et al. with the motivation of using Konno’s explicit history-based prediction and trend-to-risk mapping framework to justify computing more sophisticated time-series descriptors—such as Hölder-type exponents and recurrence-quantification-analysis metrics—from urinary-parameter histories and supplying them as features to the combined Amland/Vairavan logistic-regression predictors to enhance long-term urolithiasis-risk forecasting, while still operating in an EMR-linked clinical decision-support environment. See paragraphs 8–10 and 77–79 of Konno for the discussion of using histories and future prediction models to anticipate future abnormal trends and risk levels.
Claim 2: “The system of claim 1, wherein the algorithmic predictor includes a logistic regression electronic model, and wherein the intervening action is initiated based on content corresponding to an output from the logistic regression electronic model.”
Amland et al. teaches:
Amland teaches that “a logistic regression model is built using the retrieved clinically relevant data” and that “a readmission risk prediction model is then generated using the output from the logistic regression model,” where “a readmission risk score for a patient represents a probability of readmission for the patient.” The readmission risk score is “compared against one or more thresholds,” and “treatment recommendations are determined based on the patient’s readmission risk level” and provided to a clinician. See paragraphs 3–4, 34–36, 40–43; claim 18.
Amland et al. fails to explicitly teach:
That the logistic regression model is expressly called a “logistic regression electronic model” (although the model is implemented on computer hardware).
That “the intervening action is initiated based on content corresponding to an output from the logistic regression electronic model” in those exact words (though Amland clearly uses the logistic-regression-derived risk score and thresholds to drive interventions).
Vairavan et al. teaches:
Vairavan teaches a mortality logistic regression (logR) model 52 that “uses a nonlinear mapping of… predictor variables X to the dependent… variable p (e.g., in-hospital mortality or in-hospital survival) through the logistic regression function,” where “p is the probability of mortality or survival.” The model parameters are determined by fitting to training data using maximum likelihood. The calculated risk from the model is supplied to a “clinical decision system… [that] provid[es] a clinician with clinical decision support based on the calculated risk of the physiological condition,” thereby using the logistic-regression output to guide ICU planning, scheduling, and resource allocation. See paragraphs 6–8, 45–47, 8–11.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include systems/methods as taught by Vairavan et al. within the systems/methods as taught by Amland et al. with the motivation of explicitly implementing the algorithmic predictor as a computer-executed logistic-regression model whose output probability directly triggers interventions, consistent with Vairavan’s use of logistic-regression-derived mortality risk scores to drive clinical decision support and resource allocation.
Konno further supports output-triggered notifications but is not strictly required for claim.
Claim 3: “The system of claim 1, wherein the intervening action comprises one or more of modifying treatment, ordering additional diagnostics, or scheduling diagnostics, or scheduling the particular treatment procedure.”
Amland et al. teaches:
Amland states that “treatment recommendations may be any of a variety of different interventions intended to treat the patient’s condition and reduce the likelihood that the patient would need to be readmitted,” and gives examples including “recommending alternate therapies, performing additional studies, and/or extending the patient’s length of stay.” It further explains that readmission risk may be used for discharge planning and outpatient activities, such as determining the need for and scheduling of “surveillance calls to patients and/or in-person appointments, in-home treatment, and patient education.” See paragraphs 30–32; claims 6, 9, 12–14.
Amland et al. fails to explicitly teach:
Amland does not explicitly recite the particular phrases “modifying treatment,” “ordering additional diagnostics,” “scheduling diagnostics,” or “scheduling the particular treatment procedure,” although recommending alternate therapies, additional studies, extending length of stay, and scheduling outpatient activities are functionally equivalent clinical actions.
Vairavan et al. teaches:
Vairavan explains that its medical modeling system is combined with a “clinical decision system… receiving the calculated risk of the physiological condition and providing a clinician with clinical decision support based on the calculated risk,” adding that one advantage is “the use of mortality predictions to aid intensive care unit (ICU) clinicians in clinical decision making on planning, scheduling and allocating ICU resources among critically ill patients with varying levels of mortality risks.” See paragraphs 8–11.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include systems/methods as taught by Vairavan et al. within the systems/methods as taught by Amland et al. with the motivation of using predicted risk to drive the full range of conventional clinical actions—including explicitly modifying treatment and ordering and scheduling diagnostic tests and treatment procedures—because both references describe risk-driven planning, scheduling, and intervention selection based on predictive model output.
Claim 4: “The system of claim 1, wherein the plurality of measurements comprises a sequence of measurements, and wherein each subsequent measurement in a sequence of measurements is received after at least a minimum time interval has elapsed from a previous measurement in a sequence of measurements.”
Amland et al. teaches:
Amland’s FIG. 4 displays “LAB RESULTS 02/23–02/25” showing BUN, CRCL, and CR values at discrete timestamps (e.g., 02/23 07:00, 02/23 14:00, 02/24 07:00, 02/24 14:00, 02/25 07:00), indicating that lab values are measured at successive times and stored as multiple data points for a patient. Amland also explains that the readmission risk score “may be recalculated over the length of hospitalization” based on updated patient data, which implies repeated measurement events over time. See paragraph 11 (FIG. 4) and paragraphs 30–31; see FIG. 4.
Amland et al. fails to explicitly teach:
That the plurality of measurements “comprises a sequence of measurements” in those words.
That “each subsequent measurement… is received after at least a minimum time interval has elapsed from a previous measurement,” with any explicit definition of a minimum interval.
Vairavan et al. teaches:
Vairavan describes that “the record for an ICU patient includes one or more measurements over time, typically a plurality of measurements over time (i.e., a time series), for each variable… spanning… 48 hours” and that “vital signs and lab tests [are] measured every hour.” It further explains that “each time at least one predictor variable of the patient is measured, or in response to some other event (e.g., a periodic event), the mortality logR model 52 updates the prediction using the most recent measurements.” See paragraphs 47–49.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include systems/methods as taught by Vairavan et al. within the systems/methods as taught by Amland et al. with the motivation of explicitly treating repeated clinical measurements as time-ordered sequences with a defined minimum sampling interval (e.g., hourly or another selected period), to support systematic time-series modeling and periodic risk updates, as shown in the ICU mortality-risk framework.
Claim 5: “The system of claim 4, wherein the algorithmic predictor corresponds to a logistic regression machine learning algorithm, and wherein the minimum time interval is about 4 hours.”
Amland et al. teaches:
Amland teaches that “a logistic regression model is built using the retrieved clinically relevant data” and that “a readmission risk prediction model is then generated using the output from the logistic regression model,” stating that readmission risk prediction models may be generated “using linear regression techniques” (including logistic regression) over clinically relevant data. See paragraphs 3–4 and 34–36; claim 18.
Amland et al. fails to explicitly teach:
That the algorithmic predictor is described as a “logistic regression machine learning algorithm” (though the model is learned from data).
That “the minimum time interval is about 4 hours” (no explicit numerical sampling interval is given).
Vairavan et al. teaches:
Vairavan teaches that the mortality logR model 52 is a logistic regression model whose parameters are determined by fitting to a training data set using “a minimization technique, such as the maximum likelihood estimator (MLE),” and that predictor variables include lab tests and physiological signals. It further describes that for each patient “vital signs and lab tests [are] measured every hour” over 48 hours, and that the model updates predictions each time new measurements are made or at other periodic intervals. See paragraphs 45–49.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include systems/methods as taught by Vairavan et al. within the systems/methods as taught by Amland et al. with the motivation of using logistic regression as the machine-learning algorithm for time-series clinical risk prediction and selecting a practical minimum measurement interval, such as about 4 hours, as an obvious design choice within the range of known ICU sampling intervals (e.g., 10 minutes, hourly) to balance data granularity and clinical workflow.
Claim 6: “The system of claim 1, wherein the one or more urinary parameters comprise urine osmolality, and wherein the plurality of measurements of the one or more urinary parameters is determined based on an estimation from a measured specific gravity of urine.”
Amland et al. teaches:
Amland indicates that clinically relevant data include laboratory test results obtained from hospital electronic medical records and, in its order sets, includes “URINALYSIS MICROSCOPIC (UA MICROSCOPIC)” and other tests in a diagnostic order list, showing that urine-related lab results are part of the patient’s data used for decision support. See paragraph 33; FIG. 5.
Amland et al. fails to explicitly teach:
That “the one or more urinary parameters comprise urine osmolality.”
That the plurality of measurements is “determined based on an estimation from a measured specific gravity of urine” (no osmolality-from-specific-gravity calculation is described).
Vairavan et al. teaches:
Vairavan’s FIG. 3 identifies “Daily Urine Output” among its predictive variables, together with renal markers (creatinine, BUN) and other physiological variables, and describes that predictor variables are selected by clinical experts based on their predictive value. See FIG. 3; paragraphs 45 and 50.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include systems/methods as taught by Vairavan et al. within the systems/methods as taught by Amland et al. with the motivation of selecting urine-concentration–related measures as predictive variables for a urinary-stone–risk model, consistent with both references’ use of urine and renal markers in clinical risk prediction.
Amland et al. and Vairavan et al. fail to explicitly teach:
The specific step of computing urine osmolality from urine specific gravity, rather than using one or the other directly.
Konno teaches:
Konno explains that the biological parameters used in its future prediction model “are not limited to the pulse rate (PR) and the heart rate (HR). The biological parameters may be other various parameters such as body temperature, respiration, pulse wave and the like,” and that “the model may be defined in a manner other than that described above” and can take into account multiple parameters. See paragraphs 80–83.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include systems/methods as taught by Konno with the systems/methods as taught by Amland et al. and Vairavan et al. with the motivation of defining urine osmolality as a derived biological parameter computed from routinely measured urine specific gravity, using known clinical relationships, and using that derived parameter as one of the selectable predictive variables in flexible future-risk models of the type described in these references.
Claim 7: “The system of claim 1, wherein the time series of measurements comprises at least 60 measurements, and wherein the future time interval corresponds to a time horizon ranging from about 3 years to about 5 years.”
Amland et al. teaches:
Amland teaches that the readmission risk may be based on readmission within “a predetermined period of time, such as within 7 days after discharge, within 30 days after discharge, within 60 days after discharge, within 90 days after discharge, etc.” It also describes recalculating readmission risk over the course of hospitalization and after discharge to plan outpatient activities and surveillance. See paragraphs 3–4 and 28–32.
Amland et al. fails to explicitly teach:
That the time series of measurements “comprises at least 60 measurements.”
That the future time interval corresponds to a time horizon “ranging from about 3 years to about 5 years” (Amland’s examples are in days).
Vairavan et al. teaches:
Vairavan teaches that, for each ICU patient, “a time series of measurements spanning from the first ICU stay of the patient for a period of 48 hours… with at least one measurement within the 48 hour period and vital signs and lab tests measured every hour” is used, and that predictor variables and time sequences for assessment are selected based on expert knowledge and modeling needs. See paragraphs 47–49 and 16.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include systems/methods as taught by Vairavan et al. within the systems/methods as taught by Amland et al. with the motivation of extending the known time-series modeling approach used for short-term ICU outcomes and near-term readmission to the long-term recurrence risk of urolithiasis by choosing a larger number of measurements (at least 60) and a clinically appropriate multi-year prediction horizon (3–5 years), as a routine optimization of time-series length and forecast interval for a chronic condition.
Claim 8: “One or more non-transitory media having computer-readable instructions that, when executed by one or more hardware processors, cause the one or more hardware processors to facilitate a plurality of operations, the operations comprising: receiving, via at least one processor of the one or more hardware processors associated with an electronic digital memory at a medical records computer system, a plurality of measurements of one or more urinary parameters; determining a time series of measurements from the received plurality of measurements; based on the time series of measurements, determining a set of Hölder exponents and an RQA recurrence rate; utilizing the RQA recurrence rate and at least a subset of the set of Hölder exponents in an algorithmic predictor, the algorithmic predictor associated with the one or more hardware processors, the electronic digital memory, the medical records computer system, or any combination thereof; and utilizing the algorithmic predictor, generating a forecast of a likelihood of urolithiasis for a target patient over a future time interval, wherein, based at least partially on the generated forecast, an intervening action comprising a particular treatment procedure is administered to the target patient in connection with treating a particular condition associated with the generated forecast and associated with the likelihood of urolithiasis for the target patient.”
Amland et al. teaches:
Amland recites “one or more computer storage media storing computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to perform a method” including receiving patient data (from an electronic medical record), computing a readmission risk score using a logistic regression-based prediction model, and providing an indication of readmission risk for presentation to a clinician, as well as methods for determining treatment recommendations based on the risk score. See paragraph 4; claims 1, 13, and 18.
Amland et al. fails to explicitly teach:
(urinary-parameter focus, Hölder exponents, RQA recurrence rate, urolithiasis, 3–5-year horizon).
Vairavan et al. teaches:
Vairavan describes its medical modeling system and methods as implemented by at least one processor executing algorithms (HMM and logistic regression) on time-series physiological variables and explicitly contemplates software implementations with model training and application steps. See paragraphs 6–8, 45–49.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include systems/methods as taught by Vairavan et al. within the systems/methods as taught by Amland et al. with the motivation of implementing the combined time-series urinary-risk prediction and intervention logic as computer-readable instructions on non-transitory media, consistent with both references’ disclosure of software-based medical risk prediction and decision-support methods.
Amland et al. and Vairavan et al. fail to explicitly teach:
Hölder/RQA/urolithiasis/3–5-year specifics.
Konno teaches:
Konno states that “a part or all of the processes in the biological information predicting section 12, the notifying section 13, and the data selecting section 14 may be implemented as computer programs which operate in the biological information predicting apparatus,” and that “the programs may be stored in a non-transitory computer readable medium.” See paragraph 84.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include systems/methods as taught by Konno with the systems/methods as taught by Amland et al. and Vairavan et al. with the motivation of encoding the history-based future-trend prediction and notification logic, together with time-series feature computation (including advanced descriptors such as Hölder exponents and recurrence-based rates), as computer-readable instructions stored on non-transitory media, in line with all three references’ implementation of their prediction workflows as software.
Claims 9–14
As per Claims 9-14, Claims 9-14 are directed to a non-transitory media. Claims 9-14 recite the same or substantially similar limitations as those addressed above for Claims 2-7 as taught by Amland et al., Vairavan et al. and Konno. Claims 9-14 are therefore rejected for the same reasons as set forth above for Claims 2-7 respectively.
Claim 15: “A computer-implemented method, comprising: receiving, via at least one processor of a set of one or more hardware processors associated with an electronic digital memory at a medical records computer system, a plurality of measurements of one or more urinary parameters; determining a time series of measurements from the received plurality of measurements; based on the time series of measurements, determining a set of Hölder exponents and an RQA recurrence rate; utilizing the RQA recurrence rate and at least a subset of the set of Hölder exponents in an algorithmic predictor, the algorithmic predictor associated with the set of one or more hardware processors, the electronic digital memory, the medical records computer system, or any combination thereof; and utilizing the algorithmic predictor, generating a forecast of a likelihood of urolithiasis for a target patient over a future time interval, wherein, based at least partially on the generated forecast, an intervening action comprising a particular treatment procedure is administered to the target patient in connection with treating a particular condition associated with the generated forecast and associated with the likelihood of urolithiasis for the target patient.”
Amland et al. teaches:
Amland describes computer-implemented methods that include identifying a patient condition, selecting a readmission risk prediction model, “receiving patient data for the patient,” computing a readmission risk score using the risk prediction model, comparing the score to thresholds, and determining and providing treatment recommendations and outpatient activities based on the score. See paragraphs 4, 28–36, 40–43; claims 1, 13, 18.
Amland et al. fails to explicitly teach:
time-series urinary parameters, Hölder exponents, RQA recurrence rate, urolithiasis, 3–5-year horizon).
Vairavan et al. teaches:
Vairavan sets out methods in which measurements of a plurality of predictive variables (including daily urine output and other labs) are received over time, and risk of a physiological condition is calculated by applying those measurements to HMM or logistic regression models, with the calculated risk output to clinicians and used by a clinical decision system. See paragraphs 6–8, 45–49.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include systems/methods as taught by Vairavan et al. within the systems/methods as taught by Amland et al. with the motivation of extending Amland’s computer-implemented readmission-risk methods by incorporating time-series urinary-parameter acquisition and logistic-regression-based physiological-risk modeling, as illustrated by Vairavan’s ICU time-series methods, to generate future urolithiasis-risk forecasts and drive interventions.
Amland et al. and Vairavan et al. fail to explicitly teach:
Hölder/RQA/urolithiasis/3–5-year specifics.
Konno teaches:
Konno provides method steps in which histories of a first biological parameter are used with a future prediction model defining relationships between parameter changes to predict future trends and risk levels of a second parameter, and then to notify clinicians based on that prediction. See paragraphs 10, 76–79.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include systems/methods as taught by Konno with the systems/methods as taught by Amland et al. and Vairavan et al. with the motivation of using Konno’s history-based future-trend prediction method as the conceptual basis for deriving advanced time-series features such as Hölder exponents and recurrence-quantification rates from urinary-parameter histories and using them in the combined logistic-regression-based urolithiasis-risk method to guide interventions.
Claims 16–20
As per Claims 16-20, Claims 16-20 are directed to a computer-implemented method. Claims 16-20 recite the same or substantially similar limitations as those addressed above for Claims 2-6 as taught by Amland et al., Vairavan et al. and Konno. Claims 16-20 are therefore rejected for the same reasons as set forth above for Claims 2-6 respectively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20080125666 A1: Biological data, such as human heart rate data, is acquired and processed in a non-linear manner to facilitate an assessment of the physiological state of the subject, and/or to assist in predicting incipient disorders or instability. Determinism, laminarity and recurrence measures are derived for a rolling sample of a time series of said data. The recurrence measure can be the Euclidean threshold (.epsilon..sub.thresh) at a given recurrence rate. A representation, such a colour coded matrix or multi-dimensional vector, is formed from a combination of the derived determinism, laminarity and recurrence measures. The representation can then be analysed to detect indicators of physiological instability, such as arrhythmia, or to discriminate between arrhythmias. The analysis may be performed visually, or in an automated manner in real time, such as in an ambulatory or implanted device, or post hoc by a bedside monitor.
US 20090098553 A1: This invention relates to methods for determining the presence of cancer in a subject based on the analysis of the expression levels of an under-expressed tumour marker (TM) and at least one other TM. Specifically, this invention relates to the determination of a cancer, particularly bladder cancer, by performing ratio, regression or classification analysis of the expression levels of at least one under-expressed TM, particularly an under-expressed bladder TM (BTM), and at least one over-expressed TM, particularly an over-expressed BTM. In various aspects, the invention relates to kits and devices for carrying out these methods.
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/E.B.W/ Examiner, Art Unit 3683
/ROBERT W MORGAN/ Supervisory Patent Examiner, Art Unit 3683
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