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 .
Response to Amendment
This Final rejection is in response to the claims filed on June 30, 2025.
Claims 1, 4, 5, 16, 17, 23-26 and 30 have been amended.
Claims 3 and 22 have been canceled.
Claims 31-32 are newly added.
Claims 1-2, 4-9 and 16-21, 23-32 are currently pending and have been examined.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-2, 4-9 and 16-21, 23-32 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 16, and 25 recite “configuring a number-m of multiple machine-learning electronic models based on content selected from factor data, condition data indicated by the factor data, and temporal data”. [0040] and [0050] of Applicant’s specification recites “applying m-number of machine-learning practices, in order identify one or more combinations of factors that produce the most utility in cohorting.” However, this does not indicate configuring a number-m of machine learning models, but rather this provides support for the “utilizing the number-m of multiple machine-learning electronic models …” limitation. The Examiner is unsure how to interpret configuring a number of machine learning models. As such, this constitutes new matter. Claims 2, 4-9, 17-21, 24 and 26-32 are rejected due to dependency.
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 4-5 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 4 recites “receiving an indication that a first factor from the first number-n of factors should be removed”. There is insufficient antecedent basis for “the first number-n of factors” in the claim.
Claim 5 recites “providing a second set of factors as suggested factors to be added to the first number-n of factors”. There is insufficient antecedent basis for “the first number-n of factors” in the claim.
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 1-2, 4-9, 16-21, 23-32 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Eligibility Step 1 (Does the subject matter fall within a statutory category?)
Claims 1-2 and 4-9 are drawn to a system, claims 16-21, 23-24 and 29-32 are directed to a non-transitory media, claims 25-28 are directed to a method, and thus, are within the four statutory categories.
Eligibility Step 2A-1 (Does the claim recite an abstract idea, law of nature, or natural phenomenon?)
Claims 1-2, 4-9 and 16-21, 23-32 are further directed to an abstract idea on the grounds set out in detail below:
The Examiner has identified independent claim 1 as the claim that represent the claimed invention for analysis and is similar to independent claims 16 and 25.
Claim 1 recites a series of steps for determining the likelihood that a patient has a condition based on multiple sets of information, which, under the broadest reasonable interpretation, is an abstract idea that falls within the “Certain Method of Organizing Human Activity” grouping of abstract ideas such as managing behavior or relationships or interactions between people (i.e., following a set of rules or instructions).
Claim 1 recites the following limitations which set forth the abstract idea:
accepting a first selection of an individual;
accepting a second selection of a condition;
accessing a first set of information relating to the first individual and corresponds to a first date;
determining, based on the first set of information and the condition, a first likelihood of the individual being associated with the condition at a first time frame corresponding to the first date;
accessing a second set of information that relates to the individual and corresponds to a second date;
determining, based on the second set of information and the condition, a second likelihood of the individual being associated with the condition at a second time frame corresponding to the second date; and
determining a rate of change utilizing (a)the first likelihood of the individual being associated with the condition, (b)the second likelihood of the individual being associated with the condition, (c) the first date, and (d) the second date;
determining whether the rate of change meets a threshold rate of change; and
in response to determining that the rate of change meets the threshold rate of change:
generating output information for presentation, wherein the output information indicates one or both of (a) a particular likelihood of the individual having the condition, the particular likelihood being based on the first likelihood of the individual being associated with the condition and the second likelihood of the individual being associated with the condition and (b) the rate of change.
Eligibility Step 2A-2 (Does the claim recite additional elements that integrate the judicial exception into a practical application?):
This judicial exception is not integrated into a practical application.
Claims 1, 16, and 25 recite the following additional elements:
One or more hardware processors configured to facilitate a plurality of operations (claim 1)
An electronic interface
one or more non-transitory media having instructions that, when executed by one or more hardware processors, cause the one or more hardware processor to facilitate a plurality of operations (claim 16)
configuring a number-m of multiple machine-learning electronic models based on content selected from factor data, condition data indicated by the factor data, and temporal data;
utilizing the number-m of multiple machine-learning electronic models via the one or more hardware processors to select, from stored information, a corresponding number-n of multiple factors relating to the condition;
an analysis via the one of more hardware processors of two or more of the number-n of multiple factors
The noted above additional elements are recited a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea by adding the words ‘apply it’ (or an equivalent) with the judicial exception. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (see MPEP 2106.04 (d)(I) which states that merely having the words “apply it” and/or “generally linking” the claimed invention to a particular technological environment or field of use is insufficient to provide a practical application or significantly more). Therefore, claims 1, 16, and 25 are directed to an abstract idea without a practical application.
The use of additional elements noted above as tools to implement/automate the abstract idea does not render claims 1, 16, and 25 to be patent eligible because it does not provide meaningful limitations and requires no more than a computer performing functions that correspond to acts required to carry out the abstract idea.
Eligibility Step 2B (Does the claim amount to significantly more?):
Claims 1, 16, and 25 do not include additional elements that are sufficient to amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, using the additional elements noted above to perform the generic computer functions amount to no more than mere instructions to apply the abstract idea using a generic computer component or generally link the claimed invention to a particular technological environment or field of use (see MPEP 2106.05 (I)(A)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Claims 1, 16, and 25 are, therefore, not patent eligible.
The dependent claims 2, 4-9, 17-21, 23-24, & 26-32 further define the abstract idea that is present in their respective independent claims and hence are abstract for at least the reasons presented above.
Dependent claims 2, 4, 5-9, 17-20, 28-31 do not recite any additional elements.
Dependent claims 23-24 recite the following:
Training the one or more machine-learning electronic models
Following Example 47(claim 2), training of a machine learning model constitutes a mathematical concept which is an abstract idea. This abstract idea is interpreted to be subsumed within the identified abstract idea recited in the independent claims, supra.
Dependent claims 26-27 recite the following additional elements:
One or more processors
The noted above additional elements are recited a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea by adding the words ‘apply it’ (or an equivalent) with the judicial exception. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (see MPEP 2106.04 (d)(I) which states that merely having the words “apply it” and/or “generally linking” the claimed invention to a particular technological environment or field of use is insufficient to provide a practical application or significantly more). Therefore, claims 26-27 are directed to an abstract idea without a practical application.
Dependent claim 32 recites the following:
Wherein at least one of the set of two or more machine-learning processes corresponds to a quadratic regression machine-learning model
A quadratic regression machine-learning model constitutes a mathematical concept which is an abstract idea. This abstract idea is interpreted to be subsumed within the identified abstract idea recited in the respective independent claims, supra.
The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims are directed to an abstract idea. Claims 2, 4-9, 17-21, 23-24, and 26-32 are, therefore, not patent eligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 5-8, 16-19, 23, 25, and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Freeman et al. (US 20190385744 A1), hereinafter Freeman, in view of Razavian et al. (US 20170308981 A1), hereinafter Razavian, and further in view of Grichnick et al. (US 20090055217 A1), hereinafter Grichnick.
Regarding claim 1, Freeman teaches a system having one or more hardware processor configured to facilitate a plurality of operations ([0021] discloses a system for assessing patient deterioration over time with one or more processors), the operations comprising:
accepting a first selection of an individual ([0023] discloses the system receives an initial patient assessment and medical history and demographic information for a patient);
accepting a second selection of a condition ([0024] discloses a first clinical condition);
accessing a first set of information that relates to the individual and corresponds to a first date ([0023] discloses the system receives the medical history, demographic information, ECG data, and physiological data of the patient);
determining, based on the first set of information and the condition, […] a first likelihood of the individual being associated with the condition at a time frame corresponding to the first date ([0024] discloses generating the first risk estimate of the clinical condition of the patient by executing the first risk assessment process by identifying one or more associations between the first clinical condition and the one or more patient parameters. The physiological data received initially is considered to be acquired at a time frame corresponding to a first date);
accessing a second set of information relating to the individual and corresponds to a second date ([0045] discloses generating updated ECG data and updated physiologic data acquired subsequently to execution of the first risk assessment process, [0105]-[0106] disclose a wearable medical device to monitor patient data over hours, days, weeks, etc. indicating that updated information is acquired on a second date);
determining, based on the second set of information and the condition, a second likelihood of the individual being associated with the condition at a time frame corresponding to the second date ([0045], [0050] disclose re-executing the first risk assessment process associated with the first clinical condition using continuously/periodically monitored patient parameters/updated physiologic and ECG data to generate a second risk estimate of the clinical condition of the patient. [0105]-[0106] discloses that the updated data is received over days or weeks, indicating that the second likelihood corresponds to a second date); and
Freeman may not explicitly teach the following limitations; however, Razavian teaches:
configuring a number-m of multiple machine-learning electronic models based on content selected from factor data, condition data indicated by the factor data, and the temporal data ([0035] discloses using machine learning to fit models predicting the onset of a condition to develop an enhanced model using a dataset comprising an initial variable set and confirmed medical diagnosis of the condition. [0033] discloses temporal data is included in the initial variable dataset.) ;
utilizing the number-m of multiple machine learning electronic models via the one or more hardware processors to select, from stored information, a corresponding number-n of multiple factors relating to the condition ([0035] discloses the enhanced model is utilized and trained to develop a feature set comprising predictive variables and surrogate variables);
[…] an analysis via the one or more hardware processors of two or more of the number-n of multiple factors ([0048] discloses application of the enhanced model to a particular patient using the feature vectors to identify the risk for each patient).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to be motivated to modify the risk prediction system of Freeman to include utilizing machine learning models to select multiple factors relating to a condition as taught by Razavian in order to “discover relevant features and combinations of features that predict disease risk in ways that less sophisticated approaches cannot” (Razavian, [0006]).
Freeman/Razavian may not explicitly teach the following limitations; however, Grichnick teaches:
determining a rate of change utilizing (a) the first likelihood of the individual being associated with the condition, (b) the second likelihood of the individual being associated with the condition, (c) the first date, and (d) the second date ([0035] discloses the system calculates a rate of change of a risk of disease using data over time such as years or months);
determining whether the rate of change meets a threshold rate of change ([0035] discloses if the rate of change of risk is sufficiently high (e.g. risk increasing by 10% or more per year. [0037] discloses the system may determine if the risk is above a threshold.); and
in response to determining that the rate of change meets the threshold rate of change ([0035] discloses if the rate of change of risk is sufficiently high (e.g. risk increasing by 10% or more per year), generating output information for presentation via an electronic interface, wherein the output information indicates one or both of (a) a particular likelihood of the individual having the condition, the particular likelihood being based on the first likelihood of the individual being associated with the condition and the second likelihood of the individual being associated with the condition and (b) the rate of change ([0035] discloses the system may contact the individual to warn them of the health risk. [0038] discloses the system may use any type of communication to contact the individual such as email. [0040] discloses if the system identifies the individual as having a risk above a threshold, it will contact the individual to communicate the risk).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to be motivated to modify the risk prediction system of Freeman/Razavian to include determining a rate of change of the likelihood of developing a condition and outputting information in response to the rate of change exceeding a threshold as taught by Grichnick in order to “identify and predict people who are likely to be diagnosed with a disease, allowing preventative treatments or corrective actions to occur prior to disease onset (Grichnick, [0042]).
Regarding claim 5, Freeman/Razavian/Grichnick teach the system of claim 1. Razavian further teaches wherein the operations further comprise: providing a second set of factors as suggested factors to be added to the first number-n of multiple factors ([0028], [0034], [0038] disclose identifying surrogate variables as additional variables to be included in the disease variable set to address issues arising from missing patient data).
Regarding claim 6, Freeman/Razavian/Grichnick teach the system of claim 1. Freeman further teaches wherein determining the first likelihood of the condition corresponding to the individual is further based on a first regression analysis ([0172] discloses that the prediction model can be a linear regression model).
Regarding claim 7, Freeman/Razavian/Grichnick teach the system of claim 6. Freeman further teaches:
wherein determining the first likelihood of the condition corresponding to the individual is further based on information associated with one or more user-interface interactions ([0008]-[0009] discloses a user interface coupled to the processor that receives an initial patient assessment, medical history, and demographic information and identifies risk of first clinical condition based on received information).
Regarding claim 8, Freeman/Razavian/Grichnick teach the system of claim 6. Freeman further teaches:
wherein determining the first likelihood of the condition corresponding to the first patient is further based on a second regression analysis ([0172] discloses the prediction processes of the deterioration risk assessor is a linear regression), wherein the second regression analysis is based at least in part on the information associated with one or more user-interface interactions ([0170]-[0171] discloses the risk assessor receives patient parameters from a user interface, sensor interface, etc.)
Regarding claim 16,
The claim limitations are analogous to the limitations in claim 1. As such, claim 16 is rejected for the same reasons given for claim 1.
Regarding claim 17, Freeman/Razavian/Grichnick teaches the one or more non-transitory media of claim 16, Freeman further teaches:
Wherein the second likelihood of the individual being associated with the condition is determined based on the same analysis via the one or more hardware processors of the same number-n of multiple factors ([0045], [0050] disclose re-executing the first risk assessment process associated with the first clinical condition using continuously/periodically monitored patient parameters/updated physiologic and ECG data to generate a second risk estimate of the clinical condition of the patient), and
wherein the operations further comprise identifying an upward trend between the first likelihood of the individual being associated with the condition and the second likelihood of the individual being associated with the condition. ([0026] discloses a trajectory of the clinical condition that indicates a risk of deterioration of the patient’s condition (an upward trend in deterioration, the Examiner noting that these is no indication as to what “upward” is in reference to)).
Regarding claim 18, Freeman/Razavian/Grichnick teach the one or more non-transitory media of claim 17. Freeman further teaches wherein the operations further comprise: providing an output based on the upward trend ([0026] discloses generating a notification in response to the risk of deterioration).
Regarding claim 19, Freeman/Razavian/Grichnick teach the one or more non-transitory media of claim 16, wherein the rate of change indicates a change in likelihood per change in date (Grichnick [0036] discloses calculating a slope or rate of change for the risk connecting several datapoints. [0041] discloses graph 320 has a rate of change in risk from year 2000 through year 2004), and wherein the second set of information includes one or more events not present in the first set of information (Freeman [0048]-[0050] discloses re-executing risk assessment using a subset of robust set of patient parameters including irregularly monitored patient parameters such as discrete events. Grichnick [0035] discloses if an individual starts smoking, becomes obese, etc. the system may identify a high rate of change of a risk of contracting heart disease).
Regarding claim 23, Freeman/Razavian/Grichnick teach the one or more non-transitory media of claim 22. Razavian further teaches:
wherein the operations further comprise training the number-m of multiple machine-learning electronic models based on a training technique selected from a group comprising supervised machine learning, reinforcement machine learning, and unsupervised machine learning ([0035] discloses training machine learning model using a dataset comprising the initial set of variables and confirmed diagnosis of the condition (which is supervised learning)).
Regarding claim 25,
The claim limitations are analogous to the limitations in claim 1. As such, claim 25 is rejected for the same reasons given for claim 1.
Regarding claim 28,
The claim limitations are analogous to the limitations in claim 6-7. As such, claim 28 is rejected for the same reasons given for claim 6-7.
Claims 2 and 21 is rejected under 35 U.S.C. 103 as being unpatentable Freeman et al. (US 20190385744 A1), hereinafter Freeman, in view of Razavian et al. (US 20170308981 A1), hereinafter Razavian, in view of Grichnick et al. (US 20090055217 A1), hereinafter Grichnick as applied to claims 1 and 16 above, and further in view of Reisman et al. (US 20090216558 A1), hereinafter Reisman.
Regarding claim 2, Freeman/Razavian/Grichnick teach the system of claim 1. Grichnick further teaches wherein the operations further comprise:
Prior to the individual receiving a recognized diagnosis of the condition ([0008] discloses a system for obtaining health information for an individual and predict a risk of the individual contracting a disease. This suggests that the patient does not yet have the disease and has therefore not been diagnosed), and
Freeman/Razavian/Grichnick may not explicitly teach the following limitations; however, Reisman teaches:
updating an electronic health record associated with the individual based on the first likelihood of the condition corresponding to the individual ([0016] discloses analyzing patient specific data assessing a risk for future conditions and presents the risk score to the patient and healthcare provider. [0017] discloses transmitting alerts and risk score to the personal health record and/or health care provider applications (interpreted as electronic health record)).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to be motivated to modify the risk assessment system of Freeman/Razavian/Grichnick to include updating the electronic health record based on the determined risk as taught by Reisman in order to “expeditiously identify potential medical issues that may require attention” and “ensure prompt follow up on the results of the analysis” (Reisman, [0003]).
Regarding claim 21,
The claim limitations are analogous to the limitations in claim 2. As such, claim 21 is rejected for the same reasons given for claim 2.
Claims 4 and 20, 26-27 are rejected under 35 U.S.C. 103 as being unpatentable over Freeman et al. (US 20190385744 A1), hereinafter Freeman, in view of Razavian et al. (US 20170308981 A1), hereinafter Razavian, in view of Grichnick et al. (US 20090055217 A1), hereinafter Grichnick, as applied to claims 1 and 16 above, and further in view of Aliferis et al. (US 7117185 B1), hereinafter Aliferis.
Regarding claim 4, Freeman/Razavian/Grichnick teaches the system of claim 1, Freeman further teaches wherein the operations further include:
determining a second likelihood of the condition corresponding to the individual based on a subset of the first number-n of multiple factors that does not include the first factor ([0045], [0050] disclose re-executing the first risk assessment process associated with the first clinical condition using continuously/periodically monitored patient parameters/updated physiologic and ECG data to generate a second risk estimate of the clinical condition of the patient. [0099] discloses a deviation from an expected trajectory and switching patient assessment to an updated set of parameters upon approval by a caregiver);
Freeman/Razavian/Grichnick may not explicitly teach the following limitations; however, Aliferis teaches:
receiving an indication that a first factor from the first number-n of multiple factors should be removed (Aliferis [Col. 3, line 66] discloses removing false positive variables (interpreted to include a first factor; one of the parameters of Freeman) ) from the candidate set);
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to be motivated to modify the first set of factors as taught by Freeman/Razavian/Grichnick to include ignoring false positive variables from the set as taught by Aliferis in order to “identify a reduced, yet predictive variable set and significantly improve statistical decision support models in terms of understandability, user acceptance, speed of computation and smoother integration into clinical practice” (Aliferis, [Col. 2, lines 32-37]).
Regarding claim 20, Freeman/Razavian/Grichnick teach the one or more computing devices of claim 16. Freeman further teaches wherein accessing the second set of information includes […] (different aspects) identified by a user-interface interaction ([0008]-[0009] discloses a user interface coupled to the processor that receives an initial patient assessment, medical history, and demographic information. [0099] discloses a deviation from an expected trajectory and switching patient assessment to an updated set of parameters upon approval by a caregiver).
Freeman may not explicitly teach the following limitations; however, Aliferis teaches:
ignoring one or more aspects of the second set of information ([Col. 3, line 66] discloses removing false positive variables from the candidate set (interpreted as an aspect of the information of Freeman))
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to be motivated to modify the second set of information as taught by Freeman to include ignoring false positive variables from the set as taught by Aliferis in order to “identify a reduced, yet predictive variable set and significantly improve statistical decision support models in terms of understandability, user acceptance, speed of computation and smoother integration into clinical practice” (Aliferis, [Col. 2, lines 32-37]).
Regarding claim 26, Freeman/Razavian/Grichnick teach the method of claim 25. Freeman further teaches wherein the method is performed at least partially via one or more processors ([0021] discloses one or more processors). Grichnick teaches determining (an updated) rate of change based on the processing ([0035] discloses the system calculates a rate of change of a risk of disease using data over time such as years or months). Freeman/Razavian/Grichnick do not explicitly teach the following limitations; however, Aliferis teaches:
processing an indication that an aspect associated with one or both of the first likelihood of the individual being associated with the condition and the second likelihood of the individual being associated with the condition should be removed ([Col. 3, line 66] discloses removing false positive variables from the candidate set (interpreted as an aspect of the information of Freeman)).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to be motivated to modify the sets of information as taught by Freeman to include ignoring false positive variables from the set as taught by Aliferis in order to “identify a reduced, yet predictive variable set and significantly improve statistical decision support models in terms of understandability, user acceptance, speed of computation and smoother integration into clinical practice” (Aliferis, [Col. 2, lines 32-37]). Further, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to be motivated to determine an updated rate of change as taught by Grichnick based on the new information sets because it is merely a duplication of parts.
Regarding claim 27, Freeman/Razavian/Grichnick/Aliferis teach the method of claim 26. Grichnick further comprising: determining whether the updated rate of change meets the threshold rate of change ([0035] discloses if the rate of change of risk is sufficiently high (e.g. risk increasing by 10% or more per year),; and in response to determining that the updated rate of change meets the threshold rate of change, generating an output for presentation via the electronic interface indicating one or both of an updated first likelihood of the individual being associated with the condition and an updated second likelihood of the individual being associated with the condition and further indicating the updated rate of change ([0035] discloses the system may contact the individual to warn them of the health risk. [0038] discloses the system may use any type of communication to contact the individual such as email.).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Freeman et al. (US 20190385744 A1), hereinafter Freeman, in view of Razavian et al. (US 20170308981 A1), hereinafter Razavian, in view of Grichnick et al. (US 20090055217 A1), hereinafter Grichnick, as applied to claim 1 above, in view of Nevo et al. (US 20150313529 A1), hereinafter Nevo.
Regarding claim 9, Freeman/Razavian/Grichnick teaches the system of claim 1. Freeman/Razavian/Grichnick do not explicitly teach the following limitations; however, Nevo teaches:
wherein the condition includes an opioid use disorder condition ([0100] discloses that the abnormal condition corresponds to a substance-related disorder such as opioid-related disorders).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the noted features of Nevo with the teachings of Freeman/Razavian/Grichnick since the combination of the two references is merely simple substitution of one known element for another producing a predictable result (KSR rationale B). Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself—that is, in the substitution of the opioid use disorder condition of the secondary reference(s) for the clinical condition of the primary reference. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Claim(s) 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Freeman et al. (US 20190385744 A1), hereinafter Freeman, in view of Razavian et al. (US 20170308981 A1), hereinafter Razavian, in view of Grichnick et al. (US 20090055217 A1), hereinafter Grichnick, as applied to claim 1 above, in view of Clifton et al. (US 20210391079 A1), hereinafter Clifton.
Regarding claim 24, Freeman/Razavian/Grichnick teaches the one or more non-transitory media of claim 22. Freeman/Razavian/Grichnick do not explicitly teach the following limitations; however, Clifton teaches wherein the operations further comprise training the number-m of multiple machine-learning electronic models based on data that includes factors from three or more different time periods where a condition corresponding to the condition data was discovered or diagnosed (Fig. 7 discloses training machine learning models using five features over multiple days since admission).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to be motivated to modify the machine learning models as taught by Freeman to include training the machine learning models based on data from different time periods after diagnosis as taught by Clifton in order to “broaden the range of training data that can be used to train the machine learning unit, thereby increasing accuracy” (Clifton [0007]).
Claim(s) 29-30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Freeman et al. (US 20190385744 A1), hereinafter Freeman, in view of Razavian et al. (US 20170308981 A1), hereinafter Razavian, in view of Grichnick et al. (US 20090055217 A1), hereinafter Grichnick, as applied to claim 1 above, in view of Gross et al. (US 20160328525 A1), hereinafter Gross.
Regarding claim 29, Freeman/Razavian/Grichnick teach the one or more non-transitory media of claim 16. Freeman/Razavian/Grichnick may not explicitly teach the following limitations; however, Gross teaches wherein determining the first likelihood of the individual being associated with the condition comprises identifying a first probability that the individual has the condition at the first date ([0008] discloses determining a first probability and a first severity of the condition of interest based on the initial received data).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to be motivated to modify the likelihood that an individual has a condition as taught by Freeman to include determining a probability as taught by Gross in order to improve diagnostic accuracy and patient management.
Regarding claim 30, Freeman/Razavian/Grichnick teach the one or more non-transitory media of claim 16. Grichnick further teaches comparing the first likelihood of the individual and the second likelihood of the individual being associated with the condition to determine the rate of change ([0034] discloses comparing a first risk of disease and a second risk of disease to calculate a rate of change.)
Freeman/Razavian/Grichnick may not explicitly teach the following limitations; however, Gross teaches wherein determining the second likelihood of the individual being associated with the condition comprises identifying a second probability that the individual has the condition at the second date ([0049] discloses updating the probability of the condition based on newly acquired data.)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to be motivated to modify the likelihood that an individual has a condition as taught by Freeman to include determining a probability as taught by Gross in order to improve diagnostic accuracy and patient management.
Claim(s) 31-32 is/are rejected under 35 U.S.C. 103 as being unpatentable over Freeman et al. (US 20190385744 A1), hereinafter Freeman, in view of Razavian et al. (US 20170308981 A1), hereinafter Razavian, in view of Grichnick et al. (US 20090055217 A1), hereinafter Grichnick, as applied to claim 16 above, in view of Gossens et al. (US 20220285027 A1), hereinafter Gossens.
Regarding claim 31, Freeman/Razavian/Grichnick teach the one or more non-transitory media of claim 16. Freeman/Razavian/Grichnick do not explicitly teach the following limitation; however, Gossens teaches wherein a set of two or more machine-learning processes is used for selecting from the stored information the number-n of multiple factors relating to the condition ([0017] discloses a machine learning system comprising at least one machine learning model comprising an algorithm and an analysis model for predicting the target variable on the test data set).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to be motivated to modify the machine learning models of Razavian to include two or more machine learning models as taught by Gossens in order to generate more accurate and reliable predictors for disease prediction.
Regarding claim 32, Freeman/Razavian/Grichnick/Gossens teach the one or more non-transitory media of claim 31, wherein the two or more machine-learning processes differ, and wherein at least one of the set of two or more machine-learning processes corresponds to a quadratic machine-learning electronic model ([0077] discloses the machine learning model unit may comprise a plurality of machine learning models, where the models differ (an analysis model and an algorithm which can include quadratic discriminant analysis)).
Response to Arguments
Rejections under 35 U.S.C. § 101
Regarding the 101 rejection of claims 1-9 and 16-30, Applicant has canceled claims 3 and 22 rendering the rejection of those claims moot. Further, the Applicant has amended the independent claims, however, the amendments were not sufficient to overcome the 101 rejection as indicated in the new 101 rejection above.
Issue #1:
Applicant argues: Claim 1, as amended to address statutory subject matter issues asserted by the Office, is self-evidently statutory in compliance with the provisions of § 101. For instance, claim 1 recites a combination of elements including "one or more processors ... configuring a number-m of multiple machine learning electronic models based on content selected from factor data, condition data, and temporal data; utilizing the number-m of multiple machine learning electronic models via the one or more hardware processors to select, from store information, a corresponding number-n of multiple factors relating to the condition; determining ... a first likelihood of (an) individual being associated with (a) condition (based on a first date) ... (and) a second likelihood of the individual being associated with the condition (based on a second date); determining a rate of change utilizing (a) the first likelihood ... (b) the second likelihood ... (c) the first date, and (d) the second date ...; and in response to determining that the rate of change meets the threshold rate of change: generating output information for presentation via an electronic interface ... indicat(ing) ... the first likelihood ... the second likelihood ... and ... the rate of change," which does not seek to tie up any judicial exception. In particular, the above claim elements corresponding to claim 1, when viewed as a whole, clearly do not tie up any judicial exception such that others cannot practice it. Since the claim clearly does not to tie up any judicial exception, the claim does not need to proceed through the full § 101 analysis as its eligibility is self-evident. See MPEP Section 2106.06(a) ("Eligibility is Self Evident").
The Examiner respectfully disagrees. The claimed invention pre-empts (ties up) the identified abstract idea by definition. Full eligibility analysis is absolutely required.
Issue #2:
Applicant argues: With respect to claim 1, the Office asserted an abstract idea grouping of "organizing human activity" as basis for the § 101 rejections against the claims. This "organizing human activity" abstract idea grouping asserted in the Office Action is improper. According to MPEP Section 2106.04(a), "[t]he phrase 'methods of organizing human activity' is used to describe concepts relating to: fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); and managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions)." The MPEP further clarifies, in the same section, that the sub-grouping of "managing personal behavior or relationships or interactions between people" pertains to human social activities, human teaching, and a human following rules or instructions. None of this "organizing human activity" language applies to claim 1. Amended claim 1 recites electronic microprocessors that determine a rate of change (for a likelihood) utilizing first and second computed likelihoods of an individual being associated with a condition and utilizing respective first and second dates, and in response to the rate of change meeting a threshold rate of change, generate output information for presentation via an electronic interface to indicate the first and second likelihoods and the rate of change. Based at least on this content, claim 1 does not recite people and does not recite organizing activity between people as defined in MPEP Section 2106.04(a)(2)(ll).
Applicant further argues: Claim 1 does NOT recite a "human" but instead recites processors, accessing sets of (stored) information, creating new information, and generating output information for presentation via an electronic user interface. Further, contrary to the assertions by the Office at pages 4-5 of the Office Action, this claim does NOT recite "people," does NOT recite "person," and does NOT recite "a first person." Claim 1 further does NOT recite: "managing personal behavior" since no human is recited and no human behavior is recited, does NOT recite "interactions between people” since no human is recited and no inter-human interactions are recited, does NOT recite "interactions between people (including ... teaching ...)" since no inter-human interactions are recited and no teaching between people is recited, and does NOT recite "interactions between people (including ... following rules or instructions)" since no inter-human interactions are recited, no instructions are recited, and no following of instructions is recited. In contrast to "managing ... interactions between people (including social activities, teaching, and following rules or instructions)," claim 1 is directed to a system that accesses models, e.g., to predict likelihoods associated with dates, applies model processing operations to determine likelihood predictions corresponding to the dates, and generates rate of change information, i.e., utilizing the likelihood predictions and the dates, to present at an electronic interface. The system is NOT in any way a method of managing personal behavior, NOR is it a method of managing interactions between people. Consequently, by virtue of at least the foregoing reasons, claim 1 beyond doubt does NOT recite an abstract idea grouping of organizing human activity, and is statutory.
The Examiner respectfully disagrees. MPEP 2106.04(a)(2)(11) states that a claimed invention is directed to certain methods of organizing human activity if the identified claim elements contain limitations that encompass fundamental economic principles or practices, commercial or legal interactions or managing personal behavior or relationships, or interactions between people (including social activities. teaching and following rules or instructions,). The Examiner submits that the identified claim elements represent a series of rules or instructions that a person or persons , with or without the aid of a computer , would follow to predict likelihood of a condition, generate rate of change of the likelihoods, and present information. Furthermore, the Examiner submits that healthcare itself inherently represents the organization of human activity. Applicant has not pointed to anything in the claims that fall outside of this characterization. The listing of processors in the arguments/amendments is the application of a computer to follow the steps of the claimed invention. Furthermore, Multiple CAFC decisions that the Office has characterized as Certain Methods of Organizing Human Activity did not actively recite person or persons performing the steps of the claims (see, e.g., EPG, TLI communications, Ultramercial). Because whether a human is required to perform the step of the claim is not a requirement for claims to encompass certain method of organizing human activity, this argument is not persuasive. Because the claim elements fall under a series of rules or instructions that a person or persons would follow to determine likelihood of an individual having a condition based on information and determining a rate of change of the likelihoods over time, the claimed invention is directed to an abstract idea.
Issue #3:
Applicant argues: Claim 1 integrates the judicial exception purported by the Office into a practical application including applying electronic microprocessors to generate unique information and to present uniquely created information via an electronic user interface. Turning to MPEP Section 2106.05(e), in determining whether a claim integrates a judicial exception into a practical application, as in Step 2A, Prong Two, analysis should be made as to whether the additional elements amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use. Claim 1 as amended in view of the subject-matter rejections recites a plurality of specific, technologically grounded operations for creating in a pioneering way new, useful electronic data. For instance, in reference to the § 101 rejections, independent claim 1 is directed to an IMPROVEMENT IN TECHNOLOGY by virtue of reciting a veritable cornucopia of technological improvements" (See instances in application including "advantage,” "benef," effici," “improv,” “less,”, “more”, “unique,” which content does NOT constitute an activity performable in the human mind as explained above and which INTEGRATES any purported judicial exception into a practical application.
The Examiner respectfully disagrees. MPEP 2106.04(d)(1) states “the word ‘improvements’ in the context of this consideration is limited to improvements to the functioning of a computer or any other technology/technical field, whether in Step2A Prong Two or in Step 2B.” Here, there is no improvement to the computer (the processors in the claims are not being improved) nor is there an improvement to another technology (the machine learning models are not being improved on). Because neither type of improvement is present in the claims, an improvement to technology is not present and there is not practical application.
Rejections under 35 U.S.C. § 103
Regarding the prior art rejection of claims 1-9 and 16-30, Applicant has canceled claims 3 and 22, rendering rejection of those claims moot. Further, the Applicant has amended the independent claims. Applicant’s arguments with respect to claim(s) 1, 16, and 25 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. The dependent claims are included in the new prior art rejection.
Rejections under 35 U.S.C. § 112
Regarding the 112a rejections, the Examiner has considered Applicant’s arguments and deemed them persuasive. As such, the previous 112a rejection has been withdrawn. However, a new 112a rejection is applied for new matter issues.
Regarding the 112b rejections of claims 4, 5, 26-27, Applicant has amended the claims. The claim amendments regarding claim 26 have been considered sufficient to overcome the 112b rejection for claims 26 and 27. However, the amendments for claims 4-5 still constitute antecedent basis issues for the first number-n of factors, as the independent claim 1 does not include a first number-n of factors, only a number-n of factors. As such, the rejections of claims 4-5 are maintained.
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure and is listed in the attached form PTO-892 (Notice of References Cited). Unless expressly noted otherwise by the Examiner, all documents listed on form PTO-892 are cited in their entirety.
Ghanem (US 20110301479 A1) teaches: System and Method for Assessing a Likelihood of a Patient to Experience a Future Cardiac Arrhythmia using Dynamic Changes in a Biological Parameter.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MALAK M NASSER whose telephone number is (703)756-4610. The examiner can normally be reached M-F 8:00 AM-5:00 PM.
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/MALAK M NASSER/Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687