Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Response to Amendment
In the amendment dated 05/12/2026, the following occurred: Claims 1-5, 7-10, 12-15 and 17-21 have been amended. Claims 6, 11 and 16 have been canceled. Claims 22-24 are new.
Claims 1-5, 7-10, 12-15 and 17-24 are currently pending.
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 1-5, 7-10, 12-15 and 17-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 7, 13, 20 and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a method and one or more non-transitory computer readable media for identification of health risk and impact assessment, which are within a statutory category.
Regarding claims 1, 7, 13, 20 and 21, the limitation of (claim 7 being representative) obtaining patient information for a plurality of patients, the patient information including at least an industry standard risk score and a diagnosis code for each patient of the plurality of patients; clustering the plurality of patients based on the obtained patient information into a plurality of cluster assignments using a [..], including at least one [..]; segmenting the plurality of cluster assignments into a plurality of risk categories; assigning a risk category of the plurality of risk categories to each patient based on the segmenting; identifying, for each patient and using an impact model, an impact score of at least one clinical intervention based on the respective diagnosis code for each patient and a predicted benefit of the at least one clinical intervention and a likelihood of patient response to outreach; combining, for each patient, the respective assigned risk category and the impact score to generate a dual-model prioritization rank; and prioritizing a first patient of the plurality of patients over a second patient of the plurality of patients based on the respective dual-mode prioritization ranks of the first and second patients and regarding claim 1- the limitation of outputting a recommended action for a first patient over a second patient of the plurality of patients based on a comparison of the risk categories assigned to the first and second patient respectively and regarding claim 21- the limitation of presenting the patient profile, including the risk category for the patient as drafted, is a process that, under the broadest reasonable interpretation, covers a method organizing human activity but for the recitation of generic computer components. That is other than reciting a processor (in claim 1), a method (in claims 7, 20 and 21), one or more non-transitory computer readable media, one or more processors and a segmentation system (in claim 13), the claimed invention amounts to managing personal behavior or interaction between people (i.e., rules or instructions). For example, but for the processor, one or more non-transitory computer readable media, one or more processors and the segmentation system, the claims encompass obtaining patient information, clustering the plurality of patients based on the patient information, assigning a risk category to each patient, identifying an impact of at least one intervention based on a diagnosis, prioritizing in a health services database at least a patient of the plurality of patients based on the risk category and the impact, outputting a recommended action and presenting the patient profile in the manner described in the identified abstract idea, supra. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People (e.g. social activities, teaching, following rules or instructions)” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. Claims 7, 20 and 21 are not tied to any particular technological environment that implements the identified abstract idea. In particular, claim 1 recites the additional elements of a processor. Claim 13 recites the additional element of one or more non-transitory computer readable media, one or more processors and a segmentation system. These additional elements are not exclusively defined by the applicant and are recited at a high-level of generality (i.e., a generic computer components for enabling access to medical information or for performing generic computer functions, see Spec. at para. [030], [036] and [059]) such that they amounts to no more than mere instructions to apply the exception using a generic computer component. As set forth in MPEP 2106.04(d) “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claims 1 further recite the additional elements of interface display of a computing device and a patient database. Claim 7 further recites the additional element of a health service database. Claim 13 further recite the additional element of a database. Claim 20 further recite the additional element of a health service database. Claim 21 further recite the additional element of display of a user interface. These additional element are recited at a high level of generality (i.e. a general means to access/obtain/prioritize/output/receive/transmit data) and amount to extra solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
Claim 1 also recites the additional elements of a categorization model and an unsupervised learning algorithm. Claim 7 also recites the additional elements of an impact model, at least one unsupervised learning algorithm, a categorization model and a dual-mode prioritization model. Claim 13 also recites the additional elements of a categorization model and an unsupervised learning k-means clustering algorithm. Claim 20 also recites the additional element of a machine learning impact model. Claim 21 also recites the additional elements of a categorization model and an unsupervised machine learning model and a clustering algorithm The models and algorithms are interpreted to be (“apply it”) to the abstract idea. MPEP 2106.04(d)(I) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application. Accordingly, even in combination, these additional elements does not integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of the processor, one or more non-transitory computer readable media, one or more processors and the segmentation system to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Moreover, using generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention”). Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
As discussed with respect to integration of the abstract idea into a practical application, the additional elements of interface display of a computing device, a patient database, a health service database, a database and a user interface were considered extra-solution activity. This has been re-evaluated under “significantly more” analysis and determined to be well-understood, routine and conventional in the field of healthcare. Receiving and/or transmitting data over a network (“a communications network”) has also been recognized by the courts as a well - understood, routine and conventional function (see, e.g., buySAFE v. Google; MPEP 2016(d)(II)). Well-understood, routine and conventional activity cannot provide an inventive concept (“significantly more”). As such the claim is not patent eligible.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a categorization model, an unsupervised learning algorithm, an impact model, at least one unsupervised learning algorithm, a dual-mode prioritization model, an unsupervised learning k-means clustering algorithm, a machine learning impact model, an unsupervised machine learning model and a clustering algorithm were determined to be the application of machine learning models to the identified abstract idea. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP2106.05(1)(A) indicates that merely saying “apply it’ or equivalent to the abstract idea cannot provide an inventive concept (“significantly more’). Accordingly, even in combination, these additional elements do not provide significantly more. As such the claim is not patent eligible.
The examiner notes that: A well-known, general-purpose computer has been determined by the courts to be a well-understood, routine and conventional element (see, e.g., Alice Corp. v. CLS Bank; see also MPEP 2106.05(d)); and Performing repetitive calculations is/are also well-understood, routine and conventional computer functions when they are claimed in a merely generic manner (see, e.g., Parker v. Flook; MPEP 2016.05(d)).
Claims 2-5, 8-10, 12 and 14, 15, 19 and 22-24 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim(s) 2 and 8 further merely describe(s) the patient information. Claim(s) 3, 4, 9, 10 and 15 further merely describe(s) the risk score. Claim(s) 5 and 17 further merely describe(s) calculating at least one risk score. Claim(s) 19 further merely describe(s) prioritizing the plurality of patients. Claim(s) 12 further merely describe(s) the impact. Claim(s) 14 further merely describe(s) accessing patient information. Claim(s) 18 further merely describe(s) the patient information and identifying an impact of at least one intervention. Claim(s) 22 further merely describe(s) the unsupervised learning algorithm. Claim(s) 23 further merely describe(s) the at least four patient risk clusters. Claim(s) 24 further merely describe(s) the plurality of clusters. Claims 2-5, 8-10, 12 and 14, 15, 19 and 22-24 further define the abstract idea and are rejected for the same reason presented above with respect to claims 1, 7, 13, 20 and 21.
Claim(s) 2 also include the additional element of “at least one remote health care database” which is interpreted in the same manner as the health service database above. This additional elements, when considered alone or in combination, are recited at high level generality and amount to extra solution activity. They do not provide practical application or significantly more. MPEP 2106.04(d)(I) indicates that extra-solution data gathering activity cannot provide a practical application.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-21 are rejected under 35 U.S.C. 103 as being unpatentable over Andrews (US 2022/0406466), in view of Cox US (US 2017/0286622) and in further view of Vaccaro (US 2013/0096934).
REGARDING CLAIM 7
Andrews discloses a method for managing biased-reduced health services, comprising: obtaining patient information for a plurality of patients, the patient information including at least an industry standard risk score and a diagnosis code for each patient of the plurality of patients ([0006] teaches allow for all relevant medical information for the individual to be included when defining the present state of the individual. [0007] teaches periodically generating health scores based on more recent health vector information, the system also constructs a trend of the individual's health changes as the individual's health score varies over time (the “health score trend”) and [0016] teaches “set of individual parameters indicative of a present or a previous state of the patient” should be interpreted broadly and include any type of relevant information that has been or is collected about the patient. Such information may for example include patient's clinical data collected over a predetermined time period (including anything from seconds/hours to over the lifetime of the patient, for example collected at different doctors' appointments and/or hospitalizations). [0011] teaches receiving a first set of individual parameters indicative of a present or a previous state of the patient and determining the risk score for the patient (interpreted by examiner as obtaining patient information for a plurality of patients including at least an industry standard risk score and a diagnosis code, of Cox below, for each patient of the plurality of patients)); a categorization model including at least one unsupervised learning algorithm ([0013] teaches matching the specific behavior of the patient with a “cluster” of patients that have appeared/behaved in a similar manner (interpreted as the clustering of Cox below) [0032] teaches the machine learning process may possibly be an unsupervised machine learning process)
Andrews does not explicitly disclose, however Cox discloses:
clustering the plurality of patients, based on the obtained patient information, into a plurality of cluster assignments using a categorization model (Cox at [abstract] teaches perform a machine learning operation to train a risk scoring algorithm for scoring a risk of adverse conditions for the patient population using the patient information and [0129] teaches retrieve information from the patient cohort database 417 to classify the patient into a patient cohort. The patient cohort is a grouping of patients that have similar characteristics, e.g., similar demographics, similar medical diagnoses, etc. Patient cohorts may be generated using any known or later developed grouping mechanism. One example mechanism may be using a clustering algorithm that clusters patients based on key characteristics of the patient, e.g., age, gender, race, medical diagnosis, etc. As another example, rules in the resources database 418 may be defined for application to patient information in the EMR and demographics sources 420 and lifestyle information sources for identifying patients that have specified characteristics, e.g., patients that have diabetes and are in the age range of 18-45 (interpreted by examiner as clustering the plurality of patients, based on the obtained patient information, into a plurality of cluster assignments using a categorization model)); segmenting the plurality of cluster assignments into a plurality of risk categories (Cox at [0243] teaches segmenting of patients into various risk categories); assigning a risk category of the plurality of risk categories to each patient based on the segmenting (Cox at [abstract] teaches classify each patient into a risk classification category, in a plurality of risk classifications categories, based on a risk score generated by the application of the risk scoring algorithm to the patient information for the patient (interpreted by examiner as assigning a risk category to each patient based on the clustering)); diagnosis code (Cox at [0155] teaches medical codes (interpreted by examiner as diagnosis code));
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the clustering methods of Andrews to incorporate the clustering method and assigning a risk category as taught by Cox, with the motivation of providing an improved data processing apparatus and method and more specifically to mechanisms for performing patient risk assessment based on machine learning of health risks of a patient population. (Cox at [0001]).
Andrews and Cox do not explicitly disclose, however Vaccaro discloses:
identifying, for each patient and using an impact model, an impact score of at least one clinical intervention based on the respective diagnosis code for each patient and a predicted benefit of the at least one clinical intervention and a likelihood of patient response to outreach (Vaccaro at [0018] teaches the system may rank all members individually to assure that members deemed to have the most urgent or impactable needs are reached out to first, [0019] teaches the percolator system may involve three basic steps: validation of a health condition, risk assignment (which may determine disease burden and complexity), and identification of actionable gaps in healthcare and contractually required intervention with the members, [0020] teaches the percolator system may produce priority rankings for outreach based on urgency, impactability, or client-specific preferences and requirements. [0053] teaches for example, as a deadline approaches, the weight of the associated trigger can increase according to a predetermined sliding scale. Regarding time to most recent event: For example, the closer in time a past acute event is to an intervention, the higher the weight of the associated trigger, because the impact of that event on the member may be higher than if the event had occurred further in the past. [0054] The "number of occurrences" counts, for example, how many times an "unfavorable event" has occurred, such as hospitalization, and assigns higher weight relative to the count and [0057] teaches targeting only those members who would likely benefit from an intervention (interpreted by examiner as identifying, for each patient and using an impact model, an impact score of at least one clinical intervention based on the respective diagnosis code, of Cox above, for each patient and a predicted benefit of the at least one clinical intervention)); and prioritizing in a health services database a first patient of the plurality of patients over a second patient of the plurality of patients based on the respective dual-model prioritization ranks of the first and second patients (Vaccaro at [0005] teaches prioritizing high-risk patients and their health problems and [0006] teaches risk scores of members for the client and ranking the priority of members and their issues that need to be addressed for each individual member associated with the client and [0018] teaches the percolator system may also provide rankings of issues for each member, to assure that issues are addressed in a prioritized manner and may direct health coaches to assess and intervene based on each member's specific needs and, in some embodiments, may provide a specific sequencing of tasks for the member so as to address the member's most urgent needs first (interpreted by examiner as prioritizing in a health services database a first patient of the plurality of patients over a second patient of the plurality of patients based on the respective dual-model prioritization ranks of the first and second patients)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the clustering method of Andrews and the clustering and assigning a risk category method of Cox to incorporate identifying an impact of at least one intervention based on a diagnosis and prioritizing patients based on the risk category and the impact as taught by Vaccaro, with the motivation of reducing future insurance costs and complications, and so as to improving insurance utilization, clinical outcomes, and prevention of disease. (Vaccaro at [0015]).
REGARDING CLAIM 8
Andrews, Cox and Vaccaro disclose the limitation of claim 7.
Andrews further discloses:
The method of claim 7, wherein obtaining the patient information includes accessing the patient information from at least one remote health care system (Andrews at [0047] teaches a database (interpreted by examiner as at least one remote health care system)).
REGARDING CLAIM 9
Andrews, Cox and Vaccaro disclose the limitation of claim 7.
Andrews and Cox do not explicitly disclose, however Vaccaro further discloses:
The method of claim 7, wherein the industry standard risk score is based on insurance claims data (Vaccaro at [0006] teaches the client is an insurance carrier and stratifying and assigning risk scores to members for the client (interpreted by examiner as wherein the industry standard risk score is based on insurance claims data)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the risk score of Andrews and Cox to incorporate the risk score is based on insurance claims data as taught by Vaccaro, with the motivation of reducing future insurance costs and complications, and so as to improving insurance utilization, clinical outcomes, and prevention of disease. (Vaccaro at [0015]).
REGARDING CLAIM 10
Andrews, Cox and Vaccaro disclose the limitation of claim 7.
Andrews and Cox do not explicitly disclose, however Vaccaro further discloses:
The method of claim 7, wherein the industry standard risk score is agnostic to healthcare costs (Vaccaro at [0007] teaches the risk-evaluation unit may rank a plurality of members according to one or more factors. These factors may include, without limitation, predictive modeling of health risk, actual member cost per month, utilization patterns, count of identified gaps in care, and complexity. Using a predetermined algorithm based on the chosen factors, the risk-evaluation unit may determine a risk score (also known as acuity) for each member (interpreted by examiner as wherein the industry standard risk score is agnostic to healthcare costs)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the risk score of Andrews and Cox to incorporate the risk score is agnostic to healthcare costs as taught by Vaccaro, with the motivation of reducing future insurance costs and complications, and so as to improving insurance utilization, clinical outcomes, and prevention of disease. (Vaccaro at [0015]).
REGARDING CLAIM 12
Andrews, Cox and Vaccaro disclose the limitation of claim 7.
Andrews and Cox do not explicitly disclose, however Vaccaro further discloses:
The method of claim 7, wherein the impact score is further identified based on a severity level related to the diagnosis code (Vaccaro at [0018] teaches rank all members individually to assure that members deemed to have the most urgent or impactable needs are reached out to first (interpreted by examiner as wherein the impact score is further identified based on a severity level related to the diagnosis code of Cox above)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the clustering method of Andrews and the clustering and assigning a risk category method of Cox to incorporate the impact identified further based on a severity of the diagnosis as taught by Vaccaro, with the motivation of reducing future insurance costs and complications, and so as to improving insurance utilization, clinical outcomes, and prevention of disease. (Vaccaro at [0015]).
REGARDING CLAIMS 1-6 and 13-21
Claims 1-6 and 13-21 are analogous to Claim 7-12 thus Claims 1-6 and 13-21 are similarly analyzed and rejected in a manner consistent with the rejection of Claim 7-12. Furthermore,
REGARDING CLAIM 1, Andrews teaches outputting to an interface of a computing device, a recommended action for a first patient over a second patient of the plurality of patients based on a comparison of the risk categories assigned to the first and second patients respectively ([0003] teaches provide a recommendation to the individual with the purpose of making contextual changes that are likely to have a positive health impact on the individual, thereby reducing the risk for the individual to have to seek treatment within the healthcare system).
REGARDING CLAIM 21, Andrews teaches presenting, to a display of a user interface, the patient profile including the risk category for the patient (Andrews at [0049] teaches a graphical user interface and displaying information in regards to the risk score for the patient and/or a risk category for the patient.).
Claims 22-24 are rejected under 35 U.S.C. 103 as being unpatentable over Andrews (US 2022/0406466), in view of Cox US (US 2017/0286622), in view of Vaccaro (US 2013/0096934) and in further view of Cohen (WO 2016/094330).
REGARDING CLAIM 22
Andrews, Cox and Vaccaro disclose the limitation of claim 1.
Andrews, Cox and Vaccaro do not explicitly disclose, however Cohen further discloses:
The method of claim 1, wherein the unsupervised learning algorithm is a k-means algorithm configured to generate at least four patient risk clusters (Cohen at [00251] teaches in an unsupervised learning process, a neural net may also be provided with a large training data set, the neural net may use statistical means, e.g., K- means clustering and [00264] teaches neural network used to determine risk. [00235] teaches discover naturally occurring grouping patterns (e.g., a cluster of individuals developing cancer at a given age and based on a similar smoking history), the grouping patterns may be identified and analyzed to determine an optimal cohort for a given patient and clustering to find relevant groupings (interpreted by examiner wherein the unsupervised learning algorithm is a k-means algorithm configured to generate at least four patient risk clusters)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the methods of Andrews, Cox and Vaccaro to incorporate the unsupervised learning algorithm is a k-means algorithm configured to generate at least four patient risk clusters by Cohen, with the motivation of predicting the likelihood or risk for having a disease such as cancer, especially in an otherwise asymptomatic or vaguely symptomatic patient. (Cohen at [0002]).
REGARDING CLAIM 23
Andrews, Cox and Vaccaro disclose the limitation of claim 7.
Andrews, Cox and Vaccaro do not explicitly disclose, however Cohen further discloses:
The method of claim 22, wherein the at least four patient risk clusters are based on a low behavioral health risk, a low physical health risk, a high behavioral health risk, and a high physical health risk (Cohen at [0074] teaches low medium and high risk categories. [0082] teaches evaluating physical conditions and [00266] teaches recommendations to behavioral changes (interpreted by examiner as means for the four patient risk clusters to be based on a low behavioral health risk, a low physical health risk, a high behavioral health risk, and a high physical health risk)).
REGARDING CLAIM 24
Andrews, Cox and Vaccaro disclose the limitation of claim 7.
Andrews, Cox and Vaccaro do not explicitly disclose, however Cohen further discloses:
The method of claim 1, wherein the plurality of clusters is segmented into the plurality of respective risk categories according to segmentation rules based on a percentile likelihood of inpatient or emergency department patient admission corresponding to at least a high risk category, a rising risk category, and a low risk category (Cohen at [0063] teaches "cohort" or "cohort population" which refers to a group or segment of human subjects with shared factors or influences, a normal population group matched, for example by age, to the cancer risk cohort; also referred to herein as a "normal cohort". A "same cohort" refers to a group of human subjects having the same shared cancer risk factors as the individual undergoing assessment for a risk of having a disease such as cancer. [0073] teaches machine learning system to determine the "risk score" for each human subject tested wherein the numerical value (e.g., a multiplier, a percentage, etc.) indicating increased likelihood of having the cancer for the stratified grouping becomes the "risk score".).
Response to Arguments
Rejection under 35 U.S.C. § 101
Regarding the rejection of claims 1-8, the Examiner has considered the Applicant’s arguments, but does not find them persuasive. Applicant argues:
More particularly, the rejection erroneously asserts (Office Action, page 3), with specific reference to independent claim 7, that the recited method "is a process that, under the broadest reasonable interpretation, covers a method [of] organizing human activity but for the recitation of generic computer components." This assertion is incorrect in light of the Federal Circuit's explicit warning against characterizing limitations at such an unreasonably high level of abstraction, untethered to the actual claim language of the claim taken as a whole, and in light of the teachings of the specification…
Regarding 1, The Examiner respectfully disagrees. Claim 7 recites a method for managing biased-reduced health services which is a process that, under the broadest reasonable interpretation, covers a method organizing human activity but for the recitation of generic computer components. A person can follow a set of rules/instructions to perform the limitations of claim 7, such as obtaining patient information, clustering patient information by applying a model, segmenting the cluster assignments, assigning risk categories, identifying an impact by applying a model, combining risk categories and prioritizing patients. As such, the claim recites an abstract idea.
For example, although claims 1-21 may involve human activities, the claims - taken as a whole - are directed to significantly more. As expressly described in the present specification, conventional patient modeling processes (many of which have been patented) utilize data that is inherently biased, and conventional learning models that train on biased patient data are thus necessarily subject to some level of bias as well. This problem is mitigated, according to the present claims, by taking existing industry standard patient risk scores from available patient information and then assigning a new segmented risk category to each patient using an unsupervised learning algorithm, such as a k-means clustering algorithm to mitigate the bias that is typically found from only the existing industry standard risk scores. Patients may then be advantageously better categorized and prioritized within a health service database from the obtained/calculated risk score in combination with the new risk category assignment.
Regarding 2, The Examiner respectfully disagrees. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of the processor, one or more non-transitory computer readable media, one or more processors and the segmentation system to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). The additional elements of interface display of a computing device, a patient database, a health service database, a database and a user interface were considered extra-solution activity. This has been re-evaluated under “significantly more” analysis and determined to be well-understood, routine and conventional in the field of healthcare. Receiving and/or transmitting data over a network (“a communications network”) has also been recognized by the courts as a well - understood, routine and conventional function (see, e.g., buySAFE v. Google; MPEP 2016(d)(II)). Well-understood, routine and conventional activity cannot provide an inventive concept (“significantly more”). Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a categorization model, an unsupervised learning algorithm, an impact model, at least one unsupervised learning algorithm, a dual-mode prioritization model, an unsupervised learning k-means clustering algorithm, a machine learning impact model, an unsupervised machine learning model and a clustering algorithm were determined to be the application of machine learning models to the identified abstract idea. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP2106.05(1)(A) indicates that merely saying “apply it’ or equivalent to the abstract idea cannot provide an inventive concept (“significantly more’). Accordingly, even in combination, these additional elements do not provide significantly more. As such the claim is not patent eligible.
With respect to claim 7, this claim includes an additional feature of combining the new risk category assignment from the unsupervised categorization model with an impact score from a separate impact model to generate a dual-model ranking within a health services database for prioritizing patients for clinical intervention. Accordingly, even if the pending claims could be described as "including" an abstract idea (which Applicant does not concede), the present claims clearly integrate the entirety of the claim limitations into a practical application that significantly improves patient health service outcomes, as well as the efficient allocation of health service resources among a plurality of patients. Accordingly, for at least these reasons, the outstanding Section 101 rejection is traversed.
Regarding 3, The Examiner respectfully disagrees. Claim 7 does not provide practical application. Prioritizing patients is an abstract idea. Improving patient health service outcomes and providing efficient allocation of health service resources are all non-technical benefits/improvements and do not render a practical application.
Rejection under 35 U.S.C. § 103
Regarding the rejection of claims 1-8, the Examiner has considered the Applicant’s arguments, but does not find them persuasive. Applicant argues:
…Cox, however, fails to teach or suggest a clustering algorithm using unsupervised learning to segment risk-scored patient data into additional risk categories...
Regarding 1, The Examiner respectfully disagrees. Andrews teaches at [0032] machine learning process may possibly be an unsupervised machine learning process and Cox at [abstract] teaches perform a machine learning operation to train a risk scoring algorithm for scoring a risk of adverse conditions for the patient population using the patient information and [0129] teaches retrieve information from the patient cohort database to classify the patient into a patient cohort. The patient cohort is a grouping of patients that have similar characteristics, e.g., similar demographics, similar medical diagnoses, etc. Patient cohorts may be generated using any known or later developed grouping mechanism. One example mechanism may be using a clustering algorithm that clusters patients based on key characteristics of the patient, e.g., age, gender, race, medical diagnosis, etc. As another example, rules in the resources database 418 may be defined for application to patient information in the EMR and demographics sources 420 and lifestyle information sources for identifying patients that have specified characteristics, e.g., patients that have diabetes and are in the age range of 18-45, which is interpreted by examiner as clustering the plurality of patients, based on the obtained patient information, into a plurality of cluster assignments using a categorization model. Given the broadest reasonable interpretation, the cited references in combination teach the claimed features.
Conclusion
Applicant’s amendment necessitated the new grounds of rejection presented in this Office action. 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.
The prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Slater (US 2018/0108432) discloses system and method for providing a drug therapy coordination risk score and improvement model-of-care.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIZA TONY KANAAN whose telephone number is (571)272-4664. The examiner can normally be reached on Mon-Thu 9:00am-6:00pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached on 571-272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from the Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docs for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/L.T.K./Examiner, Art Unit 3683 /ROBERT W MORGAN/Supervisory Patent Examiner, Art Unit 3683
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