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 .
Prosecution History Summary
Claims 10-11, 13, 16-18, and 20 are cancelled.
Claims 1, 5, and 14-15 are amended.
Claims 1-9, 12, 14-15, 19, and 21-27 are 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 1-9, 12, 14-15, 19, and 21-27 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.
Subject Matter Eligibility Criteria – Step 1:
The claims recite subject matter within a statutory category as a machine (claims 1-9, 12, 14-15, 19, and 21-27). Accordingly, claims 1-9, 12, 14-15, 19, and 21-27 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria – Step 2A – Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP 2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts. MPEP 2106.04(a).
Representative independent claim 1 includes limitations that recite at least one abstract idea. Specifically, independent claim 1 recites:
A system for automatically building a control population closely matched to a member population and applying the control population in establishing medical outcome comparison metrics with the member population, the system comprising:
-a non-volatile computer readable storage medium storing an association between each of a plurality of demographic group classifications and a plurality of medical condition group classifications and one or more individuals within a control population comprising a plurality of individuals, wherein
-each classification of the plurality of demographic group classifications corresponds to a respective demographic category of a plurality of demographic categories, each demographic category comprising two or more demographic groups, and
-each medical condition group classification of the plurality of medical condition group classifications corresponds to a respective medical condition category of a plurality of medical condition categories, each medical condition category comprising two or more groups, wherein
-each medical condition category comprises a hierarchical sub-categorization structure, wherein
-the hierarchical sub-categorization structure comprises a plurality of medical condition sub-categories arranged in progressively finer levels of sub-categorization,
-each classification of a portion of the plurality of medical condition group classifications corresponds to a respective medical condition sub-category of one of the plurality of medical condition categories, and
-a total number of the plurality of medical condition categories aggregated with a plurality of medical condition sub-categories hierarchically layered beneath the plurality of medical condition categories is at least one thousand medical condition group classifications;
-one or more machine learning classifiers, each machine learning classifier configured to analyze at least one medical code taxonomy of one or more medical code taxonomies, and group medical codes of the at least one medical code taxonomy representing same or similar elements into hierarchical layers of types and sub-types of at least one medical condition of a set of medical conditions,
-wherein each medical code taxonomy of the one or more medical code taxonomies is designed to classify at least one of medical procedures, interventions, or pharmaceuticals using a plurality of medical codes;
-processing circuitry configured to perform a plurality of operations, the operations comprising,
-creating the hierarchical sub-categorization structure of at least a portion of the plurality of medical condition categories by
-parsing, by the one or more machine learning classifiers, each respective medical code taxonomy of one or more medical code taxonomies into a plurality of sets of medical codes organized, for each medical condition category of the portion of the plurality of medical condition categories, into a plurality of increasingly narrower sub-categories to capture layers of granularity in the respective medical taxonomy, and
-organizing respective sets of medical codes of the plurality of sets of medical codes into the hierarchical sub-categorization structure according to the plurality of increasingly narrower sub-categories,
-for each given member of a plurality of members of a member population,
i) accessing respective demographic information of the given member,
ii) using the respective demographic information of the given member, classifying the given member into a respective demographic group of each given demographic category of at least a portion of the plurality of demographic categories according to the respective two or more demographic groups of the given respective demographic category, wherein
-the plurality of demographic categories in which the given member is classified comprise at least one of age, gender, geography, one or more socio-economic factors, and/or one or more environmental exposure factors, and
-classifying comprises storing, to a member record of the given member, a set of demographic group indicators comprising a respective demographic group indicator of each demographic category of the at least the portion of the plurality of demographic categories according to the classifying,
iii) accessing respective medical data of the given member, and
iv) using a plurality of medical codes within the respective medical data of the given member, classifying the given member into at least one respective medical condition group of each given medical condition category of at least a portion of the plurality of medical condition categories according to a respective set of medical condition groups of the given respective medical condition category, wherein
-for each respective medical condition category of at least a portion of the plurality of medical condition categories, classifying comprises classifying the given member to at least one medical condition sub-category of the respective medical condition category, and
-classifying comprises storing, to the member record of the given member, a set of medical condition group indicators comprising a respective medical condition group indicator of each medical condition category of the at least the portion of the plurality of medical condition categories according to the classifying, and
-accessing at least one of a) costs data, b) medical outcomes data, or c) insurance claims data associated with each member of the member population,
-analyzing a respective set of medical condition group indicators stored to the member record of each respective member of the plurality of members of the member population in view of at least one of a) highest representative costs among the plurality of medical condition group classifications based on the costs data, b) greatest reduction in mortality, lowest remission rates, or highest recovery rates among the plurality of medical condition group classifications based on the medical outcomes data, or c) highest representative costs among the plurality of medical condition group classifications based on the insurance claims data, wherein dimensionally reducing the plurality of medical condition group classifications comprises
-quantifying, using the at least one of a) the costs data, b) the medical outcomes data, or c) the insurance claims data, a respective health care impact corresponding to each medical condition group classification of the portion of the plurality of medical condition group classifications, and
-selecting, at least in part according to the quantifying the subset of medical condition group classifications,
-using the set of demographic group indicators and a portion of the subset of medical condition group indicators stored to the member record of the given member, comparing the given member to at least a portion of the plurality of individuals of the control population to identify whether one or more matching individuals exist in the control population, wherein
-each medical condition group indicator of the portion of the set of medical condition group indicators corresponds to one of the subset of medical condition group classifications,
-the one or more matching individuals each have i) a corresponding set of demographic group classifications corresponding to at least a portion of the set of demographic group indicators comprising at least one match within each demographic category of the plurality of demographic categories that is represented in the set of demographic group indicators, and ii) a corresponding set of medical condition group classifications corresponding to the portion of the set of medical condition group classifications represented in the set of medical condition indicators of the respective member, and
-when the comparing results in the identifying of the one or more matching individuals,
-the given member is included in an analysis member population comprising a subset of the member population, and
-at least one of the one or more matching individuals is included in a benchmark population, and
-providing the analysis member population, the benchmark population, and at least one of a) the costs data, b) the medical outcomes data, or c) the insurance claims data associated with each member of the analysis member population and each member of the benchmark population for comparison analysis.
Examiner states submits that the foregoing underlined limitations constitute: a “mental process” because accessing, analyzing, classifying, and comparing information to determine medical outcome based on metrics can all be performed in the human mind.
Accordingly, the claim recites at least one abstract idea.
Subject Matter Eligibility Criteria – Step 2A – Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether
the claim as a whole integrates the abstract idea into a practical application. As noted at MPEP
§$2106.04(1D(A)(2), it must be determined whether any additional elements in the claim beyond
the abstract idea integrate the exception into a practical application in a manner that imposes a
meaningful limit on the judicial exception. The courts have indicated that additional elements
merely using a computer to implement an abstract idea, adding insignificant extra solution
activity, or generally linking use of a judicial exception to a particular technological environment
or field of use do not integrate a judicial exception into a “practical application.” MPEP
§2106.05(1(A).
In the present case, the additional limitations beyond the above-noted at least one abstract
idea recited in the claim are as follows (where the bolded portions are the “additional
limitations” while the underlined portions continue to represent the at least one “abstract idea”):
A system for automatically (using computers as mere tools to perform the abstract idea, see MPEP 2106.05(f)) building a control population closely matched to a member population and applying the control population in establishing medical outcome comparison metrics with the member population, the system comprising:
-a non-volatile computer readable storage medium storing an association between each of a plurality of demographic group classifications and a plurality of medical condition group classifications and one or more individuals within a control population comprising a plurality of individuals (using computers as mere tools to perform the abstract idea, see MPEP 2106.05(f); para. 121), wherein
-each classification of the plurality of demographic group classifications corresponds to a respective demographic category of a plurality of demographic categories, each demographic category comprising two or more demographic groups (mere field of use limitation, see MPEP 2106.05(h)), and
-each medical condition group classification of the plurality of medical condition group classifications corresponds to a respective medical condition category of a plurality of medical condition categories, each medical condition category comprising two or more groups (mere field of use limitation, see MPEP 2106.05(h)), wherein
-each medical condition category comprises a hierarchical sub-categorization structure (mere field of use limitation, see MPEP 2106.05(h)), wherein
-the hierarchical sub-categorization structure comprises a plurality of medical condition sub-categories arranged in progressively finer levels of sub-categorization (mere field of use limitation, see MPEP 2106.05(h)),
-each classification of a portion of the plurality of medical condition group classifications corresponds to a respective medical condition sub-category of one of the plurality of medical condition categories (mere field of use limitation, see MPEP 2106.05(h)), and
-a total number of the plurality of medical condition categories aggregated with a plurality of medical condition sub-categories hierarchically layered beneath the plurality of medical condition categories is at least one thousand medical condition group classifications (mere field of use limitation, see MPEP 2106.05(h));
-one or more machine learning classifiers, each machine learning classifier configured to analyze at least one medical code taxonomy of one or more medical code taxonomies, and group medical codes of the at least one medical code taxonomy representing same or similar elements into hierarchical layers of types and sub-types of at least one medical condition of a set of medical conditions (using computers as mere tools to perform the abstract idea, see MPEP 2106.05(f); para. 99),
-wherein each medical code taxonomy of the one or more medical code taxonomies is designed to classify at least one of medical procedures, interventions, or pharmaceuticals using a plurality of medical codes (mere field of use limitation, see MPEP 2106.05(h));
-processing circuitry configured to perform a plurality of operations (using computers as mere tools to perform the abstract idea, see MPEP 2106.05(f); para. 116-121), the operations comprising,
-creating the hierarchical sub-categorization structure of at least a portion of the plurality of medical condition categories by
-parsing, by the one or more machine learning classifiers (using computers as mere tools to perform the abstract idea, see MPEP 2106.05(f); para. 99), each respective medical code taxonomy of one or more medical code taxonomies into a plurality of sets of medical codes organized, for each medical condition category of the portion of the plurality of medical condition categories, into a plurality of increasingly narrower sub-categories to capture layers of granularity in the respective medical taxonomy, and
-organizing respective sets of medical codes of the plurality of sets of medical codes into the hierarchical sub-categorization structure according to the plurality of increasingly narrower sub-categories,
-for each given member of a plurality of members of a member population,
i) accessing respective demographic information of the given member,
ii) using the respective demographic information of the given member, classifying the given member into a respective demographic group of each given demographic category of at least a portion of the plurality of demographic categories according to the respective two or more demographic groups of the given respective demographic category, wherein
-the plurality of demographic categories in which the given member is classified comprise at least one of age, gender, geography, one or more socio-economic factors, and/or one or more environmental exposure factors, and
-classifying comprises storing, to a member record of the given member, a set of demographic group indicators comprising a respective demographic group indicator of each demographic category of the at least the portion of the plurality of demographic categories according to the classifying,
iii) accessing respective medical data of the given member, and
iv) using a plurality of medical codes within the respective medical data of the given member, classifying the given member into at least one respective medical condition group of each given medical condition category of at least a portion of the plurality of medical condition categories according to a respective set of medical condition groups of the given respective medical condition category, wherein
-for each respective medical condition category of at least a portion of the plurality of medical condition categories, classifying comprises classifying the given member to at least one medical condition sub-category of the respective medical condition category, and
-classifying comprises storing, to the member record of the given member, a set of medical condition group indicators comprising a respective medical condition group indicator of each medical condition category of the at least the portion of the plurality of medical condition categories according to the classifying, and
-accessing at least one of a) costs data, b) medical outcomes data, or c) insurance claims data associated with each member of the member population,
-analyzing a respective set of medical condition group indicators stored to the member record of each respective member of the plurality of members of the member population in view of at least one of a) highest representative costs among the plurality of medical condition group classifications based on the costs data, b) greatest reduction in mortality, lowest remission rates, or highest recovery rates among the plurality of medical condition group classifications based on the medical outcomes data, or c) highest representative costs among the plurality of medical condition group classifications based on the insurance claims data, wherein dimensionally reducing the plurality of medical condition group classifications comprises
-quantifying, using the at least one of a) the costs data, b) the medical outcomes data, or c) the insurance claims data, a respective health care impact corresponding to each medical condition group classification of the portion of the plurality of medical condition group classifications, and
-selecting, at least in part according to the quantifying the subset of medical condition group classifications,
-using the set of demographic group indicators and a portion of the subset of medical condition group indicators stored to the member record of the given member, comparing the given member to at least a portion of the plurality of individuals of the control population to identify whether one or more matching individuals exist in the control population, wherein
-each medical condition group indicator of the portion of the set of medical condition group indicators corresponds to one of the subset of medical condition group classifications,
-the one or more matching individuals each have i) a corresponding set of demographic group classifications corresponding to at least a portion of the set of demographic group indicators comprising at least one match within each demographic category of the plurality of demographic categories that is represented in the set of demographic group indicators, and ii) a corresponding set of medical condition group classifications corresponding to the portion of the set of medical condition group classifications represented in the set of medical condition indicators of the respective member, and
-when the comparing results in the identifying of the one or more matching individuals,
-the given member is included in an analysis member population comprising a subset of the member population, and
-at least one of the one or more matching individuals is included in a benchmark population, and
-providing the analysis member population, the benchmark population, and at least one of a) the costs data, b) the medical outcomes data, or c) the insurance claims data associated with each member of the analysis member population and each member of the benchmark population for comparison analysis.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the limitations reciting the at least one abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(IID(A)(2).
The remaining dependent claim limitations not addressed above fail to integrate the
abstract idea into a practical application as set forth below:
Claims 2-4, 7, 24: The claims specify the different categories, which further narrows the abstract idea.
Claim 5: The claim specifies the non-volatile computer readable medium to receive, classify, and store information, which uses the computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Claim 6: The claim specifies identifying closest matching individuals based on subcategory level if unable, which is a mental process.
Claim 8: The claim specifies analyzing medical outcomes in view of benchmark to derive statistical differences, which is a mental process using mathematical concepts.
Claim 9: The claim specifies analyzing insurance claims data to derive statistical differences, which is a mental process using mathematical concepts.
Claim 12, 14, 18: The claim specifies analyzing data to identify indicators having the greatest impact, which is a mental process.
Claim 15: The claim specifies indicators having greatest impact are selected at least 90% of variation in outcome, which does no more than generally link use of the abstract idea to a particular technological environment or field of use without altering or affecting how the use of at least one abstract idea is performed (see MPEP 2106.05(h)).
Claim 19: The claim specifies applying a machine learning classifiers, which uses the computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Claim 21, 26: The claim specifies applying statistical analysis, which is a mathematical process.
Claim 22, 27: the claim specifies dimensionally reducing by ranking values, which further narrows the abstract idea of mathematical concepts.
Claim 25: The claim specifies comparing individuals to seek matching individuals, which further narrows the abstract idea.
Thus, when the above additional limitations are considered as a whole along with the limitations directed to the at least one abstract idea, the at least one abstract idea is not integrated into a practical application. Therefore, the claims are directed to at least one abstract idea.
Subject Matter Eligibility Criteria – Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claims 1 and 23 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim 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 discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such as accessing demographic information, accessing medical data, accessing cost, medical outcomes, insurance claims data, providing the analysis, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); storing an association between demographic group indicators and medical condition indicators, classifying demographic group indicators, storing demographic group indicators, classifying medical data, classifying into sub-category, storing medical data, machine learning classifiers, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv)).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 5-6, 8-9, additional limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, claims 5 (receiving demographic category), e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); claims 8-9 (derive statistical differences), e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii); claims 5 (classifying demographic information), 6 (identify selecting a coarser demographic subcategory) e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-9, 12, 14-15, 19, and 21-27 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant’s amendment, filed 07/23/2025, with respect to 35 U.S.C. 112 have been fully considered and are persuasive. The 35 U.S.C. 112 rejection has been withdrawn.
Applicant's arguments filed for claims 1-9, 12, 14-15, 19, and 21-27 under 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant argues that the claims provide a concrete technical solution to the technical problem of large data analysis of consecutive years of collected data where “inconsistency of variables” creates difficulties in making “comparisons…between prior years and a present year” because they will “not necessarily identify benefits or detriments due to change in the health initiatives alone.” Examiner states that while having a large number of codes is cumbersome and time-consuming, using a generic dimension reduction technique (i.e. filtering) does not provide a practical application as it does not actually provide a technical solution to a technical problem, but rather an administrative problem solved by a scheme. While the claimed method purports to accelerate the process of analyzing tissue analysis data, the speed increase comes from the capabilities of a general-purpose computer, rather than the patented method itself. See Bancorp Servs., L.L.C. v. Sun Life Assur. Co. of Can. (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012) (“[The fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter.”).
Applicant argues that similar to DDR, the present claims provide a particular solution that is rooted in computer technology. Examiner disagrees. The patent at issue in DDR provided an Internet-based solution to solve a problem unique to the Internet that (1) did not foreclose other ways of solving the problem, and (2) recited a specific series of steps that resulted in a departure from the routine and conventional sequence of events after the click of a hyperlink advertisement. Id. at 1256–57, 1259. The patent claims here do not address problems unique to the Internet, so DDR has no applicability. Page 20 of the DDR Holdings, LLC v. Hotels.com Federal Circuit decision states, "But these claims stand apart because they do not merely recite the performance of some business practice known from the pre-Internet world along with the requirement to perform it on the Internet. Instead, the claimed solution is necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computer networks." The Examiner respectfully disagrees with Applicant’s assertion that the claims present a solution necessarily rooted in the technology in order to overcome a problem. Applicant’s claims seek to address a problem that existed and continues to exist outside of the realm of the technology associated with the additionally recited elements. The proposed solution is one that could have been implemented directly by a human performing analogous functions by hand and/or with the assistance of a general purpose computer applied to facilitate the functions at a high level of generality or with the assistance of additional elements performing well-known, conventional functions. In Applicant’s claims, the central processor could be substituted with a human user and the underlying invention would result in a similar solution to the problem at hand. The rejected claims do not adhere to the same fact pattern seen in the DDR Holdings, LLC v. Hotels.com decision. In the DDR Holdings decision, the manner in which the network itself operated was changed to improve network operations. There is no actual improvement made to the operations or physical structure of the additional elements claimed in the instant application.
Applicant states that the features are not directed to a generic filter technique and represent “an improvement to computer functionality itself’ by translating medical coding taxonomies into “a specific type of data structure designed to improve” computer-enabled analysis of medically-coded data in “identifying and quantifying actionable health care impacts, thus providing an avenue to direct future medical case studies related to various healthcare initiatives and interventions.” Enfish, LLC v. Microsoft Corp., 822 F. 3d 1327, 1336, 1339. Examiner disagrees. “Translating” medical coding taxonomies into a specific type of data structure is mere data manipulation and does not provide an improvement to computer functionality. Examiner fails to see the improvement and asks the Applicant for specific objective evidence regarding the improvement.
Applicant argues that the claims recite an improvement to “the way the computer stores and retrieves [medical classification] data in memory in combination with the specific data structure,” similar to the claims in Enfish. The Federal Circuit stated that the Enfish claims were not ones in which general-purpose computer components are added after the fact to a fundamental economic practice or mathematical equation, but were directed to a specific implementation of a solution to a problem in the software arts, and concluded that the Enfish claims were thus not directed to an abstract idea (configuring a computer memory in accordance with a self-referential table). Furthermore, Enfish specification also supported the benefits it held over conventional databases, such as increased flexibility, faster search times, and smaller memory requirements. Here, the focus of the claims is not any improved computer or network, and indeed, the specification makes clear that off-the-shelf computer technology is usable to carry out the analysis. There is a fundamental difference between computer functionality improvements, on the one hand, and uses of existing computers as tools to perform a particular task, on the other. Indeed, the Federal Circuit applied this very distinction in rejecting the § 101 challenge in Enfish because the claims at issue there focused on a specific type of data structure, i.e., a self-referential table for a computer database, designed to improve the way a computer carries out its basic functions of storing and retrieving data, and not merely on asserted advances in uses to which existing computer capabilities could be put. Enfish, 822 F.3d at 1335—36. The alleged improvement that Applicants tout does not concern an improvement to computer capabilities but instead relates to an alleged improvement in classifying information for which a computer is used as a tool in its ordinary capacity.
Applicant argues that similar to Example 47, the claims recite machine learning classifiers. The use of machine learning classifiers only utilizes the machine learning model as extra solution activity incidental to the primary process that is merely a nominal or tangential addition to the claim (MPEP § 2106.05(g) - insignificant pre/post-solution activity) and is therefore not a practical application of the recited judicial exception. While generating, training with specific training data, and utilizing a particular machine learning model algorithm would be considered meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment (see MPEP § 2106.05(e)), here, any generic machine learning model and its outputs are used. Examiner states that applying the output from the machine learning classifiers is using the machine learning in its ordinary capacity to generate a result.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Silva – U.S. Publication No. 2023/0409926 – Teaches using machine learning models to extract information from health information including training of multiple dimensionality reduction models.
Fischer et al. – U.S. Publication No. 2019/0237199 – Teaches a method of classifying population by analyzing medical claims data.
THIS ACTION IS MADE FINAL. 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 SHEETAL R. PAULSON whose telephone number is (571)270-1368. The examiner can normally be reached M-F 8am-5pm.
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/SHEETAL R PAULSON/Primary Examiner, Art Unit 3686