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 .
Status of the Claims
Claims 1, 11 and 16-19 have been amended.
Claims 1, 3-9 and 11-21 as presented February 23, 2026 are currently pending and considered below.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on February 23, 2026 has been entered.
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, 3-9 and 11-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step 1
Claims 1, 3-9 and 11-21 are within the four statutory categories. Claims 1, 3-9 and 19-21 are drawn to methods for classifying medical providers, which is within the four statutory categories (i.e. process). Claims 11-18 are drawn to a system for classifying medical providers, which is within the four statutory categories (i.e. machine).
Prong 1 of Step 2A
Claim 1 recites: A method comprising:
analyzing, via at least one processor, claims data representing services performed by providers within a selected specialty;
identifying, at least partially via the analysis of the claims data representing differentiated services, wherein the differentiated services are services exhibiting non-uniform distribution of performance among providers within the selected specialty and within a selected market,
wherein the differentiated services are identified based, at least in part, on: an analysis of the claims data and a threshold percentage of providers performing or not performing a service within the selected specialty and the selected market;
a determination that the differentiated services are not performed by all providers within the selected specialty; and
a determination that the differentiated services exhibit at least a threshold percentage variation in frequency of performance within a predetermined time-frame among two or more providers within the selected specialty;
curating a list of differentiated services from the identified differentiating services;
generating a data package including the curated list of differentiated services;
analyzing claims data representing practices of providers within the selected specialty and the selected market using search queries based, at least partially, on one or more services from the curated list of differentiated services;
generating at least partially via the analysis of the claims data representing practices of providers within the selected specialty and the selected market, a data package including a distribution of a volume of at least one differentiated service of the list of differentiated services and performed by providers within the selected specialty and the selected market;
determining a threshold within the distribution of the volume of the at least one differentiated service for classifying a provider as a specialist in performing the at least one differentiated service;
classifying one or more providers within the selected specialty and the selected market as a specialist in performing the at least one differentiated service at least partially based on the determined threshold, wherein the classifying one or more providers comprises utilizing machine-learning techniques that analyze the volume of the at least one differentiated service performed by each provider relative to the determined threshold to generate and apply labels representing the classifications; and
generating one or more data packages representing service or specialty-specific directories of the classified one or more providers.
The limitations of analyzing claims data, identifying services within a specialty and market, curating and generating a list of the identified services, analyzing practices of providers, generating a distribution of volume of services within the specialty and market, determining a threshold for the distribution of volume of services, and classifying the providers within the specialty as a specialist based on the threshold, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this case the aforementioned steps are directed towards following rules or instructions to classify medical providers by specialty, wherein a “specialty” represents a relationship between people because it defines the relationship between a provider and his or her field of expertise and his or her colleagues and/or his or her patients), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements,” and will be discussed in further detail below.
Furthermore, the abstract idea for Claims 11-18 and 19-21 is identical as the abstract idea for Claims 1, 3-9, because the only difference between Claims 1 and 11 is that Claim 1 recites a method, whereas Claim 11 recites a system and further requires assigning providers to a percentage ranking, and because the only difference between Claims 1 and 19 is that Claim 1 explicitly recites the steps of generating the curated list of at least one differentiated service.
Dependent Claims 3-9, 12-18, and 20-21 include other limitations, for example Claim 3 recites adding a label to a directory of providers and/or determining whether to keep or remove providers from the directory, Claims 4 and 14 recite coding by specialty, Claims 5 and 15 recite analyzing claims data for different services, Claims 6 and 20 recite assigning each provider a percentage ranking, Claims 7-9 and 16-18 recite determining performance scores for providers, Claim 12 recites generating specialty directories, Claim 13 recites analyzing practices of providers within a particular time period, and Claim 21 recites that the analysis of the claims data is based on search queries based on services, but these only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04. Hence dependent Claims 3-9, 12-18, and 20-21 are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1, 11, and 19.
Prong 2 of Step 2A
Claims 1, 3-9 and 11-21 are not integrated into a practical application because the additional elements (i.e. any limitations that are not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of a processor, a non-transitory computer-readable storage medium, machine learning and generating a “data package” implying that the data represents electronic/digital data to be processed by a computer, which amount to merely invoking a computer as a tool to perform the abstract idea, e.g. see paragraphs [0058] and [0112]-[0114] of the present Specification, see MPEP 2106.05(f);
generally link the abstract idea to a particular technological environment or field of use – for example, the claim language limiting the data being processed to claims data and services data for medical providers, which amounts to limiting the abstract idea to the field of insurance and/or healthcare, see MPEP 2106.05(h); and/or
Additionally, dependent Claims 3-9, 12-18, and 20-21 include other limitations, but these limitations do not include any additional elements beyond those already recited in independent Claims 1, 11, and 19, and hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B
Claims 1, 3-9 and 11-21 do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the elements other than the abstract idea), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, and/or generally link the abstract idea to a particular technological environment or field of use, which even when reevaluated under the considerations of Step 2B of the analysis, do not amount to “significantly more” than the abstract idea.
Dependent Claims 3-9, 12-18, and 20-21 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because, as stated above, the aforementioned dependent claims do not recite any additional elements not already recited in independent Claims 1, 11, and 18, and hence do not amount to “significantly more” than the abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1, 3-9 and 11-21 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Subject Matter Free of Prior Art
The following is a statement of reason(s) for indication of subject matter free of prior art: As per claim 1, the primary reason for the indication of the allowable subject matter is the inclusion of the following limitations in the combinations as recited and not found in the closest available prior of record:
wherein the differentiated services are identified based, at least in part, on: a determination that the differentiated services are not performed by all providers within the selected specialty; and a determination that the differentiated services exhibit at least a threshold percentage variation in frequency of performance within a predetermined time-frame among two or more providers within the selected specialty;
classifying one or more providers within the selected specialty and the selected market as a specialist in performing the at least one differentiated service at least partially based on the determined threshold, wherein the classifying one or more providers comprises utilizing machine-learning techniques that analyze the volume of the at least one differentiated service performed by each provider relative to the determined threshold to generate and apply labels representing the classifications;
The closest available prior art is understood to be Wennberg (US 2007/0078680 A1), Berk (US 2006/0294095 A1), Bauder (“Predicting Medical Provider Specialties to Detect Anomalous Insurance Claims”, 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), 2016), Yoo (US 2018/0165062 A1), Cave (US 2019/0287677 A1), Kharraz Tavakol (US 2010/0228564 A1) and Fry (US 2016/0371706 A1).
Wennburg teaches measuring unwarranted healthcare service variations by identifying services with “unwarranted variation” and “preference-sensitive care”. Wennburg discusses calculating the frequency of a procedure among peer doctors, e.g. an observed to expected ratio, to see if a specific procedure exhibits a massive statistical variance across a population. This involves calculating an expected volume threshold and comparing it to the observed volume to get the ratio. See [0011], [0057], [0051], [0055]. However, Wennburg does not fairly teach or suggest the recited features.
Berk teaches detecting behaviors from patterns of data where sets of thresholds and ranges used within detection scenarios can be created and applied while the system is in active operation. Berk discusses mining databases including healthcare claims using “parametrized patterns” and “runtime thresholds and ranges”. See abstract, [0002], [0005]-[0008]. However, Berk does not fairly teach or suggest the recited features.
Bauder teaches building a machine learning model to detect when physicians exhibit anomalous behavior in their medical insurance claims. A machine learning classifier where “The inputs to the model are physician specialties and the number of times each provider performs a particular procedure.” The model outputs a classification label representing the physician’s medical specialty, e.g. “Cardiology”. See abstract, “Introduction”, “Methodology” and Fig. 3. However, Bauder does not fairly teach or suggest the recited features.
Yoo teaches a system that includes an analysis engine that accesses a claims database to determine a total number of procedures performed by a plurality of physicians. The analysis engine determines the type and number of procedures performed from the claims data by physicians at a particular hospital The analysis engine may also determine a clinical effectiveness requirement comprising a percentage of the total number of procedures performed by a physician and a threshold for the clinical effectiveness requirement indicating a minimum clinical fluency that is critical in providing appropriate patient care. The analysis engine ranks physicians according to the degree to which they exceed the threshold. See [0113], [0115]-[0119] and [0122]-[0124]. However, Yoo does not fairly teach or suggest the recited features.
Cave teaches a system to identify medical care providers meeting a desired practice pattern. “MedMarkers” are used to establish a practice pattern for specialty-specific health care providers in a given region. In describing the properties required for a service to qualify as a “MedMarker”, the service should have the property that “More than a given percentage of the health care providers (within the specialty type of interest) perform one or more of the service code of interest.” See [0092] and [0094]-[0095]. However, Cave does not fairly teach or suggest the recited features.
Kharraz Tavakol teaches the collection and management of healthcare practitioner profile information prior to inclusion in a hospital directory of affiliated practitioners or insurance provider directory of participating practitioners. An aggregator provides a list of procedures corresponding to specialties and sub-specialties, where the list is utilized to respond to a query regarding which physicians are able to perform which procedures. See abstract, [0146], [0179]-[0181] and Fig. 7. However, Kharraz Tavakol does not fairly teach or suggest the recited features.
Fry teaches a system for measuring and analyzing provider utilization. This utilization data may be used to identify problem providers that have high, low, or unusual utilization patterns. Reports for “distribution of providers by utilization ratios” involves plotting the performance of numerous individuals on a distribution graph. The distributions are used to assign ranks to each provider. See abstract, [0015]-[0016] and [0026]. However, Fry does not fairly teach or suggest the recited features.
The aforementioned references are understood to be the closest prior art. Various aspects of the present invention are known individually, but for the reasons disclosed above, the particular manner in which the elements are claimed, when considered as an ordered combination, distinguishes from the aforementioned references and hence the present invention is not considered non-novel and/or an obvious variant of the inventions taught by the closest prior art references.
As per claims 11 and 19, a statement of reason(s) for indication of subject matter free of prior art for at least the same rationale as applied to claim 1.
Response to Arguments
Regarding the rejection under 35 U.S.C. § 101 of Claims 1, 3-9 and 11-21, the Examiner has considered the Applicant’s arguments; however the arguments are not persuasive. The Examiner has modified the grounds of rejection of Claims 1, 3-9 and 11-21 under 35 U.S.C. 101 to correspond with the present claim amendments.
Applicant argues that the claims “merely involve” human activity because they recite data processing operations performed by a processor, rather than explicitly setting forth rules for how patients should select providers or how referrals are made.
The Examiner respectfully disagrees. The claims do not merely involve human activity, they explicitly recite a method of organizing human activity. The claims analyze the work output of human providers (claims data volume), compare that output against administrative thresholds, classify human providers based on the output and organize them into a published list (generating directories). These are a series of rules and steps that a person would take to organize the human activity of classifying medical providers by specialty. In addition, Applicant is reminded that the “methods of organizing human activity” includes a person’s interaction with a computer (see October 2019 Update: Subject Matter Eligibility at Pg. 5). The computer merely serves as a tool for executing the abstract idea. Therefore, if the 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 “method of organizing human activity” grouping of abstract ideas.
Further, Applicant’s argument the claims are parallel to Example 39 are unpersuasive. The claims in Example 39 were found to not be directed to any of the enumerated types of abstract ideas and were thus eligible under step 2A - Prong 1 of the Alice Corp. test for subject matter eligibility. MPEP 2106.04(a)(1) states that "examiners should keep in mind that while all inventions at some level embody, use, reflect, rest upon, or apply laws of nature, natural phenomenon, or abstract ideas, not all claims recite an abstract idea" (internal quotations omitted). Example 39 is a hypothetical illustration of this principle. The training, use, and subsequent retraining of the Neural Network model in Example 39 are all functions that are outside of the ambit of an abstract idea (see MPEP 2106.04(a)(1)(vii)). This is contrasted with Applicant's claimed invention that recites the abstract idea of classifying medical providers by specialty that represents Certain Methods of Organizing Human activity as described above.
Applicant argues that “specialist” is a data-derived label, not a social construct.
The Examiner acknowledges the term “specialist” is data-derived label based on volume thresholds as defined in the specification. However, even when interpreting “specialist” as a data-derived label, the claims remain directed to the abstract idea of certain methods of organizing human activity. While the definition of the label is algorithmic, the claimed process is directed to managing personal behaviors, relationships and interactions between people. The claims recite a series of steps to follow to evaluate human work output, categorize medical providers and organize them into a directory.
Applicant argues the claims must be viewed as a whole and that the specific “analytical pipeline” improves the technical field of healthcare informatics, akin to the improvements in Enfish and Example 47.
The Examiner respectfully disagrees. Applicant’s argument that the claims improve a technical field is not persuasive. It is noted that the present guidance states that a limitation that is indicative of integration into a practical application includes those “improvements to the functioning of a computer, or to any other technology or technical field” (See MPEP 2106.05(a)). The identified “additional elements” of a processor, a non-transitory computer-readable storage medium, machine learning and generating a “data package” amount to no more than mere instructions to apply the exception using a generic computer. Applicant’s own specification describe the various components as general purpose, common, standard, known to one of ordinary skill, and at a high level of generality, and in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy the statutory disclosure requirements (see specification [0058] and [0112]-[0114]). See MPEP 2106.04(d) and 2106.05(f) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application nor significantly more.
Furthermore, MPEP 2106.04(d)(1) and MPEP 2106.05(a) indicates that a practical application may be present where the claimed invention provides a technical solution to a technical problem. See, e.g. DDR Holdings, LLC. v. Hotels.com, L.P., 773 F.3d 1245, 1259 (Fed. Cir. 2014) (finding that claiming a website that retained the “look and feel” of a host webpage provided a technological solution to the problem of retention of website visitors by utilizing a website descriptor that emulated the “look and feel” of a host webpage, where the problem arose out of the internet and was thus a technical problem). Here, the Applicant’s argued problem is not a technological problem caused by the computer (the technological environment to which the claims are confined). The problem of inaccuracy with existing provider directories because they rely on self-reported data and web-crawling is not a problem caused by the computer, it is a problem that existed and/or exists regardless of whether a computer is involved the process. At best, Applicant’s identified problem is a business or administrative problem. Solving a business or administrative problem by applying standard machine learning techniques is not a technical solution to a technical problem.
In Enfish, the claims were eligible because the self-referential table improved the way the computer functioned by improving memory storage and retrieval time. Here, the claims do not improve computer functionality. The computer operates in a standard manner by receiving data, calculating thresholds, applying an algorithm and outputting a data package. Any improvement in the claims is directed to the accuracy of the data (i.e. directory) and not to computer functionality.
Applicant argues the “apply it” analysis has been oversimplified by ignoring the specific ordered combination of the pipeline.
The Examiner respectfully disagrees. The “pipeline” consists of the abstract process of gathering data, analyzing data and displaying/outputting results (analyzing claims data, determining thresholds, generating volume distributions and generating data package of the classified providers). While these steps are specific to claims data and service data, limiting the use of an abstract idea to a particular technological environment (i.e. insurance and/or healthcare) does not integrate the exception into a practical application. The machine learning step is claimed functionally by claiming the result of generating a label based on the volume without reciting a specific, unconventional technical implementation of the machine learning architecture itself.
Regarding the rejection under 35 U.S.C. § 103 of Claims 1, 3-9 and 11-21, Applicant’s arguments have been fully considered and are persuasive. The rejection has been withdrawn.
Conclusion
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/A.A./Examiner, Art Unit 3686
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681