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 .
Priority
Applicant claims priority to provisional U.S. Patent Application No. 63/188,730, filed 5/14/2021.
Information Disclosure Statement
The IDSs submitted on 8/30/2022 and 1/19/2023 were previously considered.
Status of Claims
Applicant’s amended claims, filed 12/22/2025, have been entered. Claims 1, 3, 16-18, and 21 have been amended. Claim 22 is new. Claims 4-6 were previously canceled. Claims 1-3 and 7-22 are currently pending in this application and have been examined.
Potential Allowable Subject Matter
As noted in the previous office actions, claims 1-3 and 7-22 are novel in view of the prior art and would be allowable if rewritten to overcome the claim rejection(s) under 35 U.S.C. 101 set forth in this Office Action.
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 and 7-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) an abstract idea. This judicial exception is not integrated into a practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Under Step 1 of the Alice/Mayo test the claims are directed to statutory categories. Specifically, the system, as claimed in claims 1-3 and 7-22, are directed to a machine (see MPEP 2106.03).
Under Step 2A (prong 1), claim 1, taken as representative, recites at least the following limitations (emphasis added) that recite an abstract idea:
A system for providing subscription product recommendations, the system comprising:
grouping subscription product claims data into a set of utilization categories, wherein
the subscription product claims data represents a successive time period of two or more years, and
the utilization categories comprise one or more of a type of provider, a type of insurance product plan, or a type of claim,
using the grouped set of subscription product claims data, a first set of data models to identify cost-driving factors, wherein
each data model of the first set of data models is configured to analyze claims data related to a first year of the two or more years to determine at least one attribute of the claims data predictive of increased future health care costs for at least a next year of the two or more years,
identifying, by at least one data model of the first set of trained data models, a plurality of cost-driving factors impacting costs of a plurality of subscription products offered by a provider, wherein
the plurality of cost-driving factors correspond to attributes of the claims data in the first year of the two or more years that predict future costs from the claims data in at least one next year of the two or more years,
defining a plurality of cluster groupings using the plurality of cost-driving factors, each cluster grouping of the plurality of cluster groupings corresponding to a respective one or more member attributes associated with one or more cost-driving factors of the plurality of cost-driving factors, wherein
each respective cluster grouping of the plurality of cluster groupings comprises a respective projected cost associated with each respective subscription product of a plurality of subscription products, wherein the respective projected cost is based on cost data comprising actual costs incurred by a respective portion of a plurality of members belonging the respective cluster grouping in connection with each of the plurality of subscription products, and
the respective one or more member attributes comprise at least one demographic attribute or at least one medical history attribute,
using the plurality of cluster groupings, the grouped set of subscription product claims data, and a set of questionnaire response training data, a second set of data models to classify an individual into a respective cluster grouping of the plurality of cluster groupings based at least in part on questionnaire responses from the individual relevant to the plurality of cost-driving factors, wherein
the questionnaire response training data comprises at least one of
a) for at least a subset of the plurality of members represented by the grouped set of subscription product claims data, responses to a plurality of questionnaire questions corresponding to the plurality of cost-driving factors, or
b) for each respective member of at least a portion of the plurality of members, a set of items of claims data, each item of the set of items correlated to a respective questionnaire question of the plurality of questionnaire questions,
ingesting a plurality of product plan designs offered by the provider, wherein
each product plan design of the plurality of product plan designs comprises one or more coverage tiers and/or one or more pricing tiers of a subscription product plan of one or more subscription product plans offered by the provider,
applying each respective product plan design of the plurality of product plan designs to each respective cluster grouping of the plurality of cluster groupings to determine, based at least in part on a given coverage tier of the one or more coverage tiers and/or a given pricing tier of the one or more pricing tiers corresponding to the respective product plan design, at least one expected cost per combination of respective product plan design and respective cluster grouping, and
receiving, from a respective individual of the plurality of individuals, a request for subscription product recommendations offered by the provider, the request including responses to up to ten questions, each question of the up to ten questions associated with a respective factor of the plurality of cost-driving factors,
determining the request lacks a respective response to one or more questions of the up to ten questions,
to complete information required by at least one trained data model of the second set of data models, for each respective question of the one or more questions, estimating the response to the respective question based at least in part on similarities between claims data attributes corresponding to the cost driving factor associated with the respective question and member information of the respective individual, wherein the member information comprises demographic information,
identifying, by the at least one trained data model of the second set of data models based at least in part on the responses to the up to ten questions, an identified cluster grouping of the plurality of cluster groupings for the individual,
determining, in real-time based on the identified cluster grouping and the at least one expected cost per combination of product plan designs and cluster groupings, one or more subscription product recommendations for the individual, each subscription product recommendation corresponding to a recommended product plan design of the plurality of product plan designs and
causing presentation of, in real-time responsive to receiving the request, of a subscription product recommendation, the subscription product recommendation presenting the one or more subscription product recommendations for viewing or selection by the individual.
These limitations recite certain methods of organizing human activity, such as performing commercial interactions (see MPEP 2106.04(a)(2)(II)). Certain methods of organizing human activity are defined by MPEP 2106.04 as including “fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).” In this case, the abstract ideas recited in representative claim 1 are certain methods of organizing human activity because providing recommendations is a commercial or legal interaction because it is a advertising, marketing or sales activity, or business relations.
Additionally, the broadest reasonable interpretation of “grouping” information, “identifying” information, “defining” information, and “determining” information is that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion.
Thus, claim 1 recites an abstract idea.
Under Step 2A (prong 2), if it is determined that the claims recite a judicial exception, it is then necessary to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of that exception (see MPEP 2106.04). As stated in the MPEP, when “an additional element merely recites the words ‘apply it (or an equivalent) with the judicial exception, or merely uses a computer as a tool to perform an abstract idea,” the judicial exception has not been integrated into a practical application. In this case, representative claim 1 includes additional elements such as (additional elements are bolded):
A system for providing subscription product recommendations, the system comprising:
a pre-scoring platform comprising first software logic for executing on first processing circuitry and/or first hardware logic, the pre-scoring platform configured to perform first operations comprising
grouping subscription product claims data into a set of utilization categories, wherein
the subscription product claims data represents a successive time period of two or more years, and
the utilization categories comprise one or more of a type of provider, a type of insurance product plan, or a type of claim,
using the grouped set of subscription product claims data, training a first set of machine learning data models to identify cost-driving factors, wherein
each machine learning data model of the first set of machine learning data models is configured to analyze claims data related to a first year of the two or more years to determine at least one attribute of the claims data predictive of increased future health care costs for at least a next year of the two or more years,
identifying, by at least one machine learning data model of the first set of trained machine learning data models, a plurality of cost-driving factors impacting costs of a plurality of subscription products offered by a provider, wherein
the plurality of cost-driving factors correspond to attributes of the claims data in the first year of the two or more years that predict future costs from the claims data in at least one next year of the two or more years,
defining a plurality of cluster groupings using the plurality of cost-driving factors, each cluster grouping of the plurality of cluster groupings corresponding to a respective one or more member attributes associated with one or more cost-driving factors of the plurality of cost-driving factors, wherein
each respective cluster grouping of the plurality of cluster groupings comprises a respective projected cost associated with each respective subscription product of a plurality of subscription products, wherein the respective projected cost is based on cost data comprising actual costs incurred by a respective portion of a plurality of members belonging the respective cluster grouping in connection with each of the plurality of subscription products, and
the respective one or more member attributes comprise at least one demographic attribute or at least one medical history attribute,
using the plurality of cluster groupings, the grouped set of subscription product claims data, and a set of questionnaire response training data, training a second set of machine learning data models to classify an individual into a respective cluster grouping of the plurality of cluster groupings based at least in part on questionnaire responses from the individual relevant to the plurality of cost-driving factors, wherein
the questionnaire response training data comprises at least one of
a) for at least a subset of the plurality of members represented by the grouped set of subscription product claims data, responses to a plurality of questionnaire questions corresponding to the plurality of cost-driving factors, or
b) for each respective member of at least a portion of the plurality of members, a set of items of claims data, each item of the set of items correlated to a respective questionnaire question of the plurality of questionnaire questions,
ingesting a plurality of product plan designs offered by the provider, wherein
each product plan design of the plurality of product plan designs comprises one or more coverage tiers and/or one or more pricing tiers of a subscription product plan of one or more subscription product plans offered by the provider,
applying each respective product plan design of the plurality of product plan designs to each respective cluster grouping of the plurality of cluster groupings to determine, based at least in part on a given coverage tier of the one or more coverage tiers and/or a given pricing tier of the one or more pricing tiers corresponding to the respective product plan design, at least one expected cost per combination of respective product plan design and respective cluster grouping, and
an online processing platform comprising second software logic for executing on second processing circuitry and/or second hardware logic, the online processing platform configured to perform second operations comprising
receiving, from a remote computing device of a respective individual of the plurality of individuals via a network, a request for subscription product recommendations offered by the provider, the request including responses to up to ten questions, each question of the up to ten questions associated with a respective factor of the plurality of cost-driving factors,
determining the request lacks a respective response to one or more questions of the up to ten questions,
to complete information required by at least one trained machine learning data model of the second set of machine learning data models, for each respective question of the one or more questions, estimating the response to the respective question based at least in part on similarities between claims data attributes corresponding to the cost driving factor associated with the respective question and member information of the respective individual, wherein the member information comprises demographic information,
identifying, by the at least one trained machine learning data model of the second set of machine learning data models based at least in part on the responses to the up to ten questions, an identified cluster grouping of the plurality of cluster groupings for the individual,
determining, in real-time based on the identified cluster grouping and the at least one expected cost per combination of product plan designs and cluster groupings, one or more subscription product recommendations for the individual, each subscription product recommendation corresponding to a recommended product plan design of the plurality of product plan designs and
causing presentation of, in real-time responsive to receiving the request, of a subscription product recommendation user interface screen at the remote computing device, the subscription product recommendation user interface screen presenting the one or more subscription product recommendations for viewing or selection by the individual.
Although reciting these additional elements, taken alone or in combination these elements are not sufficient to integrate the abstract idea into a practical application. This is because the additional elements of claim 1 are recited at a high level of generality (i.e., as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform the abstract idea) (see Figs. 10-11; ¶¶0091-0119). The Examiner underscores that these limitations are being performed by a generic processor and merely confines the use of the abstract idea to a particular technological environment and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). The background also states that the generic processor performs these limitations at a high level of generality (see Figs. 10-11; ¶¶0091-0119) and machine learning is recited at a high level of generality (see ¶0021 [“use data science training techniques to generate predictive models”], ¶0027 [“model training engine 126 for training data models”], ¶¶0031-0040 [“a model training engine 126 that takes a set of claims data 140 for a population and trains machine learning data models”], ¶0045 [“used by model training engine 126 as feedback to re-train and update the first and second data models to use learned knowledge”]). This description demonstrates that these additional elements are merely generic devices such as a generic computer and generic machine learning. Further, the additional elements do no more than generally link the use of a judicial exception to a particular environment or field of use (such as the Internet or computing networks).
In addition to the above, the recited receiving and presenting steps (even assuming arguendo they do not form part of the abstract idea, which the Examiner does not acquiesce), are at best little more than extra-solution activity (e.g., data gathering, presentation of data) that contributes nominally or insignificantly to the execution of the claimed system (see MPEP 2106.05(g)).
In view of the above, under Step 2A (prong 2), claim 1 does not integrate the recited exception into a practical application.
Under Step 2B, examiners should evaluate additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Returning to representative claim 1, taken individually or as a whole the additional elements of claim 1 do not provide an inventive concept (i.e. they do not amount to “significantly more” than the exception itself). As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment.
Furthermore, the additional elements fail to provide significantly more also because the claim simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. For example, the additional elements of claim 1 utilize operations the courts have held to be well-understood, routine, and conventional (see: MPEP 2106.05(d)(II)), including at least:
receiving or transmitting data over a network,
storing or retrieving information from memory,
presenting offers
Even considered as an ordered combination (as a whole), the additional elements of claim 1 do not add anything further than when they are considered individually.
In view of the above, representative claim 1 does not provide an inventive concept (“significantly more”) under Step 2B, and is therefore ineligible for patenting.
Dependent claim(s) 2, 3, and 7-22, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because they do not add “significantly more” to the abstract idea. More specifically, dependent claims 2, 3, and 7-22 merely further define the abstract limitations of claim 1 or provide further embellishments of the limitations recited in independent claim 1. Dependent claims 2, 3, and 7-22 do not introduce any further additional elements.
Thus, dependent claims 2, 3, and 7-22 are ineligible.
Response to Arguments
Applicant’s arguments, on pages 9-12 of the Remarks filed 12/22/2025, with respect to the previous 35 USC §101 rejections have been fully considered but they are not persuasive. Applicant argues the amended claims recite patent-eligible subject matter. Examiner respectfully disagrees.
Examiner notes that the Applicant’s argument the that combination of features can “predict which cluster a member requesting a recommendation belongs to based on [limited] questionnaire responses” is an improvement to the abstract idea and does not recite a technical improvement.
While the Examiner agrees that the amended limitations including training and applying “machine learning models” using “a pre-scoring platform” and further including an “online processing platform” do not fall within the abstract idea, the Examiner disagrees that these elements impose meaningful limits on the judicial exception. As claimed, these elements represent the mere use of generic computing components to facility the abstract idea. Notably, the specification provides only a high level description of generic processors performing these limitations at a high level of generality (see Figs. 10-11; ¶¶0091-0119) and machine learning is recited at a high level of generality (see ¶0021 [“use data science training techniques to generate predictive models”], ¶0027 [“model training engine 126 for training data models”], ¶¶0031-0040 [“a model training engine 126 that takes a set of claims data 140 for a population and trains machine learning data models”], ¶0045 [“used by model training engine 126 as feedback to re-train and update the first and second data models to use learned knowledge”]). This description demonstrates that these additional elements are merely generic devices such as a generic computer and generic machine learning. Further, the additional elements do no more than generally link the use of a judicial exception to a particular environment or field of use (such as the Internet or computing networks).
Additionally, while the applicant argus on page 11 that the operations “backfill missing question responses with estimated responses based on claims data attributes that share similarities with member attributes,” again Examiner notes, as claimed, this limitation is directed towards the abstract idea and is an improvement to the abstract idea and not a technological improvement. “Backfilling” information, as claimed and argued in the instant application, is not technical as it could be merely providing an “answer” to the questionnaire question using a questionnaire answer provided by a similar user. The claims and Specification are merely a broad assertion of a technical solution without providing a technical explanation as to how the solution is accomplished (see paragraph [0038]).
If it is asserted that the invention improves upon conventional function of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Although the specification need not explicitly set forth the improvement, it must describe the invention such that eh improvement would be apparent to one of ordinary sill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology (see MPEP 2106.05(a); MPEP 2106.04(d)(1)).
Applicant’s specification does not provide the requisite detail necessary such that one of ordinary skill in the art could recognize the claimed invention as providing an improvement. Applicant’s specification does not provide sufficient detail with respect to either an improvement to training and applying machine learning models or to “backfilling” information, and is specific only in their use in facilitating the abstract idea of providing recommendations.
The manner in which the currently pending claims are written is akin to ineligible decisions such as Affinity Labs of Texas v. DirecTV, LLC (Fed. Cir. 2016) (the court relied on the specification’s failure to provide details regarding the manner in which the invention accomplished the alleged improvement when holding the claimed methods of delivering broadcast content to cellphones ineligible), or, Internet Patents Corp. v. Active Network, Inc. (Fed. Cir. 2015) (claims contained no restriction on the manner in which the additional elements perform these claimed functions). While the Specification recites in paragraph [0038] that
“the questionnaire management engine 130 can be configured to backfill missing question responses with estimated responses based on claims data attributes that share similarities with member attributes (e.g., demographic information, medical attributes, risk preferences, other questionnaire responses). This solution provides a technical solution to the technical problem of improving processing efficiency by minimizing the number of models that have to be trained by automatically inferring question responses based on pattern recognition of other similar attributes”,
Examiner notes the alleged improvement by Applicant is at best a bare assertion of an improvement sans sufficient detail to demonstrate that Applicant has provided the alleged improvement to the technical field.
While Applicant argues the instant claims are similar to the eligible claims in McRO, Examiner respectfully disagrees. In McRO, the claimed improvement, as confirmed by the originally filed specification, was “…allowing computers to produce ‘accurate and realistic lip synchronization and facial expressions in animated characters…” and it was “…the incorporation of the claimed rules, not the use of the computer, that “improved [the] existing technological process” by allowing the automation of further tasks.” McRO, Inc. v. Bandai Namco Games America Inc., 837 F3d 1299, (Fed. Cir. 2016). Backfilling information such as providing answers to a question based on answers that a similar user provided does not improve a technological process though automation as it does not recite any technical improvement.
There is no indication from either the claims or the specification that the invention seeks to modify conventional operation of any such technology as that in McRO. Here again, the Examiner emphasizes the failure of the disclosure to set forth or describe the amended features, or any improvements that are achieved from or made relative to another technology or technical field. Contrary to Applicant’s assertion, the improvements manifested by the claimed invention are improvements to the abstract idea itself, not the computer or another technology or technical field.
The character of the claims as a whole is not directed to improving computer performance and do not recite any such benefit. The claims of the instant application, however, merely represent the use of generic computing technology used as a tool to perform the abstract idea in an online environment. The manner in which the currently pending claims are written is much more akin to the myriad of ineligible court decisions that employed generic computer components at a high-level to achieve improvements in commercial processes, rather than McRO.
In review of the claimed invention, and in consideration of the specification as originally filed, the Examiner asserts that:
(i) the claimed invention does not reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, but instead improves an abstract, commercial process, and,
(ii) the specification, as originally filed, does not provide sufficient discloser or technical explanation such that one of ordinary skill in the art would have determined that the disclosed invention provided an improvement to the functioning of a computer or another technology or technical field.
The problem of “predicting which cluster a member requesting recommendation belongs based on [limited] questionnaire responses” and “backfilling missing question responses with estimated responses based on claims data attributes that share similarities with member attributes” (as argued on page 11 of the Remarks) is one that arises squarely in the commercial realm, and does not rise to improving the functioning of the computer or another technology or technical field. As understood from the specification, the intention of Applicant’s invention is to provide “subscription product recommendations” (¶0003). The improvement manifested by the claimed invention is an improvement to the abstract idea itself, rather than the functioning of the computer or another technology or technical field, and is achieved leveraging generic computing hardware and software set forth at a high level of generality.
Even assuming a relationship of the claimed invention to another technology or technical field, if it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological process (such as that argued), a technical explanation as to how to implement the invention should be present in the specification (of which Examiner notes paragraph [0038] of the Specification is merely a broad assertion of a technical solution without providing a technical explanation as to how the solution is accomplished). That is, the disclosure most provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement (see MPEP 2106.05(a)). Even when a specification explicitly asserts an improvement, examiner should not determine a claim improves technology when only a bare assertion of an improvement is present without the detail necessary to be apparent to a person of ordinary skill in the art (see MPEP 2106.04(d)(1)).
Further, the instant claims are not directed to improving “the existing technological process” requiring the generic components to operate in an unconventional manner to achieve an improvement in computer functionality or requiring the non-conventional and non-generic arrangement of known, conventional pieces to improve a technical process. As currently recited, the instant claims are directed to improving the business task of providing recommendations (i.e., the abstract idea).
Therefore, the instant claims are unlike the claims in McRO and the Examiner maintains the claims do not recite additional elements that integrate the judicial exception into a practical application of that exception and maintains the rejection Step 2A, Prong Two.
Accordingly, the Examiner maintains the 101 rejection of the claims.
Examiner’s Comment
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Reference A of the Notice of References Cited Krughoff et al. (US 2016/0314521 A1) discloses a tool for comparing different health insurance plans based on a user selected options and similar households based on user characteristics which are matched with profiles of similar individuals/families.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LINDSEY B SMITH whose telephone number is (571)272-0519. The examiner can normally be reached Monday - Friday 9-6 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeff Smith can be reached at 571-272-6763. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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LINDSEY B. SMITH
Examiner
Art Unit 3688
/LINDSEY B SMITH/Examiner, Art Unit 3688
/Jeffrey A. Smith/Supervisory Patent Examiner, Art Unit 3688