DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 7/8/2026 has been entered.
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.
Interview
Examiner invites the representative of this application to contact the Examiner to schedule an interview to expedite prosecution of this application.
Status of Claims
Applicant’s amended claims, filed 7/8/2026, have been entered. Claims 1, 8-10, 14, and 16-22 have been amended. 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 the claim objections and the 35 U.S.C. 101 set forth in this Office Action.
Claim Objections
Claims 1-3 and 7-22 are objected to because of the following informalities:
Claim 1 recites “receiving, from a remote computing device of via a network….” in lines 25-26 of page 4 and should recite “receiving, from a remote computing device [[of]] via a network….”
Claims 2, 3, and 7-22 inherit the objections of claim 1.
Appropriate correction is required.
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:
preparing first training data for a first set of models by automatically 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 of medical claims from a plurality of members, the plurality of members being identified using a plurality of demographic attributes and a plurality of medical history attributes, and
the set of utilization categories comprises one or more of a type of provider, a type of insurance product plan, or a type of claim,
using the first training data, the 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 data models, a plurality of cost-driving factors impacting future healthcare costs of subscribers using 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,
preparing second training data by
a) automatically 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
the respective one or more member attributes comprise at least one demographic attribute or at least one medical history attribute, and
b) collecting a set of questionnaire response training data comprising at least one of
i) for at least a subset of the plurality of members represented by the first training data, responses to a plurality of questionnaire questions corresponding to the plurality of cost-driving factors, or
ii) 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 of the plurality of questionnaire questions,
using the subscription product claims data, calculating for each respective cluster grouping of the plurality of cluster groupings, a plurality of projected costs comprising, for each respective subscription product of a plurality of subscription products, a respective projected cost, wherein
the respective projected cost is based on cost data comprising actual costs incurred by a respective portion of the plurality of members belonging the respective cluster grouping in connection with each of the plurality of subscription products,
using the second training data and the first 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,
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, and
applying each respective product plan design of the plurality of product plan designs to the plurality of projected costs of 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, a request for subscription product recommendations offered by the provider, the request including responses to up to ten questions presented to an individual, 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 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 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.
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
preparing first training data for training a first set of machine learning data models by automatically 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 of medical claims from a plurality of members, the plurality of members being identified using a plurality of demographic attributes and a plurality of medical history attributes, and
the set of utilization categories comprises one or more of a type of provider, a type of insurance product plan, or a type of claim,
using the first training data, training the 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 machine learning data models, a plurality of cost-driving factors impacting future healthcare costs of subscribers using 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,
preparing second training data by
a) automatically 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
the respective one or more member attributes comprise at least one demographic attribute or at least one medical history attribute, and
b) collecting a set of questionnaire response training data comprising at least one of
i) for at least a subset of the plurality of members represented by the first training data, responses to a plurality of questionnaire questions corresponding to the plurality of cost-driving factors, or
ii) 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 of the plurality of questionnaire questions,
using the subscription product claims data, calculating for each respective cluster grouping of the plurality of cluster groupings, a plurality of projected costs comprising, for each respective subscription product of a plurality of subscription products, a respective projected cost, wherein
the respective projected cost is based on cost data comprising actual costs incurred by a respective portion of the plurality of members belonging the respective cluster grouping in connection with each of the plurality of subscription products,
using the second training data and the first 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,
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, and
applying each respective product plan design of the plurality of product plan designs to the plurality of projected costs of 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 via a network, a request for subscription product recommendations offered by the provider, the request including responses to up to ten questions presented to an individual, 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.
Regarding claims 2, 3, and 7-22
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 claim(s) 2, 3, and 7-22 merely further define the abstract limitations of claim(s) 1 or provide further embellishments of the limitations recited in independent claim claim(s) 1.
Claims 2, 3, and 7-22 set forth:
wherein the plurality of cost-driving factors comprise one or more factors associated with health characteristics, chronic illness, prescription medication use, family planning, in-patient hospitalization, and/or medical treatment.
wherein the respective one or more member attributes of each cluster grouping of at least one cluster grouping comprise one or more attributes based on at least one of claims data or risk preferences.
wherein determining the one or more subscription product recommendations comprises :identifying one or more subscription products based at least in part on scoring each subscription of the plurality of subscription products for each respective cluster grouping of the plurality of cluster groupings, wherein each subscription of the one or more subscription product recommendations has a favorable score for the cluster grouping, and the scoring is based at least in part on the respective projected cost of the respective cluster grouping, wherein lower expected out of pocket costs of a respective subscription to members of a respective cluster grouping corresponds to a favorable score.
wherein to calculate the plurality of projected costs comprises calculating the plurality of projected costs based further in part on a set of actuarial value data.
wherein determining the one or more subscription product recommendations comprises identifying one or more subscription products based at least in part on scoring each subscription of the plurality of subscription products for each cluster grouping of the plurality of cluster groupings, wherein each subscription of the one or more subscription product recommendations has a favorable score for the cluster grouping.
wherein the scoring is based at least in part on the respective one or more member attributes of each cluster grouping of the plurality of cluster groupings.
wherein the scoring is based at least in part on the respective projected cost of each cluster grouping.
wherein the respective projected cost includes expected out of pocket costs, wherein lower expected out of pocket costs of a respective subscription to members of a respective cluster grouping corresponds to a favorable score.
wherein determining the one or more subscription product recommendations is based at least in part on an economic equivalent score for each respective subscription product of the plurality of subscription products, wherein the economic equivalent score is based on the respective projected cost for the respective subscription product and one or more adjustment factors indicating an impact of one or more qualitative factors on subscription product selection choices made by the individual.
wherein determining the economic equivalent score comprises identifying, by the second set of machine learning data models, the respective projected cost of the identified cluster grouping, wherein the second set of machine learning data models are further trained with a set of cost data comprising actual costs incurred by members of each cluster grouping in connection with each of the plurality of subscription products.
wherein the respective projected cost includes projected out of pocket costs, wherein lower projected out of pocket costs of a respective subscription to members of a respective cluster grouping corresponds to a favorable economic equivalent score.
wherein the second operations comprise determining the respective projected cost for each respective cluster grouping by predicting, using the second set of machine learning data models, the respective projected cost based at least in part on claims data for members of the respective cluster grouping associated with each subscription.
wherein the second operations comprise determining the respective projected cost for each respective cluster grouping by predicting, using the second set of machine learning data models, the respective projected cost based at least in part on a set of actuarial value data.
wherein the second operations comprise: determining at least a portion of the one or more adjustment factors based on one or more behavior related factors of the individual; and calculating the economic equivalent score using the respective projected cost for the respective subscription product and the one or more adjustment factors.
wherein the one or more behavior related factors comprise one or more of plan-design preferences, risk tolerance, referral procedures, cover of supplemental care desire to purchase additional coverage, desire of having doctors in- network, or willingness to pay for higher Centers for Medicare and Medicaid Services star rating.
wherein: each of the one or more cost-driving factors is applied a corresponding weighting factor in the first set of machine learning data models; and the first operations comprise converting, in real-time upon receipt, claims data generated from a claim submitted under a recommended subscription product into training data, and processing the training data in the first set of machine learning data models to validate the corresponding weighting factor of at least one of the one or more cost-driving factors.
wherein the second operations comprise predicting, by the second set of machine learning data models, the respective projected cost for each respective cluster grouping of the plurality of cluster groupings based at least in part on claims data for members of the respective cluster grouping associated with each subscription.
wherein to present the one or more subscription product recommendations comprises presenting, related to at least one subscription product recommendation of the one or more subscription product recommendations, a corresponding rationale for recommending the at least one subscription product recommendation.
Such recitations merely embellish the abstract idea of providing recommendations. The claims do not set forth any further additional limitations, and therefore such abstract embellishments are applied to the additional limitations recited in claim(s) 1, which do no more than generally link the use of the abstract idea to a particular technological environment, do not integrate the abstract idea into a practical application, and do not provide an inventive concept. Accordingly, the claims do not confer eligibility on the claimed invention and is ineligible for similar reasons to claim(s) 1.
Thus, dependent 2, 3, and 7-22 are ineligible.
Response to Arguments
Applicant’s arguments, on pages 10-12 of the Remarks filed 7/8/2026, 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.
Applicants arguments on pages 10-11 regarding models using “predictive models that can be used to make recommendations to members for plan selection based on projected costs”, “to predict which cluster a member requestion a recommendation belongs to based on response to a member questionnaire that includes questions directed to one or more cost driving factors”, and “determine expected costs by the member and cluster for each plan design offered by the provide” are directed to the improvement to the abstract idea and not a technical improvement.
Examiner notes that the Applicant’s argument on page 10 the that pre-scoring platform “operates in an off-line environment” is not actually claimed. While the claims recite “a pre-scoring platform” and an “online processing platform”, there is no actual limitation within the claims that the pre-scoring platform is operating in “an off-line environment”.
Examiner additionally notes that 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 the 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)).
Accordingly, the arguments directed to performing certain limitations “off-line” vs. “online” as the basis for the technical improvement are not persuasive.
On page 11 of the Remarks, Applicant argues “the online processing platform, as claimed, is configured to perform backfill estimation adjustments when one or more questionnaire questions have been suppressed by a provider.” Similarly, this too is not actually claimed. While paragraph [0038] of the Specification discloses “in order to account for question suppression without having to train additional models (which increases processing times and adds complexity to the subscription product recommendation process), 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” (emphasis added). The claims, as currently recited, estimate responses for all of the questions of the one or more questions in response to a determination that the request lacks a respective response to one or more questions.
As the argued improvement is not claimed within the claims themselves, the arguments directed to “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” as the basis for the technical improvement are not persuasive (see MPEP 2106).
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.
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, a technical explanation as to how to implement the invention should be present in the specification. 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)).
Therefore, as currently claimed, 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.
Conclusion
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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LINDSEY B. SMITH
Examiner
Art Unit 3688
/LINDSEY B SMITH/ Examiner, Art Unit 3688
Prosecution Insights