Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Status of Claims
The following is in response to the amendments and arguments filed 11/24/25 and entered with the RCE filed 12/17/2025. Claims 1-2, 9-17 are pending. Claims 3-8 and 18-20 are canceled.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/24/25 has been entered.
Claim Objection
The amendments to claim 1 have overcome the prior Objection.
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 1-2 and 9-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite an abstract idea. This is a judicial exception without significantly more. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claims 1-2 and 9-17 are directed to a system, method, or product, which are/is one of the statutory categories of invention.
The Examiner has identified independent method Claim 1 as the claim that represents the claimed invention for analysis and is similar to independent system Claim 16.
The claims recite the steps of
normalizing…data of a new insurance claim into a deterministic schema to create normalized data;
analyzing.,.,. the normalized data …to extract features and associated weights;
applying…, the weights to the extracted features of the new claim to generate a subrogation success probability;
determining…an expected subrogation recovery by combining (i) a recovery amount estimated from the normalized data and (ii) the subrogation success probability to produce a subrogation estimate;
and generating… a firm offer for the claim based on the subrogation estimate using a deterministic offer function that enforces a minimum margin parameter.
Under Step 2A Prong 1, the claim as a whole recites the series of steps instructing how to determine an insurance claim offer, which is a fundamental economic practice and thus falls within the abstract grouping of certain method of organizing human activity. Thus, the claim recites an abstract idea.
Under Step 2A prong 2, this judicial exception is not integrated into a practical application. The claim as a whole merely describes how to generally “apply” the concept of how to determine an insurance claim offer in a computer environment. The claimed computer components (a processor, a modular pipeline, a normalization engine, an analysis engine, a value engine, and an offer engine; trained supervised machine learning model) are recited at a high level of generality and are merely invoked as tools to perform an existing economic process. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Accordingly, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A prong 2, the claim describes how to generally “apply” the concept of how to determine an insurance claim offer in a computer environment. Thus, even when viewed separately and as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. The claim is ineligible.
The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Dependent claims, 2, 9-15 and 17, further define the abstract idea that is present in their respective independent claims 1 and 16. The dependent claims 2, 9-15 and 17 are abstract for the reasons presented above because there are no additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered as a whole, individually and as an ordered combination. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". Thus, the claims 1-2 and 9-17 are not patent-eligible.
Claim Rejections - 35 USC § 103 (Post-AIA )
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 9-13, 16 are rejected under 35 U.S.C. 103 as being unpatentable over US20150095071, Battcher et al, in view of US20220411495, Stanevich et al, and further in view of US9972020, Eichler et al.
As per claim(s) 1 and 16, Battcher teaches:
applying weights determined by a machine learning algorithm in the analysis engine that has analyzed past insurance claims to the features of the new claim (At least paragraph(s) 8, (score);
determining a subrogation estimate for the new claim in a value engine (At least paragraph(s) 66).
Battcher does not specifically disclose normalizing the data on the insurance claim using a normalization engine to create normalized data; however, this limitation is taught by Stanevich in at least paragraph 54. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Stanevich with those of Battcher since normalization allows for easier comparison of variables.
Stanevich then teaches:
utilizing a modular architecture comprising a normalization engine, analysis engine, value engine, and offer engine, wherein the analysis engine applies machine learning to extract features and weights from historical claims (At least paragraph(s) 5-23 and abstract);
analyzing a new claim to determine the features determined by a machine learning algorithm in an analysis engine that has analyzed past insurance claim (Abstract).
Stanevich nor Battcher specifically disclose using an offer engine to determine an offer for the claim based on the subrogation value; however, this step may be taught by Eichler in at least column 1, lines 54-67. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Eichler with those of Stanevich and Battcher since an offer engine can provide analytics, allowing the insurance company to make better decisions.
As to claim(s) 9, Stanevich recites;
The machine learning model is periodically retrained using newly closed claims having ground-truth subrogation outcomes, and the analysis engine deploys the updated model only after validation against a hold-out set measured by calibration and discrimination metrics. (At least paragraph(s) 38).
With respect to claim(s) 10, Stanevich discuses:
the claims have types selected from home, vehicle, casualty, liability, health, pet, disability, business interruption, professional liability, flood, commercial, umbrella claims, and travel insurance claims (At least paragraph(s) 37).
In reference to claim(s) 11, Stanevich disclose:
The learning model is specialized per each claim type (At least abstract).
Regarding claim(s) 12, Stanevich describes:
modular pipeline processes the claim in real time (At least paragraph(s) 55).
Concerning claim(s) 13, Eichler addresses the offer engine is specific to the claim holding company (At least column 3: lines 52-62). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Eichler with those of Stanevich and Battcher since an offer engine can provide analytics, allowing the insurance company to make better decisions.
Claims 2, 14, 15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over US20150095071, Battcher et al, US20220411495, Stanevich et al, and US9972020, Eichler et al as applied above, further in view of : US20070288273, Rojewski et al.
As per claim(s) 2 and 17, aggregating a plurality of new claims into a claim package; Rojewski in at least paragraph 6, claim 1 and abstract. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Rojewski with those of Battcher et al, Stanevich et al, and Eichler et al since this would provide consistency in analysis and the avoidance of duplication.
Rojewski also recites:
Generating by an offer engine package level offer based on the summed subrogation estimate (At least paragraph(s) 20:
Battcher teaches summing the determined subrogation estimate across the claims in the package (At least abstract).
As to claim(s) 14, Rojewski recites:
the value engine analyzes a possible subrogation recovery and a subrogation success probability from the analysis engine to determine the subrogation estimate (At least abstract).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Rojewski with those of Battcher et al, Stanevich et al, and Eichler et al since this would provide consistency in analysis and the avoidance of duplication.
With respect to claim(s) 15, Rojewski discuses:
the offer engine uses a machine learning algorithm to analyze past accepted offers to determine a minimum margin parameter used by the deterministic offer function. (At least paragraph(s) 17).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Rojewski with those of Battcher et al, Stanevich et al, and Eichler et al since this would provide consistency in analysis and the avoidance of duplication.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 2, 9-17 have been considered but are moot because the new ground of rejection necessitated by applicant' s amendments.
Applicant states that the claims are directed to a technical solution for automating subrogation valuation using machine-learning. Remarks at 9. Examiner respectfully does not agree with applicant that the invention solves a technical problem addresses a technical challenge in data heterogeneity, latency and predictive accuracy. Applicant argues that the technical solution is achieved by providing machine learning driven valuation. (Rem 9) . The claims are not directed to a technical problem but rather a business problem or insurance problem which is solved by machine learning. The Specification discloses that the operation of the steps of the claim can be performed by computer program instructions provided to a general purpose computer (para 65). As such, the Specification does not disclose an improvement to these computer components or machine learning themselves. The improvement touted by applicant is an improvement to the field of insurance claims subrogation rather than an improvement to the computer components used to make the valuation. The computer components including the machine learning are not improved. And “[n]o matter how much of an advance in the . . . field the claims recite, the advance lies entirely in the realm of abstract ideas, with no plausibly alleged innovation in the non-abstract application realm.” SAP Am., 898 F.3d at 1163. Although applicant argues that the invention’s ability to make subrogation valuation efficient and accurate is a technical improvement, there is no evidence that this ability is a result of improvement to the computer and the machine learning or any other computer component.
Mere automation of manual processes or increasing the speed of a process where these purported improvements come solely from the capabilities of a general-purpose computer are not sufficient to show an improvement in computer-functionality. See FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1095 (Fed. Cir. 2016).
As to machine-learning, the limitations of using machine learning are not technological steps, but a recitation of some generic tool to be used, which is a conventional operation. More to the point, the limitation does not recite how such machine learning is technologically used to learn, or how the recited machine learning is implemented. The claims do not recite any particular manner of training or of the underlying technological machine learning model implementation details.
Training a model per se is setting parameters for the model, and setting parameters for models is both generic and conventional. As to reciting machine learning, learning is the most important function the human mind performs, and all of the operations in the claims are analogs of what is performed in the human mind. Any training of a hypothetical model is conceptual at best. There is nothing real world about such conceptual setting of model parameters.
Concerning improving accuracy and efficiency, the Examiner finds that this resultant increase in accuracy/efficiency comes from general purpose hardware, not the instant claims themselves. FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1095 (Fed. Cir. 2016) ("While the claimed system and method certainly purport to accelerate the process of analyzing audit log data, the speed increase comes from the capabilities of a general-purpose computer, rather than the patented method itself."); OIP Tech., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015) ("relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible").
With respect to the art rejection, the Examiner has found disclosures in Stanevich which address Applicant’s amendatory language.
Accordingly, for reasons of record and as set forth above, the examiner maintains the rejection of the claims as being directed to a judicial exception without significantly more, and thereby being directed to non-statutory subject matter under 35 USC §101.
Conclusion
Additional prior art made of record and not relied upon that is considered pertinent to Applicant’s disclosure can be found on the attached PTO-892.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Kelly Campen whose telephone number is (571)272-6740. The examiner can normally be reached Monday-Thursday 6am-3pm.
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Kelly S. Campen
Primary Examiner
Art Unit 3691
/KELLY S. CAMPEN/ Primary Examiner, Art Unit 3691