`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
The following is in response to the amendments and arguments filed 5/11/26. Claims 1-2, 10-17 and 21-25 are pending. Claims 3-9 and 18-20 are canceled.
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, 10-17 and 21-25 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, 10-17 and 21-25 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 a convolutional neural network ) 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, 10-15, 17 and 21-25, further define the abstract idea that is present in their respective independent claims 1 and 16. The dependent claims 2, 10-15 and 17, 21-25 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, 10-17 and 21-25 are not patent-eligible.
Claim Rejections - 35 USC § 103
Applicant’s amendments and arguments (rem 7-8) overcome the prior rejections under 35 USC 103.
Specifically, "extracting, by a convolutional neural network executed as part of the analysis engine, visual features from one or more photographs or scans associated with the new insurance claim" and that the analysis engine analyzes the normalized data "and the extracted visual features" using the supervised machine learning model. Battcher teaches scoring and subrogation estimation but does not teach CNN-based visual feature extraction from photographs. Stanevich teaches normalization and modular ML components but does not teach a CNN that processes photographs or scans to extract visual features for integration with normalized claim data. Eichler teaches offer generation but has no disclosure of visual feature extraction. No cited reference teaches extracting visual features from claim photographs using a CNN and combining those features with normalized data in a supervised model for subrogation analysis.
Further, "the supervised machine learning model is periodically retrained using newly closed claims having ground-truth subrogation outcomes, and the analysis engine deploys a retrained model only after validation against a hold-out set measured by calibration and discrimination metrics." Stanevich does not teach deploying a retrained model only after validation against a hold-out set measured by calibration and discrimination metrics. The hold-out validation and calibration/discrimination metric requirements are specific technical safeguards that distinguish over Stanevich's generic update disclosure. The combination of CNN visual feature extraction with holdout-validated retraining in a modular subrogation pipeline is not taught or suggested by any cited reference.
Response to Arguments
Applicant’s arguments, see rem 7-8, filed 5/11/2026, with respect to 35 USC 103 have been fully considered and are persuasive. The 35 USC 103 rejection of claims 1-2, 10-17 (and new claims 21-25) has been withdrawn.
Applicant's arguments filed 5/11/2026 have been fully considered but they are not persuasive regarding 35 USC 101.
Regarding applicant’s Step 2a Prong 1 the claims are not a mental process, Examiner notes the claims were analyzed in step 2a prong 1 and found to fall within the abstract grouping of certain method of organizing human activity, specifically, the subgrouping of fundamental economic practice of a series of steps instructing how to determine an insurance claim offer.
Regarding Step 2a Prong 2, applicant argues specific technical elements that define how the system process claim data and specifies the architecture (SNN and supervised model), Examiner respectfully disagrees.
With regards to applicant’s argument directed to correlations with the Enfish court decision, Examiner disagrees. Turning to Alice step one, "[w]e must first determine whether the claims at issue are directed to a patent- ineligible concept," such as an abstract idea. See Alice, 134 S. Ct. at 2355. "At step one of the Alice framework, it is often useful to determine the breadth of the claims in order to determine whether the claims extend to cover a 'fundamental practice long prevalent in our system . . . ."' Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1369 (Fed. Cir. 2015) (quoting- Alice, 134 S. Ct. at 2356). But in determining whether the claims are directed to an abstract idea, we must be careful to avoid oversimplifying the claims because "[a]t some level, 'all inventions . . . embody, use, reflect, rest upon, or apply laws of nature, natural phenomena , or abstract ideas,"' Alice, 134 S. Ct. at 2354 (quoting Mayo, 132 S. Ct. at 1293). Cf. Diamond v. Diehr, 450 U.S. 175, 189 n.12 (1981) (cautioning that overgeneralizing claims, "if carried to its extreme, make[s] all inventions un-patentable because all inventions can be reduced to underlying principles of nature which, once known, make their implementation obvious."). However, not every claim that recites concrete, tangible components escapes the reach of the abstract-idea inquiry. See, e.g., Alice, 134 S. Ct. at 2360 (claims that recite general-purpose computer components are nevertheless "directed to" an abstract idea); Content Extraction, 776 F.3d at 1347 (claims reciting a "scanner" are nevertheless directed to an abstract idea); Mortg. Grader, Inc. v. First Choice Loan Serv. Inc., 811 F.3d 1314, 1324-25 (Fed. Cir. 2016) (claims reciting an "interface," "network," and a "database" are nevertheless directed to an abstract idea). The courts recently clarified that a relevant inquiry at step one is "to ask whether the claims are ·directed to an improvement to computer functionality versus being directed to an abstract .idea." See Enfish, LLC v. Microsoft Corp., No. 2015-2044, slip op. at *11 (Fed. Cir. May 12, 2016). The court contrasted claims "directed to an improvement in the functioning of a computer" with claims "simply adding conventional computer components to well-known business practices," or claims reciting "use of an abstract mathematical formula on any general purpose computer," or "a purely conventional computer implementation of a mathematical formula," or "generalized steps to be per- formed on a computer using conventional computer activity." Id . at *16-17. Contrary to arguments, the claims here are not directed to a specific improvement to computer functionality. Rather, they are directed to the use of conventional or generic technology in a nascent but well-known environment, without any claim that the invention reflects an inventive solution to any problem presented by combining the two.
Further, the Enfish case relates to a specific type of data structure designed to improve the way a computer stores and retrieves data in memory. The claims in Enfish are directed to improving computer functionality (not directed to an abstract idea). In other words, the computers of Enfish are not used as a tool to implement the abstract idea and instead the focus of the claims are on the specific asserted improvement in computer capabilities (i.e. self-referential table for a computer database).
The recitation of retraining machine learning model merely indicates a field of use or technological environment in which the judicial exception is performed. Although these additional elements limit the identified judicial exception, which involves generating a training machine learning model using visual data to generate an offer, this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning and/or neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore does not integrate the abstract idea into a practical application (see MPEP 2106.05(g)).
Examiner notes that the recitation of “extracting” with a convolutional neural network and combining in a supervised machine learning model (claims 1 and 16) provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. That the model is machine learned is used to generally apply the abstract idea without placing any limits on how the machine learned model functions. Rather, these limitations only recite the outcome of analyzing using a supervised machine learning model to extract features and associated weights and do not include any details about how the “analyzing” is accomplished. See MPEP 2106.05(f) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis.
Regarding Step 2B, argues the combination is not well understood, routine and conventional and cites to Berkheimer. Examiner respectfully disagrees. Examiner has not based the eligibility rejection on well understood, routine and conventional. As such, the arguments with respect to Berkheimer are moot.
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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Schitt et al. disclose raw sensor data from a device of a user is collected. Trips from the sensor data are identified as raw trip data. Metadata for the trips is identified, linked to the trips, and maintained separately from the trip data. The trips are staged, and the corresponding trip data normalized when obtained from a storage or memory location. Normalized trip data is piped or made accessible to feature enhancing applications (apps), each app associating one or more features and events with a given trip. The features and events are maintained for the trips in event and feature level of detail tables.
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 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
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