Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
2. The Amendment filed June 25, 2026 has been entered. Claims 1-7, 9-15, and 17-20 are pending and are rejected for the reasons set forth below.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 1-7, 9-15, and 17-20 are rejected under 35 U.S.C. §101 because the claimed invention recites and is directed to a judicial exception to patentability (i.e., a law of nature, a natural phenomenon, or an abstract idea) and does not include an inventive concept that is “significantly more” than the judicial exception under the January 2019 and October 2019 patentable subject matter eligibility guidance (2019 PEG) analysis which follows.
Step 1
5. Under the 2019 PEG step 1 analysis, it must first be determined whether the claims are directed to one of the four statutory categories of invention (i.e., process, machine, manufacture, or composition of matter). Applying step 1 of the analysis for patentable subject matter to the claims, it is determined that the claims are directed to the statutory category of a process (claims 17-20), a machine (claims 1-7) and a manufacture (claims 9-15). Therefore, we proceed to step 2A, Prong 1.
Step 2A, Prong 1
6. Under the 2019 PEG step 2A, Prong 1 analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories of patent ineligible subject matter (i.e., organizing human activity, mathematical concepts, and mental processes) that amount to a judicial exception to patentability.
Claim 1 recites the abstract idea of:
accumulating a dataset comprising claim files that have been processed to completion;
determine a corpus of information corresponding to a claim event, the corpus of information including incident data relating to the claim event, the incident data being obtained from a first source; and
executing [[the trained machine learning model]] on the corpus of information to generate a first optimized reserve estimate for the claim event;
transmitting data corresponding to the first optimized reserve estimate to [[a computing device associated with a first user]];
subsequent to generating the first optimized reserve estimate, generating a second optimized reserve estimate that is more accurate than the first optimized reserve estimate by:
receiving additional incident data, including image data of at least one of damage or injury caused by the claim event;
based on the simulation, determine an accuracy of the additional incident data including determining whether the additional incident data is consistent with the simulation;
updating the corpus of information based at least in part on the additional incident data; and
executing [[the trained machine learning model]] on the updated corpus of information to generate the second optimized reserve estimate; and
transmitting data corresponding to the second optimized reserve estimate to [[the computing device associated with the first user]].
Here, the recited abstract idea falls within one or more of the three enumerated 2019 PEG categories of patent ineligible subject matter, to wit: certain methods of organizing human activity, which includes fundamental economic practices or principles and/or commercial interactions (e.g., insurance -- here, determining an optimal reserve amount corresponding to an insurance claim).
Step 2A, Prong 2
7. Under the 2019 PEG step 2A, Prong 2 analysis, the identified abstract idea to which claim 1 is directed does not include limitations or additional elements that integrate the abstract idea into a practical application.
Besides reciting the abstract idea, the limitations of claim 1 also recite generic computer components (e.g., a network communication interface, one or more processors, a memory storing instructions, a computing device associated with the first user, a physics engine, and a machine learning model). In particular, the recited features of the abstract idea are merely being applied on a computer or computing device or via software programming that is simply being used as a tool (“apply it”) to implement the abstract idea. (See e.g., MPEP §2106.05(f)).
Additionally, claim 1 recites the limitations, “training a machine learning model using the dataset to predict optimal reserve estimates for claim events,” and “tuning the machine learning model based at least in part on the second optimized reserve estimate.” However, merely applying a generic machine learning model to perform the abstract idea does not integrate the abstract idea into a practical application. Claim 1 does not provide significant technical detail regarding how the machine learning model is trained and/or how it functions to provide the desired output. Therefore, these limitations amounts to no more than simply applying a generic machine learning model to implement the abstract idea.
Additionally, claim 1 recites the limitation, “based at least in part on the additional incident information, including the image data, generating, using a physics engine, a simulation of the claim event, wherein generating the simulation includes adjusting the simulation, using the physics engine, such that the simulation matches the damage shown in the image data.” This limitation simply states that the system simulates the claim event using a physics engine. However, claim 1 does not provide significant technical detail regarding how the simulation is generated and/or presented to the user. Simply stating that the simulation is adjusted to match the data shown in the image data does not provide sufficient technical detail regarding how the simulation is generated and/or how the physics engine functions. Therefore, such limitations amount to no more than merely applying a generic physics engine to implement the abstract idea on a computer.
Therefore, these additional elements are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components. In other words, the additional elements are simply used as tools to perform the abstract idea.
Thus, claim 1 does not include any limitations or additional elements that integrate the abstract idea into a practical application. As a result, claim 1 is directed to an abstract idea.
Step 2B
8. Under the 2019 PEG step 2B analysis, the additional elements of claim 1 are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea. (i.e., an innovative concept). Here, the recited additional elements (e.g., a network communication interface, one or more processors, a memory storing instructions, a computing device associated with the first user, a physics engine, and a machine learning model), do not amount to an innovative concept since, as stated above in the Step 2A, Prong 2 analysis, the claims are simply using the additional elements as a tool to carry out the abstract idea (i.e., “apply it”) on a computer or computing device and/or via software programming (See e.g., MPEP §2106.05(f)). The additional elements are specified at a high level of generality such that they are being used in the claims to simply implement the abstract idea and are not themselves being technologically improved (See e.g., MPEP §2106.05 I.A.); (See also e.g., applicant’s Specification at least Paragraphs 43-47).
Thus, claim 1 does not recite any additional elements that amount to “significantly more” than the abstract idea.
Additional Independent Claims
9. Independent claims 9 and 17 are similarly rejected under 35 U.S.C. 101 for the reasons described below:
Claim 9 recites limitations that are substantially similar to those recited in claim 1. However, the primary difference between claims 9 and 1 is that claim 9 is drafted as a computer-readable medium rather than as a system. Similarly, as described above regarding claim 1, claim 9 recites generic computer components (e.g., a non-transitory computer readable medium storing instructions, one or more processors of a computing system, a computing device associated with the first user, a physics engine, and a machine learning model) that are simply being used as a tool (“apply it”) to implement the abstract idea. Therefore, since the same analysis should be used for claims 1 and 9, claim 9 is not patent eligible (See Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2354 (2014)).
Claim 17 recites limitations that are substantially similar to those recited in claim 1. However, the primary difference between claims 17 and 1 is that claim 17 is drafted as a method rather than as a system. Similarly, as described above regarding claim 1, claim 17 recites generic computer components (e.g., one or more processors, a computing device associated with the first user, a physics engine, and a machine learning model) that are simply being used as a tool (“apply it”) to implement the abstract idea. Therefore, since the same analysis should be used for claims 1 and 17, claim 17 is not patent eligible (See Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2354 (2014)).
Dependent Claims
10. Dependent claims 2-7, 10-15, and 18-20 are also rejected under 35 U.S.C. 101 for the reasons described below:
Claims 2-4, 10-12, and 18-20 simply provide further definition to the process of “training” the machine learning model recited in claims 1, 9, and 17. However, these claims do not provide significant technical detail regarding how the training of the model is performed. For example, claims 2, 10, and 18 state that the machine learning model is trained by “causing the machine learning model to compare reserve estimates versus total payouts for each claim file in the dataset.” However, simply stating the training process includes comparing reserve estimates and total payout amounts does not amount to a technical improvement to machine learning itself. Rather, this simply recites the performance of a basic data comparison process. Therefore, such limitations do not integrate the abstract idea into a practical application. A similar argument can be made for claims 3, 4, 11, 12, 19, and 20.
Claims 5 and 13 simply provide further definition to the “claim event” recited in claims 1 and 9. Simply stating that the claim event comprises a first notice of loss does not provide any indication of an improvement to any technology or technological field. Rather, this merely defines the type of claim event analyzed by the system.
Claims 6 and 14 simply provide further definition to the process of generating the simulation recited in claims 1 and 9. These claims state that the simulation is provide on an interface of a computing device of the user, and that the user may provide contextual input to refine the simulation. However, such limitations do not provide significant technical detail regrading how the simulation is generated and/or how the simulation is presented to the user via the user interface. Rather, such limitation amount to no more than applying a generic graphical user interface to output/display the simulation.
Claims 7 and 15 simply provide further definition to the “claim files and claim event” recited in claims 1 and 9. Simply stating that the claim files and claim event correspond to historical claims stemming from one of vehicle incidents or property damage events does not provide any indication of an improvement to any technology or technological field. Rather, this merely defines the type of claim files and claim event analyzed by the system.
Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Response to Arguments
11. Applicant’s arguments filed June 25, 2026 have been fully considered.
Arguments Regarding 35 U.S.C. 101
12. Applicant’s arguments (Amendment, pages 9-14) concerning the prior rejection of the claims under 35 USC §101, including supposed deficiencies in the rejection, are not persuasive for the following reasons. Under the prior and current 101 analysis under 2019 PEG, the amended claims recite and are directed to a patent ineligible abstract idea, without something significantly more, for the reasons given above after consideration of the claimed features and elements. The abstract idea has been restated herein in line with the 2019 PEG guidance and the amended claims. Applicant is directed to the above full Alice/Mayo analysis in the 101 rejection.
Additionally, on page 11 of their remarks, the applicant argues, “Amended Claim 1 is directed to this solution, reciting… These operations are specific and technical, and are not a fundamental economic practice or a process that can be performed in the human mind.” Similar arguments are made on page 12 of the applicant’s remarks. The examiner respectfully disagrees. While the examiner agrees that the process of generating a simulation of the claim event using a physics engine is an additional element, this limitation does not integrate the abstract idea into a practical application for the reasons described in the 101 rejection above. The mere recitation of limitations that fall outside of the abstract idea (i.e., additional elements) does not prevent the claim from reciting an abstract idea. The claims clearly recite limitations for determining optimal reserve estimates for insurance claims, which fall under the category of certain methods of organizing human activity. Additionally, the examiner notes that the limitations which states, “based on the simulation, determining an accuracy of the additional incident data, including determining whether the additional incident data is consistent with the simulation” does recite an abstract idea regarding fundamental economic practices. This limitation simply states that the accuracy of the additional incident data is determined based on an analysis of the simulation. The claim does not provide significant technical detail regarding how the simulation and additional incident data are analyzed. Therefore, such limitations amount to no more than merely analyzing insurance-related data in order to generate the optimal reserve estimates, a process which falls under the category of certain methods of organizing human activity.
Additionally, on page 11 of their remarks, the applicant argues, “Under the reasoning of Recentive, Claim 1 is patent eligible because these recited features delineate how the machine learning model achieves a more accurate reserve estimate.” Similar arguments are made on page 12 of the applicant’s remarks. The examiner respectfully disagrees. The examiner notes that simply stating that the machine learning model is trained/tuned and executed to perform the abstract idea does not amount to a technical improvement to the functioning of the machine learning model itself. The claims do not provide significant technical detail regarding how the training is performed. Rather, the claims simply broadly state that the model is trained using a dataset and tuned based on the second optimized reserve estimate. Similarly, the claims do not provide significant technical detail regarding how the machine learning model performs the claimed functions. Rather, the claim simply states that the machine learning model is executed to generate the optimal reserve estimates (e.g., “executing the trained machine learning model on the corpus of information to generate a first optimized reserve estimate for the claim event”). Such limitations amount to no more than broadly applying generic machine learning technology to implement the abstract idea on a computer.
Additionally, on page 12 of their remarks, the applicant argues, “Like the eligible claim in Example 40, amended Claim 1 recites a specific operation - "generating, using a physics engine, a simulation of the claim event" and "determining whether the additional incident data is consistent with the simulation" - that is applied to improve the accuracy of the reserve estimate generated by the machine learning model, thereby integrating any alleged abstract idea into a practical application.” The examiner respectfully disagrees. Specifically, the examiner disagrees that the claims of the instant application are analogous to the claims of Example 40. As noted by the applicant, Example 40 relates that a technical improvement regarding method for collecting network traffic data. As discussed above, the claims of the instant application do not provide an indication of such a technical improvement to any technology or technological field.
Additionally, on page 13 of their remarks, the applicant argues, “The Step 2B finding also lacks the factual support required to establish that the additional elements are well-understood, routine, and conventional. Under Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018), and MPEP § 2106.05(d), the Office must provide factual support before concluding that an additional element is well-understood, routine, and conventional. The Office Action provides no such support for the physics-engine simulation and data-validation operations recited in amended Claim 1, relying instead on a general "apply it" characterization (Office Action, p. 5). The rejection therefore cannot be sustained under Step 2B.” The examiner respectfully disagrees. Specifically, it is not required for the examiner to provide Berkheimer evidence for each limitation recited in the claim. Rather, this evidence is only required when the examiner identifies a limitation as being well-understood, routine, and conventional (e.g., when identifying a limitation as reciting insignificant extra-solution activity). The examiner has not claimed that the limitations regarding the physics-engine simulation are well-understood, routine, and conventional. Rather, the examiner has asserted that these limitations amount to no more than merely applying generic computer-related technology to perform the abstract idea on a computer. These are separate analyses. As stated in MPEP2106.07(a)(III), “At Step 2A Prong Two or Step 2B, there is no requirement for evidence to support a finding that the exception is not integrated into a practical application or that the additional elements do not amount to significantly more than the exception unless the examiner asserts that additional limitations are well-understood, routine, conventional activities in Step 2B.”
Therefore, for these reasons and the reasons given above, the rejection of these claims under 35 U.S.C. §101 is maintained.
Citation of Pertinent Prior Art
13. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Swingler (U.S. Patent No. 11972489): Describes a claims processing system that includes a claims assistance component to handle the flow of data through the claim process. The data may include any documents submitted for claim processing and may be generated by disparate sources. The system may train one or more machine learning (ML) models to classify the unstructured data by insurance categories and/or determine workflow.
Malreddy (U.S. Pre-Grant Publication No. 20210342997): Describes systems and methods for vehicle damage detection and classification with reinforcement learning. An embodiment of the system generates a dataset, which can include digital images of actual vehicles or simulated (e.g., computer-generated) vehicles, and trains a neural network with a plurality of images of the dataset to learn to detect damage to a vehicle present in an image of the dataset and to classify a location of the detected damage and a severity of the detected damage utilizing segmentation processing.
Conclusion
THIS ACTION IS MADE FINAL. 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 WILLIAM D NEWLON whose telephone number is (571)272-4407. The examiner can normally be reached Mon - Fri 8:30 - 4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Gart can be reached at (571) 272-3955. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WILLIAM D NEWLON/Examiner, Art Unit 3696
/MATTHEW S GART/Supervisory Patent Examiner, Art Unit 3696
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