Notice of Pre-AIA or AIA Status
DETAILED ACTION
Introduction
1. The following is a NON-FINAL Office Action in response to the communicationreceived on 01/23/26. Claims 1-20 are now pending in this application.
2. A request for continued examination (RCE) under 37 CFR 1.114, including thefee 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1.114, and thefee set forth in 37 CFR 1.17(e) has been timely paid, the FINALITY of the previousOffice Action has been WITHDRAWN pursuant to 37 CFR 1.114. Applicant'ssubmission filed on 01/23/26 has been entered.
Response to Amendments
3. Applicants Amendment has been acknowledged in that: Claims 1,9,16 have been amended; hence such, Claims 1-20 are now pending in this application.
RESPONSE TO ARGUMENTS
Applicant argues#1
Applicant submits that, to the extent the claims recite an abstract idea, the claims are not "directed to" an alleged abstract idea because the claim elements integrate any alleged abstract idea into a practical application. In particular, the claim elements enhance the operation of a computing system by reciting specific technical steps that achieve improvements in data processing efficiency.
For example, claim 1, as amended, recites that "the one or more machine learning models implement a multi-variable approach by aggregating the one or more claim variables for each claim into a combined input and processing the combined input simultaneously through the one or more machine learning models to generate a predicted severity output, one or more eliminating data storage and network transmission requirements that would otherwise be required for individual variable analysis." This claim language specifies the precise mechanism by which the computing system achieves improved performance. The system aggregates multiple variables into a combined input structure and processes that combined input in a single operation, rather than storing and processing each variable separately. This specific data structure modification and processing approach directly reduces computational overhead.
In this regard, the specification at [0016] explains that conventional systems analyze
"severity""using a single-variable approach, where the impact of each variable is determined separately from other variables" and that "[c]onventional severity investigations therefore result in large amounts of data for each individual variable." At [0017], the specification discloses that "the systems, methods, and computer-executable media described herein provide an improved computing system for determining severity based on a multi-variable approach." At [0051], the specification describes the aggregation process in detail:"the provider computing system 110 aggregates the explainer values for each claim. The explainer values may be aggregated for a set of claims. For example, the explainer value for one type of claim variable is added up resulting in a total item damage severity for a single claim variable across the set of claims." At [0052], the specification further explains that "the provider computing system 110 sums explainer values for one or more line IDs to the claim level" and that "when the claim is a multi-coverage claim...the line IDs are aggregated such that each claim is associated with a single, aggregated value."
These technical steps achieve a concrete improvement: instead of requiring separate data storage for each variable, separate processing operations for each variable, and separate network transmissions of individual results (as required by the single-variable approach described at [0016]), the claimed system aggregates variables into a unified data structure, processes them together, and generates a single output.
Examiner Response
Examiner respectfully disagrees.
The spec paras that applicant refers to, along with other spec paras are reproduced below:
[0016] In conventional claims processing systems, item damage severity is determined retroactively-that is, when all factors that impact the item damage severity are fully known. Severity is also conventionally analyzed using a single-variable approach, where the impact of each variable is determined separately from other variables. Conventional severity investigations therefore result in large amounts of data for each individual variable, and, in some instances, may be inaccurate due to the limited single variable scope.
[0017] Accordingly, the systems, methods, and computer-executable media described herein provide an improved computing system for determining severity based on a multi-variable approach. The improved computing systems advantageously predict severity based on claims data such that severity for claims from a first time period can be predicted, rather than determined retroactively. Additionally, the systems, methods, and computer-executable media described herein provide an improved user interface that advantageously provides severity data. The improved user interface may reduce the amount of data transmissions necessary for a user to understand a determined severity, for example, by reducing the number of graphics (e.g., graphs, tables, text, etc.) needed to visually represent the determined severity. Further, the improved user interface advantageously filters and sorts the severity data such that relatively more relevant severity data is presented before and/or instead of relatively less relevant severity data. For example, relatively less relevant (e.g., lower magnitude) severity values may be automatically grouped into an "other" category and displayed as a single graphical feature. Thus the improved user interface provides at least one specific improvement over prior systems, for example, by reducing the number of graphical elements needed to understandably convey severity data. Additionally, the systems, methods, and computer-executable media described herein embody a self-correcting predictive system that is periodically re-trained using current data such that the accuracy of predictions for item damage severity is improved over time.
[0035] The item damage severity modeling circuit126 is structured to store computer- executable instructions embodying one or more machine learning models. The one or more machine learning models are configured to generate one or more statistical models of damage severity. The item damage severity modeling circuit126 may be structured to train the one or more machine learning models based on the claims information and the severity information such that the one or more machine learning models outputs and/or determines a predicted severity. As used herein "predicted severity" is severity that is estimated or predicted, using one or more statistical methods, machine learning algorithms, and the like, by estimating the factors that are not fully known when damage is reported. For example, the one or more machine learning models may be trained using training data that includes claims data (e.g., stored at the claims database134 or at the claims database172) and actual severity data stored at the severity database132. In some embodiments, the actual severity data may be provided by the item damage severity aggregation circuit124. The one or more machine learning models are trained to generate predicted severity based on the training data. In some embodiments, the one or more machine learning models generate decision trees to output and/or determine predicted severity based on input claim data. For example, the item damage severity modeling circuit126 may receive claim data and identify (e.g., parse) one or more claim variables from the received claim data. The item damage severity modeling circuit126 may utilize the one or more trained machine learning models to determine and/or output the predicted severity. As briefly described above, the one or more machine learning models may include a machine learning explanatory model (e.g., SHAP or another suitable model). Accordingly, the item damage severity modeling circuit126 may utilize the machine learning explanatory model with the one or more machine learning models to output and/or determine a base rate of expected severity and/or explainer values. For example, the machine learning explanatory model (e.g., SHAP or another suitable model) may identify the decisions made at the one or more decision trees and generate explainer values representing calculations performed at each decision juncture. The explainer values correspond to a claim variable of the claim data input into the one or more machine learning models. Specifically, the machine learning explanatory model generates explanatory values for each claim variable in the one or more decision trees. A sum of the explanatory values is equivalent to the output (e.g., the predicted severity). The base rate of severity is output and/or determined by the machine learning explanatory model by calculating an average actual severity of the training dataset.
[0051] Referring to the method300 in more detail, at step 302, the provider computing system110 generates an explainer value for each input variable of each claim received in a predetermined time period. The explainer values may be generated based on a relative impact each claim variable has on the total item damage severity. The explainer values may be generated using a machine learning explanatory model, such as SHAP. For example, the one or more machine learning models may generate one or more decision trees to arrive at an output. The provider computing system110 and/or one or more components thereof may utilize the machine learning explanatory model with the one or more machine learning models. The machine learning explanatory model may identify the decisions made at the one or more decision trees and generate explanatory values representing calculations performed at each decision juncture. The explanatory values correspond to the claim variables of the claim data input into the one or more machine learning models. In the embodiments described herein, the one or more machine learning models generate decision trees to output and/or determine a predicted severity based on one or more claim variable inputs. The machine learning explanatory model generates explanatory values for each claim variable in the one or more decision trees, and a sum of the explanatory values is equivalent to the output (e.g., the predicted severity). At step 304, the provider computing system110 aggregates the explainer values for each claim. The explainer values may be aggregated for a set of claims. For example, the explainer value for one type of claim variable is added up resulting in a total item damage severity for a single claim variable across the set of claims. In some embodiments, the provider computing system110 sums explainer values for one or more line IDs to the claim level. For example, a single claim may have one or more line IDs and/or the claim may include more than one insured item. Accordingly, one or more of the line IDs may be related to a first insured item, a second insured item, and so on. The provider computing system110 may sum the explainer values for each of the one or more line IDs of a claim. Accordingly, when the claim is a multi- coverage claim (e.g., a claim having more than one line ID and/or a claim related to more than one insured item) the line IDs are aggregated such that each claim is associated with a single, aggregated value. This process may be repeated for some or all of the claim variables. In various embodiments, the set of claims can be aggregated according to the FNOL date, loss date, insured
item type, make and/or model, geographical location of loss (e.g., GPS coordinates, zip code, etc.), or any suitable combination thereof. In an example embodiment, the set of claims is aggregated according to a claim identifier (e.g., a claim number, a claim ID, etc.).
[0052] At step 306, the provider computing system110 averages the aggregated explainer values. The average is calculated by multiplying a relative frequency (e.g., percent occurrence of each claim variable in the claims within the predetermined time period) of a claim variable by the corresponding aggregated explainer value (e.g., for the same claim variable). That is, an aggregated explainer value for a first claim variable (Xi) is multiplied by the percent occurrence of that claim variable (Yi) within the predetermined time period (e.g., within a week, a month, a quarter, a year, etc.). For example, a first claim variable may be a type of vehicle where Xi is an aggregated explainer value for the vehicle type and where Yi is a percentage of claims that include the vehicle type. The average explainer value is calculated as the product of Xi and Yi. In some embodiments, the average explainer value may be calculated for each claim variable of a plurality of claims within the predetermined time period. In some embodiments, the average explainer value may be calculated for at least one claim variable for the plurality of claims within the predetermined time period.
Examiner Response
Examiner respectfully disagrees.
Applicant is using a machine learning model implementing a multivariable approach, which is a commonly used machine learning technique when there are complex relationships between the data.
Furthermore the SHAP model or another suitable model recited in paras 35&51, is a machine learning explanatory model, that is recited at a high level of generality, operating in its ordinary capacity and as such is being used as a tool to implement the steps of the identified abstract idea.
Therefore there are no additional elements in the claim that are indicative of integration into a practical application.
The rejection is maintained.
Applicant argues#2
Consistent with this framework, recent decisions of the Office further illustrate how claims that improve the functioning of a machine or technical process are not "directed to" an abstract idea. For instance, in Ex parte Desjardins, Appeal 2024-000567, Director Review Decision (Sept. 26, 2025), the Appeals Review Panel vacated a § 101 rejection in which the Board had characterized machine-learning operations involving mathematical calculations as abstract ideas implemented on generic computer components. The panel clarified that a claim reciting a mathematical operation is not directed to an abstract idea when, viewed as a whole, it integrates the recited concept into a practical application that improves the functioning of a computer or another technology. The decision emphasized that continual-learning logic enabling a machine
learning model to "effectively learn new tasks in succession whilst protecting knowledge about previous tasks" while using "less of their storage capacity" and enabling "reduced system complexity" constitutes such an improvement to how the machine learning model itself operates. The panel further cautioned that "[e]xaminers and panels should not evaluate claims at such a high level of generality," particularly warning that "categorically excluding Al innovations from patent protection" based on overgeneralized characterizations "jeopardizes America's leadership in this critical emerging technology." The Desjardins decision thus reinforces that when claims are rooted in a specific improvement to model operation or system performance, they are not "directed to" a judicial exception under Step 2A, Prong Two of the 2019 Guidance.
Like the invention in Ex parte Desjardins that employed continual-learning logic to preserve prior task performance and reduce system complexity, the present application discloses an enhanced claims processing system that improves machine learning model operation through specific technical mechanisms. The computing system implements machine learning models that aggregate multiple claim variables into a combined input and process that combined input simultaneously to generate a predicted severity output, thereby eliminating the separate data storage and network transmission requirements that would be required by conventional single- variable approaches. As described at [0016], conventional systems analyze "the impact of each variable... separately from other variables" resulting in "large amounts of data for each individual variable." In contrast, the claimed multi-variable approach ( [0017]) processes all variables together through aggregation ( [0051]-[0052]), which directly reduces the data storage requirements (by storing one aggregated dataset rather than separate datasets for each variable) and network bandwidth requirements (by transmitting one combined result rather than individual results for each variable).
Paralleling the continual-learning approach in Ex parte Desjardins, the claimed system also includes iterative improvement mechanisms. Claim 1 recites "iteratively train one or more machine learning models to predict respective item damage severities... until the one or more machine learning models can predict the respective item damage severities to within a predetermined tolerance threshold of an actual item damage severity." This iterative training process provides a specific technical solution that continuously improves the accuracy of damage severity predictions by refining model parameters until a particular performance threshold is achieved, similar to how the Desjardins invention preserved prior task performance while reducing storage requirements. The technical approach creates a self-improving system where
each training iteration enhances the machine learning model's ability to predict damage severities with increasing precision, fundamentally transforming generic data processing into a specialized, continuously-learning damage assessment technology that improves computer system performance over successive iterations.
Therefore, for at least the reasons noted above, Applicant submits that the claimed subject matter is integrated into a practical application and, as such, that the claims are not "directed to" a judicial exception. Accordingly, Applicant respectfully requests withdrawal of the rejections under 35 U.S.C. § 101.
Examiner Response
Examiner respectfully disagrees.
Applicant argued the claims present a technical improvement. Examiner does not find this argument persuasive. Applicant’s claims do not improve technology; the underlying technology remains unaffected by the claims. Applicant is addressing a business problem (steps for determining damage severity based on claim data for multiple time periods) with a business solution. Applicant is merely using existing technology (for its intended purpose) to implement the business solution. Any improvements lie in the abstract idea itself, not in underlying technology.
As far as the comparison to Ex Parte Desjardins:
The claims of the Ex Parte Desjardins decision analyzed eligibility to determine whether
the claims were directed to an improvement in the functioning of the computer or an
improvement to other technology or technical field. It was in step 2a prong 2, it was
determined that the specification identified improvements and was reflected in the
claims as to how the machine learning model itself operates.
The specification of the Desjardins application identified the improvement to machine
learning technology.
Whereas the claims and the specification of the instant application, do not reflect the
improvement to the machine learning models.
Therefore, the claims are unlike the claims in Ex Parte Desjardins.
The rejection is maintained.
Claim Rejections- 35 U.S.C § 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.
1. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims are either directed to a method, system and computer readable medium which are one of the statutory categories of invention. (Step 1: YES).
Claim 1 recites the limitations of:
A computing system comprising:
at least one processor; and
memory storing instructions that, when executed by the at least one processor, cause the computing system to:
iteratively train one or more machine learning models to predict respective item damage severities associated with one or more claims based on one or more claim variables of the one or more claims until the one or more or more machine learning models can predict the respective item damage severities within a predetermined tolerance threshold of an actual item damage severity, wherein the one or more machine-learning models implement a multi-variable approach by aggregating the one or more claim variables for each claim into a combined input and processing the combined input simultaneously through the one or more machine learning models to generate a predicted severity output, thereby eliminating one or more data storage and network transmission requirements that would otherwise be required for individual variable analysis;
subsequently receive, for each first claim of a plurality of first claims submitted during a first period and each second claim of a plurality of second claims submitted during a second period that is before the first period, a first set of variables associated respectively with each first claim, and a second set of variables associated respectively with each second claim, wherein each second claim is associated with a respective actual damage severity and wherein a level of information specified by the first set of variables is insufficient on its own to determine respective actual damage severities associated with the first plurality of claims;
after aggregating the first set of variables into an aggregated first set of variables and aggregating the second set of variables into an aggregated second set of variables, input to the one or more machine learning models, the aggregated first set of variables and the aggregated second set of variables to receive, from the one or more machine learning models, a first predicted damage severity associated with the first plurality of claims and a second predicted damage severity associated with the second plurality of claims;
determine respective actual damage severities associated with each of the first plurality of claims based on the first predicted damage severity associated with the first plurality of claims, the second predicted damage severity associated with the second plurality of claims, and the respective actual damage severities of the second plurality of claims; and
cause a display of a computing device to generate a damage severity user interface comprising one or more selectable features , representing one or more of respective percent impact values associated with respective variables of the first set of variables and the second set of variables, the respective percent impact values being indicative of an amount of impact a particular variable has on the first predicted damage severity associated with the first plurality of claims and the second predicted damage severity associated with the second plurality of claims.
The claim recites elements that are in bold above, (e.g., predict the respective item damage severities within a predetermined tolerance threshold of an actual item damage severity to generate a predicted outpout; subsequently receive, for each first claim of a plurality of first claims submitted during a first period and each second claim of a plurality of second claims submitted during a second period that is before the first period, a first set of variables associated respectively with each first claim, and a second set of variables associated respectively with each second claim, wherein each second claim is associated with a respective actual damage severity and wherein a level of information specified by the first set of variables is insufficient on its own to determine respective actual damage severities associated with the first plurality of claims; to receive a first predicted damage severity associated with the first plurality of claims and a second predicted damage severity associated with the second plurality of claims; determine respective actual damage severities associated with each of the first plurality of claims based on the first predicted damage severity associated with the first plurality of claims, the second predicted damage severity associated with the second plurality of claims, and the respective actual damage severities of the second plurality of claims; and representing one or more of respective percent impact values associated with respective variables of the first set of variables and the second set of variables, the respective percent impact values being indicative of an amount of impact a particular variable has on the first predicted damage severity associated with the first plurality of claims and the second predicted damage severity associated with the second plurality of claims) under its broadest reasonable interpretation, covers performance of the limitation(s) as a fundamental economic practice (insurance, mitigating risk), (steps for determining damage severity based on claim data for multiple time periods).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic practice, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly claim 1 recites an abstract idea.
In addition claim 1 recites a mental process (predict the respective item damage severities within a predetermined tolerance threshold of an actual item damage severity; subsequently receive, for each first claim of a plurality of first claims submitted during a first period and each second claim of a plurality of second claims submitted during a second period that is before the first period, a first set of variables associated respectively with each first claim, and a second set of variables associated respectively with each second claim, wherein each second claim is associated with a respective actual damage severity and wherein a level of information specified by the first set of variables is insufficient on its own to determine respective actual damage severities associated with the first plurality of claims; to receive a first predicted damage severity associated with the first plurality of claims and a second predicted damage severity associated with the second plurality of claims; determine respective actual damage severities associated with each of the first plurality of claims based on the first predicted damage severity associated with the first plurality of claims, the second predicted damage severity associated with the second plurality of claims, and the respective actual damage severities of the second plurality of claims; and representing one or more of respective percent impact values associated with respective variables of the first set of variables and the second set of variables, the respective percent impact values being indicative of an amount of impact a particular variable has on the first predicted damage severity associated with the first plurality of claims and the second predicted damage severity associated with the second plurality of claims), which under its broadest reasonable interpretation, covers performance of the limitation(s) as a mental process, more specifically a concept performed mentally by a human with pen and paper (steps for determining damage severity based on claim data for multiple time periods).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a certain method of a concept performed in the human mind, then it falls within the “mental process” grouping of abstract ideas.
Claims 9,16 recite substantially the same subject matter as claim 1 and are abstract for similar reasons.
(Step 2A-Prong 1: YES. The claims are abstract)
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05.f), (2) Adding insignificant extra solution activity to the judicial exception (MPEP 2106.05.g), (8) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05.h).
Claims 1, 9, 16 includes the following additional elements:
- One or more machine learning models
-A processor
-A memory
-A computing device
-A user interface
-Iteratively training of the one or more machine learning models
-The one or more machine learning models implementing a multivariable approach by aggregating the one or more claim variables for each claim into a combined input simultaneously through the one or more machine learning models
The one or more machine learning models, processor, memory, computing device, user interface, iteratively training of the one or more machine learning models and the one or more machine learning models implementing a multivariable approach by aggregating the one or more claim variables for each claim into a combined input simultaneously through the one or more machine learning models are recited at a high level of generality and are being used in their ordinary capacity and are being used as a tool for implementing the steps of the identified abstract idea, see MPEP 2106.05(f), where applying a computer or using a computer as a tool to perform the abstract idea is not indicative of a practical application.
Therefore, there are no additional elements in the claim that amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use.
Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because
they do not impose any meaningful limits on practicing the abstract idea.
Therefore claims 1, 9, 16 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO). The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, there are no additional elements recited in the claim beyond the judicial exception.
Mere instructions to implement an abstract idea, on or with the use of generic computer components, or even without any computer components, cannot provide an inventive concept - rendering the claim patent ineligible.
Thus claims 1,9, 16 are not patent eligible. (Step 2B: NO.) The claims do not provide significantly more)
Dependent claims 2-8, 10-15, 17-20 which further define the abstract idea that is present in their respective independent claims 1, 9, 16 and thus correspond to a fundamental economic practice and a Mental process and hence are abstract for the reasons presented above.
Claims 3, 10, 17 recites, recites the additional element of “one or more sensors of a telematics device”. The one or more sensors of the telematics device are recited a high level of generality and is operating in its ordinary capacity and is being used as a tool to implement the steps of the identified abstract idea.
Therefore, the dependent claims do not include any 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 both individually and as an ordered combination.
Therefore, the dependent claims 2-8, 10-15, 17-20 are directed to an abstract idea. Thus, claims 1-20 are not patent-eligible.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD Z SHAIKH whose telephone number is (571)270-3444. The examiner can normally be reached M-T, 9-600; Fri, 8-11, 3-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, BENNETT SIGMOND can be reached at 303-297-4411. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHAMMAD Z SHAIKH/Primary Examiner, Art Unit 3694 3/7/2026