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 4/28/2026. Claims 25-48 are pending. Claims 26-28, 30-32, 34-36, 38-40, 42-44 and 46-48 have been withdrawn from consideration as directed to a non-elected invention by election by original presentation. Claims 1-24 have been canceled.
Election/Restrictions
Applicant's election with traverse in the election by original presentation in the reply filed on 4/28/2026 is acknowledged. The traversal is on the ground(s) that claims 26-27, 30, 34-35, 38, 42-43 and 46 are directed to limitations in the originally filed claims (see rem 9-10). Upon reconsideration, this is found persuasive because the limitations were considered in the originally filed independent claims 1, 10 and 19. As such, claims 26-27, 30, 34-35, 38, 42-43 and 46 are pending and will be considered.
Because applicant did not distinctly and specifically point out the supposed errors in the restriction requirement of claims 28, 31-32, 36, 39-40, 44 and 47-48, the election has been treated as an election without traverse (MPEP § 818.01(a)).
The requirement is still deemed proper and is therefore made FINAL.
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 25-27, 29-30, 33-35, 37-38, 41-43 and 45-46 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 25-27, 29-30, 33-35, 37-38, 41-43 and 45-46 are directed to a system, method and product. The claims fall within one of the four statutory categories of invention (processes, machines, manufactures and compositions of matter).
The Examiner has identified independent method Claim 33 as the claim that represents the claimed invention for analysis and is similar to independent system Claim 25 and product Claim 41.
The claims recite the steps of:
applying a …model to historical telematics data generated during operation…by the respective driver user, to receive as output from the … model (i) a respective driver operational profile associated with and identifying each of the plurality of driver users, the operational profile including an operation factor related to a likelihood of damage during operation thereof, and (ii) a predesignated policy for the plurality of users including a coverage amount based in part upon the driver operational profiles of the plurality of users;
receiving … current vehicle telematics data for a trip taken using the vehicle;
applying the… model to the current vehicle telematics data to identify, from the plurality of driver users, a confirmed driver user for the trip;
using the driver operational profile of the confirmed driver user, the current vehicle telematics data, and environmental conditions under which the vehicle was operated during the trip, classify the trip as having below standard, standard, or above standard likelihood of damage;
generating a multiplier for the trip based upon the classification;
applying the multiplier to the current vehicle telematics data to generate weighted telematics data; and
retraining the … model using the weighted telematics data to update the driver operational profile for the confirmed driver user, such updating causing a corresponding adjustment in the coverage amount to an adjusted coverage amount.
Under Step 2A Prong 1, the claim as a whole recites the series of steps instructing how to evaluate telematics data to determine an insurance policy (analyze risk), 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 determine insurance risk of damage of a driver in a computer environment (machine learning on telematics data collected). The claimed computer components (a trained machine learning model, vehicle, telematics sensors) are recited at a high level of generality and are merely invoked as tools to perform an existing risk evaluation 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 determine insurance risk of damage of a driver 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 26-27, 29-30, 34-35, 37-38, 42-43 and 45-46 further define the abstract idea that is present in their respective independent claims 25, 33 and 41 (analyze the data and further defining the type of data received for example, ). The dependent claims 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 25-27, 29-30, 33-35, 37-38, 41-43 and 45-46 are not patent eligible.
Response to Arguments
Applicant's arguments filed 4/28/2026 have been fully considered but they are not persuasive.
35 USC 101- Subject Matter Eligibility
Applicants argue that the § 101 rejection cannot be sustained because the Office has provided no evidence that the claims recite "an existing risk evaluation process" and any statement is not based in “fact or evidence” is improper (rem 10). Applicants, thus, ostensibly maintain that because there is no evidentiary support for the Examiner’s findings, the Examiner has failed to establish patent-ineligibility.
Examiner is aware of no controlling authority that requires the Office to provide factual evidence to support a finding that a claim is directed to an abstract idea. Nor, contrary to Applicant’s suggestion that there is any such requirement. Instead, the Federal Circuit has repeatedly noted that “the prima facie case is merely a procedural device that enables an appropriate shift of the burden of production.” Hyatt v. Dudas, 492 F.3d. 1365, 1369 (Fed. Cir. 2007) (citing In re Oetiker, 977 F.2d 1443, 1445 (Fed. Cir. 1992)). The court has, thus, held that the USPTO carries its procedural burden of establishing a prima facie case when its rejection satisfies the requirements of 35 U.S.C. § 132 by notifying the applicant of the reasons for rejection, “together with such information and references as may be useful in judging of the propriety of continuing the prosecution of [the] application.” See In re Jung, 637 F.3d 1356, 1362 (Fed. Cir. 2011). Thus, all that is required of the Office is that it set forth the statutory basis of the rejection in a sufficiently articulate and informative manner as to meet the notice requirement of § 132. Id.', see also Chester v. Miller,906 F.2d 1574, 1578 (Fed. Cir. 1990) (Section 132 “is violated when the rejection is so uninformative that it prevents the applicant from recognizing and seeking to counter the grounds for rejection.”).
With regards to applicant’s argument to novelty (rem 10, “no art-related rejections whatsoever”), one must keep in mind that judicial exceptions need not be old or long‐prevalent, and that even newly discovered judicial exceptions are still exceptions, despite their novelty. For example, the mathematical formula in Flook, the laws of nature in Mayo, and the isolated DNA in Myriad were all novel, but nonetheless were considered by the Supreme Court to be judicial exceptions because they were “‘basic tools of scientific and technological work’ that lie beyond the domain of patent protection.” (Parker v. Flook, 437 U.S. 584, 591‐92 (1978); and Myriad Genetics, 133 S. Ct. at 2116, quoting Mayo Collaborative Svcs. v. Prometheus Labs., 566 U.S. __, 132 S. Ct. 1289, 1293 (2012).).
Regarding applicant’s argument “the pending claims do not cover "any" solution to an identified problem. Rather, the claims recite a very specific solution to the technical drawbacks of conventional vehicle operation analysis ..,. the collection and processing of limited data that prevents unique trip analysis and accurate vehicle operation metrics.” (rem 11). Examiner respectfully disagrees.
The recitation of applying a trained machine learning model to historical telematics data … to receive as output from the trained machine learning model …applying the trained machine learning model to the current vehicle telematics data … generating a multiplier …applying the multiplier to the … data…retraining the machine learning model using the weighted telematics data …”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 retraining the machine learning model using the weighted telematics data to update the driver operational profile for the confirmed driver user 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 “applying a trained machine learning model” (claims 25, 33, and 41) 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 “to identify, from the plurality of driver users, a confirmed driver user for the trip… classify the trip … update the driver operational profile for the confirmed driver user” and do not include any details about how the evaluating, determining an insurance policy and classifying of the trip is accomplished. See MPEP 2106.05(f) and the July 2024 Subject Matter Eligibility Examples, December 2025 memo and corresponding analysis.
Regarding applicant’s argument (rem 12, lines 1-5), the results and intended use are recited in the claim without the technological implementation details. Further, as to specification paragraph 76, by applicant’s own admission, this is a technical solution to a business (insurance ) problem, see originally filed specification paragraphs 0004 and 0005.
With regards to Ex Parte Desjardins (rem 12), eligibility is rooted in the improvements to machine learning as identified in the specification. The applicant’s specification is silent to an improvement to the functioning of the computer or improvement to machine learning models.
Regarding applicant’s arguments with respect to “Additionally, the August 4, 2025 Memo indicates… to consult the specification to determine whether … invention improves technology or a technical field and evaluate the claim to ensure it reflects the disclosed improvement…claim itself does not need to explicitly recite the improvement described in the specification” Examiner consulted the specification, the specification is silent to any improvement in machine learning models. By applicant’s own admission, the machine learning models may be any type of general machine learning models (see originally filed specification at para 0140-141 “A processor … may be trained using supervised or unsupervised machine learning, …may employ a neural network, which may be a convolutional neural network, a deep learning neural network, or a combined learning module or program that learns in two or more fields or areas of interest… may involve identifying and recognizing patterns in existing data in order to facilitate making predictions for subsequent data. Models may be created based upon example inputs in order to make valid and reliable predictions for novel inputs… Additionally or alternatively, the machine learning programs may be trained by inputting sample data sets or certain data into the programs … may utilize deep learning algorithms that may be primarily focused on pattern recognition, and may be trained after processing multiple examples. The machine learning programs may include Bayesian program learning (BPL), voice recognition and synthesis, image or object recognition, optical character recognition, and/or natural language processing - either individually or in combination. The machine learning programs may also include natural language processing, semantic analysis, automatic reasoning, and/or machine learning” and para [0142] … supervised machine learning, a processing element may be provided with example inputs and their associated outputs, and may seek to discover a general rule that maps inputs to outputs, so that when subsequent novel inputs are provided the processing element may, based upon the discovered rule, accurately predict the correct output.
Here, as explained above, the claims reflect the technical improvements/solutions consistent with Applicant's Specification”) however applicant has not provided what the technical improvements include and the claims are directed to the results, omitting the technological implementation details of an improved machine learning model as asserted (rem 13). Applicant’s response fails to link the legal concepts to the facts of the application under examination.
Regarding applicant’s argument “the operations recited in the claims are not merely implemented by generic computing components to ‘apply it,’… but rather include such technical recitations as to represent meaningful limits on any alleged ‘certain method of organizing human activity’” Examiner respectfully notes applicant has not provided the facts to support this conclusory statement.
Regarding applicant’s comparison to Example 39, Examiner respectfully disagrees. In Example 39, the specification discloses the technical problem “the inability to robustly detect human faces in images where there are shifts, distortions, and variations in scale and rotation of the face pattern in the image” and the detailed technical solution “expanded training set is developed by applying mathematical transformation functions on an acquired set of facial images… include affine transformations, for example, rotating, shifting, or
mirroring or filtering transformations, for example, smoothing or contrast reduction. The
neural networks are then trained …using stochastic learning with backpropagation which is a type of machine learning algorithm that uses the gradient of a mathematical loss function to adjust the weights of the network.”
The second technical problem is “an expanded training set increases false positives when classifying non-facial images” and technical solution “minimization of these false positives by performing an iterative training algorithm, in which the system is retrained with an updated training set containing the false positives produced after face detection has been performed on a set of non-facial images. This combination of features provides a robust face detection model that can detect faces in distorted images while limiting the number of false positives.”
The claims recites the technological implementation details: “applying one or more transformations to each digital facial image including mirroring, rotating, smoothing, or contrast reduction to create a modified set of digital facial images; creating a first training set comprising the collected set of digital facial images, the modified set of digital facial images, and a set of digital non-facial images; training the neural network in a first stage using the first training set; creating a second training set for a second stage of training comprising the first
training set and digital non-facial images that are incorrectly detected as facial images after
the first stage of training; and training the neural network in a second stage using the second training set” while the applicant’s specification omits the technical problem and the claims omit the technological implementation details as the claims are results oriented.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Kwak (US 2021/0110480 A1) discloses an intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring system utilizes in-vehicle sensors, OBD outputs, and electronic driver logs from real-time monitored commercial vehicles as well as accident-causality historical statistics to produce an accurate insurance risk score per monitored vehicle and its driver. The insurance risk score generated by the intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring system incorporates multiple insurance risk factors with a variable weighting ratio per factor, which is multiplied by a numerical value per factor, wherein each weighting ratio may be autonomously machine-determined based on the significance of each insurance risk factor to a likelihood of an actual accident or another safety event. Furthermore, the insurance risk score per monitored vehicle or commercial driver is objectively comparable to peer vehicles or drivers in a commercial fleet organization, and can undergo min-max feature scaling in deriving each finalized score.
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 Kelly Campen whose telephone number is (571)272-6740. The examiner can normally be reached Monday-Thursday 6am-3pm.
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, Abhishek Vyas can be reached at 571-270-1836. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
Kelly S. Campen
Primary Examiner
Art Unit 3691
/KELLY S. CAMPEN/ Primary Examiner, Art Unit 3691
Prosecution Insights