DETAILED ACTION
Status of Claims
The following is a FINAL OFFICE ACTION in response to applicant’s amendments to and response for Application #18/182,840, filed on 05/21/2026.
Claims 1-6, 8-16, and 18-24 are now pending and have been examined.
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 1-6, 8-16, and 18-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The rationale for this finding is explained below.
Per Step 1 of the analysis, the claims are analyzed to determine if they are directed to statutory subject matter. Claims 1 and 20 claim a computing device comprising a memory unit storing. Therefore, the examiner is interpreting this as a computer, or processor, and a memory. If the applicant does not intend this interpretation, the examiner asks that they state it for the record. Therefore, the claims are interpreted as apparatuses. An apparatus is a statutory category for patentability. Claim 11 claims a method, or process. A method, or process, is a statutory category for patentability.
Per Step 2A, Prong 1 of the analysis, the examiner must now determine if the claims recite one or more abstract ideas or eligible subject matter. In the instant case, the independent claims recite an abstract idea. Specifically, the independent claims 1, 11, and 20 recite “receiving information pertaining to a weather event defined by a geographic area, determine a weather event rating based on the information pertaining to the weather event defined by the geographic area, trigger a lease deferral process in response to the weather event rating exceeding a weather event rating threshold, wherein the lease deferral process comprises obtaining, from the plurality of leased vehicles, GPS location data associated with a time interval of the weather event, determine one or more affected leased vehicles based on the GPS location data indicating location of a respective leased vehicle within the geographic area of the weather event during the time interval of the weather event, calculate an eligibility rating for each of the one or more affected lease vehicles based on the weather event rating and the lease history of the one or more affected leased vehicles, determine and eligible leased vehicle from the one or more affected leased vehicles for a lease deferral based on the eligibility rating of the one or more affected leased vehicles, notify the customer of the eligible leased vehicle of a lease deferral offer, and prompt the customer to accept the lease deferral. The claims are directed to a certain method of organizing human activity, namely a business practice. A business owner renting or leasing vehicles could become aware of a weather event and manage their lease customers by offering a deferral. This would be considered within ordinary rental agreements and customer relationship management when an unexpected event occurs. The business owner could access GPS data and make a determination about the location of a vehicle during a weather event. Therefore, the claims are directed to a business practice. The claims secondarily recite a mental process. A human operator with access to the lease and weather data could analyze the data, calculate a weather event rating and compare to a threshold for each of the lease vehicles, calculate an eligibility rating for each of the one or more affected lease vehicles, and make a judgment about what leased vehicles are eligible based on being affected by the weather event, and notify those customers. This could be done verbally, in person, or manually using known means. Fleet managers in such as a rental car office could easily make a judgment regarding a weather event based on analysis of their available rental and weather data, calculate the weather event rating and compare to a threshold to determine eligible vehicles, calculate an eligibility rating, and then communicate with customers. The computing device simply automates the abstract idea. The addition of the obtaining of the GPS location data, absent further detail, is considered accessing of data and mentally analyzing the data to reach a conclusion. Therefore, the claims also secondarily recite a mental process.
Per Step 2A, Prong 2 of the analysis, the examiner must now determine if the claims integrate the abstract idea into a practical application. The additional elements of the claims include the recitation of “a computing device comprising a memory unit,” an “automatic lease deferral system,” “automatically” triggering, and a “weather reporting system.” However, these recited elements are considered generic recitations of technical elements as they are recited at a high level of generality. These elements are being used as “tools to automate the abstract idea” (see MPEP 2106.05 (f)), and do not integrate the abstract idea into a practical application. They are not recitations of a special purpose computer or transformation (see MPEP 2106.05 (b) and (c)). The additional elements in claim 20 only also include “a machine learning model, wherein the machine learning model adjusts an algorithm that determines the eligible leased vehicle based on the leased history.” However, the use of the model is recited at a very high level of generality with no detail as to how or why the model adjusts the algorithm or what the algorithm even is. Therefore, the “machine-learning model” and the “adjusting the algorithm” is considered the equivalent of “apply it,” or using a computer as a tool to automate the abstract idea (see MPEP 2106.05(f)). Therefore, these additional elements do not integrate the abstract idea into a practical application. The claims also include retrieving “from a weather reporting system,” the actual “obtaining, from the plurality of leased vehicles, GPS data,” “transmit the electronic notification to a customer,” and prompt the customer “within the electronic notification.” These additional elements, absent further detail, are considered “receiving and/or transmission of data over a network,” which is listed in the MPEP 2106.05 (d) (II) (i) as an example of conventional computer functioning- see “receiving or transmitting data over a network” citing TLI Communications, OIP Techs v Amazon.com, buySAFE v Google. Therefore, these additional elements do not integrate the abstract idea into a practical application. The claims also include “storing a lease history of a plurality of leased vehicles.” This additional element, absent further detail, is considered “receiving, processing, and storage of data,” which is listed in the MPEP 2106.05 (d) (II) (iii-iv) as an example of conventional computer functioning- see “electronic recordkeeping,” citing Alice Corp., and “storing and retrieving information in a memory” citing Versata Dev Grp v SAP, and OIP Techs v Amazon.com. Therefore, this additional element does not integrate the abstract idea into a practical application
Per Step 2B of the analysis, the examiner must now determine if the claims include limitations that are “significantly more” than the abstract idea by demonstrating an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. The additional elements of the claims include the recitation of “a computing device comprising a memory unit,” “automatically” triggering, an “automatic lease deferral system,” and a “weather reporting system.” However, these recited elements are considered generic recitations of technical elements as they are recited at a high level of generality. These elements are being used as “tools to automate the abstract idea” (see MPEP 2106.05 (f)), and therefore, these additional elements are not considered significantly more than the abstract idea itself. They are not recitations of a special purpose computer or transformation (see MPEP 2106.05 (b) and (c)). The additional elements in claim 20 only also include “a machine learning model, wherein the machine learning model adjusts an algorithm that determines the eligible leased vehicle based on the leased history.” However, the use of the model is recited at a very high level of generality with no detail as to how or why the model adjusts the algorithm or what the algorithm even is. Therefore, the “machine-learning model” and the “adjusting the algorithm” is considered the equivalent of “apply it,” or using a computer as a tool to automate the abstract idea (see MPEP 2106.05(f)). Therefore, these additional elements are not considered significantly more than the abstract idea itself. The claims also include retrieving “from a weather reporting system,” the actual “obtaining, from the plurality of leased vehicles, GPS data,” “transmit the electronic notification to a customer,” and prompt the customer “within the electronic notification.” These additional elements, absent further detail, are considered “receiving and/or transmission of data over a network,” which is listed in the MPEP 2106.05 (d) (II) (i) as an example of conventional computer functioning- see “receiving or transmitting data over a network” citing TLI Communications, OIP Techs v Amazon.com, buySAFE v Google. Therefore, therefore, these additional elements are not considered significantly more than the abstract idea itself. The claims also include “storing a lease history of a plurality of leased vehicles.” This additional element, absent further detail, is considered “receiving, processing, and storage of data,” which is listed in the MPEP 2106.05 (d) (II) (iii-iv) as an example of conventional computer functioning- see “electronic recordkeeping,” citing Alice Corp., and “storing and retrieving information in a memory” citing Versata Dev Grp v SAP, and OIP Techs v Amazon.com. Therefore, this additional element is not considered significantly more than the abstract idea itself.
When considered as an ordered combination, the claim is still considered to be directed to an abstract idea as the claims in the ordered combination simply recite the logical steps for retrieving the weather event information, determining affected lease vehicles, determining eligibility for deferral, and notifying those customers. Therefore, the ordered combination does not lead to a determination of significantly more.
When considering the dependent claims, claim 2 is considered conventional computer functioning, and the examiner takes Official Notice that it is old and well known in the computer arts for the transmittal of content to a device to include the display of a push notification that prompts the user to perform an action. Claim 3 is considered part of the abstract idea, as “making a phone call” is considered part of a business practice or communicating of a judgment in a mental process. Claims 4-6 are considered part of the abstract idea, as the type of lease data does not change the human operator’s ability to analyze the data and make a judgment. Claim 7 is considered part of the abstract idea, as a human operator with access to the weather data could easily compare it to a threshold and determine if it exceeds the threshold. In claim 8, “receiving an acceptance” is considered “receiving or transmitting data over a network,”- see MPEP 2106.05 (d) (II) (i) citing TLI Communications, OIP Techs v Amazon.com, buySAFE v Google. Therefore, therefore, this additional element is not considered significantly more than the abstract idea itself. The claim also includes “updating the lease history….” This additional element, absent further detail, is considered “receiving, processing, and storage of data,” which is listed in the MPEP 2106.05 (d) (II) (iii-iv) as an example of conventional computer functioning- see “electronic recordkeeping,” citing Alice Corp., and “storing and retrieving information in a memory” citing Versata Dev Grp v SAP, and OIP Techs v Amazon.com. Therefore, this additional element is not considered significantly more. Claim 9 includes “a machine learning model, wherein the machine learning model adjusts an algorithm that determines the eligible leased vehicle based on the leased history.” However, the use of the model is recited at a very high level of generality with no detail as to how or why the model adjusts the algorithm or what the algorithm even is. Therefore, the “machine-learning model” and the “adjusting the algorithm” is considered the equivalent of “apply it,” or using a computer as a tool to automate the abstract idea (see MPEP 2106.05(f)). Therefore, these additional elements do not integrate the abstract idea into a practical application and are not considered significantly more. Claim 10 is considered part of the abstract idea, as a human operator with access to the weather event data and the customer data could make the geography-based determination as part of the mental process. Claim 21 does not change the analysis and is considered part of the abstract idea, as the information used to do an analysis or calculate a weather event rating including an opt-in or opt-out could still be used in the analysis or calculations as part of a mental process. Claims 22 is also considered part of the abstract idea as the type of lease history or the specific information the threshold for comparison is based on does not change the analysis. For claim 23, “predict an occurrence of a lease default if the lease deferral offer is issued on the lease history of customers, generate a confidence value associated with the lease default, determine a degree and type of electronic notification for providing the customer based on the confidence value associated with the lease default and calculate the eligibility rating for each of the one or more affected leased vehicles based on information pertaining to weather events” is considered part of the abstract idea as these steps can be performed as part of a business practice or mental process. The fact they are performed by a machine learning model that comprises a neural network that is trained to perform the steps and use two sets of training data is considered is considered the equivalent of “apply it,” or using a computer as a tool to automate the abstract idea (see MPEP 2106.05(f)). The training and use of the model is recited at a high level of generality. Therefore, these additional elements are not considered significantly more than the abstract idea itself. Claim 24 is considered part of the abstract idea, as the type or degree of electronic notification that is chosen does not change the analysis. The other dependent claims mirror those already discussed above.
Therefore, claims 1-6, 8-16, and 18-24 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. Vs. CLS Bank International et al., 2014 (please reference link to updated publicly available Alice memo at http://www.uspto.gov/patents/announce/alice_pec_25jun2014.pdf as well as the USPTO January 2019 Updated Patent Eligibility Guidance.)
Response to Arguments
Regarding the rejections based on 35 USC 101
Regarding the applicant’s argument on pages 9-11 of the response that the claims do not recited an abstract idea because the improvement is not capable of being achieved through a mental process of a human as the improvement of being able to automatically determine and offer lease deferrals to customers…ingestion of numerous data fields… analysis of the dynamic nature of these data fields and the computation of permutations of potential alternate lease deferral times/terms as well as the obtaining of GPS data…cannot be performed in the human mind:
First of all, the examiner points out that the primary determination of the claims reciting an abstract idea is that they recite a “certain method of organizing human activity, namely a business practice. The determination they recite a mental process was a secondary determination.
Further, the examiner points out that the Step 2A, Prong 1 analysis identifies if the claims recite an abstract idea such as a mental process or a business practice. This does not mean that each and every limitation in the claim can be performed in the human mind. That is why the other limitations beyond the abstract idea itself are analyzed under the Step 2A, Prong 2 and Step 2B analyses. The examiner points the applicant to Court decisions such as buySAFE V Google and OIP Techs V Amazon.com among others in which there are clearly multiple interacting technical components in these e-commerce systems yet the Court still
found the claims to recite and be directed to an abstract idea. The MPEP 2106.05 (f) lists the example of claims integrating an abstract idea into a practical application as including "an improvement to the computer itself, another technology, or the technical field." Further, it states that the claims have not been integrated into a practical application when "an additional element merely uses a computer as a tool to perform an abstract idea." The automating of an abstract idea using a computer, or being done "automatically," such as including ingesting of digital information only automates the mental process or
business practice. Regarding the applicant's argument that "the human mind cannot transmit an electronic notification or retrieve information pertaining to a weather event from a weather reporting system or transmit electronic notification to a customer': As stated above, the examiner points out that the Step 2A, Prong 1 analysis identifies if the claims recite an abstract idea such as a mental process or business practice. This does not mean that each and every limitation in the claim can be performed in the human mind or is part of the abstract business practice.
Further. The examiner points the applicant to Example 47 of the USPTO PEG Eligibility Examples, which was highlighted in the August 2024 Memorandum regarding AI and machine learning. In this example, claim 2 is given as an example of ineligible subject matter even though training and use of a machine learning model. Claim 2 is as follows:
CLAIM 2:
A method of using an artificial neural network (ANN) comprising:
(a) receiving, at a computer, continuous training data;
(b) discretizing, by the computer, the continuous training data to generate input data;
(c) training, by the computer, the ANN based on the input data and a selected training algorithm to generate a trained ANN, wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm;
(d) detecting one or more anomalies in a data set using the trained ANN;
(e) analyzing the one or more detected anomalies using the trained ANN to generate anomaly data; and
(f) outputting the anomaly data from the trained ANN.
Regarding the applicant's argument on pages 12-13 of the response that under Step 2A, Prong 2 of the analysis the claims integrate any abstract idea into a practical application because they are similar to Examples 42 and 48 of the 2019 Revised Subject Matter Eligibility Guidance:
The examiner points out that in Example 42 of the January 2019 Updated PEG, it is explained that “the claim recites a combination of additional elements including storing information, providing remote access over a network, converting updated information that was input by a user in a non-standardized form to a standardized format, automatically generating a message whenever updated information is stored, and transmitting the message to all of the users. The claim as a whole integrates the method of organizing human activity into a practical application. Specifically, the additional elements recite a specific improvement over prior art systems by allowing remote users to share information in real time in a standardized format regardless of the format in which the information was input by the user. “ The examiner sees no such technical improvement in the claimed invention. Regarding Example 48, the set of claims is a highly technical set of steps that recites a very specific set of steps for using the AI. Even then, which is as follows, is still considered ineligible:
A speech separation method comprising:
(a) receiving a mixed speech signal x comprising speech from multiple different sources sn, where n ∈ {1, . . . N};
(b) converting the mixed speech signal x into a spectrogram in a time-frequency domain using a short time Fourier transform and obtaining feature representation X, wherein X corresponds to the spectrogram of the mixed speech signal x and temporal features extracted from the mixed speech signal x; and
(c) using a deep neural network (DNN) to determine embedding vectors V using the formula V = fθ(X), where fθ(X) is a global function of the mixed speech signal x.
Only when the additional steps of claim 2 are added are the claims considered eligible:
(d) partitioning the embedding vectors V into clusters corresponding to the different sources sn;
(e) applying binary masks to the clusters to create masked clusters;
(f) synthesizing speech waveforms from the masked clusters, wherein each speech waveform corresponds to a different source sn;
(g) combining the speech waveforms to generate a mixed speech signal x' by stitching together the speech waveforms corresponding to the different sources sn, excluding the speech waveform from a target source ss such that the mixed speech signal x' includes speech waveforms from the different sources sn and excludes the speech waveform from the target source ss; and
(h) transmitting the mixed speech signal x' for storage to a remote location.
The examiner does not see technical steps such as “combining the speech waveforms to generate a mixed speech signal x by stitching together the speech waveforms corresponding to different sources sn….”
Regarding the applicant's argument on pages 13-15 of the response that the claims "amount to significantly more than an abstract idea because of processes that are not routine and conventional, and also because the non-conventional and non-generic arrangement that provides a technical improvement in the art:
The applicant seems to be using language from the BASCOM decision (non-conventional
arrangement of pieces). However, in the BASCOM decision, the Court identified the actual physical arrangement of pieces, namely the internet filter being placed on the server side but still being able to provide user-specific internet filtering, as the reason for eligibility. The arrangement of pieces was not just a set of steps. Additional elements considered improvements and not simply conventional include such as "an improvement to the computer itself, another technology, or the technical field." The examiner points
the applicant to the Enfish decision in which the claims were found to be patent eligible because the self-referential table caused the computer to be improved in its functionality in both speed and efficiency of data processing no matter what data it was processing because of the incorporation of the referential table into the computer itself. The examiner sees no such similar improvement in the current claims.
Regarding the applicant's argument on page 15 of the response based on the recent September 2025 Memorandum addressing the recent ARP decision in Ex Parte Desjardins
The current claims only recite machine learning and training and use of a neural network at a very high level. This is different than the very specific technical improvement pointed to by the ARP in the Ex parte Desjardins decision in which the MLM is trained on a second task while adjusting specific values in performing of the second task by the MLM to protect performance of the MLM on the first task in order to avoid catastrophic forgetting. The ARP states that “When evaluating the claim as a whole, we discern at least the following limitation of independent claim 1 that reflects the improvement: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task." We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation.”
The examiner sees no such improvement in the claimed invention, only the training and use of a generic neural network recited at a high level of generality in order to automate some of the steps of the abstract idea.
Therefore, the arguments are not persuasive and the rejection is sustained.
Conclusion
Applicant amendment(s) necessitated the new grounds of rejection set forth in this Office Action. Therefore, 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 extension fee 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 date of this final action.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Luis A. Brown whose telephone number is 571.270.1394. The Examiner can normally be reached on M-F 8:30am-4:30pm EST. If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, JESSICA LEMIEUX can be reached at 571.270.3445.
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/LUIS A BROWN/Primary Examiner, Art Unit 3626
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