DETAILED ACTION
Notice of AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Application
Claims 1-8, 11-12, 14-20, and 22-23 were pending and were rejected in the previous office action. Claims 1, 14, 16, 18, and 20 were amended in the 8/18/2026 response. Claims 1-8, 11-12, 14-20, and 22-23 remain pending and are examined in this office action.
Response to Arguments
Claim Objections:
Claims 1, 14, 16, 18, and 20 were previously objected to, and are overcome by amendments to claims 1, 14, 16, 18, and 20 to correct the previously identified informalities. The previous objections have been withdrawn.
35 USC § 101:
Applicant’s arguments with respect to the § 101 rejection of claims 1-8, 11-12, 14-20, and 22-23 (pgs. 10-11, remarks filed 8/18/2026) have been fully considered, but they are not persuasive.
Applicant argues that the claims have been amended to reflect an improvement to how the model(s) are trained (pgs. 10-11, remarks). However, the examiner respectfully disagrees. While claims 1, 18 and 20 are amended to recite “iteratively testing the plurality of machine-learning models while removing variables of the plurality of variables that have lowest correlation with respective known capacity statuses of the plurality of repair facilities, to identify a subset of the plurality of variables of the historical service data having a highest correlation with the respective known capacity statuses of the plurality of repair facilities” – the amendment merely describes selecting data with a highest correlation with capacity statuses by removing the variables that have the lowest correlation (which describe the abstract idea), while using this selected data for iterative training of the one or more machine learning model(s) being used to apply the abstract idea.
The examiner’s previous suggestion to further prosecution differs from the current amendment and also hinged upon reducing a training set in a way that provided a technical improvement over the prior art (e.g. a specific technical mechanism of improving performance of the machine learning model while reducing the size of the training dataset and the associated computational resources required), and which is reflected in both the claims and the specification. However, upon further review of the level of detail described in the specification and in view of the amended claims, the claimed invention only requires, at a high level of generality, selecting data that affects predictions of which repair facility to use and how busy the corresponding repair facility is by removing variables that don’t show an effect on the predictions. These limitations and the general concept of selecting training data would not suggest an improvement to technology or computer functionality to one of ordinary skill in the art, and nothing in the claims or specification describes a specific technological improvement to model training or the performance of a machine learning model itself. See MPEP 2106.05(a), showing “An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome.” Also See Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025), showing “[P]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.”. In that case, similar to here, “[t]he requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement” because “[i|terative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning.” Id. at 1212. The “removing variables of the plurality of variables that have lowest correlation with respective known capacity statuses of the plurality of repair facilities, to identify a subset of the plurality of variables” and then retraining a model using the variables, as recited in the current claims, directly corresponds to iterative training using selected training material as per Recentive Analytics above. The described methods for training one or more generically recited machine learning models does not improve the performance of the machine learning models similar to Ex Parte Desjardins.
For example, further review of paragraphs [0024] and [0033] (which were previously discussed in applicant’s 6/4/2026 remarks prior to the examiner’s suggestion) simply describe removing inputs that don’t affect the resulting prediction. This variable selection further describes the inputs used to carry out the abstract idea – and that the selected inputs being used for training/re-training a machine learning model that is applied to generate the capacity/availability prediction does not add anything more than mere instructions to apply the abstract using generic computer implementation. Paragraph [0138] describes more accurately predicting which facilities have capacity, or the speed/efficiency/accuracy in which such calculations are performed, which at most provides an improvement to the underlying abstract idea or describes the improved speed or efficiency inherent with applying the abstract idea on a computer, rather than an improvement to technology.
In summary, the instant claims do not recite a specific technical mechanism that improves how machine learning models are trained, but instead recite an abstract idea (ranking a plurality of repair facilities, receiving a repair request to repair a vehicle, adjusting the ranking based upon current service data and a probability of each repair facility being over-capacity, and determining and displaying one or more available repair facilities to service the repair request) that is being applied by a processor, and by training and implementing generically recited machine learning models to receive an input and provide an output. Notably, other than the training steps (which describe iterative training of machine learning models at a high level of generality), the claims merely input data in the at least one machine learning models to “execute” the at least machine-learning model and receive an “output from the at least one machine-learning model” that is used to further perform the abstract idea. Thus, after full consideration, it is clear that the implementation and training of the machine learning model, as described, does not provide an improvement to the performance of a machine learning model itself, or a technological improvement to model training.
The § 101 rejection of claims 1-8, 11-12, 14-20, and 22-23 is maintained and updated below to reflect the 8/18/2026 amendments.
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 1-8, 11-12, 14-20, and 22-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Step 1:
Claims 1-8, 11-12, and 14-17 recite “A computer system…” (i.e. a machine); claims 18-19 and 22-23 recite “A computer-implemented method…” (i.e. a process); and claim 20 recites “At least one non-transitory computer-readable storage media having computer-executable instructions embodied thereon…” (i.e. an article of manufacture). These claims fall under one of the four categories of statutory subject matter and as a result, pass Step 1 of the subject matter eligibility test. However, “Determining that a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 U.S.C. 101 (i.e., process, machine, manufacture, or composition of matter) in Step 1 does not end the eligibility analysis, because claims directed to nothing more than abstract ideas (such as a mathematical formula or equation), natural phenomena, and laws of nature are not eligible for patent protection.” See MPEP 2106.04. Accordingly, the examiner continues the subject matter eligibility analysis below.
Step 2A Prong One:
Independent claims 1, 18 and 20 recite limitations (additional elements omitted) for monitoring of a workload for a plurality of repair facilities (claims 1/18) and for detecting and acting upon operator reliance to vehicle alerts (claim 20), including limitations to:
generate initial workload rankings for a plurality of repair facilities…
correlate historical service data to a respective probability that any of the plurality of repair facilities is over-capacity, the historical service data including a plurality of variables including at least geographic location of the respective repair facility, vehicle damage attributes, and repair service attributes,
by removing variables of a plurality of variables that have lowest correlation with respective known capacity statuses of the plurality of repair facilities, to identify a subset of the plurality of variables of the historical service data having a highest correlation with respective known capacity statuses of the plurality of repair facilities;
collect, from the plurality of repair facilities, a plurality of current service data for the plurality of repair facilities;
receive…a query requesting service for repairing a user vehicle provided by one or more of the repair facilities, the query including a geographic location of a user computer device;
using the subset of variables of the current service data and content of the query as inputs…using an output…adjust the initial workload rankings for the plurality of repair facilities based upon the probability, for each of the plurality of repair facilities, that the respective repair facility is over-capacity;
determine one or more repair facilities most likely to have availability to provide the service for repairing the user vehicle based upon the adjusted workload rankings of the plurality of repair facilities; and
display…a list of the one or more determined repair facilities
The limitations of independent claims 1, 18 and 20 above are determined to recite an abstract idea (ranking a plurality of repair facilities and correlating historical service data to a probability of over-capacity for each repair facility, receiving a repair request to repair a vehicle, adjusting the ranking based upon current service data and the probability of each repair facility being over-capacity, and determining and displaying one or more available repair facilities to service the repair request) for the reasons discussed in the following continued Step 2A Prong One analysis. Note that “An abstract idea can generally be described at different levels of abstraction.” Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1240-41 (Fed. Cir. 2016).
As described in MPEP 2106.04(a)(2)(II), claim limitations which recite commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations) or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) fall into the “certain methods of organizing human activity” category of judicial exceptions. Therefore, since the processes described by the limitations above amount to a commercial interaction and managing interactions between people (i.e. ranking a plurality of repair facilities and correlating historical service data to a probability of over-capacity for each repair facility, receiving a repair request to repair a vehicle, adjusting the ranking based upon current service data and the probability of each repair facility being over-capacity, and determining and displaying one or more available repair facilities to service the repair request), the claims fall into the “certain methods of organizing human activity” grouping of abstract ideas.
As described in MPEP 2106.04(a)(2)(III), “[T]he "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” and “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” The limitations recited by the representative independent claims 1, 18 and 20 above, under the broadest reasonable interpretation and but for the use of generic computer components, cover concepts (e.g. observation, evaluation, judgment, and opinion) that can reasonably be performed in the human mind or by the human mind with the aid of simple tools such as pen and paper. For example, the “collect” and “receive” steps amounts to an observation, while the “generate,” “correlate,” “removing,” “identify,” “adjust,” and “determine” steps are evaluations, judgments, or opinions. Furthermore, displaying a list of the one or more repair facilities (but for the use of generic computers/computer components, which is analyzed below) is analogous to outputting a list of the one or more repair facilities by a human, e.g. writing down a list using pen and paper. Therefore, as the processes above described by the representative independent claims 1, 18 and 20 can be characterized as mental processes (i.e. observation, evaluation, judgment, and opinion), but for the recitation of generic computer components in the claims, the claims fall under the “mental processes” category of judicial exceptions (i.e. abstract ideas).
As claims 1, 18 and 20 are identified by the examiner as reciting concepts that fall under more than one abstract idea grouping (i.e. “certain methods of organizing human activity” and “mental processes”), the examiner considers the limitations together as a single abstract idea for the purposes of the Step 2A Prong Two and Step 2B analysis, in accordance with MPEP 2106.04(II)(B).
Step 2A Prong Two:
The judicial exception (i.e. abstract idea) recited in claims 1, 18 and 20 is not integrated into a practical application because the claims recite mere instructions to apply the abstract idea (i.e. ranking a plurality of repair facilities and correlating historical service data to a probability of over-capacity for each repair facility, receiving a repair request to repair a vehicle, adjusting the ranking based upon current service data and the probability of each repair facility being over-capacity, and determining and displaying one or more available repair facilities to service the repair request) using generic computers/computer components (i.e. “A computer system…comprising at least one processor in communication with at least one memory device, the computer system in communication with a user computer device associated with a user, the at least one processor is programmed to…,” “a plurality of machine-learning models”/”at least one machine-learning model of the plurality of machine-learning models,” “a user computer device,” and “an interactive user interface” displayed on the computer device of claim 1; “computer-implemented method…performed by one or more processors in communication with a memory,” “a plurality of machine-learning models”/“at least one machine-learning model of the plurality of machine-learning models,” “a user computer device,” and “an interactive user interface” displayed on the user computer device of claim 18; and “At least one non-transitory computer-readable storage media having computer-executable instructions embodied thereon, wherein when executed by a computer system…including one or more processors and a memory, the computer-executable instructions cause the one or more processors to…,”“a plurality of machine-learning models”/”at least one machine-learning model of the plurality of machine-learning models,” “a user computer device,” and “an interactive user interface” displayed on the user computer device of claim 20). See MPEP 2106.05(f), showing “[C]laims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp.,” and that the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application.
While the claims recite the execution of a plurality of machine-learning models trained on a plurality of variables for historical service data for the plurality of repair facilities to provide output used to predict a probability that respective repair facilities are over service capacity and adjust workload rankings of the plurality of repair facilities (claims 1, 18 and 20), there is no indication in the claims or the specification that the claimed invention improves machine-learning technology itself, but instead recite generic machine learning models to receive various input data and generate an output (adjust workload rankings). The claims also recite limitations to “train a plurality of machine-learning models to correlate historical service data to a respective probability that any of the plurality of repair facilities is over-capacity, the historical service data including a plurality of variables including at least geographic location of the respective repair facility, vehicle damage attributes, and repair services attributes, wherein the training includes: iteratively testing the plurality of machine-learning models while removing variables of the plurality of variables that have lowest correlation with respective known capacity statuses of the plurality of repair facilities, to identify a subset of the plurality of variables of the historical service data having a highest correlation with respective known capacity statuses of the plurality of repair facilities; and retraining the plurality of machine-learning models to execute with the subset of variables” – which indicates that the machine-learning models are iteratively trained and tested based on a selected subset of the most relevant variables. However, see Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025), showing “[P]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” In that case, similar to here, “[t]he requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement” because “[i]terative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning.” Id. at 1212. The Recentive Analytics decision makes clear that iterative training of machine learning models using selected training material (note that “removing variables…to identify a subset…,” for training the model(s) is directly analogous to selection of training material) is incident to the very nature of machine learning and does not provide a technological improvement. Further, as identified above, the step for “removing variables…to identify a subset of the plurality of variables” merely describes the abstract idea by selecting data with a highest correlation with capacity statuses by removing the variables that have the lowest correlation, and the implementation of this step as part of testing the models to identify the subset and then using this selected data for iterative training does not provide any technological improvement to how models are trained/computer functionality/any other technology, nor does it add anything more than mere instructions to apply the abstract idea using one or more generic machine learning models and the use of generic machine learning models in their ordinary capacity. Nothing in the claims or specification would suggest to one of ordinary skill in the art that the claimed invention represents an improvement to machine learning technology rather than merely using existing machine learning technology, recited at a high level of generality, to apply an abstract idea for determining/ranking repair facilities Thus, the steps for training, testing, retraining, and “executing” the machine learning models as claimed represents the use of existing machine learning technology and generic computer implementation (generic machine learning models) as a tool to apply the abstract idea.
In addition, the use of the user computer device to provide a query and display information on an interactive user interface (i.e. display one or more determined repair facilities) also merely adds generic computer implementation to output information, rather than anything that integrates the abstract idea into a practical application. The additional elements recited in the claims do not represent an improvement to machine learning technology and/or the recited machine learning models, improve the functioning of any computers, user devices, or user interfaces, or otherwise improve any other technology. Therefore, because the claims, considered as a whole, do not recite anything that integrates the abstract idea into a practical application, the claims are directed to an abstract idea.
Step 2B:
Claims 1, 18 and 20 do not include additional elements, whether considered alone or as an ordered combination, that are sufficient to amount to significantly more than the judicial exception (i.e. abstract idea) because as mentioned above, the claims recite mere instructions to apply the abstract idea (i.e. ranking a plurality of repair facilities and correlating historical service data to a probability of over-capacity for each repair facility, receiving a repair request to repair a vehicle, adjusting the ranking based upon current service data and the probability of each repair facility being over-capacity, and determining and displaying one or more available repair facilities to service the repair request) using generic computers/computer components (i.e. “A computer system…comprising at least one processor in communication with at least one memory device, the computer system in communication with a user computer device associated with a user, the at least one processor is programmed to…,” “a plurality of machine-learning models”/“at least one machine-learning model of the plurality of machine-learning models,” “a user computer device,” and “an interactive user interface” displayed on the user computer device of claim 1; “computer-implemented method…performed by one or more processors in communication with a memory,” “a plurality of machine-learning models”/”at least one machine-learning model of the plurality of machine-learning models,” “a user computer device,” and “an interactive user interface” displayed on the user computer device of claim 18; and “At least one non-transitory computer-readable storage media having computer-executable instructions embodied thereon, wherein when executed by a computer system…including one or more processors and a memory, the computer-executable instructions cause the one or more processors to…,”“a plurality of machine-learning models”/”at least one machine-learning model of the plurality of machine-learning models,” “a user computer device,” and “an interactive user interface” displayed on the user computer device of claim 20).
As mentioned above, the recited steps for training, testing, retraining (i.e. iteratively training the machine learning models), and execution of a plurality of machine-learning models trained/retrained on a plurality of selected variables (selected training material) for historical service data for the plurality of repair facilities to provide output used to predict a probability that respective repair facilities are over service capacity and adjust workload rankings of the plurality of repair facilities (claims 1, 18 and 20) does not improves machine-learning technology itself, but instead recites the use of machine learning technology in its ordinary capacity and using the machine learning models to apply the abstract idea. See Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025), showing “[P]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” In that case, similar to here, “[t]he requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement” because “[i]terative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning.” Id. at 1212. The Recentive Analytics decision makes clear that iterative training of machine learning models using selected training material (note that “removing variables…to identify a subset…,” for training the model(s) is directly analogous to selection of training material) is incident to the very nature of machine learning and does not provide a technological improvement. Further, as identified above, the step for “removing variables…to identify a subset of the plurality of variables” merely describes the abstract idea by selecting data with a highest correlation with capacity statuses by removing the variables that have the lowest correlation, and the implementation of this step as part of testing the models to identify the subset and then using this selected data for iterative training does not provide any technological improvement to how models are trained/computer functionality/any other technology, nor does it add anything more than mere instructions to apply the abstract idea using one or more generic machine learning models and the use of generic machine learning models in their ordinary capacity. Nothing in the claims or specification would suggest to one of ordinary skill in the art that the claimed invention represents an improvement to machine learning technology rather than merely using existing machine learning technology, recited at a high level of generality, to apply an abstract idea for determining/ranking repair facilities. Thus, the steps for training, testing, retraining, and “executing” the machine learning models as claimed represents the use of existing machine learning technology and generic computer implementation (generic machine learning models) as a tool to apply the abstract idea. In addition, the use of the user computer device to provide a query and display information on an interactive user interface (i.e. display one or more determined repair facilities) also merely adds generic computer implementation to output information, rather than anything that integrates the abstract idea into a practical application.
The additional elements recited in the claims do not improve the functioning of any computers, or otherwise provide an improvement to any other technology, and considering the additional elements as an ordered combination does not add significantly more than the abstract idea. Therefore, claims 1, 18 and 20 recite an abstract idea without significantly more.
Dependent Claims 2-8, 11-12, 14-17, 19, and 22-23:
Claims 2-8, 11-12, 14-17, 19, and 22-23 are directed to the same abstract idea as independent claims 1 and 18 above without significantly more, as they do not recite anything that integrates the abstract idea into a practical application or amounts to significantly more than the abstract idea.
Claims 2, 14, and 19 recite the following limitations, which further describe the abstract idea and recite mere instructions to apply the abstract idea using generic computers/generic computer implementation: “wherein the at least one processor is further programmed to receive the plurality of current service data from a plurality of computer devices associated with the plurality of repair facilities” (claim 2); “the at least one processor is further programmed to generate a user interface to provide vehicle repair information based on the geographic location of the user computer device” (claim 14 – which uses a generic user interface to display an output based on location information); and “receiving the plurality of current service data from a plurality of computer devices associated with the plurality of repair facilities” (claim 19).
Claims 3-6, 8, 11, 17, and 22 recite the following limitations, which do not add any additional elements beyond those already addressed above (e.g. at least one processor and the one or more machine learning models), but merely further describe the abstract idea being carried out above by reciting limitations for: “wherein the plurality of current service data includes a current workload of repairing a plurality of vehicles…” (claim 3); “automatically remove a repair facility from a list…” (claim 4); “remove a repair facility from the one or more determined repair facilities…” (claim 5 and similar claim 22); “receive a plurality of performance data from the plurality of repair facilities…” (claim 6); “determine a condition of the user vehicle to be repaired; and re-rank the plurality of repair facilities…” (claim 8); “reduce a workload ranking for a repair facility…” (claim 11); and “rank the plurality of repair facilities in the corresponding geographic regions” (claim 17).
Claims 6, 7, 11-12, 16, and 23 recite limitations to: “retrain the plurality of machine-learning models based upon the plurality of performance data” (claim 6); “collect a plurality of updated current service data…and iterative re-execute the plurality of machine-learning models…to iteratively generate updated workload rankings…” (claim 7 and similar claim 23); “when the one or more machine-learning models determines that the repair facility is over capacity” (claim 11); “wherein iterative testing of the plurality of machine-learning models results in identifying a variable of average time to completion as highly correlated with the probability that the respective repair facility is over-capacity, and wherein the re-training comprises re-training the plurality of machine-learning models to output a high probability that a respective repair facility is over capacity when the respective average time to completion is greater than or equal to 45 days” (claim 12); and “select the at least one machine-learning to execute based on the geographic location of the user computer device” (claim 16). These limitations do not add anything that indicates an improvement to machine-learning models or machine-learning technology, but instead describe limitations that are consistent with the ordinary functioning of basic machine learning models (training, re-training, testing, updating, executing, and selecting the machine learning models to receive an input and generate an output) and use generic machine learning and generic computer components to apply the abstract idea.
Claim 15 also further describes the field of use of the machine-learning models (“wherein each model of the plurality of machine-learning models represents a different geographic region, and wherein each machine-learning model is configured to model a respective plurality of repair facilities in the corresponding geographic region” of claim 15. Thus, the limitations of claim 15 at best generally link the performance of the abstract idea to a particular technical environment, but do not add anything that integrates the abstract idea into a practical application or adds significantly more.
Therefore, claims 1-8, 11-12, 14-20, and 22-23 are ineligible under § 101.
Novelty/Non-Obviousness
Claims 1-8, 11-12, 14-20, and 22-23 were previously indicated as novel and non-obvious over the prior art. See the 6/16/2023 Non-Final Rejection for the specific reasons for indicating novelty/non-obviousness. An updated search did not change the previous determination.
US 20200387755 A1 to Hagen et al. (Hagen) is newly cited herein as relevant to generating a reduced training data set and training/retraining a machine learning model with the reduced training data set (Hagen: ¶ 0019, ¶ 0027-0028, ¶ 0062).
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 Hunter Molnar whose telephone number is (571)272-8271. The examiner can normally be reached Monday - Friday, 7:30 - 4:00 EST.
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/HUNTER MOLNAR/Examiner, Art Unit 3628
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