DETAILED ACTION
The following Final Office action is in response to Applicant’s communications received on 2/12/2026.
Notice of Pre-AIA or 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 Claims
Claims 1 and 13 are amended and claims 5 and 16 have been canceled. Claims 1-4, 7-15, and 18-23 are pending.
Response to Arguments
Applicant’s arguments received on 2/12/2026 have been fully considered but they are not persuasive.
Applicant argues that the pending claims are eligible for reasons similar to the recent Appeals Review Panel (ARP), noting support of subject matter eligibly of the claim being based on the specification of the application and noting paragraphs [0002] and [0130]-[0131] of the instant application. Applicant also argues that the machine-learning models are trained using specific repair data for the purpose of forecasting capacity, as set forth in [0127]-[0128] of the specification.
Examiner respectfully disagrees. In Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), the benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements were disclosed in the patent application specification. Specifically, the ARP upheld that the claims recited an abstract idea (i.e., mathematical concept) and step 2A prong 1. In step 2A prong 2, it was determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems, and that the claims reflected this improvement.
In contrast, the specification of the instant application discusses real-time monitoring of repairs and determining capacity at repair facilities, overcoming the challenges of manually contacting repair facilities to determine this information (which uses additional processing and memory usage). Using automation offers advantages, including but not limited to increased speed and responsiveness and monitoring the repair process across multiple facilities. The specification also asserts improved speed, efficiency and accuracy in performing calculations, such as predicting capacity. None of the cited paragraphs disclose challenges or improvements with respect to machine learning models. Looking to the independent claims, this claim language broadly recites training and retraining using certain inputs.
Per MPEP 2106.05(f)(2), use of a computer or other machinery in its ordinary capacity for economic or other tasks does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). See also FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016), where the increased speed in the process comes solely from the capabilities of the general-purpose computer. See also MPEP 2106.05(a), discussing that mere automation of manual processes using a generic computer may not be sufficient to show an improvement in computer functionality.
Here, the additional elements are claimed in a general manner without the details of how the solution to the problem is accomplished and more in a manner that is automating the tasks of the judicial exception. Therefore, it is not agreed that the pending claims are similar to those in the Ex Parte Desjardins decision.
Applicant argues that the claims are eligible based on the amendments that reflect improvement to technology, shown in the noted paragraphs, and the technological field of machine learning – using machine-learning to assess, analyze, and monitor real-time capacity.
Examiner respectfully disagrees. The paragraphs of the specification noted in the arguments as well as [0124]-[0127] do not identify improvements as to how the machine learning model itself operates, and instead appear to discuss generally training and using such models. Thus, the claim recites training at a high level of generality, i.e., as a generic computer performing generic computer functions, and does not include details of how such training is accomplished or performed.
Applicant further argues that the training, generate, update, and retrain limitations are not reasonably characterized as falling within a grouping of abstract ideas and should be evaluated as additional elements; and that when evaluated at step 2A prong 2, these elements integrate the recited abstract idea into a practical application and this the claims are not directed to an abstract idea. See [0003] and [0037] of the specification.
Examiner respectfully disagrees. In the instant application, [0003] and [0037] describe the need for better knowledge of capacity at repair facilities and monitoring what is occurring at the facilities to not, for example, overwhelm high performers, give priority to specific customers, etc. Predicting capacity at a repair facility is part of the recited abstract idea and is not reflective of a technical field, as argued. The claims suggest improvement in predicting capacity at a repair facility for repair appointments and prevent going over capacity. Per MPEP 2106.05(a)II., an improvement in the abstract idea itself is not an improvement in technology.
Further, the machine learning model is claimed at a high level of generality and provides nothing more than mere instructions to implement the recited abstract idea on a generic computer. Per MPEP 2106.05(f), the claim recites the idea of a solution or outcome without details on how the solution is accomplished. The claim invokes computers merely as a tool to perform the claimed process. Please see further discussion above .
Applicant further argues that additional elements should not be viewed in a vacuum and all claim limitations (and how they interact and impact each other) should be considered when evaluating eligibility; claim limitations should not be oversimplified; the specification should be considered; and the preponderance of the evidence standard should be applied.
In response, examiner notes that MPEP 2106.04(d)III, 2106.07, and the August 4, 2025, memorandum on Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101 were all considered when the eligibility of each claim was evaluated in the instant application. The analysis took into consideration all claim limitations, and considered each claim as a whole, when evaluating whether the recited abstract idea was integrated into a practical application or provided an inventive concept.
Finally, it is argued that the pending claims recite more than well-understood, routine, conventional functionality systems for user mobility analytics.
Examiner is unsure what is being referred to in the argument with regard to “user mobility analytics.” It is noted that nothing in the rejection is asserted as being insignificant extra-solution activity or being well known, routine, conventional. See MPEP 2106.05(d) and 2106.05(g).
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 1-4, 7-15, and 18-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 of the subject matter eligibility test entails considering whether the claimed subject matter falls within the four categories of statutory subject matter (i.e., process, machine, manufacture, or composition of matter). In Applicant’s case, claims 1-5, 7-16, and 18-23 pass Step 1.
However, for Step 2A Prong One of the subject matter eligibility test, independent claim 1 for example recites an abstract idea of researching vehicle repair facilities. The limitations that describe an abstract idea are indicated in bold below:
A computer system for monitoring a plurality of repair facilities, the computer system including 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:
store a plurality of historical vehicle repair information for a plurality of repair facilities, the plurality of historical vehicle repair information including, for each of the plurality of repair facilities, historical capacity data for a period of time and, for each of a plurality of vehicle repairs completed at the respective repair facility during the period of time, vehicle type data, damage type data, damage location data, damage severity data, needed repairs data, date/time of receipt, and date/time of completion;
train a machine-learning model, using the plurality of historical vehicle repair information as input training sets, to identify patterns in facility capacity as a function of vehicle repairs being completed at the respective repair facility;
collect a plurality of real-time vehicle repair information from the plurality of repair facilities;
for each of the plurality of repair facilities, input the corresponding real-time vehicle repair information into the trained machine-learning model to receive, as output therefrom, a current capacity as a percentage of a total capacity of the repair facility, and a forecast future capacity for the repair facility over a future interval of time:
using the current capacity and forecast future capacity for the plurality of repair facilities, determine in real-time a first repair facility of the plurality of repair facilities has reached or is approaching total capacity;
prevent a computer device associated with the first repair facility from scheduling further repair appointments until the forecast future capacity of the first repair facility reflects a threshold availability;
receive, from a user computer device, a query for information about one or more repair facilities;
generate a user interface to include results of the query, the user interface including at least one graphical indicator of the determined current capacity, relative to the total capacity, for at least one of the plurality of repair facilities including the first repair facility; and at least one graphical indicator of the forecast future capacity for the at least one of the plurality of repair facilities including the first repair facility;
instruct the user computer device to display the user interface;
using a plurality of real-time vehicle repair information for the plurality of repair facilities collected continuously or periodically over the future interval of time, compare the forecast future capacity with an actual capacity over the future time interval;
update the user interface to identify any discrepancies between the forecast future capacity and the actual capacity over the future interval of time; and
re-train the trained machine-learning model based at least in part on the identified discrepancies.
The limitations indicated above fall under the abstract idea subject matter grouping of certain methods of organizing human activity because such researching of vehicle repair facilities and their “current capacity” (interpreted as workload associated with customers and repair appointments) relates to the sales activities or commercial interactions of the vehicle repair facilities with their customers. Therefore, the claim falls under the sub-grouping of commercial or legal interactions. This reasonably also falls within the subgrouping of managing interactions between people in that it is scheduling appoints based on historical and real-time information about a facility and its capacity.
For Step 2A Prong Two of the subject matter eligibility test, the abstract idea is not integrated into a practical application. The additional elements of a computer system with processor and memory device and a user computer device with user interface including a graphical indicator as well as train and re-train a/the machine-learning model used to implement the abstract idea are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computers or merely using computers as a tool to perform an abstract idea. See MPEP 2106.05(f) regarding mere instructions to implement on a computer and merely using a computer as a tool. Further, with respect to the training and re-training of a machine-learning model, the claim recites no details about how the trained model operates or how the model is trained / retrained. Thus, the claim recites only the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished. When considering the claim as a whole and how the additional elements individually and in combination are used, the additional elements do not reflect integration of the abstract idea into a practical application.
Regarding Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, with respect to a practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer components or merely using computers as a tool to perform an abstract idea, generally linking to a field of use or particular technological environment and insignificant extra-solution activity. Applicant’s originally filed specification (see Figs. 4, 5 and paragraphs 0066, 0067, 0072) supports this conclusion with its disclosure of general-purpose computers to perform the abstract idea. When considering the claim as a whole and how the additional elements individually and in combination are used, the additional elements do not amount to significantly more than the abstract idea itself.
The dependent claims include the limitations of the independent claim and therefore recite the same abstract idea. Accordingly, the analysis and rationale discussed above regarding the independent claim and abstract idea also apply to the dependent claims. Also, the dependent claims further limit the abstract idea to a more narrow abstract idea by further including: store the collected real-time information (claim 2), analyze the real-time and historical vehicle repair information, calculate metrics and display them (claim 3), sort and display real-time vehicle repair information by geographic area (claim 4), based on received weather determined needed future capacity (claim 7), calculate future capacity (claim 8), calculate current capacity (claims 9, 10) and provide filtered information (claims 11 and 12). While these create a narrower abstract idea but does not transform the abstract idea into patent-eligible subject matter. Additional elements recited in the dependent claims include generic processing components/functionality recited at a high-level of generality (e.g., generic displaying/user interface, filtering information (claims 3-4, 9-12)) which do not impose any meaningful limits to integrate the abstract idea into a practical application nor do they provide for an inventive concept.
Claims 13-15 and 18-23 (directed to a method) recite limitations similar to those recited in the system claims addressed above and therefore the same analysis above with respect to the system claims also applies.
Applicant’s claims are not patent-eligible.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, 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 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 BETH V BOSWELL whose telephone number is (571)272-6737. The examiner can normally be reached M-F 8AM - 4:30PM.
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/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625