DETAILED ACTION
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 Claims
This Office Action is in response to the Request for Continued Examination filed on April 20, 2026. Claims 1-10, 15 and 16 have been cancelled. Claims 21-24 have been newly added. Thus, claims 11-14 and 17-24 are pending. Claim 11 is independent.
Priority
Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on April 20, 2026 has been considered. The submission is in compliance with the provisions of 37 CFR 1.97. The Forms PTO-1449 are signed and attached hereto.
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 11-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
101 Analysis – Step 1
Claim 11 is directed to a method (i.e., a process). Therefore, claim 11 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection.
Claim 1 recites:
11. A forecasting method, which is computer implemented, for forecasting a number of residual landings corresponding to achievement of a removal threshold of an identified tire that is installed on an identified aeroplane, the forecasting method comprising the following steps:
a step of introducing parameters, influencing the identified tire, into a system that carries out the forecasting method, the system comprising a communication network that manages data input into the system, the communication network having one or more communication servers that manage parameter data corresponding to the parameters influencing the identified tire, and having at least one communication device that captures and that transmits these data to the communication servers, the step of introducing parameters comprising the following steps:
a step of identifying parameters influencing the identified tire, which step is carried out by the communication servers of the system, and in which step the parameters that are identified comprise data corresponding to historic information and to general information of the identified tire, the historic information comprising historic data corresponding to historic flights of the identified aeroplane having the identified tire installed thereon, the historic data including at least data for a departure or an arrival of the identified aeroplane, data for airports visited by the identified aeroplane, data for a manufacturer of the identified aeroplane, or data for a version of a model of the identified aeroplane; and
a step of creating a wear-state training database, which is introduced into a forecasting model comprising an Al neural network for forecasting the number of residual landings corresponding to achievement of the removal threshold of the identified tire, the wear- state training database comprising (a) at least one of 3D or 2D, or 1D images of wear profiles of example tires corresponding to known wear states of the example tires and (b) a corresponding number of landings carried out by the example tires;
a step of training the Al neural network of the forecasting model to predict the number of residual landings corresponding to achievement of the removal threshold of the identified tire:
collecting parameter data corresponding to one or more influencing parameters by at least one of (a) sensors for delivering data to the training database regarding characteristics of operation of the identified tire or (b) communication devices that capture and transmit data related to historic flights of the identified aeroplane;
associating the parameter data to known wear states corresponding to the number of landings carried out by the identified tire; and
training the Al neural network using the wear-state training database, resulting in the Al neural network being configured to predict the number of residual landings corresponding to achievement of the removal threshold of the identified tire;
a step of forecasting, using the Al neural network after the training of the Al neural network, the number of residual landings before achievement of the removal threshold of the identified tire, during which step the number of residual landings of the identified tire is computed on a basis of the parameter data a comparing step during which the number of residual landings before achievement of the removal threshold of the identified tire, which is output by the forecasting model, is compared with a value of the removal threshold of the identified tire, and
as a consequence the system creates a maintenance schedule for the identified tire;
a determination step including determining that the identified tire has to be inspected or replaced based on the comparing step;
a removing step including removing the identified tire from the identified aeroplane without removing the tire prematurely and before the tire is degraded beyond use, such that the identified tire is a removed tire when the removing step is completed; and
a replacement step, including, after removing the removed tire, inserting, into the aeroplane, a replacement tire that is new and that is of the same type as the removed tire, and updating the forecasting model to have a tread considered new.
The examiner submits that the foregoing bolded limitations constitute “mathematical concepts” because under its broadest reasonable interpretation, the claim covers training a neural network on image-based wear profiles with correlated with landing counts; using the trained network to compute a predicted residual landing count; and comparing the computed prediction to a threshold value.” The bolded limitations also constitute a “mental process” because under its broadest reasonable interpretation, the claim covers “collecting information about a tire’s wear history and flight patterns, predicting how many landings remain before replacement is needed based on that information, comparing the prediction to a threshold and deciding whether to replace the tire.” The scheduling maintenance of industrial equipment based on predicted remaining useful life derived from operation history and condition monitoring is a method of organizing human activity. More particularly “introducing parameters”. . . into a system” encompasses data collection; “identifying parameters influencing the identified tire” encompasses data collection; “creating a wear-state training database . . . the wear- state training database comprising (a) at least one of 3D or 2D, or 1D images of wear profiles” encompasses data organization; “training the Al neural” encompasses mathematical model training; “collecting parameter data . . . of (a) sensors . . . or (b) communication devices” encompasses data collection; “associating the parameter data to known wear states” encompasses data classification; “training the Al neural network using the wear-state training database” encompasses mathematical model training; “forecasting, using the Al neural network . . ., the number of residual landings . . . the number of residual landings of the identified tire is computed on a basis of the parameter data a comparing step . . . by the forecasting model, is compared with a value of the removal threshold” encompasses a mathematical prediction; “creates a maintenance schedule” encompasses information organization; “determining that the identified tire has to be inspected or replaced” encompasses a decision based on comparison output; “removing the identified tire from the identified aeroplane” encompasses a physical action on an object; “inserting, . . . a replacement tire” encompasses a physical action on an object; and “updating the forecasting model” encompasses a mathematical model update. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
11. A forecasting method, which is computer implemented, for forecasting a number of residual landings corresponding to achievement of a removal threshold of an identified tire that is installed on an identified aeroplane, the forecasting method comprising the following steps:
a step of introducing parameters, influencing the identified tire, into a system that carries out the forecasting method, the system comprising a communication network that manages data input into the system, the communication network having one or more communication servers that manage parameter data corresponding to the parameters influencing the identified tire, and having at least one communication device that captures and that transmits these data to the communication servers, the step of introducing parameters comprising the following steps:
a step of identifying parameters influencing the identified tire, which step is carried out by the communication servers of the system, and in which step the parameters that are identified comprise data corresponding to historic information and to general information of the identified tire, the historic information comprising historic data corresponding to historic flights of the identified aeroplane having the identified tire installed thereon, the historic data including at least data for a departure or an arrival of the identified aeroplane, data for airports visited by the identified aeroplane, data for a manufacturer of the identified aeroplane, or data for a version of a model of the identified aeroplane; and
a step of creating a wear-state training database, which is introduced into a forecasting model comprising an Al neural network for forecasting the number of residual landings corresponding to achievement of the removal threshold of the identified tire, the wear- state training database comprising (a) at least one of 3D or 2D, or 1D images of wear profiles of example tires corresponding to known wear states of the example tires and (b) a corresponding number of landings carried out by the example tires;
a step of training the Al neural network of the forecasting model to predict the number of residual landings corresponding to achievement of the removal threshold of the identified tire:
collecting parameter data corresponding to one or more influencing parameters by at least one of (a) sensors for delivering data to the training database regarding characteristics of operation of the identified tire or (b) communication devices that capture and transmit data related to historic flights of the identified aeroplane;
associating the parameter data to known wear states corresponding to the number of landings carried out by the identified tire; and
training the Al neural network using the wear-state training database, resulting in the Al neural network being configured to predict the number of residual landings corresponding to achievement of the removal threshold of the identified tire;
a step of forecasting, using the Al neural network after the training of the Al neural network, the number of residual landings before achievement of the removal threshold of the identified tire, during which step the number of residual landings of the identified tire is computed on a basis of the parameter data a comparing step during which the number of residual landings before achievement of the removal threshold of the identified tire, which is output by the forecasting model, is compared with a value of the removal threshold of the identified tire, and
as a consequence the system creates a maintenance schedule for the identified tire;
a determination step including determining that the identified tire has to be inspected or replaced based on the comparing step;
a removing step including removing the identified tire from the identified aeroplane without removing the tire prematurely and before the tire is degraded beyond use, such that the identified tire is a removed tire when the removing step is completed; and
a replacement step, including, after removing the removed tire, inserting, into the aeroplane, a replacement tire that is new and that is of the same type as the removed tire, and updating the forecasting model to have a tread considered new.
For the following reasons, the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of the removal and replacement tire steps are physical performance by anyone in response to any prediction. Accordingly, the steps are post-solution activity. The AI training includes collecting data, creating training database, associating data with labels, training the network consist of generic machine learning training and does not constitute a practical application. The maintenance schedule creation is based on data analysis and is abstract. The additional limitation is no more than mere physical action and model updates.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitations as an ordered combination or as a whole, the limitations add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitations do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the Revised Guidance, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
Dependent claims 12-14 and 17-24 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 12-14 and 17-24 are not patent eligible under the same rationale as provided for in the rejection of independent claim 11.
Therefore, claims 11-14 and 17-24 are ineligible under 35 USC §101.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEMETRA R SMITH-STEWART whose telephone number is (571)270-3965. The examiner can normally be reached 10am - 6pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Nolan can be reached at 571-270-7016. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DEMETRA R SMITH-STEWART/Examiner, Art Unit 3661
/PETER D NOLAN/Supervisory Patent Examiner, Art Unit 3661
