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 .
DETAILED ACTION
This is a non-final Office Action on the merits. Claims 20-38 are currently pending and are addressed below.
Priority
Acknowledgment is made of applicant's claim for priority application No. FR2202803 filed on 03/29/2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/27/2024 is being considered by the examiner.
Claim Objections
Claim 20 is objected to because of the following informalities: in line 5, the phrase “a step of performing a process of building a prediction model” is redundant. Appropriate correction is required.
Claim 32 is similarly objected to.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 20-38 are rejected under 35 U.S.C. 103 as being unpatentable over Rajkumar et al. (US 2020/0090419) in view of Laperle et al. (US 2021/0197625) and Agarwal et al. (US 2022/0219498).
Regarding claim 20:
Rajkumar teaches an estimation method implemented by a computer system for estimating a remaining useful life of an identified tire by aggregating influential parameters obtained from the identified tire and telematics information obtained from an identified vehicle having the identified tire mounted thereon (see at least abstract, ¶0001), the method comprising:
a step of performing a process of building a prediction model (see at least ¶0017, ¶0033), comprising the following:
a step of entering influential parameters of the identified tire into the system comprising data obtained by one or more communication devices of the system and transmitted to a server of the system and comprising the telematics information obtained from the identified vehicle (see at least ¶0022, ¶0028, ¶0047); and
a step of consolidating, by one or more processors of the system, the obtained influential parameters and the obtained telematics information in order to compile a plurality of independent journeys in order to establish at least one remaining useful life profile of the identified tire (determining number of days travelled, route traveled, miles, etc. see at least ¶0028. The Examiner notes that the instant claim language “in order to” is intended use language, and is not positively recited as a required limitation of the claim);
a step of training the prediction model that uses a consolidated output to establish a plurality of predictions corresponding to journeys taken by the identified vehicle having the identified tire mounted thereon and to estimate a number of journeys made by the identified tire, to predict the remaining useful life corresponding to a predicted mileage for removing the identified tire (training model, see at least ¶0030-0033);
a step of predicting, by the one or more processors, the number of journeys made in real time, wherein the predicting includes predicting a type of journey based on the telematics information including historical geographical coordinates (see at least Fig. 3B, ¶0056-0062); and
wherein the system creates a maintenance plan for the identified tire (schedule preventative maintenance, see at least ¶0015-0017).
Rajkumar does not explicitly teach the training of the model utilizing supervised learning.
Laperle teaches a system and method of monitoring vehicle tire wear, including utilizing supervised learning to determine a state of a tire, including a useful remaining life (see at least ¶0060-0063)
It would have been obvious to one of ordinary skill in the art at the time of filing of the invention to modify the tire maintenance prediction system and method as taught by Rajkumar with the well-known technique of training a prediction model utilizing supervised learning as taught by Laperle in order to provide the expected result of utilizing available data to build and train the model using conventional machine learning techniques for high accuracy and reliability.
The combination of Rajkumar and Laperle does not teach a comparison step as claimed.
Agarwal teaches a system and method for real-time health prediction of tires, including a comparison step, during which the remaining useful life before reaching the predicted mileage for removing the identified tire, derived from the prediction model, is compared with a defined removal threshold value corresponding to a predicted mileage for removing the identified tire (comparing index to manufacturers’ recommended value, see at least abstract, ¶0029, ¶0054-0058).
It would have been obvious to one of ordinary skill in the art at the time of filing of the invention to modify the system and method of predicting tire maintenance as taught by Rajkumar and Laperle with the technique of comparing a predicted value of tire life with a nominal value as taught by Agarwal in order to understand the deviation of tire from its normal usage (¶0055).
Regarding claim 21:
Rajkumar further teaches a step of storing the influential parameters of the identified tire and the telematics information in a database of the system, wherein the influential parameters of the identified tire include:
historical information of the identified tire including data corresponding to historical journeys of the identified vehicle having the identified tire mounted thereon (see at least ¶0029); and general information of the identified tire including data corresponding to the identified tire (see at least ¶0028-0029, ¶0050).
Rajkumar does not explicitly teach the information including a mounting position of a tire.
However it would have been obvious to one of ordinary skill in the art before the time of filing of the invention to include mounting location of a monitored tire in the database in order to quickly identify a monitored tire for determining which tire may need maintenance, to keep track of tire rotations, to distinguish between typically higher wear on front/steering side tires, or any other known considerations.
Regarding claim 22:
Rajkumar further teaches wherein the maintenance plan created by the system during the comparison step comprises:
a maintenance schedule, in which the identified tire remains mounted on the identified vehicle when a number of miles derived from the prediction model is greater than the defined removal threshold value for the identified tire (survival curve, see at least Fig. 4c-h, ¶0025).
Rajkumar does not explicitly teach an inspection schedule.
However, the Examiner notes that regular inspection is a conventional component of fleet management.
Therefore, It would have been obvious to one of ordinary skill in the art before the time of filing of the invention to modify the vehicle fleet component monitoring system and method as taught by Rajkumar, Laperle, and Agarwal by implementing a routine inspection of components, including tires prior to their expected service life interval in order to identify unusual wear, damage, or other potential untimely issues.
Regarding claim 23:
Rajkumar further teaches wherein the entering step further comprises a step of entering telematics data originating from one or more communication networks into the system (see at least network 106, ¶0022, Fig. 7, ¶0086).
Regarding claim 24:
Rajkumar teaches the limitations as in claim 20 above. Rajkumar further teaches entering the telematics information into the system that corresponds to each visit of the identified vehicle to a scheduled location; and entering the telematics data obtained by the location means into the system that corresponds to each journey of the identified vehicle (logging location data, including bus stops (scheduled locations), and routes traveled (each journey), see at least ¶0059, ¶0029).
Regarding claim 25:
Rajkumar teaches the limitations as in claim 24 above. Rajkumar is silent as to the particular positioning technology utilized.
Laperle further teaches determining vehicle location information utilizing one or more global positioning systems (see at least ¶0118).
It would have been obvious to one of ordinary skill in the art before the time of filing of the invention to modify the vehicle location tracking system as taught by Rajkumar with the exceedingly well-known GPS system as taught by Laperle in order to determine a location of a plurality of vehicles using a world-wide, widely available positioning system.
Regarding claim 26:
Rajkumar further teaches identifying a plurality of scheduled locations using historical data (see at least identifying number of bus stops, ¶0050, ¶0061).
Rajkumar does not explicitly teach wherein the identifying includes identifying coordinates of each scheduled location using historical global positioning system data obtained from the influential parameters incorporating the telematics information.
It would have been obvious to one of ordinary skill in the art before the time of filing of the invention to modify the data gathering with respect to historical trips and bus stops as taught by Rajkumar by identifying at least the bus stops by coordinate information as a matter of design choice in order to store more detailed data regarding scheduled stops, their frequency, their relative locations, to determine wear on the vehicle components based on time between stops, elevation changes, or other relevant data.
Regarding claim 27:
Laperle further teaches wherein the identified vehicle comprises a truck with a cab at the front and a chassis in the form of a flat body for connecting a transport container (see at least ¶0039).
Regarding claim 28:
Rajkumar further teaches wherein the historical information and/or the general information is generated and/or managed, at least partly, by one or more managers of industrial vehicles, including one or more fleet companies to which the identified vehicle belongs and/or one or more producers, including mining producers (see at least ¶0001-0002, ¶0015-0016, ¶0021).
Regarding claim 29:
Laperle further teaches wherein the entering step comprises a step of creating a reference database for the tires intended to be mounted on the identified vehicle (see at least database of inventory ¶0105).
Regarding claim 30:
Rajkumar further teaches wherein the learning model that is used to predict the remaining useful life during the training step includes a set learning method (see at least ¶0051-0054).
Regarding claim 31:
Rajkumar teaches the limitations as in claim 30 above. Rajkumar further teaches utilizing a random forest technique for determining fault criticality, but does not explicitly teach utilizing a random forest technique for training the model.
It would have been an obvious matter of design choice to one of ordinary skill before the time of filing of the invention to utilize any known machine learning technique for training the predictive model, including a random forest technique, based on available data, constraints, and any other design considerations.
Regarding claims 32-38, the combination of Rajkumar, Laperle, and Agarwal teaches a computer system for implementing the method of claim 30 above, including memory, communication servers, databases, and a communication network (see at least Rajkumar Figs. 1b, 7, ¶0047).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN J RINK whose telephone number is (571)272-4863. The examiner can normally be reached M-F 8-5.
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/Ryan Rink/ Primary Examiner, Art Unit 3619