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 application number 18/883,140 filed on 09/12/2024, in which claims 1-20 are presented for examination.
Priority
Acknowledgment is made of applicant’s claim for priority of provisional patent application No. 63/597,509, filed on 11/09/2023.
Information Disclosure Statement
The information disclosure statements (IDS(s)) submitted on 06/18/2025 & 09/03/2025 have been received and considered.
Examiner Notes
Examiner cites particular paragraphs (or columns and lines) in the references as applied to Applicant’s claims for the convenience of the Applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The prompt development of a clear issue requires that the replies of the Applicant meet the objections to and rejections of the claims. Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP §2163.06. Applicant is reminded that the Examiner is entitled to give the Broadest Reasonable Interpretation (BRI) to the language of the claims. Furthermore, the Examiner is not limited to Applicant’s definition which is not specifically set forth in the claims. See MPEP §2111.01.
Specification
The disclosure is objected to because of the following informalities:
using phrases which can be implied, i.e. "Disclosed are ...". Appropriate correction is required. Applicant is reminded of the proper language and format for an abstract of the disclosure:
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
Claim Rejections – 35 USC §101
35 USC §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 1-20 are rejected under 35 USC §101 because the claimed invention is directed to an abstract idea without significantly more. See MPEP 2106 (III)
The determination of whether a claim recites patent ineligible subject matter is a two-step inquiry.
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), See MPEP 2106.03, or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: See MPEP 2106.04
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP 2106.04(II)(A)(1)
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP 2106.04(II)(A)(2)
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP 2106.05
Claim 1. a system, comprising:
a computing device comprising a processor and a memory [applying the abstract idea using generic computing module]; and
machine-readable instructions stored in the memory that, when executed by the processor [applying the abstract idea using generic computing module], cause the computing device to at least:
obtaining segment analysis data for a given segment and a given tire, the segment analysis data comprising at least one of: tire data, vehicle data, or manual inspection data [pre-solution activity (data gathering) using generic sensors];
determine a remaining tread depth of the tire based at least in part on the segment analysis data, the remaining tread depth corresponding to a tread depth of the tire at a segment start [mental process/step];
determine a segment distance based at least in part on the segment analysis data, the segment distance corresponding to a distance the tire has traveled up to a segment end [mental process/step];
apply at least the remaining tread depth and the segment distance as inputs to a tread wear condition model [particular technological environment or field of use without telling how it is accomplished]; and
predict a tread wear condition based at least in part on an output of the tread wear condition model [mental process/step].
101 Analysis - Step 1: Statutory category – Yes
The claim recites a system (or a method) comprising a computing device, a memory and a processor that executes including at least one step. The claim falls within one of the four statutory categories. See MPEP 2106.03
Step 2A Prong one evaluation: Judicial Exception – Yes – Mental processes
In Step 2A, Prong one of the 2019 Patent Eligibility Guidance (PEG), a claim is to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the limitations can be “performed in the human mind, or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III)
The claim recites the limitation of determine a remaining tread depth of the tire based at least in part on the segment analysis data, the remaining tread depth corresponding to a tread depth of the tire at a segment start, determine a segment distance based at least in part on the segment analysis data, the segment distance corresponding to a distance the tire has traveled up to a segment end, and predict a tread wear condition based at least in part on an output of the tread wear condition model. These limitation, as drafted, are simple processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of “a computing device …”, “obtaining segment analysis data …”, and “… tread wear condition model”. That is, other than reciting “computing device”, “obtaining segment analysis data” & “tread wear condition model” nothing in the claim elements precludes the steps from practically being performed in the mind. For example, but for the “computing device”, “obtaining segment analysis data” & “tread wear condition model” language, the claim encompasses a person looking at data collected and forming a simple tread wear prediction and/or judgement. The mere nominal recitation of by a controller does not take the claim limitations out of the mental process grouping. Thus, the claim recites a mental process.
Step 2A Prong two evaluation: Practical Application - No
In Step 2A, Prong two of the 2019 PEG, a claim is to be evaluated whether, as a whole, it integrates the recited judicial exception into a practical application. As noted in MPEP 2106.04(d), 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, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The courts have indicated that additional elements such as: 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.”
The Office submits that the foregoing underlined limitation(s) recite additional elements that do not integrate the recited judicial exception into a practical application.
The claim recites additional elements or steps of: a computing device comprising a processor and a memory; and machine-readable instructions stored in the memory that, when executed by the processor, cause the computing device to at least: obtaining segment analysis data for a given segment and a given tire, the segment analysis data comprising at least one of: tire data, vehicle data, or manual inspection data; and apply at least the remaining tread depth and the segment distance as inputs to a tread wear condition model.
The obtaining “segment analysis data” step is recited at a high level of generality, i.e. as a general means of gathering vehicle and tire data for use in the determine(s) & predict steps, and amount to mere data gathering, which is a form of insignificant extra-solution activity.
The applying to “a tread wear condition model” step is recited at a high level of generality, i.e. as a general means of technological environment, and amount mere to claiming the using a particular technological environment or field of use without telling how it is accomplished, which is a form of insignificant extra-solution activity.
The “computing device”, “processor”, “memory and “machine-readable instructions” limitations merely describes how to generally and merely automates the determine(s) and predict steps, therefore acting as a generic computer to perform the abstract idea and/ or “apply” the otherwise mental judgements using a generic or general-purpose processor, i.e. a computer. The processor/ memory computing system is recited at a high level of generality and is merely automates the determine(s) and predict steps.
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B evaluation: Inventive concept - No
In Step 2B of the 2019 PEG, a claim is to be evaluated as to whether the claim, as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. See MPEP 2106.05(f).
Under the 2019 PEG, a conclusion that an additional element is insignificant extra- solution activity in Step 2A should be re-evaluated in Step 2B. Here, the obtain and/or apply steps and the computing device elements were considered to be insignificant extra-solution activity in Step 2A, and thus they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field.
The Specification (see ¶¶23 & 69-76) recites that the computing device elements, e.g., processor & memory is/are a conventional computer system mounted on the vehicle or in the cloud, and the Specification does not provide any indication that the vehicle processor/ memory is/ are anything other than a conventional computer within a vehicle or remote from the vehicle.
MPEP 2106.05(d)(II), indicate that mere collection or receipt of data over a network, i.e., cloud is/are a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here).
Accordingly, a conclusion that the obtain[ing] and/or the apply[ing] steps and the computing device elements is/are well-understood, routine, conventional activity is supported under Berkheimer. Thus, the claim is ineligible.
Independent method claim 11 recites similar limitations performed by the system of claim 1. Therefore, claim 11 is rejected under the same rationales used in the rejections of claim 1 as outlined above.
Dependent claims 2-10 & 12-20 do not recite any further limitations that cause the claim(s) 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 and amounts to mere input and/or output data manipulation. Therefore, dependent claims 2-10 & 12-20 are not patent eligible under the same rationale as provided for in the rejection of claim 1.
Thus, claims 1-20 are ineligible under 35 USC §101.
Claim Rejections - 35 USC §102
In the event the determination of the status of the application as subject to AIA 35 USC §102 and §103 (or as subject to pre-AIA 35 USC §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 the appropriate paragraphs of 35 USC §102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-7, 9-17, 19 & 20 are rejected under 35 USC §102(a)(1) as being clearly anticipated by PG Pub. No. US-2022/0017090-A1 to Sams et al. (hereinafter “Sams”), which is found in the IDS submitted on 06/18/2025
As per claim 1, Sams discloses a system (Sams, in at least Fig. 1 [reproduced here for convenience], and ¶¶119 & 196, discloses the system 100), comprising:
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Sams’s Fig. 1
a computing device comprising a processor and a memory (Sams, in at least Fig. 1, and ¶¶119 & 196, discloses the system 100 includes a computing device 102 that is onboard a vehicle, and includes a processor 104 and memory 106); and
machine-readable instructions stored in the memory that, when executed by the processor (Sams, in at least Fig. 1, and ¶¶119 & 196, discloses the system 100 includes a computing device 102 that is onboard a vehicle, and includes a processor 104 and memory 106 having program logic 108 residing thereon), cause the computing device to at least:
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obtaining segment analysis data for a given segment and a given tire, the segment analysis data comprising at least one of: tire data, vehicle data, or manual inspection data (Sams, in at least Fig(s). 1, 2 [reproduced here for convenience], 19 & 25, and ¶¶126-132 & 150, discloses tire wear (tread) measurements are made manually by the user and provided as user input into an app or equivalent interface associated with the onboard computing device 102 or directly with the hosted server 130. Sams further discloses real data 136 from a vehicle and associated location/route information is provided to generate a digital representation of the vehicle tire for estimation of tire wear, wherein the tire data 138 and/or vehicle data 136 is/are processed to provide representative data to the wear model 134A is implemented at the vehicle, for processing via the onboard system 102 and/or the tire data 138 and/or the vehicle data 136 are processed to provide representative data to the hosted server 130 for remote wear estimation. Sams also discloses to summarize data between specific/ relevant events in time and/or distance, e.g. vehicle trip);
determine a remaining tread depth of the tire based at least in part on the segment analysis data, the remaining tread depth corresponding to a tread depth of the tire at a segment start (Sams, in at least Fig(s). 1, 2, 19 & 25, and ¶¶126-132 & 150, discloses the tire data 138 and/or vehicle data 136 is/are processed to provide representative data to the wear model 134A, wherein tire wear values 150 are estimated based on a wear model 134A. Sams further discloses to summarize data between specific/ relevant events in time and/or distance, e.g. vehicle trip);
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Sams’s Fig. 11
determine a segment distance based at least in part on the segment analysis data, the segment distance corresponding to a distance the tire has traveled up to a segment end (Sams, in at least Fig(s). 1, 2, 11 [reproduced here for convenience], 12, 19, 25 & 26, and ¶¶126-132, 150, 168 & 180, discloses real data 136 from a vehicle and associated location/route information [i.e., segment distance] is provided to generate a digital representation of the vehicle tire for estimation of tire wear, wherein wear is a cumulative process it is useful to summarize data between specific events in time and/or distance, e.g., vehicle trip, tire tread depth measurement events, tire rotation events, tire mount events, vehicle maintenance events, daily/monthly/yearly summaries, mileage summaries (5 k, 10 k, 20 k miles, etc.). Sams further discloses histogram data frames 330 allow for flexible and efficient summarization, which can be used on static data in the cloud and/or on transient data on the vehicle. Sams also discloses, in Fig. 25, using this predictive method to simulate a wear reference tire compared with measured data of the same tire/vehicle/vehicle-tire system via outdoor wear testing, wherein circular markers indicate the mean tread depth of the control tire test results at each inspection mileage, whereas the underlying solid line represents the predicted tread depth as normalized with respect to an initial tread depth and further via the brush-type model);
apply at least the remaining tread depth and the segment distance as inputs to a tread wear condition model (Sams, in at least Fig(s). 1, 2, 19 & 25, and ¶¶126-132, 141 & 146, discloses to estimate tire wear values 150 based on a wear model, wherein the wear models require several inputs about the system to accurately project out the wear life of the tire); and
predict a tread wear condition based at least in part on an output of the tread wear condition model (Sams, in at least Fig(s). 1, 2, 11, 12, 19, 25 & 26, and ¶¶26-39, 126-132, 141, 146, 150, 168 & 180, discloses one or more tire conditions are measured as timeseries inputs to a predictive tire wear model, wherein a current wear rate is normalized based on said inputs with respect to the initial wear rate for the tire, then a tire wear status of the tire is predicted for one or more specified future parameters).
As per claim 2, Sams discloses the system of claim 1, accordingly, the rejection of claim 1 above is incorporated.
Sams further discloses wherein the segment corresponds to a period of time without manipulation of the tire, and the machine-readable instructions further cause the computing device to at least determine the segment start and the segment end based at least in part on a predefined traveled distance of the vehicle, a predefined period of time, or a manual inspection date (Sams, in at least Fig(s). 9 & 10, and ¶¶16,17, 29-32, 128-130, 148-158 & 166-176, discloses predicting a tire wear status at one or more future parameters for the at least one tire associated with the vehicle, e.g., the tire wear status is predicted with respect to an upcoming period of time that the vehicle is driven [i.e., predefined period of time], or with respect to an upcoming distance to be traveled [i.e., predefined traveled distance of the vehicle]. Sams further discloses predicting a replacement time for the at least one tire [i.e., segment corresponds to a period of time without manipulation of the tire] associated with the vehicle, based on a current tire wear status or the predicted tire wear status as compared with tire wear thresholds associated with the at least one tire associated with the vehicle. Sams also discloses tire wear (tread) measurements are made manually by the user and provided as user input into an app or equivalent interface associated with the onboard computing device 102 or directly with the hosted server 130, wherein, in FIG. 9, the real-time vehicle kinetics data 310 is compiled into windows 320 of time and/or distance. The compiled data is further aggregated into histogram data frames 330).
As per claim 3, Sams discloses the system of claim 1, accordingly, the rejection of claim 1 above is incorporated.
Sams further discloses wherein the tread wear condition comprises an available time to reach a replacement tread depth, a remaining available distance for the tire to reach a replacement tread depth, or an estimated current tread depth (Sams, in at least Fig(s). 9 & 10, and ¶¶16,17, 29-32, 128-130, 148-158 & 166-176, discloses predicting a replacement time for the at least one tire associated with the vehicle, based on a current tire wear status or the predicted tire wear status as compared with tire wear thresholds associated with the at least one tire associated with the vehicle).
As per claim 4, Sams discloses the system of claim 1, accordingly, the rejection of claim 1 above is incorporated.
Sams further discloses wherein the segment analysis data comprises the tire data, and the machine-readable instructions further cause the computing device to at least obtain the tire data from a sensor unit in data communication with the at least one computing device, the tire data comprising tire parameters measured by the sensor unit mounted on the tire (Sams, in at least Fig(s). 1 & 2, and ¶¶40, 48, 61, 119 & 196, discloses one or more sensors, associated with a vehicle and/or at least one tire of a plurality of tires supporting the vehicle, generating first data corresponding to real time kinetics of the vehicle and/or the at least one tire. Sams further discloses the vehicle components include one or more sensors such as, e.g., tire pressure monitoring system (TPMS) sensor transmitters 118 and associated onboard receivers, or the like, as linked for example to a controller area network (CAN) bus network and providing signals thereby to local processing units).
As per claim 5, Sams discloses the system of claim 1, accordingly, the rejection of claim 1 above is incorporated.
Sams further discloses wherein the segment analysis data comprises the vehicle data, and the machine-readable instructions further cause the computing device to at least obtain the vehicle data from a vehicle CAN bus in communication with one or more vehicle systems of a vehicle supported by the tire, the vehicle data comprising at least one of a vehicle speed, a vehicle load, an odometer value, or a brake cylinder pressure value (Sams, in at least Fig(s). 1 & 2, and ¶¶40, 48, 61, 119, 126, 133 & 196, discloses one or more sensors, associated with a vehicle and/or at least one tire of a plurality of tires supporting the vehicle, generating first data corresponding to real time kinetics of the vehicle and/or the at least one tire. Sams further discloses the vehicle components include one or more sensors such as, e.g., vehicle body accelerometers, gyroscopes, inertial measurement units (IMU), position sensors such as global positioning system (GPS) transponders 112, tire pressure monitoring system (TPMS) sensor transmitters 118 and associated onboard receivers, or the like, as linked for example to a controller area network (CAN) bus network and providing signals thereby to local processing units. Sams also discloses implements a simplified model 134B of a tire along with the tire’s wear state 150 to predict its traction capabilities 160, which is relayed to the user to promote safe driving. The simplified model predicts the forces and moments on the tire under a given friction, load, inflation pressure, speed, etc. Sams further discloses model input parameters, e.g., tire tread, inflation pressure, road surface characteristics, vehicle speed and acceleration, slip rate and angle, normal force, braking pressure and load).
As per claim 6, Sams discloses the system of claim 1, accordingly, the rejection of claim 1 above is incorporated.
Sams further discloses wherein the segment analysis data comprises the manual inspection data, the machine-readable instructions further cause the computing device to at least obtain the manual inspection data in response to one or more user interactions with a user interface rendered on a client device, the manual inspection data comprising a measure tread depth and at least one of an inspection date, an inspector name, a tire position, or a tire pressure (Sams, in at least Fig(s). 1, 2, 19 & 25, and ¶¶126-132 & 144, discloses tire wear (tread) measurements are made manually by the user and provided as user input into an app or equivalent interface associated with the onboard computing device 102 or directly with the hosted server 130. Sams further discloses the minimum resolution needed is also dependent on the tire's position on the vehicle, e.g., left-front, right-front, etc.).
As per claim 7, Sams discloses the system of claim 1, accordingly, the rejection of claim 1 above is incorporated.
Sams further discloses wherein the inputs to the tread wear condition model further comprise at least one of a tire position, an average temperature during the segment, an average pressure during the segment, or an average brake cylinder pressure value (Sams, in at least Fig(s). 1 & 2, and ¶¶40, 48, 61, 119-120, 126, 133, 140, 144 & 196, discloses the minimum resolution needed is also dependent on the tire’s position on the vehicle (e.g., left-front, right-front, etc.), wherein one or more sensors, associated with a vehicle and/or at least one tire of a plurality of tires supporting the vehicle, generating first data corresponding to real time kinetics of the vehicle and/or the at least one tire. Sams further discloses the vehicle components include one or more sensors such as, e.g., tire pressure monitoring system (TPMS) sensor transmitters 118 and associated onboard receivers, or the like, as linked for example to a controller area network (CAN) bus network and providing signals thereby to local processing unit, wherein thereby, an ambient temperature sensor 116, an engine sensor 114 configured for example to provide sensed barometric pressure signals. Sams also discloses other sensors for collecting and transmitting vehicle data such as pertaining to velocity, acceleration, braking characteristics, or the like, wherein collected with respect to numerous tire-vehicle systems and associated combinations of values for input parameters, e.g., tire tread, inflation pressure, road surface characteristics, vehicle speed and acceleration, slip rate and angle, normal force, braking pressure and load).
As per claim 9, Sams discloses the system of claim 1, accordingly, the rejection of claim 1 above is incorporated.
Sams further discloses wherein the tread wear condition model comprises a linear machine learning model, and the machine-readable instructions further cause the computing device to at least execute the tread wear condition model (Sams, in at least ¶¶131-132 & 179, discloses subsequent comparison of the estimated tire wear with a determined actual tire wear may be implemented as feedback for the machine learning algorithms, wherein an initial wear rate may be provided as an input to the system, which being provided from an FEA stage, a machine learning model, or the like, the tread depth progression for the entire life of a give tire can be predicted).
As per claim 10, Sams discloses the system of claim 1, accordingly, the rejection of claim 1 above is incorporated.
Sams further discloses wherein the at least one computing device comprises a vehicle computing device installed in a vehicle supported by the tire or a cloud computing device that is remote from the vehicle (Sams, in at least Fig(s). 1, 2, 19 & 25, and ¶¶126-132, 141, 146, 150, 156-159 & 164, discloses real data 136 from a vehicle and associated location/route information is provided to generate a digital representation of the vehicle tire for estimation of tire wear, wherein the tire data 138 and/or vehicle data 136 is/are processed to provide representative data to the wear model 134A is implemented at the vehicle, for processing via the onboard system 102 and/or the tire data 138 and/or the vehicle data 136 are processed to provide representative data to the hosted cloud server 130 for remote wear estimation).
As per claims 11-17, 19 & 20, the claim is directed towards methods that recite similar limitations performed by the systems of claims 1-7, 9 & 10, respectively. The cited portions of Sams used in the rejection of claims 1-7, 9 & 10 teach the same steps to perform the methods of claims 11-17, 19 & 20. Therefore, claims 11-17, 19 & 20 are rejected under the same rationales used in the rejections of claims 1-7, 9 & 10 as outlined above.
Claim Rejections - 35 USC §103
In the event the determination of the status of the application as subject to AIA 35 USC §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 USC §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, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 8 & 18 are rejected under 35 USC §103 as being unpatentable over Sams (US-2022/0017090-A1) in view of PG Pub. US-2022/0019212-A1 to Mars (hereinafter “Mars”)
As per claim 8, Sams discloses the system of claim 7, accordingly, the rejection of claim 7 above is incorporated.
While Sams discloses tire pressure monitoring system (TPMS) sensor transmitters 118 and an ambient temperature sensor 116, wherein collected with respect to numerous tire-vehicle systems and associated combinations of values for input parameters, e.g., tire tread, inflation pressure, road surface characteristics, vehicle speed and acceleration, slip rate and angle, normal force, braking pressure and load (See at least Fig(s). 1 & 2, and ¶¶40, 48, 61, 119-120, 126, 133, 140, 144 & 196), it does not explicitly discloses wherein the inputs further comprise the average temperature and the average pressure, and the machine-readable instructions further cause the computing device to at least:
calculate the average temperature during the segment based at least in part on temperature data included in the tire data; and
calculate the average pressure during the segment based at least in part on temperature data included in the tire data.
Mars, in at least ¶¶52-53 that is was old and well known at the time of filing in the art of vehicle control systems, teaches wherein the inputs further comprise the average temperature and the average pressure, and the machine-readable instructions further cause the computing device to at least:
calculate the average temperature during the segment based at least in part on temperature data included in the tire data; and
calculate the average pressure during the segment based at least in part on temperature data included in the tire data (Mars, in at least ¶¶52-53, teaches the residual life prediction is used to determine if the physical asset 102 is about to fail and needs to be repair/replaced, wherein the hypothetical operating history data can include a number of cycles of a hypothetical ideal load of the physical asset 102 or operational averages based upon a particular operational history of the physical asset 102. Mars further teaches the administration subsystem 110 receives the operating history data, e.g., the number of miles driven with the tire, the average temperature that the tire is exposed to, and/or the average tire pressure. Thereby, using the model implementation instructions, the operating history data and tires original life expectancy, determines that tire has remaining miles based on its original life expectancy, i.e., the residual life prediction, based on a number of cycles of a total of the operating history data).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to modify Sams in view of Mars with a reasonable expectation of success, as both inventions are directed to the same field of endeavor – Assets residual life prediction and administrative systems - and the combination would avoid potential hazards that could occur from an unexpected vehicle failure (see at least Mars’s ¶4).
As per claim 18, the claim is directed towards a method that recites similar limitations performed by the system of claim 8. The cited portions of Sams used in the rejection of claim 8 teach the same steps to perform the method of claim 18. Therefore, claim 18 is rejected under the same rationales used in the rejection of claim 8 as outlined above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See attached PTO-892 form.
Bartolotto et al. (PG Pub. US-2025/0052567-A1) discloses a method for calculating a tire wear comprising obtaining technical data of at least one tire of a vehicle and technical data of the vehicle, and calculating a tire wear rate based on the obtained technical data according to a self-tuning mathematical tire wear model.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tarek Elarabi whose telephone number is (313)446-4911. The examiner can normally be reached on Monday thru Thursday; 6:00 AM - 4:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Nolan can be reached on (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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/Tarek Elarabi, Ph.D./Primary Examiner, Art Unit 3661