DETAILED ACTION
Receipt of Applicant’s amendments filed 10/27/2025 is acknowledged.
Claims 1,3, 6-9, 11-13, and 16-20 have been amended.
Claims 4-5 and 14-15 have been canceled.
Claims 21-23 are new.
Claims 1-3, 6-13, and 16-23 are pending in this Office 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 .
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the
references as applied to the claims below 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 their 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 entire reference is considered to provide disclosure relating to the
claimed invention. The claims & only the claims form the metes & bounds of the
invention. Office personnel are to give the claims their broadest reasonable
interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the
specification are not read into the claim. Prior art was referenced using terminology
familiar to one of ordinary skill in the art. Such an approach is broad in concept and can
be either explicit or implicit in meaning. Examiner's Notes are provided with the cited
references to assist the applicant to better understand how the examiner interprets the
applied prior art. Such comments are entirely consistent with the intent & spirit of
compact prosecution.
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.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 11/4/2021 and 03/16/2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Response to Arguments
Claim Rejections under 35 U.S.C. § 101:
Acknowledgement is made of amended independent claims 1, 11, and 19.
Applicant’s arguments filed 10/27/2025 have been fully considered, but were not persuasive. Rejections to claims are maintained for at least the reasons given below and those under Claim Rejections - 35 U.S.C. § 101 section.
Applicant argues [Pg.3 Ln.1-15] “amended claim 1 does not recite any abstract idea, such as mathematical concepts or mental processes. For example, at least the following recitations are not merely directed to mathematical concepts” and lists each amended limitation as examples, yet doesn’t provide a convincing argument supporting such an assertion. The examiner respectfully disagrees that amended limitations do not recite judicial exceptions (e.g. abstract idea). As shown in Claim Rejections - 35 U.S.C. § 101 section below, the examiner has identified the following amended limitations as reciting judicial exceptions under Step 2A Prong One of the Office’s eligibility framework (Alice/Mayo test):
segmenting, by the computing system, the first time series data and second time series data into a set of two or more groups corresponding to respective undercarriage components of the first target machine and the second target machine (As drafted and under its broadest reasonable interpretation, this limitation amounts to performing Mental Processes (MPEP 2106.04(a)(2)(III)(C)) on a generic computer and/or Mathematical Concepts (MPEP 2106.04(a)(2)(I)(A)). Examples of mathematical relationships recited in a claim include: iv. organizing information and manipulating information through mathematical correlations. For instance, a person can reasonably evaluate time series data and respective undercarriage components and then segment (i.e. organize) the data into a spreadsheet or other organizational means. Also, the act of “segmenting” data by a computing system amounts to organizing information and manipulating information through mathematical correlations.)
using the two or more groups, each comprising at least a portion of the first time series data and at least a portion of the second time series data, predicting a set of wear conditions of the first undercarriage of the first target machine and the second undercarriage of the second target machine (As drafted and under its broadest reasonable interpretation, in light of the Specification [P.0003], this limitation amounts to performing Mental Processes (MPEP 2106.04(a)(2)(III)(C)) on a generic computer and/or Mathematical Concepts (MPEP 2106.04(a)(2)(I)). For instance, a person can reasonably evaluate data and then establish a statistical model (reference Spec. [P.0003]) based on that data, with/without the aid of pen/paper. Also, the act of establishing a statistical model in order to predict a set of wear conditions is interpreted as mathematical concepts.)
Regarding Applicant’s argument [Pg.3 Ln.16-18], under Step 2A Prong Two of the eligibility framework, the following amended limitations were identified as Insignificant Extra Solution Activity (mere data gathering/outputting, pre/post-solution activity) and/or Mere Instructions to Apply an Exception:
establishing a first set of communication channels between the computing system and a first target machine, wherein the first set of communication channels provides first time series data relating to operation of a first undercarriage of the first target machine in a set of target machines (The additional element amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f). Specifically, this limitation recites only the idea of a solution or outcome i.e. the limitation fails to recite details of how a first set of communication channels are established.)
establishing a second set of communication channels between the computing system and a second target machine, wherein the second set of communication channels provides second time series data relating to operation of a second undercarriage of the second target machine in the set of target machines (The additional element amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f). Specifically, this limitation recites only the idea of a solution or outcome i.e. the limitation fails to recite details of how a second set of communication channels are established.)
generating a graphical user interface component comprising a set of wear condition predictions cross- referenced to: (i) the set of target machines and (ii) the respective undercarriage components (The additional element amounts to Insignificant Extra-solution Activity (mere data outputting, post-solution activity) per MPEP 2106.05(g). The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent. The act of generating a graphical user interface component is interpreted as post-solution activity (data outputting) that is merely a nominal or tangential addition to the claim.)
Therefore, Applicant’s first arguments are not persuasive.
Applicant argues [Pg.3 Ln.19] that Claim 1 limitations do not recite extra-solution activity since the operations are “not tangential (as recited) to generating "a set of wear condition predictions cross-referenced to: (i) the set of target machines and (ii) the respective undercarriage components." Examiner respectfully disagrees. As shown in Claim Rejections - 35 U.S.C. § 101 section below, the limitations of “receiving, by a computing system, wear measurements from a plurality of source machines” and “generating a graphical user interface component” amount to Insignificant Extra-solution Activity (mere data gathering/outputting, pre/post-solution activity) per MPEP 2106.05(g), since both limitations are but nominal/tangential components of a method/system of undercarriage wear prediction. MPEP 2106.05(g) states “Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process (such as receiving wear measurements from a plurality of source machines) [...] An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent”, which is analogous to generating a graphical user interface component (i.e. outputting data) in a claimed undercarriage wear prediction process/method.
Therefore, Applicant’s second argument is not persuasive.
Applicant argues [Pg.3 Ln.24] “the recitations of amended claim 1 integrate a practical application” because [Pg.6 last paragraph] “The generated predictions can enable side-by-side comparisons of component state across different machines based on operating data, which one of skill in the art would appreciate as a technical improvement in the field”. Applicant’s argument has been fully considered, but was not persuasive. Under Step 2B of the Office’s eligibility framework (Alice/Mayo test), the examiner must determine if the claim recites additional elements that amount to significantly more than the judicial exception. As shown in 35 USC §101 analysis section below, the additional elements as described in Step 2A Prong 2 are not sufficient to amount to significantly more than the judicial exception because the additional limitations recite Insignificant Extra Solution Activity (mere data gathering/outputting, pre/post-solution activity) and/or Mere Instructions to Apply an Exception (aka “apply it”). MPEP 2106.05(g) states “As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional.” MPEP 21006.05(d) states “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network”.
Therefore, Applicant’s third argument is not persuasive.
Claim Rejections under 35 U.S.C. § 102/103:
Acknowledgement is made of amended claims 1, 3, 6-9, 11-13, and 16-20.
Applicant’s arguments filed 10/27/2025 have been fully considered, but were not persuasive. Rejections to claims are maintained for at least the reasons given below and under Claim Rejections - 35 U.S.C. § 103 section below.
Applicant argues [Pg.8 Ln.8] “the cited references (i.e. McElhinney in view of Johansen) do not disclose, teach, or suggest any of the following features of amended independent claims 1, 11, or 19”. Examiner has considered Applicant’s argument but respectfully disagrees. The amended features Applicant is referring, along with cited reference disclosures, are as follows:
establishing a first set of communication channels between the computing system and a first target machine (“a network configuration may include a communication network that facilitates communications between one or more assets, a remote computing system, one or more output systems, and one or more data sources.” McElhinney [Col.2 Ln.21-24]) , wherein the first set of communication channels provides first time series data relating to operation of a first undercarriage of the first target machine in a set of target machines; (“FIG. 6 depicts a conceptual illustration of historical operating data that the data science system 404 may analyze to facilitate defining a model. Plot 600 may correspond to a segment of historical sensor data that originated from some (e.g., Sensor A and Sensor B) or all of the sensors 204 [...] the plot 600 (see below) includes an indication of an occurrence of a failure 610 that occurred at a past time, Tf (e.g., “time of failure”), and an indication of an amount of time 612 before the occurrence of the failure, ΔT, from which sets of operating data are identified. As such, Tf−ΔT defines a timeframe 614 of data-points of interest.” McElhinney [Col.18 Ln.61-66 [...] Col.19 Ln.4-10])
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segmenting, by the computing system, the first time series data and second time series data into a set of two or more groups corresponding to respective undercarriage components of the first target machine and the second target machine; (“FIG. 6 depicts a conceptual illustration of historical operating data that the data science system 404 may analyze to facilitate defining a model. Plot 600 (see Fig.6 above) may correspond to a segment of historical sensor data that originated from some (e.g., Sensor A and Sensor B) or all of the sensors 204. As shown, the plot 600 includes time on the x-axis 602, sensor measurement values on the y-axis 604, and sensor data 606 corresponding to Sensor A and sensor data 608 corresponding to Sensor B, each of which includes various data-points representing sensor measurements at particular points in time, Ti.” McElhinney [Col.18 Ln.61]. Data science system 404 is interpreted to include a set of two or more groups because “the data science system 404 may analyze historical operating data for a group of one or more assets (e.g., fault code data) to identify past occurrences of a given failure. The group of the one or more assets may include a single asset, such as asset 200, or multiple assets of a same or similar type, such as fleet of assets. The data science system 404 may analyze a particular amount of historical operating data, such as a certain amount of time's worth of data (e.g., a month's worth) or a certain number of data-points (e.g., the most recent thousand data-points), among other examples.” McElhinney [Col.18 Ln.14-23])
predicting a set of wear condition of the first undercarriage of the first target machine and the second undercarriage of the second target machine (“at block 504, the data science system 404 may analyze historical operating data for a group of one or more assets to identify past occurrences of a given failure from the set of failures. At block 506, the data science system 404 may identify a respective set of operating data that is associated with each identified past occurrence of the given failure (e.g., sensor data from a given timeframe prior to the occurrence of the given failure). At block 508, the data science system 404 may analyze the identified sets of operating data associated with past occurrences of the given failure to define a relationship (e.g., a failure model) between (1) the values for a given set of operating metrics and (2) the likelihood of the given failure occurring within a given timeframe in the future (e.g., the next two weeks). Lastly, at block 510, the defined relationship for each failure in the defined set (e.g., the individual failure models) may then be combined into a model for predicting the overall likelihood of a failure occurring.” McElhinney [Col.17 Ln.22-39])
Applicant’s argument with respect to the amended limitation of "generating a graphical user interface component comprising a set of wear condition predictions cross-referenced to: (i) the set of target machines and (ii) the respective undercarriage components" has been considered but is moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Therefore, Applicant’s arguments are not persuasive.
Claim Objections
Claim 1 is objected to because of the following informalities:
Line 24 states “using the two or more groups, each comprising at least a portion of the first time series data and at least a portion of the second time series data, predicting a set of wear conditions” and should read “using the two or more groups, each comprising at least a portion of the first time series data and at least a portion of the second time series data, to predict .
Appropriate correction is required.
Independent claims 11 and 19 are also objected to for the same reason as claim 1. Appropriate correction is required.
Claim 9 is objected to because of the following informalities:
Line 1 states “wherein predicting set of” and should read “wherein predicting the set of”.
Appropriate correction is required.
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 1-3, 6-13, and 16-23 are rejected under 35 U.S.C. 101 because the claimed invention recites a judicial exception, is directed to that judicial exception (an abstract idea), as it has not been integrated into a practical application and the claim(s) further do/does not recite significantly more than the judicial exception. Examiner has evaluated the claim(s) under the framework provided in MPEP 2106 and has provided such analysis below.
To determine if a claim is directed to patent ineligible subject matter, the Court
has guided the Office to apply the Alice/Mayo test, which requires:
Step 1. Determining if the claim falls within a statutory category of a Process, Machine, Manufacture, or a Composition of Matter (see MPEP 2106.03);
Step 2A. Determining if the claim is directed to a patent ineligible judicial exception consisting of a law of nature, a natural phenomenon, or abstract idea (MPEP 2106.04);
Step 2A is a two-prong inquiry. MPEP 2106.04(II)(A).
Under the first prong, examiners evaluate whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. Abstract ideas include mathematical concepts, certain methods of organizing human activity, and mental processes. MPEP 2106.04(a)(2).
The second prong is an inquiry into whether the claim integrates a judicial exception into a practical application. MPEP 2106.04(d).
Step 2B. If the claim is directed to a judicial exception, determining if the claim recites limitations or elements that amount to significantly more than the judicial exception. (See MPEP 2106).
Step 1:
Claims 1-3, 6-10, and 21 are directed to a method, as such these claims fall within the statutory category of a process.
Claims 11-13, 16-18, and 22 are directed to a system, as such these claims fall within the statutory category of machine.
Claims 19, 20 and 23 are directed to a CRM, as such these claims fall within the statutory category of manufacture.
Step 2A, Prong 1:
The examiner submits that the foregoing claim limitations constitute abstract ideas, as the claims recite performing mental processes on a generic computer and/or mathematical concepts, given the broadest reasonable interpretation.
In order to apply Step 2A, a recitation of claims is copied below. The limitations of those claims which describe an abstract idea are bolded.
As per claim 1, the amended claim recites the limitations of:
;
determining, by the computing system, derived variables based on the
received wear measurements and physics-based features derived from the wear measurements, wherein the derived variables include a pedal travel difference correlating to the first travel distance and the second travel distance; (As drafted and under its broadest reasonable interpretation, this limitation amounts to Mental Processes (MPEP 2106.04(a)(2)(III)) which are defined as concepts that can practically be performed in the human mind (e.g. observations, evaluations, judgments, opinions), or by a human using pen and paper as a physical aid, and/or Mathematical Concepts (MPEP 2106.04(a)(2)(I)(C)) which are defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as subtraction) or an act of calculating using mathematical methods to determine a variable or number. Specifically, the limitation recites mental processes performed on a computer. For instance, a person can reasonably evaluate received wear measurements and physics-based features and then determine (i.e. observe, evaluate, judge) derived variables, with/without the aid of pen and paper. Also, a person can reasonably evaluate and then subtract pedal travel distances to determine a pedal travel difference as a derived variable.)
segmenting, by the computing system, the first time series data and
second time series data into a set of two or more groups corresponding to respective undercarriage components of the first target machine and the second target machine (As drafted and under its broadest reasonable interpretation, this limitation amounts to performing Mental Processes (MPEP 2106.04(a)(2)(III)(C)) on a generic computer and/or Mathematical Concepts (MPEP 2106.04(a)(2)(I)(A)). Examples of mathematical relationships recited in a claim include: iv. organizing information and manipulating information through mathematical correlations. For instance, a person can reasonably evaluate time series data and respective undercarriage components and then segment (i.e. organize) the data into a spreadsheet or other organizational means. Also, the act of “segmenting” data by a computing system amounts to organizing information and manipulating information through mathematical correlations.)
using the two or more groups, each comprising at least a portion of the
first time series data and at least a portion of the second time series data, predicting a set of wear conditions of the first undercarriage of the first
target machine and the second undercarriage of the second target machine; (As drafted and under its broadest reasonable interpretation, in light of the Specification [P.0003], this limitation amounts to performing Mental Processes (MPEP 2106.04(a)(2)(III)(C)) on a generic computer and/or Mathematical Concepts (MPEP 2106.04(a)(2)(I)). For instance, a person can reasonably evaluate data and then establish a statistical model (reference Spec. [P.0003]) based on that data, with/without the aid of pen/paper. Also, the act of establishing a statistical model in order to predict a set of wear conditions is interpreted as mathematical concepts.)
.
Step 2A, Prong 2:
As per claim 1, this judicial exception is not integrated into a practical application because the additional claim limitations outside the abstract idea only present Insignificant Extra Solution Activity (mere data gathering/outputting, pre/post-solution activity) and/or Mere Instructions to Apply an Exception. In particular, the claim recites the additional limitations:
receiving, by a computing system, wear measurements from a plurality of
source machines, wherein the wear measurements are associated with a first set of components of undercarriages of the plurality of source machines, wherein the wear measurements include a first data object corresponding to a first travel distance of a first pedal, and wherein the wear measurements include a second data object corresponding to a second travel distance of a second pedal; (The additional element amounts to Insignificant Extra-solution Activity (mere data gathering, pre-solution activity) per MPEP 2106.05(g). The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process.)
establishing a first set of communication channels between the computing system and a first target machine, wherein the first set of communication channels provides first time series data relating to operation of a first undercarriage of the first target machine in a set of target machines; (The additional element amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f). Specifically, this limitation recites only the idea of a solution or outcome i.e. the limitation fails to recite details of how a first set of communication channels are established.)
establishing a second set of communication channels between the computing system and a second target machine, wherein the second set of communication channels provides second time series data relating to operation of a second undercarriage of the second target machine in the set of target machines (The additional element amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f). Specifically, this limitation recites only the idea of a solution or outcome i.e. the limitation fails to recite details of how a second set of communication channels are established.)
and generating a graphical user interface component comprising a set of wear condition predictions cross-referenced to: (i) the set of target machines and (ii) the respective undercarriage components. (The additional element amounts to Insignificant Extra-solution Activity (mere data outputting, post-solution activity) per MPEP 2106.05(g). The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent. The act of generating a graphical user interface component is interpreted as post-solution activity (data outputting) that is merely a nominal or tangential addition to the claim.)
Accordingly, 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 when considered as an ordered combination and as a whole.
Step 2B:
For step 2B of the analysis, the Examiner must consider whether each claim limitation individually or as an ordered combination amounts to significantly more than the abstract idea. This analysis includes determining whether an inventive concept is furnished by an element or a combination of elements that are beyond the judicial exception. For limitations that were categorized as “apply it” or generally linking the use of the abstract idea to a particular technological environment or field of use, the analysis is the same.
The additional elements as described in Step 2A Prong 2 are not sufficient to amount to significantly more than the judicial exception because the additional limitations are directed towards insignificant extra solution activity and/or mere instructions to apply an exception.
Per MPEP 2106.05(d)(II), the courts have recognized the following pertinent computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: i. Receiving or transmitting data over a network, ii. Performing repetitive calculations, iii. Electronic recordkeeping, iv. Storing and retrieving information in memory.
For the foregoing reasons, claim 1 is directed to an abstract idea without significantly more and is rejected as not patent eligible under 35 U.S.C. 101.
Independent claims 11 and 19 recite substantially the same subject matter as independent claim 1 and are rejected under similar rationale. Claim 11 further recites a processor; a memory communicably coupled to the processor, the memory comprising computer executable instructions that, when executed by the processor, cause the system to: and claim 19 further recites One or more computer-readable media having computer-executable instructions stored thereon that, when executed by at least one processor of a computing system, cause the computing system to perform operations. The additional limitations are directed towards limiting the use of the abstract idea to a particular technological environment (Field of Use and Technological Environment) per MPEP 2106.05(h). Accordingly, 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 when considered as an ordered combination and as a whole.
Therefore, claims 11 and 19 are rejected as not patent eligible under 35 U.S.C 101.
Claim 2 recites wherein the wear measurements include an idle state, service hours, a travel state, a travel mode, a pedal state, a pitch angle, a roll angle, a swing angle, a body inertial measurement unit (IMU) vertical acceleration, a pump pressure, a pump dispensing state, and/or an engine speed. The additional limitation further elaborates on the wear measurements; thus, the limitation further amounts to Insignificant Extra-Solution Activity (mere data gathering, pre-solution activity) - MPEP 2106.05(g). Therefore, the claim is considered to be ineligible under 35 U.S.C 101.
Amended Claim 3 recites wherein the physics-based features of the target machine include a total travel time, an estimate odometer state, travel hours per steering, travel hours per slope, travel hours per speed, travel hours per load, and/or travel hours per ground condition. The additional limitation further elaborates on the physics-based features derived from the wear measurements; thus, the limitation further amounts to Mental Processes performed on a computer and/or Mathematical Concepts per MPEP 2106.04(a)(2)(III)/(I). Therefore, the claim is considered to be ineligible under 35 U.S.C 101
Claim 4 was canceled.
Claim 5 was canceled.
Amended Claim 6 recites wherein coefficients of a statistical model to predict the set of wear conditions are determined at least based on the first time series data or the second time series data, and wherein the statistical model is established based on the derived variables from the received wear measurements. The additional limitations are directed towards performing mental processes on a computer (MPEP 2106.04(a)(2)(III)) and/or mathematical concepts (MPEP 2106.04(a)(2)(I)). For instance, a person can reasonably evaluate received wear measurements, derive variables, then establish a statistical model, then evaluate time series data, and then determine coefficients of the statistical model, with/without the aid of pen/paper. Therefore, the claim is considered to be ineligible under 35 U.S.C 101.
Amended Claim 7 recites wherein the derived variables from the received wear measurements include an average pedal travel distance, a drive torque, an undercarriage pitch angle, a vibration level, a pump flow, and/or a steering state. The additional limitation further elaborates on the derived variables; thus, the limitation further amounts to Mental Processes performed on a computer and/or Mathematical Concepts per MPEP 2106.04(a)(2)(III)/(I). Therefore, the claim is considered to be ineligible under 35 U.S.C 101.
Amended Claim 8, which is dependent on claim 6, recites wherein the derived variables from the received wear measurements include travel hours, a travel speed, a travel slope, and/or a ground condition. The additional limitation further elaborates on the derived variables from the received wear measurements; thus, the limitation further amounts to performing mental processes on a computer (MPEP 2106.04(a)(2)(III)) and/or mathematical concepts (MPEP 2106.04(a)(2)(I)). Therefore, the claim is considered to be ineligible under 35 U.S.C 101.
Amended Claim 9 recites wherein predicting set of wear conditions of the undercarriage of the target machine based on the first time series data or the second time series data includes analyzing parameters associated with data objects associated with the wear measurements associated with the first set of components. The additional limitation further amounts mental process performed on a computer per MPEP 2106.04(a)(2)(III), since “analyzing parameters” is inherent to mental processes as it requires observation, evaluation, and judgement. Therefore, the claim is considered to be ineligible under 35 U.S.C 101.
Claim 10, which is dependent on claim 9, recites wherein the data objects associated with the wear measurements include an idle state, service hours, a travel state, a travel mode, a pedal state, a pitch angle, a roll angle, a swing angle, a vertical acceleration, a pump pressure, a pump dispensing state, and an engine speed. The additional limitation further elaborates on the data objects, thus further amounts to a mental process performed on a computer per MPEP 2106.04(a)(2)(III). Therefore, the claim is considered to be ineligible under 35 U.S.C 101.
Claims 12-13 and 16-18 (claims 14-15 have been canceled) recite significantly the same subject matter as claims 2-3 and 6-8, respectively, and are rejected under similar rationale. Therefore, claims 12-13 and 16-18 are considered ineligible under 35 U.S.C. 101.
Amended Claim 20 recites substantially the same subject matter as claims 7 and 8 and is rejected under similar rationale. Therefore, the claim is considered to be ineligible under 35 U.S.C 101.
(new) Claim 21 recites wherein the first time series data, the second time series data, or both include inspection data. The additional limitation elaborates on the time series data of claim 1, thus further amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f). Therefore, the claim is considered to be ineligible under 35 U.S.C 101.
(new) Claims 22-23 recite substantially the same subject matter as claim 21 and are rejected under similar rationale. Therefore, the claims are considered to be ineligible under 35 U.S.C 101.
Claim Rejections - 35 USC § 103
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, 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 1-3, 6, 8-13, 16, 18-23 are rejected under 35 U.S.C. 103 as being unpatentable over McElhinney et al. US Patent No. 9471452 B2 (hereinafter referred to as “McElhinney”) in view of Dixit US Patent No. 10964130 B1, included as prior art made of record in Office Action dated 02/24/2025, (hereinafter referred to as “Dixit”).
Regarding (amended) independent claim 1, McElhinney discloses A method for wear prediction of an undercarriage of a target machine, the method comprising: (“Disclosed herein are systems, devices, and methods related to assets and asset operating conditions. In particular, examples involve defining and executing predictive models for outputting health metrics that estimate the operating health of an asset or a part thereof” [Abstract]. Examiner interprets “asset” as “machine” because “Today, machines (also referred to herein as “assets”) are ubiquitous in many industries [...] some assets may include multiple subsystems that must operate in harmony for the asset to function properly (e.g., an engine, transmission, etc. of a locomotive)” [Col. 1 Ln. 25-33]. Examiner interprets “a part thereof” to include undercarriage because “a subsystem 202 may include a group of related components that are part of the asset 200...in the context of transportation assets, examples of subsystems 202 may include engines, transmissions, drivetrains, fuel systems, battery systems, exhaust systems, braking systems, electrical systems, signal processing systems, generators, gear boxes, rotors, and hydraulic systems, among numerous other examples.” [Col. 10 Ln. 9-22]. Examiner interprets “subsystem” (which includes engines, transmissions, drivetrains, fuel systems, gear boxes, rotors, hydraulic systems, among other numerous examples) as “undercarriage” per Applicant’s disclosure “The undercarriage includes various components including ground engaging members which provide propulsion to the machine” Spec Para [0002] as well as references to pump pressure, engine speed, drive torque, etc. Spec Para [0018]. Examiner also interprets health metrics “to include wear prediction” because “the remote computing system may also be configured to determine individual “health scores” for respective subsystems of the given asset based on operating data from the asset, where each individual health score indicates a single, aggregated parameter that reflects whether a failure will occur at the particular subsystem of the given asset within a certain period of time into the future.” [Col. 3 Ln. 50-57]. “Health metric” and “health score” are considered to be synonymous because “the remote computing system may be configured to determine a health metric (also referred to herein as a “health score”) of a given asset” [Col. 2 Ln. 32-34])
receiving, by a computing system, wear measurements from a plurality of source machines, wherein the wear measurements are associated with a first set of components of undercarriages of the plurality of source machines, (“In one aspect, a computing system is provided [...] (a) receive a plurality of operating data indicating one or more instances of an abnormal condition at one or more assets” [Col. 5 Ln. 56-64]. Examiner interprets “one or more assets” to include “a first set of components of undercarriages of the plurality of source machines” because of Applicant’s undercarriage disclosure (Spec [P.0017]) and “Generally, a subsystem 202 may include a group of related components that are part of the asset 200 [...] in the context of transportation assets, examples of subsystems 202 may include engines, transmissions, drivetrains, fuel systems, battery systems, exhaust systems, braking systems, electrical systems, signal processing systems, generators, gear boxes, rotors, and hydraulic systems, among numerous other examples.” [Col.10 Ln.9-23]. Examiner also interprets “operating data” to include wear measurements associated with undercarriage components because “As suggested above, the asset 200 may be outfitted with various sensors 204 that are configured to monitor operating conditions of the asset 200 [...] the group of sensors 204 may be configured to monitor operating conditions of the particular subsystem 202.” [Col.10 Ln.23-29]. “In general, a sensor 204 may be configured to detect a physical property, which may be indicative of one or more operating conditions of the asset 200 [...] In operation, the sensors 204 may be configured to obtain measurements continuously, periodically (e.g., based on a sampling frequency), and/or in response to some triggering event.” [Col.10 Ln.30-37].)
wherein the wear measurements include a first data object corresponding to a first travel distance of a first pedal, and wherein the wear measurements include a second data object corresponding to a second travel distance of a second pedal; (“aggregate response variables associated with any set of sensor measurements (i.e. first and second pedal travel distances) that occur” [Col.21 Ln.63-64]. Examiner interprets the set of sensor measurements to include a first and second pedal travel distance because “operator variables may include any variable associated with the person or persons that operate an asset ... such as average braking distance” [Col.33 Ln.54-64]. The examiner interprets “braking distance” to include brake pedal travel distance and “average” to imply multiple data objects since to calculate an average requires multiple inputs. Also see above limitation for more context.)
determining, by the computing system, derived variables based on the received wear measurements and physic-based features derived from the wear measurements, (“the analytics system 400 may be configured to determine any or all of the variables discussed above for the given asset, or the analytics system 400 may be configured to make this determination for a select subset of the variables” [Col.34 Ln.60-64]. Examiner interprets the analytics system to include wear measurements because “the method 1200 may involve the analytics system 400 receiving sensor data (i.e. wear measurements) indicating at least one operating condition of an asset” [Col.37 Ln.33-35]. Examiner interprets sensor data to include “wear measurements and physics-based features derived from wear measurements” because “sensors 204 may be configured to measure physical properties such as the location and/or movement of the asset 200, in which case the sensors may take the form of GNSS sensors, dead-reckoning-based sensors, accelerometers, gyroscopes, pedometers, magnetometers, or the like.” [Col.10 Ln.51-56])
wherein the derived variables include a pedal travel difference correlating to the first travel distance and the second travel distance; (“Examples of operator variables may include... average braking distance” [Col.33 Ln.54-63]. Examiner interprets the average braking distance to include multiple brake pedal travel distances since at least two inputs are necessary to determine an average. Examiner also interprets “travel difference” to include “average braking distance” due to Applicant’s disclosure Spec. [P.0019])
establishing a first set of communication channels between the computing system and a first target machine (“a network configuration may include a communication network that facilitates communications between one or more assets, a remote computing system, one or more output systems, and one or more data sources.” [Col.2 Ln.21-24]), wherein the first set of communication channels provides first time series data relating to operation of a first undercarriage of the first target machine in a set of target machines; (“FIG. 6 depicts a conceptual illustration of historical operating data that the data science system 404 may analyze to facilitate defining a model. Plot 600 may correspond to a segment of historical sensor data that originated from some (e.g., Sensor A and Sensor B) or all of the sensors 204 [...] the plot 600 (see below) includes an indication of an occurrence of a failure 610 that occurred at a past time, Tf (e.g., “time of failure”), and an indication of an amount of time 612 before the occurrence of the failure, ΔT, from which sets of operating data are identified. As such, Tf−ΔT defines a timeframe 614 of data-points of interest.” [Col.18 Ln.61-66 [...] Col.19 Ln.4-10])
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establishing a second set of communication channels between the computing system and a second target machine (“a network configuration may include a communication network that facilitates communications between one or more assets, a remote computing system, one or more output systems, and one or more data sources.” [Col.2 Ln.21-24]), wherein the second set of communication channels provides second time series data relating to operation of a second undercarriage of the second target machine in the set of target machines (“FIG. 6 depicts a conceptual illustration of historical operating data that the data science system 404 may analyze to facilitate defining a model. Plot 600 may correspond to a segment of historical sensor data that originated from some (e.g., Sensor A and Sensor B) or all of the sensors 204 [...] the plot 600 (see above) includes an indication of an occurrence of a failure 610 that occurred at a past time, Tf (e.g., “time of failure”), and an indication of an amount of time 612 before the occurrence of the failure, ΔT, from which sets of operating data are identified. As such, Tf−ΔT defines a timeframe 614 of data-points of interest.” [Col.18 Ln.61-66 [...] Col.19 Ln.4-10])
segmenting, by the computing system, the first time series data and second time series data into a set of two or more groups corresponding to respective undercarriage components of the first target machine and the second target machine; (“FIG. 6 depicts a conceptual illustration of historical operating data that the data science system 404 may analyze to facilitate defining a model. Plot 600 (see Fig.6 above) may correspond to a segment of historical sensor data that originated from some (e.g., Sensor A and Sensor B) or all of the sensors 204. As shown, the plot 600 includes time on the x-axis 602, sensor measurement values on the y-axis 604, and sensor data 606 corresponding to Sensor A and sensor data 608 corresponding to Sensor B, each of which includes various data-points representing sensor measurements at particular points in time, Ti.” [Col.18 Ln.61]. Data science system 404 is interpreted to include a set of two or more groups because “the data science system 404 may analyze historical operating data for a group of one or more assets (e.g., fault code data) to identify past occurrences of a given failure. The group of the one or more assets may include a single asset, such as asset 200, or multiple assets of a same or similar type, such as fleet of assets. The data science system 404 may analyze a particular amount of historical operating data, such as a certain amount of time's worth of data (e.g., a month's worth) or a certain number of data-points (e.g., the most recent thousand data-points), among other examples.” [Col.18 Ln.14-23]),
using the two or more groups, each comprising at least a portion of the first time series data and at least a portion of the second time series data, predicting a set of wear condition of the first undercarriage of the first target machine and the second undercarriage of the second target machine (“at block 504, the data science system 404 may analyze historical operating data for a group of one or more assets to identify past occurrences of a given failure from the set of failures. At block 506, the data science system 404 may identify a respective set of operating data that is associated with each identified past occurrence of the given failure (e.g., sensor data from a given timeframe prior to the occurrence of the given failure). At block 508, the data science system 404 may analyze the identified sets of operating data associated with past occurrences of the given failure to define a relationship (e.g., a failure model) between (1) the values for a given set of operating metrics and (2) the likelihood of the given failure occurring within a given timeframe in the future (e.g., the next two weeks). Lastly, at block 510, the defined relationship for each failure in the defined set (e.g., the individual failure models) may then be combined into a model for predicting the overall likelihood of a failure occurring.” [Col.17 Ln.22-39])
McElhinney fails to specifically disclose generating a graphical user interface component comprising a set of wear condition predictions cross-referenced to: (i) the set of target machines and (ii) the respective undercarriage components.
However, analogous art of Dixit, included as prior art made of record in Office Action dated 02/24/2025, discloses generating a graphical user interface component comprising a set of wear condition predictions cross-referenced to: (i) the set of target machines and (ii) the respective undercarriage components. (“when degradation/fault/failure event alarms exist, these are visualized in Screen 3605 (FIG. 44)(i.e., a table of all like aircrafts) [...] When the row is clicked, another detailed screen provides visibility of all the material assets (e.g. undercarriage components) on that particular aircraft (i.e. target machine) with their associated metrics obtained from the Metrics Module 3740 [...] In the offline mission operator/maintenance technician mode data collected in Storage 3626 is used to perform the same operation as discussed above, but offline by clicking on the “Run Predictions” button in 3601 or as discussed above by clicking on aircraft (row) in 3605 and then clicking on the asset row to view results FIG. 43 (see below), i.e., for decision on further actions to be taken. The predictive maintenance schedule shown in FIG. 43 for the degraded asset depends upon the flow diagram shown in FIG. 40.” Dixit [Col.47 Ln.20-50])
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McElhinney and Dixit are analogous art as both patents involve the use of computing systems to process data related to the operational state of physical assets. They use historical or real-time data to make predictions that inform future actions, aiming to improve operational efficiency and reduce unnecessary interventions. Both systems store and analyze data, use prediction models, and automate recommendations or actions based on those predictions.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the graphical user interface functionality, as Dixit discloses, with the asset prediction models of McElhinney, in order to visually analyze potential undercarriage failures throughout a fleet of target assets.
Regarding claim 2, McElhinney and Dixit disclose the limitations of claim 1, McElhinney further discloses wherein the wear measurements include an idle state, service hours, a travel state, a travel mode, a pedal state, a pitch angle, a roll angle, a swing angle, a body inertial measurement unit (IMU) vertical acceleration, a pump pressure, a pump dispensing state, and/or an engine speed. (“Additionally, various sensors 204 may be configured to measure other operating conditions of the asset 200, examples of which may include temperatures, pressures, speeds, friction, power usages, fuel usages, fluid levels, runtimes, voltages and currents, magnetic fields, electric fields, and power generation, among other examples. One of ordinary skill in the art will appreciate that these are but a few example operating conditions that sensors may be configured to measure. Additional or fewer sensors may be used depending on the industrial application or specific asset.” McElhinney [Col.10 Ln.57-67])
McElhinney discloses the limitations of claim 2 and maintains the same rationale for combination with Dixit as claim 1.
Regarding claim 3, McElhinney and Dixit disclose the limitations of claim 1, McElhinney further discloses wherein the physics-based features of the target machine include a total travel time, an estimate odometer state, travel hours per steering, travel hours per slope, travel hours per speed, travel hours per load, and/or travel hours per ground condition. (“the analytics system 400 may be configured to analyze historical health metric data to identify variables associated with assets”, “variables that may influence health metrics may include asset variables that indicate characteristics of a given asset and the operation thereof” McElhinney [Col.33 Ln 28-33], “Examples of asset variables may include asset brand, asset model, asset travel schedules, asset payloads, and asset environment, among others.”, “Asset travel schedules may indicate routes that a given asset traverses, which may include an indication of elevations, terrain, and/or travel durations. Asset payloads may indicate type and/or amount (e.g., weight) of cargo or the like that an asset hauls. Asset environment may indicate various characteristics about the environment in which a given asset is operated, such as geospatial location, climate, average ambient temperature or humidity, and/or proximity of sources of electrical interference, among other examples.” McElhinney [Col.33 Ln.39-53]. Examiner interprets variables as “features” because of Applicant’s disclosure “physic-based features are determined based on derived variables (e.g., Figure 5) from the wear measurements.” Spec. [P.0009])
McElhinney discloses the limitations of claim 3 and maintains the same rationale for combination with Dixit as claim 1.
Claim 4 has been canceled.
Claim 5 has been canceled.
Regarding (amended) Claim 6, McElhinney and Dixit disclose the limitations of claim 1, McElhinney further discloses wherein coefficients of a statistical model to predict the set of wear conditions are determined at least based on the first time series data or the second time series data, (“defining a failure model may involve the data science system 404 generating a response variable based on the historical operating data (i.e. time-series data) identified at block 506. Specifically, the data science system 404 may determine an associated response variable for each set of sensor measurements received at a particular point in time [...] The response variable may be a binary-valued response variable (i.e. coefficient) such that if the given set of sensor measurements is within any of determined timeframes (i.e. time series), the associated response variable is assigned a value of one (i.e. coefficient), and otherwise, the associated response variable is assigned a value of zero (i.e. coefficient).” McElhinney [Col.19 Ln.55 – Col.20 Ln.5]. The response variables (i.e. coefficients) are interpreted as being of a statistical model to predict the set of wear conditions because “training with the historical operating data (i.e. time-series data) identified at block 506 and the generated response variable may result in variable importance statistics for each sensor. A given variable importance statistic may indicate the sensor's relative effect on the probability that a given failure will occur within the period of time into the future.” McElhinney [Col.20 Ln.25-30])
and wherein the statistical model is established based on the derived variables from the received wear measurements. (“the data science system 404 may then define the failure model (i.e. statistical model) that receives as inputs various sensor data (i.e. wear measurements)” McElhinney [Col.20 Ln.19-21]. Examiner interprets the failure model to include derived variables from the wear measurements because “defining a failure model based on a response variable... the generated response variable may result in variable importance statistics for each sensor” McElhinney [Col.20 Ln.15-27])
McElhinney discloses the limitations of claim 6 and maintains the same rationale for combination with Dixit as claim 1.
Regarding (amended) Claim 8, McElhinney and Dixit disclose the limitations of claim 8, McElhinney further discloses wherein the derived variables from the received wear measurements include travel hours, a travel speed, a travel slope, and/or a ground condition. (“Additionally, various sensors 204 may be configured to measure other operating conditions of the asset 200, examples of which may include temperatures, pressures, speeds, friction, power usages, fuel usages, fluid levels, runtimes, voltages and currents, magnetic fields, electric fields, and power generation, among other examples. One of ordinary skill in the art will appreciate that these are but a few example operating conditions that sensors may be configured to measure. Additional or fewer sensors may be used depending on the industrial application or specific asset.” McElhinney [Col. 10 Ln. 57-67]. Examiner interprets “speeds” as the “derived variable” per Applicant’s disclosure “As shown in Figure 5, examples of the derived variables (including travel states) can include... travel speed (e, m, n)” Spec. [P.0019]. Examiner interprets “sensors” to include “wear measurements”.)
McElhinney discloses the limitations of claim 8 and maintains the same rationale for combination with Dixit as claim 1.
Regarding (amended) claim 9, McElhinney and Dixit disclose the limitations of claim 1, McElhinney further discloses wherein predicting set of wear conditions of the undercarriage of the target machine based on the first time series data or the second time series data includes analyzing parameters associated with data objects associated with the wear measurements associated with the first set of components. (“the remote computing system may determine a failure model for the given subsystem (i.e. undercarriage of target machine) based on operating data (i.e. time-series data) that is particular to the given subsystem” McElhinney [Col. 3 Ln. 58-65], “the remote computing system may also be configured to determine individual “health scores” for respective subsystems of the given asset based on operating data from the asset, where each individual health score indicates a single, aggregated parameter that reflects whether a failure will occur (i.e. predicting a wear condition) at the particular subsystem of the given asset within a certain period of time into the future.” McElhinney [Col.3 Ln.50-57]. Examiner interprets “health score” to “include parameters associated with data objects associated with the wear measurements associated with the first set of components” because “determining a health metric (i.e. health score. See Col.2 Ln.31-33) may involve a “machine-learning” phase, during which the remote computing system may analyze historical operating data for one or more assets to define a model for predicting asset failures” McElhinney [Col. 2 Ln. 42-45])
McElhinney discloses the limitations of claim 9 and maintains the same rationale for combination with Dixit as claim 1.
Regarding claim 10, McElhinney and Dixit disclose the limitations of claim 9, McElhinney further discloses wherein the data objects associated with the wear measurements include an idle state, service hours, a travel state, a travel mode, a pedal state, a pitch angle, a roll angle, a swing angle, a vertical acceleration, a pump pressure, a pump dispensing state, and an engine speed. (“various sensors 204 may be configured to measure other operating conditions of the asset 200, examples of which may include temperatures, pressures, speeds, friction, power usages, fuel usages, fluid levels, runtimes, voltages and currents, magnetic fields, electric fields, and power generation, among other examples. One of ordinary skill in the art will appreciate that these are but a few example operating conditions that sensors may be configured to measure.” McElhinney [Col.10 Ln.57-67]. Examiner interprets “operating conditions” as “data objects” because of Applicant’s disclosure “the data objects associated with the wear measurements include [...] an engine speed.” Spec. [P.0010]. Examiner interprets “sensors” as “wear measurements” because “In operation, the sensors 204 may be configured to obtain measurements continuously, periodically (e.g., based on a sampling frequency), and/or in response to some triggering event.” McElhinney [Col.10 Ln.30-37]. Examiner also interprets “these are but a few example operating conditions that sensors may be configured to measure” to include “an idle state, service hours, a travel state, a travel mode, a pedal state, a pitch angle, a roll angle, a swing angle, a vertical acceleration, a pump pressure, a pump dispensing state, and an engine speed”.)
McElhinney discloses the limitations of claim 10 and maintains the same rationale for combination with Dixit as claim 1.
Independent Claim 11 (amended) recites substantially the same subject matter as claim 1 and is rejected under similar rationale. Additionally, McElhinney discloses A computing system comprising: a processor; a memory communicably coupled to the processor, the memory comprising computer executable instructions (“a computing system is provided. The computing system comprises at least one processor, a non-transitory computer-readable medium, and program instructions stored on the non-transitory computer-readable medium. The program instructions are executable by the at least one processor” McElhinney [Col.5 Ln.56-61])
McElhinney discloses the additional limitations of claim 11 and maintains the same rationale for combination with Dixit as claim 1.
Claims 12-13, 16 and 18 (claims 14-15 cancelled) recite substantially the same subject matter as claims 2-3, 6 and 8, respectively, and are rejected under similar rationale.
Independent Claim 19 (amended) recites substantially the same subject matter as claim 1 and is rejected under similar rationale. Additionally, McElhinney discloses One or more computer-readable media having computer-executable instructions stored thereon that, when executed by at least one processor of a computing system, cause the computing system to perform operations (“a computing system is provided. The computing system comprises at least one processor, a non-transitory computer-readable medium, and program instructions stored on the non-transitory computer-readable medium. The program instructions are executable by the at least one processor” McElhinney [Col.5 Ln.56-61])
McElhinney discloses the additional limitations of claim 19 and maintains the same rationale for combination with Dixit as claim 1.
Claim 20 recites substantially the same subject matter as claims 7 and 8 and is rejected under similar rationale.
Regarding (new) Claim 21, McElhinney and Dixit disclose the limitations of claim 1, McElhinney further discloses wherein the first time series data, the second time series data, or both include inspection data. (“during the machine-learning phase, the remote computing system may be configured to receive operating data (i.e. inspection data) from one or more assets over a certain amount of time (i.e. time series). The operating data may include sensor data” McElhinney [Col.2 Ln.50-53]. Operating data is interpreted as “inspection data” per Applicant’s disclosure “the component tracking data set 407 can serve as inspection data input (e.g., the inspection data 101) for establishing the statistical model 105.” Spec. [P.0027])
McElhinney discloses the limitations of claim 21 and maintains the same rationale for combination with Dixit as claim 1.
Claims 22 and 23 recite substantially the same subject matter as claim 21 and are rejected under similar rational.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over McElhinney et al. (US Patent No. 9471452 B2), in view of Dixit (US Patent No. 10964130 B1), in further view of Johannsen U.S. Pub. No. 2015/0332524 A1 (hereinafter referred to as “Johannsen”).
Regarding (amended) claim 7, McElhinney and Dixit disclose the limitations of claim 1, McElhinney further discloses wherein the derived variables from the received wear measurements include an average pedal travel distance, a drive torque, an undercarriage pitch angle, a vibration level, a pump flow, and/or a steering state. (“Additionally, various sensors 204 may be configured to measure other operating conditions of the asset 200, examples of which may include temperatures, pressures, speeds, friction, power usages, fuel usages, fluid levels, runtimes, voltages and currents, magnetic fields, electric fields, and power generation, among other examples. One of ordinary skill in the art will appreciate that these are but a few example operating conditions that sensors may be configured to measure. Additional or fewer sensors may be used depending on the industrial application or specific asset.” McElhinney [Col.10 Ln.57-67]. Examiner interprets “speeds” as the derived variable per Applicant’s disclosure “As shown in Figure 5, examples of the derived variables (including travel states) can include... travel speed (e, m, n)” Spec. [P.0019]. Examiner interprets the sensors to include wear measurements per reasons previously given. McElhinney further discloses “One of ordinary skill in the art will appreciate that the aforementioned asset-related variables are provided for purposes of example and explanation only and are not meant to be limiting. Numerous other variables are possible and contemplated herein.” [Col.34 Ln.18-22])
However, the McElhinney-Dixit combination fails to specifically disclose an average pedal travel distance, a drive torque, an undercarriage pitch angle, a vibration level, a pump flow, and/or a steering state.
On the other hand, Johannsen discloses an average pedal travel distance, a drive torque, an undercarriage pitch angle, a vibration level, a pump flow, and/or a steering state. (“Further, the parameter detection module 202 is communicably coupled to a third sensor 208. The third sensor 208 is configured to determine and generate signals corresponding to the drive torque, and more particularly, the final drive torque of the machine 100. In one example, the third sensor 208 may embody a torque sensor. The torque sensor may be associated with the engine or the transmission system of the machine 100 in order to measure the final drive torque. Alternatively, the third sensor 208 may include any known torque measuring means known in the art.” Johannsen [P.0017])
Johannsen is analogous art as it relates to the technical field of machine monitoring systems, specifically for tracked machines used in mining, agriculture, forestry, construction, and other industrial applications. Its intended use is to monitor operating parameters of such machines to detect operating practices that may cause excess wear or damage to machine components.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified McElhinney-Dixit to incorporate drive torque, as disclosed by Johannsen, as another derived variable to further predict undercarriage wear and tear conditions of a target machine to provide a more versatile system that incorporates further inputs.
Claim 17 (amended) recites substantially the same subject matter as claim 7 and is rejected under similar rationale.
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon is
considered pertinent to applicant's disclosure:
Fan, Qing, and Hongqin Fan. "Reliability analysis and failure prediction of construction equipment with time series models." Journal of Advanced Management Science Vol 3.3 (2015): 163-177. “time series models is a viable alternative that gives satisfactory results for both point and interval failure predictions in terms of its predictive” [Abstract]
Hounyo et al. (Systems And Methods For Establishing A Virtualized Channel And Communicating Therewith – US Pat. No 11418423 B2). “The method includes (i) generating a virtualized channel in addition to the plurality of telematics channels operatively connected to the target machine; (ii) predicting one or more values for the virtualized channel based on a mathematical function derived from a first set of telematics channels of a source machine; (iii) receiving measurements from the plurality of telematics channels from the target machine; and (iv) applying the mathematical function to the target machine based upon the one or more predicted values of the virtualized channel and the received measurements from the plurality of telematics channels from the target machine.” [Abstract]
Allison et al. (System And Method For Machine Monitoring – US Pat. No 11656595 B2). “The present disclosure relates generally to machine or work systems, and more particularly, to methods and systems for monitoring the operation of one or more machines.” [Col.1 Ln.6-8]
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Anthony Chavez whose telephone number is (571) 272-1036. The examiner can normally be reached Monday - Thursday, 8 a.m. - 5 p.m. ET.
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/ANTHONY CHAVEZ/ Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186