DETAILED ACTION
The following Final Office Action is in response to Applicant communication on 05/12/2026.
Status of Claims
Applicant has amended claims 1-14 and 17-20.
Response to Arguments
Regarding the 35 USC 101 rejection, Applicant argues [p.2] that currently amended independent claim 1 is not directed to a mental process given that the amendments pertaining to automatically and electronically causing a display to display a warning notification and generating an estimated wear level by processing data using a reduced order model (ROM) stored on a computer readable memory cannot be performed in the human mind.
Applicant asserts that the claims do not recite a mental process as generating an estimated wear level by processing data using a reduced order model cannot be reasonably performed mentally. Examiner disagrees. While certain embodiments of the claim language would be too complex to be performed mentally as indicated by Applicant, there is no evidence of such complexity in the particular claim language that would indicate that the tasks performed within the abstract idea could not practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations (see MPEP § 2106.04(a)(2)(III)(A) citing SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019)). Furthermore, the claims recite mathematical concepts - specifically, the reduced order model (ROM) using “at least one physical or mathematical expression” to represent or simulate an undercarriage component, recursively generating an updated estimated wear level using the ROM, and recursively comparing the updated estimated wear level to a wear level threshold. Thus, claim 1 still recites both a mental process performable by pen and paper and a mathematical concept at least in the form of mathematical expressions and calculations.
Applicant argues [p. 3-4] that amended independent claim 1 integrates the alleged abstract idea into a practical application via an improvement to a computing device, e.g., providing a track-type machine with self-monitoring capabilities. Specifically, Applicant points to a processor configured to access and use a reduced order model (ROM) to generate an updated estimated wear level of a component of an undercarriage of a track-type machine associated with the processor as asserting the improvement to a computing device (e.g., a track-type machine).
This argument is not persuasive in that the claim does not recite any specific improvement to the process, the ROM itself, or any other computing technology. Rather, the processor merely performs its function of executing a mathematical model on received data to produce a calculated result, which is then displayed. MPEP 2106.05(a) states that an improvement to technology must be reflected in the claimed manner of operating the technology itself, not merely in the use of a mathematical calculation to obtain information about a physical system. In this case, the ROM is simply used as a tool to calculate an estimated wear level, while the processor performs generic data processing functions. Furthermore, automatically displaying a warning based on the calculated result constitutes insignificant post-solution activity under MPEP 2106.05(g) and does not integrate the judicial exception into a practical application (See the rejection for further details). Thus, the 35 USC 101 rejection for claims 1-20 is maintained.
Regarding the 35 USC 103 rejection, Applicant argues [p. 6-7] that no combination of the cited references discloses or suggests the features specifically related to “[a] ROM comprising at least one physical or mathematical expression representing or simulating the at least one undercarriage component of [a] track-type machine, the at least one undercarriage component of the track-type machine including a track shoe, a track chain, a roller, an idler, or a sprocket.” This is because the system of Zhang utilizes and requires a machine learning model. Machine learning models and reduced order models are fundamentally different systems. The system of Zhang requires obtaining historical data and using the historical data to train a machine learning model. Such historical data may not always be available, or may be difficult or costly to obtain. Training a machine learning model may be computationally intensive. Once a machine-learning model has been trained, it may be difficult or impossible to ascertain the reasoning behind decisions, predictions, or recommendations generated by such a model. A model-based ROM does not require a) reference data from the reference system (which is not always available) or b) a vast amount of reference data from the reference system (which is even less often available) to generate the ROM. Once a ROM simulating a complex system has been generated, the ROM can be used to estimate or predict a result of the complex reference system given a particular set of inputs. Thus, modifying the system of Zhang to include a reduced order model (ROM) would change the principle of operation of Zhang.
This argument is unpersuasive in that Zhang teaches a model comprising simulation data that is associated with wear of the one or more components and further explains that a wear detection device may preprocess and/or perform dimensionality reduction along with use a variety of models in the wear detection process. In other words, Zhang at least suggests the possibility that a model such as a reduced order model (ROM) may be used in the field of wear detection. Lin is used in combination with Zhang and actually teaches a physics-based reduced order model for solving the same problem (i.e. a more effective wear informing system for worn/deteriorated parts). Zhang further teaches that the at least one undercarriage component of the track-type machine is a sprocket. For these reasons, the limitations are disclosed or at least suggested by the combination of Zhang and Lin. It has been held that a prior art reference must either be in the field of the inventor' s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). Finally, In response to applicant's arguments against the Zhang reference individually not teaching on the ROM specific model structure, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Thus, the 35 USC 103 rejection for claims 1-20 is maintained. See the rejection for further details.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. A subject matter eligibility analysis is set forth below. See MPEP 2106.
Representative claim 1 recites:
A track-type machine, comprising:
a control interface associated with the track-type machine, the control interface including a display;
and a controller including at least one processor and at least one computer-readable memory storing instructions executable by the at least one processor that, when executed, cause the at least one processor to perform steps including:
receiving an estimated wear level of at least one undercarriage component of the track-type machine, the estimated wear level having been previously determined using machine signal data generated by the track-type machine;
accessing, from the at least one computer-readable memory, a reduced order model (ROM) corresponding to the track-type machine, the ROM comprising at least one physical or mathematical expression representing or simulating the at least one undercarriage component of the track-type machine, the at least one undercarriage component of the track-type machine including a track shoe, a track chain, a roller, an idler, or a sprocket;
recursively receiving updated machine signal data generated by the track-type machine;
recursively generating an updated estimated wear level of the at least one undercarriage component of the track-type machine by processing the updated machine signal data using the ROM;
recursively comparing the updated estimated wear level of the at least one undercarriage component to a wear level threshold;
and in response to determining that the updated estimated wear level of the at least one undercarriage component exceeds the wear level threshold, automatically and electronically causing the display of the [[a]] control interface associated with the track-type machine to display a warning notification.
The claim limitations in the abstract idea have been highlighted in bold; the remaining claim limitations are “additional elements.”
Similar limitations comprise the abstract ideas of independent claims 7 and 14.
Step 1 – Statutory Categories of Invention:
Under Step 1 of the analysis, claim 1 does belong to a statutory category, namely it is a machine claim. Likewise, claim 7 is a device claim, and claim 14 is a process claim.
Step 2A – Judicial Exception Analysis, Prong 1:
Under Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Under Step 2A, Prong One, the broadest reasonable interpretation consistent with the specification of the steps recited in claim 1 includes Under the broadest reasonable interpretation of the limitations, these limitations are best characterized as applying a mental process to a generic computing environment (see MPEP § 2106.04(a)(2)(III)(c)(2)) and representing mathematical relationships (see MPEP § 2106.04(a)(2)(I)(A).)
The claim limitation “accessing a reduced order model (ROM) corresponding to the track-type machine, the ROM comprising at least one physical or mathematical expression representing or simulating the at least one undercarriage component” recites a mental process.
According to the specification, “recursively generating an updated estimated wear level…using the ROM” involves the estimated wear level may be an initial wear level of zero percent for a first iteration of the recursive process, and/or a wear level corresponding to a direct measurement or observation) and machine signal data (as described above) generated during a current interval (e.g., the kth interval) into a ROM [0029] where the ROM uses one or more mathematical or physical equations to simulate the structure or operation of a machine (e.g., a track-type machine) [0037]. This claim limitation thus recites a mathematical concept at least in the form of mathematical calculations, equations and/or functions.
According to the specification, “recursively comparing the updated estimated wear level…to a wear level threshold” involves the IWIS submitting a most recently generated estimated wear level (i.e., estimated wear level for interval k-1), machine signal data generated by the bulldozer during the most recent interval of time (i.e., machine signal data representing how the bulldozer was operated during the most recent interval of time; machine signal data for interval k), and environmental data associated with the bulldozer during the most recent interval of time (environmental data for interval k; e.g., temperature and precipitation information associated with the location(s) of the bulldozer during the most recent interval of time) into a ROM generated to simulate the bulldozer (e.g., the ROM may be a physics-based ROM generated using Reye-Archard-Khrushchov wear law and multibody dynamics to simulate the structure and operations of the bulldozer). The ROM then uses the most recently generated estimated wear level, the machine signal data, and the environmental data to generate an updated estimated wear level of the undercarriage component (i.e., an estimated wear level for interval k). The IWIS then compares the updated estimated wear level to a wear level threshold. If the updated estimated wear level exceeds the wear level threshold, the IWIS then generates a warning notification [0031]. Given the recursive nature of the comparison process and the mathematical calculations, equations and/or functions involved in this process, this claim limitation falls into the category of mathematical concept.
Claims 7 and 14 recites analogous abstract ideas, and thus those claim limitations are rejected for similar reasons as described in the analysis of claim 1.
Step 2A – Judicial Exception Analysis, Prong 2:
Step 2A, Prong Two of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. 2019 PEG Section III(A)(2), 84 Fed. Reg. at 54-55.
The additional elements in the preamble are recited in generality and represent insignificant extra-solution activity (field-of-use limitations) that is not meaningful to indicate a practical application.
Claim 1 recites the following additional elements:
“receiving an estimated wear level”
“receiving updated machine signal data”
“displaying a warning notification”
Claim 7 recites a similar additional element:
“receiving, by the at least one processor, environmental data associated with the track-type machine”
These claim limitations generically recite collecting/outputting by sensors/devices measurement data (all independent claims), which represents the insignificant extra-solution activity of mere data gathering/outputting results. According to the October update on 2019 SME Guidance such steps are “performed in order to gather data for the mental analysis step, and is a necessary precursor for all uses of the recited exception. It is thus extra-solution activity, and does not integrate the judicial exception into a practical application”.
Claim 1 also recites the additional elements:
“a control interface”
“a controller including at least one processor”
“accessing, from the at least one computer-readable memory”
These additional elements are computer components recited in generality and are not meaningful and, therefore, are not qualified as particular machines to indicate a practical application.
Claims 1 and 7 (and dependent claims 6 and 13) recite a track-type machine (with a control interface) which amounts to a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself (MPEP § 2106.05(h) similar to example vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) wherein the additional elements do not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use).
Independent claims 7 and 14 recite similar additional elements.
Step 2B – Additional Elements that Amount to Significantly More:
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 1 recites “a control interface”; “a controller including at least one processor”; “accessing, from the at least one computer-readable memory”. Claims 1 and 7 (and dependent claims 6 and 13) recite a track-type machine (with a control interface).
Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data, the computer and data processing devices to apply the algorithm, and the display device to display selected results of the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (MPEP § 2106.07(a)(III)(A) integrating the evidentiary requirements in making a § 101 rejection as established in Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Claim 1 recites the following additional elements: “receiving an estimated wear level”; “receiving updated machine signal data” and claim 7 recites a similar additional element: “receiving, by the at least one processor, environmental data associated with the track-type machine”. The courts have decided that receiving or transmitting data over a network as well-understood, routine, conventional activity when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II) other types of activities example i. receiving or transmitting data over a network, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
Claim 1 recites “automatically and electronically causing the display of a control interface. The courts have decided that presenting generated data as well-understood, routine, conventional activity when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II) other types of activities example iv. presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93).
Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that claim 1, as well as claims 7 and 14, amount to significantly more than the abstract idea.
With regards to the dependent claims, claims 2-3, 8-9, 12 and 15-20 provide additional features/steps which are part of an expanded abstract idea of the independent claim (additionally comprising abstract idea steps) and, therefore, these claims are not eligible without meaningful additional elements that reflect a practical application and/or additional elements that qualify for significantly more for substantially similar reasons as discussed with regards to Claim 1.
Regarding claims 4, 10, and 19, the claims specifically recite: Generating “an estimate of when the undercarriage component will need to be replaced and automatically and electronically causing the control interface to display the estimate.” Similarly, claims 5, 11 and 20 recite: “…in response to determining that the updated estimated wear level of the at least one undercarriage component exceeds the failure threshold, automatically and electronically cause the display of the control interface to display an indication that the at least one undercarriage component needs to be replaced.” (See para. 0033 of instant specification) where “an updated estimated wear level exceeding the near-failure threshold is an indication that the undercarriage component will soon need to be repaired or replaced… the IWIS can generate a warning notification indicating that the undercarriage component will soon need to be repaired or replaced and deliver the warning notification to a control interface associated with the undercarriage’s machine.”
The determination of an updated estimated wear level exceeding a near-failure threshold and the corresponding warning notification displayed on a control interface are merely viewed as data gathering and output steps, which under Step 2B, do not qualify as additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2. Such insignificant extra-solution activity, e.g. data gathering and output, when re-evaluated under Step 2B is further found to be well-understood, routine, and conventional as evidenced by MPEP 2106.05(d)(II) (describing conventional activities that include transmitting and receiving data over a communication network).
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception. No specific practical application is associated with the claimed process. For instance, nothing is done once the updated estimated wear level exceeds a near-failure threshold and the corresponding warning notification is displayed on a control interface.
Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that claims 4, 10, and 19, as well as claims 5, 11 and 20, amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, US 2022/0139117 A1 (hereinafter “Zhang”), in view of Niedfeldt et al., Comparison and Analysis of Recursive-RANSAC for Multiple Target Tracking, 53(1) IEEE Transactions on Aerospace and Electronic Systems 461–476 (2017) (hereinafter “Niedfeldt”), further in view of Lin et al., A methodology to develop reduced-order models to support the operation and maintenance of offshore wind turbines, 259 Applied Energy (2020)(hereinafter “Lin”).
Regarding claim 1, Zhang teaches a track-type machine, comprising: [0014]: the machine may be another track-type machine;
a control interface associated with the track-type machine, the control interface including a display [0019]: “[o]perator cabin 130 includes an integrated display and operator controls”;
and a controller including at least one processor and at least one computer-readable memory storing instructions executable by the at least one processor that, when executed, cause the at least one processor to perform steps including [0041]: “Memory 250 includes a random-access memory (RAM), a read only memory (ROM), and/or another type of dynamic or static storage device…that stores information and/or instructions for use by a processor 240 to perform a function.” [0042]: “Wear detection device 190 may include one or more devices (e.g., a server device or a group of server devices) configured to train machine learning model 230 to predict the amount of wear of the one or more components of the undercarriage…and may be implemented by one or more computing resources of a cloud computing environment.” [0008]: “A machine includes one or more memories; and one or more processors configured to: receive, from one or more sensor devices of the machine, sensor data associated with wear of one or more components of an undercarriage of the machine; predict, using a machine learning model and the sensor data, an amount wear of the one or more components based on a wear rate of the one or more components, wherein the machine learning model is trained, using training data, to predict the wear rate of the one or more components, wherein the training data includes two or more of: historical sensor data, historical inspection data, or simulation data, of a simulation model, from one or more third devices, and wherein the two or more of the historical sensor data, the historical inspection data, or the simulation data are associated with wear of the one or more components; and perform an action based on the amount of wear of the one or more components”;
receiving an estimated wear level of at least one undercarriage component of the track-type machine, the estimated wear level having been previously determined using machine signal data generated by the track-type machine ([0086]: “As further shown in FIG. 3, process 300 may include predicting, using the machine learning model and based on the sensor data, the remaining life of the one or more components (block 350). For example, the first device may predict, using the machine learning model and based on the sensor data, the remaining life of the one or more components.” [0008]: “A machine includes one or more memories; and one or more processors configured to: receive, from one or more sensor devices of the machine, sensor data associated with wear of one or more components of an undercarriage of the machine; predict, using a machine learning model and the sensor data, an amount wear of the one or more components based on a wear rate of the one or more components, wherein the machine learning model is trained, using training data, to predict the wear rate of the one or more components, wherein the training data includes two or more of: historical sensor data, historical inspection data, or simulation data, of a simulation model, from one or more third devices, and wherein the two or more of the historical sensor data, the historical inspection data, or the simulation data are associated with wear of the one or more components; and perform an action based on the amount of wear of the one or more components”;
accessing, from the at least one computer-readable memory, a model corresponding to the track-type machine, [0086]: “As further shown in FIG. 3, process 300 may include predicting, using the machine learning model and based on the sensor data, the remaining life of the one or more components (block 350). For example, the first device may predict, using the machine learning model and based on the sensor data, the remaining life of the one or more components”,
the model comprising simulating the at least one undercarriage component of the track-type machine, [0008]: “A machine includes one or more memories; and one or more processors configured to: receive, from one or more sensor devices of the machine, sensor data associated with wear of one or more components of an undercarriage of the machine; predict, using a machine learning model and the sensor data, an amount wear of the one or more components based on a wear rate of the one or more components, wherein the machine learning model is trained, using training data, to predict the wear rate of the one or more components, wherein the training data includes two or more of: historical sensor data, historical inspection data, or simulation data, of a simulation model, from one or more third devices, and wherein the two or more of the historical sensor data, the historical inspection data, or the simulation data are associated with wear of the one or more components; and perform an action based on the amount of wear of the one or more components” the at least one undercarriage component of the track-type machine including a track shoe, a track chain, a roller, an idler, or a sprocket [0024]: Wear detection device 190 may include one or more devices capable of predicting an amount of wear of the one or more components of the undercarriage (e.g., one or more tracks, track links, one or more track link bushings, one or more track link pins, one or more sprockets 182, and/or one or more segments 184);
receiving machine signal data generated by the track-type machine; …generating an estimated wear level of the at least one undercarriage component of the track-type machine by processing the machine signal data using the model [0008]: “A machine includes one or more memories; and one or more processors configured to: receive, from one or more sensor devices of the machine, sensor data associated with wear of one or more components of an undercarriage of the machine; predict, using a machine learning model and the sensor data, an amount wear of the one or more components based on a wear rate of the one or more components, wherein the machine learning model is trained, using training data, to predict the wear rate of the one or more components, wherein the training data includes two or more of: historical sensor data, historical inspection data, or simulation data, of a simulation model, from one or more third devices, and wherein the two or more of the historical sensor data, the historical inspection data, or the simulation data are associated with wear of the one or more components; and perform an action based on the amount of wear of the one or more components”);
comparing the estimated wear level of the at least one undercarriage component to a wear level threshold; and in response to determining that the estimated wear level of the at least one undercarriage component exceeds the wear level threshold, automatically and electronically causing the display of the control interface associated with the track-type machine to display a warning notification [0072]: “Wear detection device 190 may transmit the remaining life information…[0070]: when the amount of wear (of the one or more components) satisfies a threshold amount of wear…[0072]: to cause the one or more devices (e.g., controller 140) to cause machine 105 to… cause an alarm to be activated. The alarm may indicate that the one or more components are to be repaired or replaced.” [0030]: “Additionally, or alternatively, sensor system 120 may provide the historical sensor data to wear detection device 190 (e.g., to train machine learning model 230) based on a triggering event (e.g., a request from wear detection device 190, a request from controller 140, and/or a request from an operator of machine 105 (e.g., via the integrated display and/or operator controls)…where [0040] the “controller may include one or more processors…capable of being programmed to perform a function.”; [Claim 12] “…the one or more processors are further configured to: cause an operation of the machine to be adjusted based on the amount of wear of the one or more components; transmit remaining life information to a first device to cause the first device to generate, based on the amount of wear of the one or more components, a service request to at least one of repair or replace the one or more components, wherein the remaining life information indicates the amount of wear of the one or more components; or transmit the remaining life information, to a second device associated with an operator of the machine, to cause the operator to adjust the operation of the machine based on the remaining life information or to transmit the service request using the second device associated with the operator”.
Zhang does not teach using recursiveness in updating…data and submitting data to a reduced order model (ROM) to recursively update state estimates using sequential measurements.
Niedfeldt teaches a model to recursively update state estimates using sequential measurements [p.464-465]: “The recently developed R-RANSAC algorithm described in this paper extends the traditional RANSAC algorithm to recursively update state estimates using sequential measurements…where for each new measurement scan, R-RANSAC tests each measurement to see if it is an inlier to one or more existing tracks. If so, those tracks are updated”, and [p.462]: “The R-RANSAC algorithm...robustly and recursively tracks multiple targets in clutter…and stores a set of validated hypotheses, or tracks, between time steps and uses subsequent measurements to either update existing tracks or seed new tracks…and it was first developed to estimate parameters of signals.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Niedfeldt to use recursiveness in updating…data to have lower computational complexity and avoid recomputing hypotheses and have more accurate measurements of state over time.
Zhang does not teach the following; however, Lin teaches the reduced order model [Lin: Abstract]: “aero-hydro-servo-elastic (AHSE) dynamics of each wind turbine… where a reduced-order model (ROM), able to capture the relevant dynamics of the system for a specific failure” is used. [p.2, para. 2, lines 15-17]: “Reduced-order models (ROMs) are well established and widely applied in a variety of areas [10], including static reduction [11], dynamic reduction [12], balanced methods [13], and others.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Niedfeldt, further in view of Lin to use a reduced order model (ROM) because the ROM has a lower computational cost and it is more easily scalable. [Lin: Abstract]: “This paper developed a reduced-order model (ROM), able to capture the relevant dynamics of the system for a specific failure, having a lower computational cost and therefore more easily scalable.” [p.2]: “Reduced-order models (ROMs) are well established and widely applied in a variety of areas [10], including static reduction [11], dynamic reduction [12], balanced methods [13], and others.”
Regarding claim 2, Zhang teaches the track-type machine and simulating the at least one undercarriage component of the track-type machine, but does not explicitly teach a physics-based ROM configured to apply one or more physics-based equations.
Lin teaches the physics-based reduced order model configured to apply one or more physics-based equations through the modeling of [Lin: Abstract]: “aero-hydro-servo-elastic (AHSE) dynamics of each wind turbine… where a reduced-order model (ROM), able to capture the relevant dynamics of the system for a specific failure” is used. [p.2]: “Reduced-order models (ROMs) are well established and widely applied in a variety of areas [10], including static reduction [11], dynamic reduction [12], balanced methods [13], and others.” [p.2]: “All the above ROMs focus on the reduction of the system’s mass and stiffness matrix, derived from the linearization of the system equations of motion.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a physics-based ROM configured to apply one or more physics-based equations due to their lower computational cost, easy scalability and effectiveness in monitoring the physics of wear. [Lin: Abstract]: “This paper developed a reduced-order model (ROM), able to capture the relevant dynamics of the system for a specific failure, having a lower computational cost and therefore more easily scalable.” [p.2]: “Reduced-order models (ROMs) are well established and widely applied in a variety of areas [10], including static reduction [11], dynamic reduction [12], balanced methods [13], and others…All the above ROMs focus on the reduction of the system’s mass and stiffness matrix, derived from the linearization of the system equations of motion.”
Regarding claim 3, Lin teaches wherein, when executed, the instructions further cause the at least one processor to perform steps including [0041]: “Memory 250 includes a random-access memory (RAM), a read only memory (ROM), and/or another type of dynamic or static storage device…that stores information and/or instructions for use by a processor 240 to perform a function”
receiving environmental data associated with the track-type machine [0043]: “The historical inspection data may include…environmental conditions at a location associated with machine”;
and wherein generating the estimated wear level of the at least one undercarriage component further comprises processing the environmental data associated with the track-type machine and the estimated wear level of the at least one undercarriage component using the model [0008]: “A machine includes one or more memories; and one or more processors configured to: receive, from one or more sensor devices of the machine, sensor data associated with wear of one or more components of an undercarriage of the machine; predict, using a machine learning model and the sensor data, an amount wear of the one or more components based on a wear rate of the one or more components, wherein the machine learning model is trained, using training data, to predict the wear rate of the one or more components, wherein the training data includes two or more of: historical sensor data, historical inspection data, or simulation data, of a simulation model, from one or more third devices, and wherein the two or more of the historical sensor data, the historical inspection data, or the simulation data are associated with wear of the one or more components; and perform an action based on the amount of wear of the one or more components.” [Claim 12] “…the one or more processors are further configured to: cause an operation of the machine to be adjusted based on the amount of wear of the one or more components; transmit remaining life information to a first device to cause the first device to generate, based on the amount of wear of the one or more components, a service request to at least one of repair or replace the one or more components, wherein the remaining life information indicates the amount of wear of the one or more components; or transmit the remaining life information, to a second device associated with an operator of the machine, to cause the operator to adjust the operation of the machine based on the remaining life information or to transmit the service request using the second device associated with the operator”).
However, Zhang does not teach using recursiveness in updating…data and using a ROM to recursively update state estimates using sequential measurements.
Niedfeldt teaches a model to recursively update state estimates using sequential measurements; [p.464-465]: “The recently developed R-RANSAC algorithm described in this paper extends the traditional RANSAC algorithm to recursively update state estimates using sequential measurements…where for each new measurement scan, R-RANSAC tests each measurement to see if it is an inlier to one or more existing tracks. If so, those tracks are updated”, and [p.462]: “The R-RANSAC algorithm...robustly and recursively tracks multiple targets in clutter…and stores a set of validated hypotheses, or tracks, between time steps and uses subsequent measurements to either update existing tracks or seed new tracks…and it was first developed to estimate parameters of signals.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Niedfeldt to use recursiveness in updating…data to have lower computational complexity and avoid recomputing hypotheses and have more accurate measurements of state over time.
Zhang does not teach the following; Lin, however, teaches the reduced order model through the modeling of [Lin: Abstract]: “aero-hydro-servo-elastic (AHSE) dynamics of each wind turbine… where a reduced-order model (ROM), able to capture the relevant dynamics of the system for a specific failure” is used. [p.2, para. 2, lines 15-17]: “Reduced-order models (ROMs) are well established and widely applied in a variety of areas [10], including static reduction [11], dynamic reduction [12], balanced methods [13], and others.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Niedfeldt further in view of Lin to use a reduced order model (ROM) because the ROM has a lower computational cost and it is more easily scalable. [Lin: Abstract]: “This paper developed a reduced-order model (ROM), able to capture the relevant dynamics of the system for a specific failure, having a lower computational cost and therefore more easily scalable.” [p.2]: “Reduced-order models (ROMs) are well established and widely applied in a variety of areas [10], including static reduction [11], dynamic reduction [12], balanced methods [13], and others.”
Regarding claim 4, Zhang teaches wherein comparing the estimated wear level of the at least one undercarriage component to the wear level threshold further comprises comparing the estimated wear level to a near-failure threshold [0070]: “Wear detection device 190 may transmit the remaining life information when the amount of wear (of the one or more components) satisfies a threshold amount of wear. The remaining life information may indicate the amount of wear of the one or more components, indicate a wear rate of the one or more components, indicate the remaining life of the one or more components, and/or an offer associated with repairing and/or replacing the one or more components,” and [0076]: “wear detection device 190 may determine whether a failure of the one or more components is imminent (e.g., based on the predicted component wear information). If wear detection device 190 determines that the failure is imminent, wear detection device 190 may perform one or more of the actions described above. If wear detection device 190 determines that the failure is not imminent, wear detection device 190 may not perform an action”)
and wherein the instructions, when executed, further cause the at least one processor to perform steps including, in response to determining that the estimated wear level of the at least one undercarriage component exceeds the near-failure threshold, generating an estimate of when the undercarriage component will need to be replaced and automatically and electronically causing the control interface to display the estimate [Claim 12]: “…the one or more processors are further configured to: cause an operation of the machine to be adjusted based on the amount of wear of the one or more components; transmit remaining life information to a first device to cause the first device to generate, based on the amount of wear of the one or more components, a service request to at least one of repair or replace the one or more components, wherein the remaining life information indicates the amount of wear of the one or more components; or transmit the remaining life information, to a second device associated with an operator of the machine, to cause the operator to adjust the operation of the machine based on the remaining life information or to transmit the service request using the second device associated with the operator.” [0030]: “Additionally, or alternatively, sensor system 120 may provide the historical sensor data to wear detection device 190 (e.g., to train machine learning model 230) based on a triggering event (e.g., a request from wear detection device 190, a request from controller 140, and/or a request from an operator of machine 105 (e.g., via the integrated display and/or operator controls)…where [0040] the “controller may include one or more processors…capable of being programmed to perform a function.”
However, Zhang does not teach using recursiveness in updating…data.
Niedfeldt teaches a model to recursively update state estimates using sequential measurements [p.464-465]: “The recently developed R-RANSAC algorithm described in this paper extends the traditional RANSAC algorithm to recursively update state estimates using sequential measurements…where for each new measurement scan, R-RANSAC tests each measurement to see if it is an inlier to one or more existing tracks. If so, those tracks are updated”, and [p.462]: “The R-RANSAC algorithm...robustly and recursively tracks multiple targets in clutter…and stores a set of validated hypotheses, or tracks, between time steps and uses subsequent measurements to either update existing tracks or seed new tracks…and it was first developed to estimate parameters of signals.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Niedfeldt to use recursiveness in updating…data to have lower computational complexity and avoid recomputing hypotheses and have more accurate measurements of state over time.
Regarding claim 5, Zhang teaches wherein comparing the estimated wear level of the at least one undercarriage component to the wear level threshold further comprises comparing the estimated wear level to a failure threshold [0070]: “Wear detection device 190 may transmit the remaining life information when the amount of wear (of the one or more components) satisfies a threshold amount of wear. The remaining life information may indicate the amount of wear of the one or more components, indicate a wear rate of the one or more components, indicate the remaining life of the one or more components, and/or an offer associated with repairing and/or replacing the one or more components,” and [0076]: “wear detection device 190 may determine whether a failure of the one or more components is imminent (e.g., based on the predicted component wear information). If wear detection device 190 determines that the failure is imminent, wear detection device 190 may perform one or more of the actions described above. If wear detection device 190 determines that the failure is not imminent, wear detection device 190 may not perform an action)”
and wherein the instructions, when executed, further cause the at least one processor to perform steps including, in response to determining that the estimated wear level of the at least one undercarriage component exceeds the failure threshold, automatically and electronically causing the display of the control interface to display an indication that the at least one undercarriage component needs to be replaced [Claim 12]: “…the one or more processors are further configured to: cause an operation of the machine to be adjusted based on the amount of wear of the one or more components; transmit remaining life information to a first device to cause the first device to generate, based on the amount of wear of the one or more components, a service request to at least one of repair or replace the one or more components, wherein the remaining life information indicates the amount of wear of the one or more components; or transmit the remaining life information, to a second device associated with an operator of the machine, to cause the operator to adjust the operation of the machine based on the remaining life information or to transmit the service request using the second device associated with the operator.”; [0030]: “Additionally, or alternatively, sensor system 120 may provide the historical sensor data to wear detection device 190 (e.g., to train machine learning model 230) based on a triggering event (e.g., a request from wear detection device 190, a request from controller 140, and/or a request from an operator of machine 105 (e.g., via the integrated display and/or operator controls)…where [0040] the “controller may include one or more processors…capable of being programmed to perform a function.”
However, Zhang does not teach using recursiveness in updating…data.
Niedfeldt teaches a model to recursively update state estimates using sequential measurements [p.464-465]: “The recently developed R-RANSAC algorithm described in this paper extends the traditional RANSAC algorithm to recursively update state estimates using sequential measurements…where for each new measurement scan, R-RANSAC tests each measurement to see if it is an inlier to one or more existing tracks. If so, those tracks are updated”, and [p.462]: “The R-RANSAC algorithm...robustly and recursively tracks multiple targets in clutter…and stores a set of validated hypotheses, or tracks, between time steps and uses subsequent measurements to either update existing tracks or seed new tracks…and it was first developed to estimate parameters of signals.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Niedfeldt to use recursiveness in updating…data to have lower computational complexity and avoid recomputing hypotheses and have more accurate measurements of state over time.
Regarding claim 6, Zhang teaches wherein the control interface is built into the track-type machine [0019]: Operator cabin 130 includes an integrated display and operator controls; [0030]: “Additionally, or alternatively, sensor system 120 may provide the historical sensor data to wear detection device 190 (e.g., to train machine learning model 230) based on a triggering event (e.g., a request from wear detection device 190, a request from controller 140, and/or a request from an operator of machine 105 (e.g., via the integrated display and/or operator controls)…where [0040] the “controller may include one or more processors…capable of being programmed to perform a function.”
Regarding claim 7, Zhang teaches a controller for a track-type machine, the controller comprising at least one processor and at least one memory storing instructions that, when executed, cause the at least one processor to perform steps including [0041]: “Memory 250 includes a random-access memory (RAM), a read only memory (ROM), and/or another type of dynamic or static storage device…that stores information and/or instructions for use by a processor 240 to perform a function.” [0042]: “Wear detection device 190 may include one or more devices (e.g., a server device or a group of server devices) configured to train machine learning model 230 to predict the amount of wear of the one or more components of the undercarriage… and may be implemented by one or more computing resources of a cloud computing environment”,
accessing, by the at least one processor, a model corresponding to the track-type machine, wherein the model is configured to simulate one or more aspects of at least one undercarriage component of the track-type machine [0086]: “As further shown in FIG. 3, process 300 may include predicting, using the machine learning model and based on the sensor data, the remaining life of the one or more components (block 350). For example, the first device may predict, using the machine learning model and based on the sensor data, the remaining life of the one or more components.” [0008]: “A machine includes one or more memories; and one or more processors configured to: receive, from one or more sensor devices of the machine, sensor data associated with wear of one or more components of an undercarriage of the machine; predict, using a machine learning model and the sensor data, an amount wear of the one or more components based on a wear rate of the one or more components, wherein the machine learning model is trained, using training data, to predict the wear rate of the one or more components, wherein the training data includes two or more of: historical sensor data, historical inspection data, or simulation data, of a simulation model, from one or more third devices, and wherein the two or more of the historical sensor data, the historical inspection data, or the simulation data are associated with wear of the one or more components; and perform an action based on the amount of wear of the one or more components”, the at least one undercarriage component of the track-type machine including a track shoe, a track chain, a roller, an idler, or a sprocket [0024]: Wear detection device 190 may include one or more devices capable of predicting an amount of wear of the one or more components of the undercarriage (e.g., one or more tracks, track links, one or more track link bushings, one or more track link pins, one or more sprockets 182, and/or one or more segments 184);
receiving, by the at least one processor, an estimated wear level of the at least one undercarriage component of a track-type machine; receiving, by the at least one processor, machine signal data generated by the track-type machine [0086]: “As further shown in FIG. 3, process 300 may include predicting, using the machine learning model and based on the sensor data, the remaining life of the one or more components (block 350). For example, the first device may predict, using the machine learning model and based on the sensor data, the remaining life of the one or more components.” [0008]: “A machine includes one or more memories; and one or more processors configured to: receive, from one or more sensor devices of the machine, sensor data associated with wear of one or more components of an undercarriage of the machine; predict, using a machine learning model and the sensor data, an amount wear of the one or more components based on a wear rate of the one or more components, wherein the machine learning model is trained, using training data, to predict the wear rate of the one or more components, wherein the training data includes two or more of: historical sensor data, historical inspection data, or simulation data, of a simulation model, from one or more third devices, and wherein the two or more of the historical sensor data, the historical inspection data, or the simulation data are associated with wear of the one or more components; and perform an action based on the amount of wear of the one or more components”
receiving, by the at least one processor, environmental data associated with the track-type machine [0043]: “The historical inspection data may include…environmental conditions at a location associated with machine.” [0008]: “A machine includes one or more memories; and one or more processors configured to: receive, from one or more sensor devices of the machine, sensor data associated with wear of one or more components of an undercarriage of the machine; predict, using a machine learning model and the sensor data, an amount wear of the one or more components based on a wear rate of the one or more components, wherein the machine learning model is trained, using training data, to predict the wear rate of the one or more components, wherein the training data includes two or more of: historical sensor data, historical inspection data, or simulation data, of a simulation model, from one or more third devices, and wherein the two or more of the historical sensor data, the historical inspection data, or the simulation data are associated with wear of the one or more components; and perform an action based on the amount of wear of the one or more components”
generating, by the at least one processor, an estimated wear level of the at least one undercarriage component by processing the estimated wear level of the at least one undercarriage component, the machine signal data generated by the track-type machine, and the environmental data associated with the track-type machine using the model [Claim 12]: “…the one or more processors are further configured to: cause an operation of the machine to be adjusted based on the amount of wear of the one or more components; transmit remaining life information to a first device to cause the first device to generate, based on the amount of wear of the one or more components, a service request to at least one of repair or replace the one or more components, wherein the remaining life information indicates the amount of wear of the one or more components; or transmit the remaining life information, to a second device associated with an operator of the machine, to cause the operator to adjust the operation of the machine based on the remaining life information or to transmit the service request using the second device associated with the operator”, [0008]: “A machine includes one or more memories; and one or more processors configured to: receive, from one or more sensor devices of the machine, sensor data associated with wear of one or more components of an undercarriage of the machine; predict, using a machine learning model and the sensor data, an amount wear of the one or more components based on a wear rate of the one or more components, wherein the machine learning model is trained, using training data, to predict the wear rate of the one or more components, wherein the training data includes two or more of: historical sensor data, historical inspection data, or simulation data, of a simulation model, from one or more third devices, and wherein the two or more of the historical sensor data, the historical inspection data, or the simulation data are associated with wear of the one or more components; and perform an action based on the amount of wear of the one or more components”
comparing, by the at least one processor, the estimated wear level of the at least one undercarriage component to a wear level threshold; and in response to determining that the estimated wear level of the at least one undercarriage component exceeds the wear level threshold, automatically and electronically causing a control interface associated with the track-type machine to display a warning notification [0030]: “Additionally, or alternatively, sensor system 120 may provide the historical sensor data to wear detection device 190 (e.g., to train machine learning model 230) based on a triggering event (e.g., a request from wear detection device 190, a request from controller 140, and/or a request from an operator of machine 105 (e.g., via the integrated display and/or operator controls)…where [0040] the “controller may include one or more processors…capable of being programmed to perform a function.” [0072]: “Wear detection device 190 may transmit the remaining life information…[0070]: when the amount of wear (of the one or more components) satisfies a threshold amount of wear…[0072]: to cause the one or more devices (e.g., controller 140) to cause machine 105 to… cause an alarm to be activated. The alarm may indicate that the one or more components are to be repaired or replaced.”
Zhang does not teach updating…data, and Zhang does not teach the physics-based reduced order model… wherein the physics-based ROM is configured to apply one or more physics-based equations.
Niedfeldt teaches a model to recursively update state estimates using sequential measurements [p.464-465]: “The recently developed R-RANSAC algorithm described in this paper extends the traditional RANSAC algorithm to recursively update state estimates using sequential measurements…where for each new measurement scan, R-RANSAC tests each measurement to see if it is an inlier to one or more existing tracks. If so, those tracks are updated”, and [p.462]: “The R-RANSAC algorithm...robustly and recursively tracks multiple targets in clutter…and stores a set of validated hypotheses, or tracks, between time steps and uses subsequent measurements to either update existing tracks or seed new tracks…and it was first developed to estimate parameters of signals.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Niedfeldt to use updated data to have lower computational complexity and avoid recomputing hypotheses and have more accurate measurements of state over time.
Lin teaches the physics-based reduced order model…wherein the physics-based ROM is configured to apply one or more physics-based equations through the modeling of [Lin: Abstract]: “aero-hydro-servo-elastic (AHSE) dynamics of each wind turbine… where a reduced-order model (ROM), able to capture the relevant dynamics of the system for a specific failure” is used. [p.2]: “Reduced-order models (ROMs) are well established and widely applied in a variety of areas [10], including static reduction [11], dynamic reduction [12], balanced methods [13], and others.” [p.2]: “All the above ROMs focus on the reduction of the system’s mass and stiffness matrix, derived from the linearization of the system equations of motion.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Niedfeldt, further in view of Lin to use a physics-based ROM configured to apply one or more physics-based equations due to their lower computational cost, easy scalability and effectiveness in monitoring the physics of wear. [Lin: Abstract]: “This paper developed a reduced-order model (ROM), able to capture the relevant dynamics of the system for a specific failure, having a lower computational cost and therefore more easily scalable.” [p.2]: “Reduced-order models (ROMs) are well established and widely applied in a variety of areas [10], including static reduction [11], dynamic reduction [12], balanced methods [13], and others.”
Regarding claim 8, Zhang teaches receiving, by the at least one processor, machine data generated by the track-type machine; receiving, by the at least one processor, environmental data associated with the track-type machine; generating, by the at least one processor, the estimated wear level of the at least one undercarriage component [0008]: “A machine includes one or more memories; and one or more processors configured to: receive, from one or more sensor devices of the machine, sensor data associated with wear of one or more components of an undercarriage of the machine; predict, using a machine learning model and the sensor data, an amount wear of the one or more components based on a wear rate of the one or more components, wherein the machine learning model is trained, using training data, to predict the wear rate of the one or more components, wherein the training data includes two or more of: historical sensor data, historical inspection data, or simulation data, of a simulation model, from one or more third devices, and wherein the two or more of the historical sensor data, the historical inspection data, or the simulation data are associated with wear of the one or more components; and perform an action based on the amount of wear of the one or more components.” [0043]: “The historical inspection data may include…environmental conditions at a location associated with machine” and comparing, by the at least one processor, the estimated wear level of the at least one undercarriage component to the wear level threshold [0030]: “Additionally, or alternatively, sensor system 120 may provide the historical sensor data to wear detection device 190 (e.g., to train machine learning model 230) based on a triggering event (e.g., a request from wear detection device 190, a request from controller 140, and/or a request from an operator of machine 105 (e.g., via the integrated display and/or operator controls)…where [0040] the “controller may include one or more processors…capable of being programmed to perform a function.” [0072]: “Wear detection device 190 may transmit the remaining life information…[0070]: when the amount of wear (of the one or more components) satisfies a threshold amount of wear…”
However, Zhang fails to teach using recursiveness in updating…data.
Niedfeldt teaches a model to recursively update state estimates using sequential measurements [p.464-465]: “The recently developed R-RANSAC algorithm described in this paper extends the traditional RANSAC algorithm to recursively update state estimates using sequential measurements…where for each new measurement scan, R-RANSAC tests each measurement to see if it is an inlier to one or more existing tracks. If so, those tracks are updated”, and [p.462]: “The R-RANSAC algorithm...robustly and recursively tracks multiple targets in clutter…and stores a set of validated hypotheses, or tracks, between time steps and uses subsequent measurements to either update existing tracks or seed new tracks…and it was first developed to estimate parameters of signals.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Niedfeldt to use recursiveness in updating…data to have lower computational complexity and avoid recomputing hypotheses and have more accurate measurements of state over time.
Regarding claim 9, Zhang teaches wherein the estimated wear level is generated and compared to the wear level threshold on a regular interval of time [0008]: “A machine includes one or more memories; and one or more processors configured to: receive, from one or more sensor devices of the machine, sensor data associated with wear of one or more components of an undercarriage of the machine; predict, using a machine learning model and the sensor data, an amount wear of the one or more components based on a wear rate of the one or more components, wherein the machine learning model is trained, using training data, to predict the wear rate of the one or more components…” [0072]: “Wear detection device 190 may transmit the remaining life information…[0070]: when the amount of wear (of the one or more components) satisfies a threshold amount of wear…”
However, Zhang fails to teach using recursiveness in updating…data.
Niedfeldt teaches a model to recursively update state estimates using sequential measurements [p.464-465]: “The recently developed R-RANSAC algorithm described in this paper extends the traditional RANSAC algorithm to recursively update state estimates using sequential measurements…where for each new measurement scan, R-RANSAC tests each measurement to see if it is an inlier to one or more existing tracks. If so, those tracks are updated”, and [p.462]: “The R-RANSAC algorithm...robustly and recursively tracks multiple targets in clutter…and stores a set of validated hypotheses, or tracks, between time steps and uses subsequent measurements to either update existing tracks or seed new tracks…and it was first developed to estimate parameters of signals.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Niedfeldt to use recursiveness in updating…data to have lower computational complexity and avoid recomputing hypotheses and have more accurate measurements of state over time.
Regarding claim 10, Zhang teaches wherein comparing the estimated wear level of the at least one undercarriage component to the wear level threshold further comprises comparing the estimated wear level to a near-failure threshold [0070]: “wear detection device 190 may transmit the remaining life information when the amount of wear (of the one or more components) satisfies a threshold amount of wear. The remaining life information may indicate the amount of wear of the one or more components, indicate a wear rate of the one or more components, indicate the remaining life of the one or more components, and/or an offer associated with repairing and/or replacing the one or more components,” and [0076]: “wear detection device 190 may determine whether a failure of the one or more components is imminent (e.g., based on the predicted component wear information). If wear detection device 190 determines that the failure is imminent, wear detection device 190 may perform one or more of the actions described above. If wear detection device 190 determines that the failure is not imminent, wear detection device 190 may not perform an action”
and wherein the instructions, when executed, further cause the at least one processor to perform steps including, in response to determining that the estimated wear level of the at least one undercarriage component exceeds the near-failure threshold, generating an estimate of when the undercarriage component will need to be replaced and automatically and electronically causing the control interface to display the estimate [Claim 12]: “…the one or more processors are further configured to: cause an operation of the machine to be adjusted based on the amount of wear of the one or more components; transmit remaining life information to a first device to cause the first device to generate, based on the amount of wear of the one or more components, a service request to at least one of repair or replace the one or more components, wherein the remaining life information indicates the amount of wear of the one or more components; or transmit the remaining life information, to a second device associated with an operator of the machine, to cause the operator to adjust the operation of the machine based on the remaining life information or to transmit the service request using the second device associated with the operator.” [0030]: “Additionally, or alternatively, sensor system 120 may provide the historical sensor data to wear detection device 190 (e.g., to train machine learning model 230) based on a triggering event (e.g., a request from wear detection device 190, a request from controller 140, and/or a request from an operator of machine 105 (e.g., via the integrated display and/or operator controls)…where [0040] the “controller may include one or more processors…capable of being programmed to perform a function.”
However, Zhang does not teach updating…data.
Niedfeldt teaches a model to recursively update state estimates using sequential measurements [p.464-465]: “The recently developed R-RANSAC algorithm described in this paper extends the traditional RANSAC algorithm to recursively update state estimates using sequential measurements…where for each new measurement scan, R-RANSAC tests each measurement to see if it is an inlier to one or more existing tracks. If so, those tracks are updated”, and [p.462]: “The R-RANSAC algorithm...robustly and recursively tracks multiple targets in clutter…and stores a set of validated hypotheses, or tracks, between time steps and uses subsequent measurements to either update existing tracks or seed new tracks…and it was first developed to estimate parameters of signals.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Niedfeldt to use updated data to have lower computational complexity and avoid recomputing hypotheses and have more accurate measurements of state over time.
Regarding claim 11, Zhang teaches wherein comparing the estimated wear level of the at least one undercarriage component to the wear level threshold further comprises comparing the estimated wear level to a failure threshold [0070]: “Wear detection device 190 may transmit the remaining life information when the amount of wear (of the one or more components) satisfies a threshold amount of wear. The remaining life information may indicate the amount of wear of the one or more components, indicate a wear rate of the one or more components, indicate the remaining life of the one or more components, and/or an offer associated with repairing and/or replacing the one or more components,” and [0076]: “wear detection device 190 may determine whether a failure of the one or more components is imminent (e.g., based on the predicted component wear information). If wear detection device 190 determines that the failure is imminent, wear detection device 190 may perform one or more of the actions described above. If wear detection device 190 determines that the failure is not imminent, wear detection device 190 may not perform an action”
and wherein the instructions, when executed, further cause the at least one processor to perform steps including, in response to determining that the estimated wear level of the at least one undercarriage component exceeds the failure threshold, automatically and electronically causing the control interface to display an indication that the at least one undercarriage component needs to be replaced [Claim 12]: “…the one or more processors are further configured to: cause an operation of the machine to be adjusted based on the amount of wear of the one or more components; transmit remaining life information to a first device to cause the first device to generate, based on the amount of wear of the one or more components, a service request to at least one of repair or replace the one or more components, wherein the remaining life information indicates the amount of wear of the one or more components; or transmit the remaining life information, to a second device associated with an operator of the machine, to cause the operator to adjust the operation of the machine based on the remaining life information or to transmit the service request using the second device associated with the operator”, [0030]: “Additionally, or alternatively, sensor system 120 may provide the historical sensor data to wear detection device 190 (e.g., to train machine learning model 230) based on a triggering event (e.g., a request from wear detection device 190, a request from controller 140, and/or a request from an operator of machine 105 (e.g., via the integrated display and/or operator controls)…where [0040] the “controller may include one or more processors…capable of being programmed to perform a function.”
However, Zhang does not teach updating…data.
Niedfeldt teaches a model to recursively update state estimates using sequential measurements [p.464-465]: “The recently developed R-RANSAC algorithm described in this paper extends the traditional RANSAC algorithm to recursively update state estimates using sequential measurements…where for each new measurement scan, R-RANSAC tests each measurement to see if it is an inlier to one or more existing tracks. If so, those tracks are updated”, and [p.462]: “The R-RANSAC algorithm...robustly and recursively tracks multiple targets in clutter…and stores a set of validated hypotheses, or tracks, between time steps and uses subsequent measurements to either update existing tracks or seed new tracks…and it was first developed to estimate parameters of signals.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Niedfeldt to use updated data to have lower computational complexity and avoid recomputing hypotheses and have more accurate measurements of state over time.
Regarding claim 12, Zhang teaches receiving, by the at least one processor, sensor data that is indirectly indicative of wear of the at least one undercarriage component [0051]: “Wear detection device 190 (e.g., using machine learning model 230) may identify factors impacting a wear rate of the one or more components (and/or an amount wear of the one or more components). The factors may include the measure of abrasion, the location, the measure of moisture, the operator behavior (which may identify a task performed by machine 105), the distance traveled, the speed associated with the distance traveled, the track tension, the drawbar force, the measure of vibration of machine 105, and/or the measure of sound of machine 105.” [0032]: “The sensor devices may include a vibration sensor device, a sound sensor device, a track link wear sensor device, a location sensor device, a speed sensor device, a motion sensor device, a load sensor device, a pressure sensor device, a flow sensor device, and/or a temperature sensor device.” [0040-0041]: “Controller 140…which may include one or more processors 240…may obtain sensor data (e.g., from sensor system 120) and may cause wear detection device 190 to predict (e.g., using machine learning model 230) an amount of wear of the one or more components based on the sensor data”
and optimizing, by the at least one processor, the estimated wear level of the at least one undercarriage component by model with the sensor data using an adaptive fusion method [0061]: “When training machine learning model 230, wear detection device 190 may portion the training data into a training set (e.g., a set of data to train machine learning model 230), a validation set (e.g., a set of data used to evaluate a fit of machine learning model 230 and/or to fine tune machine learning model 230), a test set (e.g., a set of data used to evaluate a final fit of machine learning model 230), and/or the like. Wear detection device 190 may preprocess and/or perform dimensionality reduction to reduce the training data to a minimum feature set. Wear detection device 190 may train machine learning model 230 on this minimum feature set, thereby reducing processing to train machine learning model 230, and may apply a classification technique, to the minimum feature set.”
However, Zhang does not teach updating…data and/or a model. Zhang also does not teach the physics-based ROM.
Niedfeldt teaches a model to recursively update state estimates using sequential measurements [p.464-465]: “The recently developed R-RANSAC algorithm described in this paper extends the traditional RANSAC algorithm to recursively update state estimates using sequential measurements…where for each new measurement scan, R-RANSAC tests each measurement to see if it is an inlier to one or more existing tracks. If so, those tracks are updated”, and [p.462]: “The R-RANSAC algorithm...robustly and recursively tracks multiple targets in clutter…and stores a set of validated hypotheses, or tracks, between time steps and uses subsequent measurements to either update existing tracks or seed new tracks…and it was first developed to estimate parameters of signals.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Niedfeldt to update data and/or a model to have lower computational complexity and avoid recomputing hypotheses and have more accurate measurements of state over time.
Zhang fails to teach the following; however, Lin teaches the physics-based reduced order model through the modeling of [Lin: Abstract]: “aero-hydro-servo-elastic (AHSE) dynamics of each wind turbine… where a reduced-order model (ROM), able to capture the relevant dynamics of the system for a specific failure” is used. [p.2]: “Reduced-order models (ROMs) are well established and widely applied in a variety of areas [10], including static reduction [11], dynamic reduction [12], balanced methods [13], and others.” [p.2]: “All the above ROMs focus on the reduction of the system’s mass and stiffness matrix, derived from the linearization of the system equations of motion.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Niedfeldt, further in view of Lin to use the physics-based ROM due to their lower computational cost, easy scalability and effectiveness in monitoring the physics of wear. [Lin: Abstract]: “This paper developed a reduced-order model (ROM), able to capture the relevant dynamics of the system for a specific failure, having a lower computational cost and therefore more easily scalable.” [p.2]: “Reduced-order models (ROMs) are well established and widely applied in a variety of areas [10], including static reduction [11], dynamic reduction [12], balanced methods [13], and others…All the above ROMs focus on the reduction of the system’s mass and stiffness matrix, derived from the linearization of the system equations of motion.”
Regarding claim 13, Zhang teaches wherein the control interface is built into the track-type machine [0019]: Operator cabin 130 includes an integrated display and operator controls); [0030]: “Additionally, or alternatively, sensor system 120 may provide the historical sensor data to wear detection device 190 (e.g., to train machine learning model 230) based on a triggering event (e.g., a request from wear detection device 190, a request from controller 140, and/or a request from an operator of machine 105 (e.g., via the integrated display and/or operator controls)…where [0040] the “controller may include one or more processors…capable of being programmed to perform a function.”
Regarding claim 14, Zhang teaches accessing, by at least one processor included in a controller associated with a track-type machine, a model corresponding to the track-type machine, the model simulating at least one undercarriage component of the track-type machine, the at least one undercarriage component of the track-type machine including a track shoe, a track chain, a roller, an idler, or a sprocket; receiving, by the at least one processor, an estimated wear level of the at least one undercarriage component of a track-type machine; receiving, by the at least one processor, machine signal data generated by the track-type machine; generating, by the at least one processor, an estimated wear level of the at least one undercarriage component by submitting processing the estimated wear level of the at least one undercarriage component and the machine signal data generated by the track-type machine using the model [0086]: “As further shown in FIG. 3, process 300 may include predicting, using the machine learning model and based on the sensor data, the remaining life of the one or more components (block 350). For example, the first device may predict, using the machine learning model and based on the sensor data, the remaining life of the one or more components.” [0008]: “A machine includes one or more memories; and one or more processors configured to: receive, from one or more sensor devices of the machine, sensor data associated with wear of one or more components of an undercarriage of the machine; predict, using a machine learning model and the sensor data, an amount wear of the one or more components based on a wear rate of the one or more components, wherein the machine learning model is trained, using training data, to predict the wear rate of the one or more components, wherein the training data includes two or more of: historical sensor data, historical inspection data, or simulation data, of a simulation model, from one or more third devices, and wherein the two or more of the historical sensor data, the historical inspection data, or the simulation data are associated with wear of the one or more components; and perform an action based on the amount of wear of the one or more components”,
and comparing, by the at least one processor, the estimated wear level of the at least one undercarriage component to a wear level threshold; and in response to determining that the estimated wear level of the at least one undercarriage component exceeds the wear level threshold, automatically and electronically causing a control interface associated with the track-type machine to output display a warning notification [0030]: “Additionally, or alternatively, sensor system 120 may provide the historical sensor data to wear detection device 190 (e.g., to train machine learning model 230) based on a triggering event (e.g., a request from wear detection device 190, a request from controller 140, and/or a request from an operator of machine 105 (e.g., via the integrated display and/or operator controls)…where [0040] the “controller may include one or more processors…capable of being programmed to perform a function.” [0072]: “Wear detection device 190 may transmit the remaining life information…[0070]: when the amount of wear (of the one or more components) satisfies a threshold amount of wear…[0072]: to cause the one or more devices (e.g., controller 140) to cause machine 105 to… cause an alarm to be activated. The alarm may indicate that the one or more components are to be repaired or replaced.”
Zhang does not teach updating…data, and Zhang does not teach the reduced order model (ROM).
Niedfeldt teaches a model to recursively update state estimates using sequential measurements [p.464-465]: “The recently developed R-RANSAC algorithm described in this paper extends the traditional RANSAC algorithm to recursively update state estimates using sequential measurements…where for each new measurement scan, R-RANSAC tests each measurement to see if it is an inlier to one or more existing tracks. If so, those tracks are updated”, and [p.462]: “The R-RANSAC algorithm...robustly and recursively tracks multiple targets in clutter…and stores a set of validated hypotheses, or tracks, between time steps and uses subsequent measurements to either update existing tracks or seed new tracks…and it was first developed to estimate parameters of signals.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Niedfeldt to use updated data to have lower computational complexity and avoid recomputing hypotheses and have more accurate measurements of state over time.
Zhang fails to teach the following; however, Lin teaches the reduced order model through the modeling of [Lin: Abstract]: “aero-hydro-servo-elastic (AHSE) dynamics of each wind turbine… where a reduced-order model (ROM), able to capture the relevant dynamics of the system for a specific failure” is used. [p.2, para. 2, lines 15-17]: “Reduced-order models (ROMs) are well established and widely applied in a variety of areas [10], including static reduction [11], dynamic reduction [12], balanced methods [13], and others.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Niedfeldt, further in view of Lin to use a reduced order model (ROM) because the ROM has a lower computational cost and it is more easily scalable. [Lin: Abstract]: “This paper developed a reduced-order model (ROM), able to capture the relevant dynamics of the system for a specific failure, having a lower computational cost and therefore more easily scalable.” [p.2]: “Reduced-order models (ROMs) are well established and widely applied in a variety of areas [10], including static reduction [11], dynamic reduction [12], balanced methods [13], and others.”
Regarding claim 15, Zhang teaches receiving, by the at least one processor, machine data generated by the track-type machine; generating, by the at least one processor, and estimated wear levels of the at least one undercarriage component [0008]: “A machine includes one or more memories; and one or more processors configured to: receive, from one or more sensor devices of the machine, sensor data associated with wear of one or more components of an undercarriage of the machine; predict, using a machine learning model and the sensor data, an amount wear of the one or more components based on a wear rate of the one or more components, wherein the machine learning model is trained, using training data, to predict the wear rate of the one or more components, wherein the training data includes two or more of: historical sensor data, historical inspection data, or simulation data, of a simulation model, from one or more third devices, and wherein the two or more of the historical sensor data, the historical inspection data, or the simulation data are associated with wear of the one or more components; and perform an action based on the amount of wear of the one or more components.” [0043]: “The historical inspection data may include…environmental conditions at a location associated with machine”,
and comparing, by the at least one processor, the estimated wear level of the at least one undercarriage component to the wear level threshold [0030]: “Additionally, or alternatively, sensor system 120 may provide the historical sensor data to wear detection device 190 (e.g., to train machine learning model 230) based on a triggering event (e.g., a request from wear detection device 190, a request from controller 140, and/or a request from an operator of machine 105 (e.g., via the integrated display and/or operator controls)…where [0040] the “controller may include one or more processors…capable of being programmed to perform a function.” [0072]: “Wear detection device 190 may transmit the remaining life information…[0070]: when the amount of wear (of the one or more components) satisfies a threshold amount of wear…”
However, Zhang fails to teach using recursiveness in updating…data.
Niedfeldt teaches a model to recursively update state estimates using sequential measurements [p.464-465]: “The recently developed R-RANSAC algorithm described in this paper extends the traditional RANSAC algorithm to recursively update state estimates using sequential measurements…where for each new measurement scan, R-RANSAC tests each measurement to see if it is an inlier to one or more existing tracks. If so, those tracks are updated”, and [p.462]: “The R-RANSAC algorithm...robustly and recursively tracks multiple targets in clutter…and stores a set of validated hypotheses, or tracks, between time steps and uses subsequent measurements to either update existing tracks or seed new tracks…and it was first developed to estimate parameters of signals.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Niedfeldt to use recursiveness in updating…data to have lower computational complexity and avoid recomputing hypotheses and have more accurate measurements of state over time.
Regarding claim 16, Zhang teaches wherein the estimated wear level is generated and compared to the wear level threshold on a regular interval of time [0008]: “A machine includes one or more memories; and one or more processors configured to: receive, from one or more sensor devices of the machine, sensor data associated with wear of one or more components of an undercarriage of the machine; predict, using a machine learning model and the sensor data, an amount wear of the one or more components based on a wear rate of the one or more components, wherein the machine learning model is trained, using training data, to predict the wear rate of the one or more components…” [0072]: “Wear detection device 190 may transmit the remaining life information…[0070]: when the amount of wear (of the one or more components) satisfies a threshold amount of wear…”
However, Zhang fails to teach using recursiveness in updating…data.
Niedfeldt teaches a model to recursively update state estimates using sequential measurements [p.464-465]: “The recently developed R-RANSAC algorithm described in this paper extends the traditional RANSAC algorithm to recursively update state estimates using sequential measurements…where for each new measurement scan, R-RANSAC tests each measurement to see if it is an inlier to one or more existing tracks. If so, those tracks are updated”, and [p.462]: “The R-RANSAC algorithm...robustly and recursively tracks multiple targets in clutter…and stores a set of validated hypotheses, or tracks, between time steps and uses subsequent measurements to either update existing tracks or seed new tracks…and it was first developed to estimate parameters of signals.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Niedfeldt to use recursiveness in updating…data to have lower computational complexity and avoid recomputing hypotheses and have more accurate measurements of state over time.
Regarding claim 17, Zhang teaches wherein the model corresponding to the track-type machine is a model configured to simulate the at least one component of the undercarriage of the track-type machine [0086]: “As further shown in FIG. 3, process 300 may include predicting, using the machine learning model and based on the sensor data, the remaining life of the one or more components (block 350). For example, the first device may predict, using the machine learning model and based on the sensor data, the remaining life of the one or more components” [0008] “wherein the machine learning model is trained using… simulation data, of a simulation model… associated with wear of the one or more components.”
However, Zhang does not teach the ROM and the physics-based ROM configured to apply one or more physics-based equations.
Lin teaches the physics-based reduced order model configured to apply one or more physics-based equations through the modeling of [Lin: Abstract]: “aero-hydro-servo-elastic (AHSE) dynamics of each wind turbine… where a reduced-order model (ROM), able to capture the relevant dynamics of the system for a specific failure” is used. [p.2]: “Reduced-order models (ROMs) are well established and widely applied in a variety of areas [10], including static reduction [11], dynamic reduction [12], balanced methods [13], and others.” [p.2]: “All the above ROMs focus on the reduction of the system’s mass and stiffness matrix, derived from the linearization of the system equations of motion.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a physics-based ROM configured to apply one or more physics-based equations due to their lower computational cost, easy scalability and effectiveness in monitoring the physics of wear. [Lin: Abstract]: “This paper developed a reduced-order model (ROM), able to capture the relevant dynamics of the system for a specific failure, having a lower computational cost and therefore more easily scalable.” [p.2]: “Reduced-order models (ROMs) are well established and widely applied in a variety of areas [10], including static reduction [11], dynamic reduction [12], balanced methods [13], and others…All the above ROMs focus on the reduction of the system’s mass and stiffness matrix, derived from the linearization of the system equations of motion.”
Regarding claim 18, Zhang teaches receiving, by the at least one processor, environmental data associated with the track-type machine, wherein generating the estimated wear level of the at least one undercarriage component further comprises processing the estimated wear level of the at least one undercarriage component, the machine signal data generated by the track-type machine, and the environmental data associated with the track-type machine using the model [0043]: “The historical inspection data may include…environmental conditions at a location associated with machine.” [0008]: “A machine includes one or more memories; and one or more processors configured to: receive, from one or more sensor devices of the machine, sensor data associated with wear of one or more components of an undercarriage of the machine; predict, using a machine learning model and the sensor data, an amount wear of the one or more components based on a wear rate of the one or more components, wherein the machine learning model is trained, using training data, to predict the wear rate of the one or more components, wherein the training data includes two or more of: historical sensor data, historical inspection data, or simulation data, of a simulation model, from one or more third devices, and wherein the two or more of the historical sensor data, the historical inspection data, or the simulation data are associated with wear of the one or more components; and perform an action based on the amount of wear of the one or more components.” [Claim 12] “…the one or more processors are further configured to: cause an operation of the machine to be adjusted based on the amount of wear of the one or more components; transmit remaining life information to a first device to cause the first device to generate, based on the amount of wear of the one or more components, a service request to at least one of repair or replace the one or more components, wherein the remaining life information indicates the amount of wear of the one or more components; or transmit the remaining life information, to a second device associated with an operator of the machine, to cause the operator to adjust the operation of the machine based on the remaining life information or to transmit the service request using the second device associated with the operator.”
However, Zhang does not teach updating…data and does not teach the ROM.
Niedfeldt teaches a model to recursively update state estimates using sequential measurements [p.464-465]: “The recently developed R-RANSAC algorithm described in this paper extends the traditional RANSAC algorithm to recursively update state estimates using sequential measurements…where for each new measurement scan, R-RANSAC tests each measurement to see if it is an inlier to one or more existing tracks. If so, those tracks are updated”, and [p.462]: “The R-RANSAC algorithm...robustly and recursively tracks multiple targets in clutter…and stores a set of validated hypotheses, or tracks, between time steps and uses subsequent measurements to either update existing tracks or seed new tracks…and it was first developed to estimate parameters of signals.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Niedfeldt to use updated data to have lower computational complexity and avoid recomputing hypotheses and have more accurate measurements of state over time.
Zhang fails to teach the following; however, Lin teaches the reduced order model through the modeling of [Lin: Abstract]: “aero-hydro-servo-elastic (AHSE) dynamics of each wind turbine… where a reduced-order model (ROM), able to capture the relevant dynamics of the system for a specific failure” is used. [p.2, para. 2, lines 15-17]: “Reduced-order models (ROMs) are well established and widely applied in a variety of areas [10], including static reduction [11], dynamic reduction [12], balanced methods [13], and others.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Niedfeldt, further in view of Lin to use a reduced order model (ROM) because the ROM has a lower computational cost and it is more easily scalable. [Lin: Abstract]: “This paper developed a reduced-order model (ROM), able to capture the relevant dynamics of the system for a specific failure, having a lower computational cost and therefore more easily scalable.” [p.2]: “Reduced-order models (ROMs) are well established and widely applied in a variety of areas [10], including static reduction [11], dynamic reduction [12], balanced methods [13], and others.”
Regarding claim 19, Zhang teaches wherein comparing the estimated wear level of the at least one undercarriage component to the wear level threshold further comprises comparing the estimated wear level to a near-failure threshold [0070]: “wear detection device 190 may transmit the remaining life information when the amount of wear (of the one or more components) satisfies a threshold amount of wear. The remaining life information may indicate the amount of wear of the one or more components, indicate a wear rate of the one or more components, indicate the remaining life of the one or more components, and/or an offer associated with repairing and/or replacing the one or more components,” and [0076]: “wear detection device 190 may determine whether a failure of the one or more components is imminent (e.g., based on the predicted component wear information). If wear detection device 190 determines that the failure is imminent, wear detection device 190 may perform one or more of the actions described above. If wear detection device 190 determines that the failure is not imminent, wear detection device 190 may not perform an action”,
and the at least one processor is further configured to, in response determining that the estimated wear level of the at least one undercarriage component exceeds the near-failure threshold, generate an estimate of when the at least one undercarriage component will need to be replaced and automatically and electronically cause the control interface to display the estimate [Claim 12]: “…the one or more processors are further configured to: cause an operation of the machine to be adjusted based on the amount of wear of the one or more components; transmit remaining life information to a first device to cause the first device to generate, based on the amount of wear of the one or more components, a service request to at least one of repair or replace the one or more components, wherein the remaining life information indicates the amount of wear of the one or more components; or transmit the remaining life information, to a second device associated with an operator of the machine, to cause the operator to adjust the operation of the machine based on the remaining life information or to transmit the service request using the second device associated with the operator.” [0030]: “Additionally, or alternatively, sensor system 120 may provide the historical sensor data to wear detection device 190 (e.g., to train machine learning model 230) based on a triggering event (e.g., a request from wear detection device 190, a request from controller 140, and/or a request from an operator of machine 105 (e.g., via the integrated display and/or operator controls)…where [0040] the “controller may include one or more processors…capable of being programmed to perform a function.”
However, Zhang does not teach updating…data.
Niedfeldt teaches a model to recursively update state estimates using sequential measurements [p.464-465]: “The recently developed R-RANSAC algorithm described in this paper extends the traditional RANSAC algorithm to recursively update state estimates using sequential measurements…where for each new measurement scan, R-RANSAC tests each measurement to see if it is an inlier to one or more existing tracks. If so, those tracks are updated”, and [p.462]: “The R-RANSAC algorithm...robustly and recursively tracks multiple targets in clutter…and stores a set of validated hypotheses, or tracks, between time steps and uses subsequent measurements to either update existing tracks or seed new tracks…and it was first developed to estimate parameters of signals.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Niedfeldt to use updated data to have lower computational complexity and avoid recomputing hypotheses and have more accurate measurements of state over time.
Regarding claim 20, Zhang teaches wherein comparing the estimated wear level of the at least one undercarriage component to the wear level threshold further comprises comparing the estimated wear level to a failure threshold [0070]: “Wear detection device 190 may transmit the remaining life information when the amount of wear (of the one or more components) satisfies a threshold amount of wear. The remaining life information may indicate the amount of wear of the one or more components, indicate a wear rate of the one or more components, indicate the remaining life of the one or more components, and/or an offer associated with repairing and/or replacing the one or more components,” and [0076]: “wear detection device 190 may determine whether a failure of the one or more components is imminent (e.g., based on the predicted component wear information). If wear detection device 190 determines that the failure is imminent, wear detection device 190 may perform one or more of the actions described above. If wear detection device 190 determines that the failure is not imminent, wear detection device 190 may not perform an action”,
and further comprising, in response to determining that the estimated wear level of the at least one undercarriage component exceeds the failure threshold, automatically and electronically causing, by the at least one processor, the control interface to output display an indication that the at least one undercarriage component needs to be replaced [Claim 12]: “…the one or more processors are further configured to: cause an operation of the machine to be adjusted based on the amount of wear of the one or more components; transmit remaining life information to a first device to cause the first device to generate, based on the amount of wear of the one or more components, a service request to at least one of repair or replace the one or more components, wherein the remaining life information indicates the amount of wear of the one or more components; or transmit the remaining life information, to a second device associated with an operator of the machine, to cause the operator to adjust the operation of the machine based on the remaining life information or to transmit the service request using the second device associated with the operator.” [0030]: “Additionally, or alternatively, sensor system 120 may provide the historical sensor data to wear detection device 190 (e.g., to train machine learning model 230) based on a triggering event (e.g., a request from wear detection device 190, a request from controller 140, and/or a request from an operator of machine 105 (e.g., via the integrated display and/or operator controls)…where [0040] the “controller may include one or more processors…capable of being programmed to perform a function.”
However, Zhang does not teach updating…data.
Niedfeldt teaches a model to recursively update state estimates using sequential measurements [p.464-465]: “The recently developed R-RANSAC algorithm described in this paper extends the traditional RANSAC algorithm to recursively update state estimates using sequential measurements…where for each new measurement scan, R-RANSAC tests each measurement to see if it is an inlier to one or more existing tracks. If so, those tracks are updated”, and [p.462]: “The R-RANSAC algorithm...robustly and recursively tracks multiple targets in clutter…and stores a set of validated hypotheses, or tracks, between time steps and uses subsequent measurements to either update existing tracks or seed new tracks…and it was first developed to estimate parameters of signals.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Niedfeldt to use updated data to have lower computational complexity and avoid recomputing hypotheses and have more accurate measurements of state over time.
Pertinent Prior Art
US 20160178483 A1: A wear sensing device is disclosed. The wear sensing device may have a wear sensor. The wear sensor may have plurality of wear members, a communication device, and a processing device. The processing device may be configured to generate a signal indicative of wear, based on a state of the plurality of wear members. The processing device may be further configured transmit the signal with the communication device. The wear sensing device may further have a housing. The housing may at least partially enclose the wear sensor and include a plurality of separate slots. The plurality of wear members may be positioned in the plurality of separate slots.
US 20210339758 A1: A vehicle comprising a track system can be monitored to obtain information regarding the vehicle, including information regarding the track system, such as an indication of a physical state of a track and/or other component of the track system based on at least on 3D recognition and/or 2D recognition of image data of a track system component.
Rodrigo et al., A Reduced Order Model for Monitoring Aeroengines Condition in Real Time, 10(10) Aerospace (2023)
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/LOGAN D COONS/Examiner, Art Unit 2857
/JORDAN L JACKSON/Primary Examiner, Art Unit 2857
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