DETAILED ACTION
Claims 1-20 are pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgement is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d) to Italian Patent Application No. 102020000014944, filed on 6/23/2020.
Response to Arguments
Applicant’s arguments, filed 8/25/25, have been fully considered but are not persuasive.
Applicant’s arguments regarding the objection to claim 17 (page 7) are persuasive and the objection is withdrawn.
Applicant’s arguments regarding the rejection of claims 14 and 18 under 35 U.S.C. § 112 (page 7) are persuasive and this rejection is withdrawn.
Applicant argues, with regard to the rejection under 35 U.S.C. § 101, that the ‘that the claims as whole represent an improvement to the automatic machine for manufacturing by providing an improved and more sophisticated predictor for needed maintenance through use of the velocity error’ and that ‘by using the velocity error as a motorization metric MM, it was possible to highlight behaviors caused by friction and evaluate wear of components, thereby improving estimates for predictive maintenance’ (pages 7-8).
It is respectfully submitted that, as discussed below, the claims are directed to the abstract idea of processing data to determine whether maintenance is going to be necessary that is generally linked to a well-understood, routine, and conventional automatic machine (1) for manufacturing or packing consumer articles and to an electric motor (see MPEP 2106.05(h)) and eligibility "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself." Genetic Techs. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) as cited in MPEP 2106.04, i.e. an improvement to the abstract idea itself (determining/estimating whether maintenance is going to be necessary) is still merely an abstract idea. Applicant’s argument is therefore not persuasive.
Applicant argues that ‘Claim 12 further recites the inclusion of periodically scheduling a maintenance program, thereby further incorporating the judicial exception into a practical application of maintenance of the automatic machine’ (page 8).
It is respectfully submitted that claim 12 merely recites scheduling a maintenance program based on data (mental process). Note that no actual maintenance is performed on the machine to potentially improve the functioning of the machine, maintenance is merely scheduled. Applicant’s argument is therefore not persuasive.
Applicant argues that ‘Filev mentions nothing regarding a velocity error’ (page 9).
It is respectfully submitted that this argument is moot because Filev is not cited as teaching a velocity error. Applicant’s argument is therefore not persuasive.
Applicant argues that ‘Hosek does not teach or suggest taking the time series of the speed error, calculating statistical characteristics from them and using these statistical characteristics as dimensions for a multidimensional anomaly matrix. Instead, the combination of Filev and Hosek would at best the lead the skilled person to consider velocity error only as a direct indicator and make a direct comparison to a threshold value. This would not lead the skilled person to the claimed invention.’ (page 9).
It is respectfully submitted that Hoesk is not cited as teaching taking the time series of the speed error, calculating statistical characteristics from them and using these statistical characteristics as dimensions for a multidimensional anomaly matrix and this argument is therefore moot. Further, no reasoned argument is provided as to why the combination of Filev and Hosek would ‘at best the lead the skilled person to consider velocity error only as a direct indicator and make a direct comparison to a threshold value’, as alleged by Applicant. In addition, no reasoned argument is provided that addresses the rationale for combining Filev and Hosek to arrive at the claimed invention as detailed below in the current rejection under 35 U.S.C. § 103 and in the last office action, i.e. that it would have been obvious to a person of ordinary skill in the art to simply substitute the known velocity error of an electric motor, as taught by Hosek, for the metric of Filev, for the predicable result of a method for the predictive maintenance of an electric motor based on a velocity error. Applicant’s argument is therefore not persuasive.
For at least these reasons, the rejection of the claims is maintained.
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed to the abstract idea (mental/mathematical process) of processing data to determine whether maintenance is going to be necessary. Note that some of the steps involved may also be interpreted as abstract mathematical processes, e.g. calculating statistical features.
Claim 1 recites a method for the predictive maintenance of an automatic machine (1) for manufacturing or packing consumer articles, i.e. a process, which is a statutory category of invention. The claim recites:
defining at least one multidimensional tolerance horizon (TH) within an anomaly matrix (AM) having, as dimensions, at least two statistical features (STF) based on at least one sampling series (SS) detected and relative at least to the motorization metric (MM) detected;
calculating, for each sampling series (SS) detected, the at least two statistical features (STF) in order to define the position of an actual condition (AC) within the anomaly matrix (AM);
determining, based on the position of the actual condition (AC) in the anomaly matrix (AM) and of the multidimensional tolerance horizon (TH), the imminence of necessary maintenance;
that may be performed in the human mind, or by a human using a pen and paper. Thus the claim recites an abstract idea (mental processes), see MPEP 2106.04(a). Note that some of these steps may also be interpreted as abstract mathematical processes, e.g. calculating.
This judicial exception is not integrated into a practical application because the additional elements, i.e. applying the method to an automatic machine (1) for manufacturing or packing consumer articles and to an electric motor (generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)) and detecting and recording, periodically and at a sampling frequency (SF), at least a sampling series (SS) relating to at least one motorization metric (MM) of at least one electric actuator (4), by means of at least one respective local control unit (3, 11); transmitting, periodically and at a transmission frequency (TF), equal to or lower than the sampling frequency (SF), the recorded sampling series (SS) to a data processing unit (5); (insignificant extra-solution elements – mere data gathering, see MPEP 2106.05 I A, MPEP 2106.05(g) MPEP 2106.05(d); applied by a generic technology, see MPEP 2106.05(d) II and MPEP 2106.05(g) (e.g. receiving or transmitting data over a network)) does not impose any meaningful limits on practicing the abstract idea. The claim is therefore directed to an abstract idea.
Note that manufacturing machines and electric motors are well-understood, routine and conventional, see for example Nishiyama et al. U.S. Patent Publication No. 20180267510 [0008, 0030, Figs. 1, 3-4], Discenzo et al. U.S. Patent No. 7797062, Pepin et al. U.S. Patent Publication No. 20110316691 and Lee U.S. Patent Publication No. 20210241544 and the references cited below in the rejection under 35 U.S.C. § 103.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, applying the method to an automatic machine (1) for manufacturing or packing consumer articles and to an electric motor (generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)) and detecting and recording, periodically and at a sampling frequency (SF), at least a sampling series (SS) relating to at least one motorization metric (MM) of at least one electric actuator (4), by means of at least one respective local control unit (3, 11); transmitting, periodically and at a transmission frequency (TF), equal to or lower than the sampling frequency (SF), the recorded sampling series (SS) to a data processing unit (5); (insignificant extra-solution elements – mere data gathering, see MPEP 2106.05 I A, MPEP 2106.05(g) MPEP 2106.05(d); applied by a generic technology, see MPEP 2106.05(d) II and MPEP 2106.05(g) (e.g. receiving or transmitting data over a network)) does not impose any meaningful limits on practicing the abstract idea and are not considered significantly more. Considering the additionally elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Thus the claim is not patent eligible.
Claim 2 recites receiving a synchronization signal (data gathering). Thus this claim recites an abstract idea.
Claim 3 recites the abstract information that the synchronization signal represents. Thus this claim recites an abstract idea.
Claim 4 recite synchronizing the samples using the synchronization signal (mental process). Thus this claim recites an abstract idea.
Claim 5 recites that the abstract sample data relate to a state metric. Thus this claim recites an abstract idea.
Claim 6 recites various types of state metric. Thus this claim recites an abstract idea.
Claim 7 recites a sampling (data collection) frequency. Thus this claim recites an abstract idea.
Claim 8 recites a transmission (data collection) frequency. Thus this claim recites an abstract idea.
Claim 9 recites using an unsupervised classifier (applying the exception with a generic computer using a known algorithm – see MPEP 2106.04(a)(2) III C). Thus this claim recites an abstract idea. Note that unsupervised K-means classification is well-understood, routine and conventional, see for example Diao U.S. Patent Publication No. 20060069709 [0004-0005].
Claim 10 recites using training a model by means of a K-means algorithm (applying the exception with a generic computer using a known algorithm – see MPEP 2106.04(a)(2) III C). Thus this claim recites an abstract idea.
Claim 11 recites calculating the velocity with which successive actual conditions (AC) move (mental/mathematical process). Thus this claim recites an abstract idea.
Claim 12 recites scheduling a maintenance program based on data (mental process). Thus this claim recites an abstract idea.
Claim 13 recites transmitting the maintenance program (insignificant extra-solution elements – using generic technology, see MPEP 2106.05(d) II and MPEP 2106.05(g) (e.g. receiving or transmitting data over a network)). Thus this claim recites an abstract idea.
Claim 14 recites further details of the abstract anomaly matrix. Thus this claim recites an abstract idea.
Claim 15 recites different types of abstract motorization metric. Thus this claim recites an abstract idea.
Claim 16 recites an automatic machine (1) for manufacturing or packing consumer articles, i.e. a machine, which is a statutory category of invention. The claim recites that the machine applies the method of claim 1 and is thus rejected under the same rationale as claim 1. Note that a machine with an electric drive and actuator (generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)) and a storage unit (applying the exception with generic computer technology – see MPEP 2106.04(a)(2) III C) and these are not considered significantly more than the abstract idea. Also note that machines with an electric drive and actuator are well-understood, routine and conventional, see. Thus this claim recites an abstract idea.
Claim 17 recites a communication unit to transmit the maintenance program and networked acquisition units (insignificant extra-solution elements – using generic technology, see MPEP 2106.05(d) II and MPEP 2106.05(g) (e.g. receiving or transmitting data over a network)), a smart tag or IoT sensor, electric drives on a control cabinet or actuator and local acquisition units on a machine (generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)). Note that smart tag or IoT sensor, electric drives on a control cabinet or actuator and local acquisition units on a machine are well-understood, routine and conventional, see for example Galera et al. U.S. Patent Publication No. 20190147655 [particularly 0107], Leeman et al. U.S. Patent Publication No. 20140321067 [0003] and Payne U.S. Patent No. 10492502; and Nishiyama et al. U.S. Patent Publication No. 20180267510 [0114] AbiEzzi et al. U.S. Patent Publication No. 20190334918 [0001] and the references cited below in the rejection under 35 U.S.C. § 103. Thus this claim recites an abstract idea.
Claim 18 recites further details of the synchronization signal (used for data gathering). Thus this claim recites an abstract idea.
Claim 19 recites using an industrial network (insignificant extra-solution elements – using generic technology, see MPEP 2106.05(d) II and MPEP 2106.05(g) (e.g. receiving or transmitting data over a network)) to detect a state metric (data gathering). Thus this claim recites an abstract idea.
Claim 20 recites using a K-means algorithm (applying the exception with a generic computer using a known algorithm – see MPEP 2106.04(a)(2) III C), details of the abstract tolerance horizon and updating the abstract tolerance horizon (mental process). Thus this claim recites an 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 5-6, 9, 11-16 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Filev et al. U.S. Patent Publication No. 20070088550 (hereinafter Filev) in view of Hosek et al. U.S. Patent Publication No. 20070067678 (hereinafter Hosek).
Regarding claim 1, Filev teaches a method for the predictive maintenance of an automatic machine (1) for manufacturing [0020 — a method for predictive maintenance of a machine; 0034 — The PdM Agent may reside in a one or more controllers which are part of larger information system used to gather and process information about equipment and processes in a manufacturing, or other, facility.] or packing consumer articles;
the method comprising the steps of:
detecting and recording, periodically and at a sampling frequency (SF), at least a sampling series (SS) relating to at least one metric (MM) of at least one actuator (4), by means of at least one respective local control unit (3, 11) [0030-0032 — a machine such as a fan, compressor, pump, etc. may have attached to it one or more sensors configured to monitor vibrations as the machine operates. To monitor the vibrations, one or more accelerometers or other vibration sensing devices could be used. It is worth noting that although the exemplary illustrations contained herein use vibrations to determine machine features, other types of machine data could be used. For example, a current sensor may be used to measure changes in the amount of current the machine draws during various operations. Similarly, a thermocouple, or other type of temperature sensor, could be used to detect changes in temperature of some portion of the machine.; 0047-0056 — As new feature data continues to be collected, the PdM Agent enters the monitoring phase where all cluster parameters are recursively updated, and condition based monitoring is performed through continuous evaluation of the position of the feature vector with respect to the OM and OC clusters. Decisions for potential incipient and drastic fault conditions are also automatically generated… The vector of model parameters .phi. for each OC cluster is saved inside the PdM Agent for future updates. Multiple-steps-ahead prediction for the recently updated OC cluster centers are performed to assess the probability of the particular OC cluster to move toward the boundary of its enclosing OM cluster--something which corresponds to an incipient failure.];
transmitting, periodically and at a transmission frequency (TF), equal to or lower than the sampling frequency (SF), the recorded sampling series (SS) to a data processing unit (5) [0034-0036, Fig. 1 — The PdM Agent may reside in a one or more controllers which are part of larger information system used to gather and process information about equipment and processes in a manufacturing, or other, facility… At step 14, data is collected and features are extracted, for example, as described above. At step 16, it is determined whether the predefined number of feature vectors (N) is reached. If not, the process loops back to collect more data and extract more features. If the data count has reached (N), the process continues at step 18… as shown in FIG. 1, these may be performed in batch mode — Step 16 implies the transmission frequency is lower than the sampling frequency.; 0047-0056 — As new feature data continues to be collected, the PdM Agent enters the monitoring phase where all cluster parameters are recursively updated, and condition based monitoring is performed through continuous evaluation of the position of the feature vector with respect to the OM and OC clusters. Decisions for potential incipient and drastic fault conditions are also automatically generated… The vector of model parameters .phi. for each OC cluster is saved inside the PdM Agent for future updates. Multiple-steps-ahead prediction for the recently updated OC cluster centers are performed to assess the probability of the particular OC cluster to move toward the boundary of its enclosing OM cluster--something which corresponds to an incipient failure.];
defining at least one multidimensional tolerance horizon (TH) within an anomaly matrix (AM) having, as dimensions, at least two statistical features (STF) based on at least one sampling series (SS) detected and relative at least to the metric (MM) detected [0037-0046, Figs. 2 and 4 — the standardized feature vectors are transformed into 2-D space, resulting in 2-D OM clusters 32, 34, 36 that have multidimensional boundaries; 0010 — Time domain data statistics include such things as root mean square (RMS), crest factor, variance, skewness, and kurtosis; 0032 — Transformation of raw data into a feature vector could include the application of a statistical equation, such as determining the root mean square (RMS) of the raw data, or applying a Fast Fourier Transform (FFT) to the data];
calculating, for each sampling series (SS) detected, the at least two statistical features (STF) in order to define the position of an actual condition (AC) within the anomaly matrix (AM) [0046 — the feature vectors 24 are standardized and grouped in OM clusters 26, 28, 30, with cluster 30 being the m.sup.th cluster. The clusters 26, 28, 30 are in the feature space, which is a K-dimensional space. In the lower portion of FIG. 2, the standardized feature vectors are transformed into 2-D space, resulting in 2-D OM clusters 32, 34, 36, where cluster 36 is the m.sup.th cluster.];
determining, based on the position of the actual condition (AC) in the anomaly matrix (AM) and of the multidimensional tolerance horizon (TH), the imminence of necessary maintenance [0047, 0060, Fig. 4 — the PdM Agent enters the monitoring phase where all cluster parameters are recursively updated, and condition based monitoring is performed through continuous evaluation of the position of the feature vector with respect to the OM and OC clusters. Decisions for potential incipient and drastic fault conditions are also automatically generated; 0013-0014 — The trend of changing the OC clusters is used to predict a potential incipient fault].
But Filev fails to clearly specify a motorization metric (MM) of at least one electric actuator (4) and wherein the motorization metric (MM) is the velocity error of an electric motor detected by a respective drive.
However, Hosek teaches a motorization metric (MM) of at least one electric actuator (4) and wherein the motorization metric (MM) is the velocity error of an electric motor detected by a respective drive [0104 — Motor current values can in turn be used to compute motor torques using the motor torque-current relationships… Position and velocity tracking error; 0128 — motor currents, velocities and duty cycle values can be used to compute the electrical power consumed by each motor at any given time; 0136, 0149 — Rapid increase in position and velocity error].
Filev and Hosek are analogous art. They relate to predictive maintenance systems.
Therefore at the time the invention was made it would have been obvious to a person of ordinary skill in the art to simply substitute the known velocity error of an electric motor, as taught by Hosek, for the metric of Filev, for the predicable result of a method for the predictive maintenance of an electric motor based on a velocity error.
Regarding claim 5, the combination of Filev and Hosek teaches all the limitations of the base claims as outlined above.
Further, Filev teaches the series of recorded samples (SS) also relates to a local state metric (LSM), concerning the condition of one or more devices [0010-0011 — Time domain features can be calculated directly from raw vibration signals picked up by one or more sensors attached to the machine being monitored; 0031-0032 — a machine such as a fan, compressor, pump, etc. may have attached to it one or more sensors configured to monitor vibrations as the machine operates. ].
Further, Hosek teaches one or more devices mounted on the automatic machine (1) [0085, Fig. 4 — the robotic manipulator is built around an open cylindrical frame 401 suspended from a circular mounting flange 402… a brushless DC motor 406 via a ball-screw mechanism 407. The carriage 405 houses a pair of coaxial brushless DC motors 408, 409].
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Filev and Hosek, by incorporating the above limitations, as taught by Hosek.
One of ordinary skill in the art would have been motivated to do this modification to provide adequate mechanical support for a motor attached to a machine thus enabling effective mechanical coupling.
Regarding claim 6, the combination of Filev and Hosek teaches all the limitations of the base claims as outlined above.
Further, Filev teaches the local state metric (LSM) comprises vibrations detected in several dimensions, and/or temperatures and/or accelerations [0010-0011 — Time domain features can be calculated directly from raw vibration signals picked up by one or more sensors attached to the machine being monitored; 0031-0032 — a machine such as a fan, compressor, pump, etc. may have attached to it one or more sensors configured to monitor vibrations as the machine operates].
Regarding claim 9, the combination of Filev and Hosek teaches all the limitations of the base claims as outlined above.
Further, Filev teaches the multidimensional tolerance horizon (TH) is defined via an unsupervised classifier [0008 — A real time, unsupervised clustering algorithm is applied to identify stable patterns that constitute different operating modes of the equipment; 0068 —Like the PdM Agent, the diagnostics based on classification determines whether a given feature vector or data point lies within an existing cluster, C.sub.i, or whether it is an outlier].
Regarding claim 11, the combination of Filev and Hosek teaches all the limitations of the base claims as outlined above.
Further, Filev teaches calculating the velocity with which successive actual conditions (AC) move within the anomaly matrix (AM) [0014 — The trend of changing the OC clusters is used to predict a potential incipient fault.; 0055-0056 — The purpose of this model is to track of the dynamics of the particular cluster over time and to enable the PdM Agent to predict the probability of the OC cluster moving towards the boundary of its corresponding OM cluster. This type of event constitutes a potential drastic fault. Therefore, the dynamics of the OC clusters are used to predict incipient failures.; 0065, 0074, Fig. 7 — diagnostics based on velocity threshold (called .sup.V)].
Regarding claim 12, the combination of Filev and Hosek teaches all the limitations of the base claims as outlined above.
Further, Filev teaches periodically scheduling a maintenance program (9) based on the position or velocity of the most recent actual condition (AC) within the anomaly matrix (AM) [0075-0077, Fig. 7 — The output 90 from the diagnostics engine 78 is in communication with a decision support system (DSS) 92. The DSS 92 uses diagnostics/prognostics results and recommends necessary actions for maintenance. A DSS, such as the DSS 92, may include computers with preprogrammed algorithms configured to return certain outputs based on the information received from the DPF. As with the PdM Agent, the outputs based on the DPF information may be in the form of graphical displays or other methods useful to shop floor and other decision making personnel.; 0047-0056 — As new feature data continues to be collected, the PdM Agent enters the monitoring phase where all cluster parameters are recursively updated, and condition based monitoring is performed through continuous evaluation of the position of the feature vector with respect to the OM and OC clusters. Decisions for potential incipient and drastic fault conditions are also automatically generated… The vector of model parameters .phi. for each OC cluster is saved inside the PdM Agent for future updates. Multiple-steps-ahead prediction for the recently updated OC cluster centers are performed to assess the probability of the particular OC cluster to move toward the boundary of its enclosing OM cluster--something which corresponds to an incipient failure.].
Regarding claim 13, the combination of Filev and Hosek teaches all the limitations of the base claims as outlined above.
Further, Filev teaches periodically transmitting the updated maintenance program (9) to a maintenance resource [0075-0077, Fig. 7 — The output 90 from the diagnostics engine 78 is in communication with a decision support system (DSS) 92. The DSS 92 uses diagnostics/prognostics results and recommends necessary actions for maintenance. A DSS, such as the DSS 92, may include computers with preprogrammed algorithms configured to return certain outputs based on the information received from the DPF. As with the PdM Agent, the outputs based on the DPF information may be in the form of graphical displays or other methods useful to shop floor and other decision making personnel.; 0047-0056 — As new feature data continues to be collected, the PdM Agent enters the monitoring phase where all cluster parameters are recursively updated, and condition based monitoring is performed through continuous evaluation of the position of the feature vector with respect to the OM and OC clusters. Decisions for potential incipient and drastic fault conditions are also automatically generated… The vector of model parameters .phi. for each OC cluster is saved inside the PdM Agent for future updates. Multiple-steps-ahead prediction for the recently updated OC cluster centers are performed to assess the probability of the particular OC cluster to move toward the boundary of its enclosing OM cluster--something which corresponds to an incipient failure.].
Regarding claim 14, the combination of Filev and Hosek teaches all the limitations of the base claims as outlined above.
Further, Filev teaches the anomaly matrix (AM) comprises a plurality of groups, each of which corresponds to the state of a mechanical element of the automatic machine (1) [0046 — the feature vectors 24 are standardized and grouped in OM clusters 26, 28, 30, with cluster 30 being the m.sup.th cluster. The clusters 26, 28, 30 are in the feature space, which is a K-dimensional space. In the lower portion of FIG. 2, the standardized feature vectors are transformed into 2-D space, resulting in 2-D OM clusters 32, 34, 36, where cluster 36 is the m.sup.th cluster.; 0030-0032 — a machine such as a fan, compressor, pump, etc. may have attached to it one or more sensors configured to monitor vibrations as the machine operates. To monitor the vibrations, one or more accelerometers or other vibration sensing devices could be used. It is worth noting that although the exemplary illustrations contained herein use vibrations to determine machine features, other types of machine data could be used. For example, a current sensor may be used to measure changes in the amount of current the machine draws during various operations. Similarly, a thermocouple, or other type of temperature sensor, could be used to detect changes in temperature of some portion of the machine].
Further, Hosek teaches different mechanical element of the automatic machine (1) or of mechanical elements with similar structural features [0084-0085, Fig. 4 — 428A, 428B Mapper sensors 429 Power supply 430 Vacuum pump 431A, 431B Valves 432A, 432B Pressure sensors… the robotic manipulator is built around an open cylindrical frame 401 suspended from a circular mounting flange 402. The frame 401 incorporates a vertical rail 403 with linear bearing 404 to provide guidance to a carriage 405 driven by a brushless DC motor 406 via a ball-screw mechanism 407. The carriage 405 houses a pair of coaxial brushless DC motors 408, 409 equipped with optical encoders 410, 411 — Numerous different mechanical elements are shown in the figure.].
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Filev and Hosek, by incorporating the above limitations, as taught by Hosek.
One of ordinary skill in the art would have been motivated to increase the capabilities of a machine by not limiting the machine to a single mechanical element.
Regarding claim 15, the combination of Filev and Hosek teaches all the limitations of the base claims as outlined above.
Further, Filev teaches the metric [0030-0032 — a machine such as a fan, compressor, pump, etc. may have attached to it one or more sensors configured to monitor vibrations as the machine operates. To monitor the vibrations, one or more accelerometers or other vibration sensing devices could be used. It is worth noting that although the exemplary illustrations contained herein use vibrations to determine machine features, other types of machine data could be used. For example, a current sensor may be used to measure changes in the amount of current the machine draws during various operations. Similarly, a thermocouple, or other type of temperature sensor, could be used to detect changes in temperature of some portion of the machine.].
Further, Hosek teaches the motorization metric (MM) comprises torque/current supplied by a motor and/or motor following error and/or load percentage and/or RMS values [0104 — Motor current values can in turn be used to compute motor torques using the motor torque-current relationships… Position and velocity tracking error; 0128 — motor currents, velocities and duty cycle values can be used to compute the electrical power consumed by each motor at any given time; 0136, 0149 — Rapid increase in position and velocity error].
Therefore at the time the invention was made it would have been obvious to a person of ordinary skill in the art to simply substitute the known torque/current metric as taught by Hosek, for the metric of Filev, for the predicable result of a method for the predictive maintenance of an electric motor based on torque/current.
Regarding claim 16, the combination of Filev and Hosek teaches all the limitations of the base claims as outlined above.
Further, Filev teaches an automatic machine (1) for manufacturing or packing consumer articles [0020 — a method for predictive maintenance of a machine; 0031-0034 — the initialization and monitoring phases are preceded by a feature extraction phase wherein a set of features is extracted from the time domain sensor signal. For example, a machine such as a fan, compressor, pump, etc. may have attached to it one or more sensors configured to monitor vibrations as the machine operates. To monitor the vibrations, one or more accelerometers or other vibration sensing devices could be used. It is worth noting that although the exemplary illustrations contained herein use vibrations to determine machine features, other types of machine data could be used. For example, a current sensor may be used to measure changes in the amount of current the machine draws during various operations. Similarly, a thermocouple, or other type of temperature sensor, could be used to detect changes in temperature of some portion of the machine. The machine speed or torque could also be sensed to provide data relating to the operation of the machine… The PdM Agent may reside in a one or more controllers which are part of larger information system used to gather and process information about equipment and processes in a manufacturing, or other, facility.];
the automatic machine (1) comprising: one or more drives (3) configured to control at least one actuator (4) and to periodically detect and record, at a sampling frequency (SF), a sampling series (SS) relating to at least one metric (MM) of the at least one electric actuator (4) [0030-0032 — a machine such as a fan, compressor, pump, etc. may have attached to it one or more sensors configured to monitor vibrations as the machine operates. To monitor the vibrations, one or more accelerometers or other vibration sensing devices could be used. It is worth noting that although the exemplary illustrations contained herein use vibrations to determine machine features, other types of machine data could be used. For example, a current sensor may be used to measure changes in the amount of current the machine draws during various operations. Similarly, a thermocouple, or other type of temperature sensor, could be used to detect changes in temperature of some portion of the machine.; 0047-0056 — As new feature data continues to be collected, the PdM Agent enters the monitoring phase where all cluster parameters are recursively updated, and condition based monitoring is performed through continuous evaluation of the position of the feature vector with respect to the OM and OC clusters. Decisions for potential incipient and drastic fault conditions are also automatically generated… The vector of model parameters .phi. for each OC cluster is saved inside the PdM Agent for future updates. Multiple-steps-ahead prediction for the recently updated OC cluster centers are performed to assess the probability of the particular OC cluster to move toward the boundary of its enclosing OM cluster--something which corresponds to an incipient failure.];
a data processing unit (5), configured to periodically receive, at a transmission frequency (TF) equal to or lower than the sampling frequency (SF), the sampling series (SS) recorded at the sampling frequency (SF) [0034-0036, Fig. 1 — The PdM Agent may reside in a one or more controllers which are part of larger information system used to gather and process information about equipment and processes in a manufacturing, or other, facility… At step 14, data is collected and features are extracted, for example, as described above. At step 16, it is determined whether the predefined number of feature vectors (N) is reached. If not, the process loops back to collect more data and extract more features. If the data count has reached (N), the process continues at step 18… as shown in FIG. 1, these may be performed in batch mode — Step 16 implies the transmission frequency is lower than the sampling frequency.; 0047-0056 — As new feature data continues to be collected, the PdM Agent enters the monitoring phase where all cluster parameters are recursively updated, and condition based monitoring is performed through continuous evaluation of the position of the feature vector with respect to the OM and OC clusters. Decisions for potential incipient and drastic fault conditions are also automatically generated… The vector of model parameters .phi. for each OC cluster is saved inside the PdM Agent for future updates. Multiple-steps-ahead prediction for the recently updated OC cluster centers are performed to assess the probability of the particular OC cluster to move toward the boundary of its enclosing OM cluster--something which corresponds to an incipient failure];
an anomaly matrix (AM) having at least two statistical features (STF) based on at least one detected metric (MM) [0037-0046, Figs. 2 and 4 — the standardized feature vectors are transformed into 2-D space, resulting in 2-D OM clusters 32, 34, 36 that have multidimensional boundaries; 0010 — Time domain data statistics include such things as root mean square (RMS), crest factor, variance, skewness, and kurtosis; 0032 — Transformation of raw data into a feature vector could include the application of a statistical equation, such as determining the root mean square (RMS) of the raw data, or applying a Fast Fourier Transform (FFT) to the data; 0046 — the feature vectors 24 are standardized and grouped in OM clusters 26, 28, 30, with cluster 30 being the m.sup.th cluster. The clusters 26, 28, 30 are in the feature space, which is a K-dimensional space. In the lower portion of FIG. 2, the standardized feature vectors are transformed into 2-D space, resulting in 2-D OM clusters 32, 34, 36, where cluster 36 is the m.sup.th cluster.; 0047, 0060, Fig. 4 — the PdM Agent enters the monitoring phase where all cluster parameters are recursively updated, and condition based monitoring is performed through continuous evaluation of the position of the feature vector with respect to the OM and OC clusters. Decisions for potential incipient and drastic fault conditions are also automatically generated; 0013-0014 — The trend of changing the OC clusters is used to predict a potential incipient fault];
the automatic machine (1) being configured to carry out the method according to claim 1 [See the rejection of claim 1 under 35 U.S.C. § 103 over the combination of Filev and Hosek above].
Filev does not clearly specify a local storage unit (6), configured to contain an anomaly matrix. However, Filev teaches the anomaly matrix [0037-0046, Figs. 2 and 4 — the standardized feature vectors are transformed into 2-D space, resulting in 2-D OM clusters 32, 34, 36 that have multidimensional boundaries; 0010 — Time domain data statistics include such things as root mean square (RMS), crest factor, variance, skewness, and kurtosis; 0032 — Transformation of raw data into a feature vector could include the application of a statistical equation, such as determining the root mean square (RMS) of the raw data, or applying a Fast Fourier Transform (FFT) to the data; 0046 — the feature vectors 24 are standardized and grouped in OM clusters 26, 28, 30, with cluster 30 being the m.sup.th cluster. The clusters 26, 28, 30 are in the feature space, which is a K-dimensional space. In the lower portion of FIG. 2, the standardized feature vectors are transformed into 2-D space, resulting in 2-D OM clusters 32, 34, 36, where cluster 36 is the m.sup.th cluster.; 0047, 0060, Fig. 4 — the PdM Agent enters the monitoring phase where all cluster parameters are recursively updated, and condition based monitoring is performed through continuous evaluation of the position of the feature vector with respect to the OM and OC clusters. Decisions for potential incipient and drastic fault conditions are also automatically generated; 0013-0014 — The trend of changing the OC clusters is used to predict a potential incipient fault] and local storage units [0034 — FIG. 1, the information system includes a database 12, which is used to store gathered data for access by the PdM Agent]. Therefore at the time the invention was made it would have been obvious to a person of ordinary skill in the art to incorporate a local storage unit (6), configured to contain an anomaly matrix, at least, so that the trend of changing the OC clusters could be determined for fault prediction [0013-0014 — The trend of changing the OC clusters is used to predict a potential incipient fault].
Further, Hosek teaches one or more electric drives (3) configured to control at least one electric actuator (4) and a motorization metric [0104 — Motor current values can in turn be used to compute motor torques using the motor torque-current relationships… Position and velocity tracking error; 0128 — motor currents, velocities and duty cycle values can be used to compute the electrical power consumed by each motor at any given time; 0136, 0149 — Rapid increase in position and velocity error].
Therefore at the time the invention was made it would have been obvious to a person of ordinary skill in the art to simply substitute the known metric of an electric motor, as taught by Hosek, for the metric of Filev, for the predicable result of a method for the predictive maintenance of an electric motor based on a metric.
Regarding claim 19, the combination of Filev and Hosek teaches all the limitations of the base claims as outlined above.
Further, Filev teaches the local state metric (LSM) values are detected by means of at least one local acquisition unit (7) [0010-0011 — Time domain features can be calculated directly from raw vibration signals picked up by one or more sensors attached to the machine being monitored; 0031-0032 — a machine such as a fan, compressor, pump, etc. may have attached to it one or more sensors configured to monitor vibrations as the machine operates.].
Further, Hosek teaches the local state metric (LSM) values are detected by means of at least one local acquisition unit (7), connected to a node of a bidirectional, digital and local industrial network [0066-0068, Fig. 1 — Communication network 120 may include the Public Switched Telephone Network (PSTN), the Internet, a wireless network, a wired network, a Local Area Network (LAN), a Wide Area Network (WAN), a virtual private network (VPN) etc., and may further include other types of networks including X.25, TCP/IP, ATM, etc. In one embodiment, communication network 120 may be an IEEE 1349 network, also referred to as a "Firewire" network…. the data collection function 105 operates to acquire time histories of selected variables relating to the operation of a device being monitored. A time history refers to a collection of values for a particular variable or group of variables over time. In addition to the elements of the function controller — Includes acquisition of real time data, see Fig. 1.].
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Filev and Hosek, by incorporating the above limitations, as taught by Hosek.
One of ordinary skill in the art would have been motivated to do this modification to effectively communicate real time sensor data using industry standard networking systems.
Claim(s) 2-4 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Filev and Hosek in view of DeBotton et al. U.S. Patent Publication No. 20040236494 (hereinafter DeBotton).
Regarding claim 2, the combination of Filev and Hosek teaches all the limitations of the base claims as outlined above.
But the combination of Filev and Hosek fails to clearly specify that during the recording, each control unit (3, 11) receives, at a synchronization frequency (SFC), a synchronism signal to be included in the recording of the sampling series (SS).
However, DeBotton teaches that during the recording, each control unit (3, 11) receives, at a synchronization frequency (SFC), a synchronism signal to be included in the recording of the sampling series (SS) [0144, 0147-0158-, Figs. 1-3 — the method of monitoring the health of an engine according to the present invention is directed to determining whether or not an imbalance exists between the cylinders in the engine… 1. Providing vibration data for the engine synchronised with respect to the thermo-mechanical cycle of the engine…. Step 1 is typically subdivided into the steps of (a) monitoring the vibration of the engine, typically by means of vibration transducer (20); (b) monitoring the crankshaft angle, typically by means of triggering transducer (40); and (c) synchronising the vibration waveform of (a) with respect to the crankshaft angle obtained in (b)…. the signals obtained from the triggering transducer (40) may be accumulated and stored simultaneously with the vibration readings from the vibration transducer (20) for post acquisition synchronization].
Filev, Hosek and DeBotton are analogous art. They relate to predictive maintenance systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Filev and Hosek, by incorporating the above limitations, as taught by DeBotton.
One of ordinary skill in the art would have been motivated to do this modification to facilitate data analysis, particularly using a fast Fourier transform, as suggested by DeBotton [0159, 0004].
Regarding claim 3, the combination of Filev, Hosek and DeBotton teaches all the limitations of the base claims as outlined above.
Further, DeBotton teaches the synchronism signal is the position of a physical or virtual master axis of the automatic machine (1) [0158 — the triggering transducer (40) can be set so that the acquisition process for signals by vibration transducer (20) to provide the wavefrom will begin when the first piston of the engine (10) is at the top dead centre (TDC) during the combustion stroke, and so on.].
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Filev and Hosek, by incorporating the above limitations, as taught by DeBotton.
One of ordinary skill in the art would have been motivated to do this modification to facilitate data analysis, particularly using a fast Fourier transform, as suggested by DeBotton [0159, 0004] and to provide a known reference for the recorded data, i.e. known angle.
Regarding claim 4, the combination of Filev, Hosek and DeBotton teaches all the limitations of the base claims as outlined above.
Further, DeBotton teaches synchronizing the samples (SS) transmitted to the data processing unit (5) using, as reference, the synchronism signal to understand which sample corresponds to a given instant in time or at a given time-phase of the automatic machine (1) [0144, 0147-0158, Figs. 1-3 — the method of monitoring the health of an engine according to the present invention is directed to determining whether or not an imbalance exists between the cylinders in the engine… 1. Providing vibration data for the engine synchronised with respect to the thermo-mechanical cycle of the engine…. Step 1 is typically subdivided into the steps of (a) monitoring the vibration of the engine, typically by means of vibration transducer (20); (b) monitoring the crankshaft angle, typically by means of trigge