DETAILED ACTION
This office action is in response to application filed on April 10, 2024.
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
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 04/10/2024, 01/13/2026 and 07/23/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Response to Amendment
Preliminary amendments filed on April 10, 2024 have been entered.
The specification has been amended.
Claims 3-6, 9-10, 12-18 have been amended.
Claims 19-24 have been added.
Claims 1-24 have been examined.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description:
A grinding head ‘14’ in Figure 1, as described in the specification (see p. 10, line 35; p. 11, lines 1-3).
Workpiece axes ‘C1’ and ‘C2’, as described in the specification (see p. 11, lines 11-12).
A meshing probe ‘24’ in Figure 1, as described in the specification (see p. 13, lines 23-24).
A dressing tool ‘33’ in Figure 1, as described in the specification (see p. 14, lines 4-5).
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The abstract of the disclosure is objected to because the language “(Fig. 7)” should be removed. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
The disclosure is objected to because of the following informalities:
Page 12, lines 33-34: Language “The monitoring device 44 communicates directly or via the Internet and a web server 47 with the service server 45” should read “The monitoring device 44 communicates directly or via the Internet [[and]]with a web server 47 and with the service server 45” in order to correct minor informalities.
Appropriate correction is required.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim language should read:
“A method of monitoring a condition of a gear cutting machine having a plurality of machine axes, the method comprising the steps of:
a) performing a test cycle, wherein in the test cycle, at least a portion of the plurality of machine axes is systematically actuated and associated machine measurement data are obtained;
b) performing a spectral analysis of the associated machine measurement data, wherein machine spectral data are calculated from the associated machine measurement data; and
c) determining predicted End-of-Line (EOL) spectral data based on the machine spectral data, wherein the predicted EOL spectral data indicate at which orders excitations are to be expected in an EOL spectrum when a workpiece machined with the gear cutting machine is installed in a gear train and rolls off on a mating gear in the gear train” in order to provide appropriate antecedence basis and correct minor informalities.
Appropriate correction is required.
Claim 2 is objected to because of the following informalities:
Claim language should read:
“The method according to claim 1[[,]] comprising: d) outputting the predicted EOL spectral data or at least one quantity derived therefrom” in order to correct minor informalities.
Appropriate correction is required.
Claim 3 is objected to because of the following informalities:
Claim language should read:
“The method according to claim 1, wherein determining the predicted EOL spectral data comprises applying a propagation factor to the machine spectral data, the propagation factor depending on a kinematic linkage between [[the]]a machine axis for which the machine spectral data was determined and the workpiece” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 5 is objected to because of the following informalities:
Claim language should read:
“The method according to claim 1,
where steps a) to c) are repeated several times,
wherein workpieces are machined with the gear cutting machine between [[the]] test cycles, and the test cycles are performed in machining pauses in which [[the]]a machining tool is not in a machining engagement with [[a]]the workpiece, and
wherein in a development of the predicted EOL spectral data as a function of the test cycles performed, the workpieces machined or [[the]] time is visualized and/or analyzed” in order to provide appropriate antecedence basis and correct minor informalities.
Appropriate correction is required.
Claim 6 is objected to because of the following informalities:
Claim language should read:
“The method according to claim 1,
wherein reference machine spectral data are available for a plurality of reference machines, the reference machine spectral data having been determined by a plurality of reference test cycles performed on the plurality of reference machines,
where predicted reference EOL spectral data are determined from the reference machine spectral data,
wherein the predicted EOL spectral data, which have been determined based on the machine spectral data of the monitored gear cutting machine, are compared to the predicted reference EOL spectral data or quantities derived therefrom” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 7 is objected to because of the following informalities:
Claim language should read:
“A method of monitoring a condition of a gear cutting machine having a plurality of machine axes, the method comprising the steps of:
a) performing an End-of-Line (EOL) test on a gear train comprising a workpiece machined by the gear cutting machine, wherein in the EOL test, the workpiece in the gear train rolls off on a mating gear and associated EOL measurement data are determined;
b) performing a spectral analysis of the associated EOL measurement data, wherein EOL spectral data are calculated from the associated EOL measurement data
c) determining predicted condition data based on the EOL spectral data, wherein the predicted condition data for at least one machine axis indicates which orders of the at least one machine axis are consistent with the .
Appropriate correction is required.
Claim 9 is objected to because of the following informalities:
Claim language should read:
“The method according to claim 7[[,]] comprising:
e) performing a test cycle in which at least a portion of the plurality of machine axes are systematically actuated and associated machine measurement data are obtained;
f) performing a spectral analysis of the associated machine measurement data, wherein machine spectral data are calculated from the associated machine measurement data; and
g) determining predicted EOL spectral data based on the machine spectral data, wherein the predicted EOL spectral data indicate at which orders excitations are to be expected in an EOL spectrum when [[a]]the workpiece machined by the gear cutting machine is installed in [[a]]the gear train and rolls off on [[a]]the mating gear in the gear train,
wherein determining the predicted condition data comprises comparing the EOL spectral data calculated from the associated EOL measurement data to the predicted EOL spectral data” in order to provide appropriate antecedence basis and correct minor informalities.
Appropriate correction is required.
Claim 10 is objected to because of the following informalities:
Claim language should read:
“A method for creating a training data set of a machine learning algorithm for monitoring a condition of a gear cutting machine with a plurality of machine axes, the method comprising:
a) performing a test cycle in which at least a portion of the plurality of machine axes is systematically actuated and associated condition data are determined by measurements;
b) machining at least one workpiece with the gear cutting machine while the gear cutting machine is in a condition that corresponds to the associated condition data;
c) installing the machined at least one workpiece in a gear train;
d) performing an End-of-Line (EOL) test on the gear train, wherein in the EOL test, the at least one workpiece in the gear train rolls off on a mating gear and associated EOL data are determined;
e) storing the associated condition data and the associated EOL data in the training data set;
f) repeating steps a) to e) for a plurality of test cycles and machined workpieces,
wherein the machined workpieces have [[the]] same nominal geometry and are machined under [[the]] same machining conditions” in order to provide appropriate antecedence basis and correct minor informalities.
Appropriate correction is required.
Claim 11 is objected to because of the following informalities:
Claim language should read:
“A method of training [[a]]the machine learning algorithm, wherein the machine learning algorithm is trained using the training data set according to claim 10” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 12 is objected to because of the following informalities:
Claim language should read:
“A method of monitoring [[a]]the condition of [[a]]the gear cutting machine with [[a]]the plurality of machine axes, the method comprising using [[a]]the machine learning algorithm trained with the training data set according to claim 10” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 13 is objected to because of the following informalities:
Claim language should read:
“The method according to claim 12:
wherein the machine learning algorithm has condition data of the gear cutting machine as input variables and predicted EOL data as output variables,
the method comprising:
a) performing [[a]]the test cycle, wherein in the test cycle, at least [[a]]the portion of the plurality of machine axes is systematically actuated and the associated condition data are determined by the measurements; and
b) determining the predicted EOL data based on the condition data by feeding the condition data to the trained [[ML]]machine learning algorithm as the input variables” in order to provide appropriate antecedence basis and correct minor informalities.
Appropriate correction is required.
Claim 14 is objected to because of the following informalities:
Claim language should read:
“The method according to claim 12:
wherein the machine learning algorithm has EOL data as input variables and predicted condition data of the gear cutting machine as output variables,
the method comprising:
a) performing[[an]]the EOL test on the gear train, wherein in the EOL test, the at least one workpiece in the gear train rolls off on [[a]]the mating gear and the associated EOL data are determined; and
b) determining predicted condition data based on the EOL data by feeding the EOL data to the trained [[ML]]machine learning algorithm as the input variables” in order to provide appropriate antecedence basis and correct minor informalities.
Appropriate correction is required.
Claim 16 is objected to because of the following informalities:
Claim language should read:
“The method according to claim 10,
wherein the associated condition data correlate with [[a]]the condition of a machine axis with respect to [[its]] vibration behavior, and/or
wherein the associated EOL data correlate with [[the]] noise behavior of the gear train” in order to provide appropriate antecedence basis and correct minor informalities.
Appropriate correction is required.
Claim 17 is objected to because of the following informalities:
Claim language should read:
“A device for monitoring a condition of a gear cutting machine having a plurality of machine axes, the device comprising a processor and a storage medium on which is stored a computer program which, when executed on the processor, causes the following steps to be performed:
receiving condition data determined by a test cycle of the gear cutting machine, wherein in the test cycle, at least a portion of the plurality of machine axes has been systematically actuated and the
determining predicted End-of-Line (EOL) data correlated with a noise behavior of a gear train comprising a workpiece machined with the gear cutting machine, based on the condition data” in order to provide appropriate antecedence basis and correct minor informalities.
Appropriate correction is required.
Claim 18 is objected to because of the following informalities:
Claim language should read:
“A device for monitoring a condition of a gear cutting machine having a plurality of machine axes, the device comprising a processor and a storage medium on which is stored a computer program which, when executed on the processor, causes the following steps to be performed:
receiving End-of-Line (EOL) data determined by an EOL test on a gear train comprising a workpiece machined by the gear cutting machine, wherein in the EOL test, the workpiece in the gear train rolls off on a mating gear and the
determining predicted condition data that correlates with a condition of at least one machine axis in terms of [[its]] vibration behavior, based on the EOL data” in order to provide appropriate antecedence basis and correct minor informalities.
Appropriate correction is required.
Claim 21 is objected to because of the following informalities:
Claim language should read:
“The method according to claim 10, wherein the associated condition data comprise machine spectral data calculated by a spectral analysis of machine measurement data” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 22 is objected to because of the following informalities:
Claim language should read:
“The method according to claim 10, wherein the associated EOL data comprise EOL spectral data calculated by spectral analysis of EOL measurement data” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 23 is objected to because of the following informalities:
Claim language should read:
“The method according to claim 13, comprising:
c) outputting the predicted EOL .
Appropriate correction is required.
Claim 24 is objected to because of the following informalities:
Claim language should read:
“The method according to claim 14, comprising:
c) outputting the predicted condition data or at least one quantity derived therefrom” in order to correct minor informalities (i.e., add space in the preamble).
Appropriate correction is required.
Examiner’s Note
The examiner submits that the claims recite “machine axes” and according to the specification: “Machine 1 thus has a large number of movable components such as slides or spindles, which can be moved under the control of corresponding drives. These drives are often
referred to in the technical world as “NC axes”, “machine axes” or abbreviated as “axes”. In
some cases, this designation also includes the components driven by the drives, such as
slides or spindles” (see p. 11, lines 19-23). Therefore, for examination purposes, the claim language is interpreted as described in the specification.
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 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Regarding claim 1, the examiner submits that under Step 1 of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (see also 2019 Revised Patent Subject Matter Eligibility Guidance) for evaluating claims for eligibility under 35 U.S.C. 101, the claim is to a process, which is one of the statutory categories of invention.
Continuing with the analysis, under Step 2A - Prong One of the test:
the limitation “b) performing a spectral analysis of the machine measurement data, wherein machine spectral data are calculated from the machine measurement data” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mathematical concepts (i.e., spectral analysis) to manipulate data and obtain additional data (i.e., machine spectral data; see specification at p. 15, lines 7-16). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated) and/or the particular technological environment or field of use, the limitation in the context of the claim mainly refers to applying mathematical concepts to transform data.
the limitation “c) determining predicted EOL spectral data based on the machine spectral data, wherein the predicted EOL spectral data indicate at which orders excitations are to be expected in an EOL spectrum when a workpiece machined with the gear cutting machine is installed in a gear train and rolls off on a mating gear in the gear train” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mental processes and/or mathematical concepts to manipulate data and obtain additional data (i.e., predicted EOL spectral data; see specification at p. 3, line 17 - p. 4, line 12; p. 4, lines 26-32; p. 15, lines 20-24; p. 16, line 35 – p. 17, line 2; p. 17, line 8 - p. 18, line 22). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated) and/or the particular technological environment or field of use, the limitation in the context of the claim mainly refers to performing a mental evaluation and/or applying mathematical concepts to transform data.
Therefore, the claim recites a judicial exception under Step 2A - Prong One of the test.
Furthermore, under Step 2A - Prong Two of the test, this judicial exception is not integrated into a practical application when considering the claim as a whole. In particular, the additional elements recited in the claim:
“A method of monitoring a condition of a gear cutting machine having a plurality of machine axes” generally links the use of the judicial exception to a particular technological environment or field of use (see specification at p. 2, lines 20-23; p. 7, lines 22-30) (MPEP 2106.05(h); see also MPEP 2106.05 (b): “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not integrate a judicial exception or provide significantly more”); and
“a) performing a test cycle, wherein in the test cycle at least a portion of the machine axes is systematically actuated and associated machine measurement data are obtained” appends a transformation at a high level of generality (i.e., performing a test cycle, wherein in the test cycle at least a portion of the machine axes is systematically actuated) and contributing only nominally or insignificantly to the execution of the claimed method (e.g., transformation used for data gathering or as a field-of-use limitation; see specification at p. 14, line 9 – p. 15, line 3) (see MPEP 2106.05(c); see also MPEP 2106.05(g)).
Accordingly, these additional elements, when considered individually and in combination, do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considering the claim as a whole. The claim is directed to a judicial exception under Step 2A of the test.
Additionally, under Step 2B of the test, the claim, when considered as a whole, does not include additional elements that, when considered individually and in combination, are sufficient to amount to significantly more than the judicial exception because the additional elements:
generally link the use of the judicial exception to a particular technological environment or field of use (i.e., monitoring a condition of a gear cutting machine having a plurality of machine axes), which as indicated in the MPEP: “As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible “simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use.” Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application” (see MPEP 2106.05(h)); and “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not integrate a judicial exception or provide significantly more” (see MPEP 2106.05(b)); and
append transformations at a high level of generality (e.g., transformation used for data gathering or as a field-of-use limitation), which as indicated in the MPEP: “A transformation that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more (or integrate a judicial exception into a practical application)” (see MPEP 2106.05(c); see also MPEP 2106.05(g)).
The claim, when considered as a whole, does not provide significantly more under Step 2B of the test.
Based on the analysis, the claim is not patent eligible.
Similarly, independent claims 7, 10 and 17-18 are directed to a judicial exception (abstract idea, Step 2A – Prong One) without integrating the judicial exception into a practical application (Step 2A – Prong Two) and/or without providing significantly more (Step 2B) when considering the claimed invention as a whole as explained above with regards to claim 1.
The examiner notes that claim 10 mainly refers to performing different tests (i.e., test cycle and EOL test) under same conditions to obtain data and create a corresponding dataset for storing purposes.
With regards to the dependent claims, they are also directed to the non-statutory subject matter because:
they just extend the abstract idea of the independent claims by additional limitations (Claims 3-6, 9, 13-14, 16 and 19-22), that under the broadest reasonable interpretation in light of the specification, cover performance of the limitations using mental processes and/or mathematical concepts, and
the additional elements recited in the dependent claims, when considered individually and in combination, refer to machines that contribute only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation), transformations recited at a high level of generality and used for data gathering or as a field-of-use limitation, extra-solution activities (e.g., mere data gathering/outputting using a data type or source), generic computer components and/or a field of use (Claims 2, 5-6, 8-15 and 23-24), which as indicated in the Office’s guidance does not integrate the judicial exception into a practical application (Step 2A – Prong Two) and/or does not provide significantly more (Step 2B) when considering the claimed invention as a whole.
Subject Matter Not Rejected Over Prior Art
Claims 1-24 are distinguished over the prior art of record for the following reasons:
Regarding claim 1.
Schenk (US 20180264570 A1, IDS reference) discloses/teaches:
A method of monitoring a condition of a gear cutting machine (Fig. 1, item “M.m”) having a plurality of machine axes ([0010]-[0018], [0025]: a method for correcting machining process performed by chip-removing machines (see [0055]-[0056]) is based on correlation analysis of machine parameters and workpiece measurements (see also [0032]-[0036], [0040]); examiner interprets the machines to include a machine axes for actuation during machining process), comprising the steps of:
a) performing a test cycle, wherein in the test cycle at least a portion of the machine axes is systematically actuated and associated machine measurement data are obtained ([0081]: machine parameters are acquired during machining of a workpiece (see also [0060]-[0074]); examiner interprets machining process to include actuation of machine axes); and
b) performing a spectral analysis of the machine measurement data, wherein machine spectral data are calculated from the machine measurement data ([0099]: machine parameter are transformed to frequency signals for correlation analysis).
Hashimoto (US 20200103854 A1) discloses/teaches:
A method of monitoring a condition of a gear cutting machine (Fig. 1, item 100 - “machine tool”) having a plurality of machine axes ([0005]: a method for controlling a gear cutting machine having a plurality of axes is presented (see also [0027], [0031])), comprising the steps of:
a) performing a test cycle, wherein in the test cycle at least a portion of the machine axes is systematically actuated and associated machine measurement data are obtained ([0027]: measurement equipment (Fig. 1, item 20) measures machining accuracy of a workpiece (Fig. 1, item ‘W’; see also [0029])); and
b) performing a spectral analysis of the machine measurement data, wherein machine spectral data are calculated from the machine measurement data ([0030]: disturbance component identification unit (Fig. 1, item 6) identifies disturbances based on stored axis data and machining accuracy measurements by applying frequency analysis (see [0052])).
Kieweler (US 20220308551 A1, IDS reference) discloses:
“A method for determining a dynamic response of a machine having at least one axis, including performing a measurement run for each axis of the machine over an entire work area of each respective axis, capturing and recording data associated with each measurement run, determining a time-frequency representation of recorded data using a data processing unit, and analyzing the time-frequency representation or a related representation using an image processing algorithm” (Abstract: dynamic response of machine is determined by performing time-frequency analysis of measurements and using image processing (see also [0017], [0025], [0027], [0030], [0034])).
The closest prior art of record, taken individually or in combination, fail to teach or suggest:
“c) determining predicted EOL spectral data based on the machine spectral data, wherein the predicted EOL spectral data indicate at which orders excitations are to be expected in an EOL spectrum when a workpiece machined with the gear cutting machine is installed in a gear train and rolls off on a mating gear in the gear train”
in combination with all other limitations within the claim, as claimed and defined by the applicant (the examiner submits that the prior art of record does not disclose, teach or suggest performing spectral analysis of machine measurements to predict end-of-line spectral data).
Regarding claim 7.
Schenk (US 20180264570 A1, IDS reference) discloses/teaches:
A method of monitoring a condition of a gear cutting machine (Fig. 1, item “M.m”) having a plurality of machine axes ([0010]-[0018], [0025]: a method for correcting machining process performed by chip-removing machines (see [0055]-[0056]) is based on correlation analysis of machine parameters and workpiece measurements (see also [0032]-[0036], [0040]); examiner interprets the machines to include a machine axes for actuation during machining process), comprising the steps of:
a) performing a test comprising a workpiece machined by the gear cutting machine, wherein associated measurement data are determined ([0081]: machine parameters are acquired during machining of a workpiece (see also [0060]-[0074]); examiner interprets machining process to include actuation of machine axes); and
b) performing a spectral analysis of the measurement data, wherein spectral data from the measurement data are calculated ([0099]: machine parameter are transformed to frequency signals for correlation analysis).
Hashimoto (US 20200103854 A1) discloses/teaches:
A method of monitoring a condition of a gear cutting machine (Fig. 1, item 100 - “machine tool”) having a plurality of machine axes ([0005]: a method for controlling a gear cutting machine having a plurality of axes is presented (see also [0027], [0031])), comprising the steps of:
a) performing a test comprising a workpiece machined by the gear cutting machine, wherein associated measurement data are determined ([0027]: measurement equipment (Fig. 1, item 20) measures machining accuracy of a workpiece (Fig. 1, item ‘W’; see also [0029])); and
b) performing a spectral analysis of the measurement data, wherein spectral data from the measurement data are calculated ([0030]: disturbance component identification unit (Fig. 1, item 6) identifies disturbances based on stored axis data and machining accuracy measurements by applying frequency analysis (see [0052])).
Kieweler (US 20220308551 A1, IDS reference) discloses:
“A method for determining a dynamic response of a machine having at least one axis, including performing a measurement run for each axis of the machine over an entire work area of each respective axis, capturing and recording data associated with each measurement run, determining a time-frequency representation of recorded data using a data processing unit, and analyzing the time-frequency representation or a related representation using an image processing algorithm” (Abstract: dynamic response of machine is determined by performing time-frequency analysis of measurements and using image processing (see also [0017], [0025], [0027], [0030], [0034])).
The closest prior art of record, taken individually or in combination, fail to teach or suggest:
“the test is an EOL test on a gear train, wherein in the EOL test the workpiece in the gear train rolls off on a mating gear;
the associated measurement data is associated EOL measurement data;
the spectral data is EOL spectral data; and
c) determining predicted condition data based on the EOL spectral data, wherein the predicted condition data for at least one machine axis indicates which orders of that machine axis are consistent with the calculated EOL spectral data”
in combination with all other limitations within the claim, as claimed and defined by the applicant (the examiner submits that the prior art of record does not disclose, teach or suggest performing spectral analysis of end-of-line measurement data to predict condition data for at least one machine axis indicating which orders of that machine axis are consistent with the calculated EOL spectral data).
Regarding claim 10.
Schenk (US 20180264570 A1, IDS reference) discloses/teaches:
A method for creating a training data set for monitoring a condition of a gear cutting machine (Fig. 1, item “M.m”) with a plurality of machine axes ([0010]-[0018], [0025]: a method for correcting machining process performed by chip-removing machines (see [0055]-[0056]) is based on correlation analysis of machine parameters and workpiece measurements (see also [0032]-[0036], [0040]); examiner interprets the machines to include a machine axes for actuation during machining process), comprising:
a) performing a test cycle in which at least a portion of the machine axes is systematically actuated and associated condition data are determined by measurements ([0081]: machine parameters are acquired during machining of a workpiece (see also [0060]-[0074]); examiner interprets machining process to include actuation of machine axes);
b) machining at least one workpiece with the gear cutting machine while the gear cutting machine is in a condition that corresponds to the condition data ([0081]: machine parameters are acquired during machining of a workpiece (see also [0060]-[0074]); examiner interprets machining process to include actuation of machine axes);
e) storing the condition data in the training data set ([0082]: machine parameters are stored in central databank);
f) repeating steps a), b) and e) for a plurality of test cycles and machined workpieces, wherein the workpieces have the same nominal geometry and are machined under the same machining conditions ([0083]: steps are repeated to build database of measurements; examiner interprets tests to be repeated under same conditions).
Hashimoto (US 20200103854 A1) discloses/teaches:
“An aspect of the present disclosure provides a controller for controlling a gear cutting machine having a plurality of axes, the controller comprising an axis information storage unit configured to store data related to control of the plurality of axes during machining, and a disturbance component identification unit configured to identify a component of disturbance with respect to the plurality of axes by using the data stored by the axis information storage unit and measurement results of machining accuracy of a workpiece machined by the gear cutting machine” ([0005]: a controller is used for controlling a gear cutting machine having a plurality of axes and to identify disturbances based on stored axis data and machining accuracy measurements by applying frequency analysis (see [0052])).
Regarding “A method for creating a training data set of a machine learning algorithm for monitoring a condition of a gear cutting machine”, Kieweler (US 20220308551 A1, IDS reference) discloses:
“A method for determining a dynamic response of a machine having at least one axis, including performing a measurement run for each axis of the machine over an entire work area of each respective axis, capturing and recording data associated with each measurement run, determining a time-frequency representation of recorded data using a data processing unit, and analyzing the time-frequency representation or a related representation using an image processing algorithm” (Abstract: dynamic response of machine is determined by performing time-frequency analysis of measurements and using image processing (see also [0017], [0025], [0027], [0030], [0034])).
The closest prior art of record, taken individually or in combination, fail to teach or suggest:
“c) installing the machined workpiece in a gear train;
d) performing an EOL test on the gear train, wherein in the EOL test the workpiece in the gear train rolls off on a mating gear and associated EOL data are determined;
e) storing the corresponding EOL data in the training data set; and
f) repeating steps c) to e) for a plurality of test cycles and machined workpieces, wherein the workpieces have the same nominal geometry and are machined under the same machining conditions”
in combination with all other limitations within the claim, as claimed and defined by the applicant (the examiner submits that the prior art of record does not disclose, teach or suggest performing end-of-line tests to acquire EOL data for creating a training dataset with corresponding condition data).
Regarding claim 17.
Schenk (US 20180264570 A1, IDS reference) discloses/teaches:
A device for monitoring a condition of a gear cutting machine (Fig. 1, item “M.m”) having a plurality of machine axes ([0009]-[0018], [0025]: a device for correcting machining process performed by chip-removing machines (see [0055]-[0056]) is based on correlation analysis of machine parameters and workpiece measurements (see also [0032]-[0036], [0040]); examiner interprets the machines to include a machine axes for actuation during machining process), comprising a processor (Fig. 1, item 10 – ‘computer’; [0059]: computer includes processor capabilities) and a storage medium ([0059]: computer includes memory capabilities (see also Fig. 1, item 11 – “central databank”)) on which is stored a computer program (Fig. 1, item ‘SW’; [0058]: software is executed by computer) which, when executed on the processor, causes the following steps to be performed:
receiving condition data determined by a test cycle of the gear cutting machine, wherein in the test cycle at least a portion of the machine axes has been systematically actuated and the associated condition data have been determined by measurements ([0081]: machine parameters are acquired during machining of a workpiece (see also [0060]-[0074]); examiner interprets machining process to include actuation of machine axes).
Hashimoto (US 20200103854 A1) discloses:
“An aspect of the present disclosure provides a controller for controlling a gear cutting machine having a plurality of axes, the controller comprising an axis information storage unit configured to store data related to control of the plurality of axes during machining, and a disturbance component identification unit configured to identify a component of disturbance with respect to the plurality of axes by using the data stored by the axis information storage unit and measurement results of machining accuracy of a workpiece machined by the gear cutting machine” ([0005]: a controller is used for controlling a gear cutting machine having a plurality of axes and to identify disturbances based on stored axis data and machining accuracy measurements by applying frequency analysis (see [0052])).
Kieweler (US 20220308551 A1, IDS reference) discloses:
“A method for determining a dynamic response of a machine having at least one axis, including performing a measurement run for each axis of the machine over an entire work area of each respective axis, capturing and recording data associated with each measurement run, determining a time-frequency representation of recorded data using a data processing unit, and analyzing the time-frequency representation or a related representation using an image processing algorithm” (Abstract: dynamic response of machine is determined by performing time-frequency analysis of measurements and using image processing (see also [0017], [0025], [0027], [0030], [0034])).
The closest prior art of record, taken individually or in combination, fail to teach or suggest:
“determining predicted EOL data correlated with a noise behavior of a gear train comprising a workpiece machined with the gear cutting machine, based on the condition data”
in combination with all other limitations within the claim, as claimed and defined by the applicant (the examiner submits that the prior art of record does not disclose, teach or suggest predicting end-of-line data correlated with a noise behavior of a gear train from condition data).
Regarding claim 18.
Schenk (US 20180264570 A1, IDS reference) discloses/teaches:
A device for monitoring a condition of a gear cutting machine (Fig. 1, item “M.m”) having a plurality of machine axes ([0009]-[0018], [0025]: a device for correcting machining process performed by chip-removing machines (see [0055]-[0056]) is based on correlation analysis of machine parameters and workpiece measurements (see also [0032]-[0036], [0040]); examiner interprets the machines to include a machine axes for actuation during machining process), comprising a processor (Fig. 1, item 10 – ‘computer’; [0059]: computer includes processor capabilities) and a storage medium ([0059]: computer includes memory capabilities (see also Fig. 1, item 11 – “central databank”)) on which is stored a computer program (Fig. 1, item ‘SW’; [0058]: software is executed by computer) which, when executed on the processor, causes the following steps to be performed:
receiving data determined by a test comprising a workpiece machined by the gear cutting machine, wherein the associated data was determined ([0081]: machine parameters are acquired during machining of a workpiece (see also [0060]-[0074]); examiner interprets machining process to include actuation of machine axes).
Hashimoto (US 20200103854 A1) discloses:
“An aspect of the present disclosure provides a controller for controlling a gear cutting machine having a plurality of axes, the controller comprising an axis information storage unit configured to store data related to control of the plurality of axes during machining, and a disturbance component identification unit configured to identify a component of disturbance with respect to the plurality of axes by using the data stored by the axis information storage unit and measurement results of machining accuracy of a workpiece machined by the gear cutting machine” ([0005]: a controller is used for controlling a gear cutting machine having a plurality of axes and to identify disturbances based on stored axis data and machining accuracy measurements by applying frequency analysis (see [0052])).
Kieweler (US 20220308551 A1, IDS reference) discloses:
“A method for determining a dynamic response of a machine having at least one axis, including performing a measurement run for each axis of the machine over an entire work area of each respective axis, capturing and recording data associated with each measurement run, determining a time-frequency representation of recorded data using a data processing unit, and analyzing the time-frequency representation or a related representation using an image processing algorithm” (Abstract: dynamic response of machine is determined by performing time-frequency analysis of measurements and using image processing (see also [0017], [0025], [0027], [0030], [0034])).
The closest prior art of record, taken individually or in combination, fail to teach or suggest:
“the data is EOL data;
the test is an EOL test on a gear train comprising a workpiece machined by the gear cutting machine, wherein in the EOL test the workpiece in the gear train rolls off on a mating gear;
the associated data is associated EOL data; and
determining predicted condition data that correlates with a condition of at least one machine axis in terms of its vibration behavior, based on the EOL data”
in combination with all other limitations within the claim, as claimed and defined by the applicant (the examiner submits that the prior art of record does not disclose, teach or suggest, predicting condition data that correlates with a condition of at least one machine axis in terms of its vibration behavior from EOL data acquired during an EOL test).
Regarding claims 2-6, 8-9, 11-16 and 19-24.
They are also distinguished over the prior art of record due to their dependency.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Auble; Ronald E. et al., US 3809870 A¸ METHOD AND APPARATUS FOR MONITORING CONDITION OF CUTTING BLADES
Reference discloses monitoring wearing of blades in cutting machines by checking power consumption.
FRUTIGER; Bernhard, US 20190308297 A1, MACHINE TOOL AND METHOD FOR DETERMINING AN ACTUAL STATE OF A MACHINE TOOL
Reference discloses acquiring structure-borne sound signals from a machine in order to determine machine state.
Haghani; Adel, US 20230117055 A1, METHODS AND SYSTEMS FOR WORKPIECE QUALITY CONTROL
Reference discloses performing workpiece quality control using autoencoder functions and time-frequency domain datasets.
Jalluri; Chandra Sekhar et al., US 20210286348 A1, SYSTEM FOR MONITORING MACHINING PROCESSES OF A COMPUTER NUMERICAL CONTROL MACHINE
Reference discloses comparing CNC machine parameters to baseline measurements to determine abnormal conditions.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LINA CORDERO whose telephone number is (571)272-9969. The examiner can normally be reached 9:30 am - 6:00 pm.
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/LINA CORDERO/Primary Examiner, Art Unit 2857
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