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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on February 6, 2026 has been entered.
Response to Arguments
Applicant's arguments filed February 6, 2026 have been fully considered but they are not persuasive.
In response to Applicant's argument on page 14 pertaining to “Yamamoto (see for example, paragraphs [0071] and [0075]) discloses general acquisition and extraction of measurement data during "normal machining," but does not specify extraction based on this combined speed-torque condition for defining the stable period start and end. … Yamamoto fails to disclose extracting, from the acquired machining data, a cutting section corresponding to a stable machining period of the machining performed based on a combination of an achievement degree of a rotational speed of a main spindle motor in the machining tool to a target value and a threshold for motor torque of the main spindle motor and machining data for the cutting section.”. The Examiner respectfully disagrees.
As mentioned in this Office Action (OA), the Examiner does not rely on Yamamoto to disclose “extraction based on this combined speed-torque condition for defining the stable period start and end”. The Examiner relies on Isobe. Isobe discloses “extraction (Fig. 2, claim 3: extracting a specific spectrum) based on this combined speed-torque (Fig. 6, ¶ 66 prescribed speed pattern; ¶ 32 torque command) condition for defining the stable period start and end (Fig. 4, ¶ 45 constant sampling period)”
In response to Applicant's argument on page 14 pertaining to “Fukuda (e.g., paragraphs [0097]-[0099] and Figs. 16 and 17) discloses extraction of a waveform period (1511, 1611) based on time points (1601, 1602) related to motor driving, but relies on post-extraction analysis of waveforms to identify periods of actual machining, without the predefined stable period extraction using the claimed speed achievement and torque thresholds for start/end.”. The Examiner respectfully disagrees.
As mentioned in this OA, The Examiner does not rely on Yamamoto to disclose “the predefined stable period extraction using the claimed speed achievement and torque thresholds for start/end.”. The Examiner relies on Isobe. Isobe discloses, “the predefined stable period extraction (Fig. 4, ¶ 45 constant sampling period) using the claimed speed achievement and torque thresholds (Fig. 6, ¶ 66 prescribed speed pattern; ¶ 32 torque command) for start/end.”
In response to Applicant's argument on page 15 pertaining to “Kawanoue fails to disclose extracting, from the acquired machining data, a cutting section corresponding to a stable machining period of the machining performed based on a combination of an achievement degree of a rotational speed of a main spindle motor in the machining tool to a target value and a threshold for motor torque of the main spindle motor and machining data for the cutting section, as required by independent Claims 1, 11 and 12.”. The Examiner respectfully disagrees.
As mentioned in this OA, the Examiner does not rely on Kawanoue to disclose the limitation of “a cutting section corresponding to a stable machining period of the machining performed based on a combination of an achievement degree of a rotational speed of a main spindle motor in the machining tool to a target value and a threshold for motor torque of the main spindle motor”. The Examiner relies on Isobe. Isobe discloses, “a cutting section (Fig. 2, ¶ 33 target spectrum) corresponding to a stable machining period (Fig. 4, ¶ 45 constant sampling period) of the machining performed based on a combination of an achievement degree of a rotational speed of a main spindle motor in the machining tool to a target value and a threshold for motor torque (Fig. 6, ¶ 66 prescribed speed pattern; ¶ 32 torque command) of the main spindle motor”.
In response to Applicant's argument on page 15 pertaining to “One of ordinary skill would not combine these references to arrive at the claimed specific extraction logic, as it requires hinging the start/end on the conjunctive speed-torque conditions to isolate stable data precisely, which is absent from the art.”. The Examiner respectfully disagrees.
It would be obvious for one skilled in the art of machine diagnosis to combine Yamamoto and Kawanoue for the benefit of diagnosing a machine abnormality flexibly by using multiple determination references. And to further combine Yamamoto in view of Kawanoue with Isobe for the benefit of accurately performing machining diagnosis even when machining tools are replaced. And to further combine, Yamamoto in view of Kawanoue in view of Isobe with Fukuda for the benefit of for the benefit of accurately diagnosing a machine abnormality.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2, 6 – 8, 11 – 14, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over YAMAMOTO (US 2019/02101760 A1) (herein after Yamamoto) in view of KAWANOUE et al (US 2019/0301979 A1) (herein after Kawanoue) in view of ISOBE (US 2017/0203400 A1) (herein after Isobe), and further in view of FUKUDA et al. (US 2018/0356282 A1) (herein after Fukuda).
Regarding Claim 1, Yamamoto discloses, a machining diagnosis device (Fig. 1 abnormality-detecting device 30A), comprising: processing circuitry (Fig. 1, ¶ 76 implemented by a computer) to acquire, from a machining tool (Fig. 2, machine tool 10; Note: Fig 2 is implementation of Fig 1, see ¶ 54), machining data including a result of machining performed based on a machining condition (Fig. 1, ¶ 71 machining state), — and machining data (Fig. 1, ¶ 75 measurement data acquired during normal machining) for the cutting section —.
Yamamoto fails to disclose, — the machining data including a rotational speed of a main spindle motor in the machining tool and motor torque of the main spindle motor, to extract, from the acquired machining data, a cutting section corresponding to a stable machining period of the machining performed based on a combination of an achievement degree of a rotational speed of a main spindle motor in the machining tool to a target value and a threshold for motor torque of the main spindle motor — to acquire the machining condition and perform, in accordance with the acquired machining condition, cleansing of the extracted machining data for the cutting section to exclude data from the cutting section for which no actual machining is performed and leave data for subsections of the cutting section in which actual machining is performed, to calculate a feature based on the cleansed machining data, and to diagnose machining based on the calculated feature
In analogous art, Kawanoue discloses, — the machining data including a rotational speed of a main spindle (Fig. 2, ¶ 69 speed of the rotors 610 and 620) motor in the machining tool and motor torque of the main spindle motor (Fig. 2, ¶ 98 monitoring target (that is, when the score exceeds or falls below a predetermined threshold value)), —
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamamoto by combining the machining diagnosis device comprising processing circuitry disclosed by Yamamoto with a machining diagnosis device comprising processing circuitry comprising: wherein machining data including a rotational speed of a main spindle motor in the machining tool and motor torque of the main spindle motor; disclosed by Kawanoue for the benefit of diagnosing a machine abnormality flexibly by using multiple determination references [Kawanoue: ¶ 46 apply a plurality of determination references to the score and evaluate the presence or absence of abnormality using each determination reference].
Yamamoto in view of Kawanoue fail to disclose, — to extract, from the acquired machining data, a cutting section corresponding to a stable machining period of the machining performed based on a combination of an achievement degree of a rotational speed of a main spindle motor in the machining tool to a target value and a threshold for motor torque of the main spindle motor — to acquire the machining condition and perform, in accordance with the acquired machining condition, cleansing of the extracted machining data for the cutting section to exclude data from the cutting section for which no actual machining is performed and leave data for subsections of the cutting section in which actual machining is performed, to calculate a feature based on the cleansed machining data, and to diagnose machining based on the calculated feature.
In analogous art, Isobe discloses, — to extract (Fig. 2, claim 3: extracting a specific spectrum), from the acquired machining data, a cutting section (Fig. 2, ¶ 33 target spectrum) corresponding to a stable machining period (Fig. 4, ¶ 45 constant sampling period) of the machining performed based on a combination of an achievement degree of a rotational speed (Fig. 6, ¶ 66 prescribed speed pattern; ”Fig 6 is part of Fig 2, ¶ 56 FIG. 6, reference numeral 20 denotes an example of tool mass arrangement position data stored in the tool mass arrangement storage unit 10d”) of a main spindle motor in the machining tool to a target value (Fig. 6, ¶ 66 prescribed speed pattern) and a threshold for motor torque (Fig. 2, ¶ 32 torque command) of the main spindle motor —
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamamoto in view of Kawanoue by combining the machining diagnosis device comprising processing circuitry disclosed by Yamamoto in view of Kawanoue with machining diagnosis device comprising: processing circuitry to extract, from the acquired machining data, a cutting section corresponding to a stable machining period of the machining performed based on a combination of an achievement degree of a rotational speed of a main spindle motor in the machining tool to a target value and a threshold for motor torque of the main spindle motor; disclosed by Isobe for the benefit of accurately performing machining diagnosis even when machining tools are replaced [Isobe: ¶ 10 it is possible to accurately diagnose the abnormality of the speed reducer even when the tools of the tool magazine are replaced].
Yamamoto in view of Kawanoue in view of Isobe fail to disclose, — to acquire the machining condition and perform, in accordance with the acquired machining condition, cleansing of the extracted machining data for the cutting section to exclude data from the cutting section for which no actual machining is performed and leave data for subsections of the cutting section in which actual machining is performed, to calculate a feature based on the cleansed machining data, and to diagnose machining based on the calculated feature.
In analogous art, Fukuda discloses, — to acquire the machining condition (Fig. 16, ¶ 97 – 99 machining period, a waveform period 1512) and perform, in accordance with the acquired machining condition, cleansing of the extracted machining data (Fig. 16, ¶ 97 – 99 waveform period 1511: “waveform period 1511 is the extracted machining data”) for the cutting section to exclude data from the cutting section for which no actual machining is performed (Fig. 17, ¶ 97 – 99 time point 1601, time point 1602; “before time point 1601 and after time point 1602 is the cutting section for which no actual machining is performed”) and leave data for subsections of the cutting section in which actual machining is performed (Fig. 17, ¶ 97 – 99 extracts a period 1611; “period 1611 is the cutting section in which actual machining is performed”), to calculate a feature based on the cleansed machining data (Fig. 16, ¶ 97 – 99 feature information extracted), and to diagnose machining based on the calculated feature (Fig. 1, ¶ 45 abnormality in the operation of the process machine 200 can be detected).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamamoto in view of Kawanoue in view of Isobe by combining the machining diagnosis device comprising processing circuitry disclosed by Yamamoto in view of Kawanoue in view of Isobe with a machining diagnosis device comprising: processing circuitry to acquire the machining condition and perform, in accordance with the acquired machining condition, cleansing of the extracted machining data for the cutting section to exclude data from the cutting section for which no actual machining is performed and leave data for subsections of the cutting section in which actual machining is performed, to calculate a feature based on the cleansed machining data, and to diagnose machining based on the calculated feature; disclosed by Fukuda for the benefit of for the benefit of accurately diagnosing a machine abnormality [Fukuda: ¶ 86 This configuration enables the diagnostic device 100 to highly accurately specify the drive unit currently driving and highly accurately diagnose an abnormality].
Regarding Claim 2, Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda disclose the limitations of claim 1, which this claim depends on.
Yamamoto further discloses, the machining diagnosis device according to claim 1, further comprising: a storage device (Fig. 1, ¶ 76 storage device) to store a machining pattern (Fig. 1, ¶ 76 predetermined machining pattern) formed in accordance with the machining condition as a diagnosis model for the machining condition (Fig. 1, ¶ 75 creates a normal model), wherein; the processing circuitry reads the diagnosis model from the storage device based on the machining condition to perform matching (Fig. 1, ¶ 75 abnormality diagnosis) between the extracted machining data and the diagnosis model (Fig. 1, ¶ 75 performs abnormality diagnosis on the measurement data based on the normal model), —.
Fukuda further discloses, — and performs cleansing of the extracted machining data based on the diagnosis model (Fig. 1, ¶ models are generated by learning based on, for example, the sensing information that has been detected during a normal operation) for which the matching has been performed, and wherein the processing circuitry identifies the diagnosis model by measuring a time from a start (Fig. 16, ¶ 99 time point 1601) to an end (Fig. 16, ¶ 99 time point 1602) of data collection for the cutting section and comparing the measured time with machining times preregistered in the storage device when the machining condition is not acquired (Fig. 1, ¶ 45 compared with, for example, a model representing).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda by combining the machine abnormality diagnosis device comprising processing circuitry disclosed by Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda with a machine abnormality diagnosis device comprising processing circuitry that, performs cleansing of the extracted machining data based on the diagnosis model for which the matching has been performed, and wherein the processing circuitry identifies the diagnosis model by measuring a time from a start to an end of data collection for the cutting section and comparing the measured time with machining times preregistered in the storage device when the machining condition is not acquired; disclosed by Fukuda for the benefit of accurately diagnosing a machine abnormality [Fukuda: ¶ 86].
Regarding Claim 6, Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda disclose the limitations of claim 1, which this claim depends on.
Yamamoto further discloses, the machining diagnosis device according to claim1, wherein the processing circuitry acquires, as training data (Fig. 1, ¶ 75 measurement data acquired during normal machining), machining data during cutting and a timing to start or end collecting data (Fig. 9, ¶ 113 timing t0 to the present time t1) for the cutting section corresponding to the stable machining period and to infer (Fig. 9, ¶ 114 when diagnostic value a deviates), using the training data, a timing to start or end collecting data (Fig. 9, ¶ 114 replacement timing of the tool T) for diagnosing a wear state of a component (Fig. 9, ¶ 114 deterioration of the tool by wear) used in cutting.
Regarding Claim 7, Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda disclose the limitations of claim 1, which this claim depends on.
Yamamoto further discloses, the machining diagnosis device according to claim 1, further comprising: a learning device (Fig. 8, normal model unit 31; Note: Fig 2 is implementation of Fig 1, see ¶ 60) including processing circuitry to acquire training data (Fig. 1, ¶ 75 measurement data acquired during normal machining) in the machining diagnosis device including at least one of a torque waveform, a motor speed waveform, an acceleration waveform, a current waveform, or a voltage waveform (Fig. 8, ¶ 98 spindle rotation speed 51; ¶ 97 motor current 45, and electric power 46) during each of air-cutting and test machining and a timing to start or end collecting (Fig. 9, ¶ 113 timing t0 to the present time t1), by the machining diagnosis device, data for the cutting section corresponding to the stable machining period for at least one of the torque waveform, the motor speed waveform, the acceleration waveform, the current waveform, or the voltage waveform (Fig. 8, ¶ 98 spindle rotation speed 51; ¶ 97 motor current 45, and electric power 46) during each of the air-cutting and the test machining, and to generate a trained model to infer (Fig. 9, ¶ 114 when diagnostic value a deviates), using the training data, a timing to start or end collecting data (Fig. 9, ¶ 114 replacement timing of the tool T) for diagnosing a wear state of a component (Fig. 9, ¶ 114 deterioration of the tool by wear) used in cutting from at least one of the torque waveform, the motor speed waveform, the acceleration waveform, the current waveform, or the voltage waveform (Fig. 8, ¶ 98 spindle rotation speed 51; ¶ 97 motor current 45, and electric power 46) during each of the air-cutting and the test machining in the machining diagnosis device.
Regarding Claim 8, Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda disclose the limitations of claim 1, which this claim depends on.
Yamamoto further discloses, the machining diagnosis device according to claim 1, further comprising: an inference device (Fig. 8, abnormality diagnosing unit 32) including processing circuitry to acquire data (Fig. 1, ¶ 71 machining state) in the machining diagnosis device including at least one of a torque waveform, a motor speed waveform, an acceleration waveform, a current waveform, or a voltage waveform (Fig. 8, ¶ 98 spindle rotation speed 51; ¶ 97 motor current 45, and electric power 46) during machining, and to output, using a trained model to infer (Fig. 9, ¶ 114 when diagnostic value a deviates) a timing to start or end collecting (Fig. 9, ¶ 113 timing t0 to the present time t1), by the machining diagnosis device, data for the cutting section corresponding to the stable machining period to acquire data for diagnosing a wear state of a component (Fig. 9, ¶ 114 deterioration of the tool by wear) used in cutting from at least one of a torque waveform, a motor speed waveform, an acceleration waveform, a current waveform, or a voltage waveform (Fig. 8, ¶ 98 spindle rotation speed 51; ¶ 97 motor current 45, and electric power 46) during each of air-cutting and test machining in the machining diagnosis device, a timing to start or end collecting data (Fig. 9, ¶ 114 replacement timing of the tool T) for diagnosing the wear state of the component used in cutting from at least one of the torque waveform, the motor speed waveform, the acceleration waveform, the current waveform, or the voltage waveform (Fig. 8, ¶ 98 spindle rotation speed 51; ¶ 97 motor current 45, and electric power 46) during the machining.
Regarding Claim 11, Yamamoto discloses, a machining diagnosis method (Fig. 1, ¶ 67 an abnormality-detecting method), comprising: acquiring, from a machining tool (Fig. 2, machine tool 10; Note: Fig 2 is implementation of Fig 1, see ¶ 54), machining data including a result of machining performed based on a machining condition (Fig. 1, ¶ 71 machining state), — and machining data for the cutting section (Fig. 1, ¶ 75 measurement data) from the acquired machining data (Fig. 1, ¶ 75 measurement data acquired during normal machining); —.
Yamamoto fails to disclose, — the machining data including a rotational speed of a main spindle motor in the machining tool and motor torque of the main spindle motor; extracting a cutting section corresponding to a stable machining period of the machining performed based on a combination of an achievement degree of a rotational speed of a main spindle motor in the machining tool to a target value and a threshold for motor torque of the main spindle motor — acquiring the machining condition and cleansing the machining data for the cutting section in accordance with the acquired machining condition for the cutting section to exclude data from the cutting section for which no actual machining is performed and leave data for subsections in of the cutting section which actual machining is performed; calculating a feature based on the cleansed machining data; and diagnosing machining based on the calculated feature.
In analogous art, Kawanoue discloses, — the machining data including a rotational speed of a main spindle (Fig. 2, ¶ 69 speed of the rotors 610 and 620) motor in the machining tool and motor torque of the main spindle motor (Fig. 2, ¶ 98 monitoring target (that is, when the score exceeds or falls below a predetermined threshold value)); —
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamamoto by combining the method performed by the machining diagnosis device disclosed by Yamamoto with a method performed by a machining diagnosis device comprising: the machining data including a rotational speed of a main spindle motor in the machining tool and motor torque of the main spindle motor; disclosed by Kawanoue for the benefit of diagnosing a machine abnormality flexibly by using multiple determination references [Kawanoue: ¶ 46 apply a plurality of determination references to the score and evaluate the presence or absence of abnormality using each determination reference].
Yamamoto in view of Kawanoue fail to disclose, — extracting a cutting section corresponding to a stable machining period of the machining performed based on a combination of an achievement degree of a rotational speed of a main spindle motor in the machining tool to a target value and a threshold for motor torque of the main spindle motor — acquiring the machining condition and cleansing the machining data for the cutting section in accordance with the acquired machining condition for the cutting section to exclude data from the cutting section for which no actual machining is performed and leave data for subsections in of the cutting section which actual machining is performed; calculating a feature based on the cleansed machining data; and diagnosing machining based on the calculated feature.
In analogous art, Isobe discloses, extracting (Fig. 2, claim 3: extracting a specific spectrum) a cutting section (Fig. 2, ¶ 33 target spectrum) corresponding to a stable machining period (Fig. 4, ¶ 45 constant sampling period) of the machining performed based on a combination of an achievement degree of a rotational speed (Fig. 6, ¶ 66 prescribed speed pattern; ”Fig 6 is part of Fig 2, ¶ 56 FIG. 6, reference numeral 20 denotes an example of tool mass arrangement position data stored in the tool mass arrangement storage unit 10d”) of a main spindle motor in the machining tool to a target value (Fig. 6, ¶ 66 prescribed speed pattern) and a threshold for motor torque (Fig. 2, ¶ 32 torque command) of the main spindle motor —.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamamoto in view of Kawanoue by combining the method performed by the machining diagnosis device disclosed by Yamamoto in view of Kawanoue with a method performed by a machining diagnosis device comprising: extracting a cutting section corresponding to a stable machining period of the machining performed based on a combination of an achievement degree of a rotational speed of a main spindle motor in the machining tool to a target value and a threshold for motor torque of the main spindle motor; disclosed by Isobe for the benefit of accurately performing machining diagnosis even when machining tools are replaced [Isobe: ¶ 10 it is possible to accurately diagnose the abnormality of the speed reducer even when the tools of the tool magazine are replaced].
Yamamoto in view of Kawanoue in view of Isobe fail to disclose, — acquiring the machining condition and cleansing the machining data for the cutting section in accordance with the acquired machining condition for the cutting section to exclude data from the cutting section for which no actual machining is performed and leave data for subsections in of the cutting section which actual machining is performed; calculating a feature based on the cleansed machining data; and diagnosing machining based on the calculated feature.
In analogous art, Fukuda discloses, — acquiring the machining condition (Fig. 16, ¶ 97 – 99 machining period, a waveform period 1512) and cleansing the machining data (Fig. 16, ¶ 97 – 99 waveform period 1511: “waveform period 1511 is the extracted machining data”) for the cutting section in accordance with the acquired machining condition for the cutting section to exclude data from the cutting section for which no actual machining is performed (Fig. 17, ¶ 97 – 99 time point 1601, time point 1602; “before time point 1601 and after time point 1602 is the cutting section for which no actual machining is performed”) and leave data for subsections in of the cutting section which actual machining is performed (Fig. 17, ¶ 97 – 99 extracts a period 1611; “period 1611 is the cutting section in which actual machining is performed”); calculating a feature based on the cleansed machining data (Fig. 16, ¶ 97 – 99 feature information extracted); and diagnosing machining based on the calculated feature (Fig. 1, ¶ 45 abnormality in the operation of the process machine 200 can be detected).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamamoto in view of Kawanoue in view of Isobe by combining the method performed by the machining diagnosis device disclosed by Yamamoto in view of Kawanoue in view of Isobe with a method performed by a machining diagnosis device comprising: acquiring the machining condition and cleansing the machining data for the cutting section in accordance with the acquired machining condition for the cutting section to exclude data from the cutting section for which no actual machining is performed and leave data for subsections in of the cutting section which actual machining is performed; calculating a feature based on the cleansed machining data; and diagnosing machining based on the calculated feature; disclosed by Fukuda for the benefit of for the benefit of accurately diagnosing a machine abnormality [Fukuda: ¶ 86 This configuration enables the diagnostic device 100 to highly accurately specify the drive unit currently driving and highly accurately diagnose an abnormality].
Regarding Claim 12, Yamamoto discloses, a non-transitory computer-readable recording medium storing a program (Fig. 1, ¶ 76 storage device, programs for diagnosis, machine learning, and abnormality remedy processes), the program causing a computer to perform operations comprising: acquiring, from a machining tool (Fig. 2, machine tool 10; Note: Fig 2 is implementation of Fig 1, see ¶ 54), machining data including a result of machining performed based on a machining condition (Fig. 1, ¶ 71 machining state), — and machining data for the cutting section (Fig. 1, ¶ 75 measurement data) from the acquired machining data (Fig. 1, ¶ 75 measurement data acquired during normal machining); —.
Yamamoto fails to disclose, — the machining data including a rotational speed of a main spindle motor in the machining tool and motor torque of the main spindle motor; extracting a cutting section corresponding to a stable machining period of the machining performed based on a combination of an achievement degree of a rotational speed of a main spindle motor in the machining tool to a target value and a threshold for motor torque of the main spindle motor — acquiring the machining condition for cutting and cleansing the machining data for the cutting section in accordance with the acquired machining for the cutting section to exclude data from the cutting section for which no actual machining is performed and leave data for subsections in of the cutting section which in actual machining is performed; calculating a feature based on the cleansed machining data; and diagnosing machining based on the calculated feature.
In analogous art, Kawanoue discloses, — the machining data including a rotational speed of a main spindle (Fig. 2, ¶ 69 speed of the rotors 610 and 620) motor in the machining tool and motor torque of the main spindle motor (Fig. 2, ¶ 98 monitoring target (that is, when the score exceeds or falls below a predetermined threshold value)); —.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamamoto by combining the non-transitory computer-readable recording medium that stores the program to perform operations disclosed by Yamamoto with a non-transitory computer-readable recording medium to store a program to perform operations comprising: machining data including a rotational speed of a main spindle motor in the machining tool and motor torque of the main spindle motor; disclosed by Kawanoue for the benefit of diagnosing a machine abnormality flexibly by using multiple determination references [Kawanoue: ¶ 46 apply a plurality of determination references to the score and evaluate the presence or absence of abnormality using each determination reference].
Yamamoto in view of Kawanoue fail to disclose, — extracting a cutting section corresponding to a stable machining period of the machining performed based on a combination of an achievement degree of a rotational speed of a main spindle motor in the machining tool to a target value and a threshold for motor torque of the main spindle motor — acquiring the machining condition for cutting and cleansing the machining data for the cutting section in accordance with the acquired machining for the cutting section to exclude data from the cutting section for which no actual machining is performed and leave data for subsections in of the cutting section which in actual machining is performed; calculating a feature based on the cleansed machining data; and diagnosing machining based on the calculated feature.
In analogous art, Isobe discloses, — extracting (Fig. 2, claim 3: extracting a specific spectrum) a cutting section (Fig. 2, ¶ 33 target spectrum) corresponding to a stable machining period (Fig. 4, ¶ 45 constant sampling period) of the machining performed based on a combination of an achievement degree of a rotational speed (Fig. 6, ¶ 66 prescribed speed pattern; ”Fig 6 is part of Fig 2, ¶ 56 FIG. 6, reference numeral 20 denotes an example of tool mass arrangement position data stored in the tool mass arrangement storage unit 10d”) of a main spindle motor in the machining tool to a target value (Fig. 6, ¶ 66 prescribed speed pattern) and a threshold for motor torque (Fig. 2, ¶ 32 torque command) of the main spindle motor —.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamamoto in view of Kawanoue by combining the non-transitory computer-readable recording medium that stores the program to perform operations disclosed by Yamamoto in view of Kawanoue with a non-transitory computer-readable recording medium to store a program to perform operations comprising: extracting a cutting section corresponding to a stable machining period of the machining performed based on a combination of an achievement degree of a rotational speed of a main spindle motor in the machining tool to a target value and a threshold for motor torque of the main spindle motor; disclosed by Isobe for the benefit of accurately performing machining diagnosis even when machining tools are replaced [Isobe: ¶ 10 it is possible to accurately diagnose the abnormality of the speed reducer even when the tools of the tool magazine are replaced].
Yamamoto in view of Kawanoue in view of Isobe fail to disclose, — acquiring the machining condition for cutting and cleansing the machining data for the cutting section in accordance with the acquired machining for the cutting section to exclude data from the cutting section for which no actual machining is performed and leave data for subsections in of the cutting section which in actual machining is performed; calculating a feature based on the cleansed machining data; and diagnosing machining based on the calculated feature.
In analogous art, Fukuda discloses, — acquiring the machining condition (Fig. 16, ¶ 97 – 99 machining period, a waveform period 1512) for cutting and cleansing the machining data (Fig. 16, ¶ 97 – 99 waveform period 1511: “waveform period 1511 is the extracted machining data”) for the cutting section in accordance with the acquired machining for the cutting section to exclude data from the cutting section for which no actual machining is performed (Fig. 17, ¶ 97 – 99 time point 1601, time point 1602; “before time point 1601 and after time point 1602 is the cutting section for which no actual machining is performed”) and leave data for subsections in of the cutting section which in actual machining is performed (Fig. 17, ¶ 97 – 99 extracts a period 1611; “period 1611 is the cutting section in which actual machining is performed”); calculating a feature based on the cleansed machining data (Fig. 16, ¶ 97 – 99 feature information extracted); and diagnosing machining based on the calculated feature (Fig. 1, ¶ 45 abnormality in the operation of the process machine 200 can be detected).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamamoto in view of Kawanoue in view of Isobe by combining the non-transitory computer-readable recording medium that stores the program to perform operations disclosed by Yamamoto in view of Kawanoue in view of Isobe with a non-transitory computer-readable recording medium to store a program to perform operations comprising: acquiring the machining condition for cutting and cleansing the machining data for the cutting section in accordance with the acquired machining for the cutting section to exclude data from the cutting section for which no actual machining is performed and leave data for subsections in of the cutting section which in actual machining is performed; calculating a feature based on the cleansed machining data; and diagnosing machining based on the calculated feature; disclosed by Fukuda for the benefit of for the benefit of accurately diagnosing a machine abnormality [Fukuda: ¶ 86 This configuration enables the diagnostic device 100 to highly accurately specify the drive unit currently driving and highly accurately diagnose an abnormality].
Regarding Claim 13, Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda disclose the limitations of claim 1, which this claim depends on.
Yamamoto further discloses, a learning device (Fig. 8, normal model unit 31; Note: Fig 2 is implementation of Fig 1, see ¶ 60), comprising: processing circuitry to acquire training data (Fig. 1, ¶ 75 measurement data acquired during normal machining) in the machining diagnosis device according to claim1 including at least one of a torque waveform, a motor speed waveform, an acceleration waveform, a current waveform, or a voltage waveform (Fig. 8, ¶ 98 spindle rotation speed 51; ¶ 97 motor current 45, and electric power 46) during each of air-cutting and test machining and a timing to start or end collecting (Fig. 9, ¶ 113 timing t0 to the present time t1), by the machining diagnosis device, data for the cutting section corresponding to the stable machining period for at least one of the torque waveform, the motor speed waveform, the acceleration waveform, the current waveform, or the voltage waveform (Fig. 8, ¶ 98 spindle rotation speed 51; ¶ 97 motor current 45, and electric power 46) during each of the air-cutting and the test machining; and to generate a trained model to infer (Fig. 9, ¶ 114 when diagnostic value a deviates), using the training data, a timing to start or end collecting data (Fig. 9, ¶ 114 replacement timing of the tool T) for diagnosing a wear state of a component (Fig. 9, ¶ 114 deterioration of the tool by wear) used in cutting from at least one of the torque waveform, the motor speed waveform, the acceleration waveform, the current waveform, or the voltage waveform (Fig. 8, ¶ 98 spindle rotation speed 51; ¶ 97 motor current 45, and electric power 46) during each of the air-cutting and the test machining in the machining diagnosis device.
Regarding Claim 14, Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda disclose the limitations of claim 1, which this claim depends on.
Yamamoto further discloses, an inference device (Fig. 8, abnormality diagnosing unit 32), comprising: processing circuitry to acquire data (Fig. 1, ¶ 71 machining state) in the machining diagnosis device according to claim1 including at least one of a torque waveform, a motor speed waveform, an acceleration waveform, a current waveform, or a voltage waveform (Fig. 8, ¶ 98 spindle rotation speed 51; ¶ 97 motor current 45, and electric power 46) during machining; and to output, using a trained model to infer (Fig. 9, ¶ 114 when diagnostic value a deviates) a timing to start or end collecting (Fig. 9, ¶ 113 timing t0 to the present time t1), by the machining diagnosis device, data for the cutting section corresponding to the stable machining period to acquire data for diagnosing a wear state of a component (Fig. 9, ¶ 114 deterioration of the tool by wear) used in cutting from at least one of a torque waveform, a motor speed waveform, an acceleration waveform, a current waveform, or a voltage waveform (Fig. 8, ¶ 98 spindle rotation speed 51; ¶ 97 motor current 45, and electric power 46) during each of air-cutting and test machining in the machining diagnosis device, a timing to start or end collecting data (Fig. 9, ¶ 114 replacement timing of the tool T) for diagnosing the wear state of the component used in cutting from at least one of the torque waveform, the motor speed waveform, the acceleration waveform, the current waveform, or the voltage waveform (Fig. 8, ¶ 98 spindle rotation speed 51; ¶ 97 motor current 45, and electric power 46) during the machining.
Regarding Claim 22, Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda disclose the limitations of claim 1, which this claim depends on.
Yamamoto and Kawanoue fail to disclose, 22. (New) the machining diagnosis device according to claim 1, wherein the processing circuitry extracts the cutting section corresponding to the stable machining period of the machining performed after the rotational speed of the main spindle motor in the machining tool reaches the achievement degree to the target value and the motor torque of the main spindle motor decreases from a peak to the threshold, and performs the cleansing by acquiring actual cutting signals directly from the machining tool and specifying the subsections in which actual machining is performed based on the actual cutting signals.
Isobe further discloses, the machining diagnosis device according to claim 1, wherein the processing circuitry extracts (Fig. 2, claim 3: extracting a specific spectrum) the cutting section corresponding to the stable machining period (Fig. 4, ¶ 45 constant sampling period) of the machining performed after the rotational speed (Fig. 6, ¶ 66 prescribed speed pattern; ”Fig 6 is part of Fig 2, ¶ 56 FIG. 6, reference numeral 20 denotes an example of tool mass arrangement position data stored in the tool mass arrangement storage unit 10d”) of the main spindle motor in the machining tool reaches the achievement degree to the target value (Fig. 6, ¶ 66 prescribed speed pattern) and the motor torque of the main spindle motor decreases from a peak to the threshold (Fig. 2, ¶ 32 torque command), —
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda by combining the machining diagnosis device comprising processing circuitry disclosed by Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda with machining diagnosis device comprising: processing circuitry, wherein the processing circuitry extracts the cutting section corresponding to the stable machining period of the machining performed after the rotational speed of the main spindle motor in the machining tool reaches the achievement degree to the target value and the motor torque of the main spindle motor decreases from a peak to the threshold ; disclosed by Isobe for the benefit of accurately performing machining diagnosis even when machining tools are replaced [Isobe: ¶ 10 it is possible to accurately diagnose the abnormality of the speed reducer even when the tools of the tool magazine are replaced].
Yamamoto, Kawanoue, Isobe fail to disclose, and performs the cleansing by acquiring actual cutting signals directly from the machining tool and specifying the subsections in which actual machining is performed based on the actual cutting signals.
Fukuda further discloses, and performs the cleansing by acquiring actual cutting signals directly from the machining tool (Fig. 16, ¶ 97 – 99 waveform period 1511: “waveform period 1511 is the extracted machining data”) and specifying the subsections in which actual machining is performed (Fig. 17, ¶ 97 – 99 extracts a period 1611; “period 1611 is the cutting section in which actual machining is performed”) based on the actual cutting signals.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda by combining the machining diagnosis device comprising processing circuitry disclosed by Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda with machining diagnosis device comprising: processing circuitry, and performs the cleansing by acquiring actual cutting signals directly from the machining tool and specifying the subsections in which actual machining is performed based on the actual cutting signals; disclosed by Fukuda for the benefit of for the benefit of accurately diagnosing a machine abnormality [Fukuda: ¶ 86 This configuration enables the diagnostic device 100 to highly accurately specify the drive unit currently driving and highly accurately diagnose an abnormality].
Claim(s) 5, 18, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over YAMAMOTO (US 2019/02101760 A1) (herein after Yamamoto) in view of KAWANOUE et al (US 2019/0301979 A1) (herein after Kawanoue) in view of ISOBE (US 2017/0203400 A1) (herein after Isobe), in view of FUKUDA et al. (US 2018/0356282 A1) (herein after Fukuda), and further in view of Humphrey (US 2013/0245883 A1) (herein after Humphrey).
Regarding Claim 5, Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda disclose the limitations of claim 1, which this claim depends on.
Yamamoto, Kawanoue, Isobe, Fukuda fail to disclose, the machining diagnosis device according to claim1, wherein the storage device stores the feature calculated by the processing circuitry as trend data, and the processing circuitry extracts trend data for analysis to be a diagnosis target from the feature stored in the storage device and performs diagnosis using a target diagnosis model.
In analogous art, Humphrey discloses, the machining diagnosis device according to claim1, wherein the storage device stores the feature calculated by the processing circuitry as trend data (Fig. 4, ¶ 50 trend in a time series), and the processing circuitry extracts trend data for analysis (Fig. 4, ¶ 50 analyze the trend in a time series) to be a diagnosis target (Fig. 4, time 440) from the feature stored in the storage device and performs diagnosis using a target diagnosis model (Fig. 4, ¶ 47 historical data points).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda by combining the machining diagnosis device comprising processing circuitry disclosed by Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda with a machining diagnosis device comprising processing circuitry, wherein the storage device stores the feature calculated by the processing circuitry as trend data, and the processing circuitry extracts trend data for analysis to be a diagnosis target from the feature stored in the storage device and performs diagnosis using a target diagnosis model; disclosed by Humphrey for the benefit of predicting machine problems at changing intervals by monitoring the rate of change of sensed values [Humphrey: ¶ 49].
Regarding Claim 18, Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda disclose the limitations of claim 1, which this claim depends on.
Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda fail to disclose, the machining diagnosis device according to claim 1, wherein the processing circuitry determines a component replacement time by calculating a fitting curve based on a series of predicted machining qualities and identifying a time at which the fitting curve intersects a dimensional tolerance.
In analogous art, Humphrey discloses, the machining diagnosis device according to claim 1, wherein the processing circuitry determines a component replacement time (Fig. 4, ¶ 51 projected machine maintenance) by calculating a fitting curve (Fig. 4, ¶ 51 curve 420) based on a series of predicted machining qualities (Fig. 4, ¶ 50 a time series of points 410a-410/ that each have a command value that was collected at a particular time) and identifying a time (Fig. 4, time 440) at which the fitting curve intersects a dimensional tolerance (Fig. 4, threshold command value 430).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda by combining the machine abnormality diagnosis device comprising: processing circuitry disclosed by Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda with a machine abnormality diagnosis device comprising: processing circuitry that determines a component replacement time by calculating a fitting curve based on a series of predicted machining qualities and identifying a time at which the fitting curve intersects a dimensional tolerance; disclosed by Humphrey for the benefit of predicting machine problems at changing intervals by monitoring the rate of change of sensed values [Humphrey: ¶ 49].
Regarding Claim 21, Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda disclose the limitations of claim 12, which this claim depends on.
Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda fail to disclose, the non-transitory computer-readable recording medium according to claim 12, wherein the operations further comprise determining a component replacement time by calculating a fitting curve based on a series of predicted machining qualities and identifying a time at which the fitting curve intersects a dimensional tolerance.
In analogous art, Humphrey discloses, the non-transitory computer-readable recording medium according to claim 12, wherein the operations further comprise determining a component replacement time (Fig. 4, ¶ 51 projected machine maintenance) by calculating a fitting curve (Fig. 4, ¶ 51 curve 420) based on a series of predicted machining qualities (Fig. 4, ¶ 50 a time series of points 410a-410/ that each have a command value that was collected at a particular time) and identifying a time (Fig. 4, time 440) at which the fitting curve intersects a dimensional tolerance (Fig. 4, threshold command value 430).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda by combining the non-transitory computer-readable recording medium disclosed by Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda with a non-transitory computer-readable recording medium causing a computer to perform operations wherein, the operations further comprise determining a component replacement time by calculating a fitting curve based on a series of predicted machining qualities and identifying a time at which the fitting curve intersects a dimensional tolerance; disclosed by Humphrey for the benefit of predicting machine problems at changing intervals by monitoring the rate of change of sensed values [Humphrey: ¶ 49].
Claim(s) 9, 10, 15, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over YAMAMOTO (US 2019/02101760 A1) (herein after Yamamoto) in view of KAWANOUE et al (US 2019/0301979 A1) (herein after Kawanoue) in view of ISOBE (US 2017/0203400 A1) (herein after Isobe), in view of FUKUDA et al. (US 2018/0356282 A1) (herein after Fukuda), and further in view of MOTEGI et al (US 2021/0319368 A1) (herein after Motegi).
Regarding Claim 9, Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda disclose the limitations of claim 1, which this claim depends on.
Yamamoto further discloses, the machining diagnosis device according to claim 1, further comprising: a learning device (Fig. 8, normal model unit 31; Note: Fig 2 is implementation of Fig 1, see ¶ 60) including processing circuitry to acquire training data (Fig. 8, ¶ 75 measurement data acquired during normal machining) including a diagnosis condition (Fig. 8, ¶ 75 abnormality diagnosis) for the machining diagnosis device and machining data after maintenance (Fig. 8, ¶ 111 replacement timing based on temporal changes in diagnostic value) in the machining diagnosis device, —
Yamamoto, Kawanoue, Isobe, and Fukuda fail to disclose, — and to generate, using the training data, a trained model to infer a correction value for each piece of diagnosis threshold data after maintenance to determine an abnormality in diagnosis based on machining data after maintenance in the machining diagnosis device.
In analogous art, Motegi discloses, — and to generate, using the training data, a trained model to infer a correction value (Fig. 1, ¶ 51 fault level scores) for each piece of diagnosis threshold data (Fig. 1, ¶ 51 the diagnosis threshold) after maintenance to determine an abnormality in diagnosis based on machining data after maintenance (Fig. 1, ¶ 84 maintenance of the machine system 2 is performed) in the machining diagnosis device.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda by combining the machining diagnosis device comprising processing circuitry disclosed by Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda with a machine diagnosis device comprising processing circuitry to generate, using the training data, a trained model to infer a correction value for each piece of diagnosis threshold data after maintenance to determine an abnormality in diagnosis based on machining data after maintenance in the machining diagnosis device; disclosed by Motegi for the benefit of creating learning information to perform machine fault diagnosis with reduced time [Motegi: ¶ 14].
Regarding Claim 10, Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda disclose the limitations of claim 1, which this claim depends on.
Yamamoto further discloses, the machining diagnosis device according to claim 1, further comprising: an inference device (Fig. 8, abnormality diagnosing unit 32) including processing circuitry to acquire machining data after maintenance (Fig. 8, ¶ 111 replacement timing based on temporal changes in diagnostic value) in the machining diagnosis device, —
Yamamoto, Kawanoue, Isobe, Fukuda fail to disclose, — and to output, using a trained model to infer a correction value for each piece of diagnosis threshold data after maintenance to determine an abnormality in diagnosis based on machining data after maintenance in the machining diagnosis device, a correction value for each piece of diagnosis threshold data after maintenance based on machining data after maintenance in the machining diagnosis device.
In analogous art, Motegi discloses, — and to output, using a trained model to infer a correction value (Fig. 1, ¶ 51 fault level scores) for each piece of diagnosis threshold data (Fig. 1, ¶ 51 the diagnosis threshold) after maintenance to determine an abnormality in diagnosis based on machining data after maintenance (Fig. 1, ¶ 84 maintenance of the machine system 2 is performed) in the machining diagnosis device, a correction value (Fig. 1, ¶ 51 fault level scores) for each piece of diagnosis threshold (Fig. 1, ¶ 51 the diagnosis threshold) data after maintenance based on machining data after maintenance (Fig. 1, ¶ 84 maintenance of the machine system 2 is performed) in the machining diagnosis device.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda by combining the machining diagnosis device comprising processing circuitry disclosed by Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda with a machine diagnosis device comprising processing circuitry to output, using a trained model to infer a correction value for each piece of diagnosis threshold data after maintenance to determine an abnormality in diagnosis based on machining data after maintenance in the machining diagnosis device, a correction value for each piece of diagnosis threshold data after maintenance based on machining data after maintenance in the machining diagnosis device; disclosed by Motegi for the benefit of creating learning information to perform machine fault diagnosis with reduced time [Motegi: ¶ 14].
Regarding Claim 15, Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda disclose the limitations of claim 1, which this claim depends on.
Yamamoto further discloses, a learning device (Fig. 8, normal model unit 31; Note: Fig 2 is implementation of Fig 1, see ¶ 60), comprising: processing circuitry to acquire training data (Fig. 8, ¶ 75 measurement data acquired during normal machining) including the diagnosis condition (Fig. 8, ¶ 75 abnormality diagnosis) for a machining diagnosis device according to claim1 and machining data after maintenance (Fig. 8, ¶ 111 replacement timing based on temporal changes in diagnostic value) in the machining diagnosis device; —
Yamamoto, Kawanoue, Isobe, Fukuda fail to disclose, — and to generate, using the training data, a trained model to infer a correction value for each piece of diagnosis threshold data after maintenance to determine an abnormality in diagnosis based on machining data after maintenance in the machining diagnosis device.
In analogous art, Motegi discloses, — and to generate, using the training data, a trained model to infer a correction value (Fig. 1, ¶ 51 fault level scores) for each piece of diagnosis threshold data (Fig. 1, ¶ 51 the diagnosis threshold) after maintenance to determine an abnormality in diagnosis based on machining data after maintenance (Fig. 1, ¶ 84 maintenance of the machine system 2 is performed) in the machining diagnosis device.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda by combining the learning device disclosed by Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda with a learning device to generate, using the training data, a trained model to infer a correction value for each piece of diagnosis threshold data after maintenance to determine an abnormality in diagnosis based on machining data after maintenance in the machining diagnosis device.; disclosed by Motegi for the benefit of creating learning information to perform machine fault diagnosis with reduced time [Motegi: ¶ 14].
Regarding Claim 16, Yamamoto in view of Kawanoue in view of Isobe disclose the limitations of claim 1, which this claim depends on.
Yamamoto further discloses, an inference device (Fig. 8, abnormality diagnosing unit 32), comprising: processing circuitry to acquire machining data after maintenance (Fig. 8, ¶ 111 replacement timing based on temporal changes in diagnostic value) in the machining diagnosis device according to claim1; —
Yamamoto, Kawanoue, Isobe, Fukuda fail to disclose, — and to output, using a trained model to infer a correction value for each piece of diagnosis threshold data after maintenance to determine an abnormality in diagnosis based on machining data after maintenance in the machining diagnosis device, a correction value for each piece of diagnosis threshold data after maintenance based on machining data after maintenance in the machining diagnosis device.
In analogous art, Motegi discloses, — and to output, using a trained model to infer a correction value (Fig. 1, ¶ 51 fault level scores) for each piece of diagnosis threshold data (Fig. 1, ¶ 51 the diagnosis threshold) after maintenance to determine an abnormality in diagnosis based on machining data after maintenance (Fig. 1, ¶ 84 maintenance of the machine system 2 is performed) in the machining diagnosis device, a correction value (Fig. 1, ¶ 51 fault level scores) for each piece of diagnosis threshold (Fig. 1, ¶ 51 the diagnosis threshold) data after maintenance based on machining data after maintenance (Fig. 1, ¶ 84 maintenance of the machine system 2 is performed) in the machining diagnosis device.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamamoto in view of Fukuda by combining the inference device disclosed by Yamamoto in view of Fukuda with an inference device to output, using a trained model to infer a correction value for each piece of diagnosis threshold data after maintenance to determine an abnormality in diagnosis based on machining data after maintenance in the machining diagnosis device, a correction value for each piece of diagnosis threshold data after maintenance based on machining data after maintenance in the machining diagnosis device; disclosed by Motegi for the benefit of creating learning information to perform machine fault diagnosis with reduced time [Motegi: ¶ 14].
Claim(s) 17, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over YAMAMOTO (US 2019/02101760 A1) (herein after Yamamoto) in view of KAWANOUE et al (US 2019/0301979 A1) (herein after Kawanoue) in view of ISOBE (US 2017/0203400 A1) (herein after Isobe), in view of FUKUDA et al. (US 2018/0356282 A1) (herein after Fukuda), and further in view of UNUMA et al. (US 2019/0171199 A1) (herein after Unuma).
Regarding Claim 17, Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda disclose the limitations of claim 1, which this claim depends on.
Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda fail to disclose, the machining diagnosis device according to claim 1. wherein the processing circuitry diagnoses machining by predicting machining quality using a regression equation generated through a least squares method, and the regression equation uses the calculated feature as an explanatory variable and a difference between a design value and a measured machining dimension as a response variable.
In analogous art, Unuma discloses, the machining diagnosis device according to claim 1. wherein the processing circuitry diagnoses machining by predicting machining quality using a regression equation generated through a least squares method (Fig. 1, ¶ 164 regression analysis using a least-square method), and the regression equation uses the calculated feature as an explanatory variable (Fig. 1, ¶ 164 regression analysis using a least-square method; Examiner interpretation: a regression least squares equation has an explanatory variable in light of the specification ¶ 38, 39) and a difference (Fig. 1, ¶ 172 comparison targets) between a design value and a measured machining dimension as a response variable (Fig. 1, ¶ 164 regression analysis using a least-square method; Examiner interpretation: a regression least squares equation has a response variable in light of the specification ¶ 38, 39).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda by combining the machine abnormality diagnosis device comprising: processing circuitry, disclosed by Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda with a machine abnormality diagnosis device comprising: processing circuitry, that diagnoses machining by predicting machining quality using a regression equation generated through a least squares method, and the regression equation uses the calculated feature as an explanatory variable and a difference between a design value and a measured machining dimension as a response variable; disclosed by Unuma for the benefit of detecting a machine abnormal state from multiple operation states of the machine [Unuma: ¶ 6].
Regarding Claim 19, Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda disclose the limitations of claim 11, which this claim depends on.
Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda fail to disclose, the machining diagnosis method according to claim 11, wherein the machining is diagnosed by predicting machining quality using a regression equation generated through a least squares method, and the regression equation uses the calculated feature as an explanatory variable and a difference between a design value and a measured machining dimension as a response variable.
In analogous art, Unuma discloses, the machining diagnosis method according to claim 11, wherein the machining is diagnosed by predicting machining quality using a regression equation generated through a least squares method (Fig. 1, ¶ 164 regression analysis using a least-square method), and the regression equation uses the calculated feature as an explanatory variable (Fig. 1, ¶ 164 regression analysis using a least-square method; Examiner interpretation: a regression least squares equation has an explanatory variable in light of the specification ¶ 38, 39) and a difference (Fig. 1, ¶ 172 comparison targets) between a design value and a measured machining dimension as a response variable (Fig. 1, ¶ 164 regression analysis using a least-square method; Examiner interpretation: a regression least squares equation has a response variable in light of the specification ¶ 38, 39).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda by combining the method performed by the machine abnormality diagnosis device disclosed by Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda with a method performed by a machine abnormality diagnosis device wherein, the machining is diagnosed by predicting machining quality using a regression equation generated through a least squares method, and the regression equation uses the calculated feature as an explanatory variable and a difference between a design value and a measured machining dimension as a response variable; disclosed by Unuma for the benefit of detecting a machine abnormal state from multiple operation states of the machine [Unuma: ¶ 6].
Regarding Claim 20, Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda disclose the limitations of claim 12, which this claim depends on.
Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda fail to disclose, the non-transitory computer-readable recording medium according to claim 12, wherein the machining is diagnosed by predicting machining quality using a regression equation generated through a least squares method, and the regression equation uses the calculated feature as an explanatory variable and a difference between a design value and a measured machining dimension as a response variable.
In analogous art, Unuma discloses, the non-transitory computer-readable recording medium according to claim 12, wherein the machining is diagnosed by predicting machining quality using a regression equation generated through a least squares method (Fig. 1, ¶ 164 regression analysis using a least-square method), and the regression equation uses the calculated feature as an explanatory variable (Fig. 1, ¶ 164 regression analysis using a least-square method; Examiner interpretation: a regression least squares equation has an explanatory variable in light of the specification ¶ 38, 39) and a difference (Fig. 1, ¶ 172 comparison targets) between a design value and a measured machining dimension as a response variable (Fig. 1, ¶ 164 regression analysis using a least-square method; Examiner interpretation: a regression least squares equation has a response variable in light of the specification ¶ 38, 39).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda by combining the non-transitory computer-readable recording medium disclosed by Yamamoto in view of Kawanoue in view of Isobe in view of Fukuda with a non-transitory computer-readable recording medium wherein, the machining is diagnosed by predicting machining quality using a regression equation generated through a least squares method, and the regression equation uses the calculated feature as an explanatory variable and a difference between a design value and a measured machining dimension as a response variable; disclosed by Unuma for the benefit of detecting a machine abnormal state from multiple operation states of the machine [Unuma: ¶ 6].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. UENO (US 2021/0019958 A1) teaches, a machining diagnosis device (Fig. 1, ¶ 29 To reduce a loss of the workpiece due to machining abnormality).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH O. NYAMOGO whose telephone number is (469)295-9276. The examiner can normally be reached 9:00 A to 5:00 P CT.
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/JOSEPH O. NYAMOGO/
Examiner
Art Unit 2858
/EMAN A ALKAFAWI/Supervisory Patent Examiner, Art Unit 2858 3/2/2026