DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim limitations “acquisition unit”, “conversion unit”, “calculation unit”, and “determination unit” in claim 11 invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Nowhere in the specification are these limitations given any further explanation. Therefore, claim 13 is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, 10-11 is/are rejected under 35 U.S.C. 102(a)(1) as being unpatentable by Hironori Murase et al. (hereinafter Murase) (JP2020144619A, 2020-09-10).
Regarding claim 1, Murase teaches;
A non-transitory computer readable recording medium storing a computer program causing a computer to execute processing of detecting an abnormality of a machine having a movable part, wherein the computer executes the processing of:
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NOTE: Murase teaches a device storing instructions to perform the methods presented in their disclosure.
acquiring physical quantity data on a time-series basis output from a sensor detecting a physical quantity related to a motion of the movable part;
([Abstract] sensor data obtaining means 11 for obtaining the signals from sensors 2 in time series as sensor data;)
NOTE: Teaches acquiring data on a time-series basis output from a sensor.
([pg.2] FIG. 1 shows the configuration of the abnormality detection device 1. The type of the sensor 2 is not limited, and examples thereof include a temperature sensor, a pressure sensor, a speed sensor, and a flow velocity sensor. Further, the number, combination, position, etc. of the sensors 2 are not particularly limited.)
NOTE: Teaches a sensor that detects a physical quantity related to a motion (speed) of a part of the machine. If you are measuring the speed of a part of a machine, then that part is a movable part.
converting the physical quantity data on a time-series basis acquired to a time-series data image representing the physical quantity data;
([Abstract] converting means 13 for converting the sensor data into a five-dimensional array combined so as to be a dimensionless number; image creating means 15 for creating a color image on the basis of the five-dimensional array;)
NOTE: Teaches converting the acquired physical quantity data on a time-series basis (the aforementioned sensor data) to a time-series data image representing the physical quantity data (the sensor data representing the time-series physical quantity data is converted into a five-dimensional array, which is then converted to a color image, which is therefore a time-series data image representing the physical quantity data).
inputting the time-series data image converted to a learning model to calculate a feature of the time-series data image,
([Abstract] feature-quantity extracting means 17 for inputting the color image into a convolution neural network, and which extracts the outputs by all coupled layers as a feature quantity;)
NOTE: Teaches inputting the converted time-series data image to a learning model (inputting the color image into a convolutional neural network) to calculate a feature of the time-series data image (extracts the outputs as a feature quantity) the learned model being trained with a feature of the time-series data related to the movable part in a normal condition
the learning model being trained with a feature of the time-series data image related to the movable part in a normal condition;
[pg.2] In general, the parameters of each CNN layer trained using a large-scale supervised image data set are highly versatile, and the target image is obtained by applying fine tuning that tunes the parameters using the training data of the target task. The accuracy of recognition can be improved.
NOTE: Teaches the learning model being trained with a feature of the time-series data image related to the movable part in the normal condition (The CNN is trained using supervised image data, where the training data is related to the target task. The target task of the disclosure, as taught above, is to monitor sensor values of machines, which can include motion data. The time-series data images used to train the learning model are therefore related to the movable part in a normal condition)
and determining a presence or an absence of an abnormality of the industrial machine based on the feature calculated.
([Abstract] and exclusive identifying means 19 for performing an exclusive identification with the extracted feature quantity being input thereto to detect an abnormality.)
NOTE: Teaches determining a presence or absence of an abnormality of the machine (detect an abnormality) based on the feature calculated (with the extracted feature quantity)
Regarding claim 2, Murase teaches;
The non-transitory computer readable recording medium according to claim 1, wherein the learning model is a model generated by machine learning.
([Abstract] feature-quantity extracting means 17 for inputting the color image into a convolution neural network, and which extracts the outputs by all coupled layers as a feature quantity;)
NOTE: The learning model is a CNN, which is an ML model and is therefore generated by ML
Regarding claim 3, Murase teaches;
The non-transitory computer readable recording medium according to claim 1, wherein the learning model is a one-class classification model generated by machine learning.
([pg.8] The feature amount of each image is extracted by CNN, and the feature amount is input to the 1-class SVM.)
NOTE: The learning model is a one class classification model generated by machine learning (includes a one-class SVM).
Claim 10 is a method claim directly corresponding to apparatus claim 1, and is therefore rejected using the same reasoning.
Regarding claim 11, Murase teaches;
An abnormality detection apparatus detecting an abnormality of a machine having a movable part, comprising:
([pg.2] The anomaly detection device (1) according to this embodiment is a time-series measurement measured by each sensor using deep learning in various plants, equipment, devices, etc. in which a large number of sensors are arranged. It is a system that detects abnormalities in equipment from data.)
NOTE: The disclosure of Murase pertains to an ‘abnormality detecting device’ with sensors and is therefore an apparatus.
a sensor that detects a physical quantity related to a motion of the movable part;
([pg.2] FIG. 1 shows the configuration of the abnormality detection device 1. The type of the sensor 2 is not limited, and examples thereof include a temperature sensor, a pressure sensor, a speed sensor, and a flow velocity sensor. Further, the number, combination, position, etc. of the sensors 2 are not particularly limited.)
NOTE: Teaches a sensor that detects a physical quantity related to a motion (speed) of a part of the machine. If you are measuring the speed of a part of a machine, then that part is a movable part.
an acquisition unit that acquires physical quantity data on a time-series basis output from the sensor;
([Abstract] sensor data obtaining means 11 for obtaining the signals from sensors 2 in time series as sensor data;)
NOTE: Teaches an acquisition unit that acquires physical quantity data (the sensor data is physical quantity data [speed, etc.], taught above) on a time-series basis output from the sensor (signals from sensors in time series).
a conversion unit that converts the physical quantity data on a time-series basis acquired by the acquisition unit to a time-series data image representing the physical quantity data;
([Abstract] converting means 13 for converting the sensor data into a five-dimensional array combined so as to be a dimensionless number; image creating means 15 for creating a color image on the basis of the five-dimensional array; )
NOTE: Teaches a conversion unit that converts the physical quantity data on a time-series basis (sensor data represents physical quantity data on a time-series basis, as taught above) acquired by the acquisition unit to a time-series data image representing the physical quantity data (creating a color image based on the five-dimensional array which is created using the sensor data)
a calculation unit that inputs the time-series data image converted to a learning model to calculate a feature of the time-series data image,
([Abstract] feature-quantity extracting means 17 for inputting the color image into a convolution neural network, and which extracts the outputs by all coupled layers as a feature quantity;)
NOTE: Teaches a calculation unit that inputs the time-series data image to a leaning model (inputting the color image [which is a time-series data image as taught above] into a convolutional neural network) to calculate a feature of the time series data image (extracts the outputs as a feature quantity)
the learning model being trained with a feature of the time-series data image related to the movable part in a normal condition;
([pg.2] In general, the parameters of each CNN layer trained using a large-scale supervised image data set are highly versatile, and the target image is obtained by applying fine tuning that tunes the parameters using the training data of the target task. The accuracy of recognition can be improved.)
NOTE: Teaches the learning model being trained with a feature of the time-series data image related to the movable part in the normal condition (The CNN is trained using supervised image data, where the training data is related to the target task. The target task of the disclosure, as taught above, is to monitor sensor values of machines, which includes the sensor values of the movable part [taught above], which is converted to time-series data images [taught above]. The time-series data images used to train the learning model are therefore related to the movable part in a normal condition)
and determining a presence or an absence of an abnormality of the machine based on the feature calculated.
([Abstract] and exclusive identifying means 19 for performing an exclusive identification with the extracted feature quantity being input thereto to detect an abnormality.)
NOTE: Teaches determining a presence or absence of an abnormality of the industrial machine based on the feature calculated (using the extracted feature quantity to identify an abnormality of the machine)
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Murase (JP2020144619A, 2020-09-10) in view of Noriyuki Aoki et al. (hereinafter Aoki) (JP2018092453A, 2018-06-14).
Regarding claim 4, Murase teaches;
The non-transitory computer readable recording medium according to claim 1,
(Using the same reason as the rejection for claim 1)
wherein the physical quantity data includes time-series data indicating a first physical quantity and time- series data indicating a second physical quantity,
([Abstract] sensor data obtaining means 11 for obtaining the signals from sensors 2 in time series as sensor data;)
NOTE: Teaches time-series data from a sensor.
([pg.2] FIG. 1 shows the configuration of the abnormality detection device 1. The type of the sensor 2 is not limited, and examples thereof include a temperature sensor, a pressure sensor, a speed sensor, and a flow velocity sensor. Further, the number, combination, position, etc. of the sensors 2 are not particularly limited.)
NOTE: The sensor data includes physical quantities related to a motion of the movable part (speed sensor, etc.) at a time series basis (as taught directly above). Any combination and number of sensors indicates that there can be at least time series data indicating a first and second physical quantity.
Murase fails to teach but Aoki teaches;
and the time-series data image includes a first image rendering the first physical quantity and a second image rendering the second physical quantity in a single image
([pg.5] Here, an example is given and demonstrated about the image data which the data conversion means 13 produces | generates. FIG. 6 is an example (sensor data table 101) of a table in which a plurality of sensor data used by the learning apparatus 1 is collected. The sensor data table 101 in FIG. 6 is a collection of sensor data acquired by a plurality of sensors numbered 1 to 12. For example, the sensor data table 101 is a collection of sensor data acquired by each sensor at a predetermined timing.)
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NOTE: Aoki discloses a single data image which is composed of multiple image renderings (each square in 201 above is an individual sensor value image rendering, and together the collection of sensor image renderings creates a single time series image) for each of the sensors data values at a specific time interval (time-series). Therefore, teaches the time-series data image including a first image rendering the first sensor data and a second image rendering the second sensor data in a single image.
OBVIOUSNESS TO COMBINE AOKI WITH MURASE:
Aoki and Murase are both analogous art to each other and to the present invention as they both pertain to converting sensor data into time-series data images. Specifically, Aoki pertains to an apparatus with a non-transitory computer readable medium which acquires and converts sensor data, and Murase pertains to an abnormality detecting device and method using time-series data images.
Rendering both of the images in a single image would allow for concurring processing of the first and second sensor data at that particular time step.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to render an image of a first and second physical quantity collected by the sensors of Murase in a single image using the method of rendering multiple sensor data images into one time-series data image disclosed by Aoki to capture both physical quantities in a single image at each step in the time-series.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Murase (JP2020144619A, 2020-09-10) in view of Aoki (JP2018092453A, 2018-06-14) further in view of David Stanley (Hereinafter Stanley) (US20160379387A1, 2016-12-29).
Regarding claim 5, Murase in view of Aoki teach;
The non-transitory computer readable recording medium according to claim 4,
(Using the rejection for claim 4 by Murase in view of Aoki)
Murase and Aoki fail to teach but Stanley teaches
wherein the first physical quantity and the second physical quantity are each a two-dimensional physical quantity indicated by two numerical vales.
([0035] FIG. 1 shows an example system 10 for modeling displacement and vibration of a shaft 30 rotating about a rotational axis 15, according to embodiments. The system 10 includes pairs of displacement sensors 12a-12b, 12c-12d, 12e-12f, and 12g-12h attached to sleeve bearings 14a, 14b, 14c and 14d. FIG. 2 shows an example perspective view of the shaft 30, displacement sensors 12a-12b, 12c-12d, 12e-12f, and 12g-12h attached to sleeve bearings 14a, 14b, 14c and 14d. These sensors are disposed on displacement axes that are referred to herein as the x-axis (horizontal axis) and y-axis (vertical axis).)
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NOTE: Teaches a first and second physical quantity (displacement and vibration) which are each a two-dimensional quantity indicated by two numerical values (measured on both the x and y axis). The above image teaches numerical values corresponding to each axis (two numerical values) which are used to measure the physical quantities.
OBVIOUSNESS TO COMBINE STANLEY WITH MURASE AND AOKI:
Stanley is analogous art to Aoki, Murase, and the present invention as they all pertain to monitoring and analyzing sensor data. Specifically, Stanley pertains to monitoring a machine using sensor data pertaining to the movements of the parts.
Stanley further states:
([0002] When a shaft is rotating at its nominal operating speed, the shaft usually rides on a hydrodynamic wedge of oil between the bearing and the shaft. If the shaft develops an abnormal vibrational mode while rotating, the outer surface of the shaft may contact the inner surface of the bearing and cause damage to the shaft and the bearing. To avoid such situations, it is important for operational personnel to be able to monitor the position of a rotating shaft with respect to the surfaces of the sleeve bearings in which it rotates.)
NOTE: Stanley explains that by monitoring the position of the rotating shaft using two coordinates, they are able to prevent abnormalities such as the shaft being too close to other parts and getting damaged.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to record the two physical quantities using two dimensions to monitor component positions and prevent abnormalities and collisions between components.
Claim(s) 6-7, 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Murase (JP2020144619A, 2020-09-10) in view of Hideo Naito et al. (Hereinafter Naito) (JPH01168421A, 1989-07-03).
Regarding claim 6, Murase teaches;
The non-transitory computer readable recording medium according to claim 1,
Physical quantity data including time-series data
(Using the same reasoning as in claim 1 rejection by Murase)
Murase fails to teach but Naito teaches;
wherein the movable part is a rotational shaft of a molding machine,
([Abstract] rotating torque is transmitted to a motor shaft 7…)
NOTE: Teaches a movable part being a rotational shaft (rotation torque transmitted to motor shaft) of a molding machine (disclosure pertains to molding machines)
and the physical quantity data includes [taught by Murase above] indicating a displacement of the rotational shaft of the molding machine, a torque, a rotational speed or a rotational acceleration.
([Abstract] …and a rotating displacement produces in a servo-motor 8, a pulse coder 9 detects the rotating displacement and the error rate of the present position to the servo-motor stopping position is stored in the error resister of a servo-circuit 10. As the error rate becomes above the limitation of the tolerance (epsilon), CPU 19 for PMC outputs an alarm via BAC 18 and performs an abnormality indication)
NOTE: Teaches the physical quantity data indicating a displacement of the rotational shaft (rotating displacement from the rotational shaft is produced and detected)
OBVIOUSNESS TO COMBINE NAITO WITH MURASE:
Naito is analogous art to Murase and the present invention as they all pertain to machine abnormality detection. Specifically, Naito pertains to detecting defective components in a molding machine.
As taught by Naito, there already exists a method of sensing the displacement of a rotational shaft in a molding machine and indicating an abnormality in this sensed data. In claim 1 of the present invention, the first limitation pertains to obtaining data, then every other limitation pertains to processing the obtained data. Simply replacing what the obtained data is would allow the processing of the obtained data to be applied to many different contexts and problems.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to perform the data processing limitations of claim 1 (taught by Murase) using the sensed displacement data of a rotational shaft of a molding machine (taught by Naito) on a time series basis to allow the methods of data processing in claim 1 (taught by Murase) to be used to detect abnormalities in a molding machine related to a rotational shaft.
Regarding claim 7, Murase teaches;
The non-transitory computer readable recording medium according to claim 1,
Physical quantity data including time-series data
(Using the same reasoning as the claim 1 rejection by Murase)
Murase fails to teach but Naito teaches;
wherein the movable part is a screw of a molding machine,
([Abstract] so that the rotating torque opposite to the one in its metering time produces in the screw…)
NOTE: The moveable part is a screw (rotating torque… produces in the screw) of a molding machine
and the physical quantity data includes
([Abstract] 2. Inasmuch as this rotating torque is transmitted to a motor shaft 7 and a rotating displacement produces in a servo-motor 8, a pulse corder 9 detects the rotating displacement)
NOTE: The physical quantity data includes data indicating a screw displacement of the molding machine (torque produces in the screw, torque is transmitted to the shaft, from which a rotating displacement is produced and detected).
Using the same reasoning to combine Murase and Naito from claim 6, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to perform the data processing limitations of claim 1 (taught by Murase) using the sensed displacement data of a screw of a molding machine (taught by Naito) on a time series basis to allow the methods of data processing in claim 1 (taught by Murase) to be used to detect abnormalities in a molding machine related to a screw.
Regarding claim 12, Murase teaches;
the abnormality detection apparatus according to claim 11;
(using the same reasoning as the rejection for claim 11)
Murase fails to teach but Naito teaches;
and a molding machine,
([pg. 2] In FIG. 1 showing the main parts of a servo motor-driven injection molding machine)
NOTE: The disclosure pertains to a molding machine.
wherein the abnormality detection apparatus is adapted to detect an abnormality of the molding machine.
([pg. 1] The present invention relates to an abnormality detection device for an injection device that detects abnormalities in an injection device, particularly an abnormality in a screw. In conventional injection molding machines, strong stress is applied to the tip of the screw due to the reaction force of the compressed molten resin during injection and during maintenance, causing the molten resin to flow backward along the groove of the screw.)
NOTE: Teaches an apparatus for detecting abnormalities in molding machines (injection molding machines).
Using the same reasoning to combine Murase and Naito from claim 6, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to perform the processes presented in claim 11 (taught by Murase) using sensor data that pertains to components of a molding machine to apply the apparatus to be used to detect abnormalities in a molding machine environment.
Claim(s) 8, 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Murase (JP2020144619A, 2020-09-10) in view of Aoki (JP2018092453A, 2018-06-14) further in view of Kazuya Mori et al. (Hereinafter Mori) (JP2018051783A, 2018-04-05).
Regarding claim 8, Murase teahces;
The non-transitory computer readable recording medium according to claim 1,
(Using the rejection for claim 1 by Murase)
Murase fails to teach but Aoki teaches;
a time-series data image including, in a single image, a first and second image rendering of sensor data.
([pg.5] Here, an example is given and demonstrated about the image data which the data conversion means 13 produces | generates. FIG. 6 is an example (sensor data table 101) of a table in which a plurality of sensor data used by the learning apparatus 1 is collected. The sensor data table 101 in FIG. 6 is a collection of sensor data acquired by a plurality of sensors numbered 1 to 12. For example, the sensor data table 101 is a collection of sensor data acquired by each sensor at a predetermined timing.)
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NOTE: Teaches a time-series data image (data from each sensor at a predetermined timing) including, in a single image, a first and second image rendering of sensor data (the two leftmost cells in 201 above a first and second image rendering of the sensor data from sensors 1 and 2, respectively, and both image renderings are included in the single time-series data image).
Murase and Aoki fail to teach but Mori teaches;
wherein the movable part is a first screw and a second screw of a twin-screw kneading extruder, the physical quantity data includes and
([pg.7] In the multi-axis kneading extruder, a vibration measuring means 60 for measuring vibration in the direction between the screw shafts (y direction in FIG. 2) and vibration in the direction included in the second plane (zx plane in FIGS. 1 and 2). When used, excessive vibration due to mounting angle failure is measured as vibration in the direction between the screw shafts, but not as vibration in the direction included in the second plane. Therefore, the multi-screw kneading extruder provided with the first screw 14 and the second screw 16 and the vibration measuring means 60 for measuring at least vibration in the direction between the screw shafts and in the direction included in the second plane is vibration measurement. It is possible to easily determine whether or not the cause of excessive vibration measured by the means 60 is a problem in the mounting angle.)
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([pg.3] In the present specification, the “longitudinal direction” means a direction along the rotation axis of the screw 12 unless otherwise specified. The x-axis direction shown in FIGS. 1 to 4 is the longitudinal direction, and in this specification, the “x-axis direction” means the longitudinal direction. In the present specification, the “radial direction” means a direction orthogonal to the rotation axis (that is, the longitudinal direction) of the screw 12 unless otherwise specified. The y-axis direction and the z-axis direction shown in FIGS. 1 and 2 are radial directions.)
NOTE: Teaches the movable part being a first screw and second screw in a twin-screw kneading extruder (multi-screw kneading extruder described as having two screws), and physical quantity data indicating displacements (vibrations) in a first and third axis direction (y) and a second and fourth axis direction (z) which intersect a rotation center axis of the first screw and second screw (x, which is the rotation axis of the screws).
OBVIOUSNESS TO COMBINE MORI WITH MURASE AND AOKI:
Mori is analogous art to Murase, Aoki, and the present invention as it pertains to collecting measurements from a system, and analyzing these measurements to obtain insights about said system. Specifically, it pertains to obtaining measurements related to the screws in a kneading extruder to determine if an abnormality exists.
As taught by Mori, there already exists a method of sensing displacement in multiple directions for the screws of a twin-screw extruder. In claim 1 of the present invention, the first limitation pertains to acquiring data, then every other limitation pertains to processing the obtained data. The process of converting data into time-series data images can also be performed using different types of data. Simply replacing what the obtained data is would allow the processing of the obtained data to be applied to many different contexts and problems.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to perform the data processing limitations of claim 1 (taught by Murase) using the time-series data image generation method presented in this claim (taught by Aoki) using the sensed displacement data of the screws in a twin-screw extruder (taught by Mori) on a time series basis to allow these methods to be utilized to detect abnormalities pertaining to the screws in a twin-screw kneading extruder.
Regarding claim 13, Murase teaches;
and based on training data sets including the plurality of time-series data images generated and a plurality of reference images having any feature,
generating a learning model that outputs a feature according to a normal operation and an abnormal operation
([Abstract] feature-quantity extracting means 17 for inputting the color image into a convolution neural network, and which extracts the outputs by all coupled layers as a feature quantity; and exclusive identifying means 19 for performing an exclusive identification with the extracted feature quantity being input thereto to detect an abnormality.)
NOTE: Teaches generating a learning model which outputs a feature (feature quantity) according to a normal and abnormal operation (using the feature quantity to detect an abnormality)
([pg.2] the parameters of each CNN layer trained using a large-scale supervised image data set are highly versatile, and the target image is obtained by applying fine tuning that tunes the parameters using the training data of the target task.)
NOTE: The learning model (CNN) is generated based on training data including the plurality of time-series data images generated (data of the target task) and a plurality of reference images having any feature (using supervised image data).
Reasoning as to why it would be obvious for the output feature of the model to be generated according to a normal operation and an abnormal operation of the twin-screw kneading extruder in a case where a time-series data image including a first image and a second image rendering displacements of the rotation center axes of the first screw and the second screw is input will be explained further below.
Murase fails to teach but Aoki teaches;
generating a plurality of time-series data images each including, in a single image, a first image rendering, and a second image rendering
([pg.5] Here, an example is given and demonstrated about the image data which the data conversion means 13 produces | generates. FIG. 6 is an example (sensor data table 101) of a table in which a plurality of sensor data used by the learning apparatus 1 is collected. The sensor data table 101 in FIG. 6 is a collection of sensor data acquired by a plurality of sensors numbered 1 to 12. For example, the sensor data table 101 is a collection of sensor data acquired by each sensor at a predetermined timing.)
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NOTE: Aoki discloses a single time-series data image which is composed of multiple image renderings (each square in 201 above is an individual sensor value image rendering at a given time, and together the collection of sensor image renderings creates a single time series image). Therefore, teaches a first and second image rendering (at least two image rendering are shown in the image) in a single image.
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NOTE: The above images show that a plurality of these time series data images are generated.
Murase and Aoki fail to teach but Mori teaches;
A method of [taught by Murase] detecting an abnormality of a twin-screw kneading extruder having a first screw and a second screw, the method [taught by Murase]
([pg.4] As the vibration measuring means 60 used in the kneading extruder 10 according to the present embodiment, vibrations in three directions orthogonal to each other are measured from the viewpoint of reducing the number of parts provided in the kneading extruder 10 and specifying an abnormal part. It is preferable to use a three-axis vibrometer. At least one of the three directions in which the triaxial vibrometer used as the vibration measuring means 60 measures vibration is the longitudinal direction of the rotational axis of the screw 12 or the radial direction of the rotational axis of the first screw 14. The direction included in the first plane including the rotation axis of the first screw 14 and the rotation axis of the second screw 16.)
NOTE: Mori pertains to a method of detecting abnormalities of a twin-screw kneading extruder having a first and second screw.
[taught by Aoki] data indicating displacements in a first-axis direction and a second-axis direction that intersect a rotation center axis of the first screw of the twin-screw kneading extruder
and[taught by Aoki] data indicating displacements in a third-axis direction and a fourth axis direction that intersect a rotation center axis of the second screw of the twin-screw kneading extruder,
([pg.7] In the multi-axis kneading extruder, a vibration measuring means 60 for measuring vibration in the direction between the screw shafts (y direction in FIG. 2) and vibration in the direction included in the second plane (zx plane in FIGS. 1 and 2). When used, excessive vibration due to mounting angle failure is measured as vibration in the direction between the screw shafts, but not as vibration in the direction included in the second plane. Therefore, the multi-screw kneading extruder provided with the first screw 14 and the second screw 16 and the vibration measuring means 60 for measuring at least vibration in the direction between the screw shafts and in the direction included in the second plane is vibration measurement. It is possible to easily determine whether or not the cause of excessive vibration measured by the means 60 is a problem in the mounting angle.)
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([pg.3] In the present specification, the “longitudinal direction” means a direction along the rotation axis of the screw 12 unless otherwise specified. The x-axis direction shown in FIGS. 1 to 4 is the longitudinal direction, and in this specification, the “x-axis direction” means the longitudinal direction. In the present specification, the “radial direction” means a direction orthogonal to the rotation axis (that is, the longitudinal direction) of the screw 12 unless otherwise specified. The y-axis direction and the z-axis direction shown in FIGS. 1 and 2 are radial directions.)
NOTE: Discloses data indicating displacements (vibrations) in a first and third axis direction (y) and a second and fourth axis direction (z) which intersect a rotation center axis of the first screw and second screw (x, which is the rotation axis of the first and second screws of the twin-kneading extruder).
OBVIOUSNESS TO COMBINE:
Below is the reasoning to combine Murase, Aoki, and Mori for limitation: outputs a feature according to a normal operation and an abnormal operation of the twin-screw kneading extruder in a case where a time-series data image including a first image and a second image rendering displacements of the rotation center axes of the first screw and the second screw is input.
generating a learning model that outputs a feature according to a normal operation and an abnormal operation [Murase]
([Abstract] feature-quantity extracting means 17 for inputting the color image into a convolution neural network, and which extracts the outputs by all coupled layers as a feature quantity; and exclusive identifying means 19 for performing an exclusive identification with the extracted feature quantity being input thereto to detect an abnormality.)
NOTE: Teaches generating a learning model which outputs a feature (feature quantity) according to a normal and abnormal operation (using the feature quantity to detect an abnormality)
of the twin-screw kneading extruder [Mori]
([pg.4] As the vibration measuring means 60 used in the kneading extruder 10 according to the present embodiment, vibrations in three directions orthogonal to each other are measured from the viewpoint of reducing the number of parts provided in the kneading extruder 10 and specifying an abnormal part. It is preferable to use a three-axis vibrometer. At least one of the three directions in which the triaxial vibrometer used as the vibration measuring means 60 measures vibration is the longitudinal direction of the rotational axis of the screw 12 or the radial direction of the rotational axis of the first screw 14. The direction included in the first plane including the rotation axis of the first screw 14 and the rotation axis of the second screw 16.)
NOTE: Mori pertains to a method of detecting abnormalities of a twin-screw kneading extruder having a first and second screw.
in a case where a time-series data image including a first image and a second image rendering [Aoki]
([pg.5] Here, an example is given and demonstrated about the image data which the data conversion means 13 produces | generates. FIG. 6 is an example (sensor data table 101) of a table in which a plurality of sensor data used by the learning apparatus 1 is collected. The sensor data table 101 in FIG. 6 is a collection of sensor data acquired by a plurality of sensors numbered 1 to 12. For example, the sensor data table 101 is a collection of sensor data acquired by each sensor at a predetermined timing.)
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NOTE: Aoki discloses a single time-series data image which is composed of multiple image renderings (each square in 201 above is an individual sensor value image rendering at a given time, and together the collection of sensor image renderings creates a single time series image), which includes a first and second image rendering
displacements of the rotation center axes of the first screw and the second screw is input [Mori].
([pg.7] In the multi-axis kneading extruder, a vibration measuring means 60 for measuring vibration in the direction between the screw shafts (y direction in FIG. 2) and vibration in the direction included in the second plane (zx plane in FIGS. 1 and 2). When used, excessive vibration due to mounting angle failure is measured as vibration in the direction between the screw shafts, but not as vibration in the direction included in the second plane. Therefore, the multi-screw kneading extruder provided with the first screw 14 and the second screw 16 and the vibration measuring means 60 for measuring at least vibration in the direction between the screw shafts and in the direction included in the second plane is vibration measurement. It is possible to easily determine whether or not the cause of excessive vibration measured by the means 60 is a problem in the mounting angle.)
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([pg.3] In the present specification, the “longitudinal direction” means a direction along the rotation axis of the screw 12 unless otherwise specified. The x-axis direction shown in FIGS. 1 to 4 is the longitudinal direction, and in this specification, the “x-axis direction” means the longitudinal direction. In the present specification, the “radial direction” means a direction orthogonal to the rotation axis (that is, the longitudinal direction) of the screw 12 unless otherwise specified. The y-axis direction and the z-axis direction shown in FIGS. 1 and 2 are radial directions.)
NOTE: Mori teaches data indicating displacements (vibrations) of the rotation center axes of the first screw and the second screw.
JUSTIFICATION: As taught by Mori above, there already exists a method of sensing displacements of the rotation center axes of the first screw and the second screw.
This data can be collected at different time intervals to be used to generate a plurality of time series data images using the methods taught by Aoki above.
The plurality of time series data images produced from the process of Aoki could then be used as input for the generated learning model taught by Murase above (the model taught by Murase is configured to take time-series data images as input: [Abstract] feature-quantity extracting means 17 for inputting the color image {which is a time-series data image} into a convolution neural network, and which extracts the outputs by all coupled layers as a feature quantity), which would allow the generated model to output a feature according to a normal operation and an abnormal operation (the model taught by Murase is configured to output a feature according to a normal and abnormal operation in a system: [Abstract] and exclusive identifying means 19 for performing an exclusive identification with the extracted feature quantity being input thereto to detect an abnormality) of the twin-screw kneading extruder in a case where a time-series data image including a first image and a second image rendering displacements of the rotation center axes of the first screw and the second screw is input.
Each of these combinations is simply substituting the data which is being used as input for each process with another appropriate data source (Screw displacement data from Mori being used as input to the time-series data image rendering process taught above by Aoki, which takes sensor data as input, to generate time series data images -> the generated time-series data images being used as input to the learning model taught by Murase which is configured to take time-series data images as input).
Additionally, Murase, Aoki, and Mori are analogous art (using the same reasoning as in claim 8).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to generate the learning model configured to detect abnormalities [taught by Murase] using a generated plurality of time series data images including a first and second rendering [taught by Aoki] of displacements of the screws in a twin-screw kneading extruder [taught by Mori] as input in order to tailor the learning model be used in the context of detecting abnormalities of a twin-screw kneading extruder system.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Murase (JP2020144619A, 2020-09-10) in view of Aoki (JP2018092453A, 2018-06-14) further in view of Mori (JP2018051783A, 2018-04-05) further in view of Anthony Thyssen (hereinafter Thyssen) (“ImageMagick Examples -- Montage, Arrays of Images”, 2009-12-27).
Regarding claim 9, Murase in view of Aoki further in view of Mori teaches;
The non-transitory computer readable recording medium according to claim 8,
(Using the reasoning of the claim 8 rejection by Murase in view of Aoki further in view of Mori)
Murase, Aoki, and Mori fail to teach but Thyssen teaches;
wherein the
and includes the first image and the second image as well as a blank image that fills a part other than the first image and the second image.
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NOTE: Thyssen discloses that if you specify ‘-tile 2x2’ but only use 2 images, you will get a substantially square image including the 2 images, where the part of the image other than the 2 input images is a blank image. This therefore teaches a substantially square image including a first image and a second image as well as a blank image that fills a part other than the first image and the second image.
OBVIOUSNESS TO COMBINE THYSSEN:
Thyssen is analogous art to the present disclosure as it solves the same problem of combining two images in a single image and making the single image square by filling the remaining space with a blank image.
In order to combine the two time series data images into a single image and fill the remaining space with a blank image to create a single square image, there would need to be a utilization of some kind of image altering software.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine and process the two time-series data images using the image editing software presented by Thyssen to allow the time series image data image to meet the requirements disclosed in claim 9 of the present disclosure.
CONCLUSION
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/MATTHEW ALAN CADY/ Examiner, Art Unit 2145
/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145