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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. KR10-2021-0109436, filed on 08/19/2021.
Response to Arguments
Applicant's arguments filed 08/28/2025 regarding the rejection under 35 USC 101 have been fully considered and they are persuasive.
Applicant argues, see especially pages 7-12, that claims 1 and 13, are -patent eligible because “However, independent claim 1, e.g., recites “A processor-implemented method of predicting an anomaly in a manufacturing process, the method comprising: receiving, by an anomaly prediction device, time-series equipment data comprising one or both of sensor data and specification data; converting the time-series equipment data into an image; dividing the image into a plurality of patch images; outputting a probability for each class associated with a sign of an anomaly in the time-series equipment data by inputting the plurality of patch images to a pretrained artificial neural network (ANN); and predicting the sign of the anomaly in the time-series equipment data by adjusting a probability weight for each class based on a preset standard.” Accordingly, Applicants respectfully submit that the above-noted claimed features are not, and/or would/could not be, practically performed in the human mind and/or correspond to mental activities, since the claim specifically recites that the method is a processor- implemented method, and that the processes are performed by an anomaly prediction device. Further, Applicants respectfully submit that the present claims are not directed to “a mathematical concept,” “fundamental economic principles,” or “managing personal behavior or relationships or interactions between people,” or “mental process,” as indicated in the above- noted Guidance.” Examiner respectfully disagrees. The court cases and guidance referenced preceding the arguments have been considered and do not affect the 101 rejections on the presented claims. The amended claim language still recites a mental process even though it is claiming it is being done by a generic processor and generic device. “Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer").” (MPEP 2106.04(a)(III)(C)).
Applicant argues, see especially pages 7-12, that claims 1 and 13, are -patent eligible because “As demonstrated above, both the USPTO and the PTAB have explicitly stated that while the claimed features may be performed by a human (such as with pen and paper), when the claimed features cannot be practically performed mentally, it is not abstract idea. It is respectfully submitted that one of ordinary skill in the art would not understand how to mentally perform receiving, by an anomaly prediction device, time-series equipment data comprising one or both of sensor data and specification data; converting the time-series equipment data into an image; dividing the image into a plurality of patch images; outputting a probability for each class associated with a sign of an anomaly in the time-series equipment data by inputting the plurality of patch images to a pretrained artificial neural network (ANN); and predicting the sign of the anomaly in the time-series equipment data by adjusting a probability weight for each class based on a preset standard.” Thus, Applicant respectfully submits that the present claims are not directed to a “mental process,” as indicated in the above-noted Guidance. Specifically, the claims cannot practically be performed in the human mind, as the human mind is not equipped to perform such complex operations, which, in the case of dividing the image into a plurality of patch images; outputting a probability for each class associated with a sign of an anomaly in the time- series equipment data by inputting the plurality of patch images to a pretrained artificial neural network (ANN); and predicting the sign of the anomaly in the time-series equipment data by adjusting a probability weight for each class based on a preset standard, might typically involve (i) individual operations with numbers too large to be practical for human computation, (ii) a number of such operations that multiplies the difficulty, and (iii) computation of functions (e.g., activation functions) which are not feasible for the unassisted human mind. Accordingly, it is respectfully submitted that the Office Action has improperly asserted that the claims are directed to an abstract idea. Applicants respectfully submit that the claims, considered as a whole without overgeneralization, are clearly not directed to an abstract idea. Based on the specific detail of the claims, Applicants respectfully submit that the claims are not directed to an abstract idea.” Examiner respectfully disagrees. The court cases and guidance referenced preceding the arguments have been considered and do not affect the 101 rejections on the presented claims. The claimed invention does not state an amount of data that is being processed that is unreasonable to be analyzed by the human mind. The claims simply state “receiving, by an anomaly prediction device, time-series equipment data comprising one or both of sensor data and specification data:” this limitation does not state an amount of data and could be any amount of data under the claim’s broadest reasonable interpretation.
Applicant argues, see especially pages 12-17, that claims 1 and 13, are -patent eligible because “Thus, the pending claims, as amended herewith, and the present disclosure clearly demonstrate that the claims are directed to a technological solution to a technological problem. Accordingly, it is respectfully submitted that it is impossible to readily conclude that the numerous elements, individually, as a whole, in an ordered combination, or in inventive concept implementation do not amount to significantly more than an abstract idea. Specifically, the claims amount to significantly more than an abstract idea when considering the claims as a whole. Thus, claims 1-21 are not directed to abstract ideas and are patent eligible. As the claim features of claims 1-21 offer improvements to another technology/technical field, offer improvements to the functioning of the computer itself, or apply the judicial exception with, or by use of, a particular machine/device, Applicants submit that they are thus significantly more than a mere abstract idea. Because claims 1-21 are not directed to an abstract idea, it is respectfully requested that the 35 U.S.C. § 101 rejections be withdrawn.” Examiner respectfully disagrees. The court cases and guidance referenced preceding the arguments have been considered and do not affect the 101 rejections on the presented claims. The claimed invention does not provide sufficient details for one of ordinary skill in the art to recognize it as an improvement. “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology.” (MPEP 2106.05(a)). The paragraphs 0046-0059 that are given as examples of the improvement are not sufficient to demonstrate said improvement because they are merely stating that the invention may do those things and are not explicitly stating that the invention will do those things.
The rejection under 35 USC 101 is maintained.
Applicant's arguments filed 08/28/2025 regarding the rejection under 35 USC 103 have been fully considered but they are not persuasive.
Applicant argues, see especially pages 8-9, that claims 1 and 7 are -patent eligible because “Nowhere does Al Shehri teach or suggest that a probability weight for each class associated with a sign of an anomaly in the time-series equipment data may be adjusted based on a preset standard, nor does the Office indicate in any manner how this claimed feature allegedly corresponds to the teachings of the cited references. Accordingly, Applicants respectfully submit that there is no teaching or suggestion in the cited references, alone or in combination, of the feature “predicting the sign of the anomaly in the time-series equipment data by adjusting a probability weight for each class based ona preset standard,” as recited in independent claim 1. Without acquiescing to any of the assertions set forth by the Office, Applicants respectfully submit that, based on at least the reasons outlined above, Ryan and Al Shehri fail to teach each and every limitation as respectively recited in independent claim 1. With regard to claims 4-10 and 12, Applicants respectfully submit that Ryan and Al Shehri also fail to teach or suggest all of the features of claims 4-10 and 12, and claims 4-10 and 12 are allowable in view of their individual recitations, and also in view of their respective dependencies on an allowable base claim. Moreover, in view of the actual disclosures of Ryan and Al Shehri, Applicants respectfully submit that Ryan and Al Shehri also do not teach or suggest the features of independent claim 13. It is respectfully submitted that Ryan and Al Shehri also fail to disclose all the respective features of claims 16-21, and claims 16-21 are also allowable at least by virtue of their respective dependencies on a patentable base claim.” Examiner respectfully disagrees. Al Sheri further explains the adjusting of weighting based on a preset standard in page 17, column 8, paragraph 3. Al Sheri explains how the weighting is adjusted to improve the predictions made by the model. The weights are adjusted based on if the temperature maps are trending towards a greater temperature contrast (i.e. a preset standard).
The rejection under 35 USC 103 is maintained.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process) without significantly more.
Regarding claim 1, in Step 1 of the 101 analyses set forth in MPEP 2106, the claim recites A method of predicting an anomaly in a manufacturing process. A method is one of the four statutory categories.
In Step 2a Prong 1 of the 101 analyses set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a mental process and a mathematical concept but for recitation of generic computer components:
converting the time-series equipment data into an image; dividing the image into a plurality of patch images; (a person can mentally convert data to an image then divide the image into patch images by a process of simply evaluating the data and making a judgement on how the data should be made into and image and how it should be split up. Further, the converting of data into images and dividing images into a plurality of patch images is a mathematical calculation which falls within the mathematical concept grouping of abstract ideas 2106.04(a)(2)).)
predicting the sign of the anomaly in the time-series equipment data by adjusting a probability weight for each class based on a preset standard. (a person can mentally predict the sign of the anomaly by a process of simply evaluating the time series data and making a judgement on if it is an anomaly)
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process/mathematical concept but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. According, the claim “recites” an abstract idea.
In Step 2a Prong 2 of the 101 analyses set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
A processor-implemented method of predicting an anomaly in a manufacturing process, the method comprising: receiving, by an anomaly prediction device, time-series equipment data comprising one or both of sensor data and specification data; (Adding insignificant extra-solution activity (mere data gathering) to the judicial exception (MPEP 2106.05(g))).
outputting a probability for each class associated with a sign of an anomaly in the time series equipment data by inputting the plurality of patch images to a pretrained artificial neural network (ANN); and (Adding insignificant extra-solution activity (mere data output) to the judicial exception (MPEP 2106.05(g))).
Since the claim does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In Step 2b of the 101 analyses set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, additional elements (iii.) and (v.) recite insignificant extra solution activities. Further, elements (iii.) and (v.) recite a step that that stores and retrieves information in memory or sends data of the network, which has been determined by the courts to recite a well understood, routine and conventional activity which is not indicative of significantly more. Additional elements (iv.) recite generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of significantly more.
Regarding claim 2 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 2 recites wherein the converting of the time series equipment data into the image comprises separating and converting one or both of the sensor data and the specification data. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more).
Regarding claim 3 it is dependent upon claim 2, and thereby incorporates the limitations of, and corresponding analysis applied to claim 2. Further, claim 3 recites wherein the separating and converting comprises converting one or both of the sensor data and the specification data into images each having a different color. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more).
Regarding claim 4 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 4 recites wherein the outputting of the probability for each class (In step 2A, prong 2, this recites insignificant extra solution activity of mere data output, which is not indicative of integration into a practical application (MPEP 2106.05(g)). In step 2B, this recites presenting offers which is a well-understood, routine and conventional activity, which is not indicative of significantly more). comprises inputting one or both of the sensor data and the specification data to different channels of the ANN. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more).
Regarding claim 5 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 5 recites wherein the ANN comprises a plurality of nodes differentiated to detect each class, and (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more). wherein the outputting of the probability for each class comprises outputting the probability for each class by calculating a weighted sum of an output for each of the plurality of nodes. (In step 2A, prong 2, this recites insignificant extra solution activity of mere data output, which is not indicative of integration into a practical application (MPEP 2106.05(g)). In step 2B, this recites presenting offers which is a well-understood, routine and conventional activity, which is not indicative of significantly more).
Regarding claim 6 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 2 recites wherein the dividing the image into the plurality of patch images comprises dividing the image based on a time flow, (In step 2A, prong 1, this recites a mathematical concept without significantly more. Dividing images into a plurality of patch images is a mathematical calculation which falls within the mathematical concept grouping of abstract ideas 2106.04(a)(2)) wherein the outputting of the probability for each class comprises outputting the probability for each class by inputting the divided patch images to the ANN based on the time flow. (In step 2A, prong 2, this recites insignificant extra solution activity of mere data output, which is not indicative of integration into a practical application (MPEP 2106.05(g)). In step 2B, this recites presenting offers which is a well-understood, routine and conventional activity, which is not indicative of significantly more).
Regarding claim 7 it is dependent upon claim 6, and thereby incorporates the limitations of, and corresponding analysis applied to claim 6. Further, claim 7 recites wherein the ANN is trained to focus on a feature of recent data. (In step 2A prong 2, merely training a generic machine learning operation constitutes “applying” the machine learning operation (MPEP 2106.05(f)). In step 2B, merely applying a generic machine learning operation is not indicative of significantly more.)
Regarding claim 8 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 8 recites further comprising: converting the plurality of patch images into a three-dimensional (3D) tensor, (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more). wherein the outputting of the probability for each class comprises outputting the probability for each class by inputting the 3D tensor to a 3D convolutional neural network (CNN)-based ANN. (In step 2A, prong 2, this recites insignificant extra solution activity of mere data output, which is not indicative of integration into a practical application (MPEP 2106.05(g)). In step 2B, this recites sending data over a network which is a well-understood, routine and conventional activity, which is not indicative of significantly more).
Regarding claim 9 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 9 recites wherein the predicting of the sign of the anomaly comprises predicting the sign of the anomaly in the time-series equipment data by comparing final output data in which the probability weight is adjusted for each class and a preset threshold value. (In step 2A, prong 1, this recites a mental process without significantly more. a person can mentally predict the sign of the anomaly by a process of simply evaluating the time series data and making a judgement on if it is an anomaly.)
Regarding claim 10 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 10 recites wherein the ANN is trained based on training time-series equipment data in which a class associated with the sign of the anomaly is labeled such that the sign of the anomaly is predicted. (In step 2A prong 2, merely training a generic machine learning operation constitutes “applying” the machine learning operation (MPEP 2106.05(f)). In step 2B, merely applying a generic machine learning operation is not indicative of significantly more.)
Regarding claim 11 it is dependent upon claim 10, and thereby incorporates the limitations of, and corresponding analysis applied to claim 10. Further, claim 11 recites wherein the ANN is trained based on data added by random shuffling of a preset region of the training time-series equipment data that has been labeled. (In step 2A prong 2, merely training a generic machine learning operation constitutes “applying” the machine learning operation (MPEP 2106.05(f)). In step 2B, merely applying a generic machine learning operation is not indicative of significantly more.)
Regarding claim 12 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 12 recites A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the method of claim 1 (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more).
Regarding claim 13, in Step 1 of the 101 analyses set forth in MPEP 2106, the claim recites A device for predicting an anomaly in a manufacturing process. A device is one of the four statutory categories.
In Step 2a Prong 1 of the 101 analyses set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a [ mental process/mathematical concept] but for recitation of generic computer components:
convert the time-series equipment data into an image, divide the image into a plurality of patch images, (a person can mentally convert data to an image then divide the image into patch images by a process of simply evaluating the data and making a judgement on how the data should be made into and image and how it should be split up. Further, the converting of data into images and dividing images into a plurality of patch images is a mathematical calculation which falls within the mathematical concept grouping of abstract ideas 2106.04(a)(2)).)
and predict the sign of the anomaly in the time-series equipment data by adjusting a probability weight for each class based on a preset standard. (a person can mentally predict the sign of the anomaly by a process of simply evaluating the time series data and making a judgement on if it is an anomaly)
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process/mathematical concept but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. According, the claim “recites” an abstract idea.
In Step 2a Prong 2 of the 101 analyses set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
the device comprising: a processor configured (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))).
to receive time-series equipment data comprising one or both of sensor data and specification data, (Adding insignificant extra-solution activity (mere data gathering) to the judicial exception (MPEP 2106.05(g))).
output a probability for each class associated with a sign of an anomaly in the time-series equipment data by inputting the plurality of patch images to a pretrained artificial neural network (ANN), (Adding insignificant extra-solution activity (mere data output) to the judicial exception (MPEP 2106.05(g))).
Since the claim does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In Step 2b of the 101 analyses set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, additional elements (iii.) and (v.) recite generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of significantly more. Additional elements (iv.) and (vi.) recites insignificant extra solution activities. Further, elements (iv.) and (vi.) recites a step that stores and retrieves information in memory or sends data of the network, which has been determined by the courts to recite a well understood, routine and conventional activity which is not indicative of significantly more.
Regarding claim 14 it is dependent upon claim 13, and thereby incorporates the limitations of, and corresponding analysis applied to claim 13. Further, claim 14 recites wherein the processor is configured to separate and convert one or both of the sensor data and the specification data. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more).
Regarding claim 15 it is dependent upon claim 14, and thereby incorporates the limitations of, and corresponding analysis applied to claim 14. Further, claim 15 recites wherein the processor is configured to convert one or both of the sensor data and the specification data into images each having a different color. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more).
Regarding claim 16 it is dependent upon claim 13, and thereby incorporates the limitations of, and corresponding analysis applied to claim 13. Further, claim 16 recites wherein the processor is configured to input one or both of the sensor data and the specification data to different channels of the ANN. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more).
Regarding claim 17 it is dependent upon claim 13, and thereby incorporates the limitations of, and corresponding analysis applied to claim 13. Further, claim 17 recites wherein the ANN comprises a plurality of nodes differentiated for detecting each class, (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more). wherein the processor is configured to output the probability for each class by calculating a weighted sum of an output for each of the plurality of nodes. (In step 2A, prong 2, this recites insignificant extra solution activity of mere data output, which is not indicative of integration into a practical application (MPEP 2106.05(g)). In step 2B, this recites sending data over a network which is a well-understood, routine and conventional activity, which is not indicative of significantly more).
Regarding claim 18 it is dependent upon claim 13, and thereby incorporates the limitations of, and corresponding analysis applied to claim 13. Further, claim 18 recites wherein the processor is configured to divide the image based on a time flow and output the probability for each class by inputting the divided patch images to the ANN based on the time flow, (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more). wherein the ANN is trained to focus on a feature of recent data. (In step 2A prong 2, merely training a generic machine learning operation constitutes “applying” the machine learning operation (MPEP 2106.05(f)). In step 2B, merely applying a generic machine learning operation is not indicative of significantly more.)
Regarding claim 19 it is dependent upon claim 13, and thereby incorporates the limitations of, and corresponding analysis applied to claim 13. Further, claim 19 recites wherein the processor is configured to predict the sign of the anomaly in the time-series equipment data by comparing final output data in which the probability weight is adjusted for each class and a preset threshold value. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more)., merely applying a generic machine learning operation is not indicative of significantly more.)
Regarding claim 20 it is dependent upon claim 13, and thereby incorporates the limitations of, and corresponding analysis applied to claim 13. Further, claim 20 recites wherein the ANN is trained based on training time-series equipment data in which a class associated with the sign of the anomaly is labeled such that the sign of the anomaly is predicted. (In step 2A prong 2, merely training a generic machine learning operation constitutes “applying” the machine learning operation (MPEP 2106.05(f)). In step 2B, merely applying a generic machine learning operation is not indicative of significantly more.)
Regarding claim 21 it is dependent upon claim 20, and thereby incorporates the limitations of, and corresponding analysis applied to claim 20. Further, claim 21 recites wherein the ANN is trained based on data added by random shuffling of a preset region of the training time-series equipment data that has been labeled. (In step 2A prong 2, merely training a generic machine learning operation constitutes “applying” the machine learning operation (MPEP 2106.05(f)). In step 2B, merely applying a generic machine learning operation is not indicative of significantly more.)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4-10, 12-13, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ryan et al. Pub. No.: US 20200387797 A1 in view of Al Shehri et al. Patent No.: US 10871444 B2.
Regarding claim 1 Ryan teaches a processor-implemented method of predicting an anomaly in a manufacturing process, comprising: receiving, by an anomaly prediction device, time-series equipment data comprising one or both of sensor data and specification data; (Ryan, page 46 - 47, paragraph 0061 – 0062, teaches the use of time-series data that can be gathered or collected from a variety of different sources including manufacturing. It further states that the data is gathered from sensors and can be gathered from multiple sensors simultaneously representing time-series equipment data comprising sensor data.) converting the time-series equipment data into an image; (Ryan, page 51-52, paragraph 0101, teaches the conversion of time-series data into an image for use in a CNN) dividing the image into a plurality of patch images; (Ryan, page 51 - 52, paragraph 0101, and Fig. 5A-B, teaches the use of sliding windows that go over each part of the image depending on the specified parameters which is equivalent to dividing the image into a plurality of patch images in that it splits the image into windows as the algorithm is running.)
Ryan does not teach outputting a probability for each class associated with a sign of an anomaly in the time series equipment data by inputting the plurality of patch images to a pretrained artificial neural network (ANN); However, Al Shehri in analogous art teaches this limitation (Al Shehri, page 16, column 5 paragraph 4, teaches the inputting of an image into a neural network and outputting a probability that the image contains anomalies.) and predicting the sign of the anomaly in the time-series equipment data by adjusting a probability weight for each class based on a preset standard. Further, Al Shehri in analogous art teaches this limitation (Al Shehri, page 18, column 9 paragraph 1, teaches the use of weights in the prediction process that are adjusted over time to continually improve the function of the neural network.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Al Shehri’s teaching of the inputting of a plurality of images to a convolutional neural network and outputting a probability of an anomaly based on the weights of the CNN with Ryans teaching of converting time-series equipment data into images for use in a CNN. The motivation to do so would be to be able to produce a probability value as to if the time-series data contains an anomaly rather than a classification as to whether or not it is anomalous data, allowing for further analysis of predictions that are on the edge of being anomalous data.
Regarding claim 4 the combination of Ryan and Al Shehri teaches the method of claim 1 (and thus the rejection of Claim 1 is incorporated), wherein the outputting of the probability for each class comprises inputting one or both of the sensor data and the specification data to different channels of the ANN. (Al Shehri, page 16, column 5 paragraph 4, teaches the inputting of an image, that was converted from time-series data retrieved from a sensor, into a neural network and outputting a probability that the image contains anomalies. Since a convolutional neural network (CNN) is used to process image data the CNN inherently splits the data into different channels in order to process the data).
Regarding claim 5 the combination of Ryan and Al Shehri teaches the method of claim 1, wherein the ANN comprises a plurality of nodes differentiated to detect each class, and wherein the outputting of the probability for each class comprises outputting the probability for each class by calculating a weighted sum of an output for each of the plurality of nodes. (Al Shehri, page 16, column 5 paragraph 4, teaches the inputting of an image into a convolutional neural network and outputting a probability that the image contains anomalies. Convolutional neural networks contain nodes that are differentiated to detect each class and use weighed sums to calculate the output of each node.)
Regarding claim 6 the combination of Ryan and Al Shehri teaches the method of claim 1 (and thus the rejection of Claim 1 is incorporated), wherein the dividing the image into the plurality of patch images comprises dividing the image based on a time flow, wherein the outputting of the probability for each class comprises outputting the probability for each class by inputting the divided patch images to the ANN based on the time flow. (Ryan, page 51 - 52, paragraph 0101, and Fig. 5A-B, teaches the use of a sliding window which is taking small parts of an image and individually feeding those smaller images into the CNN (i.e., patch images). These images where generated from time series data and are in chronological order based on the flow of time (i.e., dividing the image based on a time flow). The sliding window splits the image up in chronological order from left to right (i.e., outputting the probability for each class by inputting the divided patch images to the ANN based on the time flow.))
Regarding claim 7 the combination of Ryan and Al Shehri teaches the method of claim 6 (and thus the rejections of Claim 1 and 6 are incorporated), wherein the ANN is trained to focus on a feature of recent data. (Ryan, page 55, paragraph 0138, teaches the assigning of higher weights to more recent data allowing for the model to focus on the features of said recent data.)
Regarding claim 8 the combination of Ryan and Al Shehri teaches the method of claim 1 (and thus the rejection of Claim 1 is incorporated), further comprising: converting the plurality of patch images into a three-dimensional (3D) tensor, (Ryan, page 52, paragraph 0107, teaches the ability of the CNN to be used with multi-dimensional matrixes (i.e., 3D Tensors).) wherein the outputting of the probability for each class comprises outputting the probability for each class by inputting the 3D tensor to a 3D convolutional neural network (CNN)-based ANN. (Ryan, page 58, paragraph 0164, teaches the use of a CNN to process multiple dimension CNNs (i.e., a 3D convolutional neural network)).
Regarding claim 9 the combination of Ryan and Al Shehri teaches the method of claim 1 (and thus the rejection of Claim 1 is incorporated), wherein the predicting of the sign of the anomaly comprises predicting the sign of the anomaly in the time-series equipment data by comparing final output data in which the probability weight is adjusted for each class and a preset threshold value. (Ryan, page 47, paragraph 0066, teaches a forecasting of threshold crossings meaning that it is both looking for if a threshold is crossed to predict an anomaly and it is also looking at where thresholds might be crossed in the future (i.e., predicting the signs of anomalies.). The forecasting of threshold crossings is based on past time-series data while the threshold crossing alarms are based on the current time series data based on the output of the convolutional neural network that uses weighed nodes representing each class to determine its final output. (i.e., comparing the final output data in which the probability weight is adjusted for each class and a preset threshold)).
Regarding claim 10 the combination of Ryan and Al Shehri teaches the method of claim 1 (and thus the rejection of Claim 1 is incorporated), wherein the ANN is trained based on training time-series equipment data in which a class associated with the sign of the anomaly is labeled such that the sign of the anomaly is predicted. (Ryan, page 57, paragraph 0160, teaches the use of a CNN that is trained on labeled time-series data that is both anomalous and normal in order to be able to produce predicted labels for the prediction of anomalies when using a in new time-series data.)
Regarding claim 12 the combination of Ryan and Al Shehri teaches a non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the method of claim 1 (and thus the rejection of Claim 1 is incorporated). (Ryan, page 60, paragraph 180, teaches the use of non-transitory computer-readable storage medium that stores instructions that when executed by a process cause the processor to perform the specified task.)
Regarding claim 13 Ryan teaches A device for predicting an anomaly in a manufacturing process, the device comprising: a processor (Ryan, page 59 - 60, paragraph 175-180, teaches the use of a processor that is configured to do outlier detection (i.e., anomaly detection) on time-series equipment data.) configured to receive time-series equipment data comprising one or both of sensor data and specification data, (Ryan, page 46 - 47, paragraph 0061 – 0062, teaches the use of time-series data that can be gathered or collected from a variety of different sources including manufacturing. It further states that the data is gathered from sensors and can be gathered from multiple sensors simultaneously representing time-series equipment data comprising sensor data.) convert the time-series equipment data into an image, (Ryan, page 51-52, paragraph 0101, teaches the conversion of time-series data into an image for use in a CNN) divide the image into a plurality of patch images, (Ryan, page 51 - 52, paragraph 0101, and Fig. 5A-B, teaches the use of sliding windows that go over each part of the image depending on the specified parameters which is equivalent to dividing the image into a plurality of patch images in that it splits the image into windows as the algorithm is running.)
Ryan does not teach output a probability for each class associated with a sign of an anomaly in the time-series equipment data by inputting the plurality of patch images to a pretrained artificial neural network (ANN), However, Al Shehri in analogous art teaches this limitation (Al Shehri, page 16, column 5 paragraph 4, teaches the inputting of an image into a neural network and outputting a probability that the image contains anomalies.) and predict the sign of the anomaly in the time-series equipment data by adjusting a probability weight for each class based on a preset standard. Further, Al Shehri in analogous art teaches this limitation (Al Shehri, page 18, column 9 paragraph 1, teaches the use of weights in the prediction process that are adjusted over time to continually improve the function of the neural network.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Al Shehri’s teaching of the inputting of a plurality of images to a convolutional neural network and outputting a probability of an anomaly based on the weights of the CNN with Ryans teaching of converting time-series equipment data into images for use in a CNN. The motivation to do so would be to be able to produce a probability value as to if the time-series data contains an anomaly rather than a classification as to whether or not it is anomalous data, allowing for further analysis of predictions that are on the edge of being anomalous data.
Regarding claim 16 the combination of Ryan and Al Shehri teaches the device of claim 13 (and thus the rejection of Claim 13 is incorporated), wherein the processor is configured to input one or both of the sensor data and the specification data to different channels of the ANN. (Al Shehri, page 16, column 5 paragraph 4, teaches the inputting of an image, that was converted from time-series data retrieved from a sensor, into a neural network and outputting a probability that the image contains anomalies. Since a convolutional neural network (CNN) is used to process image data the CNN inherently splits the data into different channels in order to process the data).
Regarding claim 17 the combination of Ryan and Al Shehri teaches the device of claim 13 (and thus the rejection of Claim 13 is incorporated), wherein the ANN comprises a plurality of nodes differentiated for detecting each class, wherein the processor is configured to output the probability for each class by calculating a weighted sum of an output for each of the plurality of nodes. (Al Shehri, page 16, column 5 paragraph 4, teaches the inputting of an image into a convolutional neural network and outputting a probability that the image contains anomalies. Convolutional neural networks contain nodes that are differentiated to detect each class and use weighed sums to calculate the output of each node.)
Regarding claim 18 the combination of Ryan and Shehri teaches the device of claim 13 (and thus the rejection of Claim 13 is incorporated), wherein the processor is configured to divide the image based on a time flow and output the probability for each class by inputting the divided patch images to the ANN based on the time flow, (Ryan, page 51 - 52, paragraph 0101, and Fig. 5A-B, teaches the use of a sliding window which is taking small parts of an image and individually feeding those smaller images into the CNN (i.e., patch images). These images where generated from time series data and are in chronological order based on the flow of time (i.e., dividing the image based on a time flow). The sliding window splits the image up in chronological order from left to right (i.e., outputting the probability for each class by inputting the divided patch images to the ANN based on the time flow.)) wherein the ANN is trained to focus on a feature of recent data. (Ryan, page 55, paragraph 0138, teaches the assigning of higher weights to more recent data allowing for the model to focus on the features of said recent data.)
Regarding claim 19 the combination of Ryan and Al Shehri teaches the device of claim 13 (and thus the rejection of Claim 13 is incorporated), wherein the processor is configured to predict the sign of the anomaly in the time-series equipment data by comparing final output data in which the probability weight is adjusted for each class and a preset threshold value. (Ryan, page 47, paragraph 0066, teaches a forecasting of threshold crossings meaning that it is both looking for if a threshold is crossed to predict an anomaly and it is also looking at where thresholds might be crossed in the future (i.e., predicting the signs of anomalies.). The forecasting of threshold crossings is based on past time-series data while the threshold crossing alarms are based on the current time series data based on the output of the convolutional neural network that uses weighed nodes representing each class to determine its final output. (i.e., comparing the final output data in which the probability weight is adjusted for each class and a preset threshold)).
Regarding claim 20 the combination of Ryan and Al Shehri teaches the device of claim 13 (and thus the rejection of Claim 13 is incorporated), wherein the ANN is trained based on training time-series equipment data in which a class associated with the sign of the anomaly is labeled such that the sign of the anomaly is predicted. (Ryan, page 57, paragraph 0160, teaches the use of a CNN that is trained on labeled time-series data that is both anomalous and normal in order to be able to produce predicted labels for the prediction of anomalies when using a in new time-series data.)
Claims 2 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Ryan et al. Pub. No.: US 20200387797 A1 in view of Al Shehri et al. Patent No.: US 10871444 B2, and further in view of Lee Pub. No: US 20200249651 A1.
Regarding claim 2 the combination of Ryan and Al Shehri teaches the method of claim 1 (and thus the rejection of Claim 1 is incorporated), wherein the converting of the time series equipment data into the image comprises ((Ryan, page 51 - 52, paragraph 0101, teaches the conversion of time-series data into an image for use in a CNN)
The combination of Ryan and Al Shehri does not teach separating and converting one or both of the sensor data and the specification data. However, Lee in analogous art teaches this limitation (Lee, page 13-14, paragraph 0048, teaches the separating of time-series data, that has been retrieved from multiple sensors, before the time-series data is converted into an image.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Lee’s teaching of separating the time series data before it is converted into images with the combination of Ryan and Al Shehri’s teaching of converting time-series equipment data into images. The motivation to do so would be to separate out different sensor data from each other in order to be able to learn which sensor is recording the anomalous data rather than learning that one of the sensors is recording anomalous data without knowing which one.
Regarding claim 14 the combination of Ryan and Al Shehri teaches the device of claim 13 (and thus the rejection of Claim 13 is incorporated), wherein
The combination of Ryan and Al Shehri does not teach the processor is configured to separate and convert one or both of the sensor data and the specification data. However, Lee in analogous art teaches this limitation (Lee, page 13-14, paragraph 0048, teaches the separating of time-series data, that has been retrieved from multiple sensors, before the time-series data is converted into an image.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Lee’s teaching of separating the time series data before it is converted into images with the combination of Ryan and Al Shehri’s teaching of converting time-series equipment data into images. The motivation to do so would be to separate out different sensor data from each other in order to be able to learn which sensor is recording the anomalous data rather than learning that one of the sensors is recording anomalous data without knowing which one.
Claims 3 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Ryan et al. Pub. No.: US 20200387797 A1 in view of Al Shehri et al. Patent No.: US 10871444 B2, and Lee Pub. No: US 20200249651 A1, and further in view of Yuki Pub No.: JP2018124639A.
Regarding claim 3 the combination of Ryan, Al Shehri and Lee teaches the method of claim 2 (and thus the rejections of Claims 1 and 2 are incorporated), wherein the separating and converting comprises converting one or both of the sensor data and the specification data into images (Ryan, page 51 - 52, paragraph 0101, teaches the conversion of time-series data, that has been retrieved from multiple sensors, into an image for use in a CNN))
The combination of Ryan, Al Shehri, and Lee does not teach each having a different color. However, Yuki in analogous art teaches this limitation (Yuki, page 10, paragraph 0091 -0092, teaches as converting time-series data, gathered from sensors, into images and giving each image its own distinct color.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Yuki’s teaching of giving each different converted image its own color with the combination of Ryan, Al Shehri and Lee’s teaching of splitting and converting time-series equipment data. The motivation to do so would be to enable the neural network to learn the different sensors based on the color of the images that are coming in, in order to better predict which sensor is producing the anomalous data.
Regarding claim 15 the combination of Ryan, Al Shehri, and Lee teaches the device of claim 14 (and thus the rejections of Claims 13 and 14 are incorporated),
wherein the processor is configured to convert one or both of the sensor data and the specification data into images (Ryan, page 51 - 52, paragraph 0101, teaches the conversion of time-series data, that has been retrieved from multiple sensors, into an image for use in a CNN)).
The combination of Ryan, Al Shehri, and Lee does not teach each having a different color. However, Yuki in analogous art teaches this limitation (Yuki, page 10, paragraph 0091 -0092, teaches as converting time-series data into images and giving each image its own distinct color.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Yuki’s teaching of giving each different converted image its own color with the combination of Ryan, Al Shehri and Lee’s teaching of splitting and converting time-series equipment data. The motivation to do so would be to enable the neural network to learn the different sensors based on the color of the images that are coming in, in order to better predict which sensor is producing the anomalous data.
Claims 11 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ryan et al. Pub. No.: US 20200387797 A1 in view of Al Shehri et al. Patent No.: US 10871444 B2, and further in view of Kashiparekh et al. “ConvTimeNet: A Pre-trained Deep Convolutional Neural Network for Time Series Classification”.
Regarding claim 11 the combination of Ryan and Al Shehri teaches the method of claim 10, (and thus the rejections of Claims 1 and 10 are incorporated)
The combination of Ryan and Al Shehri does not teach wherein the ANN is trained based on data added by random shuffling of a preset region of the training time-series equipment data that has been labeled. However, Kashiparekh in analogous art teaches this limitation (Kashiparekh, page 3-4, Section III-C, paragraph 2, teaches the training of a CNN using a plurality of randomly sampled batches of labeled data from a plurality of time-series datasets. The order in which the plurality of datasets are iterated through in each epoch is decided randomly (i.e., random shuffling of a preset region of the training time-series equipment data that has been labeled.))
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Kashiparekh’s teaching of training a CNN with random batches of data from randomly ordered datasets with the combination of Ryan and Al Shehri’s teaching of A pretrained CNN that is trained to predict anomalies in time-series equipment data that has been converted to images. The motivation to do so would be to train the CNN to be able to work with a large range of data from a multitude of sources and to prevent overfitting of the CNN to one particular data set or type of data.
Regarding claim 21 the combination of Ryan and Al Shehri teaches the device of claim 20 (and thus the rejections of Claims 13 and 20 are incorporated),
wherein the ANN is trained based on data added by random shuffling of a preset region of the training time-series equipment data that has been labeled. (Kashiparekh, page 3-4, Section III-C, paragraph 2, teaches the training of a CNN using a plurality of randomly sampled batches of labeled data from a plurality of time-series datasets. The order in which the plurality of datasets are iterated through in each epoch is decided randomly (i.e., random shuffling of a preset region of the training time-series equipment data that has been labeled.))
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Kashiparekh’s teaching of training a CNN with random batches of data from randomly ordered datasets with the combination of Ryan and Al Shehri’s teaching of A pretrained CNN that is trained to predict anomalies in time-series equipment data that has been converted to images. The motivation to do so would be to train the CNN to be able to work with a large range of data from a multitude of sources and to prevent overfitting of the CNN to one particular data set or type of data.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS B LANE whose telephone number is (571)272-1872. The examiner can normally be reached M-Th: 7am-5pm; F: Out of Office.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, MARIELA REYES can be reached at (571) 270-1006. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/THOMAS BERNARD LANE/ Examiner, Art Unit 2142
/HAIMEI JIANG/ Primary Examiner, Art Unit 2142
Prosecution Insights