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 .
Status of Claims
Claims 1-13 are pending.
Information Disclosure Statement
The Information Disclosure Statements filed on 02/17/2025 and 05/28/2025 are in compliance with the provisions of 37 CFR 1.97 and have been considered. An initialed copy of the Form 1449 is enclosed herewith.
Specification
The title of the invention is not descriptive. Examiner suggests that title maybe changed to provide more description regarding the instant invention. Therefore, a new title is required that is clearly indicative of the invention to which the claims are directed.
Objection
Claim 5 is objected to because of the following informalities: Delete the extra parenthesis “variable)” in line 2 of the claim. Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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 1-13 are rejected under 35 U.S.C. 103 as being unpatentable over Lohweg et al., US 2008/0295724 in view of Guennemann-Gholizadeh et al., EP 3896543A1.
Regarding claim 1, Lohweg discloses a computer-implemented method for the evaluation of data (method that is suited to be implemented to facilitate operation of the printing press adapted to predict the occurrence of printing errors and/or provide an explanation of the likely cause of printing errors, paragraph 11), wherein the method comprises:
receiving a data set from at least one component of a printing machine or a print-processing machine (determination of whether the sensed operational parameters of the functional components of the printing press are indicative of a faulty or abnormal behavior of the printing press is carried out by monitoring the operational parameters of the functional components of the printing press during processing of the printed substrates on the printing press and by determining whether the monitored operational parameters are indicative of any one of the modelled characteristic behaviors of the printing press, paragraph 22),
wherein the data set comprises a first variable (sensors 51a, 51b for monitoring the behavior of printing press parameters such as vibrations or processing speed of the printing press, etc., paragraphs 101, 54-57) with first data points (rather than looking at the behavior of the printing press at a certain point in time, the analysis is performed over a long duration hinting multiple data points within time interval since signals from the sensors are preprocessed to calculate their spectral content, paragraphs 85, 89) and at least one second variable (sensors 52a, 52b for monitoring the behavior of printing press parameters such as rotational speed of a cylinder or roller of the printing press or current drawn by an electric motor driving cylinders of the printing unit of the printing press or wiping pressure, etc., paragraphs 101, 54-59, 76-77) with second data points (rather than looking at the behavior of the printing press at a certain point in time, the analysis is performed over a long duration hinting multiple data points within time interval since signals from the sensors are preprocessed to calculate their spectral content, paragraphs 85, 89),
carrying out a computer-implemented anomaly detection of the first data points of the first variable for determining at least one anomaly (by utilizing fuzzy logic on the monitored operational parameters P1 to PN provided by the multiple-sensor arrangement, the classification is performed into the pre-defined pattern classes and the associated classes of printing errors to determine and assign a corresponding class of printing errors, such as the sensed operational parameters might be so characterizing of a faulty or abnormal behavior of the printing press that it is possible to immediately draw conclusions that the detected faulty or abnormal behavior will lead to printing errors, paragraphs 90, 94, 123),
characterized in that at least the second data points of the second variable are considered during the computer-implemented anomaly detection of the first data points of the first variable (characterizing printing errors due to an inadequate wiping pressure occur after a certain period following decrease of the wiping pressure such as by monitoring the current drawn by the electric motor typically driving the printing unit, it is possible to detect a decrease in the wiping pressure, such decrease of wiping pressure being reflected as a decrease in the current consumption. Associated with a monitoring of the constraints (e.g. vibrations) detected on the bearings of the wiping cylinder, it is possible to define a characteristic model of the faulty behavior of the printing and predict the occurrence of the printing errors, paragraphs 94, 90).
Lohweg fails to explicitly disclose wherein a first variable with a plurality of first data points and second variable with a plurality of second data points.
However, Guennemann-Gholizadeh teaches wherein data set comprises a first variable with a plurality of first data points and at least one second variable with a plurality of second data points (for performing anomaly detection, the device can be used to evaluate classifications of measured data points originating from printing press comprising parameters like rotating speed, contact pressure to electrical power, etc., wherein, measured data points comprise several elements, each element representing a measured parameter, like pressure, temperature, mass flow, etc. Further on the measured data point is usually one data point in a sequence of data points measured over time, paragraphs 25, 27, 37).
Lohweg and Guennemann-Gholizadeh are combinable because they both are in the same field of endeavor dealing with evaluating measured data for finding printing anomalies/errors.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lohweg to incorporate the teachings of Guennemann-Gholizadeh to provide a data set comprising a first variable with a plurality of first data points and at least one second variable with a plurality of second data points for the benefit of enabling the user of an anomaly detector to effectively detect any print related anomalies and understand the reasons for the classification result, which was output by machine learning model, especially output by an isolation forest model as taught by Guennemann-Gholizadeh in paragraph 6.
Regarding claim 2, Combination of Lohweg with Guennemann-Gholizadeh further teaches characterized in that the at least one anomaly of the first variable with the corresponding data point of the at least second variable is displayed (Guennemann-Gholizadeh, in case the measured data point was classified as anomaly the mixed integer linear program function outputs those parameters which must be changed so that classification of the data point changes to normal when used as input into the isolation forest model. In addition, an average threshold can be derived from the output of the mixed integer linear program which is used to classify a measured date point as an anomaly. The described output of the mixed integer linear program is displayed on the output interface 306 which is optionally configured as a graphical user interface may additionally comprise an input unit which allows the user to concentrate on the most critical parameters influencing the classification of the measure data point which is displayed as anomaly, paragraphs 57-58).
Lohweg and Guennemann-Gholizadeh are combinable because they both are in the same field of endeavor dealing with evaluating measured data for finding printing anomalies/errors.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lohweg to incorporate the teachings of Guennemann-Gholizadeh to provide a data set comprising a first variable with a plurality of first data points and at least one second variable with a plurality of second data points for the benefit of enabling the user of an anomaly detector to effectively detect any print related anomalies and understand the reasons for the classification result, which was output by machine learning model, especially output by an isolation forest model as taught by Guennemann-Gholizadeh in paragraph 6.
Regarding claim 3, Combination of Lohweg with Guennemann-Gholizadeh further teaches characterized in that the temporal course of the first variable before and/or after a detected anomaly and the temporal course of at least the second variable before and/or after a detected anomaly is represented (Guennemann-Gholizadeh, measured data point as a time series data point for example measuring parameters like temperature, pressure of the monitored apparatus at subsequent points in time or time intervals, further on the measured data point is usually one data point in a sequence of data points measured over time. Measured is used in this context not only as physically measured but can also be derived by one or several information characterizing the monitored apparatus at the considered point in time, paragraphs 25, 37).
Lohweg and Guennemann-Gholizadeh are combinable because they both are in the same field of endeavor dealing with evaluating measured data for finding printing anomalies/errors.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lohweg to incorporate the teachings of Guennemann-Gholizadeh to provide a data set comprising a first variable with a plurality of first data points and at least one second variable with a plurality of second data points for the benefit of enabling the user of an anomaly detector to effectively detect any print related anomalies and understand the reasons for the classification result, which was output by machine learning model, especially output by an isolation forest model as taught by Guennemann-Gholizadeh in paragraph 6.
Regarding claim 4, Combination of Lohweg with Guennemann-Gholizadeh further teaches characterized in that the at least one anomaly of the first variable is characterized as error message when it exceeds a predetermined threshold value (Lohweg, sets of fuzzy "if-then" rules emulating human thinking which are designed to draw links between the printing press behavior represented by the inputted operational parameters P1 to PN and several determined pattern classes which are each assigned a corresponding class of printing errors, wherein, fuzzy systems use relative "if-then" rules of the type "if parameter alpha is equal to/greater than/less than value beta, then event A always/often/sometimes/never happens", paragraphs 87, 90).
(Guennemann-Gholizadeh also additionally teaches, output an average threshold value being used to classify an instance of the measured data point as an anomaly to output the path within a tree leading to a certain decision which has a higher length than the predefined threshold, paragraphs 22, 40, 57).
Lohweg and Guennemann-Gholizadeh are combinable because they both are in the same field of endeavor dealing with evaluating measured data for finding printing anomalies/errors.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lohweg to incorporate the teachings of Guennemann-Gholizadeh to provide a data set comprising a first variable with a plurality of first data points and at least one second variable with a plurality of second data points for the benefit of enabling the user of an anomaly detector to effectively detect any print related anomalies and understand the reasons for the classification result, which was output by machine learning model, especially output by an isolation forest model as taught by Guennemann-Gholizadeh in paragraph 6.
Regarding claim 5, Combination of Lohweg with Guennemann-Gholizadeh further teaches characterized in that an analysis data set with the first variable and the at least second variable is generated for the at least one detected anomaly (Lohweg, data sets of fuzzy "if-then" rules emulating human thinking which are designed to draw links between the printing press behavior represented by the inputted (and optionally pre-processed) operational parameters P1 to PN and several determined pattern classes which are each assigned a corresponding class of printing errors such that the sensed operational parameters might be so characterizing of a faulty or abnormal behavior of the printing press that it is possible to immediately draw conclusions that the detected faulty or abnormal behavior will lead to printing errors, paragraphs 90, 123).
(Guennemann-Gholizadeh also additionally teaches, monitoring using anomaly detection by evaluating classifications of measured data points originating from printing press comprising parameters like rotating speed and contact pressure to electrical power and anomaly detector outputting whether the measured data point is classified as whether anomaly is detected or not, paragraphs 27, 38).
Lohweg and Guennemann-Gholizadeh are combinable because they both are in the same field of endeavor dealing with evaluating measured data for finding printing anomalies/errors.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lohweg to incorporate the teachings of Guennemann-Gholizadeh to provide a data set comprising a first variable with a plurality of first data points and at least one second variable with a plurality of second data points for the benefit of enabling the user of an anomaly detector to effectively detect any print related anomalies and understand the reasons for the classification result, which was output by machine learning model, especially output by an isolation forest model as taught by Guennemann-Gholizadeh in paragraph 6.
Regarding claim 6, Combination of Lohweg with Guennemann-Gholizadeh further teaches characterized in that an AI-based software module is used for the anomaly detection (Lohweg, define a characteristic model of the faulty behavior of the printing and predict the occurrence of the printing errors by having fuzzy logic techniques which have been discussed in connection with the modelling and pattern classification issues, other approaches might be envisaged including modelling techniques making use of so-called neural networks, paragraphs 130, 91, 95).
(Guennemann-Gholizadeh also additionally teaches, anomaly detector 200 comprises a trained anomaly detection model here especially an isolation forest algorithm as an unsupervised ensemble-based anomaly detection method, paragraphs 36-38).
Lohweg and Guennemann-Gholizadeh are combinable because they both are in the same field of endeavor dealing with evaluating measured data for finding printing anomalies/errors.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lohweg to incorporate the teachings of Guennemann-Gholizadeh to provide a data set comprising a first variable with a plurality of first data points and at least one second variable with a plurality of second data points for the benefit of enabling the user of an anomaly detector to effectively detect any print related anomalies and understand the reasons for the classification result, which was output by machine learning model, especially output by an isolation forest model as taught by Guennemann-Gholizadeh in paragraph 6.
Regarding claim 7, Combination of Lohweg with Guennemann-Gholizadeh further teaches characterized in that an AI-based software module an autoencoder, such as, for example, a dense autoencoder or an LSTM autoencoder or an isolation forest is used (Guennemann-Gholizadeh also additionally teaches, anomaly detector 200 comprises a trained anomaly detection model here especially an isolation forest algorithm as an unsupervised ensemble-based anomaly detection method, paragraphs 36-38).
Lohweg and Guennemann-Gholizadeh are combinable because they both are in the same field of endeavor dealing with evaluating measured data for finding printing anomalies/errors.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lohweg to incorporate the teachings of Guennemann-Gholizadeh to provide a data set comprising a first variable with a plurality of first data points and at least one second variable with a plurality of second data points for the benefit of enabling the user of an anomaly detector to effectively detect any print related anomalies and understand the reasons for the classification result, which was output by machine learning model, especially output by an isolation forest model as taught by Guennemann-Gholizadeh in paragraph 6.
Regarding claim 8, Combination of Lohweg with Guennemann-Gholizadeh further teaches characterized in that for the anomaly detection, the data set is divided into a plurality of time intervals d (Lohweg, spectral transformation of signals requires that data sets first be divided into multiple time intervals, paragraphs 89, 85, and Guennemann-Gholizadeh, measured data point as a time series data point for example measuring parameters at subsequent points in time or time intervals to provide a good overview on the parameter value intervals which a data point needs to have to be classified as normal which is used in isolation forest algorithm for anomaly detection, paragraphs 25, 21, 27-28).
Lohweg and Guennemann-Gholizadeh are combinable because they both are in the same field of endeavor dealing with evaluating measured data for finding printing anomalies/errors.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lohweg to incorporate the teachings of Guennemann-Gholizadeh to provide a data set comprising a first variable with a plurality of first data points and at least one second variable with a plurality of second data points for the benefit of enabling the user of an anomaly detector to effectively detect any print related anomalies and understand the reasons for the classification result, which was output by machine learning model, especially output by an isolation forest model as taught by Guennemann-Gholizadeh in paragraph 6.
Regarding claim 9, Combination of Lohweg with Guennemann-Gholizadeh further teaches characterized in that the time intervals d have a first time period d1 and/or a second time period d2 and/or a third time period d3 (Guennemann-Gholizadeh, measured data point as a time series data point for example measuring parameters at subsequent points in time or time intervals such as one data point in a sequence of data points measured over time. Measured is used in this context not only as physically measured but can also be derived by one or several information characterizing the monitored apparatus at the considered point in time, paragraphs 24-25, 37).
Lohweg and Guennemann-Gholizadeh are combinable because they both are in the same field of endeavor dealing with evaluating measured data for finding printing anomalies/errors.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lohweg to incorporate the teachings of Guennemann-Gholizadeh to provide a data set comprising a first variable with a plurality of first data points and at least one second variable with a plurality of second data points for the benefit of enabling the user of an anomaly detector to effectively detect any print related anomalies and understand the reasons for the classification result, which was output by machine learning model, especially output by an isolation forest model as taught by Guennemann-Gholizadeh in paragraph 6.
Regarding claim 10, Combination of Lohweg with Guennemann-Gholizadeh further teaches characterized in that the time intervals d have a first number n1 and/or a second number n2 and/or a third number n3 of first data points (Lohweg, spectral transformation of signals requires that data sets first be divided into multiple time intervals, paragraphs 89, 85, and Guennemann-Gholizadeh, measured data point as a time series data point for example measuring parameters at subsequent points in time or time intervals such as one data point in a sequence of data points measured over time. Measured is used in this context not only as physically measured but can also be derived by one or several information characterizing the monitored apparatus at the considered point in time, paragraphs 24-25, 37).
Lohweg and Guennemann-Gholizadeh are combinable because they both are in the same field of endeavor dealing with evaluating measured data for finding printing anomalies/errors.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lohweg to incorporate the teachings of Guennemann-Gholizadeh to provide a data set comprising a first variable with a plurality of first data points and at least one second variable with a plurality of second data points for the benefit of enabling the user of an anomaly detector to effectively detect any print related anomalies and understand the reasons for the classification result, which was output by machine learning model, especially output by an isolation forest model as taught by Guennemann-Gholizadeh in paragraph 6.
Regarding claim 11, Combination of Lohweg with Guennemann-Gholizadeh further teaches characterized in that the first variable and/or the at least second variable are determined by means of sensors or are calculated (Lohweg, sensors 51a, 51b or sensors 52a, 52b for monitoring the behavior of printing press parameters such as vibrations or processing speed of the printing press, rotational speed of a cylinder or roller of the printing press or current drawn by an electric motor driving cylinders of the printing unit of the printing press or wiping pressure, etc., paragraphs 101, 54-59, 76-77). (Guennemann-Gholizadeh also teaches, measured date point is part of the sequence of the data points measured over time by at least one sensor applied to the apparatus, paragraph 24).
Lohweg and Guennemann-Gholizadeh are combinable because they both are in the same field of endeavor dealing with evaluating measured data for finding printing anomalies/errors.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lohweg to incorporate the teachings of Guennemann-Gholizadeh to provide a data set comprising a first variable with a plurality of first data points and at least one second variable with a plurality of second data points for the benefit of enabling the user of an anomaly detector to effectively detect any print related anomalies and understand the reasons for the classification result, which was output by machine learning model, especially output by an isolation forest model as taught by Guennemann-Gholizadeh in paragraph 6.
Regarding claim 12, Combination of Lohweg with Guennemann-Gholizadeh further teaches characterized in that a drive torque of a motor or a power consumption of a motor or a rotational speed of a motor or a web tension of a substrate to be processed or a lateral course of a substrate to be processed or a register deviation is used as first variable (Lohweg, sensors might be provided on the printing press in order to sense any combination of the following operational parameters: rotational speed of a cylinder or roller of the printing press; current drawn by an electric motor driving cylinders of the printing unit of the printing press; position or presence of the processed substrates in the printing press, paragraphs 56-64; Guennemann-Gholizadeh, evaluate classifications of measured data points originating from printing press comprising parameters like rotating speed and contact pressure to electrical power of electrical motor, paragraphs 27, 31).
Lohweg and Guennemann-Gholizadeh are combinable because they both are in the same field of endeavor dealing with evaluating measured data for finding printing anomalies/errors.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lohweg to incorporate the teachings of Guennemann-Gholizadeh to provide a data set comprising a first variable with a plurality of first data points and at least one second variable with a plurality of second data points for the benefit of enabling the user of an anomaly detector to effectively detect any print related anomalies and understand the reasons for the classification result, which was output by machine learning model, especially output by an isolation forest model as taught by Guennemann-Gholizadeh in paragraph 6.
Regarding claim 13, Combination of Lohweg with Guennemann-Gholizadeh further teaches characterized in that a production speed or a printing-on position of a printing cylinder or a maintenance process, such as the blanket washing or an activity of a roll changer or an activity of a downstream aggregate is used as second variable (Lohweg, sensors might be provided on the printing press in order to sense any combination of the following operational parameters: processing speed of the printing press, i.e. the speed at which the printing press processes the printed substrates; position or presence of the processed substrates in the printing press (this latter information is particularly useful in the context of printing presses comprising several printing plates and/or printing blankets as the printing behavior changes from one printing plate or blanket to the next, paragraph 56-64 with wet wiping/washing, paragraph 92).
Lohweg and Guennemann-Gholizadeh are combinable because they both are in the same field of endeavor dealing with evaluating measured data for finding printing anomalies/errors.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lohweg to incorporate the teachings of Guennemann-Gholizadeh to provide a data set comprising a first variable with a plurality of first data points and at least one second variable with a plurality of second data points for the benefit of enabling the user of an anomaly detector to effectively detect any print related anomalies and understand the reasons for the classification result, which was output by machine learning model, especially output by an isolation forest model as taught by Guennemann-Gholizadeh in paragraph 6.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Frankenberger, US 2002/0096077
Ming-Shong et al., GB 2283940
Takano et al., US 2025/0216459
Moriyama, US 2025/0190297
Maeda et al., US 2014/0195184
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/PAWAN DHINGRA/Examiner, Art Unit 2683
/ABDERRAHIM MEROUAN/Supervisory Patent Examiner, Art Unit 2683
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