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 1-15 are pending.
Information Disclosure Statement
The Information Disclosure Statements filed on 05/02/2025, 05/06/2025 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
Claims 1 and 10 are objected to because of the following informalities: delete the typo “(20)” in line 9 of claim 1 and delete the typo “(1)” in line 2 of claim 10. 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 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Lohweg et al., US 2008/0295724 in view of Bierweiler et al., EP 4060441A1.
Regarding claim 1, Lohweg discloses a computer-implemented method for detecting and analyzing data of a printing machine (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) during or after a production for the evaluation and for the productivity optimization (inspection of the quality of printed substrates which are processed on printing presses with performing in-line inspection of printed sheets for detection of occurrence of printing errors on printed substrates during processing thereof on a printing press, paragraph 1),
wherein a defined number of printed products with at least one specified target printing technology value are produced by the printing machine during at least one print job (printed products such as banknotes are printed with technology for printing of security documents during print job while detecting occurrence of printing errors on printed substrates for the production of security documents, paragraph 1, and while the printing press described hereinafter is adapted to process substrates in the form of successive sheets, the invention is also applicable to web-fed printing presses or offset printing presses, paragraph 41),
wherein one or several actual machine data and/or one or several actual printing technology values and/or one or several production data and/or one or several machine interference signals and/or at least one control signal and/or at least one operating signal are detected and/or stored over a period of time by a computing device across at least a portion of the at least one print job (actual machine data such as processing speed of the intaglio printing press with actual printing technology such as web-fed printing (webs), and several examples of production data, signals as discussed in paragraphs 67-73),
characterized in that the one or several actual machine data during the production of printed products of the at least one print job as one or several actual production data (performing an in-line analysis of the actual machine data of the printing press during the production or processing of the printed sheets of at least one print job to monitor the behavior of the printing press during processing of the printed substrates to determine actual production data in terms 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 to draw links between the printing press behavior represented by the inputted operational parameters and several determined pattern classes which are each assigned a corresponding class of printing errors or whether the monitored operational parameters are indicative of any one of the modelled characteristic behaviors of the printing press, paragraphs 22, 54, 90),
and one or several standard machine values are formed as a function of the production data (analysis of the behavior of the printing press is preferably performed by modelling characteristic behaviors of the printing press using appropriately located sensors to sense operational parameters of the functional components of the printing press such as defined behaviors (standard or normal values) of the printing press that lead or are likely to lead to a function of good printing quality production data, paragraphs 18-20)
and/or for comparable production data by using artificial intelligence from the actual production data for at least one time span (by utilizing fuzzy logic (fuzzy logic using artificial intelligence, paragraph 130) on the monitored operational parameters 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 while 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).
Lohweg fails to explicitly disclose characterized in that one or several actual machine data are stored and/or saved during production of job as one or several actual production data, and/or for comparable production data by using artificial intelligence from the actual production data for at least one time span of a defined minimum duration of an at least disruption-free production.
However, Bierweiler teaches characterized in that one or several actual machine data are stored and/or saved during production of job as one or several actual production data (anomalies are detected during operation of a technical installation for dynamic processes such that a separate self-organizing map (SOM) is trained per process step or batch using historical data sets which characterize fault-free operation to train neural network. For this map, the symptom threshold values (i.e., actual or standard machine data) and their tolerances are subsequently determined and stored for each neuron, paragraphs 20-21),
and one or several standard machine values are formed as a function of production data (symptom threshold value expressed by a vector of the dimension of the neuron relevant of the SOM to determine whether periods/process steps/batches are to be rated as good or as poor as standard, paragraphs 22-25, 39),
and/or for comparable production data by using artificial intelligence from the actual production data for at least one time span of a defined minimum duration of an at least disruption-free production (SOM is a type of artificial neural network that is learned by an unsupervised learning process to generate a two-dimensional, discretized representation of the input space of the learned data, referred to as a map, wherein, a plurality of data sets characterizing a fault-free operation of the system are stored for each node of the self-organizing map in a data memory such that a separate self-organizing map (SOM) is trained per process step or batch using historical data sets which characterize fault-free operation, paragraphs 10-11, 21, for at least one timestamp of a minimum duration such as it is checked whether the distance of a data vector from the boiling neuron lies within the symptom threshold value of all neurons from the stored curves at the current point in time and performing time stamps comparison, paragraphs 24-27, 39).
Lohweg and Bierweiler are combinable because they both are in the same field of endeavor dealing with detecting anomalies/errors during a production process.
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 Bierweiler to provide storing of actual machine data for the benefit of efficiently detecting anomalies during operation so that it detects reliably and early deviations from normal operation while requiring less computing power and making the process faster as taught by Bierweiler in paragraphs 17, 32, 38.
Regarding claim 2, Combination of Lohweg with Bierweiler further teaches characterized in that one or several actual machine data are stored and/or saved as one or several actual anomaly data, for which deviations are formed for the one or several standard machine values (Bierweiler, symptom threshold values (i.e., actual or standard machine data) and their tolerances are subsequently determined and stored for each neuron such that for each neuron, the maximum and minimum deviations are stored. In addition, these tolerances determined can be extended by applying a factor or the standard deviation to them for detecting anomalies during operation, which is designed such that at least one unit is provided for training SOMs, for storing relevant data, and one unit is provided for evaluating current data sets with the aid of the previously trained SOMs, paragraphs 21, 27, 33, 43).
Lohweg and Bierweiler are combinable because they both are in the same field of endeavor dealing with detecting anomalies/errors during a production process.
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 Bierweiler to provide storing of actual machine data for the benefit of efficiently detecting anomalies during operation so that it detects reliably and early deviations from normal operation while requiring less computing power and making the process faster as taught by Bierweiler in paragraphs 17, 32, 38.
Regarding claim 3, Combination of Lohweg with Bierweiler further teaches characterized in that those actual machine data, for which an anomaly is shown compared to the previous and/or subsequent actual machine data, are stored and/or saved as actual anomaly data (Bierweiler, SOM is trained per process step or batch using historical data sets which characterize fault-free operation such that the symptom threshold values and their tolerances are subsequently determined and stored for each neuron for different periods of time of the process steps in the historical and current data sets are taken into account during the anomaly detection and learning of the maps, threshold values, seven neuron profiles and further variables are determined in the learning phase and stored in the memory S for further use, wherein, for instance, for detecting anomalies during operation, one unit is provided for training SOMs and storing relevant data, and one unit is provided for evaluating current data sets with the aid of the previously trained SOMs data sets of the current time stamp comparing it to the previously determined neuron, paragraphs 21, 26-27, 41-43, 55).
Lohweg and Bierweiler are combinable because they both are in the same field of endeavor dealing with detecting anomalies/errors during a production process.
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 Bierweiler to provide storing of actual machine data for the benefit of efficiently detecting anomalies during operation so that it detects reliably and early deviations from normal operation while requiring less computing power and making the process faster as taught by Bierweiler in paragraphs 17, 32, 38.
Regarding claim 4, Combination of Lohweg with Bierweiler further teaches characterized in that those actual machine data, in the case of which at least one specified target machine value is exceeded, are stored and/or saved as actual anomaly data (Bierweiler, SOM is trained per process step or batch using historical data sets which characterize fault-free operation such that the symptom threshold values and their tolerances are subsequently determined and stored for each neuron for different periods of time of the process steps in the historical and current data sets are taken into account during the anomaly detection and learning of the maps, threshold values, seven neuron profiles and further variables are determined in the learning phase and stored in the memory S for further use such that the current time stamp is determined by forming differences from the data sets of the current time stamp to the previously determined neuron to be compared, taking into account only the differences of process variables that exceed a predetermined threshold value, paragraphs 21, 27, 41, 55).
Lohweg and Bierweiler are combinable because they both are in the same field of endeavor dealing with detecting anomalies/errors during a production process.
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 Bierweiler to provide storing of actual machine data for the benefit of efficiently detecting anomalies during operation so that it detects reliably and early deviations from normal operation while requiring less computing power and making the process faster as taught by Bierweiler in paragraphs 17, 32, 38.
Regarding claim 5, Combination of Lohweg with Bierweiler further teaches characterized in that those actual machine data, in the case of which at least one specified threshold printing technology (plant/device of Bierweiler could be the printing press of Lohweg) value is exceeded, are stored and/or saved as one or several actual anomaly data (Bierweiler, SOM is trained per process step or batch using historical data sets which characterize fault-free operation such that the symptom threshold values and their tolerances are subsequently determined and stored for each neuron for different periods of time of the process steps in the historical and current data sets are taken into account during the anomaly detection and learning of the maps, threshold values, seven neuron profiles and further variables are determined in the learning phase and stored in the memory S for further use such that the current time stamp is determined by forming differences from the data sets of the current time stamp to the previously determined neuron to be compared, taking into account only the differences of process variables that exceed a predetermined threshold value, paragraphs 21, 27, 41, 55).
Lohweg and Bierweiler are combinable because they both are in the same field of endeavor dealing with detecting anomalies/errors during a production process.
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 Bierweiler to provide storing of actual machine data for the benefit of efficiently detecting anomalies during operation so that it detects reliably and early deviations from normal operation while requiring less computing power and making the process faster as taught by Bierweiler in paragraphs 17, 32, 38.
Regarding claim 6, Combination of Lohweg with Bierweiler further teaches characterized in that the computing device additionally detects and/or saves one or several machine interference signals (Lohweg, sensor outputted signals representative of vibrations or noises produced by the printing press are detected, paragraphs 89, 112, 120 and Bierweiler, SOM consists of neurons, where each neuron corresponds to a dimension of the vector corresponding to the number n of input signals such as measurable interference variables/signals for detecting anomalies during operation, which is designed such that at least one unit is provided for training SOMs, for storing relevant data, paragraphs 10, 43).
Lohweg and Bierweiler are combinable because they both are in the same field of endeavor dealing with detecting anomalies/errors during a production process.
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 Bierweiler to provide storing of actual machine data for the benefit of efficiently detecting anomalies during operation so that it detects reliably and early deviations from normal operation while requiring less computing power and making the process faster as taught by Bierweiler in paragraphs 17, 32, 38.
Regarding claim 7, Combination of Lohweg with Bierweiler further teaches characterized in that the one or several actual machine data, in the case of which at least one machine interference signal is generated in the temporal advance and/or in the temporal follow-up, are stored and/or saved as one or several actual anomaly data (Bierweiler, for all time stamps, all permissible temporal profiles of the seven-digit neurons are determined and stored per process step or batch taking into account the neuron similarity such that neuron to which the distance of the good data set is minimal is the borrow neuron. If this minimum distance measure is determined time stamp for time stamps, a temporal sequence of the segregation neurons arises, the so-called segregation neuron course or also reference path and to fulfil the condition of neuron similarity, the neuron profiles are determined and stored separately per SOM and thus for each process step or batch, paragraphs 24-25).
Lohweg and Bierweiler are combinable because they both are in the same field of endeavor dealing with detecting anomalies/errors during a production process.
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 Bierweiler to provide storing of actual machine data for the benefit of efficiently detecting anomalies during operation so that it detects reliably and early deviations from normal operation while requiring less computing power and making the process faster as taught by Bierweiler in paragraphs 17, 32, 38.
Regarding claim 8, Combination of Lohweg with Bierweiler further teaches characterized in that at least one error time period, in the case of which one or several actual anomaly data lie in the range of the corresponding one or several standard machine values, is determined and/or saved (Bierweiler, it is assessed over a predefined time period (e.g. 10 time stamps) in how many time steps (here time stamps) an anomaly was detected such that in the time range 02:13:30 to about 02:15:30, the presence of an anomaly is indicated, the signal INPUT_ 1 indicates a normal state in this time range, the signal INPUT_ 2, on the other hand, indicates an overshoot twice in this time range, the signals sys 1_pos and sys 2_neg, on the other hand, each indicate a negative deviation, i.e. an undershoot of the normal value, in part in this time range, paragraphs 40, 61-62).
Lohweg and Bierweiler are combinable because they both are in the same field of endeavor dealing with detecting anomalies/errors during a production process.
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 Bierweiler to provide storing of actual machine data for the benefit of efficiently detecting anomalies during operation so that it detects reliably and early deviations from normal operation while requiring less computing power and making the process faster as taught by Bierweiler in paragraphs 17, 32, 38.
Regarding claim 9, Combination of Lohweg with Bierweiler further teaches characterized in that one or several actual anomaly data and/or the at least one error time period are analyzed with regard to the occurrence of at least one operating signal and/or at least one anomaly and/or at least one control signal (Lohweg, 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).
(Bierweiler also teaches, if an anomaly is detected in the plurality of time steps, then all 10 time steps are evaluated as an anomaly. The time tolerance for anomaly detection would be 10 time stamps here. For example, a time tolerance of 10 time stamps can also be used in the determination of symptoms. In visualizing an anomaly or symptom, the anomaly or symptom can be suppressed until the set or predefined time tolerance is completed, paragraphs 40-41).
Lohweg and Bierweiler are combinable because they both are in the same field of endeavor dealing with detecting anomalies/errors during a production process.
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 Bierweiler to provide storing of actual machine data for the benefit of efficiently detecting anomalies during operation so that it detects reliably and early deviations from normal operation while requiring less computing power and making the process faster as taught by Bierweiler in paragraphs 17, 32, 38.
Regarding claim 10, Combination of Lohweg with Bierweiler further teaches characterized in that at least one data set of one or several actual machine data and/or at least one operating signal and/or at least one control signal is determined and/or output and/or visualized as potential cause for one or several actual anomaly data and/or for at least one error time period and/or for at least one machine interference signal (Lohweg, 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) (Bierweiler also teaches, if an anomaly is detected in the plurality of time steps, then all 10 time steps are evaluated as an anomaly. The time tolerance for anomaly detection would be 10 time stamps here. For example, a time tolerance of 10 time stamps can also be used in the determination of symptoms, paragraphs 40-41 and measurable interference variables/signals for detecting anomalies, paragraph 10).
Lohweg and Bierweiler are combinable because they both are in the same field of endeavor dealing with detecting anomalies/errors during a production process.
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 Bierweiler to provide storing of actual machine data for the benefit of efficiently detecting anomalies during operation so that it detects reliably and early deviations from normal operation while requiring less computing power and making the process faster as taught by Bierweiler in paragraphs 17, 32, 38.
Regarding claim 11, Combination of Lohweg with Bierweiler further teaches characterized in that a plausibility check (prediction or likely to occur) of the at least one data set is carried out (Lohweg, 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 or predict the likely occurrence of printing errors with issuance of an early warning and/or indication of likely cause of the likely occurrence of printing errors upon determination of a faulty or abnormal behavior of the printing press, paragraphs 94, 123-126).
Lohweg and Bierweiler are combinable because they both are in the same field of endeavor dealing with detecting anomalies/errors during a production process.
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 Bierweiler to provide storing of actual machine data for the benefit of efficiently detecting anomalies during operation so that it detects reliably and early deviations from normal operation while requiring less computing power and making the process faster as taught by Bierweiler in paragraphs 17, 32, 38.
Regarding claim 12, Combination of Lohweg with Bierweiler further teaches characterized in that the plausibility check is used (Lohweg, prediction of the likely occurrence of printing errors with issuance of an early warning and/or indication of likely cause of the likely occurrence of printing errors upon determination of a faulty or abnormal behavior of the printing press, paragraphs 94, 123-126) to optimize the used artificial intelligence (Lohweg, while fuzzy logic techniques 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. One difference between the two methods is that a fuzzy pattern classifier can be set up by a learning process and a skilled designer (the so-called "expert") based on experimental data and knowledge of the involved processes, whereas neural networks are based on learning processes only. The expert is able to tune the system with the help of "linguistic modifiers, paragraph 130).
Lohweg and Bierweiler are combinable because they both are in the same field of endeavor dealing with detecting anomalies/errors during a production process.
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 Bierweiler to provide storing of actual machine data for the benefit of efficiently detecting anomalies during operation so that it detects reliably and early deviations from normal operation while requiring less computing power and making the process faster as taught by Bierweiler in paragraphs 17, 32, 38.
Regarding claim 13, Combination of Lohweg with Bierweiler further teaches characterized in that the at least one standard machine value is divided with respect to the production data (Bierweiler, data sets comprising threshold values with process steps data, tolerances and temporal profiles) for different production phases and production states (Bierweiler, learning phase and evaluation phase, paragraph 20) and is stored in a memory device and/or output by the latter (Bierweiler, in a learning phase, a plurality of data sets characterizing a fault-free operation of the system are stored for each node of the self-organizing map in a data memory such that a separate self-organizing map (SOM) is trained per process step or batch using historical data sets which characterize fault-free operation of the plant. For this map, the symptom threshold values and their tolerances are subsequently determined and stored for each neuron. In the next step of the learning phase, for all time stamps, all permissible temporal profiles of the seven-digit neurons are determined and stored per process step or batch, taking into account the neuron similarity and in contrast to the learning phase with current data sets to be monitored, the temporal profiles of the segregation neurons are determined in the evaluation phase, where crop data obtained after the learning phase are used in evaluation phase for further improving the threshold values and for determining the segregation neuron characteristics by evaluating the current data records of a process step or batch process with the aid of the self-organizing map trained in the learning phase with learning of the maps, threshold values, as determined in the learning phase being stored in the memory as well the time tolerances with respect to the time stamps in the evaluation phase during the anomaly detection are also permanently stored in the memory, paragraphs 21, 24, 26, 38, 40, 53, 55).
Lohweg and Bierweiler are combinable because they both are in the same field of endeavor dealing with detecting anomalies/errors during a production process.
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 Bierweiler to provide storing of actual machine data for the benefit of efficiently detecting anomalies during operation so that it detects reliably and early deviations from normal operation while requiring less computing power and making the process faster as taught by Bierweiler in paragraphs 17, 32, 38.
Regarding claim 14, Combination of Lohweg with Bierweiler further teaches characterized in that with respect to the production data (Lohweg, sensor data representative of characteristic faulty/abnormal behaviors of the printing press and image data, paragraph 127), the standard machine values (Lohweg, defined behaviors (standard or normal values) of the printing press that lead or are likely to lead to a function of good printing quality production data, paragraphs 18-20) are used as one or several preset values for print jobs with identical or comparable production data (Lohweg, the in-line analysis of the behavior of the printing press is based on fuzzy pattern classification is to define the common features or properties among a set of patterns and classify them into different predetermined classes according to a determined classification model. More precisely, the idea is to define a classification model that permits classification of a given job for the given printing press with identical or comparable features to be classified into different classes of behaviors corresponding to specific classes of printing errors, paragraph 86).
Lohweg and Bierweiler are combinable because they both are in the same field of endeavor dealing with detecting anomalies/errors during a production process.
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 Bierweiler to provide storing of actual machine data for the benefit of efficiently detecting anomalies during operation so that it detects reliably and early deviations from normal operation while requiring less computing power and making the process faster as taught by Bierweiler in paragraphs 17, 32, 38.
Regarding claim 15, Combination of Lohweg with Bierweiler further teaches characterized in that a warning message is generated by the computing device in the event of the occurrence of one or several actual anomaly data and/or an intervention in the control of the printing machine takes place when one or several actual anomaly data exceed at least one anomaly threshold value (Lohweg, prediction of the likely occurrence of printing errors with issuance of an early warning and/or indication of likely cause of the likely occurrence of printing errors upon determination of a faulty or abnormal behavior of the printing press, paragraphs 94, 123-126).
Lohweg and Bierweiler are combinable because they both are in the same field of endeavor dealing with detecting anomalies/errors during a production process.
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 Bierweiler to provide storing of actual machine data for the benefit of efficiently detecting anomalies during operation so that it detects reliably and early deviations from normal operation while requiring less computing power and making the process faster as taught by Bierweiler in paragraphs 17, 32, 38.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Arifuku, US 2020/0104078
Geibelsoder et al., US 2023/0376024
Weaver et al., US 2016/0098234
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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