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 .
2. This action is in response to the amendment filed on 01/15/2026. Claims 1-20 are pending in the case. This action is Final.
Applicant Response
3. In Applicant’s response dated 01/15/2026, Applicant amended Claims 1-2, 6-8, and 13-20 and argued against all objections and rejections previously set forth in the Office Action dated 10/16/2025.
Double Patenting
4. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
5. Claims 1-20 of the instant application are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-20 of Co-pending Application No. 18/116, 932 ( patent Number not issued yet). This is a provisional nonstatutory double patenting rejection.
Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the instant application can be met by claims 1-20 of co-pending application 18/116,932 as shown below.
Instant Application
18/116, 938
Co-Pending Application Number :
18/116,932 ( patent Number not Issued Yet)
1. A printhead maintenance supervisor, comprising: at least one processor and memory; the at least one processor is configured to cause the printhead maintenance supervisor at least to:
identify deployed printhead data for a plurality of deployed printheads;
maintain data logs corresponding with the deployed printheads, wherein for each deployed printhead of the deployed printheads, the at least one processor is further configured to cause the printhead maintenance supervisor at least to perform a process of:
operating a first neural network trained to generate deployment anomaly scores by inputting the deployed printhead data for the deployed printhead into the first neural network over multiple time units to produce the deployment anomaly scores for the deployed printhead for the deployed printhead;
operating a recurrent second neural network trained to scale the deployment anomaly score generated by the first neural network to produce scaled anomaly scores for the deployed printhead;
storing the scaled anomaly score in a data log corresponding with the deployed printhead; and repeating the process over a time period to store scaled anomaly scores for the deployed printhead over the time period in the data log, wherein the at least one processor is further configured to cause the printhead maintenance supervisor at least to:
provide a replacement recommendation for one or more of the deployed printheads based on the scaled anomaly scores for the deployed printheads stored in the data logs.
4. (Original) The printhead maintenance supervisor of claim 1, wherein the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
identify deployed printhead data for a plurality of deployed printheads; operate the first neural network to generate deployment anomaly scores for the deployed printheads;
operate the recurrent second neural network to scale the deployment anomaly scores generated by the first neural network to produce the scaled anomaly scores for the deployed printheads; and
provide a replacement recommendation for one or more of the deployed printheads based on the scaled anomaly scores for the deployed printheads.
2. The printhead maintenance supervisor of claim 1, wherein the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
store the replacement recommendation in a data log corresponding with the deployed printhead.
5. (Original) The printhead maintenance supervisor of claim 4, wherein the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
store the replacement recommendation in a data log corresponding with a deployed printhead.
3. The printhead maintenance supervisor of claim 1, wherein the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
transmit a message indicating the replacement recommendation via a network interface.
6. (Original) The printhead maintenance supervisor of claim 4, wherein the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
transmit a message indicating the replacement recommendation via a network interface.
4. The printhead maintenance supervisor of claim 1, wherein the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
display the replacement recommendation via a user interface.
7. (Original) The printhead maintenance supervisor of claim 4, wherein the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
display the replacement recommendation via a user interface.
5. The printhead maintenance supervisor of claim I, wherein the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
perform a comparison of one or more of the scaled anomaly scores for each of the deployed printheads to a threshold value;
determine whether the one or more of the deployed printheads are experiencing a failure condition based on the comparison of the scaled anomaly scores; and
provide the replacement recommendation for the one or more of the deployed printheads when the failure condition is determined.
8. (Original) The printhead maintenance supervisor of claim 4, wherein the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
perform a comparison of one or more of the scaled anomaly scores for each of the deployed printheads to a threshold value;
determine whether the one or more of the deployed printheads are experiencing a failure condition based on the comparison of the scaled anomaly scores; and
provide the replacement recommendation for the one or more of the deployed printheads when the failure condition is determined.
6. The printhead maintenance supervisor of claim 1, wherein to operate the first neural network,
inputting first input samples of the deployed printhead data into the first neural network to generate the deployment anomaly scores for the deployed printhead over a first plurality of the time units, wherein the first neural network is trained on conforming printhead data.
9. (Original) The printhead maintenance supervisor of claim 4, wherein to operate the first neural network, the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
input first input samples of the deployed printhead data into the first neural network to generate the deployment anomaly scores for the deployed printheads over a second plurality of time units.
7. The printhead maintenance supervisor of claim 6,
wherein to operate the recurrent second neural network,
formating second input samples for the deployed printheads, wherein each of the second input samples comprises a first time-series of deployment data objects over a number of consecutive time units for a deployed printhead, and wherein each of the deployment data objects includes a deployment anomaly score generated by the first neural network for the deployed printhead, and at least a subset of the deployed printhead data for the deployed printhead; and
inputting the second input samples for the deployed printheads into the recurrent second neural network to output the scaled anomaly scores for the deployed printhead, wherein the recurrent second neural network is trained based on a pool of training printheads.
10. (Original) The printhead maintenance supervisor of claim 9,
wherein to operate the recurrent second neural network, the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
format second input samples for the deployed printheads, wherein each of the second input samples comprises a second time-series of deployment data objects over the number of consecutive time units for a deployed printhead, and wherein each of the deployment data objects includes a deployment anomaly score generated by the first neural network for the deployed printhead, and at least a subset of the deployed printhead data for the deployed printhead; and
input the second input samples for the deployed printheads into the recurrent second neural network to output the scaled anomaly scores for the deployed printheads.
8. The printhead maintenance supervisor of claim 7, wherein the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
train the first neural network using an unsupervised learning algorithm based on first training samples of the conforming printhead data from a pool of conforming printheads;
generate a training dataset for the recurrent second neural network by:
identifying training printhead data for the pool of training printheads;
inputting second training samples of the training printhead data into the first neural network to generate training anomaly scores for the training printheads over a second plurality of time units; and
formatting third training samples for the training printheads, wherein each of the third training samples comprises a second time-series of training data objects over the number of consecutive time units for a training printhead, and a label for the training printhead, and wherein each of the training data objects includes a training anomaly score generated by the first neural network for the training printhead, and at least a subset of the training printhead data for the training printhead; and
train the recurrent second neural network using a supervised learning algorithm based on the training dataset.
1. (Original) A printhead maintenance supervisor, comprising: at least one processor and memory; the at least one processor is configured to cause the printhead maintenance supervisor at least to:
train a first neural network to generate anomaly scores for printheads using an unsupervised learning algorithm based on first training samples of conforming printhead data from a pool of conforming printheads;
generate a training dataset for a recurrent second neural network by:
identifying training printhead data for a pool of training printheads;
inputting second training samples of the training printhead data into the first neural network to generate training anomaly scores for the training printheads over a first plurality of time units; and
formatting third training samples for the training printheads, wherein each of the third training samples comprises a first time-series of training data objects over a number of consecutive time units for a training printhead, and a label for the training printhead, and wherein each of the training data objects includes a training anomaly score generated by the first neural network for the training printhead, and at least a subset of the training printhead data for the training printhead; and
train the recurrent second neural network to generate scaled anomaly scores for printheads using a supervised learning algorithm based on the training dataset.
9. The printhead maintenance supervisor of claim 8, wherein:
the label represents a printhead condition of a corresponding training printhead.
2. (Original) The printhead maintenance supervisor of claim 1, wherein:
the label represents a printhead condition of a corresponding training printhead.
10. The printhead maintenance supervisor of claim 7, wherein:
the subset of the deployed printhead data in the second input samples comprises a nozzle failure value indicating a number of failed nozzles.
3. (Original) The printhead maintenance supervisor of claim 1, wherein:
the subset of the training printhead data in the third training samples comprises a nozzle failure value indicating a number of failed nozzles.
11. The printhead maintenance supervisor of claim 1, wherein:
the first neural network comprises an autoencoder; and the recurrent second neural network comprises a Long Short-Term Memory (LSTM) neural network.
11. (Original) The printhead maintenance supervisor of claim 1, wherein:
the first neural network comprises an autoencoder; and the recurrent second neural network comprises a Long Short-Term Memory (LSTM) neural network.
12. A cloud computing platform comprising the printhead maintenance supervisor of claim 1.
12. (Original) A cloud computing platform comprising the printhead maintenance supervisor of claim 1.
13. A printhead maintenance supervisor, comprising: at least one processor and memory; the at least one processor is configured to cause the printhead maintenance supervisor at least to:
identify deployed printhead data for a plurality of deployed printheads;
maintain data logs corresponding with the deployed printheads, wherein for each deployed printhead of the deployed printheads, the at least one processor is further configured to cause the printhead maintenance supervisor at least to perform a process of:
operating a first neural network trained to generate deployment anomaly scores by inputting the deployed printhead data for the deployed printhead into the first neural network over multiple time units to produce the deployment anomaly scores for the deployed printhead for the deployed printhead;
operating a recurrent second neural network trained to scale the deployment anomaly score generated by the first neural network to produce scaled anomaly scores for the deployed printhead;
storing the scaled anomaly score in a data log corresponding with the deployed printhead; and repeating the process over a time period to store scaled anomaly scores for the deployed printhead over the time period in the data log, wherein the at least one processor is further configured to cause the printhead maintenance supervisor at least to:
provide a replacement recommendation for one or more of the deployed printheads based on the scaled anomaly scores for the deployed printheads stored in the data logs.
4. (Original) The printhead maintenance supervisor of claim 1, wherein the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
identify deployed printhead data for a plurality of deployed printheads; operate the first neural network to generate deployment anomaly scores for the deployed printheads;
operate the recurrent second neural network to scale the deployment anomaly scores generated by the first neural network to produce the scaled anomaly scores for the deployed printheads; and
provide a replacement recommendation for one or more of the deployed printheads based on the scaled anomaly scores for the deployed printheads.
14. The method of claim 13, wherein the providing of the replacement recommendation comprises:
storing the replacement recommendation in a data log corresponding with a deployed printhead.
5. (Original) The printhead maintenance supervisor of claim 4, wherein the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
store the replacement recommendation in a data log corresponding with a deployed printhead.
15. The method of claim 13, wherein providing the replacement recommendation comprises:
transmitting a message indicating the replacement recommendation via a network interface
6. (Original) The printhead maintenance supervisor of claim 4, wherein the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
transmit a message indicating the replacement recommendation via a network interface.
16. The method of claim 13, wherein providing the replacement recommendation comprises:
displaying the replacement recommendation via a user interface.
7. (Original) The printhead maintenance supervisor of claim 4, wherein the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
display the replacement recommendation via a user interface.
17. The method of claim 13, further comprising:
performing a comparison of one or more of the scaled anomaly scores for each of the deployed printheads to a threshold value; and
determining whether the one or more of the deployed printheads are experiencing a failure condition based on the comparison of the scaled anomaly scores;
wherein providing the replacement recommendation comprises providing the replacement recommendation for the one or more of the deployed printheads when the failure condition is determined.
8. (Original) The printhead maintenance supervisor of claim 4, wherein the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
perform a comparison of one or more of the scaled anomaly scores for each of the deployed printheads to a threshold value;
determine whether the one or more of the deployed printheads are experiencing a failure condition based on the comparison of the scaled anomaly scores; and
provide the replacement recommendation for the one or more of the deployed printheads when the failure condition is determined.
18. The method of claim 13, wherein operating the first neural network comprises: inputting first input samples of the deployed printhead data for the deployed printhead into the first neural network to generate the deployment anomaly scores for the deployed printheads over a plurality of time units, wherein the first neural network is trained on conforming printhead data.
9. (Original) The printhead maintenance supervisor of claim 4, wherein to operate the first neural network, the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
input first input samples of the deployed printhead data into the first neural network to generate the deployment anomaly scores for the deployed printheads over a second plurality of time units.
19. The method of claim 18, wherein operating the recurrent second neural network comprises:
formatting second input samples for the deployed printheads, wherein each of the second input samples comprises a first time-series of deployment data objects over a number of consecutive time units for a deployed printhead, and wherein each of the deployment data objects includes a deployment anomaly score generated by the first neural network for the deployed printhead, and at least a subset of the deployed printhead data for the deployed printhead; and
inputting the second input samples for the deployed printheads into the recurrent second neural network to output the scaled anomaly scores for the deployed printheads, wherein the recurrent second neural network is trained based on a pool of training printheads.
10. (Original) The printhead maintenance supervisor of claim 9,
wherein to operate the recurrent second neural network, the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
format second input samples for the deployed printheads, wherein each of the second input samples comprises a second time-series of deployment data objects over the number of consecutive time units for a deployed printhead, and wherein each of the deployment data objects includes a deployment anomaly score generated by the first neural network for the deployed printhead, and at least a subset of the deployed printhead data for the deployed printhead; and
input the second input samples for the deployed printheads into the recurrent second neural network to output the scaled anomaly scores for the deployed printheads.
20. A non-transitory computer readable medium embodying programmed instructions executed by a processor, wherein the instructions direct the processor to implement a method comprising:
identify deployed printhead data for a plurality of deployed printheads;
maintain data logs corresponding with the deployed printheads, wherein for each deployed printhead of the deployed printheads, the at least one processor is further configured to cause the printhead maintenance supervisor at least to perform a process of:
operating a first neural network trained to generate deployment anomaly scores by inputting the deployed printhead data for the deployed printhead into the first neural network over multiple time units to produce the deployment anomaly scores for the deployed printhead for the deployed printhead;
operating a recurrent second neural network trained to scale the deployment anomaly score generated by the first neural network to produce scaled anomaly scores for the deployed printhead;
storing the scaled anomaly score in a data log corresponding with the deployed printhead; and repeating the process over a time period to store scaled anomaly scores for the deployed printhead over the time period in the data log, wherein the at least one processor is further configured to cause the printhead maintenance supervisor at least to:
provide a replacement recommendation for one or more of the deployed printheads based on the scaled anomaly scores for the deployed printheads stored in the data logs.
4. (Original) The printhead maintenance supervisor of claim 1, wherein the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
identify deployed printhead data for a plurality of deployed printheads;
operate the first neural network to generate deployment anomaly scores for the deployed printheads;
operate the recurrent second neural network to scale the deployment anomaly scores generated by the first neural network to produce the scaled anomaly scores for the deployed printheads; and
provide a replacement recommendation for one or more of the deployed printheads based on the scaled anomaly scores for the deployed printheads.
Therefore, claims 1-20 are rejected on the ground of nonstatutory double patenting over claim 1-20 of copending application 18/116, 932 .
Claim Rejections - 35 USC § 101
6. 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.
Claim 20 is rejected under 35 U.S.C. 101 because claims 20 is directed to a “computer readable storage medium” that could be non-transitory medium or transitory medium since the specification is silent with respect to which the “computer readable storage medium” includes or excludes. The specification in paragraph [0050] states ambiguous definition of the storage medium as “processor or values obtained from arithmetic processing performed by the processor may be stored in a transitory and/or non-transitory computer-readable medium.” As such, in a broadest reasonable interpretation, the claimed medium can include signal per se which is non-statutory. Examiner recommends that the claims be amended to “non-transitory computer readable storage medium” in order to overcome these 101 rejections.
Appropriate correction is required.
Examiner Comments
9. 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.
Claim Rejections - 35 USC § 103
10. 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.
11. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over REDDY (Pat. No.: US 20240157652 B1, Pub. Date 2024-05-16) in view of SATO (Pub. No US 20200361210 A1, Pub. Date 2020-11-19.) in further view of MALHOTRA (US 20200012918A1, 2020-01-09)
REDDY teaches a printhead maintenance supervisor (see REDDY: Fig.2, illustrating “a system 200 for triangulation-based anomaly detection in a print job performed by a 3D printer 202”), comprising: at least one processor and memory; the at least one processor (see REDDY: Fig.4, [0031], describing memory 212, processor 104), is configured to cause the printhead maintenance supervisor at least to:
identify deployed printhead data for … deployed printhead (see REDDY: Fig.3, [0061], “block 302, a data set may be obtained from the 3D printer. In an example, a processor (not shown) of the system may obtain the data set from the 3D printer. The data set pertains to a sequence of layers printed by the 3D printer. … In addition, real-time data pertaining to a layer being printed by the 3D printer may be obtained from the 3D printer. In an example implementation, the prediction engine may obtain the data set and the real-time data from the 3D printer.”)
maintain data logs corresponding with the deployed printheads, wherein for each deployed printhead of the deployed printheads, the at least one processor is further configured to cause the printhead maintenance supervisor (see REDDY: Fig.4, [0064], “At block 404, the method 400 may include extracting a layer surface data pertaining to a sequence of layers, printed by the 3D printer. In an example, the processor may employ some image processing techniques to extract layer surface data pertaining to each layer during layer-by-layer printing.”) at least to perform a process of:
operating a first neural network trained to generate deployment anomaly scores (see REDDY: Fig.3, [0062], “real-time data of the layer being printed, the encoder-decoder based model and the time-series decomposition model may generate a respective predicted anomaly score for the layer being printed.”), by inputting the deployed printhead data for the deployed printhead into the first neural network over multiple time units to produce the deployment anomaly scores for the deployed printhead (see REDDY: Fig.3, [0061], “at block 302, a data set may be obtained from the 3D printer. In an example, a processor (not shown) of the system may obtain the data set from the 3D printer. The data set pertains to a sequence of layers printed by the 3D printer. For example, the data set may include, but is not limited to, layer thickness, drop in a print platform, post drop surface of a layer, print surface of a layer, post spread surface of a layer, and disturbance in print. In an example implementation, the sequence of layers may be pre-defined. In addition, real-time data pertaining to a layer being printed by the 3D printer may be obtained from the 3D printer. In an example implementation, the prediction engine may obtain the data set and the real-time data from the 3D printer”)
storing the scaled anomaly score in a data log corresponding with the deployed printhead (see REDDY: Fig.1, [0022], “the prediction engine 106 and the anomaly detection engine 108, amongst other things, include routines, programs, objects, components, and data structures, which perform particular tasks or implement particular abstract data types. The anomaly detection engine 108 may also be implemented as, signal processor(s), state machine(s), logic circuitries, and/or any other device or component that manipulates signals based on operational instructions. Further, the prediction engine 106 and the anomaly detection engine 108 can be implemented by hardware, by computer-readable instructions executed by a processing unit, or by a combination thereof.”); and
repeating the process over a time period to store scaled anomaly scores for the deployed printhead over the time period in the data log (see REDDY: Fig.4B, [0073], “At block 418, the method 400 may include comparing the predicted anomaly score of the encoder-decoder based model and the time-series decomposition model based on triangulation of the predicted anomaly scores to detect an anomaly in the layer being printed. Based on the triangulation, the anomaly detection engine may detect the layer being printed as anomalous if a cross-correlation score of the predicted anomalies is above a statistically derived threshold.”)
REDDY does not teach the system wherein:
printhead data for a plurality of deployed printheads;
at least one processor is further configured to cause the printhead maintenance supervisor at least to provide a replacement recommendation for one or more of the deployed printheads based on the scaled anomaly scores for the deployed printheads stored in the data logs, and
operating a recurrent second neural network trained to scale the deployment anomaly scores by inputting the deployment anomaly scores, generated by the first neural network over a number of the time units, into the recurrent second neural network to produce a scaled anomaly score for the deployed printhead.
However, SATO teaches the system wherein:
printhead data for a plurality of deployed printheads (see SATO: Fig.3, [0030], “The head unit 30 has a plurality of print heads 31 placed in a staggered arrangement along the sheet width direction as illustrated in FIG. 3. The head unit 30 also has a head control section HC that controls the print heads 31 in response to a head control signal from the controller 100.”)
at least one processor is further configured to cause the printhead maintenance supervisor at least to provide a replacement recommendation for one or more of the deployed printheads based on the scaled anomaly scores for the deployed printheads stored in the data logs,(see SATO: Fig.17, [0112] “example of a model of a neural network in this embodiment. The neural network accepts failure state information and use environment information as an input, and outputs information representing a recommended action as output data. Information representing an action is specifically information that represents whether the recommended action is “cleaning”, “nozzle complement”, “head replacement”, or “unnecessary”. The output layer in the neural network may be a widely known softmax layer. In this case, the neural network produces four outputs, probability data representing “cleaning”, probability data representing “nozzle complement”, probability data representing “head replacement”, and probability data representing “unnecessary”.”)
Because both REDDY and SATO are analogous and attempt to solve the same problem of anomaly detection for machines to assist operation decision, accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of REDDY to include the a system that collect printhead data for a plurality of deployed printheads to detect anomaly score using machine learning model to provide a replacement recommendation for one or more of the deployed printheads based on the scaled anomaly scores for the deployed printhead as taught by MALHOTRA. After modification of REDDY, the anomaly score that is generate based machine learning model based on the printer can also incorporate the printer side preventive maintenance prediction recommendation teaching of SATO. One would have been motivated to make such a combination in order to reduce downtime and production loss by triggering timely and effective maintenance actions.
REDDY and SATO does not teach the system wherein:
operating a recurrent second neural network trained to scale the deployment anomaly scores by inputting the deployment anomaly scores , generated by the first neural network over a number of the time units, into the recurrent second neural network to produce a scaled anomaly score for the deployed printhead.
However, MALHOTRA teaches the system that :
operating a recurrent second neural network trained to scale the deployment anomaly scores by inputting the deployment anomaly scores (see MALHOTRA: Fig.2, [0039], “at step 206, the one or more hardware processors 104 estimate, via the recurrent neural network (RNN) encoder-decoder model, the multi-dimensional time series using the reduced-dimensional time series obtained by the dimensionality reduction model as illustrated in FIG. 3B. More specifically, FIG. 3B, with reference to FIGS. 1 through 3A, depicts a recurrent neural network encoder-decoder (RNN-ED) model implemented by the system 100 of FIG. 1 in accordance with some embodiments of the present disclosure.”), generated by the first neural network over a number of the time units, into the recurrent second neural network to produce a scaled anomaly score for the deployed printhead (see MALHOTRA: Fig.2, [0040], “the one or more hardware processors 104 generate a one or more anomaly score based on the plurality of the error vectors. In an embodiment, an anomaly score is computed once the system 100 is trained. In an embodiment, each of the plurality of parameters in the reduced-dimensional time series is a non-linear function of a subset of the plurality of parameters of the multi-dimensional time series.”)
Because REDDY and SATO and MALHOTRA are in the same/similar field of endeavor of anomaly detection and attempt to solve the same problem of anomaly detection for machines to assist operation decision, accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of REDDY to include the a system that operate a recurrent second neural network trained to scale the deployment anomaly scores, generated by the first neural network to produce scaled anomaly scores for the deployed printheads as taught by MALHOTRA. After modification of REDDY, the anomaly score that is generate based machine learning model based on the printer can also incorporate the printer side preventive maintenance prediction recommendation teaching of MALHOTRA. One would have been motivated to make such a combination in order to reduce downtime and production loss by triggering timely and effective maintenance actions.
Regarding Claim 2,
As shown above, REDDY, SATO and MALHOTRA teaches all the limitations of claim 1. SATO further teaches the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
store the replacement recommendation in the data log corresponding with a deployed printhead (see SATO: Fig.16, [0106], “ An action represented by action information may be, for example, any one of “cleaning”, “nozzle complement”, “head replacement”, and “unnecessary” as described above. Action information is, for example, information obtained according to failure state information and use environment information. For example, “unnecessary” indicated by C1 in FIG. 16 is obtained according to a.sub.1 representing the failure count and b.sub.1 representing a failure frequency. Here, a point representing (a.sub.1, b.sub.1) is plotted in the area A1 in FIG. 12, so action information represents “unnecessary”.)”
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of REDDY to store the replacement recommendation in a data log corresponding with a deployed printhead as taught by MALHOTRA. One would have been motivated to make such a combination in order to reduce downtime and production loss by triggering timely and effective maintenance actions for future actions.
Regarding Claim 3,
As shown above, REDDY, SATO and MALHOTRA teaches all the limitations of claim 2. SATO further teaches the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
transmit a message indicating the replacement recommendation via a network interface (see SATO: Fig.1,0024, “The training continues progressing through all of the instances until the network is fully trained. In some embodiments the training makes multiple passes through the training database, as indicated through the optional loopback 109. In some embodiments the neural network may change the order of the instances as they appear in the training dataset to avoid training biases.”),
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of REDDY to transmit a message indicating the replacement recommendation via a network interface as taught by MALHOTRA. One would have been motivated to make such a combination in order to provide effective and timely communication to reduce downtime and production loss by triggering timely and effective maintenance actions for future actions.
Regarding Claim 4,
As shown above, REDDY, SATO and MALHOTRA teaches all the limitations of claim 1. SATO further teaches the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
display the replacement recommendation via a user interface (see REDDY: Fig.1, [0135], “display a screen suggesting an action or a screen prompting the user to execute an action on the display section (not illustrated) of the printing apparatus 1 or the display section of a computer CP. However, informing processing is not limited to the displaying of a screen, but may be processing to cause a light emitting section such as a light emitting diode (LED) to emit light or processing to output a warning sound or a voice from a speaker.”)
Regarding Claim 5,
As shown above, REDDY, SATO and MALHOTRA teaches all the limitations of claim 1. REDDY further teaches the wherein the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
perform a comparison of one or more of the scaled anomaly scores for each of the deployed printheads to a threshold value (see REDDY: Fig.4B, [0083], “a decoder may reconstruct the sequence of layers from the last hidden state representation. Upon comparison of the reconstructed layer surface data with a layer surface data of the layer being printed, the encoder-decoder based model may identify a deviation between the reconstructed layer surface data and the layer surface data of the layer being printed. Such a deviation is indicated as a predicted anomaly score.”) ;
determine whether the one or more of the deployed printheads are experiencing a failure condition based on the comparison of the scaled anomaly scores (see REDDY: Fig.4B, [0073], “The method 400 may include comparing the predicted anomaly score of the encoder-decoder based model and the time-series decomposition model based on triangulation of the predicted anomaly scores to detect an anomaly in the layer being printed. Based on the triangulation, the anomaly detection engine may detect the layer being printed as anomalous if a cross-correlation score of the predicted anomalies is above a statistically derived threshold.”); and
provide the replacement recommendation for the one or more of the deployed printheads when the failure condition is determined (see REDDY: Fig.1, [0071, “notifying a user whether a deviation in the real-time data with respect to the reconstructed data is above a predefined matching threshold value or not. If the reconstructed data and the real-time data obtained from the 3D printer indicates a mismatch, the decoder may detect an anomaly in the layer being printed. The prediction engine may accordingly generate a predicted anomaly score for the layer being printed.”)
Regarding Claim 6,
As shown above, REDDY, SATO and MALHOTRA teaches all the limitations of claim 1. REDDY further teaches the system wherein:
inputting first input samples of the deployed printhead data for the deployed print head into the first neural network to generate the deployment anomaly scores for the deployed printhead over a first plurality of the time units, wherein the first neural network is trained on conforming printhead data (see REDDY: Fig.2, [0048], “the anomaly detection engine 210 may obtain an output from the time-series decomposition model 220 defining a second probability score. In an example, the second probability score is calculated by performing normalization of multiple outputs from the time-series decomposition model 220. For example, the second probability score is calculated by performing normalization of differentials of multiple outputs from the time-series decomposition model 220. The second probability score may define a degree of deviation of the data pertaining to the layer from robust trend obtained for the sequence of layers 226. For example, a higher value of the second probability score may represent a higher degree of deviation and a lower value of the second probability score may represent a lower degree of deviation.”)
Regarding Claim 7,
As shown above, REDDY, SATO and MALHOTRA teaches all the limitations of claim 6. REDDY further teaches the printhead maintenance supervisor at least to method wherein:
formating second input samples for the deployed printhead, wherein each of the second input samples comprises a first time-series of deployment data objects over a number of consecutive time units for a deployed printhead (see REDDY: Fig.1, [0036], “the time-series decomposition model 220 may be trained over six attributes of layer surface data for all previously printed layers associated with a plurality of sequence records. The six attributes of layer surface data may include a median value of layer thickness, a median value of post drop surface, a median value of post spread surface, and a median value of print surface. In an example, the time-series decomposition model 220 may be trained over attributes other than surface statistical attributes.”) and wherein each of the deployment data objects includes a deployment anomaly score generated by the first neural network for the deployed printhead, and at least a subset of the deployed printhead data for the deployed printhead (see REDDY: Fig.3, [0062], “the method 300 may include providing the data set pertaining to the sequence of printed layers and the real-time data of the layer being printed as an input to an encode-decoder based model and a time-series model. Based on the sequence of printed layers and the real-time data of the layer being printed, the encoder-decoder based model and the time-series decomposition model may generate a respective predicted anomaly score for the layer being printed.”); and
inputting the second input samples for the deployed printhead into the recurrent second neural network to output the scaled anomaly score for the deployed printhead, wherein the recurrent second neural network is trained based on a pool of training printheads (see REEDY: Fig.2, [0049], “Upon obtaining the first and second probability scores from the encoder-decoder based model 218 and the time-series decomposition model 220, the anomaly detection engine 210 may perform triangulation of the first and second probability scores to detect an anomaly in the subsequent layer 228 being printed. Triangulation facilitates validation of data through cross-verification from multiples sources. For example, the triangulation may involve concurrent or parallel utilization of the two or more machine learning models for carrying out separate predictions and obtaining respective prediction results.”)
Regarding Claim 8,
As shown above, REDDY, SATO and MALHOTRA teaches all the limitations of claim 7. REDDY further wherein the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
train the first neural network using an unsupervised learning algorithm based on first training samples of the conforming printhead data from a pool of conforming printheads (see REEDY: Fig.2, [0041], “he prediction engine 208 may input the data set to the first trained machine learning model, such as the encoder-decoder based model 218 and the second trained machine learning model, such as the time-series decomposition model 220 to detect an anomaly in a subsequent layer 228 being printed. Based on the data set of the sequence of layers 226, the encoder of the encoder-decoder based model 218 may generate a hidden state representation of the data set.”);
generate a training dataset for the recurrent second neural network (see REEDY: Fig.2, [0069], “the encoder-decoder based model may be a machine learned recurrent neural network (RNN) based model. The encoder may generate a hidden state representation of the sequence of printed layers, such as 10 layers. The hidden state representation may be indicative of a comprehensive summary of the sequence of printed layers.”) by:
identifying training printhead data for the pool of training printheads (see REEDY: Fig.2, [0061], “a data set may be obtained from the 3D printer. In an example, a processor (not shown) of the system may obtain the data set from the 3D printer.”);
inputting second training samples of the training printhead data into the first neural network to generate training anomaly scores for the training printheads over a second plurality of time units (see REEDY: Fig.2, [0041], “the prediction engine 208 may input the data set to the first trained machine learning model, such as the encoder-decoder based model 218 and the second trained machine learning model, such as the time-series decomposition model 220 to detect an anomaly in a subsequent layer 228 being printed.”); and
formatting third training samples for the training printheads, wherein each of the third training samples comprises a second time-series of training data objects over the number of consecutive time units for a training printhead, and a label for the training printhead, and wherein each of the training data objects includes a training anomaly score generated by the first neural network for the training printhead, and at least a subset of the training printhead data for the training printhead (see REEDY: Fig.4B, [0072], “ay include detecting a deviation in the real-time data with respect to a trend of the data set pertaining to the sequence of layers. In an example, the time-series decomposition model may predict future values based on previously observed values. Data pertaining to all previously printed layers is provided as an input to the time-series decomposition model. As per the time-series decomposition model, an anomaly may be termed as a data point which is not following a common collective trend or seasonal or cyclic pattern of entire data pertaining to all previously printed layers. Upon detection of such a data point, the time-series decomposition model may generate a predicted anomaly score.”); and
train the recurrent second neural network using a supervised learning algorithm based on the training dataset (see REEDY: Fig.4B, [0074] “may include upon completion of the print job, triangulating predictions of the encoder-decoder based model and the time-series decomposition model to detect the anomaly in an object printed by the 3D printer.”)
Regarding Claim 9,
As shown above, REDDY, SATO and MALHOTRA teaches all the limitations of claim 8. REDDY further teaches the method wherein:
the label represents a printhead condition of a corresponding training printhead (see SATO: Fig.1, [0060], “detecting a failure in the print head 31 is known as described above for the first inspection unit 70 and second inspection unit 80. A failure in the print head 31 is specifically a discharge failure in the nozzle Nz. In this embodiment, it is only necessary to be able to detect a failure in the print head 31. Any one of the first inspection unit 70 and second inspection unit 80 may be omitted from the printing apparatus 1. Alternatively, a third inspection unit may be added that detects a failure in the print head 31 by a different method.”)
Regarding Claim 10,
As shown above, REDDY, SATO and MALHOTRA teaches all the limitations of claim 7. SATO further teaches the method wherein:
the subset of the deployed printhead data in the second input samples comprises a nozzle failure value indicating a number of failed nozzles ( see SATO: Fig.1, [0145], “The failure state information may be at least one of failure count information about the nozzles included in the print head and failure frequency information about the nozzles.”)
Regarding Claim 11,
As shown above, REDDY, SATO and MALHOTRA teaches all the limitations of claim 1. REDDY further teaches the method wherein:
the first neural network comprises an autoencoder (see REDDY: Fig.1, [0041], “The prediction engine 208 may input the data set to the first trained machine learning model, such as the encoder-decoder based model 218 and the second trained machine learning model, such as the time-series decomposition model 220 to detect an anomaly in a subsequent layer 228 being printed.”); and
the recurrent second neural network comprises a Long Short-Term Memory (LSTM) neural network (see REDDY: Fig.1, [0035], “the encoder-decoder based deep learning architecture may include long short-term memory (LSTM) units 222. The LSTM units 222 remember past data by generating a comprehensive summary of sequence in the form of last hidden state representation in reduced dimensional space. The LSTM units 222 of the encoder may summarize the sequence keeping salient information of all previous layers in the sequence. Based on the summary, the decoder may reconstruct the sequence from the last hidden state representation.”)
Regarding Claim 12,
As shown above, REDDY, SATO and MALHOTRA teaches all the limitations of claim 1. REDDY further teaches the method wherein:
a cloud computing platform comprising the printhead maintenance supervisor (see REDDY: Fig.1, [0077], “processing resource 504 and the non-transitory computer-readable medium 502 may also be communicatively coupled to data sources 510 over the network 508. The data sources 510 may include, for example, a database. The data sources 510 may be used by the database administrators and other users to communicate with the processing resource 504.”)
Regarding independent Claim 13,
Claim 13 is a method claim and has similar/same claim limitation as claim 1 and is rejected under the same rationale
Regarding Claim 14,
As shown above, REDDY, SATO and MALHOTRA teaches all the limitations of claim 13. REDDY further teaches the method wherein:
providing the replacement recommendation comprises: storing the replacement recommendation in the data log corresponding with a deployed printhead (see REDDY: Fig.1, [0024], “back propagation to modify weights and biases within the neural network so that its next prediction will be closer to the original data value. In the example of Table 1, the inputs to the network are the simulated results (Node 1 Predicted voltage, Node 2 Predicted Voltage, etc.), and the predicted output is a predicted value for R1, R2, and R3, which is compared, during training, to the original values of R1, R2, and R3 that were used to create the simulated results. The training continues progressing through all of the instances until the network is fully trained. In some embodiments the training makes multiple passes through the training database, as indicated through the optional loopback 109. In some embodiments the neural network may change the order of the instances as they appear in the training dataset to avoid training biases.”)
Regarding Claim 15,
As shown above, REDDY, SATO and MALHOTRA teaches all the limitations of claim 1. REDDY further teaches the method wherein:
providing the replacement recommendation comprises: transmitting a message indicating the replacement recommendation via a network interface ( see REDDY: Fig.1, “The machine learning network could then be improved (i.e., updated or retrained) in an operation 206 with the additional instances or new training dataset as described above. The result after re-training the machine learning network in operation 206 would be a set of nominal model parameters, each of which would have a certain range of uncertainty due to measurement errors.”)
Regarding Claim 16,
As shown above, REDDY, SATO and MALHOTRA teaches all the limitations of claim 1. REDDY further teaches the method wherein:
providing the replacement recommendation comprises: displaying the replacement recommendation via a user interface ( see REDDY: Fig.1, [0025], “ Operation 108 may use supervised machine learning or unsupervised machine learning. Supervised machine learning as used herein generally refers to machine learning that is based upon training sets that contain labeled data. Unsupervised machine learning generally refers to ‘learning’ on training sets that contain mostly unlabeled data to train the neural network. The machine learning facility may apply a particular technique, such as a Bayesian approach, Random Forest, regression models, or classification models.”)
Regarding Claim 17,
Claim 17 is a method claim and has similar/same claim limitation as claim 5 and is rejected under the same rationale.
Regarding Claim 18,
As shown above, REDDY, SATO and MALHOTRA teaches all the limitations of claim 17. REDDY further teaches the method wherein:
operating the first neural network comprises: inputting first input samples of the deployed printhead data for the deployed printhead into the first neural network to generate the deployment anomaly scores for the deployed printhead over a plurality of time units, wherein the first neural network is trained on conforming printhead data (see REDDY: Fig.1, [0013], “the machine learning facility receives its training data in operation 106, the trained network is created in operation 108. In operation 108, for example, a neural network may read the inputs from the first instance and generate a predicted outcome. Then the neural network compares its generated predicted outcome to the data used to create the simulated results, also included in the instance, and uses back propagation to modify weights and biases within the neural network so that its next prediction will be closer to the original data value.”)
Regarding Claim 19,
Claim 19 is a method claim and has similar/same claim limitation as claim 7 and is rejected under the same rationale.
Regarding Claim independent 20,
Claim 20 is a 7 is a non-transitory computer readable medium and has similar/same claim limitation as claim 1 and 13 and is rejected under the same rationale.
Response to Arguments
Claim Rejections - 35 U.S.C. § 103,
Applicant’s arguments with respect to claim amendments have been considered but are moot considering the new combination of references being used in the current rejection. The new combination of references was necessitated by Applicant’s claim amendments. Therefore, the claims are rejected under the new combination of references as indicated above.
Claim Rejections - 35 U.S.C. § 101,
For claim 1-20 , Regarding the 35 U.S.C. 101 rejection for being directed non-statutory subject matter has been withdrawn based on applicant amendments and. Therefore, the 35 U.S.C. 101 rejection has been withdrawn.
For claim 20, Regarding the 35 U.S.C. 101 rejection for being directed to signal per se has been sustained and updated.
Claim Rejections – Double Patenting,
The provisionally rejected claims 1-20 on the ground of non-statutory double patenting over the claims of US application 18/116,932 is sustained until the applicant consider filing a terminal disclaimer, per applicant statement.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
PGPUB
NUMBER:
INVENTOR-INFORMATION:
TITLE / DESCRIPTION
US 11301563 B2
Huang; Heqing
Title: Recurrent Neural Network Based Anomaly Detection
Description: The present application relates generally to an improved data processing apparatus and method and more specifically to mechanisms for performing anomaly detection based on recurrent neural network operations.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 ZELALEM W SHALU whose telephone number is (571)272-3003. The examiner can normally be reached M- F 0800am- 0500pm.
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/Zelalem Shalu/Examiner, Art Unit 2145
/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145
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