DETAILED ACTION
Final Rejection
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 .
Response to Amendment
Applicant’s amendments, filed 06/15/2026 to claims are accepted. In this amendment, claims 1 and 21 have been amended.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-24 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Each of claims 1-24 falls within one of the four statutory categories. See MPEP § 2106.03. For example, each of claim 1-21 fall within category of process; For example, each of claims 22-24 fall within category of machine, i.e., a “concrete thing, consisting of parts, or of certain devices and combination of devices.” Digitech, 758 F.3d at 1348–49, 111 USPQ2d at 1719 (quoting Burr v. Duryee, 68 U.S. 531, 570, 17 L. Ed. 650, 657 (1863)).
Regarding Claims 1-17
Step 2A – Prong 1
Exemplary claim 1 is directed to an abstract idea of predict failure of an industrial machine.
The abstract idea is set forth or described by the following italicized limitations:
1. A computer-implemented method to predict failure of an industrial machine, the method using an arrangement of processing modules, the method comprising:
operating the industrial machine in a selected operation mode among a plurality of different operations modes;
receiving machine data from the industrial machine by first, second and third sub-ordinated processing modules that are arranged to provide intermediate data to an output processing module, wherein the arrangement has been trained in advance by cascaded training,
by the first sub-ordinated processing module, processing the machine data to determine a first intermediate status indicator;
by the second sub-ordinated processing module, processing the machine data to determine a second intermediate status indicator;
by the third sub-ordinated processing module being an operation mode classifier module, processing the machine data to determine an operation mode indicator of the industrial machine; and
processing the first and second intermediate status indicators and the operation mode indicator by the output processing module, wherein the output processing module predicts failure of the industrial machine by providing prediction data.
switching the operation mode of the industrial machine based on to the prediction data.
The italicized limitations above represent a mental step (i.e., a process that can be performed by can be performed mentally and/or with pen and paper or a mental judgment) . Therefore, the italicized limitations fall within the subject matter groupings of abstract ideas enumerated in Section I of the 2019 Revised Patent Subject Matter Eligibility Guidance.
For example, the limitations “[..]determine a first intermediate status indicator;[.. ]determine a second intermediate status indicator;[..] to determine an operation mode indicator of the industrial machine; and [,,] predicts failure of the industrial machine by providing prediction data; .” are mental step (i.e., a process that can be performed by can be performed mentally and/or with pen and paper or a mental judgment), see 2106.04(a)(2). Limitations are considered together as a single abstract idea for further analysis. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)).
Step 2A – Prong 2
Claims 1 does not include additional elements (when considered individually, as an ordered combination, and/or within the claim as a whole) that are sufficient to integrate the abstract idea into a practical application.
For example, first additional first element is “ receiving machine data from the industrial machine by first, second and third sub-ordinated processing modules that are arranged to provide intermediate data to an output processing module, wherein the arrangement has been trained in advance by cascaded training,” to be performed, at least in-part, these additional elements appear to only add insignificant extra-solution activity (e.g., data gathering and /or pre-solution activity) and only generally link the abstract idea to a particular field. Therefore, this element individually or as a whole does not provide a practical application. See MPEP 2106.05(g).
For example, 2nd additional first element is “ operating the industrial machine in a selected operation mode among a plurality of different operations modes; switching the operation mode of the industrial machine based on to the prediction data. ” to be performed, at least in-part, these additional elements appear to only add insignificant post solution activity in the pertaining field and only generally link the abstract idea to a particular field. Therefore, this element individually or as a whole does not provide a practical application. See MPEP 2106.05(g).
For example, 3rd additional first element is “by the first sub-ordinated processing module, by the second sub-ordinated processing module, by the third sub-ordinated processing module”. This element amounts to mere use of a generic computer components, which is well understood routine and conventional (see background of current discloser and IDS and PTO 892) and this element individually does not provide a practical application. In view of the above, the “additional element” individually or combine does not provide a practical application of the abstract idea. see MPEP 2106.05(d).
In view of the above, the “additional elements” individually do not provide a practical application of the abstract idea. Furthermore, the “additional elements” in combination amount to a generic computer components with computer software, where such computers and software amount to mere instructions to implement the abstract idea on a computer(s) and/or mere use of a generic computer component(s) as a tool to perform the abstract idea. Therefore, these elements in combination do not provide a practical application. The combination of additional elements does no more than generally link the use of the abstract idea to a particular technological environment, and for this additional reason, the combination of additional elements does not provide a practical application of the abstract idea..
Step 2B
Claims1 does not include additional elements, when considered individually and as an ordered combination, that are sufficient to amount to significantly more than the abstract idea. The reasons for reaching this conclusion are substantially the same as the reasons given above in § Step 2A – Prong 2. For brevity only, those reasons are not repeated in this section. See MPEP §§ 2106.05(g) and MPEP §§2106.05(II).
Dependent Claims 2-17
Dependent claims 2-18 fail to cure this deficiency of independent claim 1 (set forth above) and are rejected accordingly. Particularly, claims 2-17 recite limitations that represent (in addition to the limitations already noted above) either the abstract idea or an additional element that is merely extra-solution activity, mere use of instructions and/or generic computer component(s) as a tool to implement the abstract idea, and/or merely limits the abstract idea to a particular technological environment.
For example, the limitations of Claims 2-8 and 10-14 are insignificant extra-solution activity (e.g., data gathering).
For example, the limitations of Claims 9 and 15-17 are a mental step a combination of mathematical concept (i.e., a process that can be performed by mathematical relationships or rules or idea) and mental step (i.e., a process that can be performed by can be performed mentally and/or with pen and paper or a mental judgment) with a computer component at a high level of generality.
Claims 18-20 and 22-24
Claims 18-20 and 22-24 contains language similar to claims 1-17 as discussed in the preceding paragraphs, and for reasons similar to those discussed above, claims 18-20 and 22-24 are also rejected under 35 U.S.C. § 101(abstract idea).
Furthermore, claim 18 recites an additional element is an industrial machine adapted to provide machine data to a computer that is adapted to perform a method according to claim 1 and that is further adapted to receive prediction data from the computer, wherein the industrial machine is associated with a machine controller that switches the operation mode of the industrial machine according to pre-defined optimization goals” and claim 24 “industrial machine comprising a computer that is adapted to process machine data by performing a method according to claim 1 and that is further adapted to provide prediction data, wherein the computer switches the operation mode of the industrial machine in response to the prediction data and according to pre-defined optimization goals, selected from the following: avoid maintenance as long as possible, operate in a mode for which failure is predicted to occur at the latest. ”which are well understood routine and conventional (see background of current discloser and IDS and PTO 892) and this element individually does not provide a practical application. In view of the above, the “additional element” individually or combine does not provide a practical application of the abstract idea. see MPEP 2106.05(d).
Regarding Claim 21
Step 2A – Prong 1
Exemplary claim 21 is directed to an abstract idea of predict failure of an industrial machine.
The abstract idea is set forth or described by the following italicized limitations:
21. A computer-implemented method for training a module arrangement having first, second and third sub-ordinated processing modules coupled to an output processing module to enable the module arrangement to provide a failure indicator with a failure prediction for an industrial machine, and to enable operating the industrial machine in a selected operation mode among a plurality of different operations modes and enable to switch the operation mode of the industrial machine based on to the failure prediction, the method comprising the application of cascaded training with training the sub-ordinated processing modules, subsequently operating the trained sub-ordinated processing modules, and subsequently training the output processing module, wherein the cascaded training comprises:
train the third sub-ordinated processing module with historical machine data;
run the trained third sub-ordinated processing module to obtain an historical mode indicator by processing historical machine data;
train the first and second sub-ordinated processing modules with historical machine data and with the historical mode indicator;
run the trained first and second sub-ordinated processing modules to obtain the first and second intermediate status indictors by processing historical machine data; and
train the output processing module by the historical mode indicator, by historical machine data and by historical failure data;
operating the industrial machine in a selected operation mode among a plurality of different operations modes;
receiving machine data from the industrial machine by first, second and third sub-ordinated processing modules that are arranged to provide intermediate data to an output processing module, wherein the arrangement has been trained in advance by the cascaded training,
by the first sub-ordinated processing module, processing the machine data to determine a first intermediate status indicator;
by the second sub-ordinated processing module, processing the machine data to determine a second intermediate status indicator;
by the third sub-ordinated processing module being an operation mode classifier module, processing the machine data to determine an operation mode indicator of the industrial machine; and
processing the first and second intermediate status indicators and the operation mode indicator by the output processing module, wherein the output processing module predicts failure of the industrial machine by providing prediction data; and
switching the operation mode of the industrial machine based on to the prediction data.
The italicized limitations above represent a mental step (i.e., a process that can be performed by can be performed mentally and/or with pen and paper or a mental judgment) . Therefore, the italicized limitations fall within the subject matter groupings of abstract ideas enumerated in Section I of the 2019 Revised Patent Subject Matter Eligibility Guidance.
For example, the limitations “[..]to provide a failure indicator with a failure prediction for an industrial machine; [..]determine a first intermediate status indicator;[.. ]determine a second intermediate status indicator;[..] to determine an operation mode indicator of the industrial machine; and [,,] predicts failure of the industrial machine by providing prediction data; .” are mental step (i.e., a process that can be performed by can be performed mentally and/or with pen and paper or a mental judgment), see 2106.04(a)(2). Limitations are considered together as a single abstract idea for further analysis. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)).
Step 2A – Prong 2
Claim 21 does not include additional elements (when considered individually, as an ordered combination, and/or within the claim as a whole) that are sufficient to integrate the abstract idea into a practical application.
For example, first additional first element is “ train the third sub-ordinated processing module with historical machine data; run the trained third sub-ordinated processing module to obtain an historical mode indicator by processing historical machine data; train the first and second sub-ordinated processing modules with historical machine data and with the historical mode indicator; run the trained first and second sub-ordinated processing modules to obtain the first and second intermediate status indictors by processing historical machine data; and train the output processing module by the historical mode indicator, by historical machine data and by historical failure data; receiving machine data from the industrial machine by first, second and third sub-ordinated processing modules that are arranged to provide intermediate data to an output processing module, wherein the arrangement has been trained in advance by the cascaded training,” to be performed, at least in-part, these additional elements appear to only add insignificant extra-solution activity (e.g., data gathering/ pre solution activity) and only generally link the abstract idea to a particular field. Therefore, this element individually or as a whole does not provide a practical application. See MPEP 2106.05(g).
For example, 2nd additional first element is “ operating the industrial machine in a selected operation mode among a plurality of different operations modes; switching the operation mode of the industrial machine based on to the prediction data. ” to be performed, at least in-part, these additional elements appear to only add insignificant post solution activity in the pertaining field and only generally link the abstract idea to a particular field. Therefore, this element individually or as a whole does not provide a practical application. See MPEP 2106.05(g).
For example, 3rd additional first element is “training a module arrangement having first, second and third sub-ordinated processing modules coupled to an output processing module to enable the module arrangement to provide a failure indicator with a failure prediction for an industrial machine, and to enable operating the industrial machine in a selected operation mode among a plurality of different operations modes and enable to switch the operation mode of the industrial machine based on to the failure prediction, the method comprising the application of cascaded training with training the sub-ordinated processing modules, subsequently operating the trained sub-ordinated processing modules, and subsequently training the output processing module, wherein the cascaded training comprises by the first sub-ordinated processing module, by the second sub-ordinated processing module, by the third sub-ordinated processing module”. This element amounts to mere use of a generic computer components, which is well understood routine and conventional (see background of current discloser and IDS and PTO 892) and this element individually does not provide a practical application. In view of the above, the “additional element” individually or combine does not provide a practical application of the abstract idea. see MPEP 2106.05(d).
In view of the above, the “additional elements” individually do not provide a practical application of the abstract idea. Furthermore, the “additional elements” in combination amount to a generic computer components with computer software, where such computers and software amount to mere instructions to implement the abstract idea on a computer(s) and/or mere use of a generic computer component(s) as a tool to perform the abstract idea. Therefore, these elements in combination do not provide a practical application. The combination of additional elements does no more than generally link the use of the abstract idea to a particular technological environment, and for this additional reason, the combination of additional elements does not provide a practical application of the abstract idea..
Step 2B
Claim 21 does not include additional elements, when considered individually and as an ordered combination, that are sufficient to amount to significantly more than the abstract idea. The reasons for reaching this conclusion are substantially the same as the reasons given above in § Step 2A – Prong 2. For brevity only, those reasons are not repeated in this section. See MPEP §§ 2106.05(g) and MPEP §§2106.05(II).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 3-20 and 22-24 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cella et al. (US 2023/0135882).
Regarding Claims 1 and 22-23. Cella teaches a computer-implemented method to predict failure of an industrial machine, the method using an arrangement of processing modules, the method comprising(33610: fig. 190):
operating the industrial machine in a selected operation mode among a plurality of different operations modes(operational information: [2385], [2387], [1000], [0167]);
receiving machine data from the industrial machine by first(industrial machines, such as parts, images, configurations, internal structures, use schedules: fig. 190), second (service procedures parts: fig. 190) and third sub-ordinated processing modules (failures parts conditions, timing, repair rates /PM action: fig. 190) that are arranged to provide intermediate data to an output processing module (33606: fig. 190), wherein the arrangement has been trained in advance by cascaded training (update or otherwise modify information within the knowledge base 14036. The intelligent systems 14028 may use intelligence and machine learning capabilities (e.g., of the machine learning module 14034 or as described elsewhere in this disclosure) to process state-related measurements and related information based on detected conditions: [2201]; a machine learning system for automatically configuring a topology or workflow for a set of hybrid neural networks (e.g., series, parallel, data flows, etc.) based on a training data set .Based on a training data set of outcomes from industrial IoT processes (e.g., maintenance, repair, service, prediction of faults, optimization of operation of a machine, system of facility, etc.), or other intelligence or machine learning aspects: [2202]; the Examiner considered “set of hybrid neural networks based on a training data set” to be cascaded training),
by the first sub-ordinated processing module, processing the machine data to determine a first intermediate status indicator(industrial machines, such as parts, images, configurations, internal structures, use schedules: fig. 190);
by the second sub-ordinated processing module, processing the machine data to determine a second intermediate status indicator(industrial machines, such as parts, images, configurations, internal structures, use schedules: fig. 190);
by the third sub-ordinated processing module being an operation mode classifier module, processing the machine data to determine an operation mode indicator of the industrial machine(failures parts conditions, timing, repair rates /PM action: fig. 190);
processing the first and second intermediate status indicators and the operation mode indicator by the output processing module(33606: fig. 190), wherein the output processing module predicts failure of the industrial machine by providing prediction data(33610/ 33614: fig. 190; failure when it occurs on a specific machine: [2386]); and
switching the operation mode of the industrial machine based on to the prediction data (A goal of initiating and updating such a knowledge graph, among other things may be to further improve for collecting, discovering, capturing, disseminating, managing, and processing information about industrial machines, including factual information (such as about internal structures, parts and components), operational information and procedural information, including know-how and other information relevant to maintenance, service and repairs.: [2385]; fig. 192).
Regarding Claim 3. Cella further teaches determining the operation mode indicator is performed by the operation mode classifier having been trained based on historical machine data that have been annotated by a human expert([2765]).
Regarding Claim 4. Cella further teaches expert-annotated historical machine data are sensor data([2765]).
Regarding Claim 5. Cella further teaches the operation mode classifier has been trained based on historical machine data so that during training, the operation mode classifier has clustered operation time of the machine into clusters of time-series segments(distribution of values of the sensor data:[0925]; [2765]).
Regarding Claim 6. Cella further teaches the clusters of time-series segments are being assigned to operation modes indicators, selected from being assigned automatically or by interaction with a human expert([0925]; [2765]).
Regarding Claim 7. Cella further teaches the operation mode indicator is provided by the number of mode changes over time(timing repair rates: fig, 190).
Regarding Claim 8. Cella further teaches the status indicators are selected from current indicators that indicate the current status(images: fig. 190), and predictor indicators that indicate the status in the future(service procedures parts: fig. 190).
Regarding Claim 9. Cella further teaches the output processing module predicts failure of the industrial machine, selected from the following: time to failure, failure type, remaining useful life, failure interval (failure when it occurs on a specific machine: [2386]; 33804: fig. 192; [2395]).
Regarding Claim 10. Cella further teaches the operation mode indicator further serves as a bias (parts conditions: fig. 190) that is processed by both the first (parts image: fig. 190) and the second sub-ordinated processing modules(service procedures parts: fig. 190).
Regarding Claim 11. Cella further teaches receiving machine data is performed by receiving a sub-set with sensor data and wherein determining the first and second intermediate status indicators is performed by the first and second sub-ordinated processing modules that process sub-sets with sensor data([2384]).
Regarding Claim 12. Cella further teaches receiving machine data comprises receiving machine data through data harmonizers that depending on contribution of machine data to the failure prediction provide virtual machine data by a virtual sensor or filter incoming machine data (33804: fig. 192; [2395]).
Regarding Claim 13. Cella further teaches receiving machine data through the data harmonizers comprises receiving machine data from harmonizers with processing modules that have been trained in advance by transfer learning([2395]).
Regarding Claim 14. Cella further teaches receiving machine data comprises to receiving machine data that is at least partially enhanced by data resulting from simulation([2395]).
Regarding Claim 15. Cella further teaches forwarding the prediction data to a machine controller that controls the machine([01488], [2783]).
Regarding Claim 16. Cella further teaches the machine controller lets the industrial machine assume a mode for wherein the time to fail is predicted to occur at the latest([01488], [2783]).
Regarding Claim 17. Cella further teaches the machine controller lets the industrial machine assume a mode for which the time to maintain the machine occurs at the latest([01488], [2783]).
Regarding Claim 18. Cella teaches an industrial machine adapted to provide machine data to a computer that is adapted to perform a method according to claim 1 (see rejection of Claim 1) and that is further adapted to receive prediction data from the computer, wherein the industrial machine is associated with a machine controller that switches the operation mode of the industrial machine according to pre-defined optimization goals([0013], [2783],[2395], [2483]-[2484]).
Regarding Claim 19. Cella further teaches the pre-defined optimization goals are selected from the following: avoid maintenance as long as possible, operate in a mode for which failure is predicted to occur at the latest ([0013], [2395], [2783], [2483]-[2484]).
Regarding Claim 20. Cella further teaches from chemical reactors, metallurgical furnaces, vessels, pumps, motors, and engines ([2514]).
Regarding Claim 24. Cella teaches An industrial machine comprising a computer that is adapted to process machine data by performing a method according to claim 1 (see the rejection of Claim 1) and that is further adapted to provide prediction data, wherein the computer switches the operation mode of the industrial machine in response to the prediction data and according to pre-defined optimization goals([0013], [2783], [2483]-[2484]), selected from the following: avoid maintenance as long as possible, operate in a mode for which failure is predicted to occur at the latest([0013], [2783],[1565], [2316], [2483]-[2484], [2395]).
Response to Argument
Applicant’s arguments with respect 101 rejection, specially claims 1 and 21, The applicant did not agree with it, see, pages 8-10. Applicant Argus that “The Claims Are Not Directed to an abstract idea; The Claims merely involve a Judicial Exception; The Claims Integrate the Alleged Judicial Exception into a Practical Application”
In response, the Examiner respectfully disagree because the amended limitations of claims, specifically , claims 1 and 21 represent combination of mathematical concepts (i.e., a process that can be performed by mathematical relationships or rules or idea) and mental step (i.e., a process that can be performed by can be performed mentally and/or with pen and paper or a mental judgment)). Therefore, the limitations, specifically claims1 and 21, above fall within the subject matter groupings of abstract ideas enumerated in Section I of the 2019 Revised Patent Subject Matter Eligibility Guidance. In view of the above, the “additional elements” individually do not provide a practical application of the abstract idea. Furthermore, the “additional elements” in combination amount to a plurality of generic device with computer component with software, where such computers and software amount to mere instructions to implement the abstract idea on a computer(s) and/or mere use of a generic computer component(s) as a tool to perform the abstract idea. Therefore, these elements in combination do not provide a practical application. The combination of additional elements does no more than generally link the use of the abstract idea to a particular technological environment, and for this additional reason, the combination of additional elements does no more than generally link the use of the abstract idea to a particular technological environment, and for this additional reason, the additional element does not provide a practical application of the abstract idea. Claim invention only recite the idea of a solution or outcome “outputting an analysis result” and do not include any details about how the “outputting a analysis result” is accomplished. See MPEP 2106.05(f). As such 101 rejection is maintained.
Applicant’s arguments with respect 102, see pages 10-13, Applicant argus that Cella, does not disclose processing a first intermediate status indicator, a second intermediate status indicator, and an operation mode indicator by an output processing module. Nor do the cited portions disclose an operation mode indicator of the industrial machine. Cella does not disclose an arrangement that has been trained in advance by cascaded training.
In response, the Examiner respectfully disagree because Cella’s “hybrid neural networks (e.g., series, parallel, data flows, etc.) based on a training data set” consider to be cascaded training and the first sub-ordinated processing module and status (industrial machines, such as parts, images, configurations, internal structures, use schedules: fig. 190); the second sub-ordinated processing module and status indicator(industrial machines, such as parts, images, configurations, internal structures, use schedules: fig. 190); the third sub-ordinated processing module being an operation mode classifier module, an operation mode indicator (failures parts conditions, timing, repair rates /PM action: fig. 190). As such 102 rejection is maintained.
Examiner Notes
There is no prior art rejection over claims 2 and 21, however there is 101 rejection and closets prior arts fail to teach the limitations of “ training sequence: train the third sub-ordinated processing module with historical machine data; run the trained third sub-ordinated processing module to obtain an historical mode indicator by processing historical machine data; train the first and second sub-ordinated processing modules with historical machine data and with the historical mode indicator; run the trained first and second sub-ordinated processing modules to obtain the first and second intermediate status indictors by processing historical machine data; and train the output processing module by the historical mode indicator, by historical machine data and by historical failure data.
Conclusion
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
a) Fisher (US 2022/0400251) disclose the predicted future state comprises a predicted operational state of a machine and the calculated prediction error comprises a difference between the predicted operational state of the machine and an actual operational state of the machine at a future point in time. A computer-implemented method for training machine learning models, comprising: receiving a training data set including a plurality of time-series data sequences; training a first machine learning model to extract a feature data set representing characteristics of how objects behave in an environment in which the training data set was captured based on a contextual model specifying the characteristics of how objects behave the environment; and training a second machine learning model to predict a future state of an object in the environment based on the training data set and the feature data set representing the characteristics of how objects behave in the environment.
b) Mozer et al. (US 2022/0253695) disclose Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing a network input using a parallel cascaded neural network that includes multiple neural network blocks that each have a skip connection and a propagation delay. Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training parallel cascaded neural networks using temporal difference learning are also described.
c) Bayraktar et al. (US 2021/0396903) disclose a trained system of cascaded feedforward ANNs, which is configured to predict and output a number of parameters that characterize the mud and formation electromagnetic properties as well as tool standoff for a specific button given the set of inputs. For example, the trained system of cascaded feedforward ANNs can predict.
d) Wang et al. (US 2021/0166445) disclose system for reconstructing magnetic resonance images includes a processor that is configured to obtain, from a magnetic resonance scanner, sub-sampled k-space data; apply an inverse fast fourier transform to the sub-sampled k-space data to generate a preliminary image; and process the preliminary image via a trained cascaded recurrent neural network to reconstruct a magnetic resonance image.
e) Lee et al. (US 2020/0135220) disclose the training model is derived by connecting the N autoencoders in a cascade form, and training a subsequent autoencoder using a residual signal not learned by a previous autoencoder.
f) US 20180350065 A1
g) US 20180268284 A1 disclose provided which generates a convolutional neural network (CNN), including training a CNN having three or more layers and performing cascade training on the trained CNN to insert one or more intermediate layers into the CNN until a training error is less than a threshold, where the cascade training is an iterative process of one or more stages, in which each stage includes: training the current CNN; determining whether the training error is converging; and, if the training error is converging, inserting a preset number of intermediate layers in the CNN, the weights of each new layer being set to a predetermined setting; and starting a new stage.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD K ISLAM whose telephone number is (571)270-0328. The examiner can normally be reached M-F 9:00 a.m. - 5:00 p.m..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby A Turner can be reached at 571-272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHAMMAD K ISLAM/ Primary Examiner, Art Unit 2857
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