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 .
Response to Arguments
Claims 1-20 are pending. Claims 2 and 13 are amended.
Applicant’s arguments, filed 07/17/2026, with respect to specification objections have been fully considered. The objections of the specification have been withdrawn.
Applicant’s arguments, filed 07/17/2026, with respect to claim interpretation under 35 U.S.C 112(f) for Claims 1, 6, 12, and 17 have been fully considered and are persuasive. The claim interpretation of Claims 1, 6, 12, and 17 have been withdrawn.
Applicant’s arguments, filed 07/17/2026, with respect to claim rejections under 35 U.S.C 101 for Claims 1-9 and 12-18 have been fully considered and are persuasive. The rejection of Claims 1-9 and 12-18 for 35 U.S.C 101 have been withdrawn.
Applicant’s arguments, filed 07/17/2026, with respect to claim rejections under 35 U.S.C 103 for Claims 1-20 have been fully considered and are not persuasive. A new rejection under 35 U.S.C 103 is applied for Claims 2 and 13 under prior arts Samad (US 20090091441 A1), and Jain (US 20220245011 A1), in light of applicant’s amendments.
With regards to Claims 1 and 12, applicant argues that Samad, in Figure 4, does not disclose the status data being obtained by the proxy processor from the data acquisition module as the status data y is sent from the process 54 to the PNC 53 through the feedback loop and separately routes the status data y to the Criterion Evaluation Unit 58 and the Gradient Computation 59.
Examiner respectfully disagrees. Claims 1 and 12 are directed to the status data being received by the proxy processor from the data acquisition module, which is taught by Samad in Figure 4, with the additional step of training via the PNC Training Algorithm 60. Therefore, Claims 1 and 12 recite a transfer of the status data to the proxy processor, which is also done in Samad with additional processing.
Applicant further argues that the Criterion Evaluation Unit 58 and the Gradient Computation 59 are not a data acquisition module as they evaluate a control criterion and compute the gradient of that criterion with respect to the network weights, and therefore do not acquire the status data and provide it to the proxy processor as the status data is sent to the PNC 53 through the feedback of the closed loop simulation, independent of the Criterion Evaluation Unit 58 and the Gradient Computation 59.
Examiner respectfully disagrees. The Criterion Evaluation Unit 58 and the Gradient Computation 59 both acquire the status data y as an initial step to evaluate a control criterion and compute the gradient of that criterion with respect to the network weights.
Applicant further argues “that the Office Action fails to articulate a sufficient reason why a person of ordinary skill in the art would have combined the embodiment of Figure 3 with the embodiment of Figure 4 of Samad”. Applicant argues that Figure 3 of Samad discloses a real-time operation of the PNC 33 alongside the extant controller 31 which actively controls process 32. In contrast, Figure 4 of Samad discloses an offline training simulation that involves the training of PNC 53. Applicant argues that the Office Action’s stated rational is a conclusory restatement of the desired result rather than an articulated reason grounded in its teachings.
Examiner respectfully disagrees. The rational provided by the Office Action is supported by "some articulated reasoning with some rational underpinning" under KSR International Co. v. Teleflex Inc., 550 U.S. 398, 418 (2007) as the rationale of “in order to build a control system that can efficiently analyze a processor and its performance by training a secondary processor to perform the same as the original processor” combines Figures 3 and 4 of Samad for the purpose of efficiently analyzing a processor’s performance, which serves as “some articulated reasoning with some rational underpinning”.
With regards to Claims 2 and 13, Applicant’s arguments are directed to claim amendments that are addressed in the 35 U.S.C 112 and 35 U.S.C 103 rejections for Claims 2 and 13 below.
With regards to Claims 7 and 18 applicant argues that “The Office Action characterized the signal on line 41 of Figure 3 of Samad as a fault in the proxy processor, and a fault of the proxy processor is not a fault of the control system processor or the operating apparatus.”
Examiner respectfully disagrees. The signal output from the training is also sent to the operating apparatus P in Figure 4 of Samad, see the training weights w in Figure 4, which is an input to the process P. Therefore, the training step of Samad does teach that the fault condition is reflective of a fault in the process P.
Applicant further argues that “the signal on line 41 in Figure 3 of Samad is a weight adjustment returned to the PNC 33 during training, and is not an alert indicating a fault condition, and Samad describes no alert of any kind in connection with that signal”.
Examiner respectfully disagrees. The training step in Samad acts as an optimizer to improve the weights of the PNC in Figures 3 and 4 of Samad, as a previous weights in the training step for the PNC are updated as they do not accurately reproduce the output of the machine learning dictated by the PNC in Figures 3 and 4. As such, the erroneous weights are updated in the training, making the signal containing the weights reflective of an alert indicating a fault condition.
With regards to Claims 8, applicant argues that Samad does not teach that the proxy processor is configured to “process the status data contemporaneously with processing of the status data by the control system processor, at a same processing speed”, where the PNC in Figures 3 and 4 is the proxy processor, as duplicating the extant controller refers to “reproducing its input-output control law, not matching its computational execution speed. Samad nowhere states that the PNC and the extant controller operate at the same processing speed.”. The Applicant points out that Samad mentions that the PNC is “likely” to result in faster execution than the extant controller, not the same execution speed.
Examiner respectfully disagrees. Samad does teach that the PNC is configured to “process the status data contemporaneously with processing of the status data by the control system processor, at a same processing speed” as duplicating the extant controller’s behavior involves duplicating the processing speed, and this is further supported by the applicant’s recitation that the proxy processor “mimics” the control system processor in independent Claim 1. In addition, Samad teaches that “The neural network can be "trained" to mimic an existing controller” in the Abstract. Under a broadest reasonable interpretation, mimicking between two processors involves matching performance standards such as processing times.
With regards to Claim 9 applicant argues that Samad does not teach that the proxy processor is configured to "process the status data at a processing speed that is one of faster or slower than a processing speed of the control system processor" , which is the PNC in Figures 3 and 4, as the background of Samad discloses that the neural network controllers are compared to a class of conventional “algorithmic controllers”, but not to the extant controller operating alongside the PNC within the same deployed system.
Examiner respectfully disagrees. Samad does teach that the PNC is configured to "process the status data at a processing speed that is one of faster or slower than a processing speed of the control system processor" as the Abstract states “The neural network can be "trained" to mimic an existing controller which may or not receive inputs of control system parameters. The trained neural network controller may have advantages of faster execution and reduced code size. ” Therefore, the neural network, which is the PNC in Figures 3 and 4, is specifically trained to have faster execution as a candidate for improvement upon the existing controller.
With regards to Claims 4, 10, 11, 15, 19, and 20 applicant argues “that Mishra fails to disclose, teach, or suggest the limitations asserted by Applicant to be omitted from Samad”, making Claims 4, 10, 11 which are dependent on independent Claim 1, and Claims 15, 19, and 20, which are dependent on independent Claim 12, are patentably distinguishable over Samad in view of Mishra and should also be allowed for the reasons above and due to the limitations in the dependent claims.
Examiner respectfully disagrees. Samad does teach the limitations of Claims 1 and 12, see arguments above and the 35 U.S.C. 103 rejections for Claims 1 and 12 below. In addition, Samad and Mishra teach the limitations of Claims 4, 10, 11, 15, 19, and 20 (see the 35 U.S.C. 103 rejections for Claims 4, 10, 11, 15, 19 and 20 below).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2 and 13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2 lines 2-3 and Claim 13 lines 2-3 recite that the proxy processor “does not provide the control data to the operating apparatus”. It is unclear how the proxy processor can send the control data to the operating apparatus, as the control data is recited in Claim 1 and 12 to come from the control system processor only, so the proxy processor does not have the control data. Therefore, Claims 2 and 13 are indefinite.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1,3, 5-9, 12,14, and 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Samad (US 5486996 A).
With regards to Claim 1, Samad teaches (in the embodiment of Fig. 3)
an operating apparatus (See Fig. 3 the controlled process P);
a control system processor (See Fig. 3 the extant controller C, which processes input signals y and yr) configured to control the operating apparatus by dynamically providing control data to the operating apparatus (See Fig. 3, where the extant controller C (i.e. the control system processor) sends a control signal output u (i.e. control data) via line 34, therefore controlling P (i.e. the operating apparatus), see Column 6 Lines 52-53 “wherein the control signal output (u) is forwarded to the process (P) on line 34”. This is considered dynamically providing as the signal u is input to P, and the output y is further used as feedback into C (i.e. the control system processor), therefore updating C to output a new signal u (i.e. a dynamically provided control signal)), the control system processor further configured to dynamically receive status data from the operating apparatus (See Column 6 Lines 52-57 “wherein the control signal output (u) is forwarded to the process (P) (i.e. the operating apparatus) on line 34 and the output y (i.e. status data) from the process is output on line 35. The output y is also fed on line 36 back into the extant controller 31 (i.e. the control system processor) and the PNC 33. Both produce their control signals in response to the output y and the desired output yr received from the input line 37”. This is considered dynamically receiving as the signal y is input to C, and the signal y is further used as feedback into C (i.e. the control system processor), therefore updating C to output a new signal u, therefore the signal y is dynamically received as it can cause a change in the next output signal u.), wherein an operational state of the control system processor is defined in accordance with the status data received from the operating apparatus (See Column 6 Lines 52-57 “wherein the control signal output (u) is forwarded to the process (P) (i.e. the operating apparatus) on line 34 and the output y (i.e. status data) from the process is output on line 35. The output y is also fed on line 36 back into the extant controller 31 (i.e. the control system processor) and the PNC 33. Both produce their control signals (i.e. control data) in response to the output y and the desired output yr received from the input line 37”. The operational state is referring to the performance of the C (i.e. the control system processor) outputting a control signal, where the control signal is defined in accordance with the input signal y (i.e. the status data), hence affecting the operational state of C.);
a proxy processor (See Fig. 3 the PNC 33, which processes input signals y, yr, and the parameters 43) configured to:
obtain the status data from the operating apparatus (See Fig. 3, where the PNC (i.e. the proxy processor) is configured to obtain the output signal y (i.e. the status data)); and
mimic, using the status data, the operational state of the control system processor (See Column 7 Lines 13-15 “It should be noted that if an existing controller is available that provides satisfactory, robust performance (i.e. an operational state), a neural network can be trained to mimic it (i.e. the operational state of the control system processor, as the neural network wants to mimic the robust performance), as shown here.”. See also Fig. 3, where the output signal y (i.e. the status data) is input to the PNC 33 (i.e. the neural network, See Column 5 lines 15-17 “There has also been discussion concerning the various forms the individual neural network for the PNC's could take”)).
Samad is silent to the language of (in the embodiment of Fig. 3)
a data acquisition module configured to also dynamically receive the status data from the operating apparatus; and
a proxy processor communicatively coupled to the data acquisition module, and configured to:
obtain the status data from the data acquisition module; and
using the status data obtained from the data acquisition module.
Samad teaches in an alternate embodiment (in the embodiment of Fig. 4)
a data acquisition module configured to also dynamically receive the status data from the operating apparatus (See Fig. 4, where the Criterion Evaluation 58 and Gradient Evaluation 59 combined serve as a data acquisition module, as they dynamically receive the output signal y (i.e. the status data, and they receive any other signals necessary for training the PNC, where this is a dynamic receiving as the Criterion Evaluation 58 and Gradient Evaluation 59 use the received signal y and the other signals to constantly train the PNC, see Column 9 lines 7-9 “The output y and all the other signals relevant to evaluation by criterion set by the user are entered into the criteria evaluation unit 58 and the gradient computation unit 59.”); and
a proxy processor communicatively coupled to the data acquisition module (See Fig. 4, the PNC 53 (i.e. the proxy processor, as it processes input data y, 55s, and the input signal in Fig. 4), which is connected to the Criterion Evaluation 58 and Gradient Evaluation 59 (i.e. the data acquisition module), via the output w, which serves as an input to P, and correspondingly the output of P (i.e. the signal y) serves as an input to the PNC 53), and configured to:
obtain the status data from the data acquisition module (See Fig. 4, the P has only 2 outputs for the signal y (one to the PNC (the first branch) and the second to the Criterion Evaluation 58 and Gradient Evaluation 59 (the second branch) (i.e. the data acquisition module)). Since the output y and the Criterion Evaluation 58 and Gradient Evaluation 59 are enclosed in the Closed-Loop Simulation, the input signal y can be output by the Criterion Evaluation 58 and Gradient Evaluation 59 before being input into the PNC in Fig. 4, as this is already recited in the first branch in Fig. 4.); and
using the status data obtained from the data acquisition module (See Fig. 4, the P has only 2 outputs for the signal y (one to the PNC (the first branch) and the second to the Criterion Evaluation 58 and Gradient Evaluation 59 (the second branch) (i.e. the data acquisition module)). Since the output y and the Criterion Evaluation 58 and Gradient Evaluation 59 are enclosed in the Closed-Loop Simulation, the input signal y can be output by the Criterion Evaluation 58 and Gradient Evaluation 59 before being input into the PNC in Fig. 4, as this is already recited in the first branch in Fig. 4.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Fig. 3 in Samad wherein a data acquisition module configured to also dynamically receive the status data from the operating apparatus; and a proxy processor communicatively coupled to the data acquisition module, and configured to: obtain the status data from the data acquisition module; and
using the status data obtained from the data acquisition module like in Fig. 4 of Samad in order to build a control system that can efficiently analyze a processor and its performance by training a secondary processor to perform the same as the original processor.
With regards to Claim 3, Samad teaches the limitations of Claim 1. Samad is silent to the language of (in the embodiment of Fig. 3)
wherein the proxy processor is a virtual processor implemented in software.
Samad teaches (in the embodiment of Fig. 4)
wherein the proxy processor is a virtual processor implemented in software (See Column 9 lines 42-43 “This training simulator system 50 (which contains the proxy processor PNC) may be built as some combination of hardware and software, or all software”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Samad in Fig. 3 wherein the proxy processor is a virtual processor implemented in software like in Fig. 4 of Samad in order to have a more compact design for the control system, as software will not use any physical space.
With regards to Claim 5, Samad teaches the limitations of Claim 1. Samad further teaches (in the embodiment of Fig. 3)
the control system processor (See Fig. 3 the extant controller C, which processes input signals y and yr).
Samad is silent to the language of (in the embodiment of Fig. 3)
wherein the data acquisition module is further configured to receive the control data provided to the operating apparatus.
Samad teaches (in the embodiment of Fig. 4)
wherein the data acquisition module (See Fig. 4, where the Criterion Evaluation 58 and Gradient Evaluation 59 combined serve as a data acquisition module, as they dynamically receive the output signal y (i.e. the status data) from P (i.e. the operational apparatus), and they receive any other signals necessary for training the PNC, where this is a dynamic receiving as the Criterion Evaluation 58 and Gradient Evaluation 59 use the received signal y and the other signals to constantly train the PNC, see Column 9 lines 7-9 “The output y and all the other signals relevant to evaluation by criterion set by the user are entered into the criteria evaluation unit 58 and the gradient computation unit 59.”) is further configured to receive the control data provided to the operating apparatus (See Column 9 lines 7-9 “The output y and all the other signals relevant to evaluation by criterion set by the user are entered into the criteria evaluation unit 58 and the gradient computation unit 59.” Therefore, the data acquisition module can receive the control data (i.e. the signal u in Fig. 4) provided to the operating apparatus (i.e. P in Fig. 4)).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Fig. 3 of Samad wherein the data acquisition module is further configured to receive the control data provided to the operating apparatus like in Fig. 4 of Samad in order to have a more organized circuitry for directing the control data to a single module, which in this case is the data acquisition module.
With regards to Claim 6, Samad teaches the limitations of Claim 5. Samad further teaches (in the embodiment of Fig. 3)
a performance monitoring module communicatively coupled to the proxy processor (See Fig. 3, where processor 39 (i.e. the performance monitoring module) is connected to the PNC (i.e. the proxy processor), see Column 7 lines 11-13 “The result of the learning algorithm processing is a signal (line 41) to the PNC 33 to adjust the internal weights or, in fact, its configuration, if that is desired.”), the performance monitoring module configured to:
receive proxy output data generated by the proxy processor based on the status data obtained (See Fig. 3 where the proxy output data is from line 34a generated by the PNC (i.e. the proxy processor), and this data is dependent on the incoming data y (i.e. the status data) obtained by the PNC):
obtain the control data (See Fig. 3, where the processor 39 (i.e. the performance monitoring module) obtains the signal u (i.e. the control data) via line 38)
compare the proxy output data to the control data (See Fig.3, where the comparing step is done in the “+” component by subtracting two signals, see Column 7 lines 3-4 “ processor 39 (i.e. the performance monitoring module) takes the difference of the outputs from line 34a(uPNC) (i.e. the proxy output data) and 38(u) (i.e. control data)”.); and
validate the proxy processor based on the comparing (See Column 7 lines 11-13 “The result of the learning algorithm processing is a signal (line 41) to the PNC 33 (i.e. the proxy processor) to adjust the internal weights or, in fact, its configuration, if that is desired.” In other words the learning algorithm outputs a signal to check or validate the performance of the PNC.).
Samad is silent to the language of (in the embodiment of Fig. 3)
coupled to the data acquisition module and obtained from the data acquisition module.
Samad teaches (in the embodiment on Fig. 4)
coupled to the data acquisition module (See Fig. 4, where the Criterion Evaluation 58 and Gradient Evaluation 59 (i.e. the data acquisition module when they are combined) inputs the signal y (i.e. the status data) and multiple other signals see Column 9 lines 7-9 “The output y and all the other signals relevant to evaluation by criterion set by the user are entered into the criteria evaluation unit 58 and the gradient computation unit 59.”, allowing it to be coupled to multiple components, as such signals can come from separate components) and obtained from the data acquisition module (See Fig. 4, where the Criterion Evaluation 58 and Gradient Evaluation 59 (i.e. the data acquisition module when they are combined) outputs two signals, allowing the signals to be obtained by another component(s).).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Fig. 3 of Samad wherein Samad couples to the data acquisition module and obtains from the data acquisition module like in Fig. 4 of Samad in order to have a more organized circuitry for directing the control data to a single module, which in this case is the data acquisition module.
With regards to Claim 7, Samad teaches the limitations of Claim 6. Samad further teaches (in the embodiment of Fig. 3)
detect, using the proxy output data, a fault condition of at least one of the control system processor or the operating apparatus (See Column 7 lines 11-13 “The result of the learning algorithm processing (which is part of processor 39 (i.e. the performance monitoring module)) is a signal (line 41) to the PNC 33 (i.e. the proxy processor) to adjust the internal weights or, in fact, its configuration, if that is desired.” In other words the fault condition is whether to adjust the weights or its configuration (as this would correspond to a fault in the proxy processor in Fig. 3, the PNC.); and
generate an alert indicating the fault condition (See Column 7 lines 11-13 “The result of the learning algorithm processing (which is part of processor 39 (i.e. the performance monitoring module)) is a signal (line 41) (i.e. the alert) to the PNC 33 (i.e. the proxy processor) to adjust the internal weights or, in fact, its configuration, if that is desired.”).
With regards to Claim 8, Samad teaches the limitations of Claim 1. Samad further teaches (in the embodiment of Fig. 3)
process the status data contemporaneously with processing of the status data by the control system processor (See Fig. 3, where the output y is inputted into the PNC (i.e. the proxy processor) and C (i.e. the control system processor) at the same time, allowing them to process the signal y (i.e. the status data) contemporaneously), at a same processing speed (See Column 7 lines 25-27 “The PNC (i.e. the proxy processor) will then attempt to duplicate the extant controller (i.e. the control system processor) behavior as accurately as possible given the new parameters.” Therefore, the PNC will perform at the same processing speed as the extant controller, as this falls under the duplication of the extant controller’s behavior.).
With regards to Claim 9, Samad teaches the limitations of Claim 1. Samad further teaches (in the embodiment of Fig. 3)
process the status data at a processing speed that is one of faster or slower than a processing speed of the control system processor (See Abstract “The trained neural network controller (i.e. the proxy processor, which is the PNC in Fig. 3, see Column 5 lines 15-17 “There has also been discussion concerning the various forms the individual neural network for the PNC's could take”)) may have advantages of faster execution (i.e. process the status data (i.e. the output y in Fig. 3 which is input to the PNC) at a processing speed that is faster) and reduced code size.”).
With regards to Claim 12, Samad teaches (in the embodiment of Fig. 3)
dynamically providing, by the control system processor, control data to the operating apparatus (See Fig. 3, where the extant controller C (i.e. the control system processor, which processes input signals y and yr) sends a control signal output u (i.e. control data) via line 34, therefore controlling P (i.e. the operating apparatus), see Column 6 Lines 52-53 “wherein the control signal output (u) is forwarded to the process (P) on line 34”. This is considered dynamically providing as the signal u is input to P, and the output y is further used as feedback into C (i.e. the control system processor), therefore updating C to output a new signal u (i.e. dynamically provided control data));
dynamically receiving, by the control system processor, the status data from the operating apparatus (See Column 6 Lines 52-57 “wherein the control signal output (u) is forwarded to the process (P) (i.e. the operating apparatus) on line 34 and the output y (i.e. status data) from the process is output on line 35. The output y is also fed on line 36 back into the extant controller 31 (i.e. the control system processor) and the PNC 33. Both produce their control signals (i.e. control data) in response to the output y and the desired output yr received from the input line 37”. This is considered dynamically receiving as the signal y is input to C, and the signal y is further used as feedback into C (i.e. the control system processor), therefore updating C to output a new signal u, therefore the signal y is dynamically received as it can cause a change in the next output signal u.);
obtaining, by the proxy processor, the status data (See Fig. 3, where the PNC (i.e. the proxy processor, which processes input signals y, yr, and the parameters 43) is configured to obtain the output signal y (i.e. the status data)); and
mimicking, by the proxy processor using the status data, the operational state of the control system processor (See Column 7 Lines 13-15 “It should be noted that if an existing controller is available that provides satisfactory, robust performance (i.e. an operational state), a neural network can be trained to mimic it (i.e. the operational state of the control system processor, as the neural network wants to mimic the robust performance), as shown here.”. See also Fig. 3, where the output signal y (i.e. the status data) is input to the PNC 33 (i.e. the proxy processor, which is a neural network, See Column 5 lines 15-17 “There has also been discussion concerning the various forms the individual neural network for the PNC's could take”)).
Samad is silent to the language of (in the embodiment of Fig. 3)
the status data from the data acquisition module.
Samad teaches (in the embodiment of Fig. 4)
the status data from the data acquisition module (See Fig. 4, the P has only 2 outputs for the signal y (one to the PNC (the first branch) and the second to the Criterion Evaluation 58 and Gradient Evaluation 59 (the second branch) (i.e. where the two combined is the data acquisition module)). Since the output y and the Criterion Evaluation 58 and Gradient Evaluation 59 are enclosed in the Closed-Loop Simulation, the input signal y can be output by the Criterion Evaluation 58 and Gradient Evaluation 59 before being input into the PNC in Fig. 4, as this is already recited in the first branch in Fig. 4.);
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Fig. 3 in Samad wherein the status data from the data acquisition module is obtained like in Fig. 4 of Samad in order to build a control system that can efficiently analyze a processor and its performance by training a secondary processor to perform the same as the original processor.
With regards to Claim 14, Samad teaches the limitations of Claim 12. Samad is silent to the language of (in the embodiment of Fig. 3)
wherein the proxy processor is a virtual processor implemented in software.
Samad teaches (in the embodiment of Fig. 4)
wherein the proxy processor is a virtual processor implemented in software (See Column 9 lines 42-43 “This training simulator system 50 (which contains the proxy processor PNC) may be built as some combination of hardware and software, or all software”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Samad in Fig. 3 wherein the proxy processor is a virtual processor implemented in software like in Fig. 4 of Samad in order to have a more compact design for the control system, as software will not use any physical space.
With regards to Claim 16, Samad teaches the limitations of Claim 12. Samad further teaches (in the embodiment of Fig. 3)
the control system processor (See Fig. 3 the extant controller C, which processes input signals y and yr).
Samad is silent to the language of (in the embodiment of Fig. 3)
receiving, by the data acquisition module, the control data provided to the operating apparatus.
Samad teaches (in the embodiment of Fig. 4)
receiving, by the data acquisition module (See Fig. 4, where the Criterion Evaluation 58 and Gradient Evaluation 59 combined serve as a data acquisition module, as they dynamically receive the output signal y (i.e. the status data) from P (i.e. the operational apparatus), and they receive any other signals necessary for training the PNC, where this is a dynamic receiving as the Criterion Evaluation 58 and Gradient Evaluation 59 use the received signal y and the other signals to constantly train the PNC, see Column 9 lines 7-9 “The output y and all the other signals relevant to evaluation by criterion set by the user are entered into the criteria evaluation unit 58 and the gradient computation unit 59.”), the control data provided to the operating apparatus (See Column 9 lines 7-9 “The output y and all the other signals relevant to evaluation by criterion set by the user are entered into the criteria evaluation unit 58 and the gradient computation unit 59.” Therefore, the data acquisition module can receive the control data (i.e. the signal u in Fig. 4) provided to the operating apparatus (i.e. P in Fig. 4)).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Fig. 3 of Samad wherein receiving, by the data acquisition module, the control data provided to the operating apparatus is done like in Fig. 4 of Samad in order to have a more organized circuitry for directing the control data to a single module, which in this case is the data acquisition module.
With regards to Claim 17, Samad teaches the limitations of Claim 16. Samad further teaches (in the embodiment of Fig. 3)
receiving, by the performance monitoring module (See Fig. 3, where processor 39 (i.e. the performance monitoring module) is connected to the PNC (i.e. the proxy processor), see Column 7 lines 11-13 “The result of the learning algorithm processing is a signal (line 41) to the PNC 33 to adjust the internal weights or, in fact, its configuration, if that is desired.”), proxy output data generated by the proxy processor based on the status data (See Fig. 3 where the proxy output data is from line 34a generated by the PNC (i.e. the proxy processor), and this data is dependent on the incoming data y (i.e. the status data) obtained by the PNC);
obtaining, by the performance monitoring module, the control data (See Fig. 3, where the processor 39 (i.e. the performance monitoring module) obtains the signal u (i.e. the control data) via line 38);
comparing, by the performance monitoring module, the proxy output data to the control data (See Fig.3, where the comparing step is done in the “+” component (which is inside processor 39 (i.e. the performance monitoring module)) by subtracting two signals, see Column 7 lines 3-4 “ processor 39 (i.e. the performance monitoring module) takes the difference of the outputs from line 34a(uPNC) (i.e. the proxy output data) and 38(u) (i.e. control data)”.); and
validating, by the performance monitoring module, the proxy processor based on the comparing (See Column 7 lines 11-13 “The result of the learning algorithm processing (which is inside processor 39 (i.e. the performance monitoring module) is a signal (line 41) to the PNC 33 (i.e. the proxy processor) to adjust the internal weights or, in fact, its configuration, if that is desired.” In other words the learning algorithm outputs a signal to check or validate the performance of the PNC.).
Samad is silent to the language of (in the embodiment of Fig. 3)
obtained from the data acquisition module.
Samad teaches (in the embodiment on Fig. 4)
obtained from the data acquisition module (See Fig. 4, where the Criterion Evaluation 58 and Gradient Evaluation 59 (i.e. the data acquisition module when they are combined) outputs two signals, allowing the signals to be obtained by another component.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Fig. 3 of Samad wherein Samad obtains from the data acquisition module like in Fig. 4 of Samad in order to have a more organized circuitry for directing the control data to a single module, which in this case is the data acquisition module.
With regards to Claim 18, Samad teaches the limitations of Claim 17. Samad further teaches (in the embodiment of Fig. 3)
detecting, by the performance monitoring module and using the proxy output data, a fault condition of at least one of the control system processor or the operating apparatus (See Column 7 lines 11-13 “The result of the learning algorithm processing (which is part of processor 39 (i.e. the performance monitoring module)) is a signal (line 41) to the PNC 33 (i.e. the proxy processor) to adjust the internal weights or, in fact, its configuration, if that is desired.” In other words the fault condition is whether to adjust the weights or its configuration (as this would correspond to a fault in the proxy processor in Fig. 3, the PNC.); and
generating, by the performance monitoring module, an alert indicating the fault condition (See Column 7 lines 11-13 “The result of the learning algorithm processing (which is part of processor 39 (i.e. the performance monitoring module)) is a signal (line 41) (i.e. the alert) to the PNC 33 (i.e. the proxy processor) to adjust the internal weights or, in fact, its configuration, if that is desired.”).
Claim(s) 2 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Samad (US 5486996 A) in view of Jain (US 20220245011 A1).
With regards to Claim 2, Samad teaches the limitations of Claim 1. Samad further teaches (in the embodiment of Fig. 3)
wherein the proxy processor is a duplicate of the control system processor (See Column 7 lines 25-27 “The PNC (i.e. the proxy processor) will then attempt to duplicate the extant controller (i.e. the control system processor) behavior as accurately as possible given the new parameters.”).
Samad (in the embodiment of Fig. 3) is silent to the language of
but does not provide the control data to the operating apparatus.
Jain teaches
but does not provide the control data to the operating apparatus (See Para[0018] “Based on the received signals, the dual-core lockstep system 110 generates a plurality of functional output signals.” See also Figure 1, where the functional outputs, which is the control data to the operating apparatus, see Para[0002] “Reliability is a critical concern for meeting the safety requirements of a range of automotive applications including advanced driver assistance systems, electric power steering systems, adaptive cruise control systems, braking control systems and the like.”, is not output from the Processor II, which is the proxy processor. Therefore the functional output signals from Processor I is used for a range of automotive applications like braking control systems, which function as the operating apparatus. ).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Samad (in the embodiment of Fig. 3) wherein but does not provide the control data to the operating apparatus is done like in Jain in order to separately evaluate the performance of the control system processor and the proxy processor without interference of control instructions from the proxy processor to the operating apparatus.
With regards to Claim 13, Samad teaches the limitations of Claim 12. Samad further teaches (in the embodiment of Fig. 3)
wherein the proxy processor is a duplicate of the control system processor (See Column 7 lines 25-27 “The PNC (i.e. the proxy processor) will then attempt to duplicate the extant controller (i.e. the control system processor) behavior as accurately as possible given the new parameters.”).
Samad (in the embodiment of Fig. 3) is silent to the language of
but does not provide the control data to the operating apparatus.
Jain teaches
but does not provide the control data to the operating apparatus (See Para[0018] “Based on the received signals, the dual-core lockstep system 110 generates a plurality of functional output signals.” See also Figure 1, where the functional outputs, which is the control data to the operating apparatus, see Para[0002] “Reliability is a critical concern for meeting the safety requirements of a range of automotive applications including advanced driver assistance systems, electric power steering systems, adaptive cruise control systems, braking control systems and the like.”, is not output from the Processor II, which is the proxy processor. Therefore the functional output signals from Processor I is used for a range of automotive applications like braking control systems, which function as the operating apparatus. ).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Samad (in the embodiment of Fig. 3) wherein but does not provide the control data to the operating apparatus is done like in Jain in order to separately evaluate the performance of the control system processor and the proxy processor without interference of control instructions from the proxy processor to the operating apparatus.
Claim(s) 4, 10-11, 15, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Samad (US 5486996 A) in view of Mishra (US 20220100180 A1).
With regards to Claim 4, Samad teaches the limitations of Claim 1. Samad further teaches (in the embodiment of Fig. 3)
status data received by the control system processor (See Fig. 3 where the status data is y and it is received by the control system processor C).
Samad is silent to the language of (in the embodiment of Fig. 3)
wherein the data acquisition module is configured to persistently store all of the status data received by the control system processor from the operating apparatus.
Samad teaches (in the embodiment of Fig. 4)
wherein the data acquisition module is configured to persistently receive all of the status data received from the operating apparatus (See Fig. 4, where the Criterion Evaluation 58 and Gradient Evaluation 59 combined serve as a data acquisition module, as they dynamically receive the output signal y (i.e. the status data, and this is dynamically receiving as y constantly changes based on input signal u for P, and since this is done in a Closed Loop Simulation in Fig. 4, the receiving is done persistently) from P (i.e. the operational apparatus).).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Samad in Fig. 3 wherein the data acquisition module is configured to persistently receive all of the status data received from the operating apparatus like in Fig. 4 of Samad in order to have a module that can be in a constant state of receiving data for continuous training of the proxy controller.
Samad is silent to the language of (in the embodiments of Figs. 3 and 4)
persistently store.
Mishra teaches
persistently store (See Abstract “the SIS collects and stores health and diagnostic status data” and “retrieving, from the diagnostic status aggregator, the status data collected from the SIS responsive to the query”. Therefore, the SIS persistently stores the status data and this is done persistently because it is later retrieved due to a query.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Samad (in the embodiments of Figs. 3 and 4) wherein Samad persistently stores like in Mishra in order to have a module that can store data for later use.
With regards to Claim 10, Samad teaches the limitations of Claim 1. Samad further teaches (in the embodiment of Fig. 3)
the control system processor (See Fig. 3 the extant controller C, which processes input signals y and yr).
Samad (in the embodiment of Fig. 3) is silent to the language of
wherein the processor comprises a programmable logic controller (PLC).
Mishra teaches
wherein the processor comprises a programmable logic controller (PLC) (See Para[0015] “The safety system 120 comprises at least one safety programmable logic controller (PLC) main processor module 202 (i.e. the processor is a PLC)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Samad (in the embodiment of Fig. 3) wherein the processor comprises a programmable logic controller (PLC) like in Mishra in order to have a processor that can be programmed to function efficiently the method in the instant application.
With regards to Claim 11, Samad teaches the limitations of Claim 1. Samad further teaches (in the embodiment of Fig. 3)
the system (see Fig. 3 the entire circuit).
Samad (in the embodiment of Fig. 3) is silent to the language of
wherein the system is part of one of a baggage claim carousel, an automated warehouse, a theme park attraction, a vehicle assembly line machine, or an aviation system.
Mishra teaches
wherein the system is part of one of a baggage claim carousel, an automated warehouse, a theme park attraction, a vehicle assembly line machine, or an aviation system (See Para[0001] “Such fault tolerant controllers (i.e. a system) are often used in hazard protection systems (i.e., systems that regulate nuclear plant operation, turbomachinery (i.e. part of an aviation system), fire and gas sensing systems, and the like).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Samad (in the embodiment of Fig. 3) wherein the system is part of one of a baggage claim carousel, an automated warehouse, a theme park attraction, a vehicle assembly line machine, or an aviation system like in Mishra in order to clearly define a real-world use for the instant application.
With regards to Claim 15, Samad teaches the limitations of Claim 12. Samad further teaches (in the embodiment of Fig. 3)
status data received by the control system processor (See Fig. 3 where the status data is y and it is received by the control system processor C).
Samad is silent to the language of (in the embodiment of Fig. 3)
persistently storing, by the data acquisition module, all of the status data received by the control system processor from the operating apparatus.
Samad further teaches (in the embodiment of Fig. 4)
persistently receiving, by the data acquisition module, all of the status data received from the operating apparatus (See Fig. 4, where the Criterion Evaluation 58 and Gradient Evaluation 59 combined serve as a data acquisition module, as they dynamically receive the output signal y (i.e. the status data, and this is dynamically receiving as y constantly changes based on input signal u for P, and since this is done in a Closed Loop Simulation in Fig. 4, the receiving is done persistently) from P (i.e. the operational apparatus).).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Samad in Fig. 3 wherein persistently receiving, by the data acquisition module, all of the status data received from the operating apparatus is done like in Fig. 4 of Samad in order to have a module that can be in a constant state of receiving data for continuous training of the proxy controller.
Samad is silent to the language of (in the embodiment of Figs. 3 and 4)
persistently storing.
Mishra teaches
persistently storing (See Abstract “the SIS collects and stores health and diagnostic status data” and “retrieving, from the diagnostic status aggregator, the status data collected from the SIS responsive to the query”. Therefore, the SIS persistently stores the status data and this is done persistently because it is later retrieved due to a query.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Samad (in the embodiments of Figs. 3 and 4) wherein persistently storing is done like in Mishra in order to have a module that can store data for later use.
With regards to Claim 19, Samad teaches the limitations of Claim 12. Samad further teaches (in the embodiment of Fig. 3)
the control system processor (See Fig. 3 the extant controller C, which processes input signals y and yr).
Samad (in the embodiment of Fig. 3) is silent to the language of
wherein the processor comprises a programmable logic controller (PLC).
Mishra teaches
wherein the processor comprises a programmable logic controller (PLC) (See Para[0015] “The safety system 120 comprises at least one safety programmable logic controller (PLC) main processor module 202 (i.e. the processor is a PLC)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Samad (in the embodiment of Fig. 3) wherein the processor comprises a programmable logic controller (PLC) like in Mishra in order to have a processor that can be programmed to function efficiently the method in the instant application.
With regards to Claim 20, Samad teaches the limitations of Claim 12. Samad further teaches (in the embodiment of Fig. 3)
the system (see Fig. 3 the entire circuit).
Samad (in the embodiment of Fig. 3) is silent to the language of
wherein the system is part of one of a baggage claim carousel, an automated warehouse, a theme park attraction, a vehicle assembly line machine, or an aviation system.
Mishra teaches
wherein the system is part of one of a baggage claim carousel, an automated warehouse, a theme park attraction, a vehicle assembly line machine, or an aviation system (See Para[0001] “Such fault tolerant controllers (i.e. a system) are often used in hazard protection systems (i.e., systems that regulate nuclear plant operation, turbomachinery (i.e. part of an aviation system), fire and gas sensing systems, and the like).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Samad (in the embodiment of Fig. 3) wherein the system is part of one of a baggage claim carousel, an automated warehouse, a theme park attraction, a vehicle assembly line machine, or an aviation system like in Mishra in order to clearly define a real-world use for the instant application.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOSTOFA AHMED HISHAM whose telephone number is (571)272-8773. The examiner can normally be reached Monday - Friday, 7:00 a.m. - 4 p.m. ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Rastovski can be reached at (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOSTOFA AHMED HISHAM/Examiner, Art Unit 2857 /YOSHIHISA ISHIZUKA/Primary Examiner, Art Unit 2857
