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 .
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 1-18 are rejected under 35 U.S.C. 101
because the claimed invention is directed to an abstract idea without significantly
more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be
determined whether the claim is directed to one of the four statutory categories of
invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the
claim does fall within one of the statutory categories, the second step in the analysis is
to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A
analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined
whether or not the claims recite a judicial exception (e.g., mathematical concepts,
mental processes, certain methods of organizing human activity). If it is determined in
Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the
second prong (Step 2A, Prong 2), where it is determined whether or not the claims
integrate the judicial exception into a practical application. If it is determined at step 2A,
Prong 2 that the claims do not integrate the judicial exception into a practical
application, the analysis proceeds to determining whether the claim is a patent-eligible
application of the exception (Step 2B). If an abstract idea is present in the claim, any
element or combination of elements in the claim must be sufficient to ensure that the
claim integrates the judicial exception into a practical application, or else amounts to
significantly more than the abstract idea itself. Applicant is advised to consult the 2019
PEG for more details of the analysis.
Step 1
According to the first part of the analysis, in the instant case, claims 1-15, 16-17, 18 are directed to a method, a system and medium of monitoring a state of a ML model. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). Step 2A,
Step 2A, Prong 1
Following the determination of whether or not the claims fall within one of the four
categories (Step 1), it must be determined if the claims recite a judicial exception (e.g.
mathematical concepts, mental processes, certain methods of organizing human
activity) (Step 2A, Prong 1). In this case, the claims are determined to recite a judicial
exception as explained below.
Regarding Claims 1, 16 and 18 these claims recite
providing a model of the aircraft system, wherein the model comprises representations of components of the aircraft system and connections between the components, each component associated with an operational mode status, the connections between components defining a plurality of series component paths within the model, each path having an order of priority of the respective components and a set of operational mode status combinations;
applying to each input node of the machine learning classifier an operational mode status of a respective component and a node priority determined by the order of priority of the respective series component path;
varying, for an operational mode status combination, an input node state of an input node having a relatively low node priority;
indicating a normal state of the machine learning classifier where output node states do not vary in response to the varying of the input node state; and
indicating an altered state of the machine learning classifier where a hidden layer node state and an output node state vary in response to the varying of the input node state.
The claims recite a mental process. As set forth in MPEP 2106.04(a)(2)(III)(C), “Claims can recite a mental process even if they are claimed as being performed on a computer”. These are recited at a high level such that they could be performed by a human user, simply using a computer as a tool-see spec, [0045]-[0054], Fig. 3. Thus, the claim recites abstract ideas.
Step 2A, Prong 2
Following the determination that the claims recite a judicial exception, it must be
determined if the claims recite additional elements that integrate the exception into a
practical application of the exception (Step 2A, Prong 2). In this case, after considering
all claim elements individually and as an ordered combination, it is determined that the
claims do not include additional elements that integrate the exception into a practical
application of the exception as explained below.
In Prong Two, a claim is evaluated as a whole to determine whether the recited judicial exception is integrated into a practical application of that exception. A claim is not “directed to” a judicial exception, and thus is patent eligible, if the claim as a whole integrates the recited judicial exception into a practical application of that exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d). The claims recite an abstract idea and further the claims as a whole does not integrate the recited judicial exception into a practical application of the exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d).
Regarding Claims 1, 16, 18 these claims
This limitations recite using one or more neural networks as a tool to perform an
abstract idea, which is not indicative of integration into a practical application. MPEP 2106.05(f).)
Mere Instructions to Apply an Exception. Do the additional element(s) amount to merely the words “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer? (Yes)
Step 2B
Based on the determination in Step 2A of the analysis that the claims are
directed to a judicial exception, it must be determined if the claims contain any element
or combination of elements sufficient to ensure that the claim amounts to significantly
more than the judicial exception (Step 2B). In this case, after considering all claim
elements individually and as an ordered combination, it is determined that the claims do
not include additional elements that are sufficient to amount to significantly more than
the judicial exception for the same reasons given above in the Step 2A, Prong 2
analysis. Furthermore, each additional element identified above as being insignificant
extra-solution activity is also well-known, routine, conventional as described below.
Claims 1, 16 and 18: The claims do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components and field of use/technological environment which do not amount to significantly more than the abstract idea. The underlying concept merely receives information, analyzes it, and store the results of the analysis – this concept is not meaningfully different than concepts found by the courts to be abstract (see Electric Power Group, collecting information, analyzing it, and displaying certain results of the collection and analysis; see Cybersource, obtaining and comparing intangible data; see Digitech, organizing information through mathematical correlations; see Grams, diagnosing an abnormal condition by performing clinical tests and thinking about the results; see Cyberfone, using categories to organize store and transmit information; see Smartgene, comparing new and stored information and using rules to identify options). Further the claimed invention appears to be something that can be performed by head and hand (Gottschalk v. Benson). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as a combination do not amount to significantly more than the abstract idea. For example, claim 1 recites the additional elements of applying to each input node of the machine learning classifier an operational mode status of a respective component and a node priority determined by the order of priority of the respective series component path; varying, for an operational mode status combination, an input node state of an input node having a relatively low node priority; indicating a normal state of the machine learning classifier where output node states do not vary in response to the varying of the input node state; and
indicating an altered state of the machine learning classifier where a hidden layer node state and an output node state vary in response to the varying of the input node state.
These elements are recited at a high level of generality and are well-understood, routine, and conventional activities in the computer art. Generic computers performing generic computer functions, without an inventive concept, do not amount to significantly more than the abstract idea. Looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims do not amount to significantly more than the abstract idea itself.
Step 2A/2B Prong 2 Dependent Claims
Regarding to claim 2-3
Claim 2-3 merely recite other additional elements that define indicating a normal state of the ML classifier with output varying or not with varying input node which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 4
Claim 4 merely recite other additional elements that define path priority of the ML classifier which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 5-6
Claim 5-6 merely recite other additional elements that define performing condition which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 7-8
Claim 7-8 merely recite other additional elements that define ML classifier which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 9-11
Claim 9-11 merely recite other additional elements that define variations of the output node of the ML classifier which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 12-13
Claim 12-13 merely recite other additional elements that define variations of input node of the ML classifier which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 14
Claim 14 merely recite other additional elements that define grouping of nodes of the ML classifier which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 15, 17
Claim 15, 17 merely recite other additional elements that define the aircraft system which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-10, 12-18 are rejected under 35 U.S.C. 103 as being unpatentable over Greve et al. (Greve) US 11360476 in view of Thakkar et al. (Thakkar) US 2022/0051198 and Hatami-Hanza US 2022/0245109
In regard to claim 1, Greve disclose A method of monitoring a state of a machine learning classifier configured to determine an operational mode of an aircraft system, (Fig. 2, col. 1, line 28-col. 62, col. 7, line 8-col. 52, monitoring aircraft control system using NN to determine an operation state of the aircraft system based on input)
the machine learning classifier having an input layer comprising input nodes, an output layer comprising output nodes, at least one hidden layer between the input layer and the output layer, the hidden layer comprising hidden layer nodes, and the machine learning classifier configured to provide an output for determining the operational mode, the method comprising: (Fig. 2, col. 5, line 7-col. 9, line 62, the NN include input layer with input nodes, output layer with output nodes, and hidden layers in between, the hidden layers have nodes and output the operation state of the aircraft system)
providing a model of the aircraft system, (Fig. 2, col. 5, line 7-col. 9, line 62 a NN is provided)
applying to each input node of the machine learning classifier an operational mode status of a respective component; (Fig. 2, col. 3, line 47-col. 4, line 34, col. 5, line 5-col. 6-line 37, col. 6, line 61-col. 7, line 8, col. 9, line 11-col. 9, line 37, input sensors with input conditions based on input criteria with state information)
indicating a normal state of the machine learning classifier, (col. 1, line 28-49, provide a signal related a proper operation of the NN in response to the monitor signal)
and indicating an altered state of the machine learning classifier. col. 1, line 28-49, claim 1, col. 4, line 6-34, provide a signal related an improper operation of the NN in response to the monitor signal)
But Greve fail to explicitly disclose “wherein the model comprises representations of components of the aircraft system and connections between the components, each component associated with an operational mode status, the connections between components defining a plurality of series component paths within the model, each path having a set of operational mode status combinations;”
Thakkar disclose wherein the model comprises representations of components of the aircraft system and connections between the components, each component associated with an operational mode status, the connections between components defining a plurality of series component paths within the model, each path having a set of operational mode status combinations; ([0006]-[0008] [0039]-[0047] [0055]-[0057] aircraft parts are interrelated and connected with downstream parts and each part associated with its operation state data)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Thakkar ‘s maintaining an aircraft with automated acquisition of aircraft parts into Greve’s invention as they are related to the same field endeavor of aircraft related ML training and learning. The motivation to combine these arts, as proposed above, at least because Thakkar’s ML model with aircraft parts flow path would help to provide more path related relationship into Greve’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more path related relationship associated with aircraft parts in ML model would help to improve accuracy of prediction precision.
But Greve and Thakkar fail to explicitly disclose “each path having an order of priority of the respective components, and a node priority determined by the order of priority of the respective series component path; varying, for an operational mode status combination, an input node state of an input node having a relatively low node priority; where output node states do not vary in response to the varying of the input node state; and where a hidden layer node state and an output node state vary in response to the varying of the input node state.”
Hatami-Hanza disclose each path having an order of priority of the respective components, (Fig. 4, [0007]- [0025] input the number of parts and with each connection path is grouped and having a predefined order with different value significances for the group set) and a node priority determined by the order of priority of the respective series component path; (Fig. 4, [0007]- [0025][0192]-[0199][0273]-[0278] input the number of parts and with each connection path is grouped and having a predefined order with different value significances for group set and association strengths of the state components (node of the neural net))
varying, for an operational mode status combination, an input node state of an input node having a relatively low node priority; ([0007]-[0025] [0030]-[0032] [0218]-[0225] [0273]-[0278][0346]-[0375] [0405]-[0410] change the input node state value for a connection path with lower significances)
where output node states do not vary in response to the varying of the input node state; (Fig. 19, [0077] [131]-[0134][0219]-[0225] [0273]-[0278] [0372]-[0374][0405]-[0410] the output node state would not change if a state component of the input node associated with the weight (importance) corresponding to the input node is 0, although the input state is adjusted, (v*w) is filtered out, here it discloses a trigger condition)
and where a hidden layer node state and an output node state vary in response to the varying of the input node state. (Fig. 19, [0219]-[0225][0273]-[0278] [0372]-[0374] the hidden layer node state and the output node state would change if the state component value of the input node associated with the weight (importance) corresponding to the state component input node is adjusted, here it disclose a trigger condition. Note: please further define how varying the input node state changes or not changes output node state to help move forward the prosecution)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Hatami-Hanza‘s state navigation using ML into Thakkar and Greve’s invention as they are related to the same field endeavor of ML training and learning. The motivation to combine these arts, as proposed above, at least because Hatami-Hanza state navigation using ML would help to provide state calculation using ML into Thakkar and Greve’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing state calculation using ML would help to improve the prediction of the artificial neural network.
In regard to claim 2, Greve, Thakkar and Hatami-Hanza disclose The method according to Claim 1,
Greve disclose wherein the method comprises indicating a normal state of the machine learning classifier (col. 1, line 28-49, provide a signal related a proper operation of the NN in response to the monitor signal)
But Greve, Thakkar fail to explicitly disclose “where hidden layer node states of hidden layer nodes linked to the input node do not vary in response to the varying of the input node state.”
Hatami-Hanza disclose where hidden layer node states of hidden layer nodes linked to the input node do not vary in response to the varying of the input node state.
(Fig. 19, [0077] [131]-[0134][0273]-[0278] [0372]-[0374][0405]-[0410] hidden layer node states of hidden layer nodes (outputs) connected to the input state node would not change if the input state component of the input node associated with the weight (importance) corresponding to the input node is 0, although the input state is adjusted, (v*w) is filtered out, here it discloses a trigger condition)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Hatami-Hanza‘s state navigation using ML into Thakkar and Greve’s invention as they are related to the same field endeavor of ML training and learning. The motivation to combine these arts, as proposed above, at least because Hatami-Hanza state navigation using ML would help to provide state calculation using ML into Thakkar and Greve’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing state calculation using ML would help to improve the prediction of the artificial neural network.
In regard to claim 3, Greve, Thakkar and Hatami-Hanza disclose The method according to Claim 1,
Greve disclose wherein the method comprises indicating a normal state of the machine learning classifier (col. 1, line 28-49, provide a signal related a proper operation of the NN in response to the monitor signal)
But Greve, Thakkar fail to explicitly disclose “where hidden layer node states of hidden layer nodes linked to the input node vary in response to the varying of the input node state, and output node states do not vary in response to the varying of the input node state.”
Hatami-Hanza disclose where hidden layer node states of hidden layer nodes linked to the input node vary in response to the varying of the input node state, and output node states do not vary in response to the varying of the input node state.
(Fig. 19, [0077] [131]-[0134][0273]-[0278] [0372]-[0374][0405]-[0410] the hidden layer nodes state would change if the state component value of the input node associated with the weight (importance) corresponding to the state component input node is adjusted, and if hidden layer node states of hidden layer nodes associated with the weights (importance) corresponding to the nodes are 0, (v*w) are filtered out, the output node states do not change, here it discloses a trigger condition)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Hatami-Hanza‘s state navigation using ML into Thakkar and Greve’s invention as they are related to the same field endeavor of ML training and learning. The motivation to combine these arts, as proposed above, at least because Hatami-Hanza state navigation using ML would help to provide state calculation using ML into Thakkar and Greve’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing state calculation using ML would help to improve the prediction of the artificial neural network.
In regard to claim 4, Greve, Thakkar and Hatami-Hanza disclose The method according to Claim 1,
But Greve, Thakkar fail to disclose “wherein each path comprises a respective path priority, and the operational mode determined by the machine learning classifier is based at least in part on the path priorities.”
Hatami-Hanza disclose wherein each path comprises a respective path priority, and the operational mode determined by the machine learning classifier is based at least in part on the path priorities (Fig. 4, [0007]- [0025][0192]-[0199][0245] [0273]-[0278] each connection path is grouped and having a predefined order with different value significances for group set and association strengths of the state components (node of the neural net) and the operation based on the connection path significances)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Hatami-Hanza‘s state navigation using ML into Thakkar and Greve’s invention as they are related to the same field endeavor of ML training and learning. The motivation to combine these arts, as proposed above, at least because Hatami-Hanza state navigation using ML would help to provide state calculation using ML into Thakkar and Greve’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing state calculation using ML would help to improve the prediction of the artificial neural network.
In regard to claim 5, Greve, Thakkar and Hatami-Hanza disclose The method according to Claim 1,
But Greve and Hatami-Hanza fail to explicitly disclose “wherein the method is performed when the aircraft system is non-operational.”
Thakkar disclose wherein the method is performed when the aircraft system is non-operational. ([0039] [0055]-[0056] the aircraft parts have cascade of failures, for example)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Thakkar ‘s maintaining an aircraft with automated acquisition of aircraft parts into Hatami-Hanza and Greve’s invention as they are related to the same field endeavor of aircraft related ML training and learning. The motivation to combine these arts, as proposed above, at least because Thakkar’s ML model with aircraft parts flow path would help to provide more path related relationship into Hatami-Hanza and Greve’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more path related relationship associated with aircraft parts in ML model would help to improve accuracy of prediction precision.
In regard to claim 6, Greve, Thakkar and Hatami-Hanza disclose The method according to Claim 1,
But Greve and Hatami-Hanza fail to explicitly disclose “wherein the method is performed during operation of the aircraft system.”
Thakkar disclose wherein the method is performed during operation of the aircraft system. ([0006]-[0008] [0017][0042]-[0043] during aircraft in-service operation)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Thakkar ‘s maintaining an aircraft with automated acquisition of aircraft parts into Hatami-Hanza and Greve’s invention as they are related to the same field endeavor of aircraft related ML training and learning. The motivation to combine these arts, as proposed above, at least because Thakkar’s ML model with aircraft parts flow path would help to provide more path related relationship into Hatami-Hanza and Greve’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more path related relationship associated with aircraft parts in ML model would help to improve accuracy of prediction precision.
In regard to claim 7, Greve, Thakkar and Hatami-Hanza disclose The method according to Claim1,
But Greve, Thakkar fail to explicitly disclose “wherein at least one of an input node state, a hidden layer node state, and an output node state, are bounded by maximum and minimum values.”
Hatami-Hanza disclose wherein at least one of an input node state, a hidden layer node state, and an output node state, are bounded by maximum and minimum values. (Fig. 5, Fig. 19, [0113][0213]-[0217][0228]-[0237] [0372]-[0378] [0403]-[0410] with input node state, a hidden node state and output state and the data are normalized with boundary)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Hatami-Hanza‘s state navigation using ML into Thakkar and Greve’s invention as they are related to the same field endeavor of ML training and learning. The motivation to combine these arts, as proposed above, at least because Hatami-Hanza state navigation using ML would help to provide state calculation using ML into Thakkar and Greve’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing state calculation using ML would help to improve the prediction of the artificial neural network.
In regard to claim 8, Greve, Thakkar and Hatami-Hanza disclose The method according to Claim1,
Greve disclose wherein the machine learning classifier comprises a neural network. (Fig. 2, col. 5, line 7-col. 9, line 62, the NN include input layer with input nodes, output layer with output nodes, and hidden layers in between, the hidden layers have nodes and output the operation state of the aircraft system)
In regard to claim 9, Greve, Thakkar and Hatami-Hanza disclose The method according to Claim 8,
Greve, Thakkar fail to explicitly disclose “wherein variation of output node states and/or hidden layer node states are determined based on at least one of a current output node state, a current hidden layer node state, a weight of a connection between an input layer node and a linked hidden layer node, a weight of a connection between a hidden layer node and a linked subsequent hidden layer node, and a weight of a connection between a hidden layer node and a linked output node.”
Hatami-Hanza disclose wherein variation of output node states and/or hidden layer node states are determined based on at least one of a current output node state, a current hidden layer node state, a weight of a connection between an input layer node and a linked hidden layer node, a weight of a connection between a hidden layer node and a linked subsequent hidden layer node, and a weight of a connection between a hidden layer node and a linked output node. (Fig. 19, [0113][0213]-[0217][0228]-[0237] [0372]-[0378] [0403]-[0410] the output node states based on the current output node state, current hidden layer node state and a weight of a connection between an input layer node and a linked hidden layer node, etc.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Hatami-Hanza‘s state navigation using ML into Thakkar and Greve’s invention as they are related to the same field endeavor of ML training and learning. The motivation to combine these arts, as proposed above, at least because Hatami-Hanza state navigation using ML would help to provide state calculation using ML into Thakkar and Greve’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing state calculation using ML would help to improve the prediction of the artificial neural network.
In regard to claim 10, Greve, Thakkar and Hatami-Hanza disclose The method according to Claim 8,
Greve, Thakkar fail to explicitly disclose “wherein output node states are determined not to vary in response to varying of the input node state where at least one of: a weight of a connection between the input node and a hidden layer node to which the output node is linked is zero; a weight of a connection between the output node and a hidden layer node to which the output node is linked is zero; a value of a hidden layer node state of a hidden layer node to which the output node is linked is at a maximum, and a weight of a connection between the hidden layer node and the input node is positive; a value of an output node state of the output node is at a maximum, and a weight of a connection between the output node and a hidden layer node to which the output node is linked is positive; a value of a hidden layer node state of a hidden layer node to which the output node is linked is at a minimum, and a weight of a connection between the hidden layer node and the input node is negative; and a value of an output node state of the output node is at a minimum, and a weight of a connection between the output node and a hidden layer node to which the output node is linked is negative.”
Hatami-Hanza disclose wherein output node states are determined not to vary in response to varying of the input node state where at least one of: a weight of a connection between the input node and a hidden layer node to which the output node is linked is zero; a weight of a connection between the output node and a hidden layer node to which the output node is linked is zero; a value of a hidden layer node state of a hidden layer node to which the output node is linked is at a maximum, and a weight of a connection between the hidden layer node and the input node is positive; a value of an output node state of the output node is at a maximum, and a weight of a connection between the output node and a hidden layer node to which the output node is linked is positive; a value of a hidden layer node state of a hidden layer node to which the output node is linked is at a minimum, and a weight of a connection between the hidden layer node and the input node is negative; and a value of an output node state of the output node is at a minimum, and a weight of a connection between the output node and a hidden layer node to which the output node is linked is negative.(Fig. 19, [0077] [131]-[0134][0273]-[0278] [0372]-[0374][0405]-[0410] the output node state would not change if a state component of the input node associated with the weight (importance) corresponding to the input node is 0, although the input state is adjusted, (v*w) or the weight of the links between the input and output is adjusted to 0, etc.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Hatami-Hanza‘s state navigation using ML into Thakkar and Greve’s invention as they are related to the same field endeavor of ML training and learning. The motivation to combine these arts, as proposed above, at least because Hatami-Hanza state navigation using ML would help to provide state calculation using ML into Thakkar and Greve’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing state calculation using ML would help to improve the prediction of the artificial neural network.
In regard to claim 12, Greve, Thakkar and Hatami-Hanza disclose The method according to Claim 1,
Greve, Thakkar fail to explicitly disclose “wherein an input node state of an input node having a relatively high node priority is constant whilst varying the input node state of the input node having the relatively low node priority.”
Hatami-Hanza disclose wherein an input node state of an input node having a relatively high node priority is constant whilst varying the input node state of the input node having the relatively low node priority. ([0007]-[0025] [0030]-[0032] [0185]-[0189] [0218]-[0225] [0273]-[0278][0311]-[0317] [0346]-[0375] [0405]-[0410] change the input node state value for a connection path with lower significances and an input node state of an input node having higher significances (weighting) is constant)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Hatami-Hanza‘s state navigation using ML into Thakkar and Greve’s invention as they are related to the same field endeavor of ML training and learning. The motivation to combine these arts, as proposed above, at least because Hatami-Hanza state navigation using ML would help to provide state calculation using ML into Thakkar and Greve’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing state calculation using ML would help to improve the prediction of the artificial neural network.
In regard to claim 13, Greve, Thakkar and Hatami-Hanza disclose The method according to Claim 12,
Greve, Thakkar fail to explicitly disclose “wherein varying the input node state of the input node having the relatively low node priority takes place where the input node state of the input node having the relatively high node priority is indicative of an altered operational mode status of the component associated with the input node.”
Hatami-Hanza disclose wherein varying the input node state of the input node having the relatively low node priority takes place where the input node state of the input node having the relatively high node priority is indicative of an altered operational mode status of the component associated with the input node. ([0007]-[0025] [0030]-[0032] [0185]-[0189] [0218]-[0225] [0273]-[0278][0311]-[0317] [0346]-[0375] [0405]-[0410] change the input node state value for a connection path with lower significances when an input node state of an input node having higher significance is changed)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Hatami-Hanza‘s state navigation using ML into Thakkar and Greve’s invention as they are related to the same field endeavor of ML training and learning. The motivation to combine these arts, as proposed above, at least because Hatami-Hanza state navigation using ML would help to provide state calculation using ML into Thakkar and Greve’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing state calculation using ML would help to improve the prediction of the artificial neural network.
In regard to claim 14, Greve, Thakkar and Hatami-Hanza disclose The method according to Claim 1,
Greve, Thakkar fail to explicitly disclose “wherein operational mode status combinations are grouped into sub-sets based on any of respective input node states and respective outputs of the machine learning classifier.”
Hatami-Hanza disclose wherein operational mode status combinations are grouped into sub-sets based on any of respective input node states and respective outputs of the machine learning classifier. ([0235]-[0241][0273]-0278] similar compositions states can be grouped together into set of groups based on the similarity of the input node states and outputs of the NN)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Hatami-Hanza‘s state navigation using ML into Thakkar and Greve’s invention as they are related to the same field endeavor of ML training and learning. The motivation to combine these arts, as proposed above, at least because Hatami-Hanza state navigation using ML would help to provide state calculation using ML into Thakkar and Greve’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing state calculation using ML would help to improve the prediction of the artificial neural network.
In regard to claim 15, Greve, Thakkar and Hatami-Hanza disclose The method according to Claim 1,
Greve, Thakkar and Hatami-Hanza disclose wherein the aircraft system comprises an aircraft braking system. (col. 5, line 18-35, col. 9, line 38-60, brakes of the aircraft)
In regard to claim 16, claim is an aircraft system claim corresponding to the method claim 1 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 1.
In regard to claim 17, claim is an aircraft claim corresponding to the method claim 1 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 1.
In regard to claim 18, claim is a medium claim corresponding to the method claim 1 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 1.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Greve et al. (Greve) US 11360476,Thakkar et al. (Thakkar) US 2022/0051198 and Hatami-Hanza US 2022/0245109 as applied to claim 8, further in view of Xu et al. (Xu) US 11410056
In regard to claim 11, Greve, Thakkar and Hatami-Hanza disclose The method according to Claim 8,
Greve,Thakkar fail to explicitly disclose “wherein hidden layer node states and/or output layer node states are determined to vary in response to varying of the input node state where at least one of: a value of a hidden layer node state of a hidden layer node is at a maximum, and a weight of a connection between the hidden layer node and the input node is negative; a value of a hidden layer node state of a hidden layer node is at a minimum, and a weight of a connection between the hidden layer node and the input node is positive; a value of an output node state of an output node is at a maximum, and a weight of a connection between the output node and a hidden layer node to which the output node is linked is negative; and a value of an output node state of an output node is at a minimum, and a weight of a connection between the output node and a hidden layer node to which the output node is linked is positive.”
Hatami-Hanza disclose wherein hidden layer node states and/or output layer node states are determined to vary in response to varying of the input node state where at least one of: a value of a hidden layer node state of a hidden layer node, and a weight of a connection between the hidden layer node and the input node; a value of a hidden layer node state of a hidden layer node, and a weight of a connection between the hidden layer node and the input node; a value of an output node state of an output node, and a weight of a connection between the output node and a hidden layer node to which the output node is linked; and a value of an output node state of an output node, and a weight of a connection between the output node and a hidden layer node to which the output node is linked. (Fig. 19, [0113][0213]-[0217][0228]-[0237] [0372]-[0378] [0403]-[0410] a hidden layer node state of a hidden layer node has a value and there is a weight of a connection between the hidden layer node and the input node, and a value of an output node state of an output node, and a weight of a connection between the output node and a hidden layer node to which the output node is linked, etc.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Hatami-Hanza‘s state navigation using ML into Thakkar and Greve’s invention as they are related to the same field endeavor of ML training and learning. The motivation to combine these arts, as proposed above, at least because Hatami-Hanza state navigation using ML would help to provide state calculation using ML into Thakkar and Greve’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing state calculation using ML would help to improve the prediction of the artificial neural network.
Greve,Thakkar and Hatami-Hanza fail to explicitly disclose “where at least one of: the value is at a maximum, and the weight is negative; the value is at a minimum, and the weight is positive; the value is at a maximum, and the weight is negative; and the value is at a minimum, and the weight is positive.”
Xu disclose where at least one of: the value is at a maximum, and the weight is negative; the value is at a minimum, and the weight is positive; the value is at a maximum, and the weight is negative; and the value is at a minimum, and the weight is positive. (col. 4, line 36-col. 5, line 11, col. 7, line 31-col. 8, line 44, the value is minimum and the weigh can be refined to positive, for example)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Xu‘s predictive sensor system for aircraft engines using ML into Hatami-Hanza, Thakkar and Greve’s invention as they are related to the same field endeavor of ML training and learning. The motivation to combine these arts, as proposed above, at least because Xu‘s ML model with various input weights and values would help to predict status information using ML into Hatami-Hanza, Thakkar and Greve’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that predicting status information with various input weights and values using ML would help to improve the prediction of the neural network.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
U.S. Patent Documents PATENT DATE INVENTOR(S) TITLE
US 6114976 A 2000-09-05 Vian
Vehicle Emergency Warning And Control System
Vian disclose An automatic vehicle warning and control program is provided for determining if safety enhancing actions are appropriate. The on-line determination of an action that results in a preferred outcome (e.g., aircraft ejection) is made using a neural network controller. The neural network controller is trained off-line using appropriate preferred outcome data obtained via computer simulation or experimentation. Appropriate actions are established for all conceivable sets of vehicle conditions. On-line, the neural network controller uses actual sensed vehicle conditions to determine the appropriate action. Various actions can be performed based on the preferred outcome determination. Appropriate actions can include commanding audible and visual warnings, guidance cues, automatic vehicle control, and aircraft automatic ejection… see abstract.
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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143