DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the Amendment filed on 4/22/2026. Claims 1, 4, 6-8, 10, 13-16, and 19-21 are pending in the case. Claims 2-3, 5, 9, 11-12, and 17-18 have been cancelled. Claim 21 has been added. Claims 1, 10, and 16 are independent claims.
Response to Arguments
Applicant’s amendments regarding the objections are persuasive. The objections are respectfully withdrawn.
Applicant’s amendments regarding the 35 U.S.C. § 101 rejections are persuasive. These rejections are respectfully withdrawn.
Applicant’s prior art arguments have been considered but are moot because the new grounds of rejection presented below do not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the arguments.
Claim Rejections - 35 U.S.C. § 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 of this title, 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, 4, 7-8, 10, 14-16, and 20-21 are rejected under 35 U.S.C. § 103 as being unpatentable over Zeng et al. (US 2019/0361454 A1, hereinafter Zeng) in view of Wu et al. (“The Ensemble Approach to Forecasting: A Review and Synthesis,” 5 January 2022, https://ses.library.usyd.edu.au/bitstream/handle/2123/27301/EnsembleForecasting_SynthesisPaper.pdf, hereinafter Wu), Schmidt (US 6633826 B1), and Allahdadian et al. (US 2022/0318684 A1, hereinafter Allahdadian).
As to independent claim 1, Zeng teaches an autonomous agent (“FIG. 1 is a functional block diagram illustrating an autonomous vehicle in accordance with the disclosed embodiments,” paragraph 0044 lines 1-3) comprising:
at least one computing device (“the controller 34 implements a high-level controller of an autonomous driving system (ADS) 33 as shown in FIG. 3,” paragraph 0068 lines 1-3); and
an autonomous decision system (“The high-level controller 133 includes a map generator module 130, 134 and a vehicle controller module 148. The vehicle controller module 148 includes memory 140 that stores a plurality or ensemble of sensorimotor primitive modules, a scene understanding module 150 and an arbitration and vehicle control module 170,” paragraph 0075 lines 1-6) comprising:
plurality of modules (“FIG. 4 illustrates five non-limiting examples of sensorimotor primitive modules: SuperCruise, collision imminent brake/collision imminent steering (CIB/CIS), Lane Change, Construction Zone Handling, and Intersection Handling,” paragraph 0079 lines 1-5), wherein each module is adapted to receive a plurality of input signals (“Each sensorimotor primitive module can map sensing in an environment (as represented by the navigation route data and GPS data 136, and the world representation 138) to one or more action(s) that accomplishes a specific vehicle maneuver,” paragraph 0080 lines 1-5), and to output a control signal of a plurality of control signals (“Each sensorimotor primitive module can be used to generate control signals and actuator commands that address a specific driving scenario (e.g., combination of sensed environment, location and navigation goals as represented by the navigation route data and GPS data 136, and the world representation 138, etc.) encountered during operation of an autonomous vehicle,” paragraph 0080 lines 5-11), and wherein the autonomous agent is adapted to control the operation of the autonomous agent according to the plurality of control signals (“The control signals 172 are then provided to the actuator system 190, which processes the control signals 172 to generate the appropriate commands to control various vehicle systems and subsystems. In this embodiment, the actuator system 190 includes a low-level controller 192 and a plurality of actuators 194 of the vehicle (e.g., a steering torque or angle controller, a brake system, a throttle system, etc.),” paragraph 0094 lines 1-8), and further wherein each [module] of the plurality of [modules] comprises a[n] expert (“FIG. 4 illustrates five non-limiting examples of sensorimotor primitive modules: SuperCruise, collision imminent brake/collision imminent steering (CIB/CIS), Lane Change, Construction Zone Handling, and Intersection Handling,” paragraph 0079 lines 1-5); and each expert is configured to apply [] rules to a subset of the plurality of input signals to generate an expert output (“Each sensorimotor primitive module can be used to generate control signals and actuator commands that address a specific driving scenario (e.g., combination of sensed environment, location and navigation goals as represented by the navigation route data and GPS data 136, and the world representation 138, etc.) encountered during operation of an autonomous vehicle,” paragraph 0080 lines 5-11).
Zeng does not appear to expressly teach a system wherein the plurality of modules/experts is a plurality of ensembles; and each ensemble of the plurality of ensembles [is] configured to combine associated expert outputs to generate the outputted control signal based on confidence values associated with the expert outputs.
Wu teaches a system wherein the plurality of modules/experts is a plurality of ensembles (“In this paper we review and synthesize methods of ensemble forecasting with a unifying framework, categorizing ensemble methods into two broad and not mutually exclusive categories, namely combining models, and combining data; this framework further extends to ensembles of ensembles,” abstract lines 13-16); and each ensemble of the plurality of ensembles [is] configured to combine associated expert outputs to generate the outputted control signal based on confidence values associated with the expert outputs (“Output from each base model is assigned a weight, which depends on some performance criteria of the base models; and the weights from all base models add up to one,” page 4 section “2.2 Weighted Average” line 3 to page 5 line 2).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the modules of Zeng to comprise the ensembles of Wu. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely controlling the autonomous agent with a plurality of ensembles (“In this paper we review and synthesize methods of ensemble forecasting with a unifying framework, categorizing ensemble methods into two broad and not mutually exclusive categories, namely combining models, and combining data; this framework further extends to ensembles of ensembles,” Wu abstract lines 13-16). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
Zeng/Wu does not appear to expressly teach a system wherein each expert is configured to apply fuzzy logic rules.
Schmidt teaches a system wherein each expert is configured to apply fuzzy logic rules (“the feature signal c11 is interpreted using an ensemble of binary logic and/or fuzzy logic decision rules that combine the features represented by the individual bits of the feature signal c11,” column 6 lines 31-35).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the rules of Zeng/Wu to comprise the fuzzy logic rules of Schmidt. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely using fuzzy logic to generate an expert output (“the feature signal c11 is interpreted using an ensemble of binary logic and/or fuzzy logic decision rules that combine the features represented by the individual bits of the feature signal c11,” Schmidt column 6 lines 31-35). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
Zeng/Wu/Schmidt does not appear to expressly teach a system wherein each ensemble of the plurality of ensembles comprises a gating network configured to combine associated expert outputs to generate the outputted control signal.
Allahdadian teaches a system wherein each ensemble of the plurality of ensembles comprises a gating network configured to combine associated expert outputs to generate the outputted control signal (“the proposed architecture is composed of multiple unsupervised machine learning models that each produce a score as output and a gating network that analyzes the inputs and outputs of the unsupervised machine learning models to select an optimal ensemble of unsupervised machine learning models. The gating network is trained to choose a minimal number of the multiple unsupervised machine learning models whose scores are combined to create a final score that matches or closely resembles a final score that is computed using all the scores of the multiple unsupervised machine learning models,” abstract lines 2-13).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the ensembles of Zeng/Wu/Schmidt to comprise the gating network of Allahdadian. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely choosing a minimal number of models (“the proposed architecture is composed of multiple unsupervised machine learning models that each produce a score as output and a gating network that analyzes the inputs and outputs of the unsupervised machine learning models to select an optimal ensemble of unsupervised machine learning models. The gating network is trained to choose a minimal number of the multiple unsupervised machine learning models whose scores are combined to create a final score that matches or closely resembles a final score that is computed using all the scores of the multiple unsupervised machine learning models,” Allahdadian abstract lines 2-13). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
As to dependent claim 4, the rejection of claim 1 is incorporated. Zeng/Wu/Schmidt/Allahdadian further teaches a system wherein each signal output by an expert or ensemble of experts is associated with a confidence, and wherein the control signal output by an ensemble is based on the signals output by the plurality of experts associated with the ensemble weighted by their associated confidence (“Output from each base model is assigned a weight, which depends on some performance criteria of the base models; and the weights from all base models add up to one,” Wu page 4 section “2.2 Weighted Average” line 3 to page 5 line 2).
As to dependent claim 7, the rejection of claim 1 is incorporated. Zeng/Wu/Schmidt/Allahdadian further teaches a system wherein the autonomous agent is an autonomous vehicle (“FIG. 1 is a functional block diagram illustrating an autonomous vehicle in accordance with the disclosed embodiments,” Zeng paragraph 0044 lines 1-3).
As to dependent claim 8, the rejection of claim 1 is incorporated. Zeng/Wu/Schmidt/Allahdadian further teaches a system comprising a plurality of sensors (“The sensor system 28 includes one or more sensing devices 40a-40n that sense observable conditions of the exterior environment and/or the interior environment of the autonomous vehicle 10. The sensing devices 40a-40n can include, but are not limited to, radars, lidars, optical cameras, thermal cameras, imager sensors, ultrasonic sensors, inertial measurement units, global positioning systems, navigation systems, and/or other sensors,” Zeng paragraph 0047 lines 1-8), and at least some of the plurality of input signals are received from the plurality of sensors (“Each sensorimotor primitive module can map sensing in an environment (as represented by the navigation route data and GPS data 136, and the world representation 138) to one or more action(s) that accomplishes a specific vehicle maneuver,” Zeng paragraph 0080 lines 1-5).
As to independent claim 10, Zeng teaches a method for controlling (“the controller 34 implements a high-level controller of an autonomous driving system (ADS) 33 as shown in FIG. 3,” paragraph 0068 lines 1-3) an autonomous agent (“FIG. 1 is a functional block diagram illustrating an autonomous vehicle in accordance with the disclosed embodiments,” paragraph 0044 lines 1-3) comprising:
receiving a plurality of input signals by the autonomous agent (“The sensor system 28 includes one or more sensing devices 40a-40n that sense observable conditions of the exterior environment and/or the interior environment of the autonomous vehicle 10. The sensing devices 40a-40n can include, but are not limited to, radars, lidars, optical cameras, thermal cameras, imager sensors, ultrasonic sensors, inertial measurement units, global positioning systems, navigation systems, and/or other sensors,” paragraph 0047 lines 1-8);
for each module of a plurality of modules of the autonomous agent (“FIG. 4 illustrates five non-limiting examples of sensorimotor primitive modules: SuperCruise, collision imminent brake/collision imminent steering (CIB/CIS), Lane Change, Construction Zone Handling, and Intersection Handling,” paragraph 0079 lines 1-5), generating a control signal by the module (“Each sensorimotor primitive module can be used to generate control signals and actuator commands that address a specific driving scenario (e.g., combination of sensed environment, location and navigation goals as represented by the navigation route data and GPS data 136, and the world representation 138, etc.) encountered during operation of an autonomous vehicle,” paragraph 0080 lines 5-11); and
controlling the operation of the autonomous agent according to the generated control signals (“The control signals 172 are then provided to the actuator system 190, which processes the control signals 172 to generate the appropriate commands to control various vehicle systems and subsystems. In this embodiment, the actuator system 190 includes a low-level controller 192 and a plurality of actuators 194 of the vehicle (e.g., a steering torque or angle controller, a brake system, a throttle system, etc.),” paragraph 0094 lines 1-8), wherein generating the control signal by each [module] comprises: receiving expert (“FIG. 4 illustrates five non-limiting examples of sensorimotor primitive modules: SuperCruise, collision imminent brake/collision imminent steering (CIB/CIS), Lane Change, Construction Zone Handling, and Intersection Handling,” paragraph 0079 lines 1-5) outputs generated by [the] experts that apply [] rules to subsets of the plurality of input signals (“Each sensorimotor primitive module can be used to generate control signals and actuator commands that address a specific driving scenario (e.g., combination of sensed environment, location and navigation goals as represented by the navigation route data and GPS data 136, and the world representation 138, etc.) encountered during operation of an autonomous vehicle,” paragraph 0080 lines 5-11).
Zeng does not appear to expressly teach a method wherein the plurality of modules/experts is a plurality of ensembles; and combining the expert outputs [] based on confidence values associated with the expert outputs.
Wu teaches a method wherein the plurality of modules/experts is a plurality of ensembles (“In this paper we review and synthesize methods of ensemble forecasting with a unifying framework, categorizing ensemble methods into two broad and not mutually exclusive categories, namely combining models, and combining data; this framework further extends to ensembles of ensembles,” abstract lines 13-16); and combining the expert outputs [] based on confidence values associated with the expert outputs.
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the modules of Zeng to comprise the ensembles of Wu. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely controlling the autonomous agent with a plurality of ensembles (“In this paper we review and synthesize methods of ensemble forecasting with a unifying framework, categorizing ensemble methods into two broad and not mutually exclusive categories, namely combining models, and combining data; this framework further extends to ensembles of ensembles,” Wu abstract lines 13-16). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
Zeng/Wu does not appear to expressly teach a method wherein each expert applies fuzzy logic rules.
Schmidt teaches a method wherein each expert applies fuzzy logic rules (“the feature signal c11 is interpreted using an ensemble of binary logic and/or fuzzy logic decision rules that combine the features represented by the individual bits of the feature signal c11,” column 6 lines 31-35).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the rules of Zeng/Wu to comprise the fuzzy logic rules of Schmidt. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely using fuzzy logic to generate an expert output (“the feature signal c11 is interpreted using an ensemble of binary logic and/or fuzzy logic decision rules that combine the features represented by the individual bits of the feature signal c11,” Schmidt column 6 lines 31-35). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
Zeng/Wu/Schmidt does not appear to expressly teach a method comprising combining the expert outputs using a gating network.
Allahdadian teaches a method comprising combining the expert outputs using a gating network (“the proposed architecture is composed of multiple unsupervised machine learning models that each produce a score as output and a gating network that analyzes the inputs and outputs of the unsupervised machine learning models to select an optimal ensemble of unsupervised machine learning models. The gating network is trained to choose a minimal number of the multiple unsupervised machine learning models whose scores are combined to create a final score that matches or closely resembles a final score that is computed using all the scores of the multiple unsupervised machine learning models,” abstract lines 2-13).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the ensembles of Zeng/Wu/Schmidt to comprise the gating network of Allahdadian. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely choosing a minimal number of models (“the proposed architecture is composed of multiple unsupervised machine learning models that each produce a score as output and a gating network that analyzes the inputs and outputs of the unsupervised machine learning models to select an optimal ensemble of unsupervised machine learning models. The gating network is trained to choose a minimal number of the multiple unsupervised machine learning models whose scores are combined to create a final score that matches or closely resembles a final score that is computed using all the scores of the multiple unsupervised machine learning models,” Allahdadian abstract lines 2-13). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
As to dependent claim 14, the rejection of claim 10 is incorporated. Zeng/Wu/Schmidt/Allahdadian further teaches a method wherein the autonomous agent is an autonomous vehicle (“FIG. 1 is a functional block diagram illustrating an autonomous vehicle in accordance with the disclosed embodiments,” Zeng paragraph 0044 lines 1-3).
As to dependent claim 15, the rejection of claim 10 is incorporated. Zeng/Wu/Schmidt/Allahdadian further teaches a method wherein the autonomous agent comprises a plurality of sensors (“The sensor system 28 includes one or more sensing devices 40a-40n that sense observable conditions of the exterior environment and/or the interior environment of the autonomous vehicle 10. The sensing devices 40a-40n can include, but are not limited to, radars, lidars, optical cameras, thermal cameras, imager sensors, ultrasonic sensors, inertial measurement units, global positioning systems, navigation systems, and/or other sensors,” Zeng paragraph 0047 lines 1-8), and at least some of the plurality of input signals are received from the plurality of sensors (“Each sensorimotor primitive module can map sensing in an environment (as represented by the navigation route data and GPS data 136, and the world representation 138) to one or more action(s) that accomplishes a specific vehicle maneuver,” Zeng paragraph 0080 lines 1-5).
As to independent claim 16, Zeng teaches a non-transitory computer-readable medium with computer executable instructions stored thereon (“In certain embodiments, some or all steps of these methods, and/or substantially equivalent steps, are performed by execution of processor-readable instructions stored or included on a processor-readable medium,” paragraph 0142 lines 28-31) that when executed by a computing device (“the controller 34 implements a high-level controller of an autonomous driving system (ADS) 33 as shown in FIG. 3,” paragraph 0068 lines 1-3) of an autonomous agent (“FIG. 1 is a functional block diagram illustrating an autonomous vehicle in accordance with the disclosed embodiments,” paragraph 0044 lines 1-3) cause the computing device to perform a method comprising:
receive a plurality of input signals (“The sensor system 28 includes one or more sensing devices 40a-40n that sense observable conditions of the exterior environment and/or the interior environment of the autonomous vehicle 10. The sensing devices 40a-40n can include, but are not limited to, radars, lidars, optical cameras, thermal cameras, imager sensors, ultrasonic sensors, inertial measurement units, global positioning systems, navigation systems, and/or other sensors,” paragraph 0047 lines 1-8);
for each module of a plurality of modules of the autonomous agent (“FIG. 4 illustrates five non-limiting examples of sensorimotor primitive modules: SuperCruise, collision imminent brake/collision imminent steering (CIB/CIS), Lane Change, Construction Zone Handling, and Intersection Handling,” paragraph 0079 lines 1-5), generate a control signal by the module (“Each sensorimotor primitive module can be used to generate control signals and actuator commands that address a specific driving scenario (e.g., combination of sensed environment, location and navigation goals as represented by the navigation route data and GPS data 136, and the world representation 138, etc.) encountered during operation of an autonomous vehicle,” paragraph 0080 lines 5-11); and
control the operation of the autonomous agent according to the generated control signals (“The control signals 172 are then provided to the actuator system 190, which processes the control signals 172 to generate the appropriate commands to control various vehicle systems and subsystems. In this embodiment, the actuator system 190 includes a low-level controller 192 and a plurality of actuators 194 of the vehicle (e.g., a steering torque or angle controller, a brake system, a throttle system, etc.),” paragraph 0094 lines 1-8), wherein generating the control signal by each [module] comprises: receiving expert (“FIG. 4 illustrates five non-limiting examples of sensorimotor primitive modules: SuperCruise, collision imminent brake/collision imminent steering (CIB/CIS), Lane Change, Construction Zone Handling, and Intersection Handling,” paragraph 0079 lines 1-5) outputs generated by [the] experts that apply [] rules to subsets of the plurality of input signals (“Each sensorimotor primitive module can be used to generate control signals and actuator commands that address a specific driving scenario (e.g., combination of sensed environment, location and navigation goals as represented by the navigation route data and GPS data 136, and the world representation 138, etc.) encountered during operation of an autonomous vehicle,” paragraph 0080 lines 5-11).
Zeng does not appear to expressly teach a medium wherein the plurality of modules/experts is a plurality of ensembles; and combining the expert outputs [] based on confidence values associated with the expert outputs.
Wu teaches a medium wherein the plurality of modules/experts is a plurality of ensembles (“In this paper we review and synthesize methods of ensemble forecasting with a unifying framework, categorizing ensemble methods into two broad and not mutually exclusive categories, namely combining models, and combining data; this framework further extends to ensembles of ensembles,” abstract lines 13-16); and combining the expert outputs [] based on confidence values associated with the expert outputs.
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the modules of Zeng to comprise the ensembles of Wu. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely controlling the autonomous agent with a plurality of ensembles (“In this paper we review and synthesize methods of ensemble forecasting with a unifying framework, categorizing ensemble methods into two broad and not mutually exclusive categories, namely combining models, and combining data; this framework further extends to ensembles of ensembles,” Wu abstract lines 13-16). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
Zeng/Wu does not appear to expressly teach a medium wherein each expert applies fuzzy logic rules.
Schmidt teaches a medium wherein each expert applies fuzzy logic rules (“the feature signal c11 is interpreted using an ensemble of binary logic and/or fuzzy logic decision rules that combine the features represented by the individual bits of the feature signal c11,” column 6 lines 31-35).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the rules of Zeng/Wu to comprise the fuzzy logic rules of Schmidt. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely using fuzzy logic to generate an expert output (“the feature signal c11 is interpreted using an ensemble of binary logic and/or fuzzy logic decision rules that combine the features represented by the individual bits of the feature signal c11,” Schmidt column 6 lines 31-35). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
Zeng/Wu/Schmidt does not appear to expressly teach a medium comprising instructions for combining the expert outputs using a gating network.
Allahdadian teaches a medium comprising instructions for combining the expert outputs using a gating network (“the proposed architecture is composed of multiple unsupervised machine learning models that each produce a score as output and a gating network that analyzes the inputs and outputs of the unsupervised machine learning models to select an optimal ensemble of unsupervised machine learning models. The gating network is trained to choose a minimal number of the multiple unsupervised machine learning models whose scores are combined to create a final score that matches or closely resembles a final score that is computed using all the scores of the multiple unsupervised machine learning models,” abstract lines 2-13).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the ensembles of Zeng/Wu/Schmidt to comprise the gating network of Allahdadian. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely choosing a minimal number of models (“the proposed architecture is composed of multiple unsupervised machine learning models that each produce a score as output and a gating network that analyzes the inputs and outputs of the unsupervised machine learning models to select an optimal ensemble of unsupervised machine learning models. The gating network is trained to choose a minimal number of the multiple unsupervised machine learning models whose scores are combined to create a final score that matches or closely resembles a final score that is computed using all the scores of the multiple unsupervised machine learning models,” Allahdadian abstract lines 2-13). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
As to dependent claim 20, the rejection of claim 16 is incorporated. Zeng/Wu/Schmidt/Allahdadian further teaches a medium wherein the autonomous agent is an autonomous vehicle (“FIG. 1 is a functional block diagram illustrating an autonomous vehicle in accordance with the disclosed embodiments,” Zeng paragraph 0044 lines 1-3).
As to dependent claim 21, the rejection of claim 1 is incorporated. Zeng/Wu/Schmidt/Allahdadian further teaches a system wherein the gating network generates the outputted control signal by applying recursively updated weighting values to the expert outputs (“In a recursive embodiment, there is only one step that recycles some of its output back into the one step to recursively achieve sequencing,” Allahdadian paragraph 0082 lines 10-12).
Claims 6, 13, and 19 are rejected under 35 U.S.C. § 103 as being unpatentable over Zeng in view of Wu, Schmidt, Allahdadian, and Yager (“On the Construction of Hierarchical Fuzzy Systems Models,” February 1998, https://ieeexplore.ieee.org/document/661090).
As to dependent claim 6, the rejection of claim 1 is incorporated.
Zeng/Wu/Schmidt/Allahdadian does not appear to expressly teach a system wherein each expert adapted to apply the plurality of rules to the subset of the plurality of input signals to output the signal comprises:
evaluating each rule of the plurality of rules to generate a fuzzy logic value for each rule; and
applying a defuzzification process to the generated fuzzy logic values to output the signal.
Yager teaches a system wherein each expert adapted to apply the plurality of rules to the subset of the plurality of input signals to output the signal comprises:
evaluating each rule of the plurality of rules to generate a fuzzy logic value for each rule (“1) For each rule, we find the firing level of that rule λi λi = Ai(x*)˄Bi(y*), 2) We calculate the effective output of each rule Ei,” page 56 column left lines 3-5); and
applying a defuzzification process to the generated fuzzy logic values to output the signal (“3) We then combine these individual effective rule outputs to give us an overall system output E,” page 56 column left lines 6-7).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the fuzzy logic rules of Zeng/Wu/Schmidt/Allahdadian to comprise the defuzzification process of Yager. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely defuzzing the fuzzy logic rules (“3) We then combine these individual effective rule outputs to give us an overall system output E,” Yager page 56 column left lines 6-7). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
As to dependent claim 13, the rejection of claim 10 is incorporated.
Zeng/Wu/Schmidt/Allahdadian does not appear to expressly teach a method wherein each expert applying the plurality of rules to the subset of the input signals to generate the signal comprises:
evaluating each rule of the plurality of rules to generate a fuzzy logic value for each rule; and
applying a defuzzification process to the generated fuzzy logic values to output the signal.
Yager teaches a method wherein each expert applying the plurality of rules to the subset of the input signals to generate the signal comprises:
evaluating each rule of the plurality of rules to generate a fuzzy logic value for each rule (“1) For each rule, we find the firing level of that rule λi λi = Ai(x*)˄Bi(y*), 2) We calculate the effective output of each rule Ei,” page 56 column left lines 3-5); and
applying a defuzzification process to the generated fuzzy logic values to output the signal (“3) We then combine these individual effective rule outputs to give us an overall system output E,” page 56 column left lines 6-7).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the fuzzy logic rules of Zeng/Wu/Schmidt/Allahdadian to comprise the defuzzification process of Yager. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely defuzzing the fuzzy logic rules (“3) We then combine these individual effective rule outputs to give us an overall system output E,” Yager page 56 column left lines 6-7). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
As to dependent claim 19, the rejection of claim 16 is incorporated.
Zeng/Wu/Schmidt/Allahdadian does not appear to expressly teach a medium wherein each expert applying the plurality of rules to the subset of the input signals to generate the signal comprises:
evaluating each rule of the plurality of rules to generate a fuzzy logic value for each rule; and
applying a defuzzification process to the generated fuzzy logic values to output the signal.
Yager teaches a medium wherein each expert applying the plurality of rules to the subset of the input signals to generate the signal comprises:
evaluating each rule of the plurality of rules to generate a fuzzy logic value for each rule (“1) For each rule, we find the firing level of that rule λi λi = Ai(x*)˄Bi(y*), 2) We calculate the effective output of each rule Ei,” page 56 column left lines 3-5); and
applying a defuzzification process to the generated fuzzy logic values to output the signal (“3) We then combine these individual effective rule outputs to give us an overall system output E,” page 56 column left lines 6-7).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the fuzzy logic rules of Zeng/Wu/Schmidt/Allahdadian to comprise the defuzzification process of Yager. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely defuzzing the fuzzy logic rules (“3) We then combine these individual effective rule outputs to give us an overall system output E,” Yager page 56 column left lines 6-7). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
US 2022/0012564 A1 disclosing an ensemble with a gating network
Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
Applicant’s amendments necessitated the new grounds 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 C.F.R. § 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 extension fee pursuant to 37 C.F.R. § 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 date of this final action.
In the interests of compact prosecution, Applicant is invited to contact the examiner via electronic media pursuant to USPTO policy outlined MPEP § 502.03. All electronic communication must be authorized in writing. Applicant may wish to file an Internet Communications Authorization Form PTO/SB/439. Applicant may wish to request an interview using the Interview Practice website: http://www.uspto.gov/patent/laws-and-regulations/interview-practice.
Applicant is reminded Internet e-mail may not be used for communication for matters under 35 U.S.C. § 132 or which otherwise require a signature. A reply to an Office action may NOT be communicated by Applicant to the USPTO via Internet e-mail. If such a reply is submitted by Applicant via Internet e-mail, a paper copy will be placed in the appropriate patent application file with an indication that the reply is NOT ENTERED. See MPEP § 502.03(II).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ryan Barrett whose telephone number is 571 270 3311. The examiner can normally be reached 9:00am to 5:30pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, Applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Michelle Bechtold can be reached at 571 431 0762. The fax phone number for the organization where this application or proceeding is assigned is 571 273 8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Ryan Barrett/
Primary Examiner, Art Unit 2148
Prosecution Insights