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 .
Status of Claims
Claims 1-20 pending.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 13 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 13, the phrase “modified operation model” lacks antecedent basis. Examiner’s best interpretation, used for purposes of examination in this Office Action, is that claim 13 is meant to depend from claim 12, similarly to the corresponding dependencies of the method and product claims. Appropriate correction is required.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-4, 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 10924308 B1 to Crawford in view of US 20180341262 A1 to Yeshurun.
Regarding claim 1,
US 10924308 B1 to Crawford teaches:
A method comprising:
generating, in a machine learning module, an operational model of a radar network within an environment, (Fig. 9; [col. 11, last para – col. 12, line 31] – “a plurality of RF signals is received from one or more RF emitters… characterized by a first machine learning device using a trained process to produce pulse description words (PDWs)… In block 916, the PDWs are associated with one or more RF emitters… by a second machine learning device.” [col. 10, last para] – “PDW parameters are embedded into the model.”) wherein an (lined through limitations correspond to limitations not taught by reference) agent within the environment detects the radar network; ([col. 4, lines 9-22] – “smart receiver or sensor that includes compressive sensing and machine learning capabilities for identifying and optionally locating RF signals… smart receiver or sensor that may be positioned on an airborne, ground or sea moving platform… in close proximity to the target RF emitters (e.g., radars) the signals of which are to be detected.”)
classifying the radar network as friendly or non-friendly based on the operational model; ([col. 2, lines 46-62] – “associating the PDWs with one or more RF emitters and identify the one or more RF emitters, by a second machine learning device.” [col. 2, lines 41-46] – “identifies and optionally locates radar signals and their characteristics, for example, what type of RF emitters or radars and whether they are friendly radars or “threat” radars.” [col. 7, lines 42-61] – “In some embodiments, the ML 316 may be a deep learning machine that uses multiple layers to progressively extract higher level features from the characterized pulses to associate them with certain type of emitter and identify the emitter”)
generating, in a reinforcement learning module, a counter-radar maneuver based on the operational model in response to classifying the radar network as non-friendly; (Examiner notes that the broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met. MPEP 2111.04. In this case, the “generating…” limitation is contingent upon the condition precedent “classifying the radar network as non-friendly.” Because the claimed invention may be practiced without the without the condition precedent occurring (i.e., the radar network is identified as friendly), the broadest reasonable interpretation of this claim does not require the contingent “generating…” step. In the interest of compact prosecution, Examiner additionally cites: [col. 12, lines 8-22] – “In block 916, the PDWs are associated with one or more RF emitters and the one or more RF emitters are identified by a second machine learning device. Information, including the location of identified RF emitters, can then be used to take action for the identified emitter, such as mitigate the threat, create an ingress/egress flight path that avoids the threat or is used to update the database of a radar warning receiver.” Examiner notes instant application specification [0020, 29] – “Maneuvers 208 are not necessarily limited to physical movements and actions within environment 100 as they may include, e.g., intercommunications with other agents 102, signal construction techniques for counteracting a wide variety of radars, and/or further analysis to evaluate the effectiveness of counteracting certain emitter(s) 106 and/or signals 108… Maneuver(s) 208 in some cases may simply cause agent(s) 102 to move to another location and thus evade the range of emitter(s) 106 and/or signal(s) 108.”) and
implementing the counter-radar maneuver via the (Examiner notes that the broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met. MPEP 2111.04. In this case, the “implementing…” limitation is contingent upon the condition precedent “classifying the radar network as non-friendly” (because the counter-radar maneuver is only generated when the radar network is classified as non-friendly). Because the claimed invention may be practiced without the without the condition precedent occurring (i.e., the radar network is identified as friendly), the broadest reasonable interpretation of this claim does not require the contingent “implementing…” step. In the interest of compact prosecution, Examiner additionally cites: [col. 12, lines 8-22] – “In block 916, the PDWs are associated with one or more RF emitters and the one or more RF emitters are identified by a second machine learning device. Information, including the location of identified RF emitters, can then be used to take action for the identified emitter, such as mitigate the threat, create an ingress/egress flight path that avoids the threat or is used to update the database of a radar warning receiver.”)
US 20180341262 A1 to Yeshurun teaches:
an autonomous agent within the environment detects the radar signals describing a potential threat; ([0043, 46] – “UAVs 14 may comprise surveillance equipment 20 configured to facilitate detection of an incoming threat… The surveillance equipment 20 may comprise… pulse-Doppler radar”)
implementing the counter-radar maneuver via the autonomous agent in communication with the counter measure module. ([0047-52] – “one or more of the UAVs 14 may be configured to direct deployment of the countermeasure most suited against an identified threat, and/or to direct deployment of two or more types of countermeasures against a single threat. According to some examples, the countermeasure is provided on one of the UAVs 14 itself, or on the platform 16, for deployment. According to some examples, the countermeasure is an external one, i.e., not deployed by the system 10 at all; rather, according to these examples a UAV 14 or other element of the system directs an external apparatus or system to deploy the countermeasure.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied Yeshurun’s known technique to Crawford’s known method ready for improvement to yield predictable results. Such a finding is proper because (1) Crawford teaches a base method of radar detection by an agent ([col. 4, lines 9-22] – “smart receiver or sensor that includes compressive sensing and machine learning capabilities for identifying and optionally locating RF signals… smart receiver or sensor that may be positioned on an airborne, ground or sea moving platform… in close proximity to the target RF emitters (e.g., radars) the signals of which are to be detected.”), radar network modeling, friendly/non-friendly identification, and generation and implementation of counter-radar maneuvers; (2) Yeshurun teaches a specific technique of detecting radar signals via an autonomous agent for purposes of threat identification and countermeasure deployment; (3) one of ordinary skill in the art would have recognized that applying the known technique would have yielded predictable results and resulted in a system with improved threat detection via UAVs; and (4) no additional findings based on the Graham factual inquiries are necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness (See MPEP 2143).
Regarding claim 2,
Crawford (‘308) in view of Yeshurun (‘262) teaches the invention as claimed and discussed above.
Crawford (‘308) further teaches:
The method of claim 1, wherein generating the operational model of the radar network includes:
separating a detected radio frequency (RF) signal into a set of signals, each signal of the set of signals corresponding to a respective emitter; (Figs. 3, 9; [col. 11, last para] – “As shown in block 902, a plurality of RF signals is received from one or more RF emitters, for example, one or more radars. In block 904, the RF signals are channelized into a plurality of channels, for example, by respective channelizer 304-1 to 304-N in FIG. 3”) and
estimating a descriptor for each signal of the set of signals, ([col. 12, lines 8-22] – “In block 910, pulses in each channel are detected, for example, by channelized pulse detection circuit 310 of FIG. 3… to produce pulse description words (PDWs)” [col. 10, second para] – “The output of the classifier 618 is a PDW that include the classical parameters of an RF emitter (e.g., a radar) pulse, including RF carrier, Time of Arrival (ToA), pulse width and modulation type.”) wherein the operational model is based on the estimated descriptor for each signal of the set of signals. ([col. 10, last para] – “An embedded layer 714 is where the PDW parameters are embedded into the model.”)
Regarding claim 3,
Crawford (‘308) in view of Yeshurun (‘262) teaches the invention as claimed and discussed above.
Crawford (‘308) further teaches:
The method of claim 2, wherein the descriptor includes one of a bandwidth, a modulation, a pulse width discriminator, or a pulse repetition interval for the respective emitter. ([col. 10, second para] – “The output of the classifier 618 is a PDW that include the classical parameters of an RF emitter (e.g., a radar) pulse, including RF carrier, Time of Arrival (ToA), pulse width and modulation type.”)
Regarding claim 4,
Examiner notes that the broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met. MPEP 2111.04. In this case, the additional limitations of claim 4 are contingent upon the condition precedent “generating… a counter maneuver.” Because the claimed invention may be practiced without the without the condition precedent occurring (i.e., the radar network is identified as friendly and therefore no counter-radar maneuver is generated, see also rejection and analysis of claim 1 above), the additional limitations recited in claim 4 are not required to be part of the claimed invention.
In the interest of compact prosecution, Examiner additionally cites:
Crawford (‘308) in view of Yeshurun (‘262) teaches the invention as claimed and discussed above.
Crawford (‘308) further teaches:
The method of claim 1, wherein the counter-radar maneuver includes:
a movement implemented with the autonomous agent; ([col. 12, lines 8-22] – “In block 916, the PDWs are associated with one or more RF emitters and the one or more RF emitters are identified by a second machine learning device. Information, including the location of identified RF emitters, can then be used to take action for the identified emitter, such as mitigate the threat, create an ingress/egress flight path that avoids the threat or is used to update the database of a radar warning receiver.”) and
Yeshurun (‘262) further teaches:
a signal transmitted from an RF transceiver of the autonomous agent. ([0050-51] – “According to some examples, the countermeasure is provided on one of the UAVs 14 itself, or on the platform 16, for deployment… The countermeasures may comprise one or more soft-kill countermeasures including, but not limited to, chaff, radar jamming, one or more electromagnetic pulses, a laser dazzler, radio frequency decoys, etc.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied Yeshurun’s known technique to Crawford’s known method ready for improvement to yield predictable results. Such a finding is proper because (1) Crawford teaches a base method of radar detection by an agent ([col. 4, lines 9-22] – “smart receiver or sensor that includes compressive sensing and machine learning capabilities for identifying and optionally locating RF signals… smart receiver or sensor that may be positioned on an airborne, ground or sea moving platform… in close proximity to the target RF emitters (e.g., radars) the signals of which are to be detected.”), radar network modeling, friendly/non-friendly identification, and generation and implementation of counter-radar maneuvers; (2) Yeshurun teaches a specific technique of detecting radar signals via an autonomous agent for purposes of threat identification and countermeasure deployment; (3) one of ordinary skill in the art would have recognized that applying the known technique would have yielded predictable results and resulted in a system with improved threat detection via UAVs; and (4) no additional findings based on the Graham factual inquiries are necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness (See MPEP 2143).
Regarding claim 7,
Crawford (‘308) in view of Yeshurun (‘262) teaches the invention as claimed and discussed above.
Yeshurun (‘262) further teaches:
The method of claim 1, wherein a remote operator controls the autonomous agent. ([0037-38] – “Each of the UAVs 14 may be associated with a UAV controller (not illustrated) for directing its operation, in particular the operation of its elements… The UAV controller may be provided as an element of the UAV 14 or externally thereto,”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied Yeshurun’s known technique to Crawford’s known method ready for improvement to yield predictable results. Such a finding is proper because (1) Crawford teaches a base method of radar detection by an agent ([col. 4, lines 9-22] – “smart receiver or sensor that includes compressive sensing and machine learning capabilities for identifying and optionally locating RF signals… smart receiver or sensor that may be positioned on an airborne, ground or sea moving platform… in close proximity to the target RF emitters (e.g., radars) the signals of which are to be detected.”), radar network modeling, friendly/non-friendly identification, and generation and implementation of counter-radar maneuvers; (2) Yeshurun teaches a specific technique of detecting radar signals via an autonomous agent for purposes of threat identification and countermeasure deployment; (3) one of ordinary skill in the art would have recognized that applying the known technique would have yielded predictable results and resulted in a system with improved threat detection via UAVs; and (4) no additional findings based on the Graham factual inquiries are necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness (See MPEP 2143).
Claim(s) 5-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 10924308 B1 to Crawford in view of US 20180341262 A1 to Yeshurun and further in view of US 11181634 B1 to Lue and further in view of US 20230325679 A1 to Wang.
Regarding claim 5,
Crawford (‘308) in view of Yeshurun (‘262) teaches the invention as claimed and discussed above.
Yeshurun (‘262) further teaches:
transmitting ([0037] – “transmission/receipt of data between elements of the UAV 14, other UAVs, the platform 16 (e.g., the platform controller), and/or external systems, e.g., to be directly controlled thereby.” [0071] – “the UAVs 14 may be configured to communicate among themselves in order to facilitate identifying and/or neutralizing a threat.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied Yeshurun’s known technique to Crawford’s known method ready for improvement to yield predictable results. Such a finding is proper because (1) Crawford teaches a base method of radar detection by an agent, radar network modeling, friendly/non-friendly identification, and generation and implementation of counter-radar maneuvers; (2) Yeshurun teaches a specific technique of detecting radar signals via an autonomous agent for purposes of threat identification and countermeasure deployment, and sharing threat identification information between a plurality of UAVs; (3) one of ordinary skill in the art would have recognized that applying the known technique would have yielded predictable results and resulted in a system with improved threat detection via UAVs; and (4) no additional findings based on the Graham factual inquiries are necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness (See MPEP 2143).
Crawford (‘308) in view of Yeshurun (‘262) does not explicitly teach the additional elements of the claim.
US 11181634 B1 to Lue teaches:
The method of claim 1, further comprising:
modifying the operational model of the radar network; (Fig. 6; [col. 16] – “A step (635) may include executing, by the machine learning circuit, a radar detection model to calculate an updated threat detection criteria based on the radar data signal and the at least one of the sensor data or the platform state data… The radar detection model can generate an output representative of detected entities based on received radar data… A step (640) may include providing, by the machine learning circuit to the entity detector, the updated threat detection criteria to update the entity detector.” Examiner notes instant application recites [0023] – “Each operational model 204 functions by modeling a particular radar network (e.g., using signals 108 and information about emitters 106) by isolating and identifying signals 108… Each operational model 204 allows a particular agent to classify a particular radar network as friendly or non-friendly.” For this reason, the broadest reasonable interpretation of an operational model in light of the specification includes a model which outputs a classification of the radar network and/or information with which the radar network can be classified. Therefore, a modification to the entity detector or the radar detection model may correspond to a modification of the operational model.) and
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied Lue’s known technique to Crawford’s known method ready for improvement to yield predictable results. Such a finding is proper because (1) Crawford teaches a base method of radar detection by an agent, radar network modeling, friendly/non-friendly identification based on machine / deep learning, and generation and implementation of counter-radar maneuvers; (2) Lue teaches a specific technique of deep learning for threat identification of entities; (3) one of ordinary skill in the art would have recognized that applying the known technique would have yielded predictable results and resulted in a system with improved, dynamic threat detection; and (4) no additional findings based on the Graham factual inquiries are necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness (See MPEP 2143).
US 20230325679 A1 to Wang teaches:
transmitting the modified operational model to another autonomous agent. ([0082] – “to perform the peer-to-peer federated learning, UEs (e.g., UE 112, UE 113) generate updated ML configuration information for one or more UECS DNNs by running an online or offline training procedure and exchange the updated ML configuration information directly with one another.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied Wang’s known technique to Crawford in view of Yeshurun’s known method ready for improvement to yield predictable results. Such a finding is proper because (1) Crawford in view of Yeshurun and further in view of Lue teaches a base method of radar detection by an agent, radar network modeling, friendly/non-friendly identification based on machine / deep learning, modification of operational models, and sharing of information between a plurality of autonomous agents, and generation and implementation of counter-radar maneuvers; (2) Wang teaches a specific technique of peer-to-peer federated learning; (3) one of ordinary skill in the art would have recognized that applying the known technique would have yielded predictable results and resulted in a system with more cohesive and accurate threat detection amongst a plurality of UAVs; and (4) no additional findings based on the Graham factual inquiries are necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness (See MPEP 2143).
Regarding claim 6,
Crawford (‘308) in view of Yeshurun (‘262) and further in view of Yeshurun (‘262) and further in view of Wang (‘679) teaches the invention as claimed and discussed above.
Wang further teaches:
The method of claim 5, further comprising transmitting the modified operational model of the radar network from the autonomous agent directly to another autonomous agent. ([0082] – “to perform the peer-to-peer federated learning, UEs (e.g., UE 112, UE 113) generate updated ML configuration information for one or more UECS DNNs by running an online or offline training procedure and exchange the updated ML configuration information directly with one another.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied Wang’s known technique to Crawford in view of Yeshurun’s known method ready for improvement to yield predictable results. Such a finding is proper because (1) Crawford in view of Yeshurun and further in view of Lue teaches a base method of radar detection by an agent, radar network modeling, friendly/non-friendly identification based on machine / deep learning, modification of operational models, and sharing of information between a plurality of autonomous agents, and generation and implementation of counter-radar maneuvers; (2) Wang teaches a specific technique of peer-to-peer federated learning; (3) one of ordinary skill in the art would have recognized that applying the known technique would have yielded predictable results and resulted in a system with more cohesive and accurate threat detection amongst a plurality of UAVs; and (4) no additional findings based on the Graham factual inquiries are necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness (See MPEP 2143).
Claim(s) 8-11, 14-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 10924308 B1 to Crawford in view of US 20180341262 A1 to Yeshurun and further in view of “Adaptation of Frequency Hopping Interval for Radar Anti-Jamming Based on Reinforcement Learning” to Ailiya.
Regarding claim 8,
Crawford (‘308) teaches:
A system comprising:
a machine learning module configured to:
generate an operational model of a detected radar network within an environment, (Fig. 9; [col. 11, last para – col. 12, line 31] – “a plurality of RF signals is received from one or more RF emitters… characterized by a first machine learning device using a trained process to produce pulse description words (PDWs)… In block 916, the PDWs are associated with one or more RF emitters… by a second machine learning device.” [col. 10, last para] – “PDW parameters are embedded into the model.”) and
classify the radar network as friendly or non-friendly based on the operational model; ([col. 2, lines 46-62] – “associating the PDWs with one or more RF emitters and identify the one or more RF emitters, by a second machine learning device.” [col. 2, lines 41-46] – “identifies and optionally locates radar signals and their characteristics, for example, what type of RF emitters or radars and whether they are friendly radars or “threat” radars.” [col. 7, lines 42-61] – “In some embodiments, the ML 316 may be a deep learning machine that uses multiple layers to progressively extract higher level features from the characterized pulses to associate them with certain type of emitter and identify the emitter”)
a ([col. 12, lines 8-22] – “In block 916, the PDWs are associated with one or more RF emitters and the one or more RF emitters are identified by a second machine learning device. Information, including the location of identified RF emitters, can then be used to take action for the identified emitter, such as mitigate the threat, create an ingress/egress flight path that avoids the threat or is used to update the database of a radar warning receiver.” Examiner notes instant application specification [0020, 29] – “Maneuvers 208 are not necessarily limited to physical movements and actions within environment 100 as they may include, e.g., intercommunications with other agents 102, signal construction techniques for counteracting a wide variety of radars, and/or further analysis to evaluate the effectiveness of counteracting certain emitter(s) 106 and/or signals 108… Maneuver(s) 208 in some cases may simply cause agent(s) 102 to move to another location and thus evade the range of emitter(s) 106 and/or signal(s) 108.”) and
an ([col. 4, lines 9-22] – “smart receiver or sensor that includes compressive sensing and machine learning capabilities for identifying and optionally locating RF signals… smart receiver or sensor that may be positioned on an airborne, ground or sea moving platform… in close proximity to the target RF emitters (e.g., radars) the signals of which are to be detected.”) and configured to implement the counter-radar maneuver. ([col. 12, lines 8-22] – “In block 916, the PDWs are associated with one or more RF emitters and the one or more RF emitters are identified by a second machine learning device. Information, including the location of identified RF emitters, can then be used to take action for the identified emitter, such as mitigate the threat, create an ingress/egress flight path that avoids the threat or is used to update the database of a radar warning receiver.”)
Yeshurun (‘262) teaches:
an autonomous agent ([0043, 46] – “UAVs 14 may comprise surveillance equipment 20 configured to facilitate detection of an incoming threat… The surveillance equipment 20 may comprise… pulse-Doppler radar”) configured to implement the counter-radar maneuver. ([0047-52] – “one or more of the UAVs 14 may be configured to direct deployment of the countermeasure most suited against an identified threat, and/or to direct deployment of two or more types of countermeasures against a single threat. According to some examples, the countermeasure is provided on one of the UAVs 14 itself, or on the platform 16, for deployment. According to some examples, the countermeasure is an external one, i.e., not deployed by the system 10 at all; rather, according to these examples a UAV 14 or other element of the system directs an external apparatus or system to deploy the countermeasure.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied Yeshurun’s known technique to Crawford’s known method ready for improvement to yield predictable results. Such a finding is proper because (1) Crawford teaches a base method of radar detection by an agent ([col. 4, lines 9-22] – “smart receiver or sensor that includes compressive sensing and machine learning capabilities for identifying and optionally locating RF signals… smart receiver or sensor that may be positioned on an airborne, ground or sea moving platform… in close proximity to the target RF emitters (e.g., radars) the signals of which are to be detected.”), radar network modeling, friendly/non-friendly identification, and generation and implementation of counter-radar maneuvers; (2) Yeshurun teaches a specific technique of detecting radar signals via an autonomous agent for purposes of threat identification and countermeasure deployment; (3) one of ordinary skill in the art would have recognized that applying the known technique would have yielded predictable results and resulted in a system with improved threat detection via UAVs; and (4) no additional findings based on the Graham factual inquiries are necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness (See MPEP 2143).
Ailiya (Adaptation of Frequency Hopping Interval for Radar Anti-Jamming Based on Reinforcement Learning) teaches:
a reinforcement learning module in communication with the machine learning module and configured to generate a counter-radar maneuver based on the operational model ([p. 12435-12436 – II. System Model] – “In the radar receiver, the radar pulses are integrated to improve SNR. The radar needs to adjust the frequency hopping time interval wisely to counteract the unknown jamming environment.” [p. 12435 – I. Introduction, section 3)] – “Derive the optimal frequency hopping time interval for different pulse widths under the framework of RL theory” [p. 12437] – “Fortunately, RL [10] provides a solution to the sequential decision making problems with unknown environmental models, provided that these problems can be modeled as MDPs [36].” [p. 12440 – B. Reinforcement Learning Based Problem Formulation] – “Actions taken by the radar not only influence the immediate reward but also states in the future… The impact of frequency hopping time interval on interception and jamming is considered in the process of accumulating rewards.” [p. 12441] – “Under the RL framework, (31) is the optimal anti-jamming strategy that the radar should adopt.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied Ailiya’s known technique to Crawford’s known method ready for improvement to yield predictable results. Such a finding is proper because (1) Crawford in view of Yeshurun teaches a base method of generation and implementation of counter-radar maneuvers followed by monitoring of countermeasure success and addressing countermeasure failure (see, e.g., Yeshurun [0047] – “They may be designed to accomplish this, e.g., by directing deployment of one or more countermeasures, by directly intercepting the threat, or by a combination of both. The platform 16/UAVs 14 may be further configured to monitor the threat to determine the success of the neutralization attempt, optionally directing deployment of a second countermeasure in the event of failure.”; (2) Ailiya teaches a specific technique of improving countermeasure selection via reinforcement learning; (3) one of ordinary skill in the art would have recognized that applying the known technique would have yielded predictable results and resulted in a system with improved countermeasure selection; and (4) no additional findings based on the Graham factual inquiries are necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness (See MPEP 2143).
Regarding claim(s) 9-11, 14,
The additional elements of claims 9-11, 14 is/are additional elements corresponding to claim(s) 2-4, 7, respectively. The only difference between the system claims and the method claims is that while the method claims do not require some contingent steps (see interpretation and analysis within claim rejections above), the system claims require structure that perform the functions of the contingent limitations should the condition occur. Accordingly, besides this difference in interpretation, the Examiner’s remarks and application of the prior art with respect to claim(s) 9-11, 14 are substantially the same as those made above with respect to claim(s) 2-4, 7.
Regarding claim(s) 15-18,
The additional elements of claims 15-18 is/are additional elements corresponding to claim(s) 8-11 respectively. Accordingly, the Examiner’s remarks and application of the prior art with respect to claim(s) 15-18 are substantially the same as those made above with respect to claim(s) 8-11.
Claim(s) 12-13, 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 10924308 B1 to Crawford in view of US 20180341262 A1 to Yeshurun and further in view of “Adaptation of Frequency Hopping Interval for Radar Anti-Jamming Based on Reinforcement Learning” to Ailiya and further in view of US 11181634 B1 to Lue and further in view of US 20230325679 A1 to Wang.
Regarding claim(s) 12-13,
The additional elements of claims 12-13 is/are additional elements corresponding to claim(s) 5-6, respectively. The only difference between the system claims and the method claims is that while the method claims do not require some contingent steps (see interpretation and analysis within claim rejections above), the system claims require structure that perform the functions of the contingent limitations should the condition occur. Accordingly, besides this difference in interpretation, the Examiner’s remarks and application of the prior art with respect to claim(s) 12-13 are substantially the same as those made above with respect to claim(s) 5-6.
Regarding claim(s) 19-20,
The additional elements of claims 19-20 is/are additional elements corresponding to claim(s) 12-13, respectively. Accordingly, the Examiner’s remarks and application of the prior art with respect to claim(s) 19-20 are substantially the same as those made above with respect to claim(s) 12-13.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JULIANA CROSS whose telephone number is (571)272-8721. The examiner can normally be reached Mon-Fri 9am-5pm Pacific time.
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, William Kelleher can be reached on (571) 272-7753. 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.
/JULIANA CROSS/Examiner, Art Unit 3648
/William Kelleher/Supervisory Patent Examiner, Art Unit 3648