DETAILED ACTION
Status of Claims
Claims 1-20 are currently pending and have been examined in this application. This NON-FINAL communication is the first action on the merits.
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 § 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 12-13 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 12 recites the limitation “generate electronic attack strategies to be pursed”. It is unclear what the term “attack strategies” refers to and no support for this limitation can found or implied by the instant specification. The examiner has interpreted this limitation as “generate a response based on emitter data”.
Claim 13 is rejected under 35 U.S.C. 112(b) due to its dependency on Claim 12.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 7-11, 13-14, 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Logan (US 20180313880) in view of Hjelmstad (US 20070222672).
Regarding Claims 1, Logan teaches the following limitations:
A system for emitter identification and tracking in an electronic warfare scenario, the system comprising: (Logan - [0014] Aspects of the embodiments are directed to pulse processing systems and methods. An RF pulse in the present context is a burst of energy in the monitored RF spectrum. Sources of RF pulses include communications systems, RADAR, unintentional transmitters such as arc welding tools, other industrial equipment such as microwave ovens, plasma generators, or the like, electronic warfare signals such as jamming signal transmitters, and many others. [0022] The received and digitized pulses are fed to feature correlator 128, which is constructed, programmed, or otherwise configured to extract various predefined features of the pulses and assess correlations between features of various pulses to determine and track one or more emitters of interest. According to some embodiments, feature correlator 128 extracts features such as frequency, bandwidth, pulse width, pulse repetition frequency, I-Q parameters, modulation type, angle of arrival, and the like, and generates pulse descriptor data structures, such as pulse descriptor words (PDWs) for the assessed pulses. As a subsequent layer of correlation, the PDWs are compared to determine any patterns that are indicative of relationships among the PDWs, such as whether certain pulses appear to emanate from a common emitter, such as a RADAR station, communication transmitter, etc. The result of this assessment is an emitter descriptor data structure, such as an emitter descriptor word (EDW) for each suspected emitter. The EDW may include certain features of the PDWs which have been determined to be correlated to a common emitter, for example, along with additional indicia that distinguish the emitter from among other emitters. As a further layer of assessment, the EDWs corresponding to the various emitters are stored in an active emitter data structure, such as an active emitter file (AEF), which maintains and updates features describing a known set of emitters and their various feature sets.)
an antenna array configured to receive signals from radio frequency (RF) emitters; (Logan - [0016] Pulse capture circuitry 102 may include radio receiver circuitry, including such arrangements as super heterodyne circuitry… Pulse capture circuitry 102 may also include analog-to-digital conversion circuitry that performs sampling, quantization, and digital encoding operations to interface the RF receiver circuitry with the signal processing circuitry. Logan does not explicitly teach “antenna array”.)
processing circuitry configured to: convert the signals received from the RF emitters into digital signals; (Logan – [0016], [0027] Pulse processor 135 analyzes the contents of the filtered pulses. [0033] Example computing platform 200 includes at least one processor 202 (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both, processor cores, compute nodes, etc.), a main memory 204 and a static memory 206,)
determine, from the digital signals, pulse descriptor words (PDWs) that describe characteristics of the digital signals, (Logan - [0022])
classify the PDWs using a combination of unsupervised machine learning and supervised machine learning to form classification results that indicate whether the PDWs correspond to one of a known emitter and an unknown emitter; and (Logan - [0022], [0018] In another type of embodiment, a supervised machine-learning system, such as an artificial neural network (ANN) may be utilized. Other examples include classification engines, such as a K-nearest neighbor classifier, for instance. Unsupervised machine-learning algorithms may also be used.)
update an active emitter data structure of emitter profiles based on the classification results; and (Logan – [0018], [0022] Logan does not explicitly teach “emitter library”.)
a memory configured to store the PDWs. (Logan – [0033])
Logan does not explicitly teach the following limitations, however Hjelmstad, in the same field of endeavor, teaches:
antenna array (Hjelmstad – [Fig. 1], [0003] These systems employ multiple receiving antennas and multiple receivers to derive a course direction to the emitters.)
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the radio receiver circuitry of Logan with the multiple receiving antennas of Hjelmstad in order to derive course direction to an emitter (Hjelmstad – [0003]).
emitter library (Hjelmstad – [Fig. 3], [0022] The processing unit 3 with the A/D converters 24, gate arrays 25 and processor 26, has as its task to filter out all false signal and classify, identify and find the direction to all radar sources observed within it area of sight. This is done in a multi-stage process as illustrated in FIG. 3. The process includes the following steps: [0023] 1. Pulse detection sorting wanted information from noise [0024] 2. Pulse parameter estimation, which is performed on each detected pulse. The estimated parameters may include: [0025] direction of arrival [0026] carrier frequency [0027] pulse width pulse amplitude [0028] 3. De-interleaving. De-interleaving is the process of identifying which of the incoming pulses that come from each of the observed emitters, thus sorting all pulses coming from the same emitter into one (or more) pulse trains. [0029] 4. Emitter analysis and emitter recognition. Emitter analysis means that an improved set of pulse parameters are determined, now sorted per emitter, forming a "fingerprint" characterizing each emitter. The emitters are then identified by comparison with known signatures stored in a database)
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the active emitter data structure of Logan with the database of Hjelmstad in order to perform emitter analysis and emitter recognition (Hjelmstad – [0029]).
Regarding Claim 2, Logan further teaches:
wherein the processing circuitry is configured to apply deinterleaving to the PDWs using the unsupervised machine learning to form PDW clusters of PDWs based on a combination of intrinsic and extrinsic emitter characteristics described by the PDWs. (Logan – [0022], [0050] At 522, higher-order learning operations are carried out. These may include stochastic-type machine learning, as well as neural-network, classification, association rule mining, clustering, support vector machine, or other machine-learning mechanism, whether supervised or unsupervised,)
Regarding Claim 3, Logan further teaches:
wherein the intrinsic emitter characteristics or emitter features described by the PDWs comprise at least one of Carrier Frequency (Fc), Pulse Width (PW), pulse repetition interval (PRI), signal bandwidth (BW), or a feature available in training data used to train the unsupervised machine learning and available to the system. (Logan – [0018], [0022])
Regarding Claim 4, Logan further teaches:
wherein the extrinsic characteristics comprise at least one of Pulse Amplitude (PA), Time of Arrival (TOA), Angle of Arrival (AOA). (Logan – [0018], [0022])
Regarding Claim 7, Logan further teaches:
wherein: the unsupervised machine learning includes forming PDW clusters from the PDWs, and (Logan – [0018], [0022], [0050])
the supervised machine learning includes a plurality of supervised classifiers and an arbitrator, (Logan – [0018], [0022], [0028] The pulses are further processed to further refine the pulse masks by feature grader 136. Feature grader 136 applies pulse-grading criteria 138 along with the temporal-proximity weighting such that features deemed “good” in received pulses tend to elevate the grading, and features deemed “bad” tend to suppress the grading score.)
each of the supervised classifiers configured to use the PDW clusters to generate a prediction of an emitter used to generate the PDWs, (Logan – [0018], [0022])
the arbitrator configured to weight the predictions from the supervised classifiers to generate a final decision of the emitter used to generate the PDWs. (Logan – [0018], [0022], [0028])
Regarding Claim 8, Logan further teaches:
wherein the supervised machine learning is configured to limit use of intrinsic and extrinsic emitter characteristics described by the PDWs to the intrinsic emitter characteristics described by the PDWs. (Logan – [0018], [0022] supervised learning in regards to PDWs is based off intrinsic characteristics.)
Regarding Claim 9, Logan further teaches:
wherein the supervised machine learning is configured to generate the final decision by matching the intrinsic emitter characteristics described by the PDWs to an emitter identity indicated in training data for the supervised machine learning. (Logan – [0018], [0022], [0028])
Regarding Claim 10, Logan further teaches:
wherein: the unsupervised machine learning includes forming PDW clusters from the PDWs, and (Logan – [0018], [0022], [0050])
the supervised machine is configured to use relative behavior of the PDWs within each cluster to determine whether different PDWs within one of the PDW clusters were generated by an identical emitter. (Logan – [0018], [0022], [0050])
Regarding Claim 11, Logan further teaches:
wherein the processing circuitry is further configured to: track in library PDWs from a known first emitter and out of library PDWs from an unknown first emitter; and (Logan – [0018], [0022])
merge the (Logan – [0018], [0022] Logan does not explicitly teach “in library”.)
Logan does not explicitly teach the following limitations, however Hjelmstad, in the same field of endeavor, teaches:
in library (Hjelmstad – [Fig. 3], [0022-0029])
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the active emitter data structure of Logan with the database of Hjelmstad in order to perform emitter analysis and emitter recognition (Hjelmstad – [0029]).
Regarding Claim 13, Logan further teaches:
wherein: at least one of the unsupervised machine learning or supervised machine learning is re-trained periodically using the updated active emitter data structure or a subset of features based on new information obtained from the PDWs, and (Logan – [0018], [0022] Logan does not explicitly teach “emitter library”.)
the processing circuitry is configured to use a Mission Data File Free (MDF-Free) approach in which identification and tracking do not rely exclusively on a pre-existing database of emitter characteristics but are based on learned characteristics from real-time data. (Logan – [0018], [0022])
Logan does not explicitly teach the following limitations, however Hjelmstad, in the same field of endeavor, teaches:
emitter library (Hjelmstad – [Fig. 3], [0022-0029])
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the active emitter data structure of Logan with the database of Hjelmstad in order to perform emitter analysis and emitter recognition (Hjelmstad – [0029]).
Regarding Claims 14, Logan teaches the following limitations:
A method of identifying sources of radio frequency (RF) signals, the method comprising: (Logan – [0014], [0022])
receiving the signals from the sources; (Logan – [0022])
digitizing the signals to form digital signals; (Logan – [0016])
detecting the digital signals, extracting pulse parameters of the digital signals, and (Logan – [0016], [0022])
generating pulse descriptor words (PDWs) based on the pulse parameters; (Logan – [0022])
deinterleaving the PDWs using unsupervised machine learning algorithms; (Logan – [0018], [0022], [0050])
selecting a representative set of PDWs from a cluster of PDWs that have been deinterleaved; (Logan – [0022], [0050])
determining a likelihood that the representative set of PDWs belong to a known emitter present in an active emitter data structure; and (Logan – [0022], [0050] Logan does not explicitly teach “emitter library”.)
updating the emitter library with clusters of PDWs selected for a track merge. (Logan – [0022], [0050])
Logan does not explicitly teach the following limitations, however Hjelmstad, in the same field of endeavor, teaches:
emitter library (Hjelmstad – [Fig. 3], [0022-0029])
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the active emitter data structure of Logan with the database of Hjelmstad in order to perform emitter analysis and emitter recognition (Hjelmstad – [0029]).
Regarding Claim 16, Logan further teaches:
further comprising: determining, for the cluster of PDWs, a probability of belonging to a known emitter present in training data for the supervised machine learning algorithms; and (Logan – [0018], [0022], [0050] Logan does not explicitly teach “probability of belonging to a known emitter”.)
assigning the cluster of PDWs to a group selected from: within a training library for high probability clusters, (Logan – [0022], [0050])
within the training library but with insufficient initial information to determine which class within the emitter library for medium or moderate probability PDW clusters, and (Logan – [0022], [0050] Logan does not explicitly teach “insufficient initial information… emitter library… medium or moderate probability”.)
out of the training library for low probability clusters. (Logan – [0022], [0050])
Logan does not explicitly teach the following limitations, however Hjelmstad, in the same field of endeavor, teaches:
insufficient initial information… emitter library… medium or moderate probability (Hjelmstad – [0064] Regarding the cluster of five vessels within the cluster cells, the DOA analysis will not discriminate these. Assuming a conservative estimate of the frequency spread, a uniform distribution of carrier frequency over 60 MHz, and a frequency resolution of 1 MHz in the carrier frequency determination in the receiver, there is a 16% probability that two emitters will fall into the same histogram cell. [0065] Thus, the first stage of the recommended de-interleaving process will resolve more than 90% of all vessels in the scenario. In order to increase the quality further, a second stage of de-interleaving is performed on the candidate emitter pulse trains. This stage is based on detailed PRI analysis, analysis of pulse width and antenna analysis.)
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the active emitter data structure of Logan with the de-interleaving of Hjelmstad in order to further increase direction of arrival analysis quality (Hjelmstad – [0065]).
emitter library (Hjelmstad – [Fig. 3], [0022-0029])
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the active emitter data structure of Logan with the database of Hjelmstad in order to perform emitter analysis and emitter recognition (Hjelmstad – [0029]).
Regarding Claim 17, Logan further teaches:
further comprising merging “OUT OF LIBRARY” tracks that correspond to reserve or unseen modes of (Logan – [0022], [0050] Logan does not explicitly teach “in library”.)
temporal feature analysis based on a time of arrival comparison of PDWs in an out-of-library cluster with PDWs in a tracker with PDWs from known emitters, temporal feature analysis based on a time of arrival comparison of PDWs using multiple dwells, or pulse amplitude matching between an out-of-library cluster with amplitudes of PDWs in a cluster that groups PDWS from known emitters. (Logan – [0022], [0028], [0050])
Logan does not explicitly teach the following limitations, however Hjelmstad, in the same field of endeavor, teaches:
in library (Hjelmstad – [Fig. 3], [0022-0029])
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the active emitter data structure of Logan with the database of Hjelmstad in order to perform emitter analysis and emitter recognition (Hjelmstad – [0029]).
Regarding Claim 18, Logan further teaches:
further comprising merging “OUT OF LIBRARY” tracks that correspond to reserve modes of “multiple extrinsic PDW features selected from a group that includes (Logan – [0022] Logan does not explicitly teach “in library”.)
temporal feature analysis, (Logan – [0028])
pulse amplitude matching, and (Logan – [0018], [0022], [0050])
angle of arrival or (Logan – [0022])
direction matching between an out-of-library cluster with amplitudes of PDWs in a cluster that groups PDWs from known emitters. (Logan – [0022], [0050])
Logan does not explicitly teach the following limitations, however Hjelmstad, in the same field of endeavor, teaches:
in library (Hjelmstad – [Fig. 3], [0022-0029])
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the active emitter data structure of Logan with the database of Hjelmstad in order to perform emitter analysis and emitter recognition (Hjelmstad – [0029]).
Regarding Claims 19, Logan teaches the following limitations:
A non-transitory computer-readable medium storing instructions that, when executed by a processor in an electronic warfare environment, cause the processor to: (Logan – [0014], [0033])
convert received radio frequency (RF) signals into digital signals and (Logan – [0016])
extract, from the digital signals, pulse descriptor words (PDWs) that describe characteristics of the received RF signals, (Logan – [0022])
classify the PDWs using a combination of a supervised machine learning technique and an unsupervised machine learning technique to determine whether the PDWs correspond to known emitters or whether the PDWs correspond to unknown emitters; (Logan – [0018], [0022])
update a active emitter data structure profiles to include identified emitters based on classification results; (Logan – [0018], [0022] Logan does not explicitly teach “library of emitters”.)
determine whether to track the identified emitters based on the updated library; and (Logan – [0022])
track the identified emitters in response to a determination to track the identified emitters. (Logan – [0022])
Logan does not explicitly teach the following limitations, however Hjelmstad, in the same field of endeavor, teaches:
in library (Hjelmstad – [Fig. 3], [0022-0029])
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the active emitter data structure of Logan with the database of Hjelmstad in order to perform emitter analysis and emitter recognition (Hjelmstad – [0029]).
Regarding Claim 20, Logan further teaches:
wherein the instructions, when executed, cause the processor to update the active emitter data structure by: (Logan – [0022] Logan does not explicitly teach “library of emitters”.)
incorporating new emitter profiles into the library based on PDWs classified as originating from unknown emitters, and (Logan – [0022])
refining existing emitter profiles in the library based on new information obtained from PDWs classified as originating from known emitters. (Logan – [0022])
Logan does not explicitly teach the following limitations, however Hjelmstad, in the same field of endeavor, teaches:
library of emitters (Hjelmstad – [Fig. 3], [0022-0029])
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the active emitter data structure of Logan with the database of Hjelmstad in order to perform emitter analysis and emitter recognition (Hjelmstad – [0029]).
Claims 5-6, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Logan (US 20180313880) in view of Hjelmstad (US 20070222672), as applied to claims 1, 14 above, and further in view of Ebrahimi Afrouzi (US 20210089040).
Regarding Claim 5, Logan further teaches:
wherein the unsupervised machine learning includes forming PDW clusters from the PDWs using a density-based spatial clustering of applications with noise (DBSCAN), a number of PDW clusters being less than a number of PDWs. (Logan – [0018], [0022], [0050] Logan does not explicitly teach “DBSCAN”.)
Logan does not explicitly teach the following limitations, however Ebrahimi Afrouzi, in the same field of endeavor, teaches:
DBSCAN (Ebrahimi Afrouzi – [0304] Some embodiments may execute a density-based clustering algorithm, like DBSCAN, to establish groups corresponding to the resulting clusters and exclude outliers.)
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the algorithms of Logan with the DBSCAN of Ebrahimi Afrouzi in order to establish groups corresponding to the resulting clusters and exclude outliers (Ebrahimi Afrouzi – [0304]).
Regarding Claim 6, Logan further teaches:
wherein the supervised machine learning includes use of at least one of a Bayesian Inference Engine or (Logan – [0018], [0022] Logan does not explicitly teach “Bayesian Inference Engine”.)
an ensemble learning algorithm, the ensemble learning algorithm including at least one of Random Forest, Extra Trees, Support Vector Machines (SVMs), Gradient Boosting, Adaptive Boosting, Decision Trees, Gradient Boosting Machines or another tree-based classification algorithm to classify the PDWs based on features of known emitters. (Logan – [0018], [0022] Logan does not explicitly teach “ensemble learning algorithm”.)
Logan does not explicitly teach the following limitations, however Ebrahimi Afrouzi, in the same field of endeavor, teaches:
Bayesian Inference Engine (Ebrahimi Afrouzi – [0432] In some embodiments, the processor may use a Bayesian approach, wherein the processor may form a hypothesis based on a first observation (e.g., derivative of depth color measurements) and confirm the hypothesis by a second observation (e.g., derivative of depth measurements) before making any estimation or prediction.)
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the algorithms of Logan with the Bayesian approach of Ebrahimi Afrouzi in order to improve accuracy of measuements (Ebrahimi Afrouzi – [0432]).
ensemble learning algorithm (Ebrahimi Afrouzi – [0461] Any classification system may be created, such as linear classifiers like Fisher's linear discriminant, logistic regression, naive Bayes classifier and perceptron, support vector machines like least squares support vector machines, quadratic classifiers, kernel estimation like k-nearest neighbor, boosting (meta-algorithm), decision trees like random forests, neural networks, and learning vector quantization.)
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the algorithms of Logan with the ensemble learning algorithm of Ebrahimi Afrouzi in order to classify based on reliability (Ebrahimi Afrouzi – [0461]).
Regarding Claim 15, Logan further teaches:
wherein the supervised machine learning algorithm is implemented by an ensemble of supervised machine learning algorithms and an arbitrator function that combines an output from each of the ensemble of supervised machine learning algorithms. (Logan – [0018], [0022], [0028] Logan does not explicitly teach “ensemble of supervised machine learning algorithms”.)
Logan does not explicitly teach the following limitations, however Ebrahimi Afrouzi, in the same field of endeavor, teaches:
ensemble of supervised machine learning algorithms (Ebrahimi Afrouzi – [0461])
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the algorithms of Logan with the ensemble learning algorithm of Ebrahimi Afrouzi in order to classify based on reliability (Ebrahimi Afrouzi – [0461]).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Logan (US 20180313880) in view of Hjelmstad (US 20070222672), as applied to claims 1, 14 above, and further in view of Cyr (US 20230056233).
Regarding Claim 12, Logan further teaches:
wherein the processing circuitry is configured to: generate an emitter report that includes information on a type, location, and behavior of tracked emitters, and (Logan – [0018], [0022])
use the emitter report to update the active emitter data structure to an updated emitter library and (Logan – [0018], [0022] Logan does not explicitly teach “emitter library”.)
Logan does not explicitly teach the following limitations, however Hjelmstad, in the same field of endeavor, teaches:
emitter library (Hjelmstad – [Fig. 3], [0022-0029])
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the active emitter data structure of Logan with the database of Hjelmstad in order to perform emitter analysis and emitter recognition (Hjelmstad – [0029]).
Logan does not explicitly teach the following limitations, however Cyr, in the same field of endeavor, teaches:
generate electronic attack strategies to be pursued. (Cyr – [Abstract] receiving a dataset representative of data received from a plurality of sensors at an autonomous vehicle sensor system that measure environmental conditions related to an environment of an autonomous vehicle. The system operations also perform a simulated attack on the dataset. The simulated attack includes at least one of modifying the dataset)
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the active emitter data of Logan with the modified dataset of Cyr in order simulate an attack (Cyr – [Abstract]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's
disclosure or directed to the state of art is listed on the enclosed PTO-892.
The following is a brief description for relevant prior art that was cited but not applied:
Wozny (US 20230097336) teaches antenna data collected and a neural network has been trained and installed within a direction-finding system.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON JAMES HENSON whose telephone number is (703)756-1841. The examiner can normally be reached Monday-Friday 9:00 am - 5:00 pm.
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/BRANDON JAMES HENSON/Examiner, Art Unit 3648
/VLADIMIR MAGLOIRE/Supervisory Patent Examiner, Art Unit 3648
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