DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Terminal Disclaimer
The terminal disclaimer filed on 12 June 2026 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of US Patent No. 12480898 has been reviewed and is accepted. The terminal disclaimer has been recorded.
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.
Claim(s) 1-5, 9 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Patent No. 9,625,398 to Campbell et al. (Campbell) in view of US Patent Application Publication 2020/0411513 to Jambunathan et al. (Jambunathan) and US Patent No. 11,373,839 to Hlavenka et al. (Hlavenka).
Claim 1
Campbell discloses a system for non-destructive depth-profiling of samples (see Abstract and column 2 lines 51-63, column 5 line 45 to column 6 line 11, and column 8 lines 54-67: non-destructive energy-dispersive X-ray spectroscopy used to generate a depth profile, i.e. z-profile of a sample), the system comprising:
an electron beam (e-beam) source configured to project e-beams on an inspected sample at a plurality of landing energies and at a plurality of lateral locations within a profiled region of the sample that includes lateral non-uniformity, the e-beams inducing X-ray emitting interactions within respective probed volumes whose depth varies according to the landing energy (see Fig. 1 and column 5 lines 45-64: SEM system generates an electron beam, directed at sample; and see column 2 lines 50-63 and column 6 lines 52-65: scan are performed at various beam energy levels/landing energies for probing an interaction volume; see column 7 lines 8-27: depth determined by the beams energy level/landing energy; see Fig 5 and column 8 lines 25-44: samples taken at various locations);
an X-ray sensor configured to measure the emitted X-rays to obtain X-ray emission data sets pertaining to each of the probed volumes, each associated with the landing energy and the lateral location of the inducing e-beam (see Abstract, Fig. 1, column 1 lines 21-31, and column 6 lines 11-25: EDS detectors to detect X-rays from the sample; see also column 7 lines 41-61: collect data for a plurality of different energy levels, i.e. forms a set of data; see column 7 lines 8-27: depth determined by the beams energy level/landing energy; see Fig 5 and column 8 lines 25-44: samples taken at various locations); and
processing circuitry configured to determine a set of structural parameters, which characterizes at a lateral location and depth within the profiled region of the wafer, one or both of an internal geometry and/ a composition, based on the measured optical X-ray emission data sets and taking into account reference data indicative of an intended design of the inspected sample (see Figs 1, column 2 line 64 to column 3 line 2, and 5, column 2 lines 4-24, column 6 lines 20-25, and column 8 lines 44-67: processor, generates a composition depth profile for the sample, discusses determining depth, shape, and/or composition of the layers of the sample).
Campbell does not expressly disclose wherein the sample is a patterned wafer.
Jambunathan discloses wherein the sample is a patterned wafer (see paragraph 0021, 0023, 0026, and 0047-0047: discusses patterning process on a substrate/wafer and substrate being imaged using EDX spectroscopy).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Campbell with the teachings of Jambunathan, evaluating samples such as patterned wafers, for the advantageous benefit of monitoring the manufacture of patterned wafers.
Campbell and Jambunathan do not expressly disclose characterizing a laterally non-uniform internal geometry or a laterally non-uniform composition wherein the structural parameters are determined taking into account reference data indicative of an intended design of the inspected sample.
Hlavenka teaches processing spectral components to determine concentrations of components including multiple components at single sample locations (col. 6, lines 8-60). Hlavenka teaches generating an image which displays at least multiple components which corresponds to a non-uniform composition (Figs. 5A, 5B, 6A, 6B).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the invention of Campbell to include determining multiple components at a single lateral location, as taught by Hlavenka, because then the accuracy of the composition determinations would have been improved for locations that have non-uniform composition.
Hlavenka discloses wherein structural parameters are determined taking into account reference data indicative of an intended design of the inspected sample (see column 2 lines 11-46 and lines 62-66 and column 5 line 62 to column 6 line 2: machine learning estimator may be initiated based on sample information and/or known spectral components. For example, known spectral components of possible compositions of the sample may be used as the initial spectral components, broadly interpreted, sample information and known spectral components and known spectral components of possible compositions of the sample meet limitations of design data of the sample; and see column 10 lines 40-56: determines structural and composition information of the sample).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Campbell with the teachings of Hlavenka, i.e. using sample specific information when evaluating the structural and composition makeup via machine learning, for the advantageous benefit of improving the accuracy of the structural and composition determinations of the machine learning algorithm. Once modified, the modification teaches wherein the reference data comprise design data of the inspected patterned wafer as Jambunathan previously disclosed wherein the sample is a patterned wafer.
Claim 2
Campbell and Jambunathan do not expressly disclose wherein the reference data comprise design data of the inspected patterned wafer and/or ground truth (GT) data of other patterned wafers of the same intended design as the inspected patterned wafer and/or GT data of especially prepared patterned wafers exhibiting selected variations with respect to the intended design.
Hlavenka discloses wherein the reference data comprise design data of the inspected sample (see column 2 lines 11-46 and lines 62-66 and column 5 line 62 to column 6 line 2: machine learning estimator may be initiated based on sample information and/or known spectral components. For example, known spectral components of possible compositions of the sample may be used as the initial spectral components, broadly interpreted, sample information and known spectral components and known spectral components of possible compositions of the sample meet limitations of design data of the sample; and see column 10 lines 40-56: determines structural and composition information of the sample).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Campbell with the teachings of Hlavenka, i.e. using sample specific information when evaluating the structural and composition makeup via machine learning, for the advantageous benefit of improving the accuracy of the structural and composition determinations of the machine learning algorithm. Once modified, the modification teaches wherein the reference data comprise design data of the inspected patterned wafer as Jambunathan previously disclosed wherein the sample is a patterned wafer.
Claim 3
Campbell, previously modified, further discloses wherein the set of structural parameters specifies map of a target material, which the inspected sample, i.e. previously modified patterned waver, comprises, on the depth and the lateral location (see Abstract, Fig. 5, and column 6 lines 11-25: maps out the sample, Fig.5 shows different profiles at lateral locations).
Campbell and Jambunathan do not expressly disclose wherein the map is a concentration map quantifying a dependence of a concentration of a target material.
Hlavenka discloses wherein the map is a concentration map quantifying a dependence of a concentration of a target material (see column 3 lines 6-13 and column 6 lines 12-36: discloses computing the concentration at the measurement location and generating component maps/mapping concentrations).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Campbell with the teachings of Hlavenka, i.e. mapping the concentration values, for the advantageous benefit allowing a user to visually view a map of the determined concentration values at the specific locations.
Claim 4
Campbell does not expressly disclose wherein the inspected patterned wafer comprises a bulk into which the target material has been introduced, and wherein the bulk is or comprises a semiconductor structure; and/or wherein the target material comprises fluorine, nitrogen, boron, and/or gallium.
Jambunathan discloses wherein the inspected patterned wafer comprises a bulk into which the target material has been introduced, and wherein the bulk is or comprises a semiconductor structure; and/or wherein the target material comprises fluorine, nitrogen, boron, and/or gallium (see Abstract and paragraphs 0021-0022: silicon bulk, discloses materials being evaluated such as gallium, boron, nitrogen, and fluorine; and see paragraph 0021, 0023, 0026, and 0047-0047: discusses patterning process on a substrate/wafer and substrate being imaged using EDX spectroscopy).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Campbell with the teachings of Jambunathan, evaluating substrate samples to determine concentrations of the disclosed elements, to see if the substrate has been correctly manufactured, incorrectly manufactured, or damaged during manufacturing.
Claim 5
Campbell, previously modified, further discloses wherein the set of structural parameters comprises one or more of:
one or more overall concentrations of one or more materials, respectively, that the inspected patterned wafer comprises; and
at least one width of at least one structure, respectively, which is embedded in the inspected patterned wafer; and
when the inspected patterned wafer comprises a plurality of layers:
at least one thickness of at least one of the plurality of layers, respectively;
a combined thickness of at least some of the plurality of layers; and
at least one mass density of at least one of the plurality of layers, respectively (see column 8 lines 11-24: determines thickness of the layer and/or layers; see column 6 lines 53-65 and claim 1 and 3: correlating beam energy with material density, i.e. density is a determined parameter; as such discloses the one of the one or more varieties listed above).
Claim 9
Campbell and Jambunathan do not expressly disclose wherein, in order to determine the set of structural parameters, the processing circuitry is configured to execute a trained algorithm, which is configured to receive as inputs key X-ray emission parameters extracted from the X-ray emission data sets.
Hlavenka discloses wherein, in order to determine the set of structural parameters, the processing circuitry is configured to execute a trained algorithm, which is configured to receive as inputs key X-ray emission parameters extracted from the X- ray emission data sets (see column 5 line 55 to column 6 line 35: machine learning estimator compares the input spectrum or spectral component with theoretical spectra of multiple chemical elements, and outputs quantified spectrum or quantified spectral component as well as the concentrations of the chemical elements in the component (or spectral component).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Campbell with the teachings of Hlavenka, i.e. imputing the spectral components into a machine learning algorithm, for the advantageous benefit of using a trained machine leaning algorithm to accurately determine the concentration values of the sample/wafer being analyzed.
Claim 13
Campbell discloses a computer-based method for non-destructive depth-profiling of samples (see Abstract and column 2 lines 51-63, column 5 line 45 to column 6 line 11, and column 8 lines 54-67: non-destructive energy-dispersive X-ray spectroscopy used to generate a depth profile, i.e. z-profile of a sample; and see Fig. 1 and column 3 lines 3-34: computer may implement the method), the method comprising:
a measurement operation comprising, for each of a plurality of landing energies, selected so as to allow probing an inspected sample to a plurality of depths, and at a plurality of lateral locations within a profiled region of the sample that includes lateral non-uniformity (see Fig. 1 and column 5 lines 45-64: SEM system generates an electron beam, directed at sample; and see column 2 lines 50-63 and column 6 lines 52-65: scan are performed at various beam energy levels/landing energies; see column 7 lines 8-27: depth determined by the beams energy level/landing energy; see Fig 5 and column 8 lines 25-44: samples taken at various locations show lateral non-uniformity), suboperations of:
projecting an electron beam (e-beam) on the inspected sample at the lateral location, which induces X-ray light-emitting interactions within a respective probed volume of the inspected sample, whose depth is determined by the landing energy (see Fig. 1 and column 5 lines 45-64: SEM system generates an electron beam, directed at sample; and see column 2 lines 50-63 and column 6 lines 52-65: scan are performed at various beam energy levels/landing energies for probing an interaction volume; see column 7 lines 8-27: depth determined by the beams energy level/landing energy; see Fig 5 and column 8 lines 25-44: samples taken at various locations); and
measuring the emitted X-rays to obtain an X-ray emission data set pertaining to the probed volume associated with the corresponding landing energy and lateral location of the inducing e-beam (see Abstract, Fig. 1, column 1 lines 21-31, and column 6 lines 11-25: EDS detectors to detect X-rays from the sample; see also column 7 lines 41-61: collect data for a plurality of different energy levels, i.e. forms a set of data; see column 7 lines 8-27: depth determined by the beams energy level/landing energy; see Fig 5 and column 8 lines 25-44: samples taken at various locations); and
a data analysis operation comprising determining a set of structural parameters, which characterizes one or both of an internal geometry and/ a composition, based on the measured optical X-ray emission data sets (see Figs 1, column 2 line 64 to column 3 line 2, and 5, column 2 lines 4-24, column 6 lines 20-25, and column 8 lines 44-67: processor, generates a composition depth profile for the sample, discusses determining depth, shape, and/or composition of the layers of the sample).
Campbell does not expressly disclose wherein the sample is a patterned wafer; and
wherein the structural parameters are determined taking into account reference data indicative of an intended design of the inspected sample.
Jambunathan discloses wherein the sample is a patterned wafer (see paragraph 0021, 0023, 0026, and 0047-0047: discusses patterning process on a substrate/wafer and substrate being imaged using EDX spectroscopy).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Campbell with the teachings of Jambunathan, evaluating samples such as patterned wafers, for the advantageous benefit of monitoring the manufacture of patterned wafers.
Campbell and Jambunathan do not expressly disclose characterizing a laterally non-uniform internal geometry or a laterally non-uniform composition wherein the structural parameters are determined taking into account reference data indicative of an intended design of the inspected sample.
Hlavenka teaches processing spectral components to determine concentrations of components including multiple components at single sample locations (col. 6, lines 8-60). Hlavenka teaches generating an image which displays at least multiple components which corresponds to a non-uniform composition (Figs. 5A, 5B, 6A, 6B).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the invention of Campbell to include determining multiple components at a single lateral location, as taught by Hlavenka, because then the accuracy of the composition determinations would have been improved for locations that have non-uniform composition.
Hlavenka discloses wherein structural parameters are determined taking into account reference data indicative of an intended design of the inspected sample (see column 2 lines 11-46 and lines 62-66 and column 5 line 62 to column 6 line 2: machine learning estimator may be initiated based on sample information and/or known spectral components. For example, known spectral components of possible compositions of the sample may be used as the initial spectral components, broadly interpreted, sample information and known spectral components and known spectral components of possible compositions of the sample meet limitations of design data of the sample; and see column 10 lines 40-56: determines structural and composition information of the sample).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Campbell with the teachings of Hlavenka, i.e. using sample specific information when evaluating the structural and composition makeup via machine learning, for the advantageous benefit of improving the accuracy of the structural and composition determinations of the machine learning algorithm. Once modified, the modification teaches wherein the reference data comprise design data of the inspected patterned wafer as Jambunathan previously disclosed wherein the sample is a patterned wafer.
Claim(s) 6 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Campbell in view of Jambunathan and Hlavenka as applied to claim 1 above, and further in view of US Patent Application Publication 2021/0356413 to Sakamae (Sakamae).
Claim 6
Campbell, previously modified, further discloses the X-ray sensor is configured to measure an intensity of at least a portion of the respectively emitted X-rays which the inspected sample, i.e. previously modified patterned wafer, comprises (see Abstract, Fig. 1, column 1 lines 21-31, and column 6 lines 11-25: EDS detectors to detect X-rays emitted from the sample).
Campbell, Jambunathan, and Hlavenka do not expressly disclose wherein the respective emitted X-rays have a frequency equal to, or within a frequency range about, a peak characteristic X-ray emission frequency of a target material.
Sakamae discloses wherein the portion of the respectively emitted and measured X-rays, have a frequency equal to, or within a frequency range about, a peak characteristic X-ray emission frequency of a target material, i.e. previously discussed profiled material (see paragraph 0045-0046: scanning spectral wavelength/frequency in vicinity of the peak wavelength/frequency of the target element).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Campbell with the teachings of Sakamae, i.e. measuring the wavelength/range of wavelength corresponding to the target material, for the advantageous benefit of obtaining relevant data corresponding the material of interest
Claim 7
Campbell, previously modified, discloses wherein the X-ray sensor comprises an energy-dispersive X-ray spectrometer or a wavelength-dispersive X-ray spectrometer (see Abstract and paragraph 0034: energy dispersive X-ray spectroscopy (EDX) or wavelength dispersive X-ray spectroscopy (WDX)).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Campbell in view of Jambunathan and Hlavenka as applied to claim 3 above, and further in view of US Patent Application Publication 2016/0322195 to Sender et al. (Sender).
Claim 8
Campbell discloses wherein the system is configured to allow projecting the e-beams so as to impinge on the inspected patterned wafer at each of controllably selectable lateral locations thereon (see Abstract, Fig. 5, and column lines 11-25: maps out the sample, Fig.5 shows different profiles at lateral locations).
Campbell and Jambunathan do not expressly disclose wherein the concentration map is three-dimensional.
Hlavenka discloses wherein the map is a concentration map (see column 3 lines 6-13 and column 6 lines 12-36: discloses computing the concentration at the measurement location and generating component maps/mapping concentrations).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Campbell with the teachings of Hlavenka, i.e. mapping the concentration values, for the advantageous benefit allowing a user to visually view a map of the determined concentration values at the specific locations.
Campbell, Jambunathan, and Hlavenka do not expressly disclose wherein the concentration map is three-dimensional.
Sender discloses a system that is configured to allow projecting the e-beams so as to impinge on the sample at each of controllably selectable lateral locations thereon; and wherein a generated image from the plurality of scans is three-dimensional image (see Fig. 2 and paragraphs 0047, 0056, and 0083: discloses a scan pattern, controllable in the lateral direction, to scan regions of interest of a sample, and discusses generating a 3D image from the plurality of scan measurement).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Campbell with the teachings of Sender, i.e. scanning a plurality of lateral location and using a plurality of scans to generate a 3D image, for the advantageous benefit of gathering and piecing together a plurality of scans to generate an accurate 3D representation of the sample of interest. Once modified, it would have been obvious to one with ordinary skill in the art to generate a 3D image/map of the concentration data based on the concentration data determined at the different depths.
Claim(s) 10 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Campbell in view of Jambunathan and Hlavenka as applied to claim 9 above, and further in view of US Patent Application Publication 2018/0107928 to Zhang et al. (Zhang).
Claim 10
Campbell and Jambunathan do not expressly disclose wherein weights of the trained algorithm are determined through training using the reference data and (i) key X-ray emissions parameters, which are derived from X-ray emission data sets of other patterned wafers samples of the same intended design as the inspected patterned wafer, and/or (ii) simulation data, which are derived from simulating impinging of patterned wafers of the same intended design as the inspected patterned wafer with e-beams at each of a plurality of landing energies.
Hlavenka discloses the trained algorithm is determined through training using the reference data and (i) key X-ray emissions parameters, which are derived from X-ray emission data sets of other patterned wafers samples of the same intended design as the inspected patterned wafer, and/or (ii) simulation data, which are derived from simulating impinging of patterned wafers of the same intended design as the inspected patterned wafer with e-beams at each of a plurality of landing energies (see Fig. 2A and column 2 lines 11-45: machine learning model is updated, i.e. trained, based on the measurement of the sample, known spectra, and/or theoretical spectra of the chemical elements).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Campbell with the teachings of Hlavenka, i.e. using a trained machine learning algorithm, for the advantageous benefit of improving the accuracy of the structural and composition determinations of the machine learning algorithm.
Campbell, Jambunathan, and Hlavenka do not expressly disclose wherein weights of the trained algorithm are determined through the training.
Zhang discloses wherein weights of the trained algorithm are determined through the training (see paragraph 0061-0062: trained neural network).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Campbell with the teachings of Zhang, i.e. using a neural network as a machine learning algorithm, for the advantageous benefit of using a proven, conventional machine learning algorithm for accurately analyzing energy-dispersive X-ray data sets.
Claim 11
Campbell, Jambunathan, and Hlavenka do not expressly disclose wherein the trained algorithm is or comprises a neural network, or wherein the trained algorithm is or comprises a linear model-incorporating algorithm.
Zhang discloses wherein a trained algorithm is or comprises a neural network, or wherein the trained algorithm is or comprises a linear model-incorporating algorithm (see paragraph 0061-0062: trained neural network).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Campbell with the teachings of Zhang, i.e. using a neural network as a machine learning algorithm, for the advantageous benefit of using a proven, conventional machine learning algorithm for accurately analyzing energy-dispersive X-ray data sets.
Allowable Subject Matter
Claim 12 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The prior art fails to disclose the system of claim 11, wherein the set of structural parameters specifies a concentration map quantifying a dependence of a concentration of a target material, which the inspected patterned wafer comprises, at least on the depth; and wherein the neural network is a classification neural network and at each map coordinate the concentration map specifies the density of the target material to a respective density range from a plurality of density ranges.
Response to Arguments
Applicant's arguments filed 12 June 2026 with regard to the rejection of claims 1-5, 6-7, 8, 9, 10-11 and 13 under 35 U.S.C. 103 have been fully considered but they are not persuasive.
Applicant states that none of the cited references, alone or in combination, teaches or suggests determining structural parameters that characterize a laterally non-uniform internal geometry and/or a laterally non-uniform composition within a profiled region of an inspected patterned wafer, at a lateral location and depth, while taking into account reference data indicative of an intended design of the inspected patterned wafer. As amended the claims now require characterizing one or both of (i) a laterally non-uniform internal geometry and (ii) a laterally non-uniform composition. Hlavenka teaches processing spectral components to determine concentrations of components including multiple components at single sample locations (col. 6, lines 8-60). Hlavenka teaches generating an image which displays at least multiple components which corresponds to a non-uniform composition (Figs. 5A, 5B, 6A, 6B).
Applicant states that the Examiner’s reliance on Hlavenka “sample information” as meeting the “reference data indicative of an intended design” limitation is misplaced. Applicant states that the generic information in Hlavenka at column 2, lines 11-46 is fundamentally different from reference data specifying the intended geometry, layer structure, or structural parameters of a patterned wafer with lateral non-uniformity, as required by the claims. Claim 1 recites “taking into account reference data indicative of an intended design of the inspected patterned wafer.” Hlavenka teaches known spectral components of possible compositions of the sample may be used as the initial spectral components, broadly interpreted, sample information and known spectral components and known spectral components of possible compositions of the sample meet limitations of design data of the sample; and see column 10 lines 40-56: determines structural and composition information of the sample. The known spectral components of possible compositions correspond to reference data indicative of an intended design as required by the claim. The claims do not specify the specific examples of reference data recited in the specification.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MANUEL L BARBEE whose telephone number is (571)272-2212. The examiner can normally be reached M-F: 9-5:30..
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/MANUEL L BARBEE/Primary Examiner, Art Unit 2857
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