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 .
1. Claims 1-20 are presented for examination.
Response to Amendment/Response to Arguments
2. Applicant's arguments filed 08/07/2026 have been fully considered but they are not persuasive. The rejection under 103 has been maintained since applicant’s amendments and remarks are not persuasive and fails to overcome the rejection.
Applicant has argued that the combination of Li and Nassar fail to disclose a graph comprising a plurality of nodes and a plurality of edges, wherein the plurality of nodes stores a plurality of light intensity values measured in situ during an additive manufacturing process of the additively manufactured article.
However, Li discloses that store a graph comprising a plurality of nodes and a plurality of edges ([0007], [0040], each hidden node 124 can be coupled to each input node 122, and the output node 126 can be coupled to each hidden node 220), wherein the plurality of nodes stores (Fig. 4, [0040] The neural network 120 includes a plurality of input nodes 122 for each principal component, a plurality of hidden nodes 124 (also called “intermediate nodes” below), and an output node 126 that will generate the characteristic value), a plurality of light intensity values measured in situ during an additive manufacturing process of the additively-manufactured article (Abstract, [0078], measuring by an in-situ spectrographic monitoring system a sequence of test spectra of light reflected from the substrate and measuring by an in-situ non-optical monitoring system a sequence of test values from the substrate during polishing of the test substrate). As a result, the previous rejection has been maintained.
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.
3. 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.
2.1 Claim(s) 1-3, 6-7, 10-14, 16-17 and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li (US 20220283082 A1) in view of Nassar (US 20200061710 A1).
Regarding claims 1 and 12, Li discloses computing device for detecting defects in an additively manufactured article (Abstract, [0031] The spectrographic monitoring system 70 can include a light source 72, a light detector 74, and circuitry 76 for sending and receiving signals between a controller 90, e.g., a computer,), the computing device comprising:
processing circuitry and memory storing instructions that, when executed by the processing circuitry ([0008], [0088], computer program product tangibly embodied in a non-transitory computer readable media and comprising instructions for causing a processor to carry out operations, or in a processing system, e.g., a polishing system, having a controller to carry out the operations) causes the processing circuitry to:
store a graph comprising a plurality of nodes and a plurality of edges ([0007], [0040], each hidden node 124 can be coupled to each input node 122, and the output node 126 can be coupled to each hidden node 220), wherein the plurality of nodes stores (Fig. 4, [0040] The neural network 120 includes a plurality of input nodes 122 for each principal component, a plurality of hidden nodes 124 (also called “intermediate nodes” below), and an output node 126 that will generate the characteristic value), a plurality of light intensity values measured in situ during an additive manufacturing process of the additively-manufactured article (Abstract, [0078], measuring by an in-situ spectrographic monitoring system a sequence of test spectra of light reflected from the substrate and measuring by an in-situ non-optical monitoring system a sequence of test values from the substrate during polishing of the test substrate),
generate an output describing a predicted defect in the graph using a graph neural network(Fig. 5-8, [0010], [0063], [0076]-[0077], output as the training data 200 (see Fig. 5, graph neural network) a training data generating system 250 for generating training values for the training spectra; training data 200 that is then used to train the neural network 120).
wherein the graph neural network has been trained using labeled training data generated a (Abstract, [0029]-[0030], Fig. 5-7, Label the training data of graph neural network ) by a process comprising:
storing a training graph comprising a plurality of training light intensity values measured in situ during a training additive manufacturing process of a training article (Fig. 5-7, Abstract, [0007], [0008], [0046], store by the controller a thickness predictive model that outputs a sequence of training values with each respective training value in the sequence of training values associated with a respective test spectrum from the sequence of test spectra, and training an artificial neural network using the plurality of training spectra and the plurality of training values);
determining a plurality of training sub-graphs from the training graph (Fig. 5, [0072], [0074] the training data set 200 includes the training spectra 202 and the characterizing values 204. The first and last characterizing values D.sub.0, D.sub.N can be generated by ex-situ metrology measurements); and
pairing a training sub-graph of the plurality of training sub-graphs with defect information to form a labeled pair to be included in the labeled training data ([0081],[0088],Fig. 8, the predictive model can pair each respective characterizing value with the respective test spectrum S.sub.0, S.sub.1, S.sub.2, . . . S.sub.N, that was measured at that respective time T.sub.0, T.sub.1, T.sub.2, . . . , T.sub.N. and labeled them. This data can then be output as the training data 200 (see FIG. 5) that is then used to train the neural network 120 (see FIG. 7)).
However, Li fails to disclose determining one or more defect locations of the training article, wherein the defect information describes whether a defect is spatially present in the paired training sub-graph based on the determined one or more defect locations.
Nassar discloses determining one or more defect locations of the training article ([0076]-[0077], a machine learning approach can be used wherein location-specific S.sub.Line-to-Continuum, determines the probability of flaw formation based on statistical or machine learning approaches of the output data obtained from the multi-spectral sensor); and
wherein the defect information describes whether a defect is spatially present in the paired training sub-graph based on the determined one or more defect locations (Fig. 5-Fig. 7, [0077], set as input features and one or more measurements of location-specific build quality (e.g. location-specific-density from computed tomography or metallography data) can be used as the ground truth for training).
Li and Nassar are analogous art. They relate to graph neural network for predicative defect. Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify, spectrographic monitoring, taught by Li, incorporated with multi-spectral sensor to collect spectral data, taught by Nassar, in order to measure more accurately and quickly of the thickness of a layer on a substrate; and to reduced, and reliability of an endpoint system to detect a desired processing endpoint to improve Within-wafer thickness non-uniformity and wafer-to-wafer thickness non-uniformity (WIWNU and WTWNU).
Regarding claims 2 and 13, Nassar discloses the one or more defect locations are determined using computed tomography imaging data ([0079], Fig. 5-6, identify anomalies within a region of interest 158. Knowing the region of interest 158, post-build inspection techniques (e.g., X-ray computer tomography (CT)) can be limited to the region of interest 158 instead of analyzing the entire part 104).
Regarding claims 3 and 14, combination of Nassar and Li disclose:
Nassar discloses receiving the plurality of light intensity values and associated spatial information ([0056] the multi-spectral sensor 102 can be configured to detect material interactions via received electromagnetic emission light 128 by spectral analysis. For example, the multi-spectral sensor 102 can include an optical receiver); and
constructing the graph by:
Li discloses generating a plurality of nodes, each node storing a spatially corresponding light intensity value of the plurality of light intensity values (Fig. 4, ;[0044], [0046], one or more other input nodes (e.g., node 122a) to receive other data. This other data could be from a prior measurement of the substrate by the in-situ monitoring system, e.g., spectra collected from earlier in the processing of the substrate), and generating a plurality of edges interconnecting the plurality of nodes (Fig. 4, the node N1, N2 N3, … is connected to the edges).
Regarding claims 6 and 16 Li discloses each of the plurality of nodes stores a light intensity differential value and a light intensity gradient value ([0078], [0048], linearly interpolated at a second slope, with the ratio between the first slope and second slope set by the relative polishing rate for each layer stored by the thickness predictive model).
Regarding claims 7 and 17, Nassar discloses each of the plurality of edges stores a value describing a relative alignment to a laser device used in the additive manufacturing process (Abstract, Fig. 1, [0005], [0016], The additive manufacturing process can involve use of a laser to generate a laser beam for fusion of the build material into the part. the laser is configured to generate a laser beam at an angle α relative to the surface of the build material; the optical receiver has an optical receiver axis and the optical axis forms an angle β with respect to the surface of the build material; and α does not equal β.).
Regarding claims 10 and 19, Li discloses at least one of the training sub-graphs is oriented based on a location of a laser used in the additive manufacturing process (Fig. 1, Fig. 5-Fig. 8, [0033], the rotation of the platen (shown by arrow 38), as the sensor 80 travels below the carrier head, the in-situ spectrographic monitoring system makes measurements at a sampling frequency; as a result, the measurements are taken at locations 14 in an arc that traverses the substrate).
Regarding claim 11, Nassar discloses for each of the determined one or more defect locations, determining one or more defect locations of the training article ([0076]-[0077], Fig. 5-Fig. 8, a machine learning approach can be used wherein location-specific S.sub.Line-to-Continuum, determine the probability of flaw formation based on statistical or machine learning approaches of the output data obtained from the multi-spectral sensor).
Regarding claim 20, Li discloses a method for training a graph neural network (Fig. 5-Fig. 8), the method comprising:
generating a training graph using the plurality of training light intensity values (Fig. 5 and 8, [0010], [0063], [0076]-[0077], output as the training data 200 (see Fig. 5, graph neural network) a training data generating system 250 for generating training values for the training spectra; training data 200 that is then used to train the neural network 120);
determining a plurality of training sub-graphs from the training graph ((Fig. 5-Fig. 8, [0072], [0074] the training data set 200 includes the training spectra 202 and the characterizing values 204. The first and last characterizing values D.sub.0, D.sub.N can be generated by ex-situ metrology measurements);
wherein a subset of the plurality of training sub-graphs is determined such that each of the training sub-graphs in the subset includes a defect location of the one or more defect locations (Fig. 5-Fig. 8, [0033]-[0038], the measurements are taken at locations 14 in an arc that traverses the substrate 10 (the number of points is illustrative; more or fewer measurements can be taken than illustrated, depending on the sampling frequency);
pairing each of the training sub-graphs with defect information describing whether a defect is present to generate labeled training data ([0078], 0088], Fig. 5-8, the predictive model can pair each respective characterizing value with the respective test spectrum S.sub.0, S.sub.1, S.sub.2, . . . S.sub.N, that was measured at that respective time T.sub.0, T.sub.1, T.sub.2, . . . , T.sub.N. This data can then be output as the training data 200 (see FIG. 5) that is then used to train the neural network 120 (see FIG. 7); and
training a graph neural network with the labeled training data ([0010], [0076], [0081], Fig. 8, The spectra used for training of a machine learning system, e.g., a neural network, can be labelled more accurately, thus improving the predictive performance of the machine learning system).
However, Li fails to discloses storing computed tomography imaging data of a training article and a plurality of training light intensity values measured in situ during a training additive manufacturing process of the training article.
Nassar discloses storing computed tomography imaging data of a training article and a plurality of training light intensity values measured in situ during a training additive manufacturing process of the training article ([0079], Fig. 5-6, identify anomalies within a region of interest 158. Knowing the region of interest 158, post-build inspection techniques (e.g., X-ray computer tomography (CT)) can be limited to the region of interest 158 instead of analyzing the entire part 104); determining one or more defect locations of the training article using the computed tomography imaging data (([0079], Fig. 5-6, identify anomalies within a region of interest 158).
Li and Nassar are analogous art. They relate to graph neural network for predicative defect. Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify, spectrographic monitoring, taught by Li, incorporated with multi-spectral sensor to collect spectral data, taught by Nassar, in order to measure more accurately and quickly of the thickness of a layer on a substrate; and to reduced, and reliability of an endpoint system to detect a desired processing endpoint to improve Within-wafer thickness non-uniformity and wafer-to-wafer thickness non-uniformity.
3.2 Claim(s) 4-5, 8-9, 15 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li (US 20220283082 A1) in view of Nassar (US 20200061710 A1) further in view of Chua et al. (US 20200410231 A1).
Regarding claims 4-5 and 15, the combination of Li and Nassar discloses the limitations of claims 1, 3, 12 and 14, as state above, but fails to discloses the limitations of claim 4-5 and 15. However, Chua discloses the limitations of claims 4-5 and 15, as follow:
Regarding claim 4, Chua discloses the plurality of nodes comprises layers of nodes, wherein the plurality of edges is partially generated using a Delaunay triangulation process for each of the layers of nodes (Fig. 3, [0042], [0046], [0047], The graph includes multiple nodes and multiple edges, based on the bounding boxes, using a graph construction algorithm (e.g., Delaunay triangulation)
Regarding claim 5, Chua discloses edges connecting nodes of different layers are generated based on a nearest neighbor algorithm ([0047], [0066], he prediction of the row data can include predicting a row of the lineless formatted data based on a property (e.g., one or more of: an x-coordinate, a y-coordinate, a length thereof, a distance from a nearest neighboring edge, a distance between a node thereof and at least one nearest neighbor node, etc.).
Regarding claim 15, Chua discloses the plurality of nodes comprises layers of nodes, wherein the plurality of edges is partially generated using a Delaunay triangulation process for each of the layers of nodes ([0042], [0046], [0047], The graph includes multiple nodes and multiple edges, based on the bounding boxes, using a graph construction algorithm (e.g., Delaunay triangulation); and edges connecting nodes of different layers are generated based on a nearest neighbor algorithm ([0047], [0066], the prediction of the row data can include predicting a row of the lineless formatted data based on a property (e.g., one or more of: an x-coordinate, a y-coordinate, a length thereof, a distance from a nearest neighboring edge, a distance between a node thereof and at least one nearest neighbor node, etc.).
Chua, Li and Nassar are analogous art. They relate to graph neural network. Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify, the graph with multiple nodes and edges, taught by Chua, incorporated with Li and Nassar, as state above, in order to reduced graph can be sent to a neural network to predict row labels and column labels for the table.
Regarding claims 8-9 and 18, Li discloses wherein the output describing the predicted defect is generated using the graph neural network and a sub-graph of the plurality of sub-graphs, wherein the predicted defect is a predicted defect sub-graph (Abstract, Fig. 5-8,[0019], [0076], [0077], [0081]-[0088], , the predictive model 260 can detect from the data from the second in-situ monitoring system a time during polishing of a transition between layers being polished, and outputs a sequence of training values with each respective training value in the sequence of training value), but the combination of Li and Nassar fail to disclose partition the stored graph into a plurality of sub-graphs based on a predetermined geometric shape, wherein the predetermined geometric shape is conical.
However, Chua discloses partition the stored graph into a plurality of sub-graphs based on a predetermined geometric shape, wherein the predetermined geometric shape is conical (0042], [0043], The graph can include connection lines (also referred to herein as “edges”) between neighboring vertices (also referred to herein as “nodes”) such that the graph reflects the topology and shape of the arrangement of vertices with a triangular reginal shape; graph including multiple nodes and multiple edges is generated based on the bounding boxes)
Chua, Li and Nassar are analogous art. They relate to graph neural network. Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify, the graph with multiple nodes and edges, taught by Chua, incorporated with Li and Nassar, as state above, in order to reduced graph can be sent to a neural network to predict row labels and column labels for the table.
Double Patenting
4. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 3, 12 and 14 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-2 and 11-12 of copending Application No. 19/568,549 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the instant US Application and the copending US Application has claimed similar limitation as mapping below.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Instant US Application 18/653,819
US copending Application 19/568,549
1. A computing device for detecting defects in an additively-manufactured article, the computing device comprising:
processing circuitry and memory storing instructions that, when executed by the processing circuitry, causes the processing circuitry to:
store a graph comprising a plurality of nodes and a plurality of edges, wherein the plurality of nodes stores a plurality of light intensity values measured in situ during an additive manufacturing process of the additively-manufactured article; and
generate an output describing a predicted defect in the graph using a graph neural network, wherein the graph neural network has been trained using labeled training data generated by a process comprising:
storing a training graph comprising a plurality of training light intensity values measured in situ during a training additive manufacturing process of a training article;
determining one or more defect locations of the training article; determining a plurality of training sub-graphs from the training graph; and pairing a training sub-graph of the plurality of training sub-graphs with defect information to form a labeled pair to be included in the labeled training data, wherein the defect information describes whether a defect is spatially present in the paired training sub-graph based on the determined one or more defect locations.
Claim 12 has similar limitations as claim 1
1. A computing system for detecting and classifying defects in an additively-manufactured article, the computing device comprising:
processing circuitry and memory storing instructions that, during execution by the processing circuitry, causes the processing circuitry to:
generate a graph comprising a plurality of in situ measurement values measured during an additive manufacturing process of the additively-manufactured article, wherein the plurality of in situ measurement values comprises a light intensity value and a depth value describing a three-dimensional position; and
generate an output describing a predicted defect in the graph using a graph neural network, wherein the graph neural network has been trained using labeled training data generated by a process comprising:
storing a training graph comprising a plurality of training in situ measurement values measured during a training additive manufacturing process of a training article;
determining one or more defect locations of the training article; determining a plurality of training sub-graphs from the training graph; and pairing a training sub-graph of the plurality of training sub-graphs with defect information to form a labeled pair to be included in the labeled training data, wherein the defect information describes whether a defect is spatially present in the paired training sub-graph based on the determined one or more defect locations.
Claim 11 has similar limitations as claim 1
3. The computing device of claim 1, wherein storing the graph comprises: receiving the plurality of light intensity values and associated spatial information; and constructing the graph by: generating a plurality of nodes, each node storing a spatially corresponding light intensity value of the plurality of light intensity values; and generating a plurality of edges interconnecting the plurality of nodes.
Claim 14 has similar limitations as claim 3
2. The computing system of claim 1, wherein generating the graph comprises: receiving the plurality of in situ measurement values and associated spatial information; and constructing the graph by: generating a plurality of nodes, each node storing a spatially corresponding in situ measurement value of the plurality of in situ measurement values; and generating a plurality of edges interconnecting the plurality of nodes.
Claim 12 has similar limitations as claim 2
20. A method for training a graph neural network, the method comprising:
storing computed tomography imaging data of a training article and a plurality of training light intensity values measured in situ during a training additive manufacturing process of the training article;
generating a training graph using the plurality of training light intensity values;
determining one or more defect locations of the training article using the computed tomography imaging data;
determining a plurality of training sub-graphs from the training graph, wherein a subset of the plurality of training sub-graphs is determined such that each of the training sub-graphs in the subset includes a defect location of the one or more defect locations;
pairing each of the training sub-graphs with defect information describing whether a defect is present to generate labeled training data; and training a graph neural network with the labeled training data.
20. A method for training a graph neural network, the method comprising:
storing computed tomography imaging data of a training article and a plurality of training in situ measurement values measured during a training additive manufacturing process of the training article, wherein the plurality of in situ measurement values comprises a light intensity value and a depth value describing a three-dimensional position;
generating a training graph using the plurality of training in situ measurement values;
determining one or more defect locations of the training article using the computed tomography imaging data;
determining a plurality of training sub-graphs from the training graph, wherein a subset of the plurality of training sub-graphs is determined such that each of the training sub-graphs in the subset includes a defect location of the one or more defect locations;
pairing each of the training sub-graphs with defect information describing whether a defect is present to generate labeled training data; and training a graph neural network with the labeled training data.
Citation Pertinent prior art
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wang et al. (US 20190318268 A1) discloses A training process of a machine learning model is executed at the edge node for a number of iterations to generate a model parameter based at least in part on a local dataset and a global model parameter.
A reference to specific paragraphs, columns, pages, or figures in a cited prior art reference is not limited to preferred embodiments or any specific examples. It is well settled that a prior art reference, in its entirety, must be considered for allthat it expressly teaches and fairly suggests to one having ordinary skill in the art. Stated differently, a prior art disclosure reading on a limitation of Applicant's claim cannot be ignored on the ground that other embodiments disclosed wereinstead cited. Therefore, the Examiner's citation to a specific portion of a single prior art reference is not intended to exclusively dictate, but rather, to demonstrate an exemplary disclosure commensurate with the specific limitations being addressed. In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1 009, 158 USPQ 275, 277 (CCPA 1968)). In re: Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); In re Fritch, 972 F.2d 1260, 1264, 23 USPQ2d 1780, 1782 (Fed. Cir. 1992); Merck& Co. v. Biocraft Labs., Inc., 874 F.2d804, 807, 10 USPQ2d 1843, 1846 (Fed. Cir. 1989); In re Fracalossi, 681 F.2d 792,794 n.1, 215 USPQ 569, 570 n.1 (CCPA 1982); In re Lamberti, 545 F.2d 747, 750, 192 USPQ 278, 280 (CCPA 1976); In re Bozek, 416 F.2d 1385, 1390, 163USPQ 545, 549 (CCPA 1969).
Conclusion
6. 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.
7. Any inquiry concerning this communication or earlier communications from the examiner should be directed Kidest Worku whose telephone number is 571-272-3737. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Ali Mohammad can be reached on 571-272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Examiner interviews are available via telephone 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.
Information regarding the status of an application may be obtained from the Patent Application information Retrieval IPAIRI system. Status information for published applications may be obtained from either Private PMR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAG system, contact the Electronic Business Center (EBC) at 866-217- 9197.
/KIDEST WORKU/Primary Examiner, Art Unit 2119
Prosecution Insights