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 .
Double Patenting
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.
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Claims 21-22 and 24-25 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 9-10 and 15 of U.S. Patent No. 10,438, 130. Although the claims at issue are not identical, they are not patentably distinct from each other because the US Patent discloses all the claim limitations of the Instant application. The differences between the instant application and the US Patent 10,438,130 B2 is that instant application has system embodiment claims, claims updating the graph, a database configure to store training data and a server comprising a plurality of processing units, while the instant application claim 21 cites that when assigning a particular label to a unlabeled data times generates a new labeled data item and that new label and data item are compared to threshold wherein when it is above a threshold it is used as training and when below it is not. The US patent does not explicitly cite a database configure to store training data items, however it does disclose maintaining a plurality of training data items, which would suggest that training data items are stored in some from memory or a database, thus it would obvious to do. Also, the instant application cites “at least one server comprising a plurality of processing units executed by one more processor and each processing unit associated with a private vector”, while this is not explicitly disclosed in the US Patent it is suggest by the limitation of “processing at least one unlabeled data items using a plurality of processing units executed by one or more processors, each unit associated with a private vector, comprising:”. This limitation discloses the plurality of processing units executed my one or more processors and each processing unit associated with a private vector, thus it does suggest the claim limitation of the server of as it would have been obvious to one of ordinary skill in the art to claim the system embodiment of the US Patent 10,438,130 which include a database for storing data and server containing processors for processing data. The limitation of updating a graph is disclosed by the US Patent wherein it discloses assigning a label to unlabeled data items, and as unlabeled items are assigned a label it generates new labeled data item. Thus claim 9 of the US Patent anticipated claims 21 of the instant application. Also, it would have been obvious to one of ordinary skill in the art to claim the system embodiment of claims in the US Patent. Additionally claim 22 of the instant application is anticipated by claim 9 of the US Patent as the “maintaining a plurality of training data items, each of the training data items associated with one of a plurality of label and associated with the one or more initial attributes” thus it discloses the training data items or unlabeled data items comprising initial attributes. The amended limitation of calculating a confidence for the new labeled data item and comparing the confidence to a threshold, wherein when it is above a threshold it is added to the training data for updating a classifier model and when it is below the confidence it is disclosed in claim 10 of the US Patent as the first limitation of claim 10 cites “calculating a confidence level for each of the label assignments” thus it teaches calculating a confidence for a new particular label and data item; “selecting a portion of the incoming data items associated the label assignment with the confidence level exceeding a threshold; confirming the label assignments for the selected data items and setting the data items with the confirmed labels as additional training data items, implying it is used to update or train a model; and revising the assignment of the labels to the remaining unlabeled data items using the additional training data items.”, this teaches comparing the new data item and label confidence to threshold wherein when it is above a threshold is used as additional training for later unlabeled item. While it does not explicitly cite when the confidence is below threshold it is not used for additional training data, it is implicit as it can only be used as additional training data when it is above a threshold. Claim 24 of the instant of the application is anticipated by claim 15 of the US Patent, and claim 25 of the instant application is anticipated by claim 9 and 10 of US Patent. See the chart below for comparison.
Instant Application – 18/394,656
US Patent 10,438,130 B2
21. A system for similarity classification with the aid of a digital computer, comprising:
a database configured to store a plurality of training data items, each of the training data items associated with one of a plurality of labels; and
22. The system of claim 21, wherein at least one the training data items or the unlabeled data item further comprises initial attributes.
at least one server comprising a plurality of processing units executed by one or more processors, each of the processing units associated with a private vector, the at least one server configured to:
receive an unlabeled data item;
update a graph, wherein each vertex represents the unlabeled data item or one of the training data items;
process the unlabeled data item using the plurality of processing units, each of the units associated with a private vector, comprising:
initialize the private vectors;
calculate in parallel by the processing units a similarity score between the unlabeled data item and one or more of the training data items using a similarity function;
store each of the scores into the private vector associated with each of the processing units; and
assign a particular label associated with a largest score to the unlabeled data item to generate a new labeled data item;
calculate a confidence level for the new labeled data item with the particular label;
utilize the new labeled data item and the particular label as an additional training data item for updating a classifier model that is used to perform subsequent similarity classifications for additional unlabeled data items when the confidence level exceeds a threshold;
determine that the new labeled data item and the particular label should not be used as additional training data item for the subsequent similarity classifications when the confidence level does not exceed the threshold.
9. A computer-implemented method for relational classification via maximum similarity, comprising:
receiving an incoming stream comprising one or more unlabeled data items, each associated one or more initial attributes;
maintaining a plurality of training data items, each of the training data items associated with one of a plurality of labels and associated with one or more initial attributes;
deriving additional attributes for each of the training data items based on the initial attributes;
adding the additional attributes to the initial attributes to obtain attributes of the training data items;
normalizing the attributes of the plurality of the training data items;
creating a graph comprising a plurality of vertices, each of the vertices representing one of the unlabeled data items and the training data items;
processing at least one of the unlabeled data items using a plurality of processing units executed by one or more processors, each of the units associated with a private vector, comprising:
identifying those of the training data items whose representations are within k-hops of the representations of that unlabeled data item;
initializing the private vectors;
deriving additional attributes of that unlabeled data item based on the initial attributes of that unlabeled data item;
adding the additional attributes to the initial attributes to obtain attributes of that unlabeled data item;
normalizing attributes of the unlabeled data item;
calculating in parallel by the processing units a similarity score between the unlabeled data item and each of the training data items using a similarity function and storing each of the scores into the private vector associated with each of the processing units;
weighing the similarity scores, wherein the similarity scores between that unlabeled data item and those of the training data items that are within the k-hops of that unlabeled data item are weighed heavier than the similarity scores between that incoming data item and those of that are training data items that are not within the k-hops;
summing the weighed scores from all of the private vectors into a storage vector; and
assigning the label associated with the largest score as the label of the unlabeled data item.
10. A method according to claim 9, comprising:
calculating a confidence level for each of the label assignments;
selecting a portion of the incoming data items associated the label assignment with the confidence level exceeding a threshold; and
confirming the label assignments for the selected data items and setting the data items with the confirmed labels as additional training data items; and
revising the assignment of the labels to the remaining unlabeled data items using the additional training data items.
23. A system of claim 21, wherein the at least one server is further configured to calculate a confidence level for each label associated with the unlabeled data item.
10. A method according to claim 9, comprising:
calculating a confidence level for each of the label assignments;
selecting a portion of the incoming data items associated the label assignment with the confidence level exceeding a threshold; and
confirming the label assignments for the selected data items and setting the data items with the confirmed labels as additional training data items; and
revising the assignment of the labels to the remaining unlabeled data items using the additional training data items.
24. The system of claim 21, wherein assigning the label further comprises updating the graph with an edge connecting the vertex representing the unlabeled data item to another vertex representing the label.
15. A method according to claim 14, wherein the weighing of the edges is performed using one of an exponential processing kernel, a linear processing kernel, and an inverse linear processing kernel.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 21-22 and 24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims recite a mental process that can be accomplished with the aid of pen and paper. This judicial exception is not integrated into a practical application nor amount to significantly more because the additional limitations of the claims are mere insignificant extra solution activity in combination of generic computer components performing generic functions that are implemented to perform the disclosed abstract idea above. See the analysis below:
Claims 21 and 25
Step 1: The claim recites a system, therefore, it falls into the statutory category of a machine.
Step 2A Prong 1: The claim recites, inter alia:
update a graph, wherein each vertex represents the unlabeled data item or one of the training data items; (This a mental process of user creating a graph using training data items and unlabeled items as nodes in a graph connected by edges, and updating the unlabeled data items with labels or edges with weights, can be done with the aid of pen and paper.)
process the unlabeled data item; (This amounts to a mental process of observation, evaluation and judgment wherein a user consider or observing data and making a decision.)
initialize the private vectors; (This amount to a mental process and can be done with the aid pen and paper, wherein a user creates vectors.)
calculate a similarity score between the unlabeled data item and one or more of the training data items using a similarity function; (This amounts to a mental process of observation, judgement and evaluation wherein a user is comparing two items to determine how similar they are, can be done with the aid of pen and paper.)
assign a particular label associated with a largest score to the unlabeled data item to generate a new labeled data item. (This is mental process of observation, evaluation and judgement wherein a user observing the data and assigning a class or label to item with the largest score or that is most similar.)
calculate a confidence level for the new labeled data item with the particular label; (This amounts to a mental process of a user comparing two items to determine similarity of them and using it a confidence level. This supported by specification as on page 16 line 8-10 which cites “The confidence may be predicted in a variety of ways. The most straightforward approach is to simply use the similarity score vector C (after the similarity is computed between each of the training instances)”.)
utilize the new labeled data item and the particular label as an additional training data item for subsequent similarity classifications for additional unlabeled data items when the confidence level exceeds a threshold; and determine that new labeled data time and the particular label should not be used as the additional training data item for the subsequent similarity classifications when the confidence level does not exceed the threshold. (This is a mental process of observation, evaluation and judgment wherein a user compares the confidence value associated with each data item and label to a threshold to determine if it should be used or saved as training data. If It is above the threshold its recorded, if not it is not, this can do with the aid of pen and paper.)
labeling an additional data item (claim 25) (This is a mental process of observation, evaluation and judgement wherein a user observations an unlabeled data item and makes a decision on what it should be labeled as, an done with aid of pen and paper.)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
a database configured to store a plurality of training data items, each of the training data items associated with one of a plurality of labels; and at least one server comprising a plurality of processing units executed by one or more processors, each of the processing units associated with a private vector, the at least one server configured to; using a plurality of processing units; using processing units in parallel; (Using the broadest reasonable interpretation the above limitations amount mere instructions to apply to the use of generic computer hardware to execute an abstract idea, see MPEP 2106.05(f).)
receive an unlabeled data item; and (Using the broadest reasonable interpretation the above limitations amounts to data collection or transmitting data, which is extra-solution activity, see MPEP 2106.05(g).)
utilizing new labeled data item and particular label as an additional training data item for updating a classifier model that is used to perform subsequent similarity classifications for unlabeled data; (The act of updating a classifier model using training data amounts to training model, as this (the training of the model) is cited a high level of generality it results in using machine learning model as tool or generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).)
using the updated classifier model that is trained utilizing at least the new labeled data item and the particular label; (claim 25) (This limitation amounts to using a trained classifier model and is cited at a high level of generality, result in adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f));
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer components performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “a database configured to store a plurality of training data items, each of the training data items associated with one of a plurality of labels; and at least one server comprising a plurality of processing units executed by one or more processors, each of the processing units associated with a private vector, the at least one server configured to; using a plurality of processing units; using processing units in parallel;” does not amount to significantly more as it is merely stating that the judicial exception is to be applied using a generic computer, see MPEP 2106.05(f). Also the receiving unlabeled data items and storing scores is essentially data gathering and transmitting data which is insignificant extra solution active as cited in MPEP 2106.05(g), and does not amount to significant as it is well-understood, routine and conventional as found MPEP 2106.05(d)(II), which provides that “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”. The saving additional data items and labels as training data to be used for updating a model is well-understood, routine and conventional as it amounts to saving and retrieving data from memory, see MPEP 2106.05(d)(iv). The updating a model using training data and then using the model is cited at high level of generality and results in using the machine learning model as tool to execute the abstract idea, see MPEP 2106.05(f).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of generic computer performing generic functions that are implemented to perform the disclosed abstract idea above.
Claim 22
Step 2A Prong 1: The claim recites, inter alia:
Claim 22 inherits the abstract idea of claim 21.
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
wherein at least one of the training data items or the unlabeled data time further comprises initial attributes. (This amounts to extra-solution activity, as it specifies a particular type of data to be manipulated, see MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination with the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of
wherein at least one of the training data items or the unlabeled data time further comprises initial attributes. (This amounts to extra-solution activity, as it specifies a particular type of data to be manipulated, see MPEP 2106.05(g).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Claim 24
Step 2A Prong 1: The claim recites, inter alia:
wherein assigning the label further comprise updating the graph with an edge connecting the vertex representing the unlabeled data item to another vertex presenting the label. (This amounts to a mental process of judgement and evaluation that can be accomplished by the user with the aid of paper. It amounts to user determining that two nodes of the graph (vertex) are similar and creating a connection (edge) between them or updating a weight for the edge. )
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim does not cite any additional limitations.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Aside from the limitations above, the claim does not cite any additional limitations.
Response to Arguments
Applicant's arguments filed 25 March 2026 have been fully considered but they are not persuasive. The applicant argues:
The rejection on the ground of double patenting will be addressed upon indication of allowable subject matter. The examiner notes the applicant’s stance and maintains the double patenting rejection.
The applicant argues the rejection under 35 USC 101 for being an abstract idea is improper as the amendment to claim 21 explicitly “utilizing the new labeled data item and the particular label as an additional training data item for updating a classifier model that is used perform subsequent similarity classification of additional unlabeled items when the confidence level exceeds a threshold”. Application argues that the amendment is not equivalent to saving the item in memory nor is update the model cited a high level of generality as it the updating limitation in claim 21 is recited in the context of a specific similar classification architecture that includes parallel similar score calculations by a plurality of processing units, storage of scores in private vectors, assignment of a particular label associated with a largest score, and conditional updating of the classification when the score exceeds a threshold.
The examiner respectfully traverses the applicant arguments and maintains that claims 21-22 and 24-25 are rejected under 35 USC 101 for being abstract idea. The limitations of the claim 21 and 25 that recite an abstract idea are:
update a graph, wherein each vertex represents the unlabeled data item or one of the training data items; (This a mental process of user creating a graph using training data items and unlabeled items as nodes in a graph connected by edges, and updating the unlabeled data items with labels or edges with weights, can be done with the aid of pen and paper.)
process the unlabeled data item; (This amounts to a mental process of observation, evaluation and judgment wherein a user consider or observing data and making a decision.)
initialize the private vectors; (This amount to a mental process and can be done with the aid pen and paper, wherein a user creates vectors.)
calculate a similarity score between the unlabeled data item and one or more of the training data items using a similarity function; (This amounts to a mental process of observation, judgement and evaluation wherein a user is comparing two items to determine how similar they are, can be done with the aid of pen and paper.)
assign a particular label associated with a largest score to the unlabeled data item to generate a new labeled data item. (This is mental process of observation, evaluation and judgement wherein a user observing the data and assigning a class or label to item with the largest score or that is most similar.)
calculate a confidence level for the new labeled data item with the particular label; (This amounts to a mental process of a user comparing two items to determine similarity of them and using it a confidence level. This supported by specification as on page 16 line 8-10 which cites “The confidence may be predicted in a variety of ways. The most straightforward approach is to simply use the similarity score vector C (after the similarity is computed between each of the training instances)”.)
utilize the new labeled data item and the particular label as an additional training data item for subsequent similarity classifications for additional unlabeled data items when the confidence level exceeds a threshold; and determine that new labeled data time and the particular label should not be used as the additional training data item for the subsequent similarity classifications when the confidence level does not exceed the threshold. (This is a mental process of observation, evaluation and judgment wherein a user compares the confidence value associated with each data item and label to a threshold to determine if it should be used or saved as training data. If It is above the threshold its recorded, if not it is not, this can do with the aid of pen and paper.)
labeling an additional data item (claim 25) (This is a mental process of observation, evaluation and judgement wherein a user observations an unlabeled data item and makes a decision on what it should be labeled as, an done with aid of pen and paper.)
All of the above limitations are mental processes of observation, evaluation and judgement that a user do the aid of pen and paper. In particular the “utilizing the new labeled data item and the particular label as an additional training data item that is used to perform subsequent similarity classification for unlabeled data items when the confidence level exceeds a threshold;” is a mental process of observation and evaluation wherein a user comparing similarity score of a data to item to a threshold, and evaluation and judgement is used when a user determines the score exceeds the threshold to use the item as additional training data. The act of updating the classifier is recited as high level of generality and amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training (updating) a machine learning model (classifier) with previously determined data (labeled data). As such the examiner maintains that claims cites mental processes and it not integrated into a practical application nor amount to significantly more. When determining if the additional limitations integrate the abstract idea into a practical application, the examiner considered each addition limitation. Here is an examination of the additional limitations:
a database configured to store a plurality of training data items, each of the training data items associated with one of a plurality of labels; and at least one server comprising a plurality of processing units executed by one or more processors, each of the processing units associated with a private vector, the at least one server configured to; using a plurality of processing units; using processing units in parallel; (Using the broadest reasonable interpretation the above limitations amount mere instructions to apply to the use of generic computer hardware to execute an abstract idea, see MPEP 2106.05(f).)
receive an unlabeled data item; and (Using the broadest reasonable interpretation the above limitations amounts to data collection or transmitting data, which is extra-solution activity, see MPEP 2106.05(g).)
utilizing new labeled data item and particular label as an additional training data item for updating a classifier model that is used to perform subsequent similarity classifications for unlabeled data; (The act of updating a classifier model using training data amounts to training model, as this (the training of the model) is cited a high level of generality it results in using machine learning model as tool or generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).)
using the updated classifier model that is trained utilizing at least the new labeled data item and the particular label; (claim 25) (This limitation amounts to using a trained classifier model and is cited at a high level of generality, result in adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f));
These additional elements do not integrate the abstract ideas above into a practical application as it is merely a extra-solution activity in combination with generic computer hardware performing generic computer functions to implement the abstract idea.
Also the additional limitations of the claims do not amount to significantly more, the additional elements are:
“a database configured to store a plurality of training data items, each of the training data items associated with one of a plurality of labels; and at least one server comprising a plurality of processing units executed by one or more processors, each of the processing units associated with a private vector, the at least one server configured to; using a plurality of processing units; using processing units in parallel;” does not amount to significantly more as it is merely stating that the judicial exception is to be applied using a generic computer, see MPEP 2106.05(f). Also the receiving unlabeled data items and storing scores is essentially data gathering and transmitting data which is insignificant extra solution active as cited in MPEP 2106.05(g), and does not amount to significant as it is well-understood, routine and conventional as found MPEP 2106.05(d)(II), which provides that “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”. The saving additional data items and labels as training data to be used for updating a model is well-understood, routine and conventional as it amounts to saving and retrieving data from memory, see MPEP 2106.05(d)(iv). The updating a model using training data and then using the model is cited at high level of generality and results in using the machine learning model as tool to execute the abstract idea, see MPEP 2106.05(f).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of generic computer performing generic functions that are implemented to perform the disclosed abstract idea above.
For these reason the examiner maintains that claims 21 and 25 are rejection as being an abstract idea that is not integrated into a practical application nor amounts to significantly more.
Applicant argues that parallel processing is not generic nor it is cited a high level of generality and request proof is the examiner says so. The claims “calculate, in parallel by the processing units, a similar score between the unlabeled data and training data using a similarity function.”, The limitation of calculating a similarity score between the unlabeled data and training using a similarity function is mental process wherein a user determine how alike two items are, the similarity function is mathematical function on page 12 of instant specification that user can solve. The doing this in parallel using processing units is using generic computer hardware to implement the abstract idea. If the applicant is requesting proof that parallel processing is common or generic, see the attached Goldstein reference that cites “However, what almost all modern software has in common is that it can run in parallel, meaning that it can be broken down and have different tasks run in multiple processing units at the same time. This enhances the efficient of the processors and reduces computation time.” As such the examiner maintains the rejection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAULINHO E SMITH whose telephone number is (571)270-1358. The examiner can normally be reached Mon-Fri. 10AM-6PM CST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Kawsar can be reached on 571-270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PAULINHO E SMITH/Primary Examiner, Art Unit 2127
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