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 .
Remarks
This action is in response to the application received on 2/26/25. Claims 1, 2, 4-8, 10-13, and 15-17 are pending in the application. Claims 3, 9, and 14 have been cancelled.
Claims 1, 2, 4-8, 10-13, and 15-17 are rejected under 35 U.S.C. 101.
Claims 1, 2, and 4-7 are rejected under 35 U.S.C. 103 as being unpatentable over Sutherland et al. (US 2022/0309089), and further in view of Gokalp (US 2023/0376857).
Claims 8, 10-13, and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Asermely (US 12,340,612), and further in view of Gokalp (US 2023/0376857).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-7 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 states “learning an AI model using the user-selected document assigned to the classification block.” The way in which this is worded makes it unclear as to whether the method step is learning from an AI model or teaching an AI model. “Learning an AI model” is improper English, so for purposes of examination, the limitation will be interpreted as “teaching an AI model.”
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 1, 2, 4-8, 10-13, and 15-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 2A, Prong One asks: Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? See MPEP 2106.04 Part I. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. See MPEP 2106.04(a).
With respect to claim 1, the limitations of “assigning a user-selected document among the plurality of documents,” “classifying … the remaining documents,” and “automatically generating … a new classification block… and assigning … the remaining documents”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, “assigning,” “classifying,” and “generating” in the context of this claim encompasses the user mentally determining a category.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
At step 2a, prong two, this judicial exception is not integrated into a practical application. The claims recite a computing device, however, this is recited as a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. With respect to “learning an AI model using the user-selected document assigned to the classification block,” this limitation is nonspecific and amounts to a drafting technique that places no meaningful limits on the claims. Additionally, the claim recites “reading a plurality of documents,” and “notifying… including displaying.” These elements do not integrate the abstract idea into a practical application because they do not impose a meaningful limit on the judicial exception and provide only insignificant extra solution activity that is mere data gathering in conjunction with the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
With respect to “reading a plurality of documents”, the courts have found limitations directed towards data gathering to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II). Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
With respect to “notifying … including displaying”, the courts have found limitations directed towards storing to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II). Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93.
Considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. The claim is not patent eligible.
With respect to claims 2 and 4-7, the limitations further define the limitations above and do not provide additional elements.
With respect to claims 8 and 13, the limitations of “classifying the remaining documents” and “automatically generating … a new classification block”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, “classifying” in the context of this claim encompasses the user mentally determining a category.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
At step 2a, prong two, this judicial exception is not integrated into a practical application. The claims recite a computing device, however, this is recited as a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Additionally, the claim recites “displaying on a graphical user interface.” These elements do not integrate the abstract idea into a practical application because they do not impose a meaningful limit on the judicial exception and provide only insignificant extra solution activity that is mere data gathering in conjunction with the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
With respect to “displaying on a graphical user interface”, the courts have found limitations directed towards storing to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II). Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93.
Considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. The claim is not patent eligible.
With respect to claims 10-12 and 15-17, the limitations further define the limitations above and do not provide additional elements.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, and 4-7 are rejected under 35 U.S.C. 103 as being unpatentable over Sutherland et al. (US 2022/0309089), and further in view of Gokalp (US 2023/0376857).
With respect to claim 1, Sutherland teaches an AI (Artificial Intelligence)-based document classification method performed by a computing device comprising:
reading a plurality of documents (Sutherland, pa 0136, Referring to FIG. 3, at step Sl00, the server device 20 may collect documents to be included in the training dataset 52 for training the machine learning model 50. The documents may be collected from the document DB 30.);
assigning a user-selected document among the plurality of documents to a classification block among a plurality of classification blocks, in response to a user input (Sutherland, pa 0201, a random subset may be selected from available, unlabeled, documents to be manually classified. The manual classification may then be used for training the machine learning model 50 to setup the first underlying model of the system.);
learning an AI model using the user-selected document assigned to the classification block, so that the Al model learns a user's classification intent with respect to the classification block from the user-selected document assigned by the user to the classification block (Sutherland, Fig. 6 step 202, Classify the at least one document into at least two classes using the machine learning model & pa 0162, At step the server device may classify the at least one document into at least two classes using the machine learning model);
notifying whether the AI model is capable of classifying remaining documents, among the plurality of documents, that have not been assigned by the user to any of the plurality of classification blocks into the classification block, the notifying being based on a learning state of the Al model with respect to the classification block (Sutherland, pa 0163, At step S204 the server device may determine a confidence value of the classification for each of the at least one document. The confidence value may be a numerical value representing how confident the machine learning Model is regarding a classification result for each document output by the machine learning model In other words, the confidence value may represent how credible the classification result of the machine learning model for each document can be.), …;
classifying the remaining documents using the AI model …
automatically generating, by the computing device after the classifying is performed, a new classification block corresponding to the classification block, the new classification block being separate from the classification block to which the user-selected document has been assigned by the user, and assigning, to the new classification block, the remaining documents that the AI model has classified into the classification block (Sutherland, Fig. 6 step 206, To each of the at least one document, assign one of at least two categories that are associated with different degrees of credibility of the classification pa 0190, each of the documents classified by the ANN model is assigned to a "confident" category or a "unconfident" category, based on a confidence value representing how confident the ANN model is regarding its classification result for the document)
Sutherland doesn't expressly discuss the notifying including displaying, in a chat-window-shaped classification interaction area, an indication that the Al model is in a state capable of classifying the remaining documents into the classification block; and classifying, in response to a classification request received from the user the remaining documents using the AI model that has learned the user's classification intent with respect to the classification block.
Gokalp teaches notifying whether the AI model is capable of classifying remaining documents, among the plurality of documents, that have not been assigned by the user to any of the plurality of classification blocks into the classification block (Gokalp, fig. 22, status updates 2219 & pa 0122, Status indicators or updates 2219 with respect to various classifier training metrics may be provided, e.g., automatically or in response to additional programmatic requests, to the clients), the notifying being based on a learning state of the Al model with respect to the classification block (Gokalp, pa 0150, A number of metrics may be collected with regard to individual training iterations as well as for sequences of training iterations, and such metrics may be used to generate training status indicators that can be presented visually to users/clients of the service such as data scientists that may wish to analyze/debug the training progress) and the notifying including displaying, in a chat-window-shaped classification interaction area, an indication that the Al model is in a state capable of classifying the remaining documents into the classification block (Gokalp, Fig. 20, pa 0114, In the diagnosis test list section 2016, a list of various diagnosis tests that have been identified for the current classifier development effort may be shown, along with a respective pass/fail status (indicated by a checkmark or an X respectively). & pa 0151, The presentation of a given visualization data set may include various types of panels and layout components); and
classifying, in response to a classification request received from the user the remaining documents using the AI model that has learned the user's classification intent with respect to the classification block (Gokalp, pa 0127, A given training iteration may comprise one or more epochs (passes through the training data set available for the iteration in some embodiments. Rules to determine when a given training iteration is to be considered complete (e.g., when a specified number of epochs is completed, when the difference in a metric value from a previous epoch falls below a threshold, when a specified amount of time has elapsed, when a specified amount of processing resources have been consumed, etc.) may be indicated via iteration completion criteria parameter 2326 & pa 0154, The results of the diagnosis tests may be used, for example, to automatically determine whether additional training iterations are to be initiated. Examiner Note: these teachings show that continued classification is performed after the status updates are given).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified Sutherland to have included the teachings of Gokalp because it allows the user to determine the kinds of guidance that should be provided (if any) to the classification service to enhance or accelerate future training iterations (Gokalp, pa 0102).
With respect to claim 2, Sutherland in view of Gokalp teaches the AI-based document classification method of claim 1, wherein learning the AI model comprises learning the AI model with a document selected by the user and the classification block to which the user assigns the selected document, when the user selects the selected document and assigns the selected document to the classification block (Sutherland, pa 0201, a random subset may be selected from available, unlabeled, documents to be manually classified. The manual classification may then be used for training the machine learning model 50 to setup the first underlying model of the system.).
With respect to claim 4, Sutherland in view of Gokalp teaches the AI-based document classification method of claim 3, wherein the new classification block includes a plurality of documents that the AI model has classified into the classification block (Sutherland, pa 0172, in case of assigning one of two categories (e.g., credible and uncredible; confident and unconfident, etc.), a document may be assigned a category with a higher degree of credibility if the confidence value for the classification result of that document exceeds a specified threshold value.).
With respect to claim 5, Sutherland in view of Gokalp teaches the AI-based document classification method of claim 1, wherein the notifying comprises:
determining an indication to represent whether the AI model is capable of classification for the classification block or to represent a degree to which the AI model has learned the user's classification intent for the classification block, the indication being determined by the AI model (Sutherland, pa 0186, The display of the document and the attention information at step S304 may facilitate the decision on whether the document indeed belongs to the class identified by the machine learning model 50 (e.g., whether the document indeed is relevant for the interest of the user). & Gokalp, pa 0150, Any combination of a variety of metrics may be collected and presented in different embodiments, including for example (a) a positive predictive value (PPV), (b) a negative predictive value (NPV), (c) an accuracy, (d) a prevalence, (e) a precision, (f) a false discovery rate, (g) a false omission rate, (h) a recall, (i) a sensitivity, G) a diagnostic odds ratio, and/or (k) an Fl score.).
With respect to claim 6, Sutherland in view of Gokalp teaches the AI-based document classification method of claim 1, wherein the notifying comprises:
displaying a message describing that the AI model is capable of classifying the classification block, in the classification interaction area (Sutherland, pa 0184, At step S304, the server device 20 may provide for display the document obtained at step S300 and the attention information obtained at step S302 … the server device 20 may further provide for display the classification result of the document (e.g., relevant or irrelevant; see also, step S202 of FIG. 6) and/or the category assigned to the document (e.g., classification with low credibility).
With respect to claim 7, Sutherland in view of Gokalp teaches the AI-based document classification method of claim 6, wherein the classification interaction area displays at least one of (i) a message explaining a method of classifying documents using the AI model (Sutherland, pa 0184, At step S304, the server device 20 may provide for display the document obtained at step S300 and the attention information obtained at step S302 … the server device 20 may further provide for display the classification result of the document (e.g., relevant or irrelevant; see also, step S202 of FIG. 6) and/or the category assigned to the document (e.g., classification with low credibility & pa 0185, at step S304, the attention information may be provided for display so as to display the one or more parts of the document in manners different from each other based on the significance of the respective part or parts indicated by the attention information.), (ii) a message for selecting the method of classifying documents using the AI model, or (iii) a message indicating that the AI model has completed document classification.
Claims 8, 10-13, and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Asermely (US 12,340,612), and further in view of Gokalp (US 2023/0376857).
With respect to claim 8, Asermely teaches an AI (Artificial Intelligence)-based document classification method performed by a computing device comprising:
classifying the remaining documents using the AI model, when a request for document classification with respect to the classification block selected by the user is received from the user (Asermely, Col. 15 Li. 7-11, If there are one or more unrecognized pages, the online document system assigns 850 the unrecognized pages to documents and document stacks based on user input (a manual assignment) and/or a supplementary probabilistic identification method);
automatically generating, by the computing device after the classifying is performed, a new classification block corresponding to the classification block selected by the user, the new classification block being separate from the classification block selected by the user (Asermely, Col. 15 Li. 22-25, a package administrator of the package template can update 870 the document identification rules of the package template based on the assignments of any identified unrecognized pages.), and assigning, to the new classification block, the remaining documents that the AI model has classified into the classification block selected by the user (Asermely, Col. 15 Li. 7-11, If there are one or more unrecognized pages, the online document system assigns 850 the unrecognized pages to documents and document stacks based on user input (a manual assignment) and/or a supplementary probabilistic identification method), wherein the AI model is a model trained, when the user assigns documents to a particular one of the plurality of classification blocks, on the documents assigned by the user to the particular classification block so that the AI model learns the user's classification intent with respect to that particular classification block (Asermely, Col. 12 Li. 14-23, some types of document package require a manual approval or review of the automatic sort performed by the document recognition and sorting modules 320 and 330 before the package intake module 240 can perform stack actions. For example, a package template 410, importing user 130, or document source 140 can be flagged as requiring manual approval (for an importing user 130 or other user with appropriate permissions) in cases where a document package contains high risk, sensitive, or regulated component documents.).
Asermely doesn't expressly discuss displaying on a graphical user interface a visual indication to represent whether an AI model can classify remaining documents, among a plurality of documents read by the computing device, that have not been assigned by a user to any of a plurality of classification blocks, into a classification block selected by the user from the plurality of classification blocks, the visual indication being generated based on a learning state of the AI model with respect to the classification block selected by the user.
Gokalp teaches displaying on a graphical user interface a visual indication to represent whether an AI model can classify remaining documents, among a plurality of documents read by the computing device, that have not been assigned by a user to any of a plurality of classification blocks, into a classification block selected by the user from the plurality of classification blocks, the visual indication being generated based on a learning state of the AI model with respect to the classification block selected by the user (Gokalp, fig. 22, status updates 2219 & pa 0122, Status indicators or updates 2219 with respect to various classifier training metrics may be provided, e.g., automatically or in response to additional programmatic requests, to the clients & Fig. 20, pa 0114, In the diagnosis test list section 2016, a list of various diagnosis tests that have been identified for the current classifier development effort may be shown, along with a respective pass/fail status (indicated by a checkmark or an X respectively). & pa 0151, The presentation of a given visualization data set may include various types of panels and layout components);
classifying the remaining documents using the AI model (Gokalp, pa 0127, A given training iteration may comprise one or more epochs (passes through the training data set available for the iteration in some embodiments. Rules to determine when a given training iteration is to be considered complete (e.g., when a specified number of epochs is completed, when the difference in a metric value from a previous epoch falls below a threshold, when a specified amount of time has elapsed, when a specified amount of processing resources have been consumed, etc.) may be indicated via iteration completion criteria parameter 2326 & pa 0154, The results of the diagnosis tests may be used, for example, to automatically determine whether additional training iterations are to be initiated. Examiner Note: these teachings show that continued classification is performed after the status updates are given).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified Asermely to have included the teachings of Gokalp because it allows the user to determine the kinds of guidance that should be provided (if any) to the classification service to enhance or accelerate future training iterations (Gokalp, pa 0102).
With respect to claim 10, Asermely in view of Gokalp teaches the AI-based document classification method of claim 9, wherein the new classification block includes a plurality of documents that the AI model has classified into the classification block (Asermely, Col. 12 Li. 45-47, the user 130 can associate documents generated from unrecognized pages with stacks. Examiner note: these are pre-existing classes that have other documents classified by the model).
With respect to claim 11, Asermely in view of Gokalp teaches the AI-based document classification method of claim 8, wherein the displaying comprises:
determining the visual indication to represent whether the AI model is capable of classification for the classification block or to represent a degree to which the AI model has learned the user's classification intent for the classification block selected by the user, the visual indication being determined by the AI model (Asermely, Col. 12 Li. 38-40, The unrecognized document module 340 may display to an importing user 130 (or other authorized user 130) an interface identifying unrecognized pages in the document package & Gokalp, pa 0150, Any combination of a variety of metrics may be collected and presented in different embodiments, including for example (a) a positive predictive value (PPV), (b) a negative predictive value (NPV), (c) an accuracy, (d) a prevalence, (e) a precision, (f) a false discovery rate, (g) a false omission rate, (h) a recall, (i) a sensitivity, G) a diagnostic odds ratio, and/or (k) an Fl score.).
With respect to claim 12, Asermely in view of Gokalp teaches the AI-based document classification method of claim 8, wherein the displaying comprises:
displaying a message describing that the AI model is capable of classifying the classification block, in a classification interaction area (Asermely, Col. 12 Li. 49-53, the unrecognized document module 340 also provides an importing user 130 (or other appropriate user(s) 130) an interface for reviewing the automatically recognized documents/document pages and overriding the default stack actions 425 or stack assignments for that document package).
With respect to claims 13 and 15-17, Asermely in view of Gokalp teaches a computing device performing an AI-based document classification method, wherein computing device comprising a processor (Asermely, Col. 15 Li. 52-56), with limitations similar to claims 8 and 10-12, and are rejected for the same reasons.
Response to Arguments
35 U.S.C. 101
Applicant argues that the steps of the claim cannot be performed in the human mind because they are rooted in computer technology. The Examiner respectfully disagrees. MPEP 2106.04(a) states that claims can recite a mental process even if they are claimed as being performed on a computer. The training recited in the claim nonspecific and amounts to a drafting technique that places no meaningful limits on the claims. The language “so that the Al model learns a user's classification intent with respect to the classification block from the user-selected document assigned by the user to the classification block” is intended use of the training. The determinations and classification blocks are mental data analysis steps. The claimed AI model merely teaches a machine to do what the user already knows how to do. Therefore, the claim includes limitations describing a mental process.
Applicant argues that the claims integrate the exception into a practical application by providing an improvement to the functioning of the computer. The Examiner respectfully disagrees. Applicant must specifically be able to show improvement in the functionality of the computer itself. There must also be limitations in the claim that disclose a technological solution to the identified problem. See 2106.04(d)(1). However, the claim merely uses the computer as a tool to process the information (“… the focus of the claims is not on such an improvement in computers as tools, but on certain independently abstract ideas that use computers as tools” see MPEP 2106.05(a)(I)).
Applicant argues that the claims recite significantly more than any alleged abstract idea because they provide an ordered combination that is not well-understood, routine, or conventional. The Examiner respectfully disagrees. An inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself." Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016). See also Alice Corp., 573 U.S. at 21-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 78, 101 USPQ2d at 1968 (after determining that a claim is directed to a judicial exception, "we then ask, ‘[w]hat else is there in the claims before us?") (emphasis added)); RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"). Instead, an "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. Alice Corp., 573 U.S. at 27-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966). See MPEP 2106.05 part I. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The elements Applicant has pointed to are not additional elements in a non-conventional and non-generic arrangement as described in MPEP 2106.05 part I section B. Appellant has not provided evidence (e.g., in the specification) for their assertion that elements are arranged in a non-conventional, non-generic arrangement of known, conventional elements. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually.
35 U.S.C. 103
Applicant seems to argue a newly amended limitation. Applicant’s amendment has rendered the previous rejection moot. Upon further consideration of the amendment, a new grounds of rejection is made in view of Gokalp (US 2023/0376857).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 BRITTANY N ALLEN whose telephone number is (571)270-3566. The examiner can normally be reached M-F 9 am - 5:00 pm EST.
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, Sherief Badawi can be reached at 571-272-9782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRITTANY N ALLEN/ Primary Examiner, Art Unit 2169
Prosecution Insights