DETAILED ACTION
This Action is a response to the filing received 25 October 2023. Claims 1-20 are presented for examination.
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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 9 June 2025 and 25 June 2025 are being considered by the examiner.
Allowable Subject Matter
Pending resolution of any other outstanding issues, claims 8 and 19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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 1-4 and 11-15 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
At Step 1, the claims are evaluated for whether they fall within the statutory categories (MPEP § 2106.03). Claims 1-10 recite processes, claim 11 recites a machine, and claims 12-20 recite articles of manufacture. Accordingly, the analysis proceeds to Step 2.
At Step 2A, the claims are evaluated for whether they are directed to a judicial exception, such as an abstract idea (MPEP § 2106.04). At Prong 1, the claims are evaluated for whether they recite an abstract idea, such as a mental process (MPEP §§ 2106.04(II)(A) and 2106.04(a)). Mental processes are thinking that can be performed in the human mind, or by a human using pen and paper; examples include observations, evaluations, judgments and opinions (MPEP § 2106.04(a)(2)(III)). An example includes a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality (MPEP § 2106.04(a)(2)(III)(A)). A claim that requires a computer may still recite a mental process, if the mental process is performed in a generic computer or in a computer environment, or if the computer is used as a tool to perform the mental process (MPEP § 2106.04(a)(2)(III)(C)).
Claim 1 recites the following mental process steps: (1) categorizing error information associated with the reported application error experienced by a plurality of users in a plurality of user sessions to determine a plurality of associated grouped events; (2) isolating from the plurality of associated grouped events, one or more designated grouped events causing the reported application error; and (3) identifying from the one or more designated grouped events a direct path causing the reported application error.
Element (1) recites an observation, evaluation, judgment or opinion that error information relating to a plurality of sessions relate to a reported error and are of a sufficiently similar nature such that they should be considered together. Element (2) recites an observation, evaluation, judgment or opinion that, for example, a common sequence of events is present in one or more of the grouped events causing the reported error. Element (3) recites an observation, evaluation, judgment or opinion that one or more of the designated grouped events comprises a direct path causing the reported error. Each of these data analysis steps is recited at a high level of generality, such that the analysis may be performed in the human mind, or with the aid of pen and paper. While the claim contemplates the use of a computer, the claim recites performing the identified mental process steps on a generic computer / in a computer environment and/or by using a computer as a tool to perform the mental process steps.
The analysis thus proceeds to Prong 2, wherein the claims are evaluated for whether they recite additional elements that integrate the judicial exception into a practical application (MPEP §§ 2106.04(II)(A)(2) and 2106.04(d)). Claim 1 recites the following additional elements: (1) A computer-implemented method for identifying a sequence of steps that reproduces a reported application error, comprising the mental process steps identified above; and (2) performing the categorizing, isolating, and identifying steps by a processor.
Element (1) recites the statutory category of invention, and sets forth the intended purpose of the method steps. Element (2) recites that the method steps are performed by a processor, which is not (a) an improvement in the functioning of a computer or other technology, (b) a particular machine or (c) more than merely including instructions to implement the abstract idea on a computer or using the computer as a tool to perform the abstract idea. These findings hold whether the additional elements are considered individually or in combination, or whether these elements are considered with the other elements of the claim as a whole.
The analysis proceeds to Step 2B, where the claims are evaluated for whether they recite additional elements that amount to significantly more than the judicial exception (MPEP § 2106.05). At Step 2B, the analysis performed with respect to Step 2A Prong 2 is incorporated, and the additional elements are further considered for whether they recite other than what is well-understood, routine and/or conventional activity in the field (MPEP § 2106.05(II)). The additional elements, whether considered alone or in combination, comprise well-understood, routine and/or conventional activity, to include receiving or transmitting data over a network (such as to obtain the user session data), performing repetitive calculations (such as using a computer processor to perform the process steps), electronic recordkeeping and storing and retrieving information in memory (such as temporarily or durably storing results of the process steps). Claim 1 therefore does not recite significantly more than the abstract idea.
In view of the foregoing, claim 1 is ineligible under 35 U.S.C. § 101.
Claims 11-12 are ineligible for similar reasons as those provided with respect to claim 1. Claim 11 additionally recites a computer system for performing the method that includes a processor for executing instructions and a non-volatile memory for storing the executable instructions. Claim 12 additionally recites a non-transitory computer-readable medium storing instructions for execution by a processor to carry out the method. In both cases, the claims recite the use of a generic computer / computer environment to perform the mental process steps and/or the use of a computer as a tool to perform the mental process steps. Further, these additional elements fail to integrate the abstract idea into a practical application or recite significantly more than the abstract ideas for similar reasons as those provided with respect to the computing elements of claim 1.
Claims 2 and 13 recite that the user events comprise a click, keyboard interaction, or scroll. This describes the type of information included in the user session event data, and does not change the analysis provided above with respect to claims 1 and 12.
Claims 3 and 14 recite capturing user activity from the user sessions and converting the user activity into the user events and application information. These additional elements recite the insignificant extra-solution activity of necessary pre-solution data gathering, recited at a high level of generality. These steps are performed using a generic computer, and represent the well-understood, routine and/or conventional activity of receiving data over a network and/or performing repetitive calculations.
Claims 4 and 15 recite the additional element of displaying the sequence of steps corresponding to the direct path causing the reported error; this represents displaying certain results of the collection and analysis of information, consistent with the abstract idea identified in Electric Power Group (see MPEP § 2106.04(a)(2)(III)(A)). Further, this additional element comprises mere instructions to apply an exception (reciting the solution of the idea of the outcome of displaying the sequence) and does not represent an additional meaningful limitation of the abstract idea.
Accordingly, claims 2-4 and 11-15 are likewise ineligible under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. §§ 102 and 103 (or as subject to pre-AIA 35 U.S.C. §§ 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. § 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, 7, 9, 11-16, 18 and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Lucioni et al., U.S. 2024/0256429 A1 (“Lucioni”) in view of Misra et al., U.S. 2019/0089577 A1 (“Misra”).
Regarding claim 1, Lucioni teaches: A computer-implemented method for identifying a sequence of steps that reproduces a reported application error (Lucioni, e.g., ¶¶285-290, “If at block 1204 the system determines that the issue is classified into the target impact level … system determines whether the session(s) meet one or more rules using data collected from the session(s) … That a session includes a particular type of event … proximate occurrence of the issue in the session …” See also, e.g., ¶282, “example process 1200 … may be performed by session description system 700 …” and ¶142, “Session description system 700 may comprise one or more computing devices …”), comprising the steps of:
categorizing, by a processor, error information associated with the reported application error experienced by a plurality of users in a plurality of user sessions (Lucioni, e.g., ¶¶36-38, “application may be a web application accessed by various users … in a large number of sessions every day by various user devices … session may be represented by a sequence of events … a user may experience an issue … may be improper functionality of the software application (e.g., an exception) … improper functionality of a graphical user interface element …” See also, e.g., ¶85, “issue detection module 208 may detect an issue by detecting occurrence of an error in the software application …”),
the error information comprising, for each user session, one or more user events and associated application information (Lucioni, e.g., ¶50, “generate a representation of a session using data collected from the session … may indicate events such as user actions, the rendering of content … may indicate a sequence of events …” See also, e.g., ¶¶68-80, “parameters of which values may be collected … include … (HTML) document object model (DOM) changes … CSS styles and/or stylesheets … Navigation events … User device type … Browser application type … User activity … Exceptions … User device processor and/or memory usage …” See also, e.g., ¶86, “issue detection module 208 may store data associated with the detected issue … within a time period preceding a detected issue and/or a time period after the detected issue …”),
to determine a plurality of associated grouped events (Lucioni, e.g., ¶89, “triage module 210 may determine, using data collected during the session, a classification of the issue into one of multiple impact levels …” See also, e.g., ¶90, “triage module 210 may determine a classification of an issue using data collected from multiple occurrences of the issue (e.g., in different sessions) … determine an impact level classification for an issue based on aggregated results of classifying multiple occurrences of the issue …”); and
isolating, by the processor, from the plurality of associated grouped events, one or more designated grouped events causing the reported application error (Lucioni, e.g., ¶¶285-290, “If at block 1204 the system determines that the issue is classified into the target impact level … system determines whether the session(s) meet one or more rules using data collected from the session(s) … That a session includes a particular type of event … proximate occurrence of the issue in the session …”).
Lucioni does not more particularly teach identifying from the designated grouped events a direct path causing the reported application error. However, Misra does teach: identifying, by the processor, from the one or more designated grouped events, a direct path causing the reported application error (Misra, e.g., ¶45, “log data receiver 102 is to receive as input historical incident or defect log data … with … steps to reproduce a defect …” See also, e.g., ¶46, “step action graph generator 106 may generated, based on the historical log data, the step action graphs by analyzing … data related to reproduction of the incident or defect …” and ¶47, “incident and defect action graph generator 110 may generate, based on grouping of the step action graphs … the incident and defect action graph for each of the incident and defect tickets by analyzing, for the step action graphs, directed flow edges that identify sequences of actions …” See also, e.g., ¶71, “reproduction steps for the Defect-1 are listed at 1.1 to 1.16 … ascertained, for example, from historical user input …” and ¶74, “steps from the different defects … may be analyzed to normalize (e.g., combine) the steps based on semantic similarity …” See also, e.g., ¶90, “output generator 120 may generate, based on the analysis of the machine learning model with respect to the new incident or defect, the output that includes the sequence of actions to reproduce, for the new incident, steps that result in the new incident … identify the root cause of the new incident or defect …”) for the purpose of analyzing historical and additional defect data in order to extract patterns underlying sequences of steps performed in sessions in order to reproduce incidents or identify root causes thereof (Misra, e.g., ¶¶28, 30-35).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method for triaging and describing reported application issues as taught by Lucioni to provide for identifying from the designated grouped events a direct path causing the reported application error because the disclosure of Misra shows that it was known to those of ordinary skill in the pertinent art to improve a system and method for learning-based defect resolution to provide for identifying from the designated grouped events a direct path causing the reported application error for the purpose of analyzing historical and additional defect data in order to extract patterns underlying sequences of steps performed in sessions in order to reproduce incidents or identify root causes thereof (Misra, Id.).
Claims 11-12 are rejected for the reasons given in the rejection of claim 1 above. Examiner notes that with respect to claim 11, Lucioni further teaches: A system for identifying a sequence of steps that reproduce a reported application error, comprising: a processor for executing instructions; a non-volatile memory coupled to the processor storing instructions that when executed by the processor configures the system to perform the method (Lucioni, e.g., ¶¶285-290, “If at block 1204 the system determines that the issue is classified into the target impact level … system determines whether the session(s) meet one or more rules using data collected from the session(s) … That a session includes a particular type of event … proximate occurrence of the issue in the session …” See also, e.g., ¶282, “example process 1200 … may be performed by session description system 700 …” and ¶142, “Session description system 700 may comprise one or more computing devices …” See also, e.g., ¶292, “example computer system 1500 … may include one or more computer hardware processors 1502 and non-transitory computer-readable storage media … processor(s) 1502 may execute one or more processor-executable instructions stored in one or more non-transitory computer-readable storage media …”) of: [[[the method of claim 1]]]; and with respect to claim 12, Lucioni further teaches: A non-transitory computer-readable medium having statements and instructions stored thereon to be executed by a digital processor (Lucioni, e.g., ¶¶285-290, “If at block 1204 the system determines that the issue is classified into the target impact level … system determines whether the session(s) meet one or more rules using data collected from the session(s) … That a session includes a particular type of event … proximate occurrence of the issue in the session …” See also, e.g., ¶282, “example process 1200 … may be performed by session description system 700 …” and ¶142, “Session description system 700 may comprise one or more computing devices …” See also, e.g., ¶292, “example computer system 1500 … may include one or more computer hardware processors 1502 and non-transitory computer-readable storage media … processor(s) 1502 may execute one or more processor-executable instructions stored in one or more non-transitory computer-readable storage media …”) to automatically: [[[perform the method of claim 1]]].
Regarding claim 2, the rejection of claim 1 is incorporated, and Lucioni further teaches: wherein said one or more user events comprise at least one of: a click, a keyboard interaction, or a scroll (Lucioni, e.g., ¶50, “generate a representation of a session using data collected from the session … may indicate events such as user actions, the rendering of content … may indicate a sequence of events …” See also, e.g., ¶¶68-80, “parameters of which values may be collected … include … (HTML) document object model (DOM) changes … CSS styles and/or stylesheets … Navigation events … User device type … Browser application type … User activity … Exceptions … User device processor and/or memory usage …” See also, e.g., ¶125, “how far a user scrolled a scroll bar of the GUI … coordinates 504 of an interaction with the GUI … click of the mouse …”).
Regarding claim 2, the rejection of claim 1 is incorporated, and Lucioni further teaches: further comprising the steps of, before said categorizing: capturing user activity from said plurality of user sessions; and converting said user activity into said one or more user events and associated application information (Lucioni, e.g., ¶50, “generate a representation of a session using data collected from the session … may indicate events such as user actions, the rendering of content … may indicate a sequence of events …”).
Regarding claim 4, the rejection of claim 1 is incorporated, but Lucioni does not more particularly teach displaying the sequence of steps corresponding to the direct path. However, Misra does teach: further comprising the step of: displaying, by the processor, the sequence of steps corresponding to the direct path causing the reported application error (Misra, e.g., ¶67, “For a new ticket (e.g., a new incident or defect), the user 130 may be presented the output 124 with predicted graphical sequence of actions either as incident action graph (e.g., predicted incident action graph) or as a set of action descriptions (as originally present in the incident resolution log) … user 130 may choose to update the predicted incident action graph as well as other supporting incident action graphs …”) for the purpose of analyzing historical and additional defect data in order to extract patterns underlying sequences of steps performed in sessions in order to reproduce incidents or identify root causes thereof (Misra, e.g., ¶¶28, 30-35).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method for triaging and describing reported application issues as taught by Lucioni to provide for displaying the sequence of steps corresponding to the direct path because the disclosure of Misra shows that it was known to those of ordinary skill in the pertinent art to improve a system and method for learning-based defect resolution to provide for displaying the sequence of steps corresponding to the direct path for the purpose of analyzing historical and additional defect data in order to extract patterns underlying sequences of steps performed in sessions in order to reproduce incidents or identify root causes thereof (Misra, Id.).
Regarding claim 5, the rejection of claim 1 is incorporated, and Lucioni further teaches: wherein said categorizing comprises the steps of: pre-processing, by the processor, the one or more user events and associated application information into a plurality of embeddings (Lucioni, e.g., ¶112, “feature generator 404 obtains data 204 associated with a given issue (e.g., that was collected during a session in which the issue occurred) … to generate a set of features corresponding to the issue … by: (1) determining values of parameters using the data 402; and (2) outputting the values of the parameters as the set of features … parameters indicating actions performed by a user in a session …” and ¶113, “parameters indicating operation of the software application before and/or after occurrence of the issue …”).
Lucioni does not more particularly teach clustering the plurality of embeddings into the plurality of associated grouped events by performing semantics clustering analysis via one or more machine learning models. However, Misra does teach: clustering, by the processor, the plurality of embeddings into the plurality of associated grouped events by performing semantics clustering analysis via one or more machine learning models (Misra, e.g., ¶74, “steps from the different defects … may be analyzed to normalize (e.g., combine) the steps based on semantic similarity …” See also, e.g., ¶80, “parameters for selection of steps for the incident action graph 114 for a new defect may include semantic similarity between new defect (or new incident) and existing defects in the historical log data 104, semantic relatedness between the new defect with a step appearing in one or more defects in the historical log data 104, previous step chosen (e.g., there exists control and data flow relations between consecutive steps), and dependencies among sequence of steps …” See also, e.g., ¶90, “output generator 120 may generate, based on the analysis of the machine learning model with respect to the new incident or defect, the output that includes the sequence of actions to reproduce, for the new incident, steps that result in the new incident … identify the root cause of the new incident or defect …”) for the purpose of analyzing historical and additional defect data in order to extract patterns underlying sequences of steps performed in sessions in order to reproduce incidents or identify root causes thereof (Misra, e.g., ¶¶28, 30-35).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method for triaging and describing reported application issues as taught by Lucioni to provide for clustering the plurality of embeddings into the plurality of associated grouped events by performing semantics clustering analysis via one or more machine learning models because the disclosure of Misra shows that it was known to those of ordinary skill in the pertinent art to improve a system and method for learning-based defect resolution to provide for clustering the plurality of embeddings into the plurality of associated grouped events by performing semantics clustering analysis via one or more machine learning models for the purpose of analyzing historical and additional defect data in order to extract patterns underlying sequences of steps performed in sessions in order to reproduce incidents or identify root causes thereof (Misra, Id.).
Claims 13-16 are rejected for the additional reasons given in the rejections of claims 2-5 above.
Regarding claim 7, the rejection of claim 5 is incorporated, and Lucioni further teaches: wherein said associated application information comprises at least one of: cascading style sheet (CSS) elements, hypertext markup language (HTML) elements, a uniform resource locator (URL), text, and screen recordings (Lucioni, e.g., ¶50, “generate a representation of a session using data collected from the session … may indicate events such as user actions, the rendering of content … may indicate a sequence of events …” See also, e.g., ¶¶68-80, “parameters of which values may be collected … include … (HTML) document object model (DOM) changes … CSS styles and/or stylesheets … Navigation events … User device type … Browser application type … User activity … Exceptions … User device processor and/or memory usage …” See also, e.g., ¶125, “how far a user scrolled a scroll bar of the GUI … coordinates 504 of an interaction with the GUI … click of the mouse …” See also, e.g., ¶155, “session representation module 702 may generate a session representation by generating a sequence of images of a GUI of a software application captured during a session …”).
Claim 18 is rejected for the additional reasons given in the rejection of claim 7 above.
Regarding claim 9, the rejection of claim 7 is incorporated, and Lucioni further teaches: wherein said pre-processing comprises utilizing an image source of a user-event (Lucioni, e.g., ¶155, “session representation generation module 702 may generate representations of sessions … include data indicating a sequence of events … generating a sequence of images of a GUI of a software application captured during a session …” See also, e.g., ¶169, “view a replication of a sequence of events that occurred during a session … an indication of a sequence of events … screenshots of a replay of the session …”).
Claim 20 is rejected for the additional reasons given in the rejection of claim 9 above.
Claims 6 and 17 are rejected under 35 U.S.C. § 103 as being unpatentable over Lucioni in view of Misra, and in further view of Vajjiparti et al., U.S. 2024/0420189 A1 (“Vajjiparti”).
Regarding claim 6, the rejection of claim 5 is incorporated, but Lucioni in view of Misra does not more particularly teach that said one or more machine learning models uses a transformer architecture. However, Vajjiparti does teach: wherein said one or more machine learning models uses a transformer architecture (Vajjiparti, e.g., ¶147, “Trained machine learning model 630 may … classify, cluster, determine similarity between … may take the form of one or more artificial intelligence constructs, such as … transformer-based architectures …” See also, e.g., ¶173, “Similarity and/or clustering model 816 may take the vector representation from trained machine learning model 812 as input … calculate similarity measures … between the vector representation … it can be concluded that there is a semantic similarity …”) for the purpose of allowing users to provide non-limited feedback regarding software performance, performing machine learning using similarity, clustering or sentiment analysis to generate summarizations of user experience or actionable steps for software improvement (Vajjiparti, e.g., ¶¶140, 152 et seq.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method for triaging and describing reported application issues as taught by Lucioni in view of Misra to provide that said one or more machine learning models uses a transformer architecture because the disclosure of Vajjiparti shows that it was known to those of ordinary skill in the pertinent art to improve a system and method for evaluating user feedback regarding software performance to provide that said one or more machine learning models uses a transformer architecture for the purpose of allowing users to provide non-limited feedback regarding software performance, performing machine learning using similarity, clustering or sentiment analysis to generate summarizations of user experience or actionable steps for software improvement (Vajjiparti, Id.).
Claim 17 is rejected for the additional reasons given in the rejection of claim 6 above.
Claim 10 is rejected under 35 U.S.C. § 103 as being unpatentable over Lucioni in view of Misra, and in further view of Vijayaraghavan et al., U.S. 2014/0229408 A1 (“Vijayaraghavan”).
Regarding claim 10, the rejection of claim 7 is incorporated, but Lucioni in view of Misra does not more particularly teach that said pre-processing comprises masking numerical portions of the text and CSS elements. However, Vijayaraghavan does teach: wherein said pre-processing comprises masking numerical portions of the text and CSS elements (Vijayaraghavan, e.g., ¶27, “lines of text may be preprocessed … which may involve … masking text patterns, for example, different date patterns, digit patterns, phone numbers … removing numbers …”) for the purpose of providing appropriate data to one or more machine learning models to classify user interaction information (Vijayaraghavan, e.g., ¶¶21-32).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method for triaging and describing reported application issues as taught by Lucioni in view of Misra to provide that said pre-processing comprises masking numerical portions of the text and CSS elements because the disclosure of Vijayaraghavan shows that it was known to those of ordinary skill in the pertinent art to improve a system and method for grouping and categorizing user interactions to provide that said pre-processing comprises masking numerical portions of the text and CSS elements for the purpose of providing appropriate data to one or more machine learning models to classify user interaction information (Vijayaraghavan, Id.).
Conclusion
Examiner has identified particular references contained in the prior art of record within the body of this action for the convenience of Applicant. Although the citations made are representative of the teachings in the art and are applied to the specific limitations within the enumerated claims, the teaching of the cited art as a whole is not limited to the cited passages. Other passages and figures may apply. Applicant, in preparing the response, should consider fully the entire reference as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art and/or disclosed by Examiner.
Examiner respectfully requests that, in response to this Office Action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist Examiner in prosecuting the application.
When responding to this Office Action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections. See 37 C.F.R. 1.111(c).
Examiner interviews are available via telephone and video conferencing using a USPTO-supplied web-based collaboration tool. Applicant is encouraged to submit an Automated Interview Request (AIR) which may be done via https://www.uspto.gov/patent/uspto-automated-interview-request-air-form, or may contact Examiner directly via the methods below.
Any inquiry concerning this communication or earlier communication from Examiner should be directed to Andrew M. Lyons, whose telephone number is (571) 270-3529, and whose fax number is (571) 270-4529. The examiner can normally be reached Monday to Friday from 10:00 AM to 6:00 PM ET. If attempts to reach Examiner by telephone are unsuccessful, Examiner’s supervisor, Wei Mui, can be reached at (571) 272-3708. Information regarding the status of an application may be obtained from the Patent Center system. For more information about the Patent Center system, see https://www.uspto.gov/patents/apply/patent-center. If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call (800) 786-9199 (in USA or Canada) or (571) 272-1000.
/Andrew M. Lyons/Examiner, Art Unit 2191