DETAILED ACTION
Introduction
Applicant's submission filed on 04/16/2026 has been entered. Claims 1, 4-8, 11-15 and 18-26 are pending in the application and have been examined
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 .
Response to Amendment
The response filed on 04/16/2026 has been correspondingly accepted and considered in this Office Action. Claims 1, 4-8, 11-15 and 18-26 have been examined.
Response to Arguments
Applicant's arguments filed 04/16/2026 have been fully considered as follows:
Applicant’s arguments with respect to claim 1 (also representative of claims 8 and 15) state that
“Thus, Goldberg provides at most a generalized linguistic framework for classifying interruption types in conversational discourse and does not teach "wherein the neutral interruption indicates that no drift in topic has occurred and the power interruption
associated with a decision being made indicates that a drift in topic has occurred."
The examiner respectfully disagrees, Goldberg teaches “Rapport interruptions are viewed as acts of collaboration, cooperation, and/or mutual orientation providing the interruptee with immediate feedback, filling in informational gaps, and elaborating on the interruptee's topic or theme. Thus, 'power' is assignable to those interruptions which are off-topic, which re-introduce topics, or which contain few (if any) coherent-cohesive ties with the interrupted utterance. However, the 'rapport' classification is assigned to those interruptions which stay on-topic by meeting the requirements for either holding or progressive-holding moves.” in Goldberg section 3.2, 3.2.1, the interpretive strategies for differentiating between interruption types is performed to determine topic change based on the analyzing the decisions of the conversations based on the respective speakers. Examiner note, the claims are read in light of the specifications, if there are specific speaker remarks determining the power interruption and change of topic, it is not recited in the claims. Therefore, Goldberg teaches wherein the neutral interruption indicates that no drift in topic has occurred and the power interruption associated with a decision being made indicates that a drift in topic has occurred and therefore, the rejections of Claims 1, 8 and 15 are rejected under 35 U.S.C. 103 are sustained and further updated accordingly.
Applicant’s arguments with respect to claim 1 state that
“Nowak is silent on the different types of interruptions recited in claim 1. See, Office Action at 7. Thus, Nowak does not teach or suggest "generating a summary of a topic of the one or more topics based at least in part on the interruption type included in the discourse feature," as recited in amended claim 1. Nowak also does not teach "wherein the topic is selected for summarization based on the topic being associated with a highest level of user engagement of the one or more levels of user engagement." The addition of Goldberg or Mody does not cure this deficiency in Nowak.”
Applicant’s arguments with respect to claim(s) 1, 8 and 15 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
In response to the art rejection(s) of the remainder of dependent claims are rejected under 35 U.S.C 103, in case said claims are correspondingly discussed and/or argued for at least the same rationale presented in Remarks filed 04/16/2026, Examiner respectfully notes as follows. For completeness, should the mentioned claims be likewise traversed for similar reasons to independent claims 1, 8 and 15 correspondingly, Examiner respectfully directs Applicant to the same previous supra reasons provided in the response directed towards claims 1, 8 and 15 correspondingly discussed above. For at least the same supra provided reasons, Examiner likewise respectfully disagrees, and Applicant's arguments have been fully considered but they are not persuasive.
Claim Objections
Claims 1, 8 and 15 are objected to because of the following informalities: “a drift in topic has occurred” in claim 1 lines 15 and 19. It is unclear if they are referring to the same drift in topic or different drift in topic. Appropriate correction is required.
Claims 1, 8 and 15 are objected to because of the following informalities: “one or more topics based at least in part on the type of interruption included in the discourse feature, wherein the topic is selected for summarization based on the topic being associated with a highest level of user engagement of the one or more levels of user engagement” in claim 1 lines 24. It is unclear if one or more topics are determined by the type of interruption and how these one or more topics are used for the topic selected for summarization based on the topic associated with the highest level of user engagements. The Specifications[0063] discusses different embodiments for interruptions to determine drift in topics and topic selected based on user engagements. Appropriate correction is required.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1, 4-8, 11-15 and 18-20, 24-26 are rejected under 35 U.S.C. 103 as being unpatentable over Nowak-Przygodzki et. al., US Patent 10,645,035(referred as Nowak) in in view of Julia A Goldberg. Interrupting the discourse on interruptions: An analysis in terms of relationally neutral, power and rapport-oriented acts. Journal of Pragmatics, 14(6): 883–903, 1990 further in view of Chhabra et. a. US PgPub 2020/0374146.
Regarding claim 1, Nowak teaches a computer-implemented machine learning method for generating real-time summaries, the method comprising: receiving a data during a conference session (see Nowak, col 16 lines 7-30 describes the conferencing setup and includes the data : the speech received and the agenda and calendar ); identifying a speech segment associated with the received data during the conference session using a machine learning (ML) model (see Nowak, col 16 lines 31-52 discusses identifying the speech segment in conference mode of the automated assistant ( ML model) using the semantic processing and entity tagging ); generating a real-time transcript from the speech segment identified during the conference session using an automatic speech recognition (ASR) (see Nowak, col 19 lines 1-11 discusses speech to text processing on multiple distinct spoken utterances; Nowak, Fig. 1, 116 ( Text to speech module) in the Automated assistant 120); computing a lexical feature and a discourse feature from the real-time transcript(see Nowak, col 13 lines 49-61 discusses the semantic(lexical feature) processing of the utterances and checking with action items ( discourse features) of the agenda and updating accordingly); determining whether a drift in topic has occurred based on a pattern associated with the lexical feature or based on a distribution associated with the lexical feature and the discourse feature (see Nowak, col 13 lines 35-48 discusses topic classifier that identifies, from text generated from participant utterances, one or more topics that are raised and/or identifies when discussion has transitioned between different topics ; Nowak, col 15 lines 23-26, a new topic of discussion is detected ( drift in topic has occurred) based on the semantic processing to determine the context); determining one or more topics from the real-time transcript generated from the speech segment based on whether a drift in topic and further by using an ML classifier (see Nowak, col 13 lines 35-48 discusses the topic classifier based on the participant utterances using known techniques of topic classification, a topic classifier may employ a variety of known techniques of topic classification that are often used for document classification, such as expectation maximization, term-frequency-inverse document frequency (“TF-IDF”), na?ve Bayes classification, latent semantic indexing, support vector machines, artificial neural networks, decision trees, concept mining, etc.); generating a summary of the a topic of the plurality of topics (see Nowak, col 14 lines 9-20 generating a summary of the topics discussed ); and streaming the summary of the topic during the conference session (see Nowak, col 15 lines 38-44 discusses visual or audio output of the summary of the conference session. See Nowak, col 19 lines 48-50 discusses the meeting summary is generation).
Nowak teaches computing a lexical feature and a discourse feature from the real-time transcript, however Nowak fails to teach wherein the discourse feature includes a type of interruption in the speech segment, wherein the type of interruption is a neutral interruption or a power interruption associated with a decision being made,wherein the neutral interruption indicates that no drift in topic has occurred and the power interruption associated with a decision being made indicates that a drift in topic has occurred.
However, Goldberg teaches computing a lexical feature and a discourse feature from the real-time transcript, wherein the discourse feature includes a type of interruption in the speech segment, wherein the type of interruption is a neutral interruption or a power interruption associated with a decision being made,wherein the neutral interruption indicates that no drift in topic has occurred and the power interruption associated with a decision being made indicates that a drift in topic has occurred. (see Goldberg, sect 3.1-3.2 The first step in the interpretive schema is to identify and separate relationally neutral interruptions from relationally loaded ones (i.e., those falling along the power-rapport continuum). Relationally neutral interruptions are those which address the immediate needs of the communicative situation. Neutral interruptions, in effect, initiate minor side, repair or misapprehension sequences; once completed the discourse is returned to its pre-interruption state thereby 'allowing' the interrupted speaker to continue where s/he left off ( no drift in topic). Power and rapport type interruptions are designed to satisfy listener wants at the expense of his/her own obligations to support the rights (and wants) of the speaker to an unimpeded turn. Distinguishing between these two types of interruptions is the next step (step 2) of the heuristic. Power type interruptions are designed to wrest the discourse from the speaker by gaining control of the conversational process and/or content. Such power type interrupting the discourse type interruptions typically revolve topic change (drift in topic) attempts accomplished by questions and requests (process control strategies) or by assertions or statements (content control strategies) whose proposition A content is unrelated to the specific topic at hand); determining whether a drift in topic has occurred based on a pattern associated with the lexical feature and the discourse feature or based on a distribution associated with the lexical feature and the discourse feature (see Goldberg, sect 3.2.1 Power type interruptions(discourse feature) are designed to wrest the discourse from the speaker by gaining control of the conversational process and/or content. Such power type interruptions typically revolve topic change attempts accomplished by questions and requests (process control strategies) or by assertions or statements (content control strategies) whose proposition A content is unrelated to the specific topic at hand(pattern associated with lexical features). When successful, content control interruptions constrain both the direction and substance of the discourse. Control is now in the hands of the interrupter (as in examples 9-12)).
Nowak teach the method for identifying topics and generating meeting summary, however does not teach detection of different types of interruptions during a discussion. Goldberg teaches methods of identifying different types of interruptions during a conversation with examples. Using the known technique of interrupt detection as taught by Goldberg, to provide discourse feature from a conversation and change of topic in the references Nowak and to provide interruption type information, such as improved drift in topic would have been obvious to one of ordinary skill in the art.
Nowak teaches generating a summary of the a topic of the plurality of topics, However, Nowak in view of Goldberg fails to teach determining one or more levels of user engagement for the one or more topics; generating a summary of a topic of the one or more topics based at least in part on the type of interruption included in the discourse feature, wherein the topic is selected for summarization based on the topic being associated with a highest level of user engagement of the one or more levels of user engagement.
However, Chhabra teaches determining one or more levels of user engagement for the one or more topics(see Chhabra, [0101] The routine 1000 begins at operation 1002, where a system 100 receives contextual data or sensor data indicating a level of engagement) ; generating a summary of a topic of the one or more topics based at least in part on the type of interruption included in the discourse feature, wherein the topic is selected for summarization based on the topic being associated with a highest level of user engagement of the one or more levels of user engagement (see Chhabra, [0094] discusses the summary of the topics of discussion for the relevant conversation ( selected by contextual data from Fig. 10, 1004) as shown in Fig. 9). Examiner’s note based on the claims and Specifications [0063] is unclear how the interruptions correlate with user engagement topic prioritization, interruptions are used to determine topics of the conversation but unclear how this is correlated to user engagement per the specifications.
Nowak in view of Goldberg teach the method for identifying topics, interruptions and generating meeting summary, however does not teach detection of user engagements during a discussion. Chhabra teaches methods of identifying user engagement during a meeting. Using the known technique of user engagement as taught by Chhabra, to provide topic summarization in the references Nowak in view of Goldberg would have been obvious to one of ordinary skill in the art.
Regarding claim 4, Nowak in view of Goldberg further in view of Chhabra teaches the computer-implemented machine learning method of claim 1. Chhabra further teaches generating the real-time transcript comprises tagging a speaker identity or a timestamp, and wherein generating the summary of the topic comprises generating the summary using the speaker identity or the timestamp (see Chhabra, Fig. 7 includes the summary generation which includes additional user identity that contributed to the contents of selected section based on the topic discussed, etc. [0082-0083]). The same motivation to combine as claim 1 applies here.
Regarding claim 5, Nowak in view of Goldberg further in view of Chhabra teaches the computer-implemented machine learning method of claim 1. Nowak further teaches determining another topic from the real-time transcript generated from the speech segment(see Nowak, col 19 lines 21-27 discusses semantic processing the utterance(other topic) which is not relevant to the meeting discussion.. when the relevancy score fails a threshold ); determining an irrelevancy of the other topic (see Nowak, col 19 lines 21-27 if the semantic processing to produce the relevancy score which fails the threshold is interpreted as irrelevancy of the other topic ); and filtering out the other topic based on the irrelevancy (see Nowak, col 19 lines 21-34 discusses if the utterance fails the relevance threshold then no action is taken; filtering out the other topic).
Regarding claim 6, Nowak in view of Goldberg further in view of Chhabra teaches the computer-implemented machine learning method of claim 1. Chhabra further teaches generating the summary of the topic comprises generating an abstractive summary that includes independently chosen words from existing words in the real-time transcript (see Chhabra, [0059] discusses keywords and key topics and summary of the topics). Nowak further teaches generating the summary of the topic comprises generating an abstractive summary that includes independently chosen words from existing words in the real-time transcript, and wherein streaming the summary of the topic comprises streaming the abstractive summary (see Nowak, col 13 line 63- col 14 line 20 discusses generating a meeting summary based on the content of the discussion(real time transcript) learnt through semantic processing(independently chosen words) and processed to include topics discussed, action items created/addressed/modified, outcomes of the meeting and so forth and Nowak Fig. 2D, 2062, ). The same motivation to combine as claim 1 applies here.
Regarding claim 7, Nowak in view of Goldberg further in view of Chhabra teaches the computer-implemented machine learning method of claim 1. Chhabra further teaches processing the summary in response to generating the summary(see Chhabra, [0075] discusses selected portions and summarizes of the selected portions being displayed as shown in Fig. 5C), wherein processing comprises adding a speaker identity or a timestamp(see Chhabra, Fig. 5C displays the participants of selected sections ); and wherein streaming the summary comprises streaming the summary in response to the processing(see Chhabra, [0076] discusses the topics, user activity and event summary ). The same motivation to combine as claim 1 applies here.
Regarding claim 8, is directed to a non-transitory computer-readable media claim corresponding to the method claim presented in claim 1 and is rejected under the same grounds stated above regarding claim 1.
Regarding claim 11, is directed to a non-transitory computer-readable media claim corresponding to the method claim presented in claim 4 and is rejected under the same grounds stated above regarding claim 4.
Regarding claim 12, is directed to a non-transitory computer-readable media claim corresponding to the method claim presented in claim 5 and is rejected under the same grounds stated above regarding claim 5.
Regarding claim 13, is directed to a non-transitory computer-readable media claim corresponding to the method claim presented in claim 6 and is rejected under the same grounds stated above regarding claim 6.
Regarding claim 14, is directed to a non-transitory computer-readable media claim corresponding to the method claim presented in claim 7 and is rejected under the same grounds stated above regarding claim 7.
Regarding claim 15, is directed to a machine learning system claim corresponding to the method claim presented in claim 1 and is rejected under the same grounds stated above regarding claim 1.
Regarding claim 18, is directed to a machine learning system claim corresponding to the method claim presented in claim 5 and is rejected under the same grounds stated above regarding claim 5.
Regarding claim 19, is directed to a machine learning system claim corresponding to the method claim presented in claim 6 and is rejected under the same grounds stated above regarding claim 6.
Regarding claim 20, is directed to a machine learning system claim corresponding to the method claim presented in claim 7 and is rejected under the same grounds stated above regarding claim 7.
Regarding claim 24, Nowak in view of Goldberg further in view of Chhabra teaches the computer-implemented machine learning method of claim 1. Chhabra further teaches wherein each level of user engagement is determined by performing speech analysis of the real-time transcript, audio analysis of audio data associated with one or more participants, or facial analysis of video data associated with one or more participants(see Chhabra, [0006-0010] describes various measure of determining a person’s level of engagement including video analysis and audio analysis ).The same motivation to combine as claim 1 applies here.
Regarding claim 25, Nowak in view of Goldberg further in view of Chhabra teaches the computer-implemented machine learning method of claim 1. Chhabra further teaches wherein the summary is generated in response to a user selection of a participant of the conference session (see Chhabra, [0078] Fig. 5E discusses selection of portion of summary associated with the user ). The same motivation to combine as claim 1 applies here.
Regarding claim 26, Nowak in view of Goldberg further in view of Chhabra teaches the computer-implemented machine learning method of claim 1. Chhabra further teaches tagging a speaker in the conference session(see Chhabra, the transcripts indicates a speaker based on the selected portions of content); and generating an abstractive summary associated with the speaker(see Chhabra, [0111] describes the updating of the summary section based on the analysis accordingly ). The same motivation to combine as claim 1 applies here.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Nowak-Przygodzki et. al., US Patent 10,645,035(referred as Nowak) in view of Julia A Goldberg. Interrupting the discourse on interruptions: An analysis in terms of relationally neutral, power and rapport-oriented acts. Journal of Pragmatics, 14(6): 883–903, 1990 further in view of Chhabra et. a. US PgPub 2020/0374146 further in view of Mody et al., US PgPub. 2020/0273453.
Regarding claim 21, Nowak in view of Goldberg further in view of Chhabra teaches the computer-implemented machine learning method of claim 1. However, Nowak in view of Goldberg further in view of Chhabra fail to teach generating a sentence vector for each sentence of the real-time transcript, and wherein the determining the drift in topic is further based on the sentence vector.
However, Mody teaches further comprising generating a sentence vector for each sentence of the real-time transcript(see Mody, [0064] At 504, the vector clustering module 304 generates representative vectors for each statement), and wherein the determining the drift in topic is further based on the sentence vector(see Mody, [0066], [0066] At operation 506, the clustering module 306 clusters the representative vectors into vector clusters. Each resulting vector cluster includes a subset of the representative vectors that represent statements from the text that are likely part of the same topic ; Mody [0017, 0041, 0051] The cosine similarity value describes a measure of similarity between two representative vectors based on the angle between the two vectors. The temporal distance between two distances indicates an amount of time that elapsed between occurrence of the statements. The meeting summarization system uses the cosine similarity values and the temporal distances to determine vector distances between the representative vectors, which the HAC algorithm uses to cluster the representative vectors into vector clusters. Different topics to depict delineation one topic from another are interpreted as drift in topic).
Nowak in view of Goldberg further in view of Chhabra teach the method for identifying topics and generating meeting summary, however does not teach sentence vector generation. Mody teaches methods of sentence vector generation. Using the known technique of sentence vector generation as taught by Mody, to provide topic identification in the references Nowak in view of Goldberg further in view of Chhabra to improve providing insights into contents of the meeting ( see Mody, [0002] )would have been obvious to one of ordinary skill in the art.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Nowak-Przygodzki et. al., US Patent 10,645,035(referred as Nowak) in view of Julia A Goldberg. Interrupting the discourse on interruptions: An analysis in terms of relationally neutral, power and rapport-oriented acts. Journal of Pragmatics, 14(6): 883–903, 1990 further in view of Chhabra et. a. US PgPub 2020/0374146 further in view of Curtis et. al., US Patent 8,266,534.
Regarding claim 22, Nowak in view of Goldberg further in view of Chhabra teaches the computer-implemented machine learning method of claim 1. Nowak further teaches determining whether a speaker is critical to the topic (see Nowak, col 13 lines 1-10 Not every participant utterance is worthy of a response by automated assistant 120. For example, participants may engage in informal banter during the meeting to which they may not desire automated assistant 120 react( content not critical to the topic under discussion). Accordingly, in various implementations, automated assistant 120 may analyze various criteria to determine whether to inject into meetings content it retrieves based on semantic processing of the participants' discussions. In some implementations, automated assistant 120 may determine a relevancy score associated with information it obtains responsive to a participant's utterance); and removing content associated with the speaker from the summary in response to determining that the speaker is not critical to the topic (see Nowak, col 13 lines 15-20 On the other hand, if the retrieved information has a relevancy score that fails to satisfy such a threshold, automated assistant 120 may refrain from incorporating the information into the meeting discussion because that information may not likely be useful to, or well-received by, the participants). Nowak teaches removing content associated with the speaker from the summary in response to determining that the speaker is not critical to the topic based on the speaker content not relevant to the topic of discussion. To further teach determining whether a speaker is critical to the topic, Curtis teaches determining whether a speaker is critical to the topic (see Curtis, col 10 lines 8-17 discusses Filtering component 508 can identify a speaker, such as through voice-recognition or other manners of recognition, and filter out all the other speakers ( determining critical to the topic) ) and removing content associated with the speaker from the summary in response to determining that the speaker is not critical to the topic (see Curtis, 10 lines 17-22 Alternatively or additionally, filtering component 508 can remove information contributed by one or more identified people. As such, relevant information can be presented to a user while information not relevant or not desired can be disregarded and not output to the user. ). Examiner’s note : if the claims could further details on how the critical speaker is detected and how the non critical content is removed by the invention.
Nowak, Goldberg, Chhabra and Curtis are considered to be analogous to the claimed invention because they relate to methods for automating meetings. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Nowak in view of Goldberg further in view of Chhabra on the method for identifying topics and generating meeting summary with the topic management teachings of Curtis to improve efficiency of meetings( see Curtis, col 1 lines 28-37).
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Nowak-Przygodzki et. al., US Patent 10,645,035(referred as Nowak) in view of Julia A Goldberg. Interrupting the discourse on interruptions: An analysis in terms of relationally neutral, power and rapport-oriented acts. Journal of Pragmatics, 14(6): 883–903, 1990 further in view of Chhabra et. a. US PgPub 2020/0374146 further in view of Wang et. al., US Patent 10,719,696 .
Regarding claim 23, Nowak in view of Goldberg further in view of Chhabra teaches the computer-implemented machine learning method of claim 1. However, Nowak in view of Goldberg further in view of Chhabra fails to teach determining sentiments and emotions associated with a speaker in the speech segment, wherein the summary of the topic is further based on the determined sentiments and emotions.
However, Wang teaches determining sentiments and emotions associated with a speaker in the speech segment, wherein the summary of the topic is further based on the determined sentiments and emotions(see Wang, col 3 lines 10-14 discusses determining emotions of participants to determine the interest of the topic; Wang, col 7 lines 8-10; (based on Specifications [0067]). Examiner’s note : if the claims could further details on how the sentiments and emotions speaker is processed with relevance to the topic by the invention.
Nowak, Goldberg, Chhabra and Wang are considered to be analogous to the claimed invention because they relate to methods for automating meetings. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Nowak in view of Goldberg further in view of Chhabra on the method for identifying topics and generating meeting summary with the Interrelationships Among Participants And Topics In A Videoconferencing System teachings of Wang to improve flexibility in interaction among participants( see Wang, col 2 lines 55-59).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Krishnan et. al., US PgPub. 2022/0093093 teaches user interruption to determine change of topic (see Krishnan, [0350-0351]).
Bellamy et. al., US PgPub. 2017/0243171 teaches a method to list the critical speakers for the meeting and the difference determination circuit determines that when “Jon” and “Alan” speak that “who” is speaking is different than what is stated on the agenda. Thus, the difference determination circuit determines a difference from the agenda based on who is speaking (see Bellamy, Fig. 3, 4).
Banerjee, S., & Rudnicky, A. (2006), “A TextTiling based approach to topic boundary detection in meetings” discusses automatically detect boundaries between discussions of different topics in meetings using the TextTiling algorithm to the context of meetings.
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 NANDINI SUBRAMANI whose telephone number is (571)272-3916. The examiner can normally be reached Monday - Friday 12:00pm - 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, Bhavesh M Mehta can be reached at (571)272-7453. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/NANDINI SUBRAMANI/ Examiner, Art Unit 2656
/BHAVESH M MEHTA/ Supervisory Patent Examiner, Art Unit 2656
Prosecution Insights