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 .
Response to Arguments
Applicant’s arguments, filed 01/29/2026, with respect to the rejection(s) of independent claim(s) 1, 8, and 15 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn.
Applicant argues that the newly added limitations in the amended independent claims, namely “determining a usage-history context of the error based on the historical error corrections for a presenter and a linguistic context of the error based on surrounding phonetic elements in the audio segment” and “identifying an intended word or an intended phrase to be spoken during the audio segment based on a historical correction log and the context”, are not disclosed, suggested, or rendered predictable by any of the cited references.
To teach these limitations requires analyzing a spoken error and determining a correction in the context of two sets of information: 1) a historical error correction log, and 2) the linguistic and phonetic information in the surrounding audio segment.
Linguistic context (surrounding phonetic elements):
Narayanan teaches a feedback loop in which a planned error correction may be evaluated based on the surrounding audio ([0047] “The feedback loop 438 may generally indicate… whether the replaced incorrect word and/or other contextual words were properly recognized (generally) in the input audio content 402 and translated to text,… whether the inserted correct word is in fact a correction to the incorrect word, or the extend to which the correct word is the most appropriate correction within the context of the speech…”).
Narayanan also describes in more detail the process of determining the linguistic context of an error and the reason for an error, which informs the choice of a correction ([0054] “Determining that the portion of the spoken audio content indicates an incorrect word may comprise determining a meaning of the incorrect word, as well as meanings of any associated words (e.g., the other words of the sentence, utterance, expression, etc. comprising the incorrect word). The meaning of these associated words may provide contextual information for identifying the incorrect word, such as to resolve an ambiguity between several possible meanings.”; [0055] “Having determined that a word is incorrect, as well as (optionally) the reason(s) that the word is incorrect or in what respect the word is incorrect, the correction module 424 may determine a correct word to take the place of the incorrect word in the output audio content 436. As noted above, a word may be characterized as incorrect or correct with respect to meaning (e.g., semantic meaning, including nonsensical meaning, or word choice, including profanity), pronunciation, or grammar (e.g., verb tense, plural/singular form, grammatical case, verb conjugation, or sentence structure)”). Understanding the semantic meaning or especially grammar of an incorrect word necessitates understanding the context in which the word exists in the sentence.
Finally, Narayanan teaches that the voice profile used to determine a correction may include linguistic information from the audio surrounding an incorrect word ([0058] “The corrected spoken audio content 430 may be determined based on the voice profile associated with the spoken audio content indicating the incorrect word (e.g., associated with the speaker). As noted above, the voice profile may indicate audio, vocal, and/or linguistic characteristics that are based on the original spoken audio content from the speaker in the input audio content 402 (which may include the portion of the spoken audio content/input audio content 402 indicating the incorrect word and/or other portions of the spoken audio content/input audio content 402) and/or entirely other spoken content audio.”) – this paragraph does not single out the incorrect word itself but includes the audio “indicating the incorrect word”, which was previously established to include the surrounding audio.
Narayanan does not explicitly state that the linguistic context of the error is determined based on surrounding phonetic elements. However, Narayanan teaches that automatic speech recognition (ASR) may be used to convert input audio to text in order to perform the aforementioned analysis, and that “The ASR 414 may employ acoustic modeling and/or language modeling.” ([0050]). While not defined in Narayanan, acoustic modeling is a known concept in the art which translates audio signals to phonemes, indicating that the analysis does take phonetic analysis into account and therefore reads upon the claim limitation. One of ordinary skill in the art would have either known or been easily able to find the definition of “acoustic modeling”; nevertheless, an additional reference defining the term has been added to the “References Cited” section.
Therefore, Narayanan does teach this set of limitations.
Historical error correction:
Narayanan teaches the use of a “voice profile” to determine a correction for a detected error ([0058] “The corrected spoken audio content 430 may be determined based on the voice profile associated with the spoken audio content indicating the incorrect word (e.g., associated with the speaker).”). The voice profile may be generated based on previous error correction data ([0045] “A voice profile may have been previously generated. The previously-generated voice profile may be already associated with the speaker. For example, the voice profile may have already been generated during an audio correction analysis of another portion of the input audio content 402 or during an audio correction analysis of a different instance of audio content.”), possibly via a machine learning model trained on the error correction data ([0048], [0079]-[0081]).
The voice profile contains historical audio information for a speaker, and Narayanan teaches at least one embodiment in which the context of the voice profile is taken into account when analyzing a spoken error and determining a correction. However, the voice profile does not contain explicit information about specific error corrections, so it cannot be considered a “historical error correction log”.
The additional references do not teach this set of limitations either. Therefore, existing rejection under 35 U.S.C. 103 has been overcome.
However, upon further consideration, a new ground(s) of rejection is made in view of Drewes (US 20170133007 A1), which teaches the use of a historical error correction log when making corrections based on spoken audio.
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.
Claim(s) 1, 2, 4, 7, 8, 9, 11, 14, 15, 16, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Narayanan et al. (US 20220417659 A1, hereinafter “Narayanan”) in view of Drewes (US 20170133007 A1), Fried et al. (“Text-based editing of talking-head video”, hereinafter “Fried”), Murzina et al. (US 20070038455 A1, hereinafter “Murzina”), and Shi (US 20160360343 A1).
Regarding claim 1, Narayanan teaches: A processor-implemented method, the method comprising:
identifying an error within a segment of a multimedia file ([0004] “Audio correction may be applied to content, such as a news program, a sports broadcast, a game show, or a talk show, to automatically identify any “incorrect” words spoken by a featured speaker in the content…”) based on a machine learning model trained on historical error corrections ([0056] “For example, based on manual and/or automated review of output audio content 436, including any word corrections therein, the feedback loop 438 may indicate whether, or to what degree, a word selected to replace a corresponding incorrect word is in fact “correct.” The feedback loop 438 may indicate an in-fact correct word that should have been selected, one or more words that would have been preferable over the selected word, and/or one or more words that would have been equally acceptable to the selected word. The feedback loop 438 may be used as training data to determine the machine learning model.”), wherein the error comprises an audio segment and a video segment ([0020] “Content may comprise audio-only content, such as a radio broadcast, or content with both audio and video components.”; an error identified within audiovisual content would also involve both audio and video components);
generating a plan to correct the error ([0023] “The audio correction module 104 may generally determine that content comprises one or more incorrect words (e.g., spoken by a speaker) and automatically initiate steps to replace the incorrect(s) word in the content with corresponding correct word(s)”), wherein the generating further comprises:
determining a usage-history context of the error based on a linguistic context of the error based on surrounding phonetic elements in the audio segment ([0047] “The feedback loop 438 may generally indicate… whether the replaced incorrect word and/or other contextual words were properly recognized (generally) in the input audio content 402 and translated to text,… whether the inserted correct word is in fact a correction to the incorrect word, or the extend to which the correct word is the most appropriate correction within the context of the speech…”; [0054] “Determining that the portion of the spoken audio content indicates an incorrect word may comprise determining a meaning of the incorrect word, as well as meanings of any associated words (e.g., the other words of the sentence, utterance, expression, etc. comprising the incorrect word). The meaning of these associated words may provide contextual information for identifying the incorrect word, such as to resolve an ambiguity between several possible meanings.”; [0050] mentions the use of acoustic modeling for automatic speech recognition, which translates spoken audio into phonemes); and
identifying an intended word or an intended phrase to be spoken during the audio segment based on the context ([0055] “Having determined that a word is incorrect, as well as (optionally) the reason(s) that the word is incorrect or in what respect the word is incorrect, the correction module 424 may determine a correct word to take the place of the incorrect word in the output audio content 436. As noted above, a word may be characterized as incorrect or correct with respect to meaning (e.g., semantic meaning, including nonsensical meaning, or word choice, including profanity), pronunciation, or grammar (e.g., verb tense, plural/singular form, grammatical case, verb conjugation, or sentence structure)”) – understanding the semantic meaning or especially grammar of an incorrect word necessitates understanding the context in which the word exists in the sentence.);
generating a corrected audio segment based on the plan ([0026] “The audio correction module 104 may generate second spoken audio content indicating the correct word. The second spoken audio content may be generated using the voice profile associated with the speaker. As such, the correct word may be represented in the second spoken audio content in a similar manner as if the correct word was actually spoken by the speaker.”); and
replacing the audio segment with the corrected audio segment ([0026] “The audio correction module 104 may replace, in the content, the initial portion of the content with the generated second portion of content (indicating the correct word)”).
Narayanan additionally teaches using historical data (a “voice profile”) to identify errors and determine corrections ([0045] “A voice profile may have been previously generated. The previously-generated voice profile may be already associated with the speaker. For example, the voice profile may have already been generated during an audio correction analysis of another portion of the input audio content 402 or during an audio correction analysis of a different instance of audio content.”; [0079]-[0080] elaborate on training a machine learning algorithm, using other audio associated with the speaker as the input, to generate a voice profile and to detect incorrect words; [0058] “The corrected spoken audio content 430 may be determined based on the voice profile associated with the spoken audio content indicating the incorrect word (e.g., associated with the speaker).”).
However, Narayanan does not explicitly teach: determining a usage-history context of the error based on the historical error corrections for a presenter; and identifying an intended word or an intended phrase to be spoken during the audio segment based on a historical correction log.
Drewes teaches: determining a usage-history context of the error based on the historical error corrections for a presenter; and identifying an intended word or an intended phrase to be spoken during the audio segment based on a historical correction log ([0015] “The vocabulary model can be created by the cumulative input of all previously spoken words (word acoustics), associated with the digital text of the word, where the spoken words have been previously correctly recognized by the voice recognition software.”; [0016] The vocabulary model will include errors in voice recognition that were corrected. In other words, recordings having words that were previously incorrectly recognized or that the software was not able to recognize (e.g., such as when the acoustics of a spoken word could not be definitively associated with any acoustic word samples in the vocabulary dictionary) that have subsequently been corrected (e.g., the acoustics of the word, as spoken by the user, is added to the vocabulary dictionary and is associated with the correct digital text of the word in the vocabulary dictionary), so that in the future the same word in the same context and/or when pronounced the same way for other reasons (and therefore has the same acoustics) will be recognized.).
Narayanan and Drewes are both analogous to the claimed invention because they are in the same field of determining corrections for spoken audio; Drewes teaches corrections in text form instead of audio, but Narayanan also teaches conversion to text in order to perform analysis ([0050]), so the inventions would feasibly be compatible. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the automatic audio error correction invention of Narayanan with the teachings of Drewes to implement a vocabulary dictionary (or “historical error correction log”) to store incorrect pronunciations and their corresponding correct words and to use this tool when determining corrections. The motivation would have been to improve performance and avoid repeating error identification (Drewes abstract).
The combination of Narayanan in view of Drewes does not explicitly teach: generating a corrected video segment by modifying a lip movement in the video segment so lip movements of the presenter correspond to respective phonetics in the corrected audio segment, wherein a start and an end of the corrected video segment is imperceptible when transitioned from or to, respectively, the audio segment;
replacing the video segment with the corrected video segment so that the corrected video segment corresponds with the corrected audio segment; and
prompting a user to confirm updates prior to saving or uploading the multimedia file with the corrected audio segment and the corrected video segment.
Fried teaches: generating a corrected video segment by modifying a lip movement in the video segment so lip movements of the presenter correspond to respective phonetics in the corrected audio segment (fig. 10, fig. 2 “We first align phonemes to the input audio and track each input frame to construct a parametric head model. Then, for a given edit operation (changing spider to fox), we find segments of the input video that have similar visemes to the new word. In the above case we use viper and ox to construct fox. We use blended head parameters from the corresponding video frames, together with a retimed background sequence, to generate a composite image, which is used to generate a photorealistic frame using our neural face rendering method. In the resulting video, the actress appears to be saying fox, even though that word was never spoken by her in the original recording.”, pg. 5 col. 1 “Given an edit operation specified as a sequence of words W, our goal is to find matching sequences of phonemes in the video that can be combined to produce W. In the matching procedure we use the fact that identical phonemes are expected to be, on average, more visually similar to each other than non-identical phonemes (despite co-articulation effects). We similarly consider visemes, groups of aurally distinct phonemes that appear visually similar to one another (Section 3.3), as good potential matches.”), wherein a start and an end of the corrected video segment is imperceptible when transitioned from or to, respectively, the audio segment (pg. 2 col. 1 “This paper presents a method that completes the suite of operations necessary for transcript-based editing of talking-head video. Specifically, based only on text edits, it can synthesize convincing new video of a person speaking, and produce seamless transitions even at challenging cut points such as the middle of an utterance.”);
replacing the video segment with the corrected video segment so that the corrected video segment corresponds with the corrected audio segment (fig. 1, pg. 1 “We propose a novel method to edit talking-head video based on its transcript to produce a realistic output video in which the dialogue of the speaker has been modified, while maintaining a seamless audio-visual flow (i.e. no jump cuts) … We demonstrate a large variety of edits, such as the addition, removal, and alteration of words, as well as convincing language translation and full sentence synthesis.”); and
prompting a user to confirm updates prior to saving or uploading the multimedia file with the corrected audio segment and the corrected video segment (Fried pg. 2 section 1.1 “Ethical Considerations”: “We also believe that it is essential to obtain permission from the performers for any alteration before sharing a resulting video with a broad audience.”).
Fried and the combination of Narayanan in view of Drewes are both analogous to the claimed invention because they pertain to the same problem of modifying and replacing spoken words in a multimedia file. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the automatic audio error correction invention of Narayanan in view of Drewes with the audio-visual editing of Fried to automatically correct a speaker’s mouth movements to match a corrected word or phrase being spoken. The original and modified transcripts generated by the invention of Narayanan in view of Drewes could be automatically supplied to the invention of Fried to generate the visual correction. The motivation would have been to allow for the generation of edits that are realistic or convincing in respect to both audio and video.
The combination of Narayanan in view of Drewes and Fried does not explicitly teach identifying an error within phonetic elements of a segment of a multimedia file, or generating a corrected audio segment wherein the corrected audio segment matches a tone, inflection, and volume of the audio segment.
Murzina teaches identifying an error within phonetic elements (Abstract “The input audio signal is analyzed for finding pre-specified unwanted speech patterns, i.e. phonemes or groups of phonemes that are to be corrected, for instance because they represent a foreign accent.”), and generating a corrected audio segment wherein the corrected audio segment matches a tone, inflection, and volume of the audio segment ([0058] “The device adjusts the replacement sound signal to match the current pitch and possibly the timbre of the speaker and fits the adjusted speech fragment into the speech stream to substitute the unwanted pattern.”; [0061] “In FIG. 5D, the correction mode, example 2, the desired sound pattern corresponding to the identified unwanted sound pattern is adjusted for pitch and volume and fit into the incoming signal to replace the unwanted pattern.”)
Murzina and the combination of Narayanan in view of Drewes and Fried are both analogous to the claimed invention because they pertain to the same problem of modifying and replacing spoken words in an audio file. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Narayanan in view of Drewes and Fried with the phoneme-based audio replacement of Murzina. The motivation would have been to perform the audio modification in a way that is complementary to the phoneme-based video modification of Fried, making it easier to generate edits that are realistic or convincing in respect to both audio and video, and to allow for more precise edits that modify parts of individual words.
The combination of Narayanan in view of Drewes, Fried, and Murzina does not explicitly teach prompting a user, verified as the presenter in the multimedia file using biometric data, to confirm updates prior to saving or uploading the multimedia file with the corrected audio segment and the corrected video segment.
Shi teaches an authentication step for uploading an image file depicting a person wherein the user performing the operation is verified as the presenter in the multimedia file using biometric data ([0006] “The step of performing by the first terminal the face recognition on the user of each of the nearby terminals may comprise: performing, by the first terminal, face recognition on a face in a picture file saved in the first terminal, so as to determine whether the user face of each of the nearby terminals is present in the picture file; and the step of automatically generating the file-transmission command if the user is recognized successfully and transmitting the file to the successfully recognized nearby terminal may comprise: if the user face is present in the picture file, transmitting the picture file to the terminal held by the user with the face present in the picture file.”).
There are known ethical issues with the ability to generate realistic edits of audiovisual recordings of real people and impersonate them for unscrupulous purposes, as discussed in Fried (pg. 2 section 1.1), and thus one of ordinary skill in the art before the effective filing date of the claimed invention would have considered cybersecurity or digital authentication to be a pertinent issue to the invention taught by the combination of Narayanan in view of Drewes, Fried, and Murzina. Therefore, it would have been obvious to combine the invention of Narayanan in view of Drewes, Fried, and Murzina with the secure data transmission system of Shi. Though Shi teaches an image file rather than a multimedia audiovisual file, one of ordinary skill in the art would have been able to apply the teachings of Shi to the multimedia file of Narayanan in view of Drewes, Fried and Murzina. The motivation would have been to discourage anyone but the presenter in the multimedia file from editing the file, preventing others from misusing the technology.
Regarding claim 2, the combination of Narayanan in view of Drewes, Fried, Murzina, and Shi teaches: The method of claim 1, wherein generating the corrected audio segment further comprises:
extracting the audio segment from the multimedia file (Narayanan fig. 2, video content 204 and audio content 206 are separated, and audio content 206 is processed separately, [0033] “The content 202 may undergo audio correction to determine corrected content 202′. The corrected content 202′ may comprise corrected audio content 206′. The video content 204 in the corrected content 202′ may remain generally unchanged from the initial content 202.”);
translating the audio segment to phonetic elements (Fried fig. 2 “We first align phonemes to the input audio…”, section 3.1 Phoneme Alignment: “Our method relies on phonemes to find snippets in the video that we later combine to produce new content. Thus, our first step is to compute the identity and timing of phonemes in the input video. To segment the video’s speech audio into phones (audible realizations of phonemes), we assume we have an accurate text transcript and align it to the audio using P2FA [Rubin et al. 2013; Yuan and Liberman 2008], a phoneme-based alignment tool… Note that if a transcript is not given as part of the input, we can use automatic speech transcription tools [IBM 2016; Ochshorn and Hawkins 2016] or crowdsourcing transcription services like rev.com to obtain it.”);
identifying one or more phonetic elements of the audio segment associated with the error (Murzina abstract “The input audio signal is analyzed for finding pre-specified unwanted speech patterns, i.e. phonemes or groups of phonemes that are to be corrected, for instance because they represent a foreign accent.”);
identifying one or more phonetic elements, in the audio segment or in a historical correction log, that correspond to a corrected word or phrase in the corrected audio segment (Murzina [0058] “For each unwanted group of sounds, the accent corrector finds the corresponding "desired" digital signal from the pre-stored library of the replacement phoneme groups.); and
generating the corrected audio segment from the one or more phonetic elements that correspond to the corrected word or phrase (Murzina [0058] “The device adjusts the replacement sound signal to match the current pitch and possibly the timbre of the speaker and fits the adjusted speech fragment into the speech stream to substitute the unwanted pattern”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Narayanan in view of Drewes, Fried, Murzina, and Shi with the additional teachings of Fried and Murzina, all of which are in the same field of endeavor as the claimed invention, to break speech into phonemes when performing the error replacement functionality. The motivation would have been to allow for more precise edits that modify parts of individual words, making the results more realistic or convincing.
Regarding claim 4, the combination of Narayanan in view of Drewes, Fried, Murzina, and Shi teaches: The method of claim 1, wherein generating the plan further comprises utilizing a machine learning model or a historical correction log to determine a best fit word or phrase to replace the error in the multimedia file (Narayanan [0025] “The correct word may be determined using rules- and/or dictionary-based techniques. For example, a machine-readable dictionary may comprise listings of incorrect words and correct words that may be cross-referenced to determine what correct word(s), if any, are associated with a given incorrect word. Additionally or alternatively, the correct word may be determined using a machine learning model configured to receive an incorrect word as an input and output an associated correct word.”).
Regarding claim 7, the combination of Narayanan in view of Drewes, Fried, Murzina, and Shi teaches: The method of claim 1, wherein the error is selected from a group consisting of a language error, a grammatical mistake, and inappropriate content (Narayanan [0055] “As noted above, a word may be characterized as incorrect or correct with respect to meaning (e.g., semantic meaning, including nonsensical meaning, or word choice, including profanity), pronunciation, or grammar (e.g., verb tense, plural/singular form, grammatical case, verb conjugation, or sentence structure)”).
Regarding claims 8 and 15, they are rejected using the same references, rationale, and motivations to combine as claim 1 because their limitations substantially correspond to the limitations of claim 1, as well as the additional limitations of:
Claim 8: A computer system (Narayanan fig. 6), the computer system comprising: one or more processors (Narayanan fig. 6 CPU 604), one or more computer-readable memories (Narayanan fig. 6 RAM 608), one or more computer-readable tangible storage media (Narayanan fig. 6 mass storage device 628), and program instructions stored on at least one of the one or more tangible storage media for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method (Narayanan [0114] “The mass storage device 628 or other computer-readable storage media may also be encoded with computer-executable instructions, which, when loaded into the computing device 600, transforms the computing device from a general-purpose computing system into a special-purpose computer capable of implementing the aspects described herein.”);
Claim 15: A computer program product, the computer program product comprising: one or more computer-readable tangible storage media and program instructions stored on at least one of the one or more tangible storage media (Narayanan [0108] “The computing device 600 may be connected to a mass storage device 628 that provides non-volatile storage for the computer. The mass storage device 628 may store system programs, application programs, other program modules, and data, which have been described in greater detail herein.”), the program instructions executable by a processor capable of performing a method (Narayanan [0114] “The mass storage device 628 or other computer-readable storage media may also be encoded with computer-executable instructions, which, when loaded into the computing device 600, transforms the computing device from a general-purpose computing system into a special-purpose computer capable of implementing the aspects described herein.”).
Regarding claims 9 and 16, they are rejected using the same references, rationale, and motivations to combine as claim 2 because their limitations substantially correspond to the limitations of claim 2.
Regarding claims 11 and 18, they are rejected using the same references, rationale, and motivations to combine as claim 4 because their limitations substantially correspond to the limitations of claim 4.
Regarding claim 14, it is rejected using the same references, rationale, and motivations to combine as claim 7 because its limitations substantially correspond to the limitations of claim 7.
References Cited
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kibre et al. (US 9589562 B2) teaches a pronunciation correction system that learns pronunciations via a user correction log that keeps track of previous corrections.
Kumar (“Acoustic Modeling (ASR Part 2)”) teaches the definition of acoustic modeling: “Acoustic Model is a classifier which predicts the phonemes given audio input.” This definition explains, but does not expand upon, the use of the term in Narayanan (see claim 1 rejection).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BENJAMIN STATZ whose telephone number is (571)272-6654. The examiner can normally be reached Mon-Fri 8am-5pm.
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, Tammy Goddard can be reached at (571)272-7773. 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.
/BENJAMIN TOM STATZ/Examiner, Art Unit 2611
/TAMMY PAIGE GODDARD/Supervisory Patent Examiner, Art Unit 2611