DETAILED ACTION
Claims 1 – 20 are pending.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant’s submission filed on 21 May 2026 has been entered.
Response to Amendment
With regard to the Final Office Action from 09 April 2026, the Applicant has filed a response on 21 May 2026.
Response to Arguments
With regard to the 35 U.S.C. 103 given to the independent claims, the Applicant states (Remarks: pages 10 – 11) that the applied prior art fails to teach the limitation of ‘replacing, by a voice replacer component, the identified sensitive data with synthetic data that is semantically and contextually meaningful in a manner so that background audio is preserved wherein a generative model is leveraged to recreate sound information of the same duration and wherein the synthetic data is voice using the synthetic voice’ as previously provided by claim 1. The Applicant goes on to state that the prior art of record is not suitable to teach the amended claim limitations either (Remarks: page 11 par 2).
Applicant’s arguments with respect to the independent claims have been considered but are moot due to the new grounds of rejection necessitated by the amendment to the claims. The claims will be addressed by their current presentation in the following rejection section.
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.
Claims 1, 7, 8, 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Dotan (US 9,507,947 B1) in view of Lahr et al. (US 2023/0386446 A1: hereafter — Lahr), further in view of Flechl, Martin, et al. (“End-to-end speech recognition modeling from de-identified data.” arXiv preprint arXiv:2207.05469 (2022): hereafter — Flechl), and further in view of Parkinson (US 2022/0108692 A1).
For claim 1, Dotan discloses a computer-implemented method (Dotan: Col 5 lines 11 – 32 — provides teaching for one or more processors, storage devices, and instructions that can be executed by the processors, these being suitable to read upon the elements of the claimed invention) comprising:
extracting, by a signal separator component, a voice from an audio file (Dotan: Col 3 lines 6–10 — extracting voice content from audio; Col 5 lines13–17 — processor for performing the required task);
transcribing, by a speech-to-text component, the voice in the audio file into text (Dotan: Col 5 line 55 – Col 6 line 3 — performing speech-to-text recognition on the input audio);
identifying, by a natural language processing and identification component, sensitive data in the text, wherein identifying the sensitive data comprises:
parsing the text and identifying the sensitive data contained within the text (Dotan: Col 2 lines 18 – 23 — performing content extraction from a content source and parsing words from the content (to indicate a natural language process of text parsing); Col 4 lines 8–11 — parsing text and labelling words or identifying words as sensitive data; Col 9 lines 14 – 17 — identifying sensitive data).
The reference of Dotan provides teaching for receiving audio data and identifying the presence of sensitive data after parsing the content of the audio. It differs from the claimed invention in that the claimed invention further provides teaching for identifying a synthetic voice that matches the voice from the audio file.
This teaching isn’t new to the art, as the reference of Lahr is now introduced to teach this as:
identifying, by a voice locator component, a synthetic voice that matches the voice from the audio file (Lahr: [0064] — modifying and providing a synthesised voice that matches a recorded one).
Hence, before the effective filing date of the claimed invention, one of ordinary skill in the art would have found it obvious to improve upon the technique of Dotan which receives audio data that contains identified sensitive data, by applying the known technique provided by Lahr which provides the identification of a synthetic voice that matches the voice from the input audio, to thereby come up with the claimed invention. The combination of both prior art elements would have provided the predictable result of presenting the intent to replace the identified sensitive data with replacement data that matches the voice from the received audio to give the semblance that a replacement audio is voice by the same speaker of the received audio. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007).
The combination of Dotan in view of Lahr provides teaching for identifying sensitive data contained in an audio file and identifying a synthetic voice that matches the voice from the audio file. This combination however differs from the claimed invention in that the claimed invention further provides teaching for replacing the identified sensitive data with semantically and contextually meaningful synthetic data.
This however isn’t new to the art as the reference of Flechl is now introduced to teach this as
replacing, by a voice replacer component, the identified sensitive data with synthetic data that is semantically and contextually meaningful [[in a manner so that the background audio is preserved]] by replacing soundwave associated with the voice in the audio file with the synthetic voice so that the synthetic voice is inserted over the identified sensitive data, [[wherein a generative model is leveraged to recreate sound information of the same duration that conveys a different message that is not sensitive data]], and wherein the synthetic data is voiced using the synthetic voice (Flechl: Page 2 Col 2 3.2 — a text-to-speech model (Cerence TTS) being used to synthesise clean speech audio that then gets concatenated with the original audio; Page 2 Col 1 3. — ‘the original PII text is replaced by a so-called surrogate’; Page 2 Col 1 3.1 — surrogates are generated such that each PII is replaced with the surrogate such as ‘Michael’ being replaced by ‘John,’ ‘New York’ by ‘Los Angeles,’ ‘6/3 2021’ by ‘7/4 2019’ and ‘April 7, 2020’ by ‘first of May, 2021’ (teaching of the replacement of the identified sensitive data with a semantically and contextually meaningful replacement information); Figure 2 — generating spliced audio such that the identified sensitive content is replaced by synthetic voice; Abstract — ‘corresponding audio is produced by text-to-speech’); and
outputting a new audio file with the sensitive data replaced by the synthetic data (Flechl: Figure 2 — audio generation by combining original speech and text-to-speech (outputting of new audio file that has the sensitive data replaced by the synthetic data)).
Hence, before the effective filing date of the claimed invention, one of ordinary skill in the art would have found it obvious to improve upon the combination of Dotan in view of Lahr which provides identifying sensitive data contained in an audio file and identifying a synthetic voice that matches the voice from the audio file, by applying the known technique of Flechl which replaces the identified sensitive data with semantically and contextually meaningful synthetic data to thereby produce a new audio file with the sensitive data replaced by synthetic data, to thereby come up with the claimed invention. The combination of both prior art techniques would have provided the predictable result of maintaining the data structure of the sentence contained in the audio file, preserving its usefulness in a manner similar to the original, without exposing the sensitive information. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007).
The combination of Dotan in view of Lahr further in view of provides teaching for identifying sensitive information in an audio and replacing the sensitive audio with synthetic data. This combination differs from the claimed invention in that the claimed invention further provides teaching for performing the replacement at the region containing sensitive data in a manner that the background noise is preserved, along with the generation of sound information of the same duration.
This is however not new to the art as the reference of Parkison is now introduced to teach this as
replacing, by a voice replacer component, the identified sensitive data with synthetic data that is semantically and contextually meaningful in a manner so that the background audio is preserved by replacing soundwave associated with the voice in the audio file [[with the synthetic voice so that the synthetic voice is inserted over the identified sensitive data]], wherein a generative model is leveraged to recreate sound information of the same duration that conveys a different message that is not sensitive data, and wherein the synthetic data is voiced using the synthetic voice (Parkinson: [0018] — a situation whereby audio data is removed by performing audio scrubbing, resulting in the removal of the voice command while maintaining the background environment audio data; [0038] — maintaining background environment audio data; [0040] — filler audio data may be embedded in the audio data between the timestamps corresponding to the determined voice command to generate scrubbed audio data (indicating teaching for replacing voiced information with filler audio such as generated background noise that fits the duration of the voice data); [0045] — a machine learning model being used to provide filler audio that has its duration equal to a duration of the utterance).
Hence, before the effective filing date of the claimed invention, one of ordinary skill in the art would have found it obvious to combine the known teaching of Parkinson which applies a machine learning model to generate an audible replacement for audio of a similar duration to audio that was removed while maintaining the background environmental audio, with the teaching of the combination of Dotan in view of Lahr further in view of Flechl which provides the replacement of an identified sensitive data portion with synthetic data that is semantically and contextually meaningful to the removed sensitive data, to thereby come up with the claimed invention. The combination of both prior art elements would have provided the predictable result of ensuring uninterrupted continuous audio that attempts to maintain the originality of the input audio, giving the listener a seamless feeling while listening to the modified audio. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007).
For claim 7, claim 1 is incorporated and the combination of Dotan in view of Lahr further in view of Flechl and further in view of Parkinson discloses the computer-implemented method, further comprising:
merging the new audio file with the audio file to generate an updated audio file (Flechl: Figure 2 — the new synthetic data containing ‘Mike’ is merged with the original audio file); and
outputting the updated audio file, wherein the updated audio file comprises audio with the sensitive data replaced with synthetic data voiced by the synthetic voice (Flechl: Figure 2 — the new synthetic data containing ‘Mike’ is merged with the original audio file to generate the new TTS TOKEN file, and this is provided as a generated new audio file).
As for claim 8, computer system claim 8 and method claim 1 are related as apparatus and the method of using same, with each claimed element’s function corresponding to the claimed method step. Dotan in Col 5 lines 11–32 provides teaching for one or more processors, storage devices, and instructions that can be executed by the processors, these being suitable to read upon the elements of the claimed invention. Accordingly, claim 8 is similarly rejected under the same rationale as applied above with respect to method claim 1.
As for claim 14, computer system claim 14 and method claim 7 are related as apparatus and the method of using same, with each claimed element’s function corresponding to the claimed method step. Accordingly, claim 14 is similarly rejected under the same rationale as applied above with respect to method claim 7.
As for claim 15, computer program product claim 15 and method claim 1 are related as computer program product storing executable instructions required for performing the claimed method steps on a computer. Dotan in Col 5 lines 11–32 provides teaching for instructions stored on storage devices and executed by the processors, these being suitable to read upon the elements of the claimed invention. Accordingly, claim 15 is similarly rejected under the same rationale as applied above with respect to method claim 1.
Claims 2, 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Dotan (US 9,507,947 B1) in view of Lahr (US 2023/0386446 A1), further in view of Flechl (“End-to-end speech recognition modeling from de-identified data.” arXiv preprint arXiv:2207.05469 (2022)), and further in view of Parkinson (US 2022/0108692 A1), as applied to claims 1, 8 and 15, and further in view of Chen et al. (US 2020/0394258 A1: hereafter — Chen).
For claim 2, claim 1 is incorporated and the combination of Dotan in view of Lahr further in view of Flechl and further in view of Parkinson provides teaching for identifying sensitive information. This combination however differs from the claimed invention in that the claimed invention further provides teaching for editing speech transcripts that constitute a query based on applying knowledge database of a domain configuration.
This isn’t new to the art as the reference of Chen is now introduced to teach this as:
the computer-implemented method, further comprising:
receiving an audio file with editable speech content, wherein audio file editing utilizes an input query, configuration, and a knowledge base (Chen: [0026] — the user says ‘when is the pink concert’ which is an audio as an input query that is determined to pertain to a Music domain (as the configuration), and the occurrence of ‘pink’ gets edited to be replaced with ‘P!nk’ as this would be present in the Music knowledge base; [0037] — replacing a token in a transcription based on a determined natural language domain).
Hence, before the effective filing date of the claimed invention, one of ordinary skill in the art would have found it obvious to improve upon the teaching of the combination of Dotan in view of Lahr further in view of Flechl and further in view of Parkinson which teaches identifying sensitive information, by applying the known technique of Chen which provides editing speech transcripts that constitute a query based on applying knowledge database of a domain configuration, to thereby come up with the claimed invention. The combination of both prior art elements would have provided the predictable result of being able to identify and replace sensitive information in speech content with replacement words that would fit the domain configuration of the speech input. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007).
As for claim 9, computer system claim 9 and method claim 2 are related as apparatus and the method of using same, with each claimed element’s function corresponding to the claimed method step. Accordingly, claim 9 is similarly rejected under the same rationale as applied above with respect to method claim 2.
As for claim 16, computer program product claim 16 and method claim 2 are related as computer program product storing executable instructions required for performing the claimed method steps on a computer. Accordingly, claim 16 is similarly rejected under the same rationale as applied above with respect to method claim 2.
Claims 3, 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Dotan (US 9,507,947 B1) in view of Lahr (US 2023/0386446 A1), further in view of Flechl (“End-to-end speech recognition modeling from de-identified data.” arXiv preprint arXiv:2207.05469 (2022)), and further in view of Parkinson (US 2022/0108692 A1), as applied to claims 1, 8 and 15, and further in view of Matthews et al. (US 10,529,336 B1: hereafter — Matthews).
For claim 3, claim 1 is incorporated and the combination of Dotan in view of Lahr further in view of Flechl and further in view of Parkinson provides teaching for identifying sensitive information in a transcript, but differs from the claimed invention in that the claimed invention further provides teaching for the provision of a timestamp location of sensitive data within and audio file.
This is however not new to the art as the reference of Matthews is now introduced to teach this as:
the computer-implemented method, further comprising:
tagging and categorizing an original user’s speech signal, transcript, and/or sensitive words (Matthews: Col 2 lines 57–65 — detecting patterns of words in a text representation that indicate sensitive information of a customer (teaching of the tagging of and categorising as sensitive information, within a transcript)); and
marking the tagged and categorized sensitive words in the text with a timestamp associated with a location of the sensitive words in the audio file (Matthews: Col 3 lines 3–5 — an identified timestamp that corresponds to sensitive information, identified in the metadata of the audio block).
Hence, before the effective filing date of the claimed invention, one of ordinary skill in the art would have found it obvious to improve upon the teaching of the combination of Dotan in view of Lahr further in view of Flechl and further in view of Parkinson which provides identifying sensitive information, by applying the known technique of Matthews which provides a timestamp location of sensitive data within and audio file, to thereby come up with the claimed invention. The combination of both prior art elements would have provided the predictable result of being able to quickly access the sensitive information within the audio file. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007).
As for claim 10, computer system claim 10 and method claim 3 are related as apparatus and the method of using same, with each claimed element’s function corresponding to the claimed method step. Accordingly, claim 10 is similarly rejected under the same rationale as applied above with respect to method claim 3.
As for claim 17, computer program product claim 17 and method claim 3 are related as computer program product storing executable instructions required for performing the claimed method steps on a computer. Accordingly, claim 17 is similarly rejected under the same rationale as applied above with respect to method claim 3.
Claims 4, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Dotan (US 9,507,947 B1) in view of Lahr (US 2023/0386446 A1), further in view of Flechl (“End-to-end speech recognition modeling from de-identified data.” arXiv preprint arXiv:2207.05469 (2022)), and further in view of Parkinson (US 2022/0108692 A1), as applied to claims 1, 8 and 15, and further in view of Fernandez Guajardo et al. (US 11,900, 914 B2: hereafter — Fernandez).
For claim 4, claim 1 is incorporated and the combination of Dotan in view of Lahr further in view of Flechl and further in view of Parkinson provides teaching for replacing sensitive sections of audio with synthetic data, but differs from the claimed invention in that the claimed invention further provides teaching for the selection of a voice-matching synthetic voice from a manifold of pre-trained GANs.
This is however not new to the art as the reference of Fernandez is now introduced to teach this as
the computer-implemented method, further comprising:
identifying, within a manifold of a pre-trained generative adversarial network (GAN) that synthesizes the voice, the synthetic voice that matches the voice from the audio file, wherein the synthetic voice that matches the voice from the audio file is determined based on predetermined metrics (Fernandez: Col 3 lines 42–50 — voice synthesis model being generated using a GAN, wherein a discriminator model of the GAN is generated based on the voice of the user (indicating that matches are to be made to fit the voice of the user from an audio file); Col 6 lines 9-25 — generating voice content selected as a voice synthesis model from a set (manifold) of voice synthesis models (a manifold of pre-trained GANs) that correspond to the user, the synthesis also including configuration parameters to instruct the voice synthesis model (teaching of the synthesis making a match based on predetermined metrics)).
Hence, before the effective filing date of the claimed invention, one of ordinary skill in the art would have found it obvious to improve upon the teaching of the combination of Dotan in view of Lahr further in view of Flechl and further in view of Parkinson which teaches replacing sensitive sections of audio with synthetic data, by applying the known technique of Fernandez which provides selecting a voice synthesis model to use for a particular situation, to thereby come up with the claimed invention. The combination of both prior art elements would have provided the predictable result of replacing the sensitive sections with synthetic voice that would sound just like the voice of the user, causing a proper flow to occur between non-sensitive sections and sensitive sections of the audio file. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007).
As for claim 11, computer system claim 11 and method claim 4 are related as apparatus and the method of using same, with each claimed element’s function corresponding to the claimed method step. Accordingly, claim 11 is similarly rejected under the same rationale as applied above with respect to method claim 4.
As for claim 18, computer program product claim 18 and method claim 4 are related as computer program product storing executable instructions required for performing the claimed method steps on a computer. Accordingly, claim 18 is similarly rejected under the same rationale as applied above with respect to method claim 4.
Claims 5, 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Dotan (US 9,507,947 B1) in view of Lahr (US 2023/0386446 A1), further in view of Flechl (“End-to-end speech recognition modeling from de-identified data.” arXiv preprint arXiv:2207.05469 (2022)), and further in view of Parkinson (US 2022/0108692 A1), as applied to claims 1, 8 and 15, and further in view of YAMAMOTO (US 2016/0086622 A1).
For claim 5, claim 1 is incorporated and the combination of Dotan in view of Lahr further in view of Flechl and further in view of Parkinson discloses replacing sensitive sections of audio with synthetic data, but differs from the claimed invention in that that claimed invention further provides teaching for dynamically adjusting the synthetic voice so that it is within a predetermined threshold of acceptance of similarity with the voice from the audio file.
This is however not new to the art as the reference of Yamamoto is now introduced to teach this as
the computer-implemented method, further comprising:
dynamically tunning, by a voice tuner component, the synthetic voice, wherein the synthetic voice is dynamically tuned until the synthetic voice is within a predetermined threshold of acceptance of similarity associated with the voice from the audio file (Yamamoto: [0056] — the processing of inputting synthetic speech to a speech analyser is repeated (dynamic tuning) so as to compare the similarity of the synthetic speech to the target reference speech (voice from the audio file) until a certain threshold is reached to determine the closeness of the synthetic speech to the reference speech).
Hence, before the effective filing date of the claimed invention, one of ordinary skill in the art would have found it obvious to improve upon the teaching of the combination of Dotan in view of Lahr further in view of Flechl and further in view of Parkinson which provides replacing sensitive sections of audio with synthetic data, by applying the known technique of Yamamoto which provides adjusting the synthetic speech to be close to a reference speech up to a certain threshold, to thereby come up with the claimed invention. The combination of both prior art elements would have provided the predictable result of replacing the sensitive sections with synthetic voice that would sound similar enough to the voice of the user, causing a proper flow to occur between non-sensitive sections and sensitive sections of the audio file. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007).
As for claim 12, computer system claim 12 and method claim 5 are related as apparatus and the method of using same, with each claimed element’s function corresponding to the claimed method step. Accordingly, claim 12 is similarly rejected under the same rationale as applied above with respect to method claim 5.
As for claim 19, computer program product claim 19 and method claim 5 are related as computer program product storing executable instructions required for performing the claimed method steps on a computer. Accordingly, claim 19 is similarly rejected under the same rationale as applied above with respect to method claim 5.
Claims 6, 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Dotan (US 9,507,947 B1) in view of Lahr (US 2023/0386446 A1), further in view of Flechl (“End-to-end speech recognition modeling from de-identified data.” arXiv preprint arXiv:2207.05469 (2022)), and further in view of Parkinson (US 2022/0108692 A1), as applied to claims 1, 8 and 15, and further in view of Allen et al. (US 2018/0276402 A1: hereafter — Allen).
For claim 6, claim 1 is incorporated and the combination Dotan in view of Lahr further in view of Flechl and further in view of Parkinson provides teaching for replacing sensitive sections of audio with synthetic data. This combination however differs from the claimed invention in that the claimed invention further provides teaching for using the sensitive data to identify their replacements from a knowledge base, these being output as replacement options.
This teaching is however not new to the art as the reference of Allen is now introduced to teach this as:
the computer-implemented method, further comprising:
utilizing the sensitive data from the text to identify replacements for the sensitive data within a knowledge base (Allen: [0039] — ‘if a SSN is included in a cell, a user might be presented with options to replace the digits in the SSN with ‘X’ characters while leaving intact a data scheme of the SSN, i.e. leaving in the familiar “3-2-4”’ showing identification of the replacement of the sensitive content; [0064] — ‘[t]he obfuscation rules may be used for replacing an internal codename with a marketing approved name, used to obfuscate personally identifiable information (PII) with boilerplate names’ (teaching of replacements from a knowledge base)); and
outputting a list of replacement options for the identified sensitive data (Allen: [0064] — the user is presented with an option to replace sensitive data with safe text).
Hence, before the effective filing date of the claimed invention, one of ordinary skill in the art would have found it obvious to improve upon the teaching of the combination of Dotan in view of Lahr further in view of Flechl and further in view of Parkinson which provides replacing sensitive sections of audio with synthetic data, by applying the known technique of Allen which provides utilising the sensitive data to find its replacements, to thereby come up with the claimed invention. The combination of both prior art elements would have provided the predictable result of providing a user with replacement options suitable to replace the sensitive sections, such that the user would be able to personally select the best-fitting replacement terms to go with the sentence while still obfuscating the sensitive information. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007).
As for claim 13, computer system claim 13 and method claim 6 are related as apparatus and the method of using same, with each claimed element’s function corresponding to the claimed method step. Accordingly, claim 13 is similarly rejected under the same rationale as applied above with respect to method claim 6.
As for claim 20, computer program product claim 20 and method claims 6 and 7 are related as computer program product storing executable instructions required for performing the claimed method steps on a computer. Accordingly, claim 20 is similarly rejected under the same rationale as applied above with respect to method claims 6 and 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure.
Ramadas et al. (US 2023/0269291 A1) provides teaching for a computing system that may obfuscate the sensitive-information utterance by replacing the sensitive-information utterance with alternative audio data based on a voice of the user [0004], going so far as to have a prediction engine that may process input utterances in real-time to predict whether a next utterance contains sensitive information, predict a duration of the sensitive information utterance, and an obfuscation module that may obfuscate the sensitive information utterances [0065].
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to OLUWADAMILOLA M. OGUNBIYI whose telephone number is (571)272-4708. The Examiner can normally be reached Monday – Thursday (8:00 AM – 5:30 PM Eastern Standard Time).
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, PARAS D. SHAH can be reached at (571) 270-1650. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/OLUWADAMILOLA M OGUNBIYI/ Examiner, Art Unit 2653
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