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 .
Oath/Declaration
The Katz Declaration pursuant to 37 CFR 1.130(a) has been received. Accordingly, the 103 rejections for claims 2-5, 9-12, and 16-19 made over Kala et al. (An Objective Measure of Signal Quality for Pediatric Lung Auscultations, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)) in view of Langnes et al. (US 20200256834 A1) have been withdrawn.
Status of Claims
Claims 1-21 are currently pending and claims 1, 3, 5, 7-8, 10, 13-15, 17, and 20-21 have been amended.
Response to Arguments
The examiner has considered the arguments pertaining to the claim objections and the rejections under 35 USC 112(b) and 102(a)(2) and finds them persuasive. Accordingly, all claim objections and all rejections under 35 USC 112(b) and 102(a)(2) have been withdrawn.
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Applicant's arguments regarding rejections under 35 USC 101, see above, filed 05/06/2026, with respect to the rejection of claims 1-21 have been fully considered and are persuasive. The examiner agrees that the independent claims do not recite a mental process or the organizing of human activity. The rejections have been withdrawn. However, the amendments to claims 1, 8, and 15 necessitate novel consideration under 35 USC 101. The new grounds for rejection are presented below, see Claim Rejections – 35 USC § 101.
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The examiner respectfully notes that the Office did not reject claims 1, 8, and 15 under 35 USC 103 as being unpatentable over Kala in view of Langnes because these claims, prior to amendment by the applicant, were rejected under 35 USC 102(a)(2) as being anticipated by Nematihosseinabadi.
Applicant’s arguments regarding rejections under 35 USC 103, see above, filed 05/06/2026, with respect to the rejections of claims 2-7, 9-14, and 16-21 have been fully considered and are persuasive. Therefore, the rejections have been withdrawn. However, a new ground(s) of rejection is made in view of further consideration of Nematihosseinabadi and in view of newly found prior art Bryan (US 20210125629 A1) and are presented below, see Claim Rejections — 35 USC § 103.
Claim Rejections - 35 USC § 101
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 1-21 are rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more.
Claim 1 (a method claim)
Step 2A-I: The claim recites the abstract idea of a mathematical algorithm comprising the following steps:
Determining a plurality of derived signals from the acoustic signals
Performing a regression analysis on the plurality of derived signals
Determining the AQM from the regression analysis
Step 2A-II: The claim language does not integrate the recited mathematical algorithm into a practical application because the mere performance of the algorithm does not improve the healthcare outcome of the patient to whom the obtained data is associated or improve the average quality of signals in a dataset. The examiner respectfully notes that an amendment of the form “…to identify and flag out low-quality acoustic sound recordings of the pulmonary sounds of the patient”, as supported in the provided specification ([0029] — “The software can be used as a triage tool to flag out low-quality recordings.”), would sufficiently integrate the recited abstract idea into a practical application, as they would together both improve the average sound quality in the dataset and enable physicians to make better-informed decisions for their patients’ treatment.
Step 2B: The claim recites the following additional elements:
Obtaining, using a digital stethoscope, acoustic signals representative of pulmonary sounds from a patient, wherein the pulmonary sounds comprise breathing patterns of the patient
Outputting the AQM to identify low-quality acoustic sound recordings of the pulmonary sounds of the patient
However, additional element (a) is merely a data gathering step required for the performance of the recited mathematical algorithm, performed using a tool that is well-understood, routine, and conventional in the art (Yamanaka et al. (20150230751 A1), [0130] — “Hitherto, digital stethoscopes which collect body sounds (such as breath sounds and heartbeats) from a body (patient or subject) and record the collected body sounds as digital signals (body sound information) are widely used.”; McLane (US 10702239 B1), Col. 4, lines 31-32 — “The digital stethoscope 110 is an acoustic device for detecting and analyzing noises from a patient's body.”), and therefore does not amount to significantly more and additional element (b) is merely a data outputting step of the recited mathematical algorithm and therefore also does not amount to significantly more.
Furthermore, claims 2 and 7 are also rejected by virtue of their dependence from claim 1 and because they do not set forth any additional elements that integrate the recited mathematical algorithm into a practical application or that amount to significantly more.
Claim 3 (a method claim)
Step 2A-I: The claim recites an abstract idea as it inherits the limitations of claim 1.
Step 2A-II: The claim language does not integrate the recited mathematical algorithm into a practical application because the mere performance of the algorithm does not improve the healthcare outcome of the patient to whom the obtained data is associated or improve the average quality of signals in a dataset.
Step 2B: The claim recites the following additional element:
A trained neural network
However, additional element (a) is merely a generic computer component and/or software element configured to perform generic computer operations on signals.
Furthermore, claims 4-5 are also rejected by virtue of their dependence from claim 3 because they merely expand upon the type of neural network and do not recite any further additional elements that integrate the recited mathematical algorithm into a practical application or amount to significantly more.
Claim 6 (a method claim)
Step 2A-I: The claim recites an abstract idea as it inherits the limitations of claim 1 and recites a further abstract idea of a mathematical algorithm comprising the following step:
Training a convolutional autoencoder from a set of high-quality acoustic signals obtained from a variety of patients
A combination of abstract ideas is an abstract idea (See MPEP 2106.05(I) – "Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract").
While the courts generally recognize that a specific method of training a neural network does not recite an abstract idea (see MPEP § 2106.04(a)(1)(vii)), the claim language as written merely recites the generic mathematical concept of training a convolutional autoencoder (the neural network) from a set of high-quality acoustic signals (the dataset) without reciting the specific steps taken in the training procedure.
Step 2A-II: The claim language does not integrate the recited mathematical algorithm into a practical application because the mere performance of the algorithm does not improve the healthcare outcome of the patient to whom the obtained data is associated or improve the average quality of signals in a dataset.
Step 2B: The claim does not recite any additional elements that amount to significantly more.
Claim 8 (an apparatus claim)
Step 2A-I: The claim recites the abstract idea of a mathematical algorithm comprising the following steps:
Determining a plurality of derived signals from the acoustic signals;
Performing a regression analysis on the plurality of derived signals;
Determining the AQM from the regression analysis;
Step 2A-II: The claim language does not integrate the recited mathematical algorithm into a practical application because the mere performance of the algorithm does not improve the healthcare outcome of the patient to whom the obtained data is associated or improve the average quality of signals in a dataset. The examiner respectfully notes that an amendment of the form “…to identify and flag out low-quality acoustic sound recordings of the pulmonary sounds of the patient”, as supported in the provided specification ([0029] — “The software can be used as a triage tool to flag out low-quality recordings.”), would sufficiently integrate the recited abstract idea into a practical application, as they would together both improve the average sound quality in the dataset and enable physicians to make better-informed decisions for their patients’ treatment.
Step 2B: The claim recites the following additional elements:
A hardware processor
A non-transitory computer readable medium comprising instructions that when executed by the hardware processor perform a method for determining an auscultation quality metric (AQM)
Obtaining, using a digital stethoscope, acoustic signals representative of pulmonary sounds from a patient, wherein the pulmonary sounds comprise breathing patterns of the patient
Outputting the AQM to identify low-quality acoustic sound recordings of the pulmonary sounds of the patient
However, additional elements (a) and (b) are generic computer components configured to perform generic computer operations and therefore do not amount to significantly more. Additional element (c) is merely a data gathering step required for the performance of the recited mathematical algorithm and therefore does not amount to significantly more. Similarly, additional element (d) is merely a data outputting step required for the application of the recited mathematical algorithm and therefore does not amount to significantly more.
Furthermore, claims 9 and 14 are also rejected by virtue of their dependence from claim 8 and because they do not set forth any further additional elements that integrate the recited mathematical algorithm into a practical application or that amount to significantly more.
Claim 10 (an apparatus claim)
Step 2A-I: The claim recites an abstract idea as it inherits the limitations of claim 8.
Step 2A-II: The claim language does not integrate the recited mathematical algorithm into a practical application because the mere performance of the algorithm does not improve the healthcare outcome of the patient to whom the obtained data is associated or improve the average quality of signals in a dataset.
Step 2B: The claim recites the following additional element:
A trained neural network
However, additional element (a) is merely a generic computer component and/or software element configured to perform generic computer operations on signals.
Furthermore, claims 11-12 are also rejected by virtue of their dependence from claim 10 because they merely expand upon the type of neural network and do not recite any further additional elements that integrate the recited mathematical algorithm into a practical application or amount to significantly more.
Claim 13 (an apparatus claim)
Step 2A-I: The claim recites an abstract idea as it inherits the limitations of claim 8 and further recites the abstract idea of a mathematical algorithm comprising the following step:
Training a convolutional autoencoder from a set of acoustic signals obtained from a variety of patients
A combination of abstract ideas is an abstract idea (See MPEP 2106.05(I) – "Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract").
While the courts generally recognize that a specific method of training a neural network does not recite an abstract idea (see MPEP § 2106.04(a)(1)(vii)), the claim language as written merely recites the generic mathematical concept of training a convolutional autoencoder (the neural network) from a set of high-quality acoustic signals (the dataset) without reciting the specific steps taken in the training procedure.
Step 2A-II: The claim language does not integrate the recited mathematical algorithm into a practical application because the mere performance of the algorithm does not improve the healthcare outcome of the patient to whom the obtained data is associated or improve the average quality of signals in a dataset.
Step 2B: The claim does not recite any additional elements that amount to significantly more.
Claim 15 (an apparatus claim)
Step 2A-I: The claim recites the abstract idea of a mathematical algorithm comprising the following steps:
Determining a plurality of derived signals from the acoustic signals;
Performing a regression analysis on the plurality of derived signals;
Determining the AQM from the regression analysis;
Step 2A-II: The claim language does not integrate the recited mathematical algorithm into a practical application because the mere performance of the algorithm does not improve the healthcare outcome of the patient to whom the obtained data is associated or improve the average quality of signals in a dataset. The examiner respectfully notes that an amendment of the form “…to identify and flag out low-quality acoustic sound recordings of the pulmonary sounds of the patient”, as supported in the provided specification ([0029] — “The software can be used as a triage tool to flag out low-quality recordings.”), would sufficiently integrate the recited abstract idea into a practical application, as they would together both improve the average sound quality in the dataset and enable physicians to make better-informed decisions for their patients’ treatment.
Step 2B: The claim recites the following additional elements:
A non-transitory computer readable medium comprising instructions that when executed by a hardware processor perform a method for determining an auscultation quality metric (AQM)
Obtaining, using a digital stethoscope, acoustic signals representative of pulmonary sounds from a patient, wherein the pulmonary sounds comprise breathing patterns of the patient
Outputting the AQM to identify low-quality acoustic sound recordings of the pulmonary sounds of the patient
However, additional elements (a) is a generic computer component configured to perform generic computer operations and therefore does not amount to significantly more. Additional element (b) is merely a data gathering step required for the performance of the recited mathematical algorithm and therefore does not amount to significantly more. Similarly, additional element (c) is merely a data outputting step required for the application of the recited mathematical algorithm and therefore also does not amount to significantly more.
Furthermore, claims 16 and 21 are also rejected by virtue of their dependence from claim 15 and because they do not set forth any further additional elements that integrate the recited mathematical algorithm into a practical application or that amount to significantly more.
Claim 17 (an apparatus claim)
Step 2A-I: The claim recites an abstract idea as it inherits the limitations of claim 15.
Step 2A-II: The claim language does not integrate the recited mathematical algorithm into a practical application because the mere performance of the algorithm does not improve the healthcare outcome of the patient to whom the obtained data is associated or improve the average quality of signals in a dataset.
Step 2B: The claim recites the following additional element:
A trained neural network
However, additional element (a) is merely a generic computer component and/or software element configured to perform generic computer operations on signals.
Furthermore, claims 18-19 are also rejected by virtue of their dependence from claim 17 because they merely expand upon the type of neural network and do not recite any further additional elements that integrate the recited mathematical algorithm into a practical application or amount to significantly more.
Claim 20 (an apparatus claim)
Step 2A-I: The claim recites an abstract idea as it inherits the limitations of claim 15 and further recites the abstract idea of a mathematical algorithm comprising the following step:
Training a convolutional autoencoder from a set of acoustic signals obtained from a variety of patients
A combination of abstract ideas is an abstract idea (See MPEP 2106.05(I) – "Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract").
While the courts generally recognize that a specific method of training a neural network does not recite an abstract idea (see MPEP § 2106.04(a)(1)(vii)), the claim language as written merely recites the generic mathematical concept of training a convolutional autoencoder (the neural network) from a set of high-quality acoustic signals (the dataset) without reciting the specific steps taken in the training procedure.
Step 2A-II: The claim language does not integrate the recited mathematical algorithm into a practical application because the mere performance of the algorithm does not improve the healthcare outcome of the patient to whom the obtained data is associated or improve the average quality of signals in a dataset.
Step 2B: The claim does not recite any additional elements that amount to significantly more.
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 factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 8-9, and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Nematihosseinabadi et al. (US 20210027893 A1, hereinafter Nematihosseinabadi) in view of Bryan (US 20210125629 A1).
Claims 1, 8, and 15
Nematihosseinabadi teaches a computer-implemented method ([0006] — “In one or more embodiments, a computer program product includes a computer readable storage medium having instructions stored thereon.”), a corresponding hardware processor ([0006] — “The instructions are executable by a processor to initiate operations.”), and a corresponding non-transitory computer readable medium comprising instructions that when executed by the hardware processor perform said method ([0006] — “In one or more embodiments, a computer program product includes a computer readable storage medium having instructions stored thereon. The instructions are executable by a processor to initiate operations.”; [0111] — “‘Computer readable storage medium,’ as defined herein, is not a transitory, propagating signal per se.”), the computer implemented method comprising: obtaining, using a digital stethoscope, acoustic signals representative of pulmonary sounds from a patient ([0005] — “In one or more embodiments, a system includes a processor configured to initiate operations. The operations include detecting one or more cough events from a time series of audio signals generated by an electronic device of a user.”; [0040] — “System 100 can include a memory 102, one or more processors 104 (e.g., image processors, digital signal processors, data processors), and interface circuitry 106.”; an electronic device which obtains acoustic signals representative of pulmonary sounds (cough events) for use by digital signal processors constitutes a digital stethoscope), wherein the pulmonary sounds comprise breathing patterns of the patient ([0028] — “The PFT determiner can provide monitoring of the user's lung functioning and breathing patterns over time.”); determining a plurality of derived signals from the acoustic signals (Fig. 7, #706; [0027] — “The features extracted by the system can include, for example, a measured cough force and/or duration of the cough. The features can include mel-frequency cepstral coefficients (MFCCs) and statistical measures (e.g., mean, standard deviation, skewness, kurtosis) of audio signals modeled as random processes.”); performing a regression analysis on the plurality of derived signals (Fig. 7, #712; [0031] — “The regression model provides a regression equation whose parameters are determined by regressing the values of various cough features extracted from audio signals…”); and determining the AQM from the regression analysis (Fig. 7, #716; [0087] — “Consistency determiner 716 of PFT determiner 700 determines whether the quality of the passively sensed cough is sufficient to provide a reliable PFT prediction.”); and identifying low-quality acoustic sound recordings of the pulmonary sounds of the patient ([0087] — “In the event that a PFT value is inconsistent with one or more values stored in PFT bank 718, or if no PFT value can be determined after sensing because of the poor quality of the sensing, PFT consistency determiner 716 responds by issuing an active measurement signal to the user.”).
Nematihosseinabadi fails to teach outputting the AQM to identify low-quality acoustic sound recordings of the pulmonary sounds of the patient.
Bryan teaches outputting the AQM of an acoustic signal ([0065] — “The acoustic improvement system can again analyze the digital audio recording, determine acoustic quality metrics, and update the interactive interface 202 to display the updated acoustic quality metric scores.”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to include outputting the AQM as taught by Bryan in identifying low-quality acoustic sound recordings of the pulmonary sounds of the patient as taught by Nematihosseinabadi to improve user experience by enabling the user to track progression and/or regression in signal quality between measurement data (Bryan, [0067] — “In one or more embodiments, a user can compare the overall acoustic quality scores as benchmark measurements to track progression and/or improvement (or regression) between audio tests.”).
Claims 2, 9, and 16
Nematihosseinabadi further discloses wherein the plurality of derived signals comprise a spectral energy signal ([0067] — “The features include, for example, MFCCs, total signal energy…”), a spectral shape signal, a temporal dynamics signal, a fundamental frequency signal, a mean error signal, a reconstruction error signal, a bandwidth signal, a spectral flatness signal, a spectral irregularity signal, a high modulation rate energy signal, or a low modulation rate energy signal.
Claims 3, 10, and 17 are rejected under 35 USC 103 as being unpatentable over Nematihosseinabadi in view of Bryan and in further view of Servajean et al. (US 20200106795 A1, hereinafter Servajean).
Claims 3, 10, and 17
Nematihosseinabadi and Bryan fail to teach wherein the plurality of derived signals comprise a mean error signal and a reconstruction error signal that are obtained from a trained neural network.
Servajean teaches obtaining a mean error signal and a reconstruction error signal from a trained neural network ([0025] — “Thus, the anomaly detector 224 determines reconstruction error(s) for the production time series…”; [0027] — “Accordingly, embodiments of the present disclosure employ a statistical model of reconstruction errors generated by the autoencoders. For example, a Gaussian probability distribution of reconstruction errors can be applied…”; a Gaussian probability distribution inherently has a mean, corresponding to the mean reconstruction error in this disclosure). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to obtain a mean error signal and reconstruction error signal from a trained neural network as derived signals as taught by Servajean in combination with the teachings of Nematihosseinabadi and Bryan in order to better understand the learned features of noisy signals in determining the AQM.
Claims 4-5, 11-12, and 18-19 are rejected under 35 USC 103 as being unpatentable over Nematihosseinabadi in view of Bryan and Servajean and in further view of Gfeller et al. (US 20210056980 A1, hereinafter Gfeller).
Claims 4, 11, and 18
Nematihosseinabadi fails to disclose wherein the trained neural network is a trained convolutional autoencoder.
Gfeller teaches a trained convolution autoencoder for audio signal processing ([0056] — “As shown, the encoder network 400 can include one or more convolutional layers 410. For example, as shown, five convolutional layers 410A-E are depicted. The encoder network 400 can be configured to analyze sampled slices of an audio signal.”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to use a convolutional autoencoder as taught by Gfeller in combination with the teachings of Nematihosseinabadi, Bryan, and Servajean in order to improve model robustness to local signal variations.
Claims 5, 12, and 19
Nematihosseinabadi fails to disclose wherein the trained convolutional autoencoder is a three-layer autoencoder, four-layer autoencoder, or a five-layer autoencoder.
Gfeller further teaches wherein the trained convolutional autoencoder is a three-layer autoencoder, four-layer autoencoder, or a five-layer autoencoder ([0056] — “As shown, the encoder network 400 can include one or more convolutional layers 410. For example, as shown, five convolutional layers 410A-E are depicted. The encoder network 400 can be configured to analyze sampled slices of an audio signal.”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to improve model accuracy and feature learning by including additional layers.
Claims 6, 13, and 20 are rejected under 35 USC 103 as being unpatentable over Nematihosseinabadi in view of Bryan and Gfeller.
Claims 6, 13, and 20
Nematihosseinabadi discloses training a machine learning model using a set of high-quality acoustic signals obtained from a variety of patients ([0071] — “The regression model implemented by regressor 212 can be trained with data collected from multiple subjects whose coughs provide a statistical sample determining correlations between features of the cough and PFT values typically obtained using pulmonary function testing…”; the training data would need to be of sufficiently high-quality in order to determine correlations between features of the cough and PFT values).
Nematihosseinabadi fails to teach training a convolutional autoencoder from a set of high-quality acoustic signals obtained from a variety of patients.
Gfeller discloses a trained convolution autoencoder for audio signal processing ([0056] — “As shown, the encoder network 400 can include one or more convolutional layers 410. For example, as shown, five convolutional layers 410A-E are depicted. The encoder network 400 can be configured to analyze sampled slices of an audio signal.”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to train a convolutional autoencoder as taught by Gfeller using the set of high-quality acoustic signals obtained from a variety of patients taught by Nematihosseinabadi in order to better identify the unsupervised/learned features of acoustic signals.
Claims 7, 14, and 21 are rejected under 35 USC 103 as being unpatentable over Nematihosseinabadi in view of Bryan and in further view of Mortazavian et al. (US 20200134148 A1, hereinafter Mortazavian).
Claims 7, 14, and 21
Nematihosseinabadi fails to teach the AQM ranging from 0 to 1.
Mortazavian teaches an AQM that ranges from 0 to 1 ([0071] — “The decision making module 321 also receives quality scores comprising the audio quality assessment score Qa and the video quality assessment score Qv. The assessment score is also a value in the range of 0 to 1, with 0 corresponding to a bad quality and 1 corresponding to a good quality.”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to implement the AQM range as taught by Mortazavian with the teachings of Nematihosseinabadi and Bryan in order to normalize the quality score given to an acoustic signal.
Prior Art
The prior art further made of record and not relied upon is considered pertinent to the applicant’s disclosure:
West et al. (US 20140126732 A1), Acoustic Monitoring System and Methods
Emmanouilidou et al. (US 20160015359 A1), Lung Sound Denoising Stethoscope, Algorithm, and Related Methods
Vatanparvar et al. (US 20210244313 A1), System and Method for Conducting On-Device Spirometry Test
The examiner used the above prior art to better contextualize the claimed invention within the current state of the art.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN JAMES STEAR whose telephone number is (571)272-8334. The examiner can normally be reached 7:30-5:30 EST/EDT.
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, Arleen Vazquez can be reached at (571) 272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RYAN JAMES STEAR/Examiner, Art Unit 2857
/ARLEEN M VAZQUEZ/Supervisory Patent Examiner, Art Unit 2857
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