Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
2. In view of Applicant’s argument [Remarks] and amendments filed 06/03/2026, claim objections with regards to claims 2-9, 11-18 and 20-26 are withdrawn
Claim Rejections - 35 USC § 112
3. In view of Applicant’s argument [Remarks] and amendments filed 06/03/2026, claim rejections with respect to 35 USC 112(b) have been fully considered and the rejection of claims 4-9, 13-18 and 22-26 under 35 U.S.C. 112(b) is withdrawn.
Response to Arguments
4. Applicant’s arguments have been fully considered by are not persuasive.
Regarding Applicant’s arguments directed to the prior § 102 rejection of claims 1, 10 and 19 over Ankishan, the arguments are moot to the extent the rejection has been modified in view of the claim amendments in particular, Applicant amended the independent claims to further recite that the voice sample comprises a predetermined phrase or randomly selected sentence displayed on a user device and that the hypertension indicator is one of a plurality of categories. The present rejection addresses those amended limitations under § 103.
Applicant argues that Ankishan’s sustained /a/ vowel recording is not a predetermined phrase or a randomly selected sentence. This argument does not overcome the present rejection because Larson is relied upon for the displayed phrase/sentence limitation. Larson explicitly teaches recording a user’s voice while the user reads a phrase displayed on a screen. Therefore, regardless of whether Ankishan’s /a/ vowel alone meets the amended phrase, the combination of Ankishan an Larson teaches the amended voice sample collection feature. Applicant’s argument is also not persuasive because it attacks Ankishan individually rather than the combination as a whole. Ankishan is relied upon for voice based blood pressure prediction using extracted acoustic features, while Larson is relied upon for the known user interface technique of displaying prompted speech content to a user and recording the resulting voice sample. A reference need not teach every limitations individually where the rejection is based on a combination of references.
Applicant further argues that Ankishan predicts systolic blood pressure/diastolic (SBP/DBP) values rather than a categorical hypertension indicator. This argument is unpersuasive. Ankishan teaches predicting blood pressure values from speech recording and also teaches subdividing blood pressure (BP) values into BP hypertension categories (emphasis added) using Table 2, including Normal, Pre-Hypertension, High VP (Hypertension Stage 1) and High BP (Hypertension Stage 2).
Applicant’s assertions that the rejection improperly collapses “predicting a blood pressure measurement” and “determining a hypertension-status category” is not persuasive. The claimed hypertension indicator is based on blood-pressure status, and Ankishan and NICE expressly defines hypertensions status by SBP/DBP threshold values. The claimed hypertension indicator is based on blood-pressure and Ankishan explicitly teaches subdividing BP values into multiple BP and hypertension categories on SBP/SPB ranges. Categorizing a predicting SBP/DBP value according to Ankishan’s disclosed BP-category framework is predictable use of prior art elements according to their established functions.
Applicant also argues that NICE cannot “convert” Ankishan’s regression output into a categorical hypertension indicator. This argument mischaracterizes the rejection. NICE is not relied upon to perform voice analysis or to replace Ankishan’s model. NICE is not needed to teach the broad plurality of categories into multiple BP/hypertension categories. To the extent dependent claims recite specific threshold values, NICE is relied upon for those recognized clinical thresholds. Ankishan provides the voice derived SBP/DBP prediction and the BP category framework, while NICE further supports the specifically claimed threshold values where recited.
Applicant’s teaching away argument is not persuasive. NICE’s use of clinic, ABPM or HBPM measurements does not criticize, discredit or discourage Ankishan’s voice based BP prediction. At most, NICE describes conventional clinical measurement contexts. A difference in measurement source is not a teaching away from applying known hypertension thresholds to blood-pressure values predicted by Ankishan.
Applicant further contends that there is no teaching or suggestion to combine Ankishan and NICE other than hindsight. This is not persuasive. Ankishan is expressly concerned with predicting blood pressure and hypertension related disease from speech recordings. NICE provides known diagnostic threshold for determining hypertension status from blood-pressure values. A person of ordinary skill would have reason to apply known hypertension thresholds to Ankishan’s predicted BP values to provide clinically interpretable hypertension categories.
Regarding the dependent feature set arguments, Applicant’s arguments are not commensurate with the claim language to the extent the claims recite features selected from alternative groups or lists. The Examiner does not need to show that Ankishan teaches every example from Applicant’s specification unless the claim actually requires every example. Ankishan teaches extracting acoustic speech features including MFCCs, entropy-based features, maximum/minimum segment values, gradients and other features vector values from speech recordings. These teachings correspond to the claimed frequency related, non-linear, low-level descriptor and time-related acoustic feature categories. Applicants attempt to require the entire exemplary feature menu from the specification improperly imports limitations into the claims.
Regarding Chen, Applicant argues Chen does not remedy the deficiencies of Ankishan and NICE. This argument is unpersuasive because Chen is not relied upon to teach the voice sample collection, hypertension threshold or SBP/DBP prediction features. Chen is relied upon to teach the claimed classifier and LDA/discriminant analysis limitation. Applicant’s argument attacks the references individually rather than addressing what the combined teachings would have suggested to a person of ordinary skill in the art. Applicant’s characterization of Chen as being directed to a discriminant forest classification system does not distinguish the claims. That is precisely why Chen is relevant to the classifier limitations. Chen teaches using discriminant analysis, including LDA, within a classification framework to predict class membership. The claims do not require that the LDA classifier originate in a hypertension specific reference.
Regarding Larson, Applicant argues that Larson is directed to identity verification and therefore is not analogous art. This argument is unpersuasive. Larson is reasonably pertinent to the problem of collecting standardized voice samples from a user through a user device. The amended claims recite displaying a predetermined phrase or randomly selected sentence on a user device and recording the subject’s vocalization. Larson teaches that exact type of prompted voice recording. The fact that Larson uses the recording voice in an identity verification context does not negate its relevance to the claimed voice sample collection technique. Applicant’s field of endeavor argument is too narrow. The relevant limitation supplied by Larson is not hypertension diagnosis, it is prompted voice capture using a user device. Ankishan already teaches collected standardized voice recordings from a user for acoustic analysis. Larson provides a known way to collect such standardized voice samples by displaying speech content to the user and recording the user’s voice while reading it. Therefore Larson is reasonably pertinent to the problem addressed by the amended limitation.
Applicant further argues that the rationale for using Larson is generic or results oriented. This is not persuasive. The rationale is specific, displaying a predetermined phrase or sentence standardizes the spoken content during voice sample collection, thereby reducing variability in the recorded speech content and allowing more consistent extraction of acoustic features. Ankishan itself demonstrates the desirability of standardizing speech content by selecting a particular vocalization, namely the /a/ vowel, for recording. Larson teaches an alternative standardized prompted speech collection technique using displayed phrases. Substituting Larson’s displayed prompted phrase for Ankishan’s selected vocalization would have been a predictable variation for collecting voice samples through a user device.
Applicant’s argument that Larson does not relate to hypertension screening is not persuasive. A secondary reference does not need to be directed to the same ultimate medical diagnosis when it is relied upon for a known sub-feature of the claimed system. Larson is used for the prompted voice recording interface, not for the hypertension model itself. The combination uses Ankishan for speech based blood pressure prediction, NICE for hypertension category thresholds and Larson for displayed prompted voice collection.
Accordingly, Applicant’s argument do no overcome the rejections. The amended claims are unpatentable because the combination of Ankishan, NICE and Larson teaches or suggests the amended independent claim limitations and the additional limitations of the dependent claims are taught or suggested by the cited references as set forth above.
Claim Rejections - 35 USC § 103
5. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
6. Claims 1, 9-10 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Ankishan et al. “Blood pressure prediction from speech recordings”, herein Ankishan, in view of Larson et al. (US 2020/0366671), herein Larson.
Regarding Claim 1:
Ankishan discloses a computer-implemented method for providing a hypertension indicator for a subject, the method comprising:
- providing, at a memory, a hypertension indicator prediction model (Ankishan: Introduction p1 discloses using trained regression/prediction models to predict blood pressure (BP) from extracted speech features);
- receiving, at a processor in communication with the memory, a voice sample from the subject, the voice sample comprises one selected from the group consisting of (Ankishan: Abstract, Section 3 p4, uses a database consisting of recorded samples for different blood pressure (BP) values):
a predetermined phrase vocalized by the subject (Ankishan: Section 2.1 discloses a mobile application that records data from a one way microphone at a 16 bit resolution and 44,100Hz sampling rate, the application can record /a/ vowel for 10 ms, also stating “the reason /a/ vowel selected is that /a/ is generally chosen”, this is a predetermined utterance)
- extracting, at the processor, at least one voice biomarker feature value from the voice sample for at least one predetermined voice biomarker feature (Ankishan: Section 2 p1 discloses a mobile application for recording microphone data and recording a vowel for 10 seconds);
- determining, at the processor, the hypertension indicator for the subject based on the at least one voice biomarker feature value and the hypertension indicator prediction model (Ankishan: Abstract, Section 3.2 p1 discloses using Convolutional neural networks (CNN-R), support vector machines (SVM-R) and multiple linear regression (MLR) to predict blood pressure from extracted features, predicted blood pressure is a hypertension related indicator (at minimum it is the underlying measure used to define hypertensive status); and
- outputting, at an output device, the hypertension indicator for the subject or an output based on the hypertension indicator, wherein the hypertension indicator for the subject is one of a plurality of categories (Ankishan: Section 3.2 Tables 6-7 and Section 4 disclose outputting prediction results and development of mobile platforms that predicts BP values from speech and sharing the results with the user).
Ankishan does not explicitly disclose:
a randomly selected sentence vocalized by the subject, the predetermined phrase or the randomly selected sentence displayed to the subject on a user device;
However, Larson discloses a randomly selected sentence vocalized by the subject, the predetermined phrase or the randomly selected sentence displayed to the subject on a user device (Larson: ¶[0236] discloses the user reads a phrase three times in a row into a microphone of their mobile device displayed on the screen).
Ankishan and Larson are combinable because they are from applicable fields of endeavor. Ankishan discloses techniques for accurately collecting voice data and determining traits/qualities about the user’s health. Larson discloses collecting user vocal data accurately to verify identity of a user. Ankishan does not disclose the predetermined phrase or the randomly selected sentence is displayed to the subject on the user device. Larson does disclose this limitation. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to disclose the predetermined phrase or the randomly selected sentence is displayed to the subject on the user device. The suggestion/motivation for doing so is discloses in ¶[0038] of Larson: “the secure portal may provide access to a dashboard GUI that allows users to see the depth and quality of their identity information, update and improve the quality of the collected identity information and collected biometrics, and provide new biographic, identity, and/or biometric data.” In other words, the secure and user friendly graphical user interface allows for collecting biometric or biographic data (such as voice print or voice state) accurately and efficiently, something that applies directly to Ankishan’s methods.
Regarding Claim 9:
The combination of Ankishan and Larson further discloses the method of claim 1, wherein the voice sample is received from an audio sensor at the user device (Ankishan: Section 2.1 discloses a mobile application that records data from a one way microphone).
Regarding Claim 10: Claim 10 has been analyzed with regard to claim 1 (see rejection above) and is rejected for the same reasons of obviousness used above.
It is noted Ankishan discloses a system at least at Section 3 and Fig. 1 disclosing raw audio data and machine learning models stored in memory for processing raw audio signals.
Regarding Claim 19:
Claim 19 has been analyzed with regard to claim 1 (see rejection above) and is rejected for the same reasons of obviousness used above.
It is noted Ankishan discloses a device running software at least at Section 3 and Fig. 1 disclosing raw audio data and machine learning models stored in memory for processing raw audio signals; it also discloses a mobile device capable of this process.
7. Claims 2-5, 11-14 and 11-23 are rejected under 35 U.S.C. 103 as being unpatentable over Ankishan in view of Larson and further in view of National Institute for Health and Care “Hypertension in adults: diagnosis and management”, herein NICE.
Regarding Claim 2:
The proposed combination of Ankishan and Larson further discloses method of claim 1, wherein the hypertension indicator for the subject is one of a plurality of categories, the plurality of categories comprising comprises:
-
-
- a non-hypertensive category (Ankishan: Table 2 discloses a normal (non hypertensive) category).
Ankishan and Larson do not explicitly disclose:
- a first hypertensive category comprising systolic blood pressure (SBP1 >= 135 OR diastolic blood pressure (DBP1 >=85;
- a second hypertensive category comprising SBP >= 140 OR DBP >=90;
However NICE discloses
- a first hypertensive category comprising systolic blood pressure (SBP1 >= 135 OR diastolic blood pressure (DBP1 >=85 (NICE: page 8 Section 1.2.8 discloses the clinically recognized hypertension thresholds of 135/85 mmHg for daytime average, therefore teaching the specific cutoff values recited for defining hypertensive categories).;
- a second hypertensive category comprising SBP >= 140 OR DBP >=90 (NICE: page 8 Section 1.2.8 discloses the clinically recognized hypertension thresholds of 140/90 mmHg for clinic blood pressure);
Ankishan and Larson in view of NICE are combinable because they both disclose pertinent information related to each other’s disclosures. Ankishan discloses using training various models on voice samples in order to categorize the likely blood pressure level in between low, normal, pre-hypertension and hypertension likely categories based on specific relation between voice samples and systolic mmHg. NICE discloses general guidelines for blood pressure and hypertension. Although Ankishan discloses categorizing voice samples into categories of blood pressure, it does not mention these categories being SBP >= 135 OR DBP >=85;- a second hypertensive category comprising SBP >= 140 OR DBP >=90. NICE discloses these categories. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the categories to be within a specific range as disclosed in NICE because NICE teaches clinically recognized diagnostic threshold values for hypertension, including a clinic blood pressure of 140/90 mmHg or higher and an ABPM daytime average or HBPM average of 135/85 or higher mmHg or higher to confirm a diagnosis of hypertension. The suggestion/motivation for doing so is: ”The updated guideline makes new recommendations in these areas, based on the evidence, that aim to improve care and reduce variation in current practice” as disclosed on page 48 of NICE.
Regarding Claim 3:
The combination of Ankishan, Larson and NICE further discloses the method of claim 2, wherein: the at least one predetermined voice biomarker feature is selected from the group consisting of temporal feature type, a spectral feature type, and a spectrotemporal feature type. (Ankishan: discloses a non-linear feature by computing entropy based measures from the derived feature/difference vectors (i.e., entropy of the extracted vectors/coefficients). Ankishan Section 1 page 2 and Section 2.2.2 page 3 further discloses a low-level descriptor , the maximum minimum vectors of signal, each segment has maximum and minimum values, these are low level descriptors, Ankishan Section 2.2.2 page 3 short range time values denoting variability of signals over time. Ankishan Section 1 page 2 explicitly computes Mel-frequency cepstral coefficients (MFCCs) are classic low level descriptors and they’re derived from the frequency spectrum (so they’re also frequency related)).
Regarding Claim 4:
The combination of Ankishan, Larson and NICE further discloses the method of claim 3, wherein:
the temporal feature type comprises at least one selected from the group consisting of: mean of the voice sample, variance of the voice sample, skewness of the voice sample, kurtosis of the voice sample, range of the voice sample, standard deviation of zero-crossing rate over time of the voice sample, Shannon entropy of the voice sample and logarithm entropy of the voice sample (Ankishan: Introduction page 2 discloses calculating entropy based measures from the voice sample derived vectors which teaches the claimed temporal feature type (Shannon/logarithm entropy));
the spectral feature type comprises at least one selected from the group of: mean and standard deviation of spectral contrast, centroid, bandwidth, flatness, and roll-off over time, fundamental frequency (FO), low-level descriptors of tempogram, bandpowers, mean, root-mean- square, skewness, range, entropies, cumulative different sum (CDS) and mean and standard deviation of the 3rd-level polynomial fitting to the curve of power spectral density (PSD) (Ankishan: Introduction page 2 discloses extracting entropy based features from voice derived feature vectors which teaches the claimed spectral feature type “entropies”)
and the spectrotemporal feature type comprises at least one selected from the group of: mean and standard deviation of a Mel-scale frequency cepstral coefficient (MFCC), a first (AMFCC) and second derivative (AAMFCC) of the MFCC, and mean and standard deviation of a Chromagram coefficient . (Ankishan Section 1 page 2 explicitly computes Mel-frequency cepstral coefficients (MFCCs) are classic low level descriptors and they’re derived from the frequency spectrum (so they’re also frequency related)).
Regarding Claim 5:
The combination of Ankishan, Larson and NICE further discloses the method of claim 4, wherein the hypertension indicator prediction model comprises a statistical classifier the statistical classifier comprises at least one selected from the group consisting of: a support vector classifier (SVC), a K-nearest neighbour (KNN) classifier, a logistic regression (LR) classifier, a linear discriminant analysis (LDA) classifier, a quadratic discriminant analysis (QDA) classifier, a Gaussian naive Bayesian (GNB) classifier, a Bernoulli naive Bayesian (BNB) classifier, a Ridge model, an extra gradient boosting (XGBoost) classifier, a decision tree (DT) classifier, and a random forest (RF) classifier. (Ankishan: Abstract, Section 3.2 p1 discloses predicting blood pressure from extracted speech features using machine-learning/statistical models, including CNN-R, SVM-R and MLR. SVM and MLR are statistical classifiers/regressors).
Regarding Claim 11:
Claim 11 has been analyzed with regard to claim 2 (see rejection above) and is rejected for the same reasons of obviousness as used above.
Regarding Claim 12:
Claim 12 has been analyzed with regard to claims 3 (see rejection above) and is rejected for the same reasons of obviousness as used above.
Regarding Claim 13:
Claim 13 has been analyzed with regard to claim 4 (see rejection above) and is rejected for the same reasons of obviousness as used above.
Regarding Claim 14:
The combination of Ankishan, Larson and NICE further discloses the system of claim 13, wherein the hypertension indicator prediction model comprises a statistical classifier, the statistical classifier comprises at least one selected from the group of: a support vector classifier (SVC), a K-nearest neighbor (KNN) classifier, a logistic regression (LR) classifier, a linear discriminant analysis (LDA) classifier, quadratic discriminant analysis (QDA)classifier, a Gaussian naive Bayesian (GNB) classifier, Bernoulli naive Bayesian (BNB) classifier, a Ridge model, an extra gradient boosting (XGBoost) classifier, a decision tree (DT) classifier, and a random forest (RF) classifier (Ankishan: Section 2.2.3.2 discloses a support vector machine may be used for the regression model).
Regarding Claim 18:
Claim 18 has been analyzed with regard to claim 9 (see rejection above) and is rejected for the same reasons of obviousness as used above
Regarding Claim 20:
Claim 20 has been analyzed with regard to claim 2 (see rejection above) and is rejected for the same reasons of obviousness as used above.
Regarding Claim 21:
Claim 21 has been analyzed with regard to claim 3 (see rejection above) and is rejected for the same reasons of obviousness as used above.
Regarding Claim 22:
Claim 22 has been analyzed with regard to claim 4 (see rejection above) and is rejected for the same reasons of obviousness as used above.
Regarding Claim 23:
Claim 23 has been analyzed with regard to claims 14 (see rejection above) and is rejected for the same reasons of obviousness as used above.
8. Claims 6-7, 15-16 and 24-25 are rejected under 35 U.S.C. 103 as being unpatentable over Ankishan in view of Larson, further in view of NICE and further in view of Chen (US 2009/0281981).
Regarding Claim 6:
The combination of Ankishan, Larson and NICE further discloses the method of claim 5, except wherein:
the hypertension indicator prediction model is an ensemble model, the ensemble model comprising n LDA classifiers; and
wherein the determining, at the processor, the hypertension indicator comprises:
determining a prediction from each of the n LDA classifiers in the ensemble model; and
determining the hypertension indicator based on an election of the predictions from the n LDA classifiers in the ensemble model.
However, Chen discloses:
the hypertension indicator prediction model is an ensemble model, the ensemble model comprising n LDA classifiers (Chen: ¶[0019] discloses a set of n LDA-based decision trees is produced for use in predicting the classification of a new sample of unknown class); and
wherein the determining, at the processor, the hypertension indicator comprises:
determining a prediction from each of the n LDA classifiers in the ensemble model (Chen: ¶[0079] discloses class predictions are performed by propagating a new sample through each tree and assigning a class label/vote); and
determining the hypertension indicator based on an election of the predictions from the n LDA classifiers in the ensemble model (Chen: ¶[0011], ¶[0017] discloses computing the relative frequency of class assignments for x in the n decision trees and determining a final class prediction based on the relative frequency of class assignments).
The combination of Ankishan and NICE in view of Chen are combinable because they are from pertinent fields of endeavor, i.e., both disclose methods for training machine learning models to classify vocal input. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Ankishan to use an ensemble of n LDA based classifiers and determine a final hypertension category by election/majority vote, as taught by Chen, because Chen explicitly teaches that in a forest, “to provide enhanced classification capability” (Chen ¶[0019]), and further teaches that a classification addressing known classifier limitation “is highly desirable for general, practical use” (Chen ¶[0006]). Applying Chen’s ensemble voting technique to Ankishan’s voice-feature hypertension classification would have been a predictable substitution to obtain the Chen-stated enhancement.
Regarding Claim 7:
The combination of Ankishan, Larson, NICE and Chen further discloses the method of claim 6, further comprising:
transmitting, to a user device in network communication with the processor, the hypertension indicator for the subject, wherein the outputting of the hypertension indicator for the subject occurs at the user device (Ankishan: Section 2 p1 and Section 4 discloses a mobile application for recording microphone data and outputting prediction results of BP values from speech and sharing the results with the user noting the possible level of hypertension from the results); and
determining the hypertension indicator for the subject based on at least one clinicopathological value for the subject, wherein the at least one clinicopathological value is at least one of height, weight, BMI, diabetes status and blood pressure (Ankishan: Table 2 discloses at least one clinicopathological value for a subject (blood pressure)).
Regarding Claim 15:
Claim 15 has been analyzed with regard to claim 6 (see rejection above) and is rejected for the same reasons of obviousness as used above.
Regarding Claim 16:
Claim 16 has been analyzed with regard to claim 7 (see rejection above) and is rejected for the same reasons of obviousness as used above.
Regarding Claim 24:
Claim 24 has been analyzed with regard to claim 6 (see rejection above) and is rejected for the same reasons of obviousness as used above.
Regarding Claim 25:
Claim 25 has been analyzed with regard to claim 7 (see rejection above) and is rejected for the same reasons of obviousness as used above.
Conclusion
9. 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 IAN SCOTT MCLEAN whose telephone number is (703)756-4599. The examiner can normally be reached "Monday - Friday 8:00-5:00 EST, off Every 2nd Friday".
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/IAN SCOTT MCLEAN/Examiner, Art Unit 2654
/HAI PHAN/Supervisory Patent Examiner, Art Unit 2654
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