Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This office action is in response the amendment filed on 06/25/2026.
Response to Arguments
Applicant's arguments filed 06/25/2026 have been fully considered but they are not persuasive. The applicant argues that the amendment of claim 1 to include the subject matter of previous claim 10 overcomes the double patenting rejection. The examiner respectfully disagrees. The secondary reference of Fleming et al. (US 2019/0006040) discloses “at least one sensor for collecting a voice sample recorded proximate to the device; and a processor in communication with the memory and the at least one sensor, the processor configured to: receive the voice sample from the at least one sensor” (Detection and/or prediction of a patient's diabetic risk level is based on patient history and analysis (e.g., using deep learning and visual analysis) of the patient's speech, facial expression, heart rate, etc. captured via one or more sensors on a user device. The device may be a stand-alone sensor, or may be a device comprising a sensor, such as a wearable device, mobile, tablet, camera, etc.) (page 1, paragraph [0008]) as recited in amended claim 1.
Additionally, the applicant argues that the amendment of claim 1 to include the subject matter of previous claim 10 overcomes the 35 USC 101 rejections for claim 1. The examiner respectfully disagrees. The additional limitation of a sensor to collect a voice sample is directed to insignificant presolution activity to collect data.
With regards to the applicant’s arguments that the prior art of Fossat et al. (WO 2022/109713) fails to teach “determine the type-II (T2DM) diabetic status prediction for the subject based on the at least one voice biomarker feature value and the diabetic status prediction model” as recited in amended claim 1. The examiner respectfully disagrees. Fossat et al, pages 81-82, paragraph [478] discloses the biomarker discovery strategy to identify biomarkers associated with blood glucose levels and diabetes development. It is being interpreted by the examiner that based on the biomarkers, type-II (T2DM) can be predicted.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-6 and 14-16 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 13-24 of copending Application No. 18/244,400 in view of Flemming et al. (US 2019/0006040). Although the claims at issue are not identical, they are not patentably distinct from each other because the claims are obvious variations of each other.
This is a provisional nonstatutory double patenting rejection.
Regarding Claim 1 (drawn to device):
Current Application
Claim 1:
A computer-implemented device for predicting a type-II (T2DM) diabetic status for a subject, the device comprising:
- a memory comprising a diabetic status prediction model; and
- a processor in communication with the memory, the processor configured to:
- receive the voice sample from the at least one sensor;
- extract at least one voice biomarker feature value from the voice sample for at least one predetermined voice biomarker feature;
- determine the type-II (T2DM) diabetic status prediction for the subject based on the at least one voice biomarker feature value and the diabetic status prediction model; and
- output, to an output device, the type-II (T2DM) diabetic status prediction for the subject or an output based on the diabetic status prediction.
‘400
Claim 13:
A computer-implemented system for predicting a type-Il (T2DM) diabetic status for a subject, the system comprising:
- a memory comprising a diabetic status prediction model; and
- a processor in communication with the memory, the processor configured to:
- receive a voice sample from the subject;
- extract at least one voice biomarker feature value from the voice sample for at least one predetermined voice biomarker feature;
- determine the type-Il (T2DM) diabetic status prediction for the subject based on the at least one voice biomarker feature value and the diabetic status prediction model; and
- output, to an output device, the type-Il (T2DM) diabetic status prediction for the subject or an output based on the diabetic status prediction.
However, copending Application 18/244,400 fails to teach at least one sensor for collecting a voice sample recorded proximate to the device; and a processor in communication with the at least one sensor.
Flemming et al teaches at least one sensor for collecting a voice sample recorded proximate to the device (Detection and/or prediction of a patient's diabetic risk level is based on patient history and analysis (e.g., using deep learning and visual analysis) of the patient's speech, facial expression, heart rate, etc. captured via one or more sensors on a user device. The device may be a stand-alone sensor, or may be a device comprising a sensor, such as a wearable device, mobile, tablet, camera, etc.) (page 1, paragraph [0008]); and a processor in communication with the at least one sensor (processor-executable instructions and a plurality of accounts each for storing at least historical sensor data of each of the users received by respective ones of the electronic user devices) (page 3, paragraph [0039]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of copending application 18/244,400 with the teachings of Flemming to use a sensor capable of collecting data by using a user’s voice so as to collect the data in a non-invasive or minimally invasive manner.
Claim 2 of the current application corresponds to claim 14 of copending application 18/244,400.
Claim 3 of the current application corresponds to claim 15 of copending application 18/244,400.
Claim 4 of the current application corresponds to claim 16 of copending application 18/244,400.
Claim 5 of the current application corresponds to claim 17 of copending application 18/244,400.
Claim 6 of the current application corresponds to claim 18 of copending application 18/244,400.
Claim 14 of the current application corresponds to claim 20 of copending application 18/244,400.
Claim 15 of the current application corresponds to claim 21 of copending application 18/244,400.
Claim 16 of the current application corresponds to claim 22 of copending application 18/244,400.
Claim 17 of the current application corresponds to claim 23 of copending application 18/244,400.
Claim 18 of the current application corresponds to claim 24 of copending application 18/244,400.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6 and 14-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The independent claim 1 relates to the statutory category of machine/apparatus. The independent claim recites “…receive the voice sample from the at least one sensor; extract at least one voice biomarker feature value from the voice sample for at least one predetermined voice biomarker feature; determine the type-II (T2DM) diabetic status prediction for the subject based on the at least one voice biomarker feature value and the diabetic status prediction model; and output, to an output device, the type-II (T2DM) diabetic status prediction for the subject or an output based on the diabetic status prediction”.
The limitations of claim 1 of “receive…”, “extract…”, “determine…”, and “output…” as drafted covers mental activity. More specifically, for claim 1, a human after receiving a spoken voice sample from a person, determining from the voice sample, a trait/attribute that are stored in a table, and from the identified trait/attribute, predicting if they have type 2 diabetes.
This judicial exception is not integrated into a practical application. In particular, claim 1 recites the additional elements of “memory”. “sensor”, and “processor” which are recited generally in the specification. For example, in paragraphs [0117], [00123], [00132], and [00134] of the as filed specification, there is a description of using a general purpose operating system. Accordingly, these additionally elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using a computer as a general computer is noted. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Further, the additional limitation of the sensor in the claim noted above is directed towards insignificant presolution activity of collecting data. The claim is not patent eligible.
With respect to claims 2 and 3, the claim relates to determining the vocal trait/attribute of the spoken voice sample. The claim relates to a mental activity of determining how the spoken word is different than previously, whether it be in pitch, or if there is a distortion in the phrase being spoken, etc. and assigning a numerical value to the trait. No additional limitations are present.
With respect to claim 4, the claim relates to keeping a log/table of the previous spoken voice samples and averaging the current sample based on the previous samples. The claim relates a mental activity of analyzing the spoken voice sample based on the previous samples. Not additional limitations are present.
With respect to claim 5, the claim relates to determining if a particular phrase is recited in the spoken voice sample. The claims relate to a mental activity of listening for a particular phrase and determining that the phrase was spoken. No additional limitations are present.
With respect to claim 6 and 8, the claim relates to determining if the particular phrase is displayed on your watch or mobile phone. The claims relate to a mental activity of determining if the phrase is displayed somewhere. No additional limitations are present.
With respect to claim 11, the claim relates to collecting the voice sample on a wireless input device. The claim relates to a mental activity of speaking into a wireless microphone. No additional limitations are present.
With respect to claim 12, the claim relates to choosing the sensor from one of being worn by the user on their body, on their clothing or somewhere in close proximity. The claim relates to a mental activity of putting the sensor in close proximity of where the user is speaking. No additional limitations are present.
With respect to claims 14 and 15, the claims relate to predicting if the speaker has diabetes depends on the which category the speaker belongs to. The claims relate to a mental activity of determining if the analysis of the vocal trait/attribute puts someone as having type 2 diabetes or having normal glucose levels. No additional limitations are present.
With respect to claim 16 and 18, the claim relates to determining if the speaker is diabetic based on the vocal trait/attribute, age, or BMI. The claims relate a mental activity of determining if someone has diabetes based on information about them. No additional limitations are present.
With respect to claim 17, the claim relates to the formula to be used to predict whether someone has diabetes by using either logistical regression, naïve bayes or support vector machine. The claim relates a mental activity of using a mathematical algorithm to determine if someone has diabetes. No additional limitations are present.
With respect to claims 19 and 20, the claims relate to prompting the user to speak a particular sentence or utterance. The claims relate to asking someone to speak a particular sentence or utterance. No additional limitations are present.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-6, 8, 9, and 11-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Fossat et al. (WO 2022/109713).
Regarding Claim 1, Fossat et al. discloses a computer-implemented device for predicting a type-II (T2DM) diabetic status for a subject, the device comprising:
a memory (Fit. 4, Memory Unit 410) (page 39, paragraph [245]) comprising a diabetic status prediction model (providing, at a memory, a blood glucose level prediction model) (page 61, paragraph [0396]);
at least one sensor for collecting a voice sample recorded proximate to the device (The user device 400 may be a passive sensor system proximate to the user, for example, a device worn on user, or on the clothing of the use) (page 28, paragraph [238]); and
a processor (Fig. 4, Processor Unit 408) (page 38, paragraph [238]) in communication with the memory (Fig. 4, Memory Unit 410) (page 38, paragraph [238]) and the at least one sensor (The processor unit 408 controls the operation of the mobile device 400) (page 38-39, paragraph [242]), the processor configured to:
receive the voice sample from the at least one sensor (the voice sample may be received from one or more sensor devices proximate to the user in network communication with the user device) (page 60, paragraph [0381]);
extract at least one voice biomarker feature value from the voice sample for at least one predetermined voice biomarker feature (At 806, extracting, at the processor, at least one voice biomarker feature value from the voice sample for at least one predetermined voice biomarker feature) (page 61, paragraph [398]);
determine the type-II (T2DM) diabetic status prediction for the subject (In another embodiment, a blood sugar level of 200 mg/dL (11.1 mmol/L) or higher in the subject is indicative of type 2 diabetes) (page 56, paragraph [345]) based on the at least one voice biomarker feature value and the diabetic status prediction model (At 808, determining, at the processor, the blood glucose level or an output based on the blood glucose level for the subject based on the at least one voice biomarker feature value and the blood glucose level prediction model) (page 62, paragraph [399]); and
output, to an output device, the type-II (T2DM) diabetic status prediction for the subject (In another embodiment, a blood sugar level of 200 mg/dL (11.1 mmol/L) or higher in the subject is indicative of type 2 diabetes) (page 56, paragraph [345]) or an output based on the diabetic status prediction (At 810, outputting, at an output device, the blood glucose level for the subject or the output based on the blood glucose level) (page 62, paragraph [400]).
Regarding Claim 2, Fossat et al discloses the device, wherein each of the at least one voice biomarker feature value is selected from a group consisting of: a statistical feature category (For each iteration, Gini impurity scores were measured from the randomly selected 29 participants in Group A, and scores were normalized to have a same range of values (normalized Gini impurity score, Ginin): where, Gini impurity, indicates Gini impurity score of voice-feature /, m and s indicate mean and standard deviation of Gini impurity scores. Each voice-feature has 1 ,000 Ginin, and finally corrected Gini impurity scores (Ginic): were measured where n indicated the number of Ginin whose absolute value ³ 1.96. Biomarkers are defined when they have Ginic > 0.5. In total, 196 voice-features were defined as voice biomarkers and fed into a predictive model to identify distinct BG groups) (pages 72-73, paragraph 463]),
a shimmer feature category (an amplitude variations (ShimmerLocal) feature) (page 67, paragraph [446]), and
a jitter feature category ((JitterLocal) feature, a difference of JitterLocal (JitterDDP) feature) (page 67, paragraph [446]).
Regarding Claim 3, Fossat et al discloses the device, the statistical feature category comprises a mean pitch feature value, a pitch standard deviation feature value, a mean intensity feature value, an intensity standard deviation feature value and a harmonic-to-noise ratio feature value ((For each iteration, Gini impurity scores were measured from the randomly selected 29 participants in Group A, and scores were normalized to have a same range of values (normalized Gini impurity score, Ginin): where, Gini impurity, indicates Gini impurity score of voice-feature /, m and s indicate mean and standard deviation of Gini impurity scores. Each voice-feature has 1 ,000 Ginin, and finally corrected Gini impurity scores (Ginic): were measured where n indicated the number of Ginin whose absolute value ³ 1.96. Biomarkers are defined when they have Ginic > 0.5. In total, 196 voice-features were defined as voice biomarkers and fed into a predictive model to identify distinct BG groups) (pages 72-73, paragraph 463]));
the shimmer feature category comprises a localShimmer feature value, a localdbShimmer feature value, an apq3Shimmer feature value, an apq5Shimmer feature value , and an apq11 Shimmer feature value (an amplitude variations (ShimmerLocal) feature) (page 67, paragraph [446]); and
the jitter feature category comprises a localJitter feature value, a localabsJitter feature value, a rapJitter feature value and a ppq5Jitter feature value (a difference of period lengths (JitterLocal) feature, a difference of JitterLocal (JitterDDP) feature) (page 67, paragraph [446]).
Regarding Claim 4, Fossat et al discloses the device, wherein the processor is further configured to: preprocess the voice sample by: storing, at a database in communication with the processor, a plurality of historical voice samples of the subject (The voice sample data received by the data store 314 from the one or more user devices 316 may be stored in the database at data store 314, or may be stored in a file system at data store 314) (page 37, paragraph [235]); and averaging the voice sample based on at least one of the plurality of historical voice samples of the subject (averaging the voice feature outputs) (page 63, paragraph [407]).
Regarding Claim 5, Fossat et al discloses the device, wherein the voice sample comprises a predetermined phrase vocalized by the subject (The glucose measurements recorded generally contemporaneously with the utterance or voicing of a sample phrase by the user 324) (page 37, paragraph [232]) .
Regarding Claim 6, Fossat et al discloses the device, wherein the predetermined phrase is displayed to the subject on a display device (In one or more embodiments, the predetermined phrase may be displayed to the subject on a mobile device) (page 64, paragraph [420]).
Regarding Claim 8, Fossat et al discloses the device, wherein the device is a mobile device, a smart speaker, or a smart watch (The user device 400 may be a laptop, gaming system, smart speaker device, mobile phone device, smart watch or others as are known) (page 38, paragraph [238]).
Regarding Claim 9, Fossat et al discloses the device, wherein the device is configured to download an application for determining the type-II (T2DM) diabetic status prediction (In an alternate embodiment, the one or more user devices 116 may download an application (including downloading from an App Store such as the Apple® App Store or the Google® Play Store) for determining BG predictions) (page 32, paragraph [209]).
Regarding Claim 11, Fossat et al discloses the device, wherein the at least one sensor is a wireless audio input device (The sensor device 120 may be a wireless audio input device, such as a wireless microphone. The sensor device 120 may transmit voice samples recorded proximate to the user 124 to the user device 116) (page 33, paragraph [215]).
Regarding Claim 12, Fossat et al discloses the device, wherein the at least one sensor is one selected from the group consisting of: a sensor worn on a body of the subject user, a sensor worn on a clothing of the subject, and a sensor disposed proximate to the subject (The user device 400 may be a passive sensor system proximate to the user, for example, a device worn on user, or on the clothing of the use) (page 28, paragraph [238]).
Regarding Claim 13, Fossat et al discloses the device, wherein the application is an application for tracking health information, a nutrition or diet tracking application, an application dedicated for type-II (T2DM) prediction tracking, or a telehealth application (In one or more embodiments, the programs 422 may include a nutrition application which may determine a diet recommendation for a user based on their blood glucose level or category) (page 41, paragraph [256]).
Regarding Claim 14, Fossat et al discloses the device, wherein the diabetic status prediction comprises a categorical prediction (The BG prediction 634 may be a categorical prediction, i.e. ‘Low’, ‘Medium’, and ‘High’ or ‘hypoglycemic’, ‘normal’ and ‘hyperglycemic’ or a quantitative level i.e. mg/dL or mmol/L) (page 52, paragraph [310]).
Regarding Claim 15, Fossat et al discloses the device, wherein the categorical prediction is one of a type-II (T2DM) diabetic category (In another embodiment, a blood sugar level of 200 mg/dL (11.1 mmol/L) or higher in the subject is indicative of type 2 diabetes) (page 56, paragraph [345]), or a normal category (The BG prediction 634 may be a categorical prediction, i.e. ‘Low’, ‘Medium’, and ‘High’ or ‘hypoglycemic’, ‘normal’ and ‘hyperglycemic’ or a quantitative level i.e. mg/dL or mmol/L) (page 52, paragraph [310]).
Regarding Claim 16, Fossat et al discloses the device, wherein the diabetic status prediction for the subject is based on at least one selected from the group consisting of: vocal parameter data of the subject (In an alternate embodiment, the prediction unit 424 of the mobile device 400 may include a voice glucose prediction model, and may operate the method as described in FIG. 8 to generate a blood glucose prediction for the subject on the mobile device itself. In this alternate unit, the voice sample data may be stored in the voice sample database 428 along with the prediction data) (page 44, paragraph [271]), age data of the subject, and Body Mass Index (BMI) data of the subject (For example, the pre-diabetic screening application may incorporate at least one screening question that provide information related to risk factors for pre diabetes or diabetes such as body mass index (BMI), weight, blood pressure, disease comorbidity, family history, age, race or ethnicity and physical activity) (pages 42-43, paragraph [263]).
Regarding Claim 17, Fossat et al discloses the device, wherein the diabetic status prediction model comprises at least one selected from the group consisting of a Logistic Regression (LR) model, a Naive Bayes (NB) model, and a Support Vector Machine (SVM) model (In one or more embodiments, the statistical classifier may comprise at least one selected from the group of a perceptron, a naive Bayes classifier, a decision tree, logistic regression, «-Nearest Neighbor, an artificial neural network, machine learning, deep learning and support vector machine) (page 34, paragraph [413]).
Regarding Claim 18, Fossat et al discloses the device, wherein the diabetic status prediction model comprises an ensemble model (In one or more embodiments, the blood glucose level prediction model may comprise an ensemble model, the ensemble model comprising n random forest classifiers; and wherein the determining, at the processor, the blood glucose level may comprise: determining a prediction from each of the n random forest classifiers in the ensemble model; and determining the blood glucose level based on an election of the predictions from the n random forest classifiers in the ensemble model) (page 64, paragraph [415]), the ensemble model comprising averaging all the prediction probabilities for an individual (To understand how each voice biomarker contributed to the prediction of a test set, Local Interpretable Model-agnostic Explanations (LIME) analysis was performed (Ribiero et al., 2016). Lime provides three types of weights per voice biomarker. Each weight represented the contribution to predict high, normal and low BG groups in a given sample. To evaluate the importance of voice biomarkers in a high BG group, only high BG weights were compiled from voice samples predicted as a high BG group, and ranked voice biomarkers based on their average weight. Importance for normal and low BG groups also followed the same procedure. LIME package (v.0.1) in Python was used for analyses) (pages 74-75, paragraph [465]), averaging a voice prediction result with a T2DM prevalence at a participant age, averaging the voice prediction result with the T2DM prevalence at a participant BMI, and/or a combination thereof.
Regarding Claim 19, Fossat et al discloses the device, wherein the processor is further configured to: generate a user interface including a prompt for prompting the subject to speak a particular prompt (The software application running on the one or more user devices 316 may prompt the user to speak a particular prompt, and record a voice sample) (page 37, paragraph [f235]); and receive the voice sample of the subject (The software application running on the one or more user devices 316 may prompt the user to speak a particular prompt, and record a voice sample) (page 37, paragraph [f235]).
Regarding Claim 20, Fossat et al discloses the device, wherein the prompt comprises: a fixed sentence, a fixed utterance, a varied sentence, and a varied utterance (The prompt may be a fixed sentence or utterance, or it may be a varied sentence or utterance) (page 37, paragraph [235]).
Cited Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Flemming et al. (US 2019/0005201) discloses determining, customizing, and communicating dietary recommendations, particularly for patients suffering from Type 2 diabetes.
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.
/SATWANT K SINGH/Primary Examiner, Art Unit 2653
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