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 .
Amendment Entered
This Office action is responsive to the Amendment filed on December 22nd, 2025. The examiner acknowledges the amendments to claims 1, 41-42, 50, and 52. Claims 1, and 35-53 remain pending in the application.
Response to Arguments
Applicant’s arguments and amendments, filed December 22nd, 2025, with respect to the claim objections have been fully considered. The claim objections are withdrawn.
Applicant’s arguments and amendments, filed December 22nd, 2025, with respect to the rejections under 35 U.S.C.112(b) have been fully considered. The rejections under 35 U.S.C.112(b) are withdrawn. However, additional rejections are added.
Applicant’s arguments and amendments, filed December 22nd, 2025, with respect to the rejections of claims 1 and 35-49 under 35 U.S.C. 103 have been fully considered and are persuasive. The rejections under 35 U.S.C.103 have been withdrawn.
Applicant’s arguments and amendments, filed December 22nd, 2025, with respect to the rejections of claims 50-53 under 35 U.S.C. 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant’s arguments and amendments, filed December 22nd, 2025, with respect to the rejections under 35 U.S.C. 101 have been fully considered but are not persuasive.
At page 11, with respect to Step 2A prong One, Applicant argues that the claims are not directed to abstract ideas because it recites a concrete system configuration for acquiring, extracting, generating, and is expressly configured for remote-clinical trial participation via system prompting. Examiner respectfully disagrees. The steps of extracting, analyzing, and generating are directed to abstract ideas. “It is essential that the broadest reasonable interpretation (BRI) of the claim be established prior to examining a claim for eligibility.” MPEP 2106 II. In light of Applicant’s specification, the claim encompasses acoustic data with as few as one or two audio samples. See, for example, [0023-0024]. “The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea.” MPEP 2106.04(a)(2) III. The claimed steps can be performed using one or two audio samples in the human mind or by using a pen and paper. Further, Applicant’s specification clearly explains that the claimed forced vital capacity generated using the predictive algorithm is a mathematical relationship. See, for example, [0042-0043, 0062, 0112]. “A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words ….” A prediction of forced vital capacity based on the analyzing acoustic features/maximum phonation time using a predictive algorithm comprising a trained machine learning regression model and a mixed-effects model is “a relationship between variables or numbers” that is “expressed in words.” Id. Furthermore, the steps of acquiring and prompting are directed to insignificant pre-solution activity e.g., mere data gathering step necessary to perform the Abstract Idea.
At page 12, Applicant argues that the claims recite significantly more than any alleged abstract idea. Examiner respectfully disagrees. When considered in combination, the additional elements (i.e. the generic computer functions and conventional equipment/steps) do not amount to significantly more than the abstract idea. Looking at the claim limitations as a whole adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and mere instructions to implement the abstract idea. Furthermore, the claim confines the use of the judicial exception to the technological environment of the predictive algorithm comprising a trained machine learning regression model and a mixed-effects model by generally linking the use of the judicial exception to the recited predictive algorithm. Therefore, this general predictive algorithm comprising the trained machine learning regression model and the mixed-effects model does not integrate the judicial exception into a practical application.
Furthermore, the claims recite no details about a particular predictive algorithm, a particular trained machine learning regression model, or a particular mixed-effects model. The predictive algorithm comprising the trained machine learning regression model and the mixed-effects model is used to generally apply the abstract idea (i.e., perform the mathematical concepts/mental processes, "extracting, analyzing, generate") without placing any limitations on how the predictive algorithm comprising the trained machine learning regression model and the mixed-effects model operates to derive the evaluation of pulmonary function including forced vital capacity. In addition, the limitation would cover every mode of implementing the recited abstract idea using the predictive algorithm comprising the trained machine learning regression model and the mixed-effects model. The claims omit any details as to how the predictive algorithm comprising the trained machine learning regression model and the mixed-effects model solves a technical problem and instead recites only the idea of a solution or outcome. See MPEP 2106.05(f). Therefore, the limitation "(c) analyzing the one or more acoustic features using a predictive algorithm comprising a trained machine learning regression model to generate an evaluation of pulmonary function for the subject including a predicted forced vital capacity (FVC),the predictive algorithm comprising a mixed-effects model trained using leave-one-participant-out cross-validation and configured to account for repeated measurements per participant by disaggregating the MPT feature into a participant-specific mean and an observation-specific deviation, the mixed-effects model using age and height with the MPT to generate the predicted FVC, wherein the trained machine learning regression model is trained using a training data set comprising acoustic features extracted from audio samples and corresponding ground-truth spirometry measurements of a pulmonary function value" represents no more than mere instructions to implement the abstract idea.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim 1 lines 14-15 should recite “predicted forced vital capacity”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 36, 38, and 41 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 36 recites “wherein the evaluation of pulmonary function comprises one or more of: a predicted forced vital capacity”. It is unclear whether this is the same or different predicted forced vital capacity as recited in claim 1. Furthermore, it is unclear whether the evaluation of pulmonary function includes or excludes the predicted forced vital capacity, with respect to claim 1. The limitation is suggested to recite: “wherein the evaluation of pulmonary function further comprises one or more of: the predicted forced vital capacity”.
Claim 38 recites “wherein the predictive algorithm comprises a trained machine learning model having been trained … selected from the group comprising: MPT …”. It is unclear whether the trained machine learning model is the same model or a different model than recited in claim 1. Furthermore, it is unclear whether the acoustic features the model is trained on includes or excludes MPT since claim 1 recites that the one or more acoustic features from the audio data includes a maximum phonation time. The limitation is suggested to recite “wherein the predictive algorithm comprises the trained machine learning model having been further trained … selected from the group comprising:
Claim 41 recites the limitation “acoustic features comprise one or more of: “maximum phonation time (MPT); …”. It is unclear whether the one or more acoustic features includes or excludes the maximum phonation time. The limitation is suggested to recite “acoustic features include the MPT and further comprise one or more of: “
Claim Rejections - 35 USC § 101
Claims 1 and 35-53 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claims 1, 50, and 52 follows.
STEP 1
Regarding claims 1, 50, and 52, the claims recite a series of structural elements and a series of steps or acts, including an electronic device. Thus, the claims are directed to a machine and/or a process, which is one of the statutory categories of invention.
STEP 2A, PRONG ONE
The claim is then analyzed to determine whether it is directed to any judicial exception. The steps of:
(b) extracting one or more acoustic features from the audio data including a maximum phonation time (MPT) feature from a sustained phonation portion of the audio data;
"(c) analyzing the one or more acoustic features using a predictive algorithm comprising a trained machine learning regression model to generate an evaluation of pulmonary function for the subject including a predicted forced vital capacity (FVC),the predictive algorithm comprising a mixed-effects model trained using leave-one-participant-out cross-validation and configured to account for repeated measurements per participant by disaggregating the MPT feature into a participant-specific mean and an observation-specific deviation, the mixed-effects model using age and height with the MPT to generate the predicted FVC, wherein the trained machine learning regression model is trained using a training data set comprising acoustic features extracted from audio samples and corresponding ground-truth spirometry measurements of a pulmonary function value;
set forth a judicial exception. These steps describe concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (the extracting step) and mathematical relationships (the analyzing and generating steps using the predictive algorithm is a mathematical relationship between maximum phonation time and forced vital capacity). Thus, the claims are drawn to Mental Processes and Mathematical Concepts, which is an Abstract Idea.
STEP 2A, PRONG TWO
Next, the claim as a whole is analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. The claim fails to recite an additional element or a combination of additional elements to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. Claims 1, 50, and 52 recites receiving audio data of a subject through an electronic device comprising a microphone and prompting by the system requesting the subject to provide the audio data as input into the system, which is merely adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)). The generated evaluation of pulmonary function including a predicted forced vital capacity does not provide an improvement to the technological field, the method does not effect a particular treatment or effect a particular change based on the generated evaluation of pulmonary function including a predicted forced vital capacity, nor does the method use a particular machine to perform the Abstract Idea.
STEP 2B
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception. Besides the Abstract Idea, the claim recites additional steps of:
A memory to store instructions;
A processor configured to execute the instructions …;
Receiving audio data of a subject through an electronic device comprising a microphone;
a predictive algorithm comprising a trained machine learning regression model … the predictive algorithm comprising a mixed-effects model trained using leave-one-participant-out cross-validation …, wherein the trained machine learning regression model is trained using a training data set comprising acoustic features extracted from audio samples and corresponding ground-truth spirometry measurements of a pulmonary function value;
wherein the subject is undergoing a clinical trial … participates in the clinical trial via prompting …;
The receiving and prompting steps are well-understood, routine and conventional activities for those in the field of medical diagnostics. Further, the receiving and prompting steps are each recited at a high level of generality such that it amounts to insignificant pre-solution activity, e.g., mere data gathering step necessary to perform the Abstract Idea. When recited at this high level of generality, there is no meaningful limitation, such as a particular or unconventional step that distinguishes it from well-understood, routine, and conventional data gathering and comparing activity engaged in by medical professionals prior to Applicant's invention. Furthermore, it is well established that the mere physical or tangible nature of additional elements such as the memory, processor, electronic device, and analyzing steps do not automatically confer eligibility on a claim directed to an abstract idea (see, e.g., Alice Corp. v. CLS Bank Int'l, 134 S.Ct. 2347, 2358-59 (2014)).
Consideration of the additional elements as a combination also adds no other meaningful limitations to the exception not already present when the elements are considered separately. Unlike the eligible claim in Diehr in which the elements limiting the exception are individually conventional, but taken together act in concert to improve a technical field, the claim here does not provide an improvement to the technical field. Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claim as a whole does not amount to significantly more than the exception itself. The claim is therefore drawn to non-statutory subject matter.
Regarding claims 1, 50, and 52, the memory, processor, and electronic device comprising a microphone recited in the claims is a generic system/device comprising generic components configured to perform the abstract idea – as evidenced by the non-patent literature of record. The recited electronic device and microphone are generic sensors configured to perform pre-solutional data gathering activity, and the processor/memory is configured to perform the Abstract Idea. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application.
The dependent claims also fail to add something more to the abstract independent claims. Claims 35-49, 51, and 53 are directed to more abstract ideas, which does not add anything significantly more. The steps recited in the independent claims maintain a high level of generality even when considered in combination with the dependent claims.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 50-51 are rejected under 35 U.S.C. 103 as being unpatentable over Nazir (Nazir et al., 2020. Lung Function Estimation from a Monosyllabic Voice Segment Captured Using Smartphones. In 22nd International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI '20). Association for Computing Machinery, New York, NY, USA, Article 10, 1–11. https://doi.org/10.1145/3379503.3403543) in view of Miri (US 20210298711 A1 – previously cited), and further in view of Gilifanov (RU2598051C2 English Translation).
Regarding claim 50, Nazir discloses a computer-implemented method (fig. 4), wherein the method comprises: (a) receiving audio data of a subject (fig. 1 & page 4, 2.2 Mobile Audio Based Lung Assessment, “blowing exhalation sound captured in a smartphone microphone”); (b) extracting one or more acoustic features from the audio data (fig. 4 & pages 6-7, 4.4 Lung Function Estimation, “extracted a set of 310 features for estimating lung function … speech features: shimmer and jitter to assess lung function”); (c) analyzing the one or more acoustic features using a predictive algorithm comprising a trained machine learning regression model to generate an evaluation of pulmonary function for the subject (figs. 4 & 7 & pages 7-8, 4.4.3 Regression Model, “evaluated six regression models to estimate the FEV1/FVC ratio” & 5.3 Performance of Estimating Lung Function, “Multi-Layer Regression (MLR) model”), wherein the trained machine learning regression model is trained using a training data set comprising acoustic features extracted from audio samples and corresponding ground-truth spirometry measurements of a pulmonary function value (pages 4-5, 3.1 “collected the ground-truth lung function parameters, such as FEV1, FVC, and FEV1/FVC ratio” & ”4.2 System Overview, “train a regression model … lab study data … and in-clinic data” & Table 2), and wherein the subject is undergoing a clinical trial that comprises the evaluation for pulmonary function to assess status of a disease area associated with pulmonary function (Abstract, pages 4-5, 2.2 Mobile Audio Based Lung Assessment & 3.2 Study-II: Clinical Study); and an electronic device comprising a microphone (fig. 1 & page 4, 2.2 Mobile Audio Based Lung Assessment, “blowing exhalation sound captured in a smartphone microphone”).
Nazir does not disclose the computer-implemented method performed by a system having at least a processor and a memory therein to execute instructions for evaluating pulmonary function and wherein the subject participates in the clinical trial at via prompting by the system requesting the subject to provide the audio data as input into the system remotely through an electronic device comprising a microphone.
However, Miri directed to virtual lung function assessment and auscultation (VLFAA) discloses computer-implemented method performed by a system (“system”, para. [0032], fig. 1) having at least a processor (processor 1040, para. [0110]) and a memory (1050, para. [0110]) (sever 1025, para. [0110]) therein to execute instructions (“program instructions”, para. [0110]) for evaluating pulmonary function (“virtual lung function assessment test”; “analyze patients speech, breathing and lung sounds”, para. [0032-0033, 0110]) and wherein the subject participates in the clinical trial at via prompting by the system requesting the subject to provide the audio data as input into the system remotely through an electronic device comprising a microphone (“coughing into a microphone 10 … mobile device 22 or 24 … requests … types of sound and record audio”; “virtual lung function tests … readily incorporated in … clinical trials”; “during each test, user is prompted”, para. [0032, 0102, 0106]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nazir such that the computer-implemented method is performed by a system having at least a processor and a memory therein to execute instructions for evaluating pulmonary function and the subject participates in the clinical trial at via prompting by the system requesting the subject to provide the audio data as input into the system remotely through an electronic device comprising a microphone, in view of the teachings of Miri, as this would aid in obtaining and analyzing audio recordings for virtual lung function assessment and auscultation (VLFAA).
Nazir, as modified by Miri hereinabove, does not disclose the extracted one or more acoustic features from the audio data including a maximum phonation time (MPT) feature from a sustained phonation portion of the audio data.
However, Gilifanov directed to the field of diagnostics and determining changes in human voice function as a criterion for the effectiveness of treatment of diseases of the upper and lower respiratory tract in patients with chronic obstructive pulmonary disease (COPD) (page 1, 1st para. after Description), discloses extracting one or more acoustic features from the audio data including a maximum phonation time (MPT) feature from a sustained phonation portion of the audio data (page 2, 7th-10th para., “maximum phonation time” & page 4, 4th para., “objective determination of changes in a person's voice function (voice) under pathological conditions such as COPD and laryngitis”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nazir, as modified by Miri hereinabove, such that the extracted one or more acoustic features from the audio data includes a maximum phonation time (MPT) feature from a sustained phonation portion of the audio data, in view of the teachings of Gilifanov, as this would aid in determining the effectiveness of treatment of diseases of the upper and lower respiratory tract in patients with chronic obstructive pulmonary disease (COPD) using changes in human voice function as a criterion.
Regarding claim 51, Nazir, as modified by Miri and Gilifanov hereinabove, discloses the computer-implemented method of claim 50: wherein the disease area is a respiratory condition or disease affecting bloody oxygenation or homeostasis or a neurodegenerative disease affecting pulmonary function (page 8, 5.3 Performance of Estimating Lung Function, “asthma COPD”); wherein the disease area comprises one of: ALS, COPD, asthma, cystic fibrosis,COVID-19 (page 8, 5.3 Performance of Estimating Lung Function, “asthma COPD”); and wherein the evaluation of pulmonary function comprises one or more of: a predicted force vital capacity, forced expiratory volume, peak expiratory flow, mid- expiratory flow rate, forced inspiratory vital capacity, forced expiratory time, respiration rate, respiration rhythm, respiration quality, pause rate, cough events, maximum phonation time, vocal quality, hypernasality, or any combination thereof (page 4, 2.2 Mobile Audio Based Lung Assessment, “estimate the FEV1/FVC ratio” & fig. 7 & Gilifanov page 2, 7th-10th para., “maximum phonation time”).
Claims 52-53 are rejected under 35 U.S.C. 103 as being unpatentable over Nazir in view of Miri.
Regarding claim 52, Nazir discloses a method for evaluating pulmonary function (fig. 4), by performing the following operations: (a) receiving audio data of a subject (fig. 1 & page 4, 2.2 Mobile Audio Based Lung Assessment, “blowing exhalation sound captured in a smartphone microphone”); (b) extracting one or more acoustic features from the audio data (fig. 4 & pages 6-7, 4.4 Lung Function Estimation, “extracted a set of 310 features for estimating lung function … speech features: shimmer and jitter to assess lung function”); (c) analyzing the one or more acoustic features using a predictive algorithm comprising a trained machine learning model to generate an evaluation of pulmonary function for the subject (figs. 4 & 7 & pages 7-8, 4.4.3 Regression Model, “evaluated six regression models to estimate the FEV1/FVC ratio” & 5.3 Performance of Estimating Lung Function, “Multi-Layer Regression (MLR) model”), wherein the trained machine learning model is trained using a training data set comprising acoustic features extracted from audio samples and corresponding ground-truth spirometry measurements of a pulmonary function value (pages 4-5, 3.1 “collected the ground-truth lung function parameters, such as FEV1, FVC, and FEV1/FVC ratio” & ”4.2 System Overview, “train a regression model … lab study data … and in-clinic data” & Table 2), and wherein the subject is undergoing a clinical trial that comprises the evaluation for pulmonary function to assess status of a disease area associated with pulmonary function (Abstract, pages 4-5, 2.2 Mobile Audio Based Lung Assessment & 3.2 Study-II: Clinical Study); and an electronic device comprising a microphone (fig. 1 & page 4, 2.2 Mobile Audio Based Lung Assessment, “blowing exhalation sound captured in a smartphone microphone”).
Nazir does not disclose a non-transitory computer readable storage media having instructions stored thereupon that, when executed by a system having at least a processor and a memory therein, the instructions cause the processor to execute instructions for evaluating pulmonary function and wherein the subject participates in the clinical trial at via prompting by the system requesting the subject to provide the audio data as input into the system remotely through an electronic device comprising a microphone.
However, Miri discloses a non-transitory computer readable storage media having instructions (“program instructions”, para. [0110]) stored thereupon that, when executed by a system (fig. 1, “system”, para. [0032]) having at least a processor (processor 1040, para. [0110]) and a memory therein (1050, para. [0110]) (sever 1025, para. [0110]), the instructions cause the processor to execute instructions for evaluating pulmonary function (“virtual lung function assessment test”; “analyze patients speech, breathing and lung sounds”, para. [0032-0033, 0110]) and wherein the subject participates in the clinical trial at via prompting by the system requesting the subject to provide the audio data as input into the system remotely through an electronic device comprising a microphone (“coughing into a microphone 10 … mobile device 22 or 24 … requests … types of sound and record audio”; “virtual lung function tests … readily incorporated in … clinical trials”; “during each test, user is prompted”, para. [0032, 0102, 0106]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nazir to comprise a non-transitory computer readable storage media having instructions stored thereupon that, when executed by a system having at least a processor and a memory therein, the instructions cause the processor to execute instructions for evaluating pulmonary function and wherein the subject participates in the clinical trial at via prompting by the system requesting the subject to provide the audio data as input into the system remotely through an electronic device comprising a microphone, in view of the teachings of Miri, as this would aid in obtaining and analyzing audio recordings for virtual lung function assessment and auscultation (VLFAA).
Regarding claim 51, Nazir, as modified by Miri hereinabove, discloses the non-transitory computer readable storage media of claim 52: wherein the disease area is a respiratory condition or disease affecting bloody oxygenation or homeostasis or a neurodegenerative disease affecting pulmonary function (page 8, 5.3 Performance of Estimating Lung Function, “asthma COPD”); wherein the disease area comprises one of: ALS, COPD, asthma, cystic fibrosis,COVID-19 (page 8, 5.3 Performance of Estimating Lung Function, “asthma COPD”); and wherein the evaluation of pulmonary function comprises one or more of: a predicted forced vital capacity, forced expiratory volume, peak expiratory flow, mid- expiratory flow rate, forced inspiratory vital capacity, forced expiratory time, respiration rate, respiration rhythm, respiration quality, pause rate, cough events, maximum phonation time, vocal quality, hypernasality, or any combination thereof (page 4, 2.2 Mobile Audio Based Lung Assessment, “estimate the FEV1/FVC ratio” & fig. 7).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Kyal (US 20150272456 A1) discloses linear mixed effect models facilitated an estimation of the difference in period-specific means, along with the SE and p-value for the difference and that leave-one-subject-out cross-validation was used to estimate the classification error rate (CER) of each method (para. [0090, 0093])
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 ANDREW ELI HOFFPAUIR whose telephone number is (571)272-4522. The examiner can normally be reached Monday-Friday 8:00-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Marmor II can be reached at (571) 272-4730. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/A.E.H./Examiner, Art Unit 3791
/AURELIE H TU/Primary Examiner, Art Unit 3791