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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/08/25 and 04/11/25 have been considered by the examiner.
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, 5, 9, 14-20, 22, 26, 30, 35-41 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 claim 1 follows.
Regarding claim 1, the claim recites a method of forming a classification model associated with a respiratory disease based on training audio data. Thus, the claim is directed to a process, which is one of the statutory categories of invention
The claim is then analyzed to determine whether it is directed to any judicial exception. The following limitations set forth a judicial exception:
wherein the training audio data is characterized by a pre-existing correlation with the respiratory disease
normalizing the training audio data;
removing portions of the training audio data below an audio energy threshold to produce thresholded audio data;
segmenting the thresholded audio data into uniform time segments;
extracting MFCC coefficients and RASTA-PLP coefficients from the segmented thresholded audio data;
generating a feature matrix using the MFCC coefficients and RASTA-PLP coefficients
and wherein the MFCC coefficients further comprise at least one of MFCC-A coefficients and MFCC-AA coefficients
These limitations set forth a judicial exception. These steps describe a concept performed in the human mind (including an observation, evaluation, judgment, opinion). Thus, the claim is drawn to a Mental Process, which is an Abstract Idea.
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. Claim 1 recites classifying the feature matrix based on the pre-existing correlation to form the classification model using a multilayer perceptron technique, which is merely adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)). The classifying of a feature matrix to form a classification model does not provide an improvement to the technological field, the system does not effect a particular treatment or effect a particular change based on the model, nor does the method use a particular machine to perform the Abstract Idea. Examiner adds that administering antiepileptic medications is analogous to “administering a suitable medication to a patient”, which is further explained in MPEP 2106.04(d)(2).
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:
classification model
Additionally, claim 22 recites the additional elements
computer
storage device
classifier
interpreter
The providing and recording steps are well-understood, routine and conventional activities for those in the field of medical diagnostics. Further, the providing and recording steps are each recited at a high level of generality such that it amounts to insignificant presolution 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 obtaining and comparing 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
Independent claims 22 is also not patent eligible for substantially similar reasons
Dependent claims 5, 9, 14-20, 26, 30, 35-41 also fail to add something more to the abstract independent claims as they merely further limit the abstract idea.
Therefore, claims 1, 5, 9, 14-20, 22, 26, 30, 35-41 are not patent eligible under 35 USC 101.
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 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.
Claims 1, 9, 18, 19, 22, 30, 39 and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Lotan (U.S. Patent Application Document 2019/0080803) and in further view of Saripalli (U.S. Patent Application Document 2020/0337648)
Lotan and Saripalli were submitted in Applicant’s IDS
Regarding claim 1, Lotan teaches a method of forming a classification model associated with a respiratory disease based on training audio data [par. 38, 48, 83], wherein the training audio data is characterized by a pre-existing correlation with the respiratory disease [par. 38, 48], the method comprising: normalizing the training audio data [par. 84]; removing portions of the training audio data below an audio energy threshold to produce thresholded audio data [par. 68]; segmenting the thresholded audio data into uniform time segments [par. 63, 68]; extracting MFCC coefficients and RASTA-PLP coefficients from the segmented thresholded audio data [par. 81]; generating a feature matrix using the MFCC coefficients and RASTA-PLP coefficients [par. 81, 84]; classifying the feature matrix based on the pre-existing correlation to form the classification model [par. 83] and wherein the MFCC coefficients further comprise at least one of MFCC-A coefficients and MFCC-AA coefficients [par. 81, 83; Examiner notes that MFCC first derivatives and second derivatives are considered the MFCC-A and MFCC-AA]
However, Lotan does not teach using a multilayer perceptron technique
Saripalli teaches using a multilayer perceptron technique [par. 42, 76]
Therefore, it would have been prima facie obvious to a person having ordinary skill in the art when the invention was filed to modify the method as taught by Lotan, to incorporate using a multilayer perceptron technique, for provides a linear activation function to map weighted inputs to an output set representing predicted machine events at a future time, as evidence by Saripalli [par. 76].
Regarding claim 9, Lotan teaches method of generating a probability of a diagnosis based on acquired audio data [par. 31, 85], the method comprising: forming the classification model of claim 1; normalizing the acquired audio data [par. 84]; removing portions of the acquired audio data below the audio energy threshold to produce thresholded acquired audio data [par. 68]; segmenting the thresholded acquired audio data into uniform time segments [par. 63, 68]; extracting MFCC coefficients and RASTA-PLP coefficients from the segmented thresholded acquired audio data [par. 81]; generating an acquired feature matrix using the MFCC coefficients and RASTA-PLP coefficients from the segmented thresholded acquired audio data [par. 81, 84]; using the classification model and the acquired feature matrix to generate the probability of the diagnosis of the respiratory disease [par. 31, 85]
Regarding claim 18, Lotan teaches at least a portion of said acquired audio data is stored [par. 46]; wherein said at least a portion of said acquired audio data is further characterized by additional data establishing an additional correlation between the stored acquired audio data and the respiratory disease [par. 46, 83]; wherein said classifying the feature matrix based on the pre-existing correlation to form the classification model further comprises classifying the feature matrix based on the pre-existing correlation and said additional correlation to form said classification model [par. 46, 83]
Regarding claims 19 and 40, Lotan teaches at least a portion of said training audio data and/or said acquired audio data is obtained through telephonic, video-conferencing, or computer-network-based communications [par. 46]
Regarding claim 22, Lotan teaches a system for forming a classification model associated with a respiratory disease based on training audio data, wherein the training audio data is characterized by a pre-existing correlation with the respiratory disease [par. 38, 46, 48, 83], said system comprising: at least one computer [par. 64]; at least one storage device [par. 64]; said at least one storage device storing instructions that, when executed by the at least one computer, cause the at least one computer to perform a method of forming said classification model [par. 40], said method comprising: normalizing the training audio data [par. 84]; removing portions of the training audio data below an audio energy threshold to produce thresholded audio data [par. 68]; segmenting the thresholded audio data into uniform time segments [par. 68]; extracting MFCC coefficients and RASTA-PLP coefficients from the segmented thresholded audio data [par. 81]; generating a feature matrix using the MFCC coefficients and RASTA-PLP coefficients [par. 81, 84]; classifying the feature matrix based on the pre-existing correlation to form the classification model [par. 83] and wherein the MFCC coefficients further comprise at least one of MFCC-A coefficients and MFCC-AA coefficients [par. 81, 83; Examiner notes that MFCC first derivatives and second derivatives are considered the MFCC-A and MFCC-AA]
However, Lotan does not teach using a multilayer perceptron technique
Saripalli teaches using a multilayer perceptron technique [par. 42, 76]
Therefore, it would have been prima facie obvious to a person having ordinary skill in the art when the invention was filed to modify the method as taught by Lotan, to incorporate using a multilayer perceptron technique, for provides a linear activation function to map weighted inputs to an output set representing predicted machine events at a future time, as evidence by Saripalli [par. 76].
Regarding claim 30, Lotan teaches a system for generating a probability of a diagnosis based on acquired audio data, the system comprising the system for forming the classification model of a system for generating a probability of a diagnosis based on acquired audio data, the system comprising the system for forming the classification model of wherein the instructions further comprise additional instructions such that, when said instructions are executed by said at least one computer [par. 40], said at least one computer performs the method of generating the probability of a diagnosis [par. 31, 85], said method comprising: forming the classification model [par. 85]; normalizing the acquired audio data [par. 84]; removing portions of the acquired audio data below the audio energy threshold to produce thresholded acquired audio data [par. 68]; segmenting the thresholded acquired audio data into uniform time segments [par. 63, 68]; extracting MFCC coefficients and RASTA-PLP coefficients from the segmented thresholded acquired audio data [par. 81]; generating an acquired feature matrix using the MFCC coefficients and RASTA-PLP coefficients from the segmented thresholded acquired audio data [par. 81, 84]; using the classification model and the acquired feature matrix to generate the probability of the diagnosis of the respiratory disease [par. 31, 85]
Regarding claim 39, Lotan teaches at least a portion of said acquired audio data is stored [par. 46]; wherein said at least a portion of said acquired audio data is further characterized by additional data establishing an additional correlation between the stored acquired audio data and the respiratory disease [par. 46, 83]; wherein said classifying the feature matrix based on the pre-existing correlation to form the classification model further comprises classifying the feature matrix based on the pre-existing correlation and said additional correlation to form said classification model [par. 46, 83]
Claims 5, 14, 15, 26, and 35-37 are rejected under 35 U.S.C. 103 as being unpatentable over Lotan and Saripalli and in further view of Hardt ("Spectral subtraction and rasta-filtering in text-dependent HMM-based speaker verification." 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing. Vol. 2. IEEE, 1997.)
Regarding claims 5, 15, 26, 36 and 37, Lotan and Saripalli teach a method and system of forming a classification model associated with a respiratory disease based on training audio data, as disclosed above
Lotan further teaches wherein the RASTA-PLP coefficients comprise at least one of: RASTA-PLP coefficients of a model order [par. 83]
However, Lotan and Saripalli do not teach at least one of: RASTA-PLP coefficients of model order 25,RASTA-PLP coefficients of model order 20, and RASTA-PLP coefficients of model order 22
Hardt teaches at least one of: RASTA-PLP coefficients of model order 25,RASTA-PLP coefficients of model order 20, and RASTA-PLP coefficients of model order 22 [fig. 4; Table 2; pg. 868 col. 2]
Therefore, it would have been prima facie obvious to a person having ordinary skill in the art when the invention was filed to modify the method as taught by Lotan and Saripalli, to incorporate at least one of: RASTA-PLP coefficients of model order 25,RASTA-PLP coefficients of model order 20, and RASTA-PLP coefficients of model order 22, as these ranges provide the best results, as evidence by Hardt [pg. 868 col. 2].
Regarding claims 14 and 35, Hardt teaches the RASTA-PLP coefficients comprise at least one of: RASTA-PLP coefficients of a model order 20 and RASTA-PLP coefficients of a model order 22 [fig. 4; Table 2; pg. 868 col. 2]
Therefore, it would have been prima facie obvious to a person having ordinary skill in the art when the invention was filed to modify the method as taught by Lotan and Saripalli, to incorporate at least one of: RASTA-PLP coefficients of model order 25,RASTA-PLP coefficients of model order 20, and RASTA-PLP coefficients of model order 22, as these ranges provide the best results, as evidence by Hardt [pg. 868 col. 2].
Although Hardt does not explicitly teach RASTA-PLP coefficients of a model order 25, this would be obvious to one skilled in the art since Hardt also teaches RASTA-PLP coefficients of a model order 20 and RASTA-PLP coefficients of a model order 22 [fig. 4; Table 2; pg. 868 col. 2]. Additionally, Hardt teaches implementing several different numbers of PLP coefficients [fig. 4] Therefore, incorporating RASTA-PLP coefficients of a model order 25, would only involve routine skilled in the art.
Claims 17 and 38 are rejected under 35 U.S.C. 103 as being unpatentable over Lotan and Saripalli and in further view of Imran (U.S. Patent Application Document 2021/0338103)
Imran was submitted in Applicant’s IDS
Regarding claims 17 and 38, Lotan and Saripalli teach a method and system of forming a classification model associated with a respiratory disease based on training audio data, as disclosed above
However, Lotan and Saripalli do not teach wherein the respiratory disease is COVID-19
Imran teaches wherein the respiratory disease is COVID-19 [par. 19, 27]
Therefore, it would have been prima facie obvious to a person having ordinary skill in the art when the invention was filed to modify the method as taught by Lotan and Saripalli, to incorporate wherein the respiratory disease is COVID-19, for providing scalable screening of individuals for respiratory infection, as evidence by Imran [par. 19].
Claims 20 and 41 are rejected under 35 U.S.C. 103 as being unpatentable over Lotan and Saripalli and in further view of Iliff (U.S. Patent Application Document 2008/0051638)
Iliff was submitted in Applicant’s IDS
Regarding claims 20 and 41, Lotan and Saripalli teach a method and system of forming a classification model associated with a respiratory disease based on training audio data, as disclosed above
However, Lotan and Saripalli do not teach said telephonic communications comprises toll-free communications
Iliff teaches said telephonic communications comprises toll-free communications [par. 212]
Therefore, it would have been prima facie obvious to a person having ordinary skill in the art when the invention was filed to modify the method as taught by Lotan and Saripalli, to incorporate said telephonic communications comprises toll-free communications, for providing access to the MDATA system 100 at no cost to the caller, as evidence by Iliff [par. 212].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GRACE L ROZANSKI whose telephone number is (571)272-7067. The examiner can normally be reached M-F 8:30am-5pm, alt F 8:30am-5pm.
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/GRACE L ROZANSKI/Examiner, Art Unit 3791
/ALEX M VALVIS/Supervisory Patent Examiner, Art Unit 3791
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