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 .
Claim Status
Claims 1-11 are currently pending and under exam herein.
Claims 1-11 are rejected.
Priority
The instant application claims priority under 35 U.S.C. §119 to Korean Patent Applications No. 10-2022-0177620, filed on 16 December 2022 and No. 10-2023- 0044734, filed on 5 April 2023. Benefit is acknowledged.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 6 September 2023 complies with 37 CFR 1.98. Accordingly, all references listed have been considered by the examiner.
Drawings
The drawings filed on 6 September 2023 have been received and are accepted.
Claim Rejections - 35 USC § 101
Claims 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract ideas and natural phenomenon) without significantly more. Under MPEP § 2106, subject matter is patent eligible when the claimed invention is to one of the four statutory categories of invention [Step 1], and the claim is not directed to a judicial exception [Step 2A] unless the claim as a whole includes additional limitations amounting to significantly more than the exception [Step 2B].
Step 1
Claims XXX describe inventions that are to one of the statutory categories. In Step 1, a claim must fall within one of the four enumerated categories of statutory subject matter (process, machine, manufacture, or composition of matter); a claim falling outside these categories is ineligible without further analysis. See MPEP § 2106.03. Claims 1-9 are properly to one of the four statutory categories because the claimed invention is a method, which falls into the process category [Step 1: Yes]. Claim 10 is properly to one of the four statutory categories because the claimed invention is a non-transitory computer-readable recording medium having recorded thereon a computer program, which falls into the manufacture category [Step 1: Yes]. Claim 11 is properly to one of the four statutory categories because the claimed invention is a Parkinson’s disease prediction apparatus, which falls into the machine category [Step 1: Yes].
Step 2A
Under Step 2A, a claim is directed to a judicial exception if, under the broadest reasonable interpretation, it recites an abstract idea, law of nature, or natural phenomena [Prong One] without the claim as a whole integrating the exception into a practical application [Prong Two]. Abstract ideas include mathematical concepts, mental processes, and certain methods of organizing human activity. Mathematical concepts encompass mathematical relationships, formulas, equations, and mathematical calculations. See MPEP § 2106.04(a)(2)(I). Mental processes involve concepts that can be performed in the human mind or by a human with the aid of pen and paper, such as observations, evaluations, judgments, or opinions. See MPEP § 2106.04(a)(2)(III). Certain methods of organizing human activity include fundamental economic principles, commercial or legal interactions, and managing personal behavior or relationships. See MPEP § 2106.04(a)(2)(II). Laws of nature and natural phenomena, include naturally occurring principles/relations and nature-based products that are naturally occurring or that do not have markedly different characteristics compared to what occurs in nature. See MPEP § 2106.04(b)-(c).
Prong One
A claim recites a judicial exception when it sets forth or describes a law of nature, natural phenomenon, or abstract idea. Claims 1-11 recite abstract ideas that fall into the groupings of mathematical concepts and mental processes.
Claims 1 and 10-11 recite the following limitations, which describe abstract ideas within the mathematical concepts and/or mental processes groupings:
extracting a syntactic combination from audio data including a speaker's speech result;
verifying accuracy of a Parkinson's disease prediction model by changing conditions for preprocessing the audio data and the syntactic combination;
determining a syntactic combination ranked in a high rank as audio data for Parkinson's disease prediction, based on a result of verifying the accuracy of the Parkinson's disease prediction model; and
inputting, to the Parkinson's disease prediction model, a speaker's speech result corresponding to the audio data for the Parkinson's disease prediction and obtaining a Parkinson's disease prediction result for the speaker as an output of the Parkinson's disease prediction model.
The limitation of extracting a syntactic combination involves observing and identifying linguistic units from speech, which is an abstract idea within the mental processes grouping. The limitation of verifying accuracy of a prediction model involves evaluating model performance under different parameters and ranking the results, which constitutes an abstract idea within the mathematical concepts and mental processes groupings. The limitation of determining a syntactic combination rank involves ranking and selecting the highest-accuracy feature set, which constitutes an abstract idea within the mental processes grouping. The limitation of inputting a speaker’s speech results involves applying a diagnostic rule/mathematical model to reach a conclusion about the presence of a disease, which constitutes an abstract idea within the mathematical concepts and mental processes groupings.
Claims 2-9 recite the following limitations, which describe or narrow abstract ideas:
Claim 2 recites wherein the syntactic combination is a plurality of first different syntactic combinations including at least one first syllable generated by combining a certain consonant with a certain vowel, the syntactic combination is a plurality of second different syntactic combinations in which a basic vowel set, a second syllable generated by combining a certain double consonant with a certain vowel, and at least one of the plurality of first different syntactic combinations are combined with each other, or the syntactic combination is a plurality of third different syntactic combinations including at least one third syllable generated by combining an arbitrary consonant with an arbitrary vowel and one syntactic combination included in the plurality of second different syntactic combinations.
Claim 3 recites wherein the certain consonant included in the plurality of first different syntactic combinations is combined with the certain vowel in a consonant order according to a language regulation, and the certain vowel included in the plurality of first different syntactic combinations is determined as a single vowel.
Claim 4 recites wherein the extracting of the syntactic combination from the audio data comprises extracting, from the audio data, the plurality of first different syntactic combinations in which the at least one first syllable generated by combining nth to (n+k)th consonants according to the consonant order with the certain vowel is repeated a preset number of times, wherein n includes a natural number and k includes 0 and a natural number.
Claim 5 recites generating the Parkinson's disease prediction model, wherein the Parkinson's disease prediction model is generated by training a deep neural network model pre-trained to predict Parkinson's disease for the speaker by using the audio data including the speaker's speech result, and the deep neural network model is a model that receives the audio data including the speaker's speech result and is trained in a supervised learning method by using training data labeled with one of normal, Parkinson, multiple system atrophy, and cerebellar atrophy included in Parkinson's disease-related diseases.
Claim 6 recites wherein the verifying of the accuracy comprises verifying the accuracy of the Parkinson's disease prediction model based on a second preprocessing condition for preprocessing the audio data and the plurality of second different syntactic combinations, and the second preprocessing condition comprises a condition for executing first acoustic preprocessing to unify channels for recording the audio data and unify sample rates of the audio data.
Claim 7 recites wherein the determining as the audio data comprises determining a basic vowel set ranked in a highest rank as the audio data for the Parkinson's disease prediction, based on the result of verifying the accuracy of the Parkinson's disease prediction model.
Claim 8 recites wherein the verifying of the accuracy comprises verifying the accuracy of the Parkinson's disease prediction model based on a seventh preprocessing condition for preprocessing the audio data, the plurality of first different syntactic combinations, and the plurality of second different syntactic combinations, and the seventh preprocessing condition comprises a condition for executing second acoustic preprocessing to unify channels for recording the audio data, unify sample rates of the audio data, normalize the audio data according to an average volume, filter the audio data in a preset band, remove a DC-offset from the audio data, and reduce noise from the audio data, and use of edge from among types of data padding that equalizes a size of the audio data by filling a silent section of the audio data with a specific value.
Claim 9 recites wherein the determining as the audio data comprises determining a first syntactic combination ranked in a highest rank and a first syntactic combination ranked in a second highest rank as the audio data for the Parkinson's disease prediction, based on a result of verifying the accuracy of the Parkinson's disease prediction model, the first syntactic combination ranked in the highest rank is a syntactic combination in which the at least one first syllable generated by combining first to ninth consonants according to the consonant order with the certain vowel is repeated a predetermined number of times, and the first syntactic combination ranked in the second highest rank is a syntactic combination in which the at least one first syllable generated by combining first to seventh consonants according to the consonant order with the certain vowel is repeated a predetermined number of times.
The limitations of claim 2 recite three alternative definitions of “syntactic combination” involving generating and combining linguistic units according to linguistic rules, which merely narrows the mental process of extracting a syntactic combination of claim 1. The limitations of claim 3 further narrows this mental process of claims 1 and 2 by specifying the process of ordering and selecting linguistic elements according to linguistic conventions. The limitation of claim 4 involves selecting a consecutive range of consonants and repeating syllables, which narrows the mental process of claim 1 and further recites a mathematical concept. The limitations of claim 5 describe abstract ideas including the mathematical concepts of supervised machine learning and the mental process of classifying disease states. The limitations of claims 6 and 8 narrow the abstract idea of verifying model accuracy of claim 1 by specifying the process of model verification. The limitations of claims 7 and 9 narrow the mental process of determining a syntactic combination rank of claim 1 by specifying the mental process of ranking and selecting specific linguistic feature sets by accuracy.
Therefore, claims 1-11 recite abstract ideas – namely mathematical concepts and mental processes [Step 2A, Prong One: Yes].
Prong Two
Claims 1-11 as a whole do not integrate the recited judicial exception into a practical application. A claim that recites a judicial exception [Prong One] is deemed to be directed to a judicial exception [Step 2A] unless the claim as a whole contains additional elements that integrate the exception into a practical application [Prong Two]. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See MPEP §§ 2106.04(d) and 2106.05(e). A claim does not integrate a judicial exception into a practical application by reciting insignificant extra-solution activity, generally linking the exception to a particular technological environment or field of use, merely reciting to apply the exception, merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea. See MPEP § 2106.04(d)(I). Insignificant extra-solution activities are nominal or tangential additions to a claim that are incidental to the primary process or product, including both pre-solution and post-solution activity (e.g. pre-solution data gathering for use in a process). If integrated into a practical application, the claim is eligible; otherwise, it is directed to the judicial exception, necessitating further analysis at Step 2B.
Claims 1 and 10-11 recite the following limitations, which are additional elements:
a processor
a Parkinson’s disease prediction apparatus
a memory operatively connected to the processor and configured to store at least one code executable by the processor
a non-transitory computer-readable recording medium having recorded thereon a computer program
These limitations describe generic computer components that amount to nothing more than mere instructions to apply the judicial exceptions, which do not integrate into a practical application. See MPEP §§ 2106.05(b) & (f). Additionally, claim 5 recites training a deep neural network model that receives the audio data including the speaker’s speech results. While the deep neural network model is an additional element, it is recited at a high level of generality that equates to mere instructions to apply the abstract ideas on a generic neural network. The limitations recite no specific architecture details, novel training technique, hardware improvement, or application that improves computer functioning. They simply recite using a known type of AI for the abstract purpose of disease prediction, which does not integrate the judicial exceptions into a practical application. See MPEP § 2106.05 (f). Finally, claims 2-4 and 6-9 do not include any additional elements.
The claims as a whole merely recite abstract ideas implemented on generic computer components without meaningful limitations that tie it to a specific technological improvement. Therefore, claims 1-11 do not contain additional elements that integrate the recited abstract ideas into a practical application [Step 2A, Prong Two: No].
Step 2B
Claims 1-11 do not include additional elements, whether considered individually or in combination, that are sufficient to amount to significantly more than the judicial exception itself. Under Step 2B, the claim is analyzed to determine whether there are any additional elements that, individually or in combination, constitute an “inventive concept" sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself. See MPEP § 2106.05; and Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 217-18, 110 USPQ2d 1976, 1981 (2014).
Claims 1 and 10-11 recite the following limitations, which are additional elements:
a processor
a Parkinson’s disease prediction apparatus
a memory operatively connected to the processor and configured to store at least one code executable by the processor
a non-transitory computer-readable recording medium having recorded thereon a computer program
These limitations describe generic and conventional computer components that amount to nothing more than mere instructions to apply the judicial exceptions, which does not add significantly more than the exceptions. See MPEP §§ 2106.05(b) & (f); and Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1976, 1984 (2014). Additionally, claim 5 recites training a deep neural network model to predict Parkinson’s classification, which is conventional and does not add significantly more than the exceptions themselves. See MPEP § 2106.05 (f); and Abdullah Caliskan et al., DIAGNOSIS OF THE PARKINSON DISEASE BY USING DEEP NEURAL NETWORK CLASSIFIER, 17(2) IU-JEEE 3311-18, 3312 col.1 para.3 (14 March 2017).
Overall, claims 1-11 amount to no more than implementing the abstract ideas on conventional computers in a routine way. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception itself because the claims recite additional elements that equate to mere instructions to apply the recited abstract ideas in a generic way or in a generic computing environment. Therefore, claims 1-11 are rejected for failing to set forth patent eligible subject matter under 35 U.S.C. 101 because the claimed invention recites abstract ideas [Step 2A, Prong One: Yes] and the additional elements do not integrate the judicial exception into a practical application [Step 2A, Prong Two: No] and do not amount to claiming significantly more than the recited exception [Step 2B: No].
Claim Rejections - 35 USC § 102
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 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 1-4, 7, and 10-11 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Juan Camilo Vásquez-Correa et al., Articulation and Empirical Mode Decomposition Features in Diadochokinetic Exercises for the Speech Assessment of Parkinson’s Disease Patients, 11896 Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications (CIARP 2019) 688-96 (22 October 2019) (hereinafter “Vásquez-Correa”), as evidenced by C3.ai., Infrastructure: Machine Learning Hardware Requirements (15 May 2021) (hereinafter “C3.ai.”). The italicized text within parenthesis corresponds to the instant claim limitations.
Regarding claims 1 and 10-11, Vásquez-Correa discloses a method to classify whether an individual has Parkinson’s and to predict the severity of the disease. At Abstract (a Parkinson's disease prediction method). Vásquez-Correa extracts articulation features from audio data of participants performing diadochokinetic (DDK) exercises. At 690 paras.1-2 (extracting a syntactic combination from audio data including a speaker's speech result). Vásquez-Correa uses three feature sets extracted using different processing choices and compares model accuracy across the different sets. At 694 para.1 (verifying accuracy of a Parkinson's disease prediction model by changing conditions for preprocessing the audio data and the syntactic combination). Vásquez-Correa compares the results for each DDK exercise for each processing choice. At Table 3 (determining a syntactic combination ranked in a high rank as audio data for Parkinson's disease prediction, based on a result of verifying the accuracy of the Parkinson's disease prediction model). Vásquez-Correa classifies individuals as healthy or having Parkinson’s by feeding features extracted from the DDK exercises into a support vector machine (SVM) classifier. At 692 para.2 (inputting, to the Parkinson's disease prediction model, a speaker's speech result corresponding to the audio data for the Parkinson's disease prediction and obtaining a Parkinson's disease prediction result for the speaker as an output of the Parkinson's disease prediction model).
Vásquez-Correa does not explicitly teach a Parkinson's disease prediction apparatus comprising a processor and a memory operatively connected to the processor and configured to store at least one code executable by the processor. However, Vásquez-Correa discloses using a SVM classifier, which is a machine learning model that necessarily involves a computer-readable medium with instructions stored thereon to be executed by one or more processors. See C3.ai., §§ Processors: CPUs, GPUs, TPUs, and FPGAs & Memory and Storage.
Regarding claims 2 and 3, Vásquez-Correa discloses that the features are extracted from DDK exercises pronounced by the subjects including the rapid repetition of the syllables /pa-ta ka/, /pa-ka-ta/, /pe-ta-ka/, /pa/, /ta/, and /ka/. At 693 para.2 (claim 2: wherein the syntactic combination is a plurality of first different syntactic combinations including at least one first syllable generated by combining a certain consonant with a certain vowel, the syntactic combination is a plurality of second different syntactic combinations in which a basic vowel set, a second syllable generated by combining a certain double consonant with a certain vowel, and at least one of the plurality of first different syntactic combinations are combined with each other, or the syntactic combination is a plurality of third different syntactic combinations including at least one third syllable generated by combining an arbitrary consonant with an arbitrary vowel and one syntactic combination included in the plurality of second different syntactic combinations; claim 3: wherein the certain consonant included in the plurality of first different syntactic combinations is combined with the certain vowel in a consonant order according to a language regulation, and the certain vowel included in the plurality of first different syntactic combinations is determined as a single vowel).
Regarding claim 4, Vásquez-Correa discloses that the features are extracted from DDK exercises pronounced by the subjects including the rapid repetition of the syllables /pa-ta ka/, /pa-ka-ta/, /pe-ta-ka/, /pa/, /ta/, and /ka/. At 693 para.2. Vásquez-Correa discloses that one extraction method involves segmenting the plosives and the vowels, the features of which are then concatenated. At 692 para.1 (wherein the extracting of the syntactic combination from the audio data comprises extracting, from the audio data, the plurality of first different syntactic combinations in which the at least one first syllable generated by combining nth to (n+k)th consonants according to the consonant order with the certain vowel is repeated a preset number of times, wherein n includes a natural number and k includes 0 and a natural number).
Regarding claim 7, Vásquez-Correa ranks the results for each DDK exercise for each processing choice. At Table 3 (wherein the determining as the audio data comprises determining a basic vowel set ranked in a highest rank as the audio data for the Parkinson's disease prediction, based on the result of verifying the accuracy of the Parkinson's disease prediction model).
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.
Claims 5-6 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Vásquez-Correa in view of Jhansi Mallela et al., Voice based classification of patients with Amyotrophic Lateral Sclerosis, Parkinson’s Disease and Healthy Controls with CNN-LSTM using transfer learning, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 6784-88 (May 2020) (hereinafter “Mallela”).
Regarding claim 5, Vásquez-Correa discloses the method of claim 4 (see 102 rejection above). Vásquez-Correa does not disclose training a deep neural network model pre-trained to predict Parkinson's disease for the speaker by using the audio data including the speaker's speech result, and the deep neural network model is a model that receives the audio data including the speaker's speech result and is trained in a supervised learning method by using training data labeled with one of normal, Parkinson, multiple system atrophy, and cerebellar atrophy included in Parkinson's disease-related diseases. However, Mallela discloses training a convolutional neural network (CNN) with long short-term memory networks (LSTM) for classifying individuals as having Parkinson’s disease based on speech data for an individual. At 6786 col.2 para.3; 6785 col.1 para.3 (training a deep neural network model pre-trained to predict Parkinson's disease for the speaker by using the audio data including the speaker's speech result). Mallela trains the CNN-LSTM using audio data from patients labeled as healthy and Parkinson’s. At 6786 col.2 para.3 (the deep neural network model is a model that receives the audio data including the speaker's speech result and is trained in a supervised learning method by using training data labeled with one of normal, Parkinson, multiple system atrophy, and cerebellar atrophy included in Parkinson's disease-related diseases). Mallela compares the performance of the CNN-LSTM with the performance of a SVM classifier, and finds that the CNN-LSTM achieves an improved performance over SVM. At 6787 col.1 para.1.
A person having ordinary skill in the art would be motivated to combine the CNN-LSTM of Mallela with the method of Vásquez-Correa, and one of ordinary skill in the art would reasonably expect success in the combination because the CNN-LSTM of Mallela outperforms SVM classifiers, such as the one used in the method of Vásquez-Correa. Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007); and MPEP § 2143, G.
Regarding claim 6, Vásquez-Correa uses three feature sets extracted using different processing choices and compares model accuracy across the different sets. At 694 para.1 (wherein the verifying of the accuracy comprises verifying the accuracy of the Parkinson's disease prediction model based on a second preprocessing condition for preprocessing the audio data and the plurality of second different syntactic combinations). Vásquez-Correa discloses that the second group of articulation features aims to model the speech rate and the regularity of the DDK exercises. At 690 para.1 (the second preprocessing condition comprises a condition for executing first acoustic preprocessing to unify channels for recording the audio data and unify sample rates of the audio data).
Regarding claim 8, Vásquez-Correa uses three feature sets extracted using different processing choices and compares model accuracy across the different sets. At 694 para.1 (wherein the verifying of the accuracy comprises verifying the accuracy of the Parkinson's disease prediction model based on a seventh preprocessing condition for preprocessing the audio data, the plurality of first different syntactic combinations, and the plurality of second different syntactic combinations). Vásquez-Correa teaches converting recordings to a consistent channel format and resampling recordings to a common sample rate before feature extraction. At 692 para.2 (the seventh preprocessing condition comprises a condition for executing second acoustic preprocessing to unify channels for recording the audio data, unify sample rates of the audio data). Vásquez-Correa further discloses amplitude/volume normalization of the audio signals, application of frequency-selective filtering, removal of constant bias present in the waveform, and noise reduction steps. At 690 para.2 (normalize the audio data according to an average volume, filter the audio data in a preset band, remove a DC-offset from the audio data, and reduce noise from the audio data, and use of edge from among types of data padding that equalizes a size of the audio data by filling a silent section of the audio data with a specific value).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Vásquez-Correa in view of Bo Mi Kim et al., Acoustic and Auditory-Perceptual Characteristics of Korean Stop Consonants in Patients with Idiopathic Parkinson’s Disease and Cerebellar-Multiple System Atrophy, 25(4) Commun Sci Disord 954-65 (16 November 2020) (hereinafter “Kim”).
Regarding claim 9, Vásquez-Correa discloses the method of claim 1 (see 102 rejection above). Vásquez-Correa systematically compares the six DDK sequences and their fusions, and ranks them by classification accuracy to select one of the higher-ranking sequences for use in the subsequent classification step. At 694 para.1; Table 3 (wherein the determining as the audio data comprises determining a first syntactic combination ranked in a highest rank and a first syntactic combination ranked in a second highest rank as the audio data for the Parkinson's disease prediction, based on a result of verifying the accuracy of the Parkinson's disease prediction model). Vásquez-Correa does not teach the first syntactic combination ranked in the highest rank is a syntactic combination in which the at least one first syllable generated by combining first to ninth consonants according to the consonant order with the certain vowel is repeated a predetermined number of times, and the first syntactic combination ranked in the second highest rank is a syntactic combination in which the at least one first syllable generated by combining first to seventh consonants according to the consonant order with the certain vowel is repeated a predetermined number of times.
However, Kim investigates Korean stop consonant production in patients with idiopathic Parkinson’s disease (IPD) and Multiple System Atrophy-cerebellar variant (MSA-C). Kim teaches that the Korean language has nine plosives. At 961 col.1 para.3. Kim further teaches that the closure intervals for individuals with MSA-C is significant in seven plosives as compared to the IPD or control group. At 957 col.2 para.1. A person having ordinary skill in the art would be motivated to combine the teachings of Kim with the teachings of Vásquez-Correa to adapt Vásquez-Correa’s method to the phonology of the Korean language. One of ordinary skill in the art would understand that applying the ranking and selection technique of Vásquez-Correa to ordered consecutive subsets of the Korean consonant inventory would construct a “first-to-ninth” repeated syllable combination as the highest-ranked sequence and a “first-to-seventh” repeated-syllable combination as the second-highest ranked sequence. One of ordinary skill in the art would reasonably expect success in the combination because Kim demonstrates that Parkinson’s and related diseases affect the speech pattern in Korean in a predictable manner. Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007); and MPEP § 2143, G.
Conclusion
No claim is allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Emily A Darrigrand whose telephone number is (571) 272-1098. The examiner can normally be reached Monday-Thursday 7:00AM-4:00PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Larry Riggs, can be reached at (571) 270-3062. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/E.A.D./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
Prosecution Insights