DETAILED ACTION
Applicant’s response, filed 24 September 2025, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 1-17 are pending and examined herein.
Claims 1-17 are rejected.
Claims 2, 5, 7, 10, and 14 are objected to.
Priority
Claims 1-17 are not granted the claim to the benefit of priority to U.S. Provisional application 62/899915 filed 13 September 2019 because there is no disclosure of calculating an index to allow for point-of-care delivery or calculating an index sufficiently quickly to allow for point-of-care delivery. Thus, the effective filling date of claims 1-17 is the filing date of the instant application 24 September 2025.
Specification
The amendment filed 24 September 2025 is objected to under 35 U.S.C. 132(a) because it introduces new matter into the disclosure. 35 U.S.C. 132(a) states that no amendment shall introduce new matter into the disclosure of the invention. The added material which is not supported by the original disclosure is as follows: For the purposes of this disclosure, real time applications refer to applications like the ongoing monitoring of a patient or the rapid testing of a patient or emergency situations. Such real time applications are in contrast to discrete tests, where data is collected from a patient and the data is then sent off for analysis, typically by specialists rather than the treating medical staff. Similarly described, the current system and method are efficient enough to allow for point-of-care result delivery.
Applicant is required to cancel the new matter in the reply to this Office Action.
Claim Objections
Claims 2, 10, and 14 objected to because of the following informalities:
Claim 2 recites “D_PD” (in line 8 of the claim) and “D_H” (in line 10 of the claim) but should read “DPD” and “DH”.
Claim 10 recites “X_PD” (in line 10 of the claim) but should read “XPD”.
Claim 14 recites “aPD” (in line 3 of the claim) but should read “aH”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The rejection on the ground of 112/b of claims 1-17 for claims 1, 6, and 11 reciting “in real time” in Office action mailed 24 April 2025 is withdrawn in view of the amendment which removes the limitation of “in real time” received 24 September 2025.
The rejection on the ground of 112/b of claim 4 in Office action mailed 24 April 2025 is withdrawn in view of the amendment of “The method of Claim 1, further comprises…” received 24 September 2025.
112/a New Matter
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-17 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites “calculating a Linear-predictive-coding EEG algorithm in PD (“LEAPD”) index for the EEG time series data with the system to allow for point-of-care delivery of the LEAPD index”, claim 6 recites “and wherein the LEAPD index is calculated sufficiently quickly to allow for point-of-care delivery of the results”, and claim 11 “calculating a LEAPD index for the EEG time series data sufficiently quickly to allow for point-of-care delivery of the LEAPD index”. There is not an adequate written description for these limitations in the originally filed disclosure. The originally filed disclosure provides that “the current system and method is highly efficient and is amenable to real time applications”, however the originally filed disclosure does not provide an adequate written description of calculating an index to allow for point-of-care delivery or calculating an index sufficiently quickly to allow for point-of-care delivery. Therefore, these limitations constitute as new matter. Dependent claims 2-5, 7-10, and 12-17 are rejected by virtue of their dependency on rejected claims.
112/b
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 6-17 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 6 recites “wherein the LEAPD index is calculated sufficiently quickly to allow for point-of-care delivery of the results” and claim 11 recites “calculating a LEAPD index for the EEG time series data sufficiently quickly to allow for point-of-care delivery of the LEAPD index” which renders the metes and bounds of the claim to be indefinite. The indefiniteness arises because it is unclear what time frame the limitation of “sufficiently quickly” is meant to encompass. The specification does not provide a clear and precise definition of the limitation, nor would one skilled in the art recognize the metes and bounds of said limitation. Dependent claims 7-10 and 12-17 are further rejected by virtue of their dependency on rejected claims without alleviating the indefiniteness. For the sake of furthering examination, these limitations will be interpreted as wherein the LEAPD index is calculated to allow for point-of-care delivery of the results (in claim 6) and calculating a LEAPD index for the EEG time series data to allow for point-of-care delivery of the LEAPD index (in claim 11) which does not require a time frame for the step of calculating the LEAPD index.
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 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
(Step 1)
Claims 1-5 fall under the statutory category of a process and claims 6-17 fall under the statutory category of a machine.
(Step 2A Prong 1)
Under the BRI, the instant claims recite judicial exceptions that are an abstract idea of the type that is in the grouping of a “mental process”, such as procedures for evaluating, analyzing or organizing information, and forming judgement or an opinion. Along with abstract ideas of the type that is in the grouping of a “mathematical concept”, such as mathematical relationships and mathematical equations.
Independent claims 1 recites mathematical concepts of “calculating a Linear-predictive-coding EEG Algorithm in PD ("LEAPD") index for the EEG time series data to allow for point-of-care delivery of the LEAPD index, wherein the LEAPD index is calculated using…”.
Independent claim 6 recites a mathematical concept of “calculate a Linear-predictive-coding EEG Algorithm in PD ("LEAPD") index for the EEG time series data, wherein the LEAPD index is calculated using…and wherein the LEAPD index is calculated to allow for point-of-care delivery of the results”.
Claim 6 recites a law of nature of the correlation between a result for a patient (in a system for diagnosing Parkinson’s Disease (PD)) and a patient’s brain activity is a naturally occurring correlation.
Independent claim 11 recites a mathematical concept of “calculating a LEAPD index for the EEG time series data to allow for point-of-care delivery of the LEAPD index, wherein the LEAPD index calculation uses…”.
Dependent claim 2 recites a mental process of “filtering said EEG time series data…”. Dependent claim 2 recites mathematical concepts of “determining Linear Predictive Coding (LPC) coefficients from the EEG times series data…”, “calculating aPD…and aH…”, “calculating the distance vector D_PD…”, “calculating the distance vector D_H…”, and “calculating LEAPD index…”. Dependent claim 3 and 8 recite mental processes “recognizing that vector of LPC coefficients…”, “finding the hyperplane for the PD patients…”, “determining whether the distance…”. Dependent claims 3 and 8 recite mathematical concepts of “generating Linear Predictive Coding (“LPC”) coefficients…” and “calculating the distances between the vector created by the LPC coefficients…”. Dependent claims 3 and 8 recite a law of nature of “a result for the patient having PD” because a patient’s diagnosis of PD and a patient’s brain activity is a naturally occurring correlation. Dependent claims 4 and 9 recite a law of nature of “a negative result for having PD…” and “a positive result for having PD…” because a patient’s health state in the context of PD and a patient’s brain activity is a naturally occurring correlation. Dependent claim 5 and 10 recite mental processes of “filtering all EEG time series data…”, “finding PD Principal Components Array (“PDPCA”) …”, and “finding Healthy Principal Components Array (“HPCA”) …”. Dependent claim 5 and 10 recite mathematical concepts of “calculating feature vector by determining LPC coefficients for each EEG…”, “creating X_PD by combining the feature vectors…”, “determining PD Mean Array…”, “determining a set of principal components from XPD…”, “creating XH by combining the LPC coefficients…”, “determining Healthy Mean Array (“HMA”) …”, and “determining a set of principal components from XH…”. Dependent claim 7 further recites mental process by performing the steps of filtering, a Burg’s method for determining LPC coefficients, and calculating. Dependent claim 7 recites mathematical concepts of “determining LPC coefficients…”, “calculating aPD from… and aH from…”, “calculating the distance vector DPD…”, “calculating the distance vector DH…”, and “calculating LEAPD index…”. Dependent claim 12 recites a mental process of “filtering said EEG time series data…”. Dependent claim 13 recites a mathematical concept of “determining LPC coefficients…”. Dependent claim 14 recites a mathematical concept of “calculating aPD from… and aH from…”. Dependent claim 15 recites a mathematical concept of “calculating the distance vector DPD…”. Dependent claim 16 recites a mathematical concept of “calculating the distance vector DH…”. Dependent claim 17 recites a mathematical concept of “calculating LEAPD index p…”.
The instant claims recite a step of organizing data by filtering EEG time series data with predetermined filter range and analyzing data through recognizing LPC vectors lie on different hyperplanes, finding the hyperplanes for PD patients and healthy controls, and determining whether a distance is smaller. The BRI of these limitations encompass parsing data into segments for further processing and analyzing numerical data. The human mind is capable of organizing data and analyzing data in this matter. The instant claims recite mathematical calculations of calculating a LEAPD index, determining LPC coefficients which is achieved through mathematical calculations (see instant disclosure [0067]-[0073]), calculating aPD and aH using equations present in the claims, calculating distances using equations present in the claims, creating variables (combined feature vectors, mean array, PCA array, and combined LPC coefficients) using mathematical equations in the claim. The instant claims further recite a law of nature because a correlation between a result for a patient and a patient’s brain activity is a naturally occurring correlation. Thus, claims 1-17 recite abstract ideas and a law of nature.
(Step 2A Prong 2)
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). Integration into a practical application is evaluated by identifying whether there are any additional elements recited in the claim and evaluating those additional elements to determine whether they integrate the exception into a practical application.
The additional element in claim 1, 6, and 11 of using a generic computer to perform judicial exceptions does not integrate the judicial exception into a practical application because this is simply applying the judicial exception to a generic computer without improvement to computer technology (see MPEP 2106.04(d)(1)).
The additional element in claim 1, 6, and 11 of receiving data and outputting data does not integrate the judicial exception into a practical application because this is adding insignificant extra solution activity of data gathering and outputting data because these steps interact with the judicial exceptions only to provide data to be processed and to provide the solution of the judicial exception as output.
Thus, the additional elements do not integrate the judicial exceptions into a practical application and claims 1-17 are directed to the abstract idea.
(Step 2B)
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because:
The additional element in claim 1, 6, and 11 of using a generic computer to perform judicial exceptions is conventional see MPEP 2106.05(b) and MPEP 2106.05(d)(II).
The additional elements in claim 1, 6, and 11 of receiving data and outputting data are conventional see MPEP 2106.05(b) and MPEP 2106.05(d)(II).
The combination of additional elements of a generic computer, receiving data, and outputting data is conventional see MPEP 2106.05(b) and MPEP 2106.05(d)(II).
Thus, the additional elements (alone or in combination) are not sufficient to amount to significantly more than the judicial exception because they are conventional.
Response to Arguments
Applicant's arguments filed 24 September 2025 have been fully considered but they are not persuasive.
Applicant argues that the LEAPD index value can be calculated “in real time”, a patient can have their LEAPD index value constantly monitored, rather than having discrete tests performed and the improved efficiency of the claimed material does not simply make an incremental change to data processing of EEG data, but instead dramatically improves technology by allowing EEG scanners and accompanying equipment to be used in entirely new applications. Applicant argues the claimed technology allows for the real-time calculation and output of LEAPD index values, which presents new and substantial uses of EEG technology in the treatment of Parkinson’s Disease. (Reply p. 11-14).
This argument has been fully considered but found to be not persuasive. The MPEP states at 2106.05(a) “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements… In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception”. The determination of an improvement to technology has two steps, the identification of additional elements (which define the technology) and the evaluation of the additional elements to determine if the improvement is realized in the additional elements either by the additional elements themselves or the additional element in combination with the judicial exception (i.e. the interaction between the judicial exceptions and the additional elements). In the instant case, the claims recite the additional elements of a generic computer and receiving data. The additional element of the computer interacts with the judicial exceptions in a manner of using a computer system as a tool to perform the judicial exception without a change (or improvement) in the functionality of the computer itself. The additional elements of receiving data and outputting data interacts with the judicial exceptions in a manner of collecting/providing data to be processed by the judicial exceptions which constitutes as insignificant extra solution activity of data gathering and outputting the data processed by the judicial exceptions. The claims do not provide additional elements in which an improvement is realized. The claims do not include using an EEG in the method nor provide an EEG in the system and receiving the EEG data encompasses a process of accessing records that were previously measured (i.e. these claims are not limited to receiving the data directly from an EEG or a process of constant monitoring of a patient using an EEG). Therefore, the argued improvement lies in the judicial exception itself (the processing of the EEG data) and does not constitute as an improvement to EEG technology.
Examiner’s Comment
It is noted the 35 USC 102 rejection below is newly applied due to the new effective filling date given to the pending claims because of the new matter added to the claim. This rejection may be overcome by removing the new matter which was added to the claim and thus restoring the earlier effective filling date.
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 1-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Anjum et al. (Parkinsonism & related disorders 79 (2020): 79-85; newly cited).
Claim 1 is directed to receiving an EEG time series data for use with the system, calculating a Linear-predictive-coding EEG Algorithm in PD (“LEAPD”) index for the EEG time series data with the system to allow for point-of-care delivery of the LEAPD index, wherein: the LEAPD index is calculated using relative distance of a vector, obtained using the vector projection, of one or more Linear Predictive Coding (LPC) coefficients that lie on a first hyperplane for healthy subjects and lie on a second hyperplane for PD subjects, and the first hyperplane and second hyperplane are determined using principal component analysis.
Anjum et al. shows implementing a LEAPD algorithm which is computationally efficient leading increased speed of computation which is interpreted as being implemented on a computing system (Anjum et al. abstract). Anjum et al. shows receiving an EEG signal into a classifier (Anjum et al. page 82 figure 2). Anjum et al. shows processing this EEG data using a classifier which calculates a LEAPD index that is calculated using the relative distance of a vector obtained using vector projections of LPC coefficients that lie on a hyperplane for healthy subjects and a hyperplane for Parkinson Disease subjects (Anjum et al. page 80 right col. and supplemental method section “classifier design”).
Claim 2 is directed to filtering said EEG time series data with predetermined filter range, determining LPC coefficients from the EEG time series data with predetermined order and creating feature vector a, calculating aPD from aPD = a - mPD and aH from aH = a – mH, calculating the distance vector D_PD from PD Principal Components Array (“PDPCA”) using the equation in the claim, calculating the distance vector D_H from Healthy Principal Components Array (“HPCA”) using the equation in the claim, and calculating LEAPD index ρ using 0 ≤ ρ = DH/ DH + DPD < 1.
Anjum et al. shows the classifier utilizes a Bandpass filter before calculating the LEAPD index (Anjum et al. page 82 figure 2 and supplementary methods section “Single-channel classification”). Anjum et al. shows a vector projection formula to determine the minimum distance from the point a to the hyperplane associated with PD subjects and the minimum distance from the point a to the hyperplane associated with healthy subjects (Anjum et al. supplemental method section “classifier design”). Anjum et al. shows using the same equation in the claim as equation 9 to calculate the distance vector and provides that a = (a – mc) (Anjum et al. supplemental method section “classifier design” and equation 9). Anjum et al. shows calculating the LEAPD index using the equation 0 ≤ ρ = D2/ D2 + D1 < 1 where D2 is the distance vector from point a to the hyperplane associated with healthy subjects and D1 is the distance vector from point a to the hyperplane associated with PD subjects (Anjum et al. supplemental method section “classifier design” and equation 10).
Claims 3 and 8 are directed to generating LPC coefficients from the EEG time series data, recognizing that vector of LPC coefficients for PD patients and healthy controls lie in separate hyperplanes, finding the hyperplane for the PD patients and the hyperplane for the healthy controls, calculating the distances between the vector by the LPC coefficients and the PD patient’s hyperplane and the healthy controls hyperplane, and determining whether the distance between the vector created by the LPC coefficients and the hyperplane for the PD patients is smaller than the distance between the vector created by the LPC coefficients and the hyperplane for the healthy controls, and if so, then outputting a positive result of having PD.
Anjum et al. shows feature extraction by calculating LPC coefficients from the EEG data (Anjum et al. page 82 figure 2 and supplemental method section “Feature extraction using LPC”). Anjum et al. shows that the rationale for the classifier was the observation that the vectors of LPC coefficients for PD and healthy subjects fall on two distinct hyperplanes (Anjum et al. page 80 right col.). Anjum et al. shows finding hyperplanes for PD patients and healthy patients using principal component analysis (Anjum et al. page 80 right col. and supplemental methods section “Classifier design”). Anjum et al. shows calculating the distances between the vector of LPC coefficients and the two hyperplanes (Anjum et al. supplemental method section “classifier design” and equation 9). Anjum et al. shows determining whether the distance between the vector is closer to the hyperplane for PD or the hyperplane for healthy patients and if the distance from the vector to the PD hyperplane is smaller (i.e. the vector is closer to the PD hyperplane) than outputting a positive result for PD (Anjum et al. supplemental method section “classifier design” and equation 10).
Claims 4 and 9 are directed to diagnosing the patient from the EEG time series data using said LEAPD index comprises: outputting a negative result for having PD for the patient if the LEAPD index value is less than 0.5; and outputting a positive result for having PD for the patient if the LEAPD index value is greater than 0.5.
Anjum et al. shows that the if the LEAPD index value is less than 0.5 than a negative result for a patient having PD is outputted by the classifier and if the LEAPD index values is greater than 0.5 a positive result for a patient having PD is outputted by the classifier (Anjum et al. page 82 figure 2).
Claims 5 and 10 are directed to utilizing a training dataset of multiple pre-diagnosed EEG time series data and a predetermined value of filter range, LPC order and number of components; filtering all EEG time series data of said training dataset with the predetermined filter range;
calculating feature vector by determining LPC coefficients for each EEG time series data of said training dataset using Burg's method with the predetermined order
Anjum et al. shows the classifier utilizes a Bandpass filter before calculating the LEAPD index (Anjum et al. page 82 figure 2). Anjum et al. shows a training phase for the classifier Anjum et al. shows using a Burg’s method to calculate LPC coefficients (Anjum et al. supplemental methods section “Feature extraction using LPC”).
creating XPD by combining the feature vectors of all EEG time series data from said training set pre-diagnosed as PD by using XPD defined by the equation in the claim, determining PD Mean Array (“PDMA”) by using mPD defined by the equation in the claim, determining a set of principal components from XPD for PD,
Anjum et al. shows a training phase which utilizes feature vectors from PD subjects and creates a set by combining feature vectors from the PD group utilizing equation 5 and determining PD mean array utilizing equation 6, and determining a set of principal compoents for the set of feature vectors from the PD group (Anjum et al. supplemental method section “classifier design” and equations 5-6 in this section).
finding PD Principal Components Array (“PDPCA”) by taking the predetermined number of components from the set of principal components, creating XH by combining the LPC coefficients of all EEG time series data from said training set pre-diagnosed as healthy by using XH defined by the equation in the claim, determining Healthy Mean Array (“HMA”) by using mH defined by the equation in the claim, determining a set of principal components from XH for healthy, finding Healthy Principal Components Array (“HPCA”) by taking the predetermined number of components from the set of principal components from XH for healthy.
Anjum et al. shows a training phase which utilizes feature vectors from healthy subjects and creates a set of feature vectors by combining feature vectors from the healthy group utilizing equation 5 and determining healthy mean array utilizing equation 6, and determining a set of principal components for the set of feature vectors from the healthy group (Anjum et al. supplemental method section “classifier design” and equations 5-6 in this section).
Claim 6 is directed to a system for diagnosing Parkinson’s Disease comprising a computer processing system with a processor, a storage medium associated with the processor, hardware associated with the processor configured to receive EEG data for diagnosis, a software module to calculate a LEAPD index for the EEG data, a software module configured to output a result for a patient from the EEG data using the LEAPD index wherein the LEAPD index is calculated using relative distance of a vector of one or more Linear Predictive Coding (LPC) coefficients that lie on a first hyperplane for healthy subjects and lie on a second hyperplane for PD subjects, wherein the first hyperplane and second hyperplane are determined using principal component analysis, and wherein the LEAPD index is calculated to allow for point-of-care delivery of the results.
Anjum et al. shows implementing a LEAPD algorithm which is computationally efficient leading increased speed of computation which is interpreted as being implemented on a computing system (Anjum et al. abstract). Anjum et al. shows receiving an EEG signal into a classifier (Anjum et al. page 82 figure 2). Anjum et al. shows processing this EEG data using a classifier which calculates a LEAPD index that is calculated using the relative distance of a vector obtained using vector projections of LPC coefficients that lie on a hyperplane for healthy subjects and a hyperplane for Parkinson Disease subjects (Anjum et al. page 80 right col. and supplemental method section “classifier design”).
Claim 7 is directed to a filtering step of the EEG data, a Burg’s method step for determining LPC coefficients form the EEG data with predetermined order and creating feature vector a, a step of calculating aPD from aPD = a - mPD and aH from aH = a – mH, a step of calculating the distance vector DPD from PDPCA using the equation in the claim, calculating the distance vector DH from HPCA using DH defined by the equation in the claim, a step of calculating LEAPD index ρ using 0 ≤ ρ = DH/ DH + DPD < 1.
Anjum et al. shows the classifier utilizes a Bandpass filter before calculating the LEAPD index (Anjum et al. page 82 figure 2 and supplementary methods section “Single-channel classification”). Anjum et al. shows using a Burg’s method to calculate LPC coefficients (Anjum et al. supplemental methods section “Feature extraction using LPC”). Anjum et al. shows a vector projection formula to determine the minimum distance from the point a to the hyperplane associated with PD subjects and the minimum distance from the point a to the hyperplane associated with healthy subjects (Anjum et al. supplemental method section “classifier design”). Anjum et al. shows using the same equation in the claim as equation 9 to calculate the distance vector and provides that a = (a – mc) (Anjum et al. supplemental method section “classifier design” and equation 9). Anjum et al. shows calculating the LEAPD index using the equation 0 ≤ ρ = D2/ D2 + D1 < 1 where D2 is the distance vector from point a to the hyperplane associated with healthy subjects and D1 is the distance vector from point a to the hyperplane associated with PD subjects (Anjum et al. supplemental method section “classifier design” and equation 10).
Claim 11 is directed to a PD EEG system comprising a computer processor arranged to be in operational communication with an electroencephalograph, a storage system for storing data on a storage medium, wherein the processor and storage system are configured for: receiving an EEG time series data, calculating a LEAPD index for the EEG time series data to allow for point-of-care delivery of the LEAPD index, wherein the wherein: the LEAPD index calculation uses first hyperplane for healthy subjects and a second hyperplane for PD subjects, and the first and second hyperplanes are determined through an algorithm based on vector projection and the use of principal components.
Anjum et al. shows implementing a LEAPD algorithm which is computationally efficient leading increased speed of computation which is interpreted as being implemented on a computing system (Anjum et al. abstract). Anjum et al. shows receiving an EEG signal into a classifier (Anjum et al. page 82 figure 2). Anjum et al. shows processing this EEG data using a classifier which calculates a LEAPD index that is calculated using the relative distance of a vector obtained using vector projections of LPC coefficients that lie on a hyperplane for healthy subjects and a hyperplane for Parkinson Disease subjects (Anjum et al. page 80 right col. and supplemental method section “classifier design”).
Claim 12 is directed to wherein the calculating the LEAPD index for the EEG time series data comprises filtering said EEG time series data with predetermined filter range.
Anjum et al. shows the classifier utilizes a Bandpass filter before calculating the LEAPD index (Anjum et al. page 82 figure 2 and supplementary methods section “Single-channel classification”).
Claim 13 is directed to wherein the calculating the LEAPD index for the EEG time series data comprises determining LPC coefficients from the EEG time series data with predetermined order and creating feature vector a. Claim 14 is directed to wherein the calculating the LEAPD index for the EEG time series data comprises calculating aPD from aPD = a - mPD and aH from aH = a – mH. Claim 15 is directed to wherein the calculating the LEAPD index for the EEG time series data comprises calculating the distance vector DPD from PDPCA using the equation in the claim. Claim 16 is directed to wherein the calculating the LEAPD index for the EEG time series data comprises calculating the distance vector DH from HPCA using DH defined by the equation in the claim.
Anjum et al. shows a vector projection formula to determine the minimum distance from the point a to the hyperplane associated with PD subjects and the minimum distance from the point a to the hyperplane associated with healthy subjects (Anjum et al. supplemental method section “classifier design”). Anjum et al. shows using the same equation in the claim as equation 9 to calculate the distance vector and provides that a = (a – mc) (Anjum et al. supplemental method section “classifier design” and equation 9).
Claim 17 is directed to wherein the calculating the LEAPD index for the EEG time series data comprises calculating LEAPD index ρ using 0 ≤ ρ = DH/ DH + DPD < 1.
Anjum et al. shows calculating the LEAPD index using the equation 0 ≤ ρ = D2/ D2 + D1 < 1 where D2 is the distance vector from point a to the hyperplane associated with healthy subjects and D1 is the distance vector from point a to the hyperplane associated with PD subjects (Anjum et al. supplemental method section “classifier design” and equation 10).
Conclusion
No claims are allowed.
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 JONATHAN EDWARD HAYES whose telephone number is (571)272-6165. The examiner can normally be reached M-F 9am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Olivia Wise can be reached at 571-272-2249. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.E.H./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685