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 Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
claim 15:
a modeling unit with corresponding structure seen in Fig. 2; and
a processing unit, with corresponding structure found in para. [0104] of applicant’s pre-publication.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claim(s) 1-5 and 7-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Grosseline et al. (WO 2020/002519).
With respect to claim 1, Grosseline et at. teaches a method of generating a prediction model configured to predict a quality value (Qix) of an electrode configuration (page 7 lines 1-2) for electrophysiological measurements (page 7 lines 16-20), wherein the electrode configuration is a positional arrangement of a plurality of electrodes (on a user), the method comprising: inputting a plurality of training electrode signals for a plurality of known electrode configurations, and with a plurality of known quality values to the prediction model (Grosseline et al. teaches a electroencephalographic training set TR1 which includes a plurality of training electrode signal TS1 used to train a predicative model, i.e. as the examiner considers the taught classification algorithms as reading on the claimed predictive model; page 19 line 15 to page 20, line 14, based on defined quality metrics used to compare a difference threshold between the training set quality and the predicted quality); receiving (120) a plurality of predicted quality values (Qix; page 23 line 9 to page 24 line 3) from the prediction model (i.e. the first and second classification models, which read on the claimed “prediction model”); and determining parameter values (i.e. weights and k values) of the prediction model (i.e. first and second classification models) based on a difference between the plurality of known quality values and the plurality of predicted quality values (as Grosseline et al teaches using features values of the signals to determine the weights and k values based on determined difference between the a known quality point and an unknown quality point during the classification training; page 20 line 20 to page 21 line 5).
With respect to claim 2, Grosseline et at. teaches the method wherein the prediction model uses a classifier (i.e. a first classifier; page 12 lines 1-3).
With respect to claim 3, Grosseline et at. teaches the method wherein the plurality of training electrode signals (found in the train sets; TR1) comprises training electrode signals with the one or more signal variables (i.e. statistical descriptors; page 18 lines 15-19), and wherein the prediction model (i.e. first and second classifiers) is configured to represent an interaction between with the one or more signal variables (i.e. the statistical descriptors) and the plurality of known quality values (as Grosseline et al. teaches the qualities are used by the prediction model classifier and are determined using statistical descriptors; page 18 lines 15-19).
With respect to claim 4, Grosseline et at. teaches the method wherein the one signal variable includes kurtosis of channels (page 18 lines 15-19).
With respect to claims 5 and 12, Grosseline et at. teaches the method wherein the quality value (Qix) comprises accuracy (as the quality equation taught on page 18 relates to measures that involve accuracy, such as performance metrics in machine learning or statistics).
With respect to claim 7, Grosseline et at. teaches a method for processing electrode signals from an electrode configuration for electrophysiological measurements (as read in the Abstract), the method comprising: generating a prediction model (i.e. the first and second classification algorithms) according to claim 1 (as rejected above); obtaining electrode signals from an electrode configuration for obtaining electrophysiological measurements of a subject; and obtaining a prediction of a quality value of the electrode configuration based on inputting the electrode signals to the generated prediction model (as Grosseline et al. teaches, the trained prediction model is used to give in real-time positional correction based on obtained signals from placed electrodes on a user and generated results from the predication model based on the electrode signals; page 4 lines 21-25).
With respect to claim 8, Grosseline et at. teaches the method further comprising: identifying a modification to the electrode configuration based on the predicted quality value of the electrode configuration (as the result from the prediction model will indicate if the electrode configuration is not providing accurate enough results, thereby prompting the user to re-position the electrodes until the prediction model indicates the quality of the signals meets a defined threshold; page 4 lines 21-25 and page 3 lines 1-7).
With respect to claim 9, Grosseline et at. teaches the method further comprising: classifying (using the taught classifier) the electrode configuration into one of a plurality of classifications (similar to CLAS1 and CLAS2 used to train the model) based on the predicted quality value of the electrode configuration (i.e. as Grosseline teaches based on the quality metric used to steer feature values from the electrode signals to their respective classification).
With respect to claim 10, Grosseline et at. teaches the method wherein classifying the electrode configuration comprises: comparing the predicted quality value of the electrode configuration against at least one threshold value; and classifying the electrode configuration into one of a plurality of classifications based on the comparison result (as taught in claim 11 of the method taught by Grosseline et al. which details of each EEG signal calculating a spectral distance between the spectrum of each EEG signal segment and a reference spectrum; and comparing said spectral distance to a predefined threshold to determine the presence of a muscular artifact in the EEG signal segment in the quality class and assign it a class).
With respect to claim 11, Grosseline et at. teaches a method for processing electrode signals from an electrode configuration for electrophysiological measurements using a prediction model (as read in the Abstract) configured to predict a quality value (i.e. a quality of the signal; Abstract) of an electrode configuration (as based on the model, a user will be presented a quality of placement based on the prediction model and received signals; page 4 lines 21-25), wherein the electrode configuration is a positional arrangement of a plurality of electrodes (as indirectly taught on page 4 lines 21-25 which details the positional location of the electrodes with be assessed based on a quality of the signal), the method comprising: obtaining electrode signals from an electrode configuration for obtaining electrophysiological measurements of a subject (as page 4 lines 21-25 details the positioned signal for obtaining electrophysical measurements from a patient); and acquiring a prediction of a quality value of the electrode configuration based on inputting the electrode signals to the generated prediction model (as the classification model with provide the user of the electrodes an indication of the quality of the placement based on the signals and their respective classification indicating a good or bad placement).
With respect to claim 13, Grosseline et at. teaches the method further comprising: identifying a modification to the electrode configuration based on the predicted quality value of the electrode configuration (as Grosseline the output of the prediction model is to indicate a proper improper position of the electrodes based the quality of the signal so to improve their respective position, if necessary, page 4 lines 21-25).
With respect to claim 14, Grosseline et at. teaches the method further comprising: classifying the electrode configuration into one of a plurality of classifications based on the predicted quality value of the electrode configuration (as taught in claim 11 of the method taught by Grosseline et al. which details of each EEG signal calculating a spectral distance between the spectrum of each EEG signal segment and a reference spectrum; and comparing said spectral distance to a predefined threshold to determine the presence of a muscular artifact in the EEG signal segment in the quality class and assign it a class).
With respect to claim 15, Grosseline et at. teaches a system for processing electrode signals from an electrode configuration (Abstract) for electrophysiological measurements using a prediction model (i.e. as Grosseline et al. teaches using classification models for prediction) configured to predict a quality value of an electrode configuration (page 4 lines 20-25), wherein the electrode configuration is a positional arrangement of a plurality of electrodes (as Grosseline et al. teaches the prediction model is used to determine in the position of the electrodes are in the right location to ensure a predefined quality threshold to ensure the most accurate signal), the system comprising: a modelling unit (i.e. a data processing system; page 8 lines 12-14) configured to generate a prediction model (i.e. first and second classification models) configured to predict a quality value (i.e. quality index, Qix) of an electrode configuration for electrophysiological measurements (as initially position on a user); an interface (i.e. EEG channel) configured to obtain electrode signals from an electrode configuration (page 12 lines 4-26); and a processing unit (as part of the data processing system; page 8 lines 12-14) configured to acquire a prediction of a quality value of the electrode configuration (as determined using the equation seen on page 18) based on inputting the electrode signals to the generated prediction model (where the model using the first and second classification algorithms determine if the quality index indicates a correctly position electrode or not based on that signals classification).
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.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Grosseline et al. (WO 2020/002519) in view of Hansen et al. (WO 2019/120438).
With respect to claim 6, Grosseline et at. teaches all that is claimed above in the rejection of claim 1, but remains silent regarding the method wherein the determining parameter values of the prediction model comprises: adjusting parameter values of the prediction model so as to decrease a difference between: a known quality value associated with training electrode signals for a known electrode configuration; and a predicted quality value received from the prediction model for the known electrode configuration.
Hansen et al. teaches a similar method that includes adjusting parameter values of a prediction model so as to decrease a difference between: a known quality value associated with training electrode signals for a known electrode configuration; and a predicted quality value received from the prediction model for the known electrode configuration (as Hansen et al. teaches [a]fter the initial convolution neural network module 313 outputs identified features, the initial weights of the convolution neural network module 313 may be adjusted so the features identified by the convolution neural network module 313 closely align with the known, verified, and/or confirmed features. For example, backpropagation can be performed on the convolution neural network module 313 to adjust the weights of the filters to minimize the differences between the features identified by the convolution neural network module 313 and the known, verified, and/or confirmed features. Over several training iterations, optimal or otherwise suitable weights can be learned for the filters of the convolution neural network module 313 and, therefore, the convolution neural network module 313 can be sufficiently trained to identify the reference locations 354, 356, the stoma 324, the ostomy appliance 2, and/or other objects included in an image. For example, a sufficiently trained convolution neural network module 313 has obtained an overall accuracy exceeding 95% when identifying reference locations 354, 356, the stoma 324, the ostomy appliance 2).
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to substitute the predictive model in Grosseline with the BPNN taught in Hansen et al. to achieve the predictable results of determining the best configuration for electrodes on a user. Further, such a modification enhances the models performance using an adjustable quality metric, thereby improving the electrode placement of Grosseline iteratively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wen et al. (2020/0121293) which teaches determining the proper placement of an electrode on a user using quality data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW G MARINI whose telephone number is (571)272-2676. The examiner can normally be reached Monday-Friday 8am-5pm.
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/MATTHEW G MARINI/Primary Examiner, Art Unit 2853