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 .
Claims Accounting
Applicant's arguments, filed 05/08/2026, have 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.
Applicant has amended claims 1, 8, and 17-18, filed 05/08/2026.
Claims 1-4, 7-14, and 17-18 are the current claims hereby under examination.
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 1-4, 7-14, and 17-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims 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.
STEP 1
Regarding claim 1, the claim recites a series of steps or acts, including generating a channel-level classification and obtaining a segment-level slowing classification. Thus, the claim is directed to a process, which is one of the statutory categories of invention.
STEP 2A, PRONG ONE
The claim is then analyzed to determine whether it is directed to any judicial exception. The steps of obtaining a first classifier, generating a channel-level classification using the first classifier based on the first feature set, obtaining a second classifier, and obtaining a segment-level slowing classification for the respective one or more segments set forth a judicial exception. These steps describe the use of mathematical relationships, mathematical formulas or equations, and/or mathematical calculations. Thus, the claim is drawn to a Mathematical Concept, which is an Abstract Idea.
Further, the steps of extracting a first feature set and aggregating the segment-level slowing classifications sets forth a judicial exception. These steps describe a concepts 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.
STEP 2A, PRONG TWO
Next, the claim as a whole is analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. The claim fails to recite an additional element or a combination of additional elements to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. Claim 1 recites displaying potential cerebral dysfunction by displaying the EEG-level scalp heatmap representing the percentages of segments exhibiting slowing on a user interface, which is merely adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)). The displaying potential cerebral dysfunction by displaying the EEG-level scalp heatmap on a user interface does not provide an improvement to the technological field, the displaying does not effect a particular treatment or effect a particular change, nor does the method use a particular machine to perform the Abstract Idea. It is noted that the display of potential cerebral dysfunction by displaying the EEG-level scalp heatmap on a user interface is based and dependent on the limitation of generating an EEG-level scalp heatmap based on the channel-level percentages of segments exhibiting slowing. This step in conjunction with the displaying of potential cerebral dysfunction by displaying the EEG-level scalp heatmap on a user interface amounts to displaying the output of the judicial exception and does not impose a meaningful limitation on the judicial exception.
STEP 2B
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception. Besides the Abstract Idea, the claim recites additional steps of performing a sequence of artifact removal processes, generating an EEG-level scalp heatmap, and displaying cerebral dysfunction. Filtering and pre-processing (i.e., performing a sequence of artifact removal processes) EEG data is well-understood, routine and conventional activity for those in the field of medical diagnostics and are considered insignificant pre-solution activity, e.g., mere data gathering steps necessary to perform the Abstract Idea. The steps of generating an EEG-level scalp heatmap and displaying the potential cerebral dysfunction amount to mere insignificant post-solution activity, e.g., insignificant application of 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 them from well-understood, routine, and conventional data processing and display activity engaged in by medical professionals prior to Applicant's invention.
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.
The dependent claims also fail to add significantly more to the abstract independent claims as they generally recite method steps pertaining to the details of data gathering and pre-processing the data. The obtaining, performing, extracting, generating, aggregating, and displaying steps recited in the independent claim maintain a high level of generality even when considered in combination with the dependent claims.
Regarding claims 17 and 18, it is well established that the mere physical or tangible nature of additional elements do not automatically confer eligibility on a claim directed to an abstract idea. Claim 17 recites the abstract ideas of claim 1 stored as computer-readable instructions in a memory with a processor to perform the abstract idea. Claim 18 recites the abstract ideas of claim 1 stored as instruction on a non-transitory computer-readable storage. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application.
Examiner’s Note
The following is a statement of reasons for the lack of prior art rejections:
Regarding claim 1, the closest prior art is identified as US Patent Publication 2015/0038869 by Simon et al. – previously cited (hereinafter “Simon”) in view of US Patent Publication 2012/0101401 by Faul et al. – previously cited (hereinafter “Faul”) in view of US Patent Publication 2018/0049896 by Connolly et al. – previously cited, (hereinafter “Connolly”).
Simon teaches a method for detecting presence of slowing patterns in an EEG sample comprising a plurality of channels of EEG signals, each channel comprising one or more segments (Epochs of the time-series [0083, 0085]) the method comprising: obtaining a first classifier that is trained to classify EEG samples as containing abnormal slow waves or not (logistic regression using an optimal cut-point as a classifier [0088]; AD brain exhibits a spectral slowing relative to CTL subjects [0182]), wherein each abnormal slow wave is a waveform present in the EEG sample exhibiting spectral slowing (AD brain exhibits a spectral slowing relative to CTL subjects [0182]); performing a sequence of artifact removal processes on the EEG sample to generate a preprocessed EEG sample (Pre-processing artifact detection algorithm detects the epochs to be removed an analysis is only performed on epochs not identified as containing artifacts [0083]); extracting a first feature set from the preprocessed EEG sample (“Once the spectral analysis code has transformed each epoch of artifact free time series EEG data, a feature extraction algorithm can assess each block of transformed data to create a list of features or variables or biomarkers extracted from each block of EEG data conducted during an individual task.”, [0085]); generating a classification, using the first classifier and based on the first feature set, of whether the EEG sample contains abnormal slow waves or not (EEG features can be used in combination as an input to a statistical predictive model, which can classify the state of the brain [0064; 0090-0116]), wherein the sequence of artifact removal processes comprises removal of one or more ocular artifacts (“Excessive Signal segments occur during eye blink”, [0161]) and removal of one or more electrode artifacts (“or non-physiological electrical noise including movement of the EEG dry electrode” [0161]).
Simon does not teach wherein the first classifier classifies the EEG sample by channel-wise classification of the one or more segments, of each of said channels in the EEG sample, as containing slow waves or not containing slow waves, based on the first feature set; obtaining a second classifier that is trained to classify the one or more segments of each said channel, as containing abnormal slow waves or not based on a second feature set that is extracted from the first feature set and/or from the plurality of channel-wise classifications of the one or more segments of each said channel; and obtaining a slowing classification for the respective one or more segments, by passing the second feature set to the second classifier to generate the slowing classification whereby a segment-level classification is obtained for each segment of each channel.
Fig. 3 of Faul teaches a method for generating features for each segment in each channel of a plurality of EEG channels and passing the features to a classifier in which the classifier generates a classification for each segment in each channel relating to the state of the EEG signal ([0033-0037]). Faul further teaches a channel fusion step (step 312; i.e., second classifier) that uses the output from the plurality of channel classifications (i.e., second feature set) to obtain a binary result (i.e., classification) for each epoch ([0188-0189]). Faul also teaches outputting the indication (i.e., outputting the classification) of the classification in step 316. Applying the classification to each channel and fusing the combined classifications provides increased classification accuracy ([0043]). It is noted that Faul teaches that a classification is obtained for each epoch (i.e., segment).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the method of Simon such that the first classifier classifies the EEG sample by channel-wise classification of the one or more segments, of each of said channels in the EEG sample, as containing slow waves or not containing slow waves, based on the first feature set, and to have modified the method to include obtaining a second classifier that is trained to classify the one or more segments, of each said channel, as containing abnormal slow waves or not based on a second feature set that is extracted from the first feature set and/or from the plurality of channel-wise classifications of the one or more segments of each said channel; obtaining a slowing classification for the respective one or more segments, by passing the second feature set to the second classifier to generate the slowing classification whereby a segment-level classification is obtained for each segment of each channel; as applying the classifier to each channel separately and then fusing the combined predictions provides increased classification accuracy ([0043]).
The combination of Simon and Faul do not teach generating an EEG-level scalp heatmap and displaying potential cerebral dysfunction by displaying the EEG-level heatmap representing percentages on a user interface.
Figs. 12-14 of Connolly teach an EEG-level heatmap generated from features derived from each channel of the EEG, illustrating the spatial distribution of the features. The values corresponding to the shades are used to represent the percentage of features of that channel that are related to abnormal brain activity normalized to the maximum number of features. The EEG-level scalp heatmaps can be used to distinguish spatial differences in EEG activity that can help localize the regions where abnormalities exist ([0097-0099]).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the method of Simon in view of Faul to include generating an EEG-level scalp heatmap and displaying potential cerebral dysfunction by displaying the EEG-level heatmap representing percentages on a user interface, as generating and displaying EEG-level heatmaps can help to distinguish spatial differences in EEG activity that can help localize the regions where abnormalities exist, as taught by Connolly ([0097-0099]).
The combination of Simon, Faul, and Connolly fails to teach aggregating the segment-level slowing classifications to determine, for each channel, a channel-level percentage of segments exhibiting slowing; generating an EEG-level scalp heatmap based on the channel-level percentage of segments exhibiting slowing, or displaying potential cerebral dysfunction by displaying the EEG-level scalp heatmap representing the percentages of segments exhibiting slowing on a user interface.
Similar limitations are present in independent claims 17 and 18. The limitations of these claims are patentably distinct over the prior art cited in this Office action and any other prior art.
Response to Arguments
Applicant’s arguments, filed 05/08/2026 have been fully considered.
The amendments to claims 1, 8, and 18 overcome the objections of record.
The amendments to the claims overcome the rejections under 35 U.S.C. 112(a) and 112(b) of claims 1, 17, and 18.
Applicant’s arguments regarding the rejections under 35 U.S.C. 101 are acknowledged.
Applicant argues that Examiner has failed to take the limitation of “generating an EEG-level scalp heatmap based on the channel-level percentages of segments exhibiting slowing” into account when evaluating claim 1 under Step 2A, Prong 2. Applicant further argues that Examiner fails to consider claim 1 as a whole in this analysis. These arguments are not found persuasive. The limitation of generating an EEG-level scalp heatmap amounts to merely preparing the outputs of the judicial exceptions for display. Therefore the effect of both limitations in combination amounts to preparing to display and displaying the outputs of the judicial exceptions, which amounts to insignificant post-solution activity. This clarification has been reflected in the rejections under 35 U.S.C. 101 above.
Applicant argues that determining the degree of slowing along each EEG channel in the form of a scalp plot allows for a determination of the degree and location of slowing in the patient, which is useful in EEG reviewing and annotation processes and better understanding of the severity of a neurological condition of cerebral dysfunction. Applicant argues that this benefit provides an improvement in the technical field. This argument is not found persuasive, as it is not commensurate in scope with the claims. Claim 1 recites a method that determines the degree (i.e., percentage) of slowing and location (i.e., the respective channel) of slowing in a patient, however, the claims are silent regarding method steps relying upon the displayed heatmap for reviewing of EEG, annotation of EEG, or a determination/understanding of a neurological condition of cerebral dysfunction of the patient.
Applicant refers Examiner to the case study of claim 2 of Example 49 of the July Subject Matter Eligibility Examples. Examiner notes that claim 2 of Example 49 of the July Subject Matter Eligibility Examples, the claim is drawn to method wherein a sample is collected, analyzed, and a specific treatment is administered based on the analysis of the sample. This example differs from the claim 1 of the instant application in that claim 1 of the instant application does not recite any specific treatment to be administered based on the identification/analysis of the collected data. Therefore the reference to the case study of claim 2 of Example 49 of the July Subject Matter Eligibility Examples is not found persuasive in the analysis of claim 1 under 35 U.S.C. 101.
Applicant argues that the generation and display of the EEG-level scalp heatmap representing the channel-level percentages of segments exhibiting slowing on a user interface is neither routine nor conventional, and therefore should pass Step 2B of the Subject Matter Eligibility Analysis. This argument is not found persuasive. It is noted that the generation and display of the EEG-level scalp heatmap representing the channel-level percentages of segments exhibiting slowing on a user interface is analogous to the generation and display of the EEG-level scalp heatmap of the judicial exception. Displaying EEG data using a scalp heatmap (i.e., topographic map) is well-understood in the field of medical diagnostics. When analyzing the claim as a whole, these limitations are drawn towards using a well-understood method of displaying a judicial exception. Applicant further argues that the combination of Simon, Faul, and Connolly fails to teach these limitations. While the limitations are identified as being patentably distinct over the prior art, the Subject Matter Eligibility Analysis is distinct from patentability over prior art references. MPEP 2106.05-I states: “Although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination”.
Conclusion
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 NELSON A GLOVER whose telephone number is (571)270-0971. The examiner can normally be reached Mon-Fri 8:00-5:00 EST.
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, Jason Sims can be reached at 571-272-7540. 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.
/NELSON ALEXANDER GLOVER/Examiner, Art Unit 3791
/ADAM J EISEMAN/Primary Examiner, Art Unit 3791
Prosecution Insights