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 .
Status Of Claims
This action is in reply to the application filed on 09/27/2024.
Claims 1-24 are currently pending and have been examined.
Claim Rejections – 35 § 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 1-24 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 1, recites in part, “characterizing the ictal-related chirp patterns”. It is unclear how the ictal-related chirp patterns, are being characterized. Is there an algorithm or formula that is being used to characterize the ictal-related chirp patterns? Claims 21 and 22 recite similar limitations. Claims 1, 21 and 22 are therefore found to be indefinite, because the resulting claims does not clearly set forth the metes and bounds of the patent protection desired. All dependent claims, namely claims 2-20 and 23-24 are rejected for at least the same reason.
Claim 2, recites in part, “confirming that the identified event is statistically significant”. It is unclear how the identified event is bring confirmed as statistically significant. Is there an algorithm or formula that is being used to confirm the statistical significances of the identified event? Claim 2 is therefore found to be indefinite, because the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. All dependent claims, namely claim 3 is rejected for at least the same reason.
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 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-24: Step 1
Claims 1-20 are drawn to a computer implemented method for identifying ictal-related chirp patterns from recordings of electrophysiological signals, which is within the four statutory categories (i.e. process). Claim 21 is drawn to an epilepsy monitoring unit, which is within the four statutory categories (i.e. machine). Claims 22-24 are drawn to a computer implemented method for electrophysiological signals, which is within the four statutory categories (i.e. process).
Claims 1-24: Step 2A Prong One
Claim 1 recites identifying ictal-related chirp patterns from recordings of electrophysiological signals by determining onset and offset times of the ictal-related chirp patterns, characterizing the ictal-related chirp patterns; classifying spectro-temporal morphology of the ictal-related chirp patterns. Claims 21 and 22 recite similar limitations.
These limitations, as drafted, given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass managing personal behavior by manually following rules or instructions, which is a subgrouping of Certain Methods of Organizing Human Activity. But for the recitation of generic computer components, these limitations encompass a user identifying ictal-related chirp patterns from recordings of electrophysiological signals by determining onset and offset times of the ictal-related chirp patterns, characterizing the ictal-related chirp patterns; classifying spectro-temporal morphology of the ictal-related chirp patterns. These steps could be carried out manually by a user following rules or instructions, which is a subgrouping of Certain Methods of Organizing Human Activity. Claims 21 and 22 recite similar limitations.
Claims 2-20 and 23-24 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea, but for the recitation of generic computer components. For example, but for the recitation of generic computer components, Claim 2 further defines determining the onset and offset times of the Ictal-related chirp patterns. Claim 3 further defines high-frequency range. Claim 4 further defines tracking chirp morphology. Claim 5 further defines characterizing the ictal-related chirp pattern. Claims 6, 15 and 16 further define classifying the spectro-temporal morphology of the ictal-related chirp patterns. Claims 7 and 10 further define generating the data about the chirp morphology. Claim 8 further defines extracting the time-frequency representation. Claim 9 further defines determining the onset and offset of chirp. Claim 11 further defines the electrophysiological signals. Claim 12 further defines a chirp. Claim 13 further defines characterizing at least one of progression of a disease and effect of a drug. Claim 14 further defines elucidating a mechanism of action of a drug. Claim 17 further defines determining whether the ictal-related chirp patterns arise from a spontaneous ictal event or an evoked discharged based on timing of the chirp within the ictal event. Claim 18 further defines verifying the spectro-temporal morphology classification of the ictal-related chirp patterns. Claim 19 further defines generating visual elements related to the data for the ictal-related chirp patterns and chirp morphology. Claim 20 further defines transmitting the data for the ictal-related chirp patterns and visual elements related to the data. Claim 23 further defines the neuromarker comprises a plurality of features derived from chirp-like patterns in the electrophysiological signals. Claim 24 further defines generating neuromarker-based evaluations or developing a personalized treatment plan to improve seizure control. Therefore, these claims are similarly drawn to Certain Methods of Organizing Human Activity.
Claims 1-24: Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas along with insignificant, extra-solution data gathering activity, and adding limitations similar to adding the words “apply it” to the abstract idea. Claim 1 and 22 do not specifically recite the additional elements other than in the preamble of “computer implemented method” steps. Claim 21 recites additional elements of an epilepsy monitoring unit comprising sensor(s) for capturing electrophysiological signals and processing systems that includes processor(s) and memories.
Claims 1-24, directly or indirectly, recite the following generic computer components: “computer-implemented method”, and “a processing system with one or more processors and one or more memories” which are similar to adding the words “apply it” to the abstract idea. The written description discloses that the recited computer components encompass generic components including “systems and methods described herein may be implemented in a combination of both hardware and software. These embodiments may be implemented on programmable computers, each computer including at least one processor, a data storage system (including volatile memory or non-volatile memory or other data storage elements or a combination thereof) “ (see at least Paragraph [00172]). Although the additional element “machine learning model” limits the identified judicial exceptions, this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning), and thus fails to add an inventive concept to the claims. See MPEP 2106.05 (h). As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely include[ing] instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application.
Claims 1-24: Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration into a practical application, the additional elements are recited at a high level of generality, and the written description indicates that these elements are generic computer components. Using generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”). As explained above, the generic computer components are at best the equivalent of merely adding the words “apply it” to the judicial exception.
Receiving and transmitting data over a network (i.e. receiving and communicating data or signals) has been recognized as well-understood, routine, and conventional activity of a general-purpose computer (see MPEP 2106.05(d) and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)).
Gathering and analyzing information using conventional techniques and displaying the result has also been found to be insufficient to show an improvement to technology, (see MPEP 2106.05(a) and TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48).
Insignificant, extra solution, data gathering activity has been found to not amount to significantly more than an abstract idea (see MPEP 2106.05(g) and Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)). Therefore, the high-level recitation of an output of results also fails to include additional elements that are sufficient to amount to significantly more than the judicial exception.
Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
Allowable Subject Matter
In the closest prior art, WO 2016/110804 A1 to Burton discloses an online “monitoring” of one or more “events of interest” and/or “biomarkers” and/or “neurology” signals, combined with automatic online processing capable of detecting seizure precursors via singular or clusters of “spectral events of interest” related to neurological or nervous system disorders such as (but not limited to) Parkinson’s, epilepsy or seizures in general (generic). In additional prior art, “Current Classification of Seizures and Epilepsies: Scope, Limitations and Recommendations for Future Action” to Sarmast et al. (hereinafter “Sarmast”) discloses new classifications for seizures types ((i) focal motor: epileptic spasms, hyperkinetic and automatism - in this new classification, seizures like myoclonic, tonic, tonic-clonic, clonic and atonic which were considered only generalized are now included in focal onset seizures; (ii) focal non-motor: emotional or behavioral arrest; and (iii) generalized: epileptic spasms, myoclonic-atonic, myoclonic-tonic-clonic, and absence with eyelid myoclonia) encourages patient characterization based on the available resources like electroencephalography (EEG) findings, video recording, neuroimaging, or genetic workup. In further additional prior art, US 2016/0228705 A1 to Crowder et al. (hereinafter “Crowder”) discloses extracts features from the electrographic signals, and when the extracted features satisfy certain criteria, detects a neurological event type.
The following is a statement of reasons for the indication of allowable subject matter: Although Burton discloses automatic online processing capable of detecting seizure precursors, and Sarmast discloses new classifications for seizures types, both fail to disclose identifying ictal-related chirp patterns from recordings of electrophysiological signals by determining onset and offset times of the ictal-related chirp patterns, characterizing the ictal-related chirp patterns and classifying spectro-temporal morphology of the ictal-related chirp patterns.
The applicants teaches a computer implemented method and system for identifying ictal-related chirp patterns from recordings of electrophysiological signals by determining onset and offset times of the ictal-related chirp patterns, characterizing the ictal-related chirp patterns and classifying spectro-temporal morphology of the ictal-related chirp patterns. These limitations in conjunction with other limitations in the claims were not shown by, would not have been obvious over, nor would have been fairly suggested by the prior art of record.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joy Chng whose telephone number is 571.270.7897. The examiner can normally be reached on Monday-Friday, 9:00am-5:00pm.
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/Joy Chng/
Primary Examiner, Art Unit 3686
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