DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
2. According to the Amendment, filed 28 August 2024, the status of the claims is as follows:
Claims 21-40 are new; and
Claims 1-20 are cancelled.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, i.e. abstract idea, without significantly more.
Step 1 of the Patent Subject Matter Eligibility Guidance (see MPEP 2106.03):
Claim 21-30 are directed to a “method”, which describes one of the four statutory categories of patentable subject matter, i.e. a process. Claims 31-40 are directed to a “system”, which describes one of the four statutory categories of patentable subject matter, i.e. a machine.
Step 2A of the Revised Patent Subject Matter Eligibility Guidance (see MPEP 2106.04 and the 2019 Revised Patent Subject Matter Eligibility Guidance, FR Vol. 84, No. 4, 07 January 2019):
Claim(s) 21-40, recite the following mental process:
classifying, using a pre-trained machine learning classifier, each of the plurality of single channel EEG time segments as one of normal or abnormal, wherein the pre-trained machine learning classifier has been trained using a training set of EEG data;
identifying a set of the plurality of single channel EEG time segments classified as abnormal to indicate a seizure event lasting longer than an individual single channel EEG time segment;
creating an annotation list comprising an ordered set of the plurality of single channel EEG time segments; and
providing an indication of the seizure event and the annotation list to facilitate an expedited review of the EEG data, …
This judicial exception is not integrated into a practical application because the additional limitations of “collecting EEG data from at least two self-contained wireless single-channel EEG sensors disposed on a scalp of a patient, the EEG data comprising a plurality of single channel EEG time segments;” in claim 21, and “at least two self-contained wireless single-channel EEG sensors configured to be disposed on a scalp of a patient and collect EEG data, the EEG data comprising a plurality of single channel EEG time segments;” in claim 31, add insignificant pre-solution activity to the abstract idea that merely collects data to be used by the mental process. Furthermore, “wherein the method is performed by one or more processors” in claim 21, and “a non-transitory computer readable medium storing program instructions that, when executed by at least one processor, cause the at least one processor to:” in claim 31, are merely parts of a computer to be used as a tool to perform the mental process.
Step 2B of the Patent Subject Matter Eligibility Guidance (see MPEP 2106.05):
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered separately and in combination.
Analyzing the additional claim limitations individually, the additional limitations that are not directed to the mental process are “collecting EEG data from at least two self-contained wireless single-channel EEG sensors disposed on a scalp of a patient, the EEG data comprising a plurality of single channel EEG time segments;” in claim 21, and “at least two self-contained wireless single-channel EEG sensors configured to be disposed on a scalp of a patient and collect EEG data, the EEG data comprising a plurality of single channel EEG time segments;” in claim 31. Such limitations are conventional and routine in the art (see Warwick et al., WO 2013/027027 A2, para. [0036]-[0039], and figs. 1 and 2), and add insignificant pre-solution activity to the abstract idea that merely collects data to be used by the abstract idea.
The additional limitations “wherein the method is performed by one or more processors” in claim 21, and “a non-transitory computer readable medium storing program instructions that, when executed by at least one processor, cause the at least one processor to:” in claim 31, are merely parts of a computer to be used as a tool to perform the mental process.
The additional limitations of dependent claims 22-30 and 32-40 are merely directed to and further narrow the scope of the mental process.
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide computer implementation of the abstract idea using collected data without: improvement to the functioning of a computer or to any other technology or technical field; applying the mental process with, or by use of, a particular machine; effecting a transformation or reduction of a particular article to a different state or thing; applying or using the mental process in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment; or adding a specific limitation other than what is well-understood, routine, conventional activity in the field.
Claim Rejections - 35 USC § 102
5. 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.
6. 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.
7. Claims 21, 22, 27-32, and 37-40 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Westover et al., U.S. Patent Application Publication No. 2017/0231519 A1 (“Westover”).
As to Claim 21, Westover teaches the following:
A method (see “The present invention generally relates to the field of electroencephalography and specifically to a system and method enabling rapid waveform annotation used to generate a high volume database.” in para. [0001]), comprising:
collecting EEG data from at least two self-contained wireless single-channel EEG sensors disposed on a scalp of a patient (see “The method initially receives raw EEG data 308. To make this raw EEG data 308 more well-suited to analysis, and thereby improve the efficiency and accuracy of the disclosed system, the sampling rate of the raw EEG data may be reduced (Step 600), the raw EEG data 308 may also be subjected to various filters (Step 610), or other modifications as part of preprocessing. In one embodiment, preprocessing may involve down-sampling the raw EEG data 308 to 128 Hz in order to reduce computational complexity (visual recognition of EDs in scalp EEG data may be uncompromised by sub-sampling to this level). Digital filters such as a 60 Hz notch filter and [0.1 Hz 64 Hz] band-pass filter can also be applied to remove artifacts such as power line interference and baseline fluctuations.” in para. [0040]), the EEG data comprising a plurality of single channel EEG time segments (see “In DTW, therefore, segments of a time series are aligned with segments of another time series, effectively allowing for matching similar waveforms in spite of small local dilations and contractions of the time axis. In the standard implementation of the DTW algorithm, optimal alignment of waveforms is accomplished through an iterative process known as dynamic programming algorithm.” in para. [0044]);
classifying, using a pre-trained machine learning classifier, each of the plurality of single channel EEG time segments as one of normal or abnormal (see “At the system's core lies a waveform analysis engine that performs template waveform matching using matching algorithms such as EuD and online machine learning and/or Dynamic Time Warping (DTW), which may substantially accelerate the task of annotating waveforms. These algorithms are described in more detail below.” in para. [0032]), wherein the pre-trained machine learning classifier has been trained using a training set of EEG data (see “Returning now to FIG. 17, the confirmed and annotated EDs may produce a database of ED profiles 304. As seen in FIG. 18, the database of ED profiles 304 may be used to train a group of classifiers within the database. Each of these profiles may include labels, identifying each of the EEG waveforms as an ED or not. Each of the profiles may also include one or more features that define the ED. A large family of generated classifiers and/or potential classifiers may be evaluated and trained to determine ranks among the classifiers.” in para. [0085]);
identifying a set of the plurality of single channel EEG time segments classified as abnormal to indicate a seizure event lasting longer than an individual single channel EEG time segment (see “After the raw EEG data has been pre-processed (Step 310) as illustrated in FIG. 6, template matching (Step 710) can be performed on the clean EEG data 620 to identify waveforms that match a template initially selected by a user. FIG. 7 is a flowchart representing a more detailed view of the steps involved in the user identifying a template 700 and then the system executing template matching (Step 710). After the raw EEG data 308 has been preprocessed (Step 310), the clean EEG data 620 can be presented to the user, for example, via the user interface shown in FIG. 14 and described below. Using the user interface, the user may provide a template ED 700 (e.g., by selecting or otherwise identifying an ED that appears in the EEG data depicted within the user interface), and the disclosed system runs a template marching algorithm 710 to find template-matching EDs within the clean EEG data 620.” in para. [0042]);
creating an annotation list (“rapid EEG annotation”) 300 comprising an ordered set of the plurality of single channel EEG time segments (see “FIG. 4 shows sub-steps of the first general step seen in FIG. 3, and depicts an algorithm for EEG review and template matching enabling rapid EEG annotation 300, thereby providing doctors and technicians with the ability to quickly template and annotate a plurality of EDs and populate a database 304 for algorithmic learning purposes. This rapid EEG annotation algorithm 304 can be accomplished through an interactive process wherein a user selects or identifies a particular ED contained within the EEG data for a subject. That identified ED then becomes a template and the system employs a similarity search algorithm 306 (e.g., DTW or EuD/online machine learning, described below) to identify a number of waveforms within the subject's EEG data that match the template. These waveforms are displayed as a list or cluster to the user, who can then select and verify that the waveforms recognized by the template matching algorithm do in fact depict EDs.” in para. [0039]); and
providing an indication of the seizure event and the annotation list to facilitate an expedited review of the EEG data (see “Returning now to FIG. 3, the confirmed and annotated EDs (Step 316) may be used in automated ED detection 202. This (preferably large) set of confirmed and annotated ED waveforms may be leveraged to develop a general purpose automated ED detection algorithm 202, seen in FIG. 5, representing the right side of the overall algorithm seen in FIG. 3. In FIG. 5, the classifiers to be tested against EEG data may be trained (Step 510) using the confirmed and annotated ED waveforms. A rest EEG 520 may also be used to determine background rejection 530 of data that is determined not to be EDs. Using the trained classifiers, the classifiers may be rested against incoming EEG data 520 in order to identify EDs.” in para. [0082]; and see “Returning now to FIG. 17, the confirmed and annotated EDs may produce a database of ED profiles 304. As seen in FIG. 18, the database of ED profiles 304 may be used to train a group of classifiers within the database. Each of these profiles may include labels, identifying each of the EEG waveforms as an ED or not. Each of the profiles may also include one or more features that define the ED. …” in para. [0085]),
wherein the method is performed by one or more processors (“computing device”) 100 (see “Returning to FIG. 1, the user interface may be displayed on a computing device such as a client or server 100 and may be any graphical, textual, scanned and/or auditory information a computer program presents to the user, and the control sequences such as keystrokes, movements of the computer mouse, selections with a touch screen, scanned information etc. used to control the program.” in para. [0070]; and see “The computing device may be any computer or program that provides services to other computers, programs, or users either in the same computer or over a computer network. Such computing devices may include, as non-limiting examples, a desktop computer, a laptop computer, a server computer etc.” in para. [0071]).
As to Claim 22, Westover teaches the following:
updating an abnormality list based on the classification of each of the plurality of single channel EEG time segments as one of normal or abnormal to facilitate automated recording of discrete seizure events (see “Returning now to FIG. 17, the confirmed and annotated EDs may produce a database of ED profiles 304. As seen in FIG. 18, the database of ED profiles 304 may be used to train a group of classifiers within the database. Each of these profiles may include labels, identifying each of the EEG waveforms as an ED or not. Each of the profiles may also include one or more features that define the ED. A large family of generated classifiers and/or potential classifiers may be evaluated and trained to determine ranks among the classifiers.” in para. [0085]); and
refining the pre-trained machine learning classifier using the abnormality list to tune the pre-trained machine learning classifier for automated recording of discrete seizure events in the patient (see “Training of classifiers may occur in a series, beginning with a simple classifier. As a simpler classifier makes mistakes, the incorrectly specified, or otherwise incorrect data may be used to train a second, more complex classifier, which will also make mistakes. These mistakes may be used to train a third, more complex classifier, and so on. Thus, the training scheme for the overall detection system may determine an order to rank how effective the classifiers are. Ranks are assigned using receiver operating characteristic (ROC) curves, derived by changing the discriminant threshold upon classification scores.” in para. [0087]).
As to Claim 27, Westover teaches the following:
wherein the training set of EEG data comprises one or more of:
EEG data collected by at least two other self-contained wireless single-channel EEG sensor or EEG data collected by a wired EEG system (“electroencephalogram”) 110 (see fig. 1 and see “The present invention provides systems and methods comprising: storing an annotated set of confirmed epileptiform discharges (ED) waveforms in a database; receiving, by a computing device, a signal encoding electroencephalograph (EEG) data from a plurality of electrodes each attached to a subject and detecting EEG data; …” in para. [0002]).
As to Claim 28, Westover teaches the following:
receiving a seizure indication input from the patient (see “This rapid EEG annotation algorithm 304 can be accomplished through an interactive process wherein a user selects or identifies a particular ED contained within the EEG data for a subject. …” in para. [0039]); and
updating an abnormality list based on the seizure indication input (see “… That identified ED then becomes a template and the system employs a similarity search algorithm 306 (e.g., DTW or EuD/online machine learning, described below) to identify a number of waveforms within the subject's EEG data that match the template. These waveforms are displayed as a list or cluster to the user, who can then select and verify that the waveforms recognized by the template matching algorithm do in fact depict EDs.” in para. [0039]).
As to Claim 29, Westover teaches the following:
creating a patient health report based on one or more of: the EEG data, the classification of EEG data, and the annotation list (see para. [0077]-[0079] and [0081]); and
providing the patient health report for being displayed (see “In both EuD and DTW, once all candidates are returned and displayed to the user, the user may select all or just some of the candidates that they confirm are in fact representatives of the candidate signal of interest, or deselect those that are not. The end result is therefore that all EDs are certified by an expert's recognition as being valid. …” in para. [0081]).
As to Claim 30, Westover teaches the following:
classifying the seizure event as a type of seizure (see para. [0044]-[0045]); and
updating the annotation list based on the classification of the seizure event as the type of seizure (see para. [0045]).
As to Claims 31, 32, and 37-40, because the subject matter of claims 31, 32, and 37-40 directed to a electroencephalogram (EEG) monitoring system is not distinct from the subject matter of claims 21, 22, and 27-30 directed to a method, Westover teaches claims 31, 32, and 37-40 for the same reasons as that provided for the rejection of claims 21, 22, and 27-30 above.
Claim Rejections - 35 USC § 103
8. 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
9. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
10. Claims 23-26 and 33-36 are rejected under 35 U.S.C. 103 as being unpatentable over Westover, as applied to claims 21 and 31, respectively, above, and further in view of Warwick et al., U.S. Patent Application Publication No. 2011/0270117 A1 (“Warwick”).
As to Claims 23 and 33, Westover teaches the subject matter of claims 21 and 31, respectively, above. Westover does not teach the following:
creating a seizure forecast based on one or more of the EEG data, the classification of the EEG data as one of normal or abnormal, and an abnormality list.
However, Warwick teaches the following:
creating a seizure forecast based on one or more of the EEG data, the classification of the EEG data as one of normal or abnormal, and an abnormality list (see “The data is examined by the system in epochs, or signal data windows with a specific duration, usually on the order of 2 to 3 seconds. Where a learning algorithm(s) is used, the signal is analyzed to extract core features for machine training. A common implementation of machine learning is the use of a Support Vector Machine (SVM), a software process that builds on training the learning system to recognize classifications of data of a patent for use in future event detection. When an epileptiform event is accurately detected, it increases the probability that a true seizure event will be detected in the future.” in para. [0054]).
Thus, it would have been obvious for one of ordinary skill in the art at the time the present application was effectively filed to modify Westover’s method, in regards to claim 21, or processor, in regards to claim 31, to create a seizure forecast (“a future event detection”), as taught by Warwick, in order to determine the probability of a future seizure event (see Warwick, para. [0054]), and allow a caregiver to “... provide observation and attention to the seizure sufferer in the event that serious side effects occur” (see Warwick, para. [0014]).
As to Claims 24 and 34, Westover in view of Warwick teaches the subject matter of claims 23 and 33, respectively, above. Warwick teaches the following:
wherein creating the seizure forecast comprises determining a likelihood that a seizure will occur for one or more time periods based on one or more of the EEG data, the classification of the EEG data as one of normal or abnormal, and the abnormality list (see “A common implementation of machine learning is the use of a Support Vector Machine (SVM), a software process that builds on training the learning system to recognize classifications of data of a patent for use in future event detection. When an epileptiform event is accurately detected, it increases the probability that a true seizure event will be detected in the future.” in para. [0054]).
Thus, it would have been obvious for one of ordinary skill in the art at the time the present application was effectively filed to modify Westover’s method, in regards to claim 21, or processor, in regards to claim 31, to create a seizure forecast (“a future event detection”), as taught by Warwick, in order to determine the probability of a future seizure event (see Warwick, para. [0054]), and allow a caregiver to “... provide observation and attention to the seizure sufferer in the event that serious side effects occur” (see Warwick, para. [0014]).
As to Claims 25 and 35, Westover in view of Warwick teaches the subject matter of claims 24 and 34, respectively, above. Warwick teaches the following:
wherein the one or more time periods comprise: a second, a minute, an hour, a day, and a week (see para. [0054]).
Thus, it would have been obvious for one of ordinary skill in the art at the time the present application was effectively filed to modify Westover’s method, in regards to claim 21, or processor, in regards to claim 31, to create a seizure forecast (“a future event detection”), as taught by Warwick, in order to determine the probability of a future seizure event (see Warwick, para. [0054]), and allow a caregiver to “... provide observation and attention to the seizure sufferer in the event that serious side effects occur” (see Warwick, para. [0014]).
As to Claims 26 and 36, Westover in view of Warwick teaches the subject matter of claims 23 and 33, respectively, above. Warwick teaches the following:
providing the seizure forecast to the patient (see “As reflected in the flowchart of FIG. 6, if a seizure is detected by correlating the filtered and conditioned signal to a pre-determined triggering threshold (e.g., by comparing the signal to data sets based on previously detected epochs at signal correlation module 33), an alarm is triggered in event handling module 34 that is conveyed via user interface 35, and optionally recorded for later review at event logging module 36. The alarm 23 (FIG. 4) may be a visual one (e.g., warning light), an audible noise (conveyed through a speaker in housing 24 of system 20; FIG. 4), a physical (e.g., vibratory) alarm or a small display (e.g., shown on an LCD screen). Alarm 23 may be provided in housing 24 or, optionally, on a remote monitor whereby the alarm is triggered by wired or wireless transmission of a seizure detection signal to a dedicated monitoring device or a non-dedicated one, such as a cellular phone or PDA.” in para. [0055]).
Thus, it would have been obvious for one of ordinary skill in the art at the time the present application was effectively filed to modify Westover’s method, in regards to claim 21, or processor, in regards to claim 31, to create a seizure forecast (“a future event detection”), as taught by Warwick, in order to determine the probability of a future seizure event (see Warwick, para. [0054]), and allow a caregiver to “... provide observation and attention to the seizure sufferer in the event that serious side effects occur” (see Warwick, para. [0014]).
Conclusion
11. Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAVIN NATNITHITHADHA whose telephone number is (571)272-4732. The examiner can normally be reached Monday - Friday 8:00 am - 8:00 am - 4:00 pm.
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/NAVIN NATNITHITHADHA/Primary Examiner, Art Unit 3791 01/05/2026