DETAILED ACTION
Acknowledgements
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending.
This action is Non-Final.
Information Disclosure Statement
The information disclosure statement filed 5/29/2026 fails to comply with 37 CFR 1.98(a)(2), which requires a legible copy of each cited foreign patent document; each non-patent literature publication or that portion which caused it to be listed; and all other information or that portion which caused it to be listed. It has been placed in the application file, but the information referred to therein has not been considered. Specifically, Foreign Patent Document 30 only submitted a 2 page with abstract and not a copy of the document being cited.
Applicant is advised that the date of any re-submission of any item of information contained in this information disclosure statement or the submission of any missing element(s) will be the date of submission for purposes of determining compliance with the requirements based on the time of filing the statement, including all certification requirements for statements under 37 CFR 1.97(e). See MPEP § 609.05(a).
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. The claim(s) recite(s):
Claims 1 and 11 (claim 1 representative)
training a machine learning model based on the first unified discrete bipolar sensor data set, the machine learning model being configured detect presence of at least one type of seizure event;
applying the machine learning model to a second unified discrete bipolar sensor data set obtained from a second set of at least two EEG sensors positioned on a scalp of a patient being evaluated to determine a plurality of likelihoods of an occurrence of a possible seizure event in a plurality of segments of the second unified discrete bipolar sensor data set, wherein the possible seizure event spans multiple segments of the plurality of segments of the second unified discrete bipolar sensor data set;
determining a start and stop time of the possible seizure event based on evaluating the plurality of likelihoods;
The abstract idea is part of the Mathematical Concepts and/or Mental Processes group(s) identified in the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019.
These claim limitations fall within the identified groupings of abstract ideas:
Mathematical Concepts:
mathematical relationships
mathematical formulas or equations
mathematical calculations
Mental Processes
concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
This judicial exception is not integrated into a practical application because:
Under the step 2A, analysis is conducted on the additional features of the claim. Under this analysis, the additional features beyond the judicial exception are:
Claims 1 and 11:
a non-transitory computer readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to (generic computer structures used as a tool)
receiving a first unified discrete bipolar sensor data set obtained from a plurality of discrete unitary wireless EEG sensors from various patient types and disease states, the plurality of EEG sensors comprising a first set of at least two EEG sensors placed in a plurality of locations spaced around a scalp of a particular patient, and each EEG sensor comprising two electrodes forming a single bipolar channel (limitations related to data gathering)
providing an indication of a region of the second unified discrete bipolar sensor data set denoted by the start and stop time that contains the possible seizure event, wherein the indication facilitates adjustment of a treatment of the patient being evaluated (insignificant post solution activities, such as output)
These features in the claim do not integrate the exception into a practical application of the exception as the additional elements in the claim do not apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is no more than a drafting effort designed to monopolize the exception.
Limitation concepts that are indicative of integration into a practical application:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
Limitation concepts that are not indicative of integration into a practical application:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)
Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
Under Step 2B, the claim limitations are evaluated for an inventive concept. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and in combination, they do not add significantly more to the exception. Analyzing the additional claim limitations individually, the additional limitation that is not directed to the abstract idea are the same as those identified above in step 2A. Such limitations related to the sensors are recognized by the courts as routine data gathering in order to input data to the mathematical algorithm/mental processes, and thus, do not add a meaningful limitation to the method/product as it would be routinely used by those of ordinary skill in the art in order to apply the mathematical algorithm/mental process. In addition, these sensor structures are known from US 2017/0215759, US 2008/0208008. The method does not contain any computing structure for the claimed processing data which further supports that the claims are directed to a judicial exception without significantly more. The computer structures cited above are claimed as performing generic computer functions routinely used in computer applications. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. The additional limitations recited in the dependent claims are directed to further details of data processing (A more specific abstraction is still an abstraction). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Therefore, analyzing the claims as an ordered combination under the Mayo/Alice analysis the features claimed are directed to patent ineligible limitations.
Claim Rejections - 35 USC § 103
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.
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.
Claims 1, 3-6, 9-11, 13-16, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over “Saputro” (Saputro et al., Seizure Type Classification on EEG Signal using Support Vector Machine, IOP Conf. Series, J Phys Conf. Series, 2019. cited by applicant on IDS) in view of Dudek et al. (Dudek, US 2017/0215759) and Guttag et al. (Guttag, US 2006/0111644) and Pless et al. (Pless, US 2003/0074033).
Regarding claims 1 and 11, Saputro teaches a non-transitory computer readable media (MATLAB software implies non-transitory computer readable media; section 3.2) and a method for classifying electroencephalogram (EEG) sensor data (see at least abstract) comprising:
receiving sensor data obtained from a plurality of EEG sensors from various patient types and disease states (see at least section 2, training data set D)
training a machine learning model based on the sensor data, the machine learning model being configured detect presence of at least one type of seizure event (see at least section 2.5);
applying the machine learning model to a second data set (see at least section 2; training and evaluation data);
However, the specific limitations of the sensor data including and from specific sensors in the limitations of receiving a first unified discrete bipolar sensor data set obtained from a plurality of discrete unitary wireless EEG sensors from various patient types and disease states, the plurality of EEG sensors comprising a first set of at least two EEG sensors placed in a plurality of locations spaced around a scalp of a particular patient, and each EEG sensor comprising two electrodes forming a single bipolar channel; a second unified discrete bipolar sensor data set obtained from a second set of at least two EEG sensors positioned on a scalp of a patient being evaluated to determine a plurality of likelihoods of an occurrence of a possible seizure event in a plurality of segments of the second unified discrete bipolar sensor data set, wherein the possible seizure event spans multiple segments of the plurality of segments of the second unified discrete bipolar sensor data set; determining a start and stop time of the possible seizure event based on evaluating the plurality of likelihoods; and providing an indication of a region of the second unified discrete bipolar sensor data set denoted by the start and stop time that contains the possible seizure event, wherein the indication facilitates adjustment of a treatment of the patient being evaluated are not directly taught.
Dudek teaches a related system for measuring EEG data (see title and abstract), and teaches that sensor data may be gathered using discreet wireless sensor patches with electrodes and reasonably teaches the limitations for gathering both data sets using such structures for the limitations receiving a first unified discrete bipolar sensor data set obtained from a plurality of discrete unitary wireless EEG sensors from various patient types and disease states, the plurality of EEG sensors comprising a first set of at least two EEG sensors placed in a plurality of locations spaced around a scalp of a particular patient, and each EEG sensor comprising two electrodes forming a single bipolar channel; a second unified discrete bipolar sensor data set obtained from a second set of at least two EEG sensors positioned on a scalp of a patient (see at least Figures 1, 13 multiple patches with ability of measuring multiple data simultaneously teaches capability to collect plurality of data from different sections using such known device structures for the needs of Saputro data receiving and processing, [0042]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine prior art elements according to known methods to yield predictable results of collecting EEG data for seizure determinations using known patch structures for collecting such information in order to assess the data for ictal activities.
Guttag teaches a related system for monitoring EEG and seizures (see at least title and abstract), and teaches the use of machine learning model trained reference EEG data and applied to a second set of data to determine the likelihood of seizure event which with the modification to the prior explanations reasonably teaches second data being evaluated to determine a plurality of likelihoods of an occurrence of a possible seizure event in a plurality of segments of the second unified discrete bipolar sensor data set, wherein the possible seizure event spans multiple segments of the plurality of segments of the second unified discrete bipolar sensor data set (see at least [0008]-[0012], [0235]-[0236]) including determining the onset, duration and termination of the event for the limitations determining a start and stop time of the possible seizure event based on evaluating the plurality of likelihoods (see at least [0075] onset, duration; [0349] identify termination of seizure event, and thus its duration). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine prior art elements according to known methods to yield predictable results of analyzing EEG data using known processes in order to identify seizure locations in time recorded data including start, duration, and termination of events.
Pless teaches a related system for measuring EEG signals and seizures (see at least title and abstract), and teaches that it is a known technique to annotate EEG recordings to identify which channels have seizures and the start and stop times and such identifications allow for adjustments of sensing/treatment to account for changes, which reasonably teaches the claimed features providing an indication of a region of the second unified discrete bipolar sensor data set denoted by the start and stop time that contains the possible seizure event, wherein the indication facilitates adjustment of a treatment of the patient being evaluated (see at least Figure 6, [0029], [0037],[0046], [0111], [0124], [0131], [0135]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a known technique to improver similar devices in the same way to include known processes of annotating EEG data in order to allow for refinement of sensing/treatment based on the identified events on known channel regions to improve monitoring and management of seizures.
Regarding claims 3 and 13, the limitations are met by Saputro in view of Dudek and Guttag and Pless, where Saputro teaches discriminating between various seizure types using the machine learning model (see at least abstract, FNSZ, GNSZ, TCSZ, Normal).
Regarding claims 4 and 14, the limitations are met by Saputro in view of Dudek and Guttag and Pless, where Saputro teaches producing a generalized seizure event prediction model for a common seizure type with the machine learning model (see at least abstract GNSZ, generalized non-specific seizure).
Regarding claims 5 and 15, the limitations are met by Saputro in view of Dudek and Guttag and Pless, where Saputro teaches storing and using EEG data from multiple patients to build a database suitable for forming future seizure event detection and prediction models (see at least Section 2, database is data stored and capable of intended use).
Regarding claims 6 and 16, the limitations are met by Saputro in view of Dudek and Guttag and Pless, where Saputro teaches wherein the first unified discrete bipolar sensor data set has been normalized to account for inter-patient differences (intended use/result bear no patentable weight to the claimed features, see at least section 3.2, normalized signals; Examiner further notes that normalization of data is well known in the art to account for variability/differences between data).
Regarding claims 9 and 19, the limitations are met by Saputro in view of Dudek and Guttag and Pless, where Guttag teaches wherein the evaluating comprises combining the plurality of likelihoods by performing at least one of individual segment thresholding, a multi-segment thresholding and windowing process, or integration windowing (see at least [0250]-[0255]).
Regarding claims 10 and 20, the limitations are met by Saputro in view of Dudek and Guttag and Pless, where Guttag teaches wherein the evaluating comprises comparing the plurality of likelihoods from each of the plurality of segments to a threshold (see at least [0250]-[0255]).
Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over “Saputro” (Saputro et al., Seizure Type Classification on EEG Signal using Support Vector Machine, IOP Conf. Series, J Phys Conf. Series, 2019. cited by applicant on IDS) in view of Dudek et al. (Dudek, US 2017/0215759) and Guttag et al. (Guttag, US 2006/0111644) and Pless et al. (Pless, US 2003/0074033) as applied to claims 1 and 11 above, and further in view of Montgomery et al. (Montgomery, US 2005/0165323).
Regarding claims 2 and 12, the limitations are met by Saputro in view of Dudek and Guttag and Pless, where Dudek teaches wherein each EEG sensor comprises a wireless transmitter configured to transmit sensed EEG signals (see at least Figure 12 EEG transmitted to base station device, it is also noted in [0049] that the patch 1 may store the EEG data).
However, the limitation that such is occurring periodically is not directly taught.
Montgomery teaches a related system for measuring EEG data (see abstract), and data transmission during periods when wireless communications are possible which reasonably teaches such claimed limitations, this being an improvement to EEG continuous transmission to allow for no lost data and to store data locally in memory when data communication is not possible, and then transmitting the data to the receiving structure when communication is possible (see [0021] “During the loss of wireless communication, the EEG signals are stored to memory for a time period and then transmitted to the host computer once wireless communication is re-established. When re-establishment of this wireless communication is detected, the apparatus preferably operates in much the same manner as when the cable of the above-described embodiment is re-connected.”). The Dudek EEG sensors contain memory 9 as discussed prior, and thus is ready for such improvement to log data during periods of no wireless communications and transmit data when communications are reestablished. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a known method to improve similar devices in the same way, with a device ready for improvement, in order to allow no data loss during periods of poor or no wireless communications between system elements.
Claims 7, 17 are rejected under 35 U.S.C. 103 as being unpatentable over “Saputro” (Saputro et al., Seizure Type Classification on EEG Signal using Support Vector Machine, IOP Conf. Series, J Phys Conf. Series, 2019. cited by applicant on IDS) in view of Dudek et al. (Dudek, US 2017/0215759) and Guttag et al. (Guttag, US 2006/0111644) and Pless et al. (Pless, US 2003/0074033) as applied to claims 1 and 11 above, and further in view of Nierenberg et al. (Nierenberg, US 2017/0172414).
Regarding claims 7 and 17, the limitations are met by Saputro in view of Dudek and Guttag and Pless, where Guttag teaches wherein the first unified discrete bipolar sensor data set is obtained by automatically configuring a longitudinal transverse montage from EEG signals by bipolar derivation and subtracting each EEG signal from one sensor relative to another to create a longitudinal transverse montage by teaching various locations where individual sensors can be placed, such that with this teaching in view of the Dudek units, can include any number and be positioned on a user in any suggested location to measure the suggested differentials across the various different electrode positions, where such differentials when more than one would include the ability the differential montage comprises a longitudinal transverse montage (see Figure 1B, Figures 15-16). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine prior art elements according to known methods to yield predictable results of including differential mode with active channels in order to analyze further aspects of a patient’s brain activity for diagnostic purposes.
For compact prosecution purposes, Nierenberg teaches creating transverse and longitudinal montages of electrode pairs by determining the difference between the pairs in order to determine the true morphology and amplitude of a waveform (see at least [0010]-[0011]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine prior art elements according to known methods to yield predictable results of including user ability to select differential mode with active channels in order to get a better determination of waveform amplitude and morphology across different sections.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over “Saputro” (Saputro et al., Seizure Type Classification on EEG Signal using Support Vector Machine, IOP Conf. Series, J Phys Conf. Series, 2019. cited by applicant on IDS) in view of Dudek et al. (Dudek, US 2017/0215759) and Guttag et al. (Guttag, US 2006/0111644) and Pless et al. (Pless, US 2003/0074033) as applied to claims 1 and 11 above, and further in view of Krumm et al. (Krumm, US 2017/0076217).
Regarding claims 8 and 18, the limitations are met by Saputro in view of Dudek and Guttag and Pless, except the limitations of wherein training the machine learning model comprises adjusting a probability threshold are not directly taught.
Krum teaches a related system in computations involving machine learning, and teaches that in a classification algorithm, the threshold probability can be adjusted to adjust the sensitivity of a machine learning training algorithm/module (see at least [0069]). It would have been obvious to one of ordinary skill in the art before the effective filing date of he claimed invention to use a known technique to improve machine learning algorithms by updating thresholds in order to allow for adjustments to be made to the sensitivity of the machine learning model.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL R BLOCH whose telephone number is (571)270-3252. The examiner can normally be reached M-F 11-8 EST.
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/MICHAEL R BLOCH/ Primary Examiner, Art Unit 3791
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