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 Objections
Claims 1, 16, 18, and 24 are objected to because of the following informalities: “for each of a plurality of electrical-activity records” in claim 1 line 2 and claim 18 line 5 should recite “for each electrical-activity record of a plurality of electrical-activity records”; “the a dominant seizure onset type” in claims 16 line 9 and 24 line 9 should recite “the dominant seizure onset type”. Appropriate correction is required.
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 1-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-25 are all within at least one of the four categories.
The independent claims recite:
for each of a plurality of electrical-activity records of a brain, applying a machine- learned electrographic seizure classification (ESC) model to the electrical-activity record to classify the electrical-activity record as one of a seizure record or a non-seizure record, wherein each of the plurality of electrical-activity records is sensed by a corresponding one of a plurality of sensing channels of an implanted medical device; for each seizure record in a set of seizure records, applying the machine-learned ESC model to the seizure record to classify the seizure record as one of a local-seizure record or a spread-seizure record, wherein the seizure record comprises a first seizure record captured by a first channel of the plurality of sensing channels and a second seizure record captured by a second channel of the plurality of sensing channels; and for each spread-seizure record in a set of spread-seizure records, applying a machine- learned seizure spread classification (SSC) model to the spread-seizure record to classify the spread-seizure record as a type of seizure spread pattern.
The above claim limitations (classify[ing]) constitute an abstract idea that is part of the Mental Processes group identified in the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019.
The claimed steps of apply[ing], classify[ing] can be practically performed in the human mind using mental steps or basic critical thinking, which are types of activities that have been found by the courts to represent abstract ideas.
“[T]he ‘mental processes’ abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” MPEP 2106.04(a)(2) III. The pending claims merely recite steps for classifying electrical activity of the brain/seizures.
Examples of ineligible claims that recite mental processes include:
a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group, LLC v. Alstom, S.A.;
claims to “comparing BRCA sequences and determining the existence of alterations,” where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind, University of Utah Research Foundation v. Ambry Genetics Corp.
a claim to collecting and comparing known information, which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC.
See p. 7-8 of October 2019 Update: Subject Matter Eligibility.
Regarding the dependent claims, the dependent claims are directed to either 1) steps that are also abstract or 2) additional data gathering/output that is well-understood, routine and previously known to the industry. Although the dependent claims are further limiting, they do not recite significantly more than the abstract idea. A narrow abstract idea is still an abstract idea and an abstract idea with additional well-known equipment/functions is not significantly more than the abstract idea.
Claims 2-17 and 19-25 are directed to more abstract ideas, and further limitations on abstract ideas is already recited.
This judicial exception (abstract idea) in Claims 1-25 is not integrated into a practical application because:
The abstract idea amounts to simply implementing the abstract idea on a computer. For example, the recitations regarding the generic computing components for apply[ing], classify[ing] merely invoke a computer as a tool.
The data-gathering step (sens[ing]) and the data-output step do not add a meaningful limitation to the method as they are insignificant extra-solution activity.
There is no improvement to a computer or other technology. “The McRO court indicated that it was the incorporation of the particular claimed rules in computer animation that "improved [the] existing technological process", unlike cases such as Alice where a computer was merely used as a tool to perform an existing process.” MPEP 2106.05(a) II. The claims recite a computer that is used as a tool for apply[ing], classify[ing].
The claims do not apply the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition. Rather, the abstract idea is utilized to classify electrical activity of the brain/seizures.
The claims do not apply the abstract idea to a particular machine. “Integral use of a machine to achieve performance of a method may provide significantly more, in contrast to where the machine is merely an object on which the method operates, which does not provide significantly more.” MPEP 2106.05(b). II. “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more.” MPEP 2106.05(b) III. The pending claims utilize a computer for apply[ing], classify[ing]. The claims do not apply the obtained calculation to a particular machine. Rather, the data is merely output in a post-solution step.
The additional elements are identified as follows: memory; processor; implanted medical device; classification model.
Those in the relevant field of art would recognize the above-identified additional elements as being well-understood, routine, and conventional means for data-gathering and computing, as demonstrated by
Applicant’s Background in the specification;
The prior art of record:
DeMazumder (US 20210272696 A1) discloses in para. [0133] deriving predictive models using a combination of conventional … supervised and unsupervised deep learning techniques;
Katsuki (US 20180018568 A1) discloses in para. [0032] conventional classification models, such as linear classification or SVM, conventional clustering models, such as K-means or K-nearest neighbor;
Ghosh (US 20190171931 A1) discloses in para. [0042] conventional 3D machine learning processors;
Umstetter (US 6862347 B1) discloses in col. 3 lines 55-65 a conventional processor and a conventional memory in a PC.
Wingeier (US 20090112280 A1) discloses in para. [0183-0184, 0209] conventional deep brain electrodes;
Hadipour-Niktarash (US 20200222700 A1) discloses in para. [0007] conventional recording and stimulation electrodes.
The Non-Patent Literature of record:
Karthick k et al., Prediction of secondary generalization from a focal onset seizure in intracerebral EEG, Clinical Neurophysiology, Volume 129, Issue 5, 2018, Pages 1030-1040, ISSN 1388-2457, https://doi.org/10.1016/j.clinph.2018.02.122.
Lieb, J.P., Engel, J., Jr. and Babb, T.L. (1986), Interhemispheric Propagation Time of Human Hippocampal Seizures. Epilepsia, 27: 286-293. https://doi.org/10.1111/j.1528-1157.1986.tb03541.x;
Yoo et al. Ictal spread of medial temporal lobe seizures with and without secondary generalization: an intracranial electroencephalography analysis. Epilepsia. 2014 Feb;55(2):289-95. doi: 10.1111/epi.12505. Epub 2014 Jan 13. PMID: 24417694; PMCID: PMC4103687;
Schindler et al., How generalised are secondarily "generalised" tonic clonic seizures? J Neurol Neurosurg Psychiatry. 2007 Sep;78(9):993-6. doi: 10.1136/jnnp.2006.108753. Epub 2007 Jan 19. PMID: 17237141; PMCID: PMC2117860;
Naftulin et al., Ictal and preictal power changes outside of the seizure focus correlate with seizure generalization. Epilepsia. 2018 Jul;59(7):1398-1409. doi: 10.1111/epi.14449. Epub 2018 Jun 13. PMID: 29897628; PMCID: PMC6031475.
Thus, the claimed additional elements “are so well-known that they do not need to be described in detail in a patent application to satisfy 35 U.S.C. § 112(a).” Berkheimer Memorandum, III. A. 3.
Furthermore, the court decisions discussed in MPEP § 2106.05(d)(lI) note the well-understood, routine and conventional nature of such additional generic computer components as those claimed. See option III. A. 2. in the Berkheimer memorandum.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the units associated with the steps do not add meaningful limitation to the abstract idea. A computer, processor, memory, or equivalent hardware is merely used as a tool for executing the abstract idea(s). The process claimed does not reflect an improvement in the functioning of the computer.
When considered in combination, the additional elements (i.e., the generic computer functions and conventional equipment/steps) do not amount to significantly more than the abstract idea. Looking at the claim limitations as a whole adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
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.
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 1-2 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Guttag (US 20110257517 A1) in view of Karthick k (Karthick k et al., Prediction of secondary generalization from a focal onset seizure in intracerebral EEG, Clinical Neurophysiology, Volume 129, Issue 5, 2018, Pages 1030-1040, ISSN 1388-2457, https://doi.org/10.1016/j.clinph.2018.02.122).
Regarding claim 1, Guttag discloses a method of assessing electrical activity of a brain (Abstract), the method comprising: for each of a plurality of electrical-activity records of a brain (plurality of EEG waveform channels, para. [0017-0018], fig. 22), applying a machine- learned electrographic seizure classification (ESC) model (“Support Vector Machine classification algorithm”; “trained classifier”, para. [0006, 0022, 0238-0239]) to the electrical-activity record to classify the electrical-activity record as one of a seizure record or a non-seizure record (step 18, fig 22, EEG sample waveforms … feature vector … classified as belonging to a seizure class or non-seizure class, para. [0235-0237], figs. 23A-B), wherein each of the plurality of electrical-activity records is sensed by a corresponding one of a plurality of sensing channels (“plurality of EEG waveform channels of the patient”, para. [0017, 0191-0192], figs. 1A & 23A-B) of an implanted medical device (“invasive EEG measurement device”, para. [0086]); for each seizure record in a set of seizure records, applying the machine-learned ESC model to the seizure record (“Support Vector Machine classification algorithm”; “trained classifier”, para. [0006, 0022, 0239]) to classify the seizure record as one of a local-seizure record (“classifying the feature vector as belonging to one of … first type”; “simple partial seizures”; “seizure sub-classes”, para. [0026-0027, 0189, 0235, 0336]) or a spread-seizure record (“classifying the feature vector as belonging to one of … second type”; “secondary generalized seizures”; “seizure sub-classes”, para. [0026-0027, 0189, 0235, 0336]), wherein the seizure record comprises a first seizure record captured by a first channel of the plurality of sensing channels (EEG channel 1, as seen in figs. 23A-B, “ two-second epoch from each of twenty-one bipolar EEG derivations is individually passed through one of the feature extractors”, para. [0237-0239]) and a second seizure record captured by a second channel of the plurality of sensing channels (EEG channel 2, as seen in figs. 23A-B, “ two-second epoch from each of twenty-one bipolar EEG derivations is individually passed through one of the feature extractors”, para. [0237-0239]).
Guttag does not expressly disclose for each spread-seizure record in a set of spread-seizure records, applying a machine- learned seizure spread classification (SSC) model to the spread-seizure record to classify the spread-seizure record as a type of seizure spread pattern.
However, Karthick directed the prediction of secondary generalization from a focal onset seizure discloses for each spread-seizure record in a set of spread-seizure records, applying a machine- learned seizure spread classification (SSC) model (fig. 1, seizure prediction system … classification models, pages 1033-1034, “second model”) to the spread-seizure record to classify the spread-seizure record as a type of seizure spread pattern (page 1031, 2. Methods, “focal to bilateral tonic-clonic seizure (FTC), seizure with spread beyond the seizure onset zone but without tonic-clonic evolution (FS) … each seizure type was identified” & pages 1033-1034, “first model predicts whether the seizure is with spread (FTC-FS) or without spread (F) and the second predicts if the FTC-FS seizure type is either FTC or FS”). Karthick further discloses that these approaches could be used to predict the seizure with FTC evolution and alert the medical staff during presurgical investigations and thereby improve patient safety (page 1040, 5. Conclusions).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Guttag such that the method further comprises for each spread-seizure record in a set of spread-seizure records, applying a machine-learned seizure spread classification (SSC) model to the spread-seizure record to classify the spread-seizure record as a type of seizure spread pattern, in view of the teachings of Karthick, for the obvious advantage of predicting the seizure with FTC evolution and alert the medical staff during presurgical investigations and thereby improve patient safety.
Regarding claim 2, Guttag, as modified by Karthick hereinabove, discloses the method of claim 1, wherein a seizure record is classified as a local- seizure record when the ESC model determines a seizure is present in only one of the first seizure record and the second seizure record (“EEG activity … limited region of the head is called focal”, para. [0201, 0267], as seen in fig. 32).
Regarding claim 18, Guttag discloses an apparatus for assessing electrical activity of a brain, the apparatus (Abstract, seizure detector 40, fig. 53) comprising: a memory (memory 44, fig. 53) having one or more machine-learned models (“Support Vector Machine classification algorithm”; “trained classifier”; “stored”, para. [0006, 0022, 0238-0239, 0318]), and a processor (processing unit 42, fig. 53) couple to the memory (as seen in fig. 53, para. [0317]) and configured to: for each of a plurality of electrical-activity records of a brain (plurality of EEG waveform channels, para. [0017-0018], fig. 22), applying a machine-learned electrographic seizure classification (ESC) model (“Support Vector Machine classification algorithm”; “trained classifier”, para. [0006, 0022, 0238-0239]) to the electrical-activity record to classify the electrical-activity record as one of a seizure record or a non-seizure record (step 18, fig 22, EEG sample waveforms … feature vector … classified as belonging to a seizure class or non-seizure class, para. [0235-0237], figs. 23A-B), wherein each of the plurality of electrical-activity records is sensed by a corresponding one of a plurality of sensing channels (“plurality of EEG waveform channels of the patient”, para. [0017, 0191-0192], figs. 1A & 23A-B) of an implanted medical device (“invasive EEG measurement device”, para. [0086]); for each seizure record in a set of seizure records, applying the machine- learned electrographic seizure classification (ESC) model to the seizure record (“Support Vector Machine classification algorithm”; “trained classifier”, para. [0006, 0022, 0239]) to classify the seizure record as one of a local-seizure record (“classifying the feature vector as belonging to one of … first type”; “simple partial seizures”; “seizure sub-classes”, para. [0026-0027, 0189, 0235, 0336]) or a spread-seizure record (“classifying the feature vector as belonging to one of … second type”; “secondary generalized seizures”; “seizure sub-classes”, para. [0026-0027, 0189, 0235, 0336]), wherein the seizure record comprises a first seizure record captured by a first channel of the plurality of sensing channels (EEG channel 1, as seen in figs. 23A-B, “ two-second epoch from each of twenty-one bipolar EEG derivations is individually passed through one of the feature extractors”, para. [0237-0239]) and a second seizure record captured by a second channel of the plurality of sensing channels (EEG channel 2, as seen in figs. 23A-B, “ two-second epoch from each of twenty-one bipolar EEG derivations is individually passed through one of the feature extractors”, para. [0237-0239]). Guttag does not expressly disclose the processor configured to: for each spread-seizure record in a set of spread-seizure records, applying a machine-learned seizure spread classification (SSC) model to the spread-seizure record to classify the spread-seizure record as a type of seizure spread pattern.
However, Karthick directed the prediction of secondary generalization from a focal onset seizure discloses for each spread-seizure record in a set of spread-seizure records, applying a machine- learned seizure spread classification (SSC) model (fig. 1, seizure prediction system … classification models, pages 1033-1034, “second model”) to the spread-seizure record to classify the spread-seizure record as a type of seizure spread pattern (page 1031, 2. Methods, “focal to bilateral tonic-clonic seizure (FTC), seizure with spread beyond the seizure onset zone but without tonic-clonic evolution (FS) … each seizure type was identified” & pages 1033-1034, “first model predicts whether the seizure is with spread (FTC-FS) or without spread (F) and the second predicts if the FTC-FS seizure type is either FTC or FS”). Karthick further discloses that these approaches could be used to predict the seizure with FTC evolution and alert the medical staff during presurgical investigations and thereby improve patient safety (page 1040, 5. Conclusions).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Guttag such that the processor is further configured to: for each spread-seizure record in a set of spread-seizure records, applying a machine-learned seizure spread classification (SSC) model to the spread-seizure record to classify the spread-seizure record as a type of seizure spread pattern, in view of the teachings of Karthick, for the obvious advantage of predicting the seizure with FTC evolution and alert the medical staff during presurgical investigations and thereby improve patient safety.
Regarding claim 19, Guttag, as modified by Karthick hereinabove, discloses the apparatus of claim 18, wherein the ESC model is configured to classify a seizure record as a local- seizure record when a seizure is present in only one of the first seizure record and the second seizure record (“EEG activity … limited region of the head is called focal”, para. [0201, 0267], as seen in fig. 32).
Claims 3 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Guttag in view of Karthick, as applied to claim 1 above, and further in view of Pless (US 20030074033 A1).
Regarding claim 3, Guttag, as modified by Karthick hereinabove, discloses the method of claim 1. Guttag, as modified by Karthick hereinabove does not expressly disclose wherein a seizure record is classified as a spread-seizure record when the ESC model determines a seizure is present in each of the first seizure record and the second seizure record.
However, Pless directed to an epileptiform activity patient-specific template creation system (Abstract) discloses wherein a seizure record is classified as a spread- seizure record when the ESC model (detection subsystem 122, fig. 1) determines a seizure is present in each of the first seizure record and the second seizure record (“seizure has “generalized,” i.e. spread … when the first detection output and the second detection output both indicate the presence of an event … detection of generalization”, para. [0201-0203]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Guttag, as modified by Karthick hereinabove, such that a seizure record is classified as a spread-seizure record when the ESC model determines a seizure is present in each of the first seizure record and the second seizure record, in view of the teachings of Pless, as such a modification would have yielded predictable results of detecting when a seizure has generalized.
Regarding claim 20, Guttag, as modified by Karthick hereinabove, discloses the apparatus of claim 18. Guttag, as modified by Karthick hereinabove does not expressly disclose wherein the ESC model is configured to classify a seizure record as a spread-seizure record when a seizure is present in each of the first seizure record and the second seizure record.
However, Pless directed to an epileptiform activity patient-specific template creation system (Abstract) discloses wherein the ESC model (detection subsystem 122, fig. 1) is configured to classify a seizure record as a spread-seizure record when a seizure is present in each of the first seizure record and the second seizure record (“seizure has “generalized,” i.e. spread … when the first detection output and the second detection output both indicate the presence of an event … detection of generalization”, para. [0201-0203]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Guttag, as modified by Karthick hereinabove, such that a seizure record is classified as a spread-seizure record when the ESC model determines a seizure is present in each of the first seizure record and the second seizure record, in view of the teachings of Pless, as such a modification would have yielded predictable results of detecting when a seizure has generalized.
Claims 4-6 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Guttag in view of Karthick, as applied to claim 1 above, further in view of Lieb (Lieb, J.P., Engel, J., Jr. and Babb, T.L. (1986), Interhemispheric Propagation Time of Human Hippocampal Seizures. Epilepsia, 27: 286-293. https://doi.org/10.1111/j.1528-1157.1986.tb03541.x), and further in view of Yoo (Yoo et al. Ictal spread of medial temporal lobe seizures with and without secondary generalization: an intracranial electroencephalography analysis. Epilepsia. 2014 Feb;55(2):289-95. doi: 10.1111/epi.12505. Epub 2014 Jan 13. PMID: 24417694; PMCID: PMC4103687).
Regarding claim 4, Guttag, as modified by Karthick hereinabove, discloses the method of claim 1.
Guttag, as modified by Karthick hereinabove, does not expressly disclose wherein: the first channel is defined by one or more electrodes implanted at a first location of the brain and the second channel is defined by one or more electrodes implanted at a second location of the brain and the spread-seizure record is classified as: a first-channel-to-second-channel seizure spread pattern when the SSC model determines a time of seizure onset in the first seizure record precedes a time of seizure onset in the second seizure record by a threshold duration, a second-channel-to-first-channel seizure spread pattern when the SSC model determines a time of seizure onset in the second seizure record precedes a time of seizure onset in the first seizure record by a threshold duration, and a non-spread seizure spread pattern when the SSC model determines a duration between a time of seizure onset in the first seizure record and a time of seizure onset in the second seizure record is within a threshold range.
However, Lieb directed to the assessment of interhemispheric seizure propagation time (IHSPT) discloses that the spread-seizure record is classified based on a threshold duration (page 288, Table 1, “time required for electrographic activity to propagate … classified into …. categories) and the spread-seizure record is classified as: a non-spread seizure spread pattern when the SSC model determines a duration between a time of seizure onset in the first seizure record and a time of seizure onset in the second seizure record is within a threshold range (page 3, Assessment of interhemispheric seizure propagation time (IHSPT), “each SEEG episode was classified into one of seven categories. Each successive category contained episodes with progressively longer IHSPTs (see Table I) … category 1 … bilaterally synchronous onsets”, Table 1, category 1, 0 ≤ t < 0.5).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Guttag, as modified by Karthick hereinabove, such that the spread-seizure record is classified based on a threshold duration and the spread-seizure record is classified as: a non-spread seizure spread pattern when the SSC model determines a duration between a time of seizure onset in the first seizure record and a time of seizure onset in the second seizure record is within a threshold range, in view of the teachings of Lieb, in order to classify SEEG episodes into categories and identify bilaterally synchronous onsets.
Guttag, as modified by Karthick and Lieb hereinabove, does not expressly disclose wherein: the first channel is defined by one or more electrodes implanted at a first location of the brain and the second channel is defined by one or more electrodes implanted at a second location of the brain and the spread-seizure record is classified as: a first-channel-to-second-channel seizure spread pattern when the SSC model determines a time of seizure onset in the first seizure record precedes a time of seizure onset in the second seizure record by a threshold duration, a second-channel-to-first-channel seizure spread pattern when the SSC model determines a time of seizure onset in the second seizure record precedes a time of seizure onset in the first seizure record by a threshold duration.
However, Yoo directed to investigating the differences in onset and propagation patterns of temporal lobe seizures, discloses wherein: the first channel is defined by one or more electrodes implanted at a first location of the brain (page 4, Results, “ipsilateral hippocampal depth electrodes) and the second channel is defined by one or more electrodes implanted at a second location of the brain (page 4, Results, “electrodes over the contralateral temporal region”) and the spread-seizure record is classified as: a first-channel-to-second-channel seizure spread pattern when the SSC model determines a time of seizure onset in the first seizure record precedes a time of seizure onset in the second seizure record (page 3, Electroencephalography analysis, “spread pattern … classified as hippocampal onset when the seizure activity started from the hippocampal depth”), a second-channel-to-first-channel seizure spread pattern when the SSC model determines a time of seizure onset in the second seizure record precedes a time of seizure onset in the first seizure record (page 3, Electroencephalography analysis, “spread pattern … seizure activity started from the medial temporal electrode contacts, the seizure onset was classified as non-hippocampal onset”). Yoo further discloses that the studies may lead to a better understanding of the mechanisms of secondary generalization, and may potentially lead to a novel treatment strategy to detect and intervene early in seizure propagation (page 6, Discussion).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Guttag, as modified by Karthick and Lieb hereinabove, such that the first channel is defined by one or more electrodes implanted at a first location of the brain and the second channel is defined by one or more electrodes implanted at a second location of the brain and the spread-seizure record is classified as: a first-channel-to-second-channel seizure spread pattern when the SSC model determines a time of seizure onset in the first seizure record precedes a time of seizure onset in the second seizure record by a threshold duration, a second-channel-to-first-channel seizure spread pattern when the SSC model determines a time of seizure onset in the second seizure record precedes a time of seizure onset in the first seizure record by a threshold duration, in view of the teachings of Yoo, for the obvious advantage of leading to a novel treatment strategy to detect and intervene early in seizure propagation.
Regarding claim 5, Guttag, as modified by Karthick, Lieb, and Yoo hereinabove, discloses the method of claim 4, wherein the first location of the brain and the second location of the brain are on or within a same hemisphere of the brain (as seen in figs. 1A-1B, 15A-B, & 32-33, “one cerebral hemisphere” para. [0184, 0222]).
Regarding claim 6, Guttag, as modified by Karthick, Lieb, and Yoo hereinabove, discloses the method of claim 4, wherein the first location of the brain and the second location of the brain are on or within opposite hemispheres of the brain (as seen in figs. 1A-1B, 15A-B, & 32-33, “both cerebral hemispheres”, para. [0179, 0222]).
Regarding claim 21, Guttag, as modified by Karthick hereinabove, discloses the apparatus of claim 18. Guttag, as modified by Karthick hereinabove, does not disclose wherein: the first channel is defined by one or more electrodes implanted at a first location of the brain and the second channel is defined by one or more electrodes implanted at a second location of the brain, and the SSC model is configured to: classify a spread-seizure record as a first-channel-to-second-channel seizure spread pattern when the SSC model determines a time of seizure onset in the first seizure record precedes a time of seizure onset in the second seizure record by a threshold duration, classify a spread-seizure record as a second-channel-to-first-channel seizure spread pattern when the SSC model determines a time of seizure onset in the second seizure record precedes a time of seizure onset in the first seizure record by a threshold duration, and classify a spread-seizure record as a non-spread seizure spread pattern when the SSC model determines a duration between a time of seizure onset in the first seizure record and a time of seizure onset in the second seizure record is within a threshold range.
However, Lieb directed to the assessment of interhemispheric seizure propagation time (IHSPT) discloses that the SSC model is configured to classify a spread-seizure record based on a threshold duration (page 288, Table 1, “time required for electrographic activity to propagate … classified into …. categories) and classify a spread-seizure record as a non-spread seizure spread pattern when the SSC model determines a duration between a time of seizure onset in the first seizure record and a time of seizure onset in the second seizure record is within a threshold range (page 3, Assessment of interhemispheric seizure propagation time (IHSPT), “each SEEG episode was classified into one of seven categories. Each successive category contained episodes with progressively longer IHSPTs (see Table I) … category 1 … bilaterally synchronous onsets”, Table 1, category 1, 0 ≤ t < 0.5).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Guttag, as modified by Karthick hereinabove, such that the SSC model is configured to: classify a spread-seizure record based on a threshold duration and classify a spread-seizure record as a non-spread seizure spread pattern when the SSC model determines a duration between a time of seizure onset in the first seizure record and a time of seizure onset in the second seizure record is within a threshold range, in view of the teachings of Lieb, in order to classify SEEG episodes into categories and identify bilaterally synchronous onsets.
Guttag, as modified by Karthick and Lieb hereinabove, does not expressly disclose wherein: the first channel is defined by one or more electrodes implanted at a first location of the brain and the second channel is defined by one or more electrodes implanted at a second location of the brain, and the SSC model is configured to: classify a spread-seizure record as a first-channel-to-second-channel seizure spread pattern when the SSC model determines a time of seizure onset in the first seizure record precedes a time of seizure onset in the second seizure record by a threshold duration, classify a spread-seizure record as a second-channel-to-first-channel seizure spread pattern when the SSC model determines a time of seizure onset in the second seizure record precedes a time of seizure onset in the first seizure record by a threshold duration.
However, Yoo directed to investigating the differences in onset and propagation patterns of temporal lobe seizures, discloses wherein: the first channel is defined by one or more electrodes implanted at a first location of the brain (page 4, Results, “ipsilateral hippocampal depth electrodes) and the second channel is defined by one or more electrodes implanted at a second location of the brain (page 4, Results, “electrodes over the contralateral temporal region”) and the SSC model is configured to: classify a spread-seizure record as a first-channel-to-second-channel seizure spread pattern when the SSC model determines a time of seizure onset in the first seizure record precedes a time of seizure onset in the second seizure record (page 3, Electroencephalography analysis, “spread pattern … classified as hippocampal onset when the seizure activity started from the hippocampal depth”), classify a spread-seizure record as a second-channel-to-first-channel seizure spread pattern when the SSC model determines a time of seizure onset in the second seizure record precedes a time of seizure onset in the first seizure record (page 3, Electroencephalography analysis, “spread pattern … seizure activity started from the medial temporal electrode contacts, the seizure onset was classified as non-hippocampal onset”). Yoo further discloses that the studies may lead to a better understanding of the mechanisms of secondary generalization, and may potentially lead to a novel treatment strategy to detect and intervene early in seizure propagation (page 6, Discussion).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Guttag, as modified by Karthick and Lieb hereinabove, such that the first channel is defined by one or more electrodes implanted at a first location of the brain and the second channel is defined by one or more electrodes implanted at a second location of the brain, and the SSC model is configured to: classify a spread-seizure record as a first-channel-to-second-channel seizure spread pattern when the SSC model determines a time of seizure onset in the first seizure record precedes a time of seizure onset in the second seizure record by a threshold duration, classify a spread-seizure record as a second-channel-to-first-channel seizure spread pattern when the SSC model determines a time of seizure onset in the second seizure record precedes a time of seizure onset in the first seizure record by a threshold duration, in view of the teachings of Yoo, for the obvious advantage of leading to a novel treatment strategy to detect and intervene early in seizure propagation.
Claims 7-8, 10-11, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Guttag in view of Karthick, as applied to claim 1 above, and further in view of Osorio (US 20040138647 A1).
Regarding claim 7, Guttag, as modified by Karthick hereinabove, further discloses the method of claim 1. Guttag, as modified by Karthick hereinabove, does not disclose the method further comprising: determining an aspect of a treatment based on the type of seizure spread pattern for each spread-seizure record in the set of spread-seizure records.
However, Osorio directed to the detection and the treatment of nervous system disorders discloses determining an aspect of a treatment based on the type of seizure spread pattern for each spread-seizure record in the set of spread-seizure records (“identifies channels that are "involved" in the seizure in order to determine an electrographic spread”; “electrode configuration in accordance with the electrographic spread and the seizure focus location … stimulation parameters … delivering stimulation to the patient”; “ratio 2203”, para. [0109, 0114-0115, 0130-0134]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Guttag, as modified by Karthick hereinabove, such that the method further comprises determining an aspect of a treatment based on the type of seizure spread pattern for each spread-seizure record in the set of spread-seizure records, in view of the teachings of Osorio, in order to determine electrode configurations and stimulation parameters in accordance with the electrographic spread and the seizure focus location to provide a sufficient reduction of a detected frequency, duration, intensity, and extent of the electrographic spread that are associated with the seizure.
Regarding claim 8, Guttag, as modified by Karthick and Osorio hereinabove, discloses the method of claim 7. Guttag, as modified by Karthick and Osorio hereinabove, does not expressly disclose wherein the aspect of a treatment corresponds to a stimulation site, and determining the stimulation site comprises: for each type of seizure spread pattern included in the set of spread-seizure records, deriving a metric based a count of the type of seizure spread pattern; processing the respective counts to determine a dominate type of seizure spread pattern; and select a stimulation site based on the dominate type of seizure spread pattern.
However, Osorio directed to the detection and the treatment of nervous system disorders discloses wherein the aspect of a treatment corresponds to a stimulation site (“electrode configuration … seizure focus location”, para. [0114]), and determining the stimulation site comprises: for each type of seizure spread pattern included in the set of spread-seizure records, deriving a metric based a count of the type of seizure spread pattern (“step 2019 … identifies channels that are "involved" in the seizure in order to determine an electrographic spread of the seizure”; “numerically indicate the electrographic spread”; “set of quantity information”, para. [0109, 0130-0134, 0147]); processing the respective counts to determine a dominate type of seizure spread pattern (“sub-step 2021 and sub-step 2025 … reports the electrographic spread, intensity, and duration to the physician in sub-step 2025 … graphical representation … highlights the location and the extent of the seizure focus”; “distinguishing an electrode corresponding to a waveform that is "involved" in the seizure”, para. [0109, 0130-0134]); and select a stimulation site based on the dominate type of seizure spread pattern (“step 2153 … inputs an electrode configuration in accordance with the electrographic spread … and the seizure focus location … delivering stimulation to the patient … step 2167”, para. [0114-0115], figs. 20 & 22).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Guttag, as modified by Karthick hereinabove, such that the aspect of a treatment corresponds to a stimulation site, and determining the stimulation site comprises: for each type of seizure spread pattern included in the set of spread-seizure records, deriving a metric based a count of the type of seizure spread pattern; processing the respective counts to determine a dominate type of seizure spread pattern; and select a stimulation site based on the dominate type of seizure spread pattern, in view of the teachings of Osorio, in order to determine electrode configurations and stimulation parameters in accordance with the electrographic spread and the seizure focus location to provide a sufficient reduction of a detected frequency, duration, intensity, and extent of the electrographic spread that are associated with the seizure.
Regarding claim 10, Guttag, as modified by Karthick hereinabove, discloses the method of claim 1. Guttag, as modified by Karthick hereinabove, does not disclose determining an aspect of treatment based on the type of seizure spread pattern and a time of seizure spread for each spread-seizure record in the set of spread-seizure records.
However, Osorio directed to the detection and the treatment of nervous system disorders discloses determining an aspect of treatment (“medical device system provides a recommendation of the electrode configuration to the physician in accordance to the electrographic spread and the seizure focus location … stimulation parameters … provide suggested values”, para. [0114-0115, 0141], fig. 20) based on the type of seizure spread pattern and a time of seizure spread for each spread-seizure record in the set of spread-seizure records (“maximal ratio … largest ratio at the given instant in time … time constraint 2215; numerically indicate the electrographic spread … time duration constraint … 0.84 seconds … electrographic spread is two electrodes”; “time period … set of quantity information”, para. [0114, 0130-0134, 0147], figs. 20 & 22).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Guttag, as modified by Karthick hereinabove, such that the method further comprises determining an aspect of treatment based on the type of seizure spread pattern and a time of seizure spread for each spread-seizure record in the set of spread-seizure records, in view of the teachings of Osorio, in order to determine electrode configurations and stimulation parameters in accordance with the electrographic spread and the seizure focus location to provide a sufficient reduction of a detected frequency, duration, intensity, and extent of the electrographic spread that are associated with the seizure.
Regarding claim 11, Guttag, as modified by Karthick and Osorio hereinabove, discloses the method of claim 10. Guttag, as modified by Karthick and Osorio hereinabove, does not disclose wherein the aspect of a treatment corresponds to at least one of a stimulation site and a location of a surgical resection, and determining the stimulation site comprises: for each type of seizure spread pattern included in the set of spread-seizure records, deriving a metric set comprising a count of the type of seizure spread pattern and a measure of time of seizure spread for the type of seizure spread; processing the respective metric sets to determine a dominate type of seizure spread pattern; and selecting at least one of a stimulation site and a location of a surgical resection based on the dominate type of seizure spread pattern and the measure of time of seizure spread for the dominate type of seizure spread.
However, Osorio discloses wherein the aspect of a treatment corresponds to at least one of a stimulation site and a location of a surgical resection (“DBS … thalamic nuclei, subthalamus, temporal lobe”; “medical device system provides a recommendation of the electrode configuration to the physician in accordance to the electrographic spread and the seizure focus location … stimulation parameters … provide suggested values”, para. [0052, 0114-0115, 0141], fig. 20), and determining the stimulation site comprises: for each type of seizure spread pattern included in the set of spread-seizure records, deriving a metric set comprising a count of the type of seizure spread pattern and a measure of time of seizure spread for the type of seizure spread (time constraint 2215 ; “location of the seizure … identifies the seizure focus location … step 2019 … identifies channels that are "involved" in the seizure in order to determine an electrographic spread of the seizure … reports at least one seizure onset location”; “numerically indicate the electrographic spread … … time duration constraint … 0.84 seconds … electrographic spread is two electrodes”; “time period … set of quantity information”, para. [0108-0109, 0114, 0130-0134, 0147], figs. 20 * 22); processing the respective metric sets to determine a dominate type of seizure spread pattern (“sub-step 2021 and sub-step 2025 … reports the electrographic spread, intensity, and duration to the physician in sub-step 2025 … graphical representation … highlights the location and the extent of the seizure focus”; “distinguishing an electrode corresponding to a waveform that is "involved" in the seizure”, para. [0109, 0133-0134]); and selecting at least one of a stimulation site and a location of a surgical resection based on the dominate type of seizure spread pattern and the measure of time of seizure spread for the dominate type of seizure spread (“DBS … thalamic nuclei, subthalamus, temporal lobe”; “step 2153 … inputs an electrode configuration in accordance with the electrographic spread … and the seizure focus location … delivering stimulation to the patient … step 2167”, para. [0052, 114-0115], figs. 20 & 22).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Guttag, as modified by Karthick and Osorio hereinabove, such that the aspect of a treatment corresponds to at least one of a stimulation site and a location of a surgical resection, and determining the stimulation site comprises: for each type of seizure spread pattern included in the set of spread-seizure records, deriving a metric set comprising a count of the type of seizure spread pattern and a measure of time of seizure spread for the type of seizure spread; processing the respective metric sets to determine a dominate type of seizure spread pattern; and selecting at least one of a stimulation site and a location of a surgical resection based on the dominate type of seizure spread pattern and the measure of time of seizure spread for the dominate type of seizure spread, in view of the teachings of Osorio, in order to determine electrode configurations and stimulation parameters in accordance with the electrogra