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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/11/2025 has been entered.
Claims 1-16 are the current claims hereby under examination.
All references to Applicant’s specification are made using the paragraph numbers assigned in the US publication of the present application US 2022/0225926 A1.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
In particular, the following limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
Claim 1 line 8, and claim 8 line 8: a stimulation unit
Claim 1 line 10, and claim 8 line 10: a determination unit
Claim 1 line 16, and claim 8 line 16: a communication unit
Claim 1 lines 31-32 and Claim 8 lines 28-29: a user-customized convulsion detection and prediction algorithm
Claim 8 line 28: a learning unit
Claim 4 line 3, and claim 11 line 3: first communication means
Claim 4 line 5, and claim 11 line 5: second communication means
Each of the above elements are being interpreted as their corresponding structure or algorithm for carrying out their recited function as taught by the specification and their equivalents. The particular interpretations for each element are as follows:
Claim 1 line 6, and claim 8 line 6: a stimulation unit is being interpreted as an electrode for providing stimulation and its equivalents. Such an interpretation is consistent with Applicant’s specification paragraph 0058.
Claim 1 line 9, and claim 8 line 9: a determination unit is being interpreted as a processor for executing received commands according to program code and its equivalents. Such an interpretation is consistent with Applicant’s specification paragraph 0072.
Claim 1 line 16, and claim 8 line 16: a communication unit is interpreted as the particular structure that carries out the recited function of wireless communication and its equivalents. The communication unit is described as comprising a first and second communication means in paragraphs 0018, 0025, and 0063 and is described using functional language in paragraphs 0022, 0063, 0066, and 0071. The communication unit is further described as using a variety of different communication methods in paragraph 0065 such as Bluetooth, ZigBee, or near field communication but these recitations describe particular methods of communication rather than a particular structure of the communication unit. The specification does not appear to describe a particular structure to perform the recited function
Claim 1 lines 18-19, and claim 8 line 16: a user-customized convulsion detection and prediction algorithm is being interpreted as the particular algorithm to detect seizures and its equivalents. The algorithm is consider to be comprised by the particular steps taken by the trained algorithm to convert its recited inputs to its recited outputs and the algorithms equivalents. Paragraphs 0076-0079 describe the algorithm in functional language but do not appear to describe the particular steps taken by the trained algorithm to convert or classify the input data of new cranial nerve signals into the output data of convulsive or normal cranial nerve signals.
Claim 8 line 16: a learning unit is being interpreted as the algorithm or method of performing the machine learning training on the user-customized convulsion detection and prediction algorithm and its equivalents. Paragraphs 0082-0087 recite a number of generic training methods but do not limit the training to any of the recited training methods. A recitation that a machine learning model is trained using a particular method (such as logistic regression or supervised learning) does not satisfy the written description requirement because the particular steps taken to train the algorithm are not disclosed.
Claim 4 line 3, and claim 11 line 3: first communication means is being interpreted as the particular structure for carrying out the recited function of performing an active communication method and its equivalents. Paragraphs 0019, 0025-0026, 0063, and 0065-0066 describe the first communication means in functional language as performing the active communication method but do not describe a particular structure for performing the recited function.
Claim 4 line 5, and claim 11 line 5: second communication means is being interpreted as the particular structure for carrying out the recited function of performing a passive communication method and its equivalents. Paragraphs 0019, 0025-0026, 0063, and 0065-0066 describe the first communication means in functional language as performing the passive communication method but do not describe a particular structure for performing the recited function.
Claim Objections
Claims 1 and 9 are objected to because of the following informalities:
Claim 8 it appears that “the determination configured to” should recite “the determination unit configured to”
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “the second unit configured to: receive… calculate… analyze… and generate …” but it is unclear which element of the second unit is performing the recited functions. It is unclear if the determination unit within the second unit performs these functions or if some other element of combination of elements performs these functions. For the purposes of this examination, the limitation will be interpreted as a processor in the second unit performing these functions. This rejection is similarly applied to claim 8.
Examiner’s Note: It is noted that should the claim be amended to recite that the determination unit carries out these functions then the interpretation of the determination unit would be changed from being the processor to being the algorithm which carries out these recited functions.
Claim 1 recites the limitations “a communication unit” and “a user-customized convulsion detection and prediction algorithm”; Claim 4 recites “a first communication means” and “a second communication means”; and claim 8 recites “a communication unit”, “a learning unit”, and “a user-customized convulsion detection and prediction algorithm”. Each of these limitations invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph interpretation. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. In particular, the limitations are interpreted as described in the above claim interpretation section which describes how the specification does not set forth the particular structure or steps taken to carry out the recited functions of these elements. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claims 2-7 and 15-16 are rejected by virtue of their dependence on claim 1.
Claim 5 is rejected by virtue of its dependence on claim 4.
Claims 9-14 are rejected by virtue of their dependence on claim 8.
Claim 15 recites “wherein the specific frequency band comprises a frequency band having a certain range based on the frequency having the greatest separation width” but it is unclear if the frequency band having a range “based on” the frequency having the greatest separation width is meant to convey that the frequency range is static and selected by determining that the frequency with the greatest separation is within the frequency range or if the frequency range is dynamic and is selected by virtue of some calculation focused on the frequency with the greatest difference. For the purposes of this examination, any frequency band including the frequency with the greatest difference will be considered “based on” the frequency with the greatest difference.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 4, and 8 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites the limitations “a communication unit” and “a user-customized convulsion detection and prediction algorithm”; Claim 4 recites “a first communication means” and “a second communication means”; and claim 8 recites “a communication unit”, “a learning unit”, and “a user-customized convulsion detection and prediction algorithm”. Each of these limitations invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph interpretation. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. In particular, the limitations are interpreted as described in the above claim interpretation section which describes how the specification does not set forth the particular structure or steps taken to carry out the recited functions of these elements. Therefore, the claim lacks sufficient written description and is rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph. In particular “the user-customized convulsion detection and prediction algorithm” of claims 1 and 8 is recited as performing the functions of determining whether the user has convulsions, and the “learning unit” of claim 8 is recited as performing the function of training the algorithm. The specification does not particularly describe how the algorithm is trained using real and virtual signals and how the trained algorithm determines whether the user has convulsions. Paragraphs 0076-0077 describes how the algorithm may detect a specific frequency band having the greatest separation width between normal and convulsive signals and that the frequency band may be detected with a Welch method. But do not describe how this detected frequency band is applied to new signals to distinguish normal versus convulsive signals. Paragraphs 0078-0079 then describe how virtual signals can be generated and the accuracy of the algorithm may be determined but do not explicitly describe how the virtual signals are used in training the algorithm. Paragraphs 0082-0088 recite a number of training methods but do not describe how the algorithm is actually trained using any of the recited methods. The specification does not appear to explicitly describe how to classify new signals between convulsive and normal using the algorithm trained using real and virtue signals. Paragraphs 0056 and 0064 provide mere conclusory statements that the determination unit detects convulsions using the user-customized convulsion detection and prediction algorithm but the particular operation of said algorithm is not seemingly disclosed.
Claim 1 recites “the user-customized convulsion detection and prediction algorithm having been pre-trained by both 1) the actual normal cranial nerve signals and the actual convulsive cranial nerve signals, and 2) the virtual normal cranial nerve signals and virtual convulsive cranial nerve signals” but the specification does not particularly describe how the algorithm is trained using real and virtual signals. In particular, paragraphs 0082-0088 recite a number of training methods but do not describe how the algorithm is actually trained using any of the recited methods. A statement that the algorithm may be trained using any number of training methods and the actual and virtual data is not considered sufficient to support the training of the algorithm because the specification does not detail how the algorithm is trained. It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement (MPEP 2161.01).
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.
Claims 1-3 8-10, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Higgins US Patent Application Publication Number US 2011/0201944 A1 hereinafter Higgins in view of Osorio US Patent Application Publication Number US 2005/0197590 A1 hereinafter Osorio and further in view of Echauz international Patent Application Publication Number WO 03/030734 A2 hereinafter Echauz.
Regarding claim 1 Higgins discloses an epilepsy monitoring device (Abstract) comprising:
a first unit (Paragraph 0152: the electrode array; Fig. 13 reference 12) comprising:
a first body portion configured to be placed on or implanted within a head of a user (Paragraph 0152; fig. 13 reference 12 depicts the electrode arrays including a surface and depth electrode array. These electrode arrays have individual connecting wires which join together into a single wire (reference 16) that leads back to the implantable assembly (reference 14). The point where the wires connect is considered to be “a first unit” and “a first body portion” because the connecting point of these two wires satisfies all requirements set for by the claim)
a sensor unit connected to the first body portion and including a first sensor configured to measure actual normal cranial nerve signals and actual convulsive cranial nerve signals (Paragraphs 0059-0060: the electrode arrays may include cranial nerve electrodes; Paragraphs 0053 and 0055: the systems monitors EEG signals during normal periods and seizure events), and
a stimulation unit connected to the first body portion and configured to apply cranial nerve stimulation treatment to a brain of the user based on a brain stimulation signal generated and provided by a determination unit (Paragraph 0063: the stimulation cuff may provide stimulation to cranial nerves; Paragraph 0066: the implanted assembly provides the electrical stimulation signal; Paragraph 0152: the electrodes implanted in the brain may be used for stimulation; Paragraphs 0161-0162, 0166, and 0170: the processing sub-assembly located within the implantable assembly may execute the detection algorithms and provide the stimulation signal); and
a second unit electrically connected to the first unit (Paragraphs 0063-0064: the implanted assembly is in wired or wireless communication with the electrode arrays) and comprising:
a second body portion configured to be placed on or implanted within a body of the user other than the head of the user (Paragraph 0064: the implanted assembly may be implanted in a sub-clavicular pocket or abdomen),
a battery unit housed within the second body portion and configured to supply power to the first unit (Paragraph 0071: the implanted assembly includes a power source; Paragraphs 0066: the implanted assembly may provide the electrical stimulation and thus supplied power to the first unit), and
a communication unit for wireless communication with an external device (Paragraphs 0054 and 0071-0073: the implanted assembly wirelessly communicates with an external assembly).
the second unit configured to:
receive the actual normal cranial nerve signals and the actual convulsive cranial nerve signals in time series from the first sensor of the first unit (Paragraphs 0059-0060: the electrode arrays may include cranial nerve electrodes; Paragraphs 0053 and 0055: the systems monitors EEG signals during normal periods and seizure events; Paragraph 0068-0069: the second unit receives signals from the electrodes of the first unit.)
the second unit further comprising:
the determination unit including or operatively connected to a storage unit that stores a user-customized convulsion detection and prediction algorithm (Paragraphs 0069 and 0072: the memory sub-assembly and processing sub-assembly; Paragraphs 0161-0162 and 0166: the processing sub-assembly located within the implantable assembly may execute the detection algorithm),
the user-customized convulsion detection and prediction algorithm having been pre-trained by the actual normal cranial nerve signals and the actual convulsive cranial nerve signals (Paragraphs 0157-0159: the feature extraction and classification models are customized to the user to characterize a subject’s condition; Paragraphs 0164-0165: the seizure detection algorithms may be trained on the annotated EEG data collected from the patient), the determination unit being configured to execute the user- customized convulsion detection and prediction algorithm to determine whether the user has convulsions (Paragraphs 0161-0162 and 0166: the processing sub-assembly located within the implantable assembly may execute the detection algorithms; Paragraphs 0157-0159: the feature extraction and classification models are customized to the user to characterize a subject’s condition)
Higgins fails to further disclose the device wherein the second unit is configured to: analyze the power spectrum density to detect a specific frequency band including a frequency having the greatest separation width between the actual normal cranial nerve signals and the actual convulsive cranial nerve signals, and generate virtual normal cranial nerve signals and virtual convulsive cranial nerve signals having the specific frequency band and the user-customized convulsion detection and prediction algorithm also being trained using virtual normal cranial nerve signals and virtual convulsive cranial nerve signals
Osorio teaches a system that analyzes signals representative of a subject's brain activity in a signal processor for information indicating the subject's current activity state and for predicting a change in the activity state. The system uses a combination of nonlinear filtering methods to perform real-time analysis of the electro-encephalogram (EEG) or electro-corticogram (ECoG) signals from a subject patient for information indicative of or predictive of a seizure, and to complete the needed analysis at least before clinical seizure onset. The preferred system then performs an output task for prevention or abatement of the seizure, or for recording pertinent data (Abstract). Thus, Osorio falls within the same field of endeavor as Applicant’s invention.
Osorio teaches the analysis of brainwave signals by performing power spectral density (PSD) analysis and matching the PSD signal to patterns representative of interictal, or normal, and ictal, or seizure, states. Osorio teaches that the system focuses on the PSD of frequency bands of past seizures that are maximally different from their respective normal segments. These bands of greatest difference are weighted more heavily in the determination of ictal states. (Paragraphs 0107 and 0144). Thus, Osorio teaches the calculation of PSD values for the normal and convulsive signal and the analysis of the PSD values to determine the frequency band with the greatest difference between normal and convulsive signals.
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to implement the PSD analysis and focus on the frequency bands with the greatest difference as taught by Osorio into the device of Higgins because weighting these frequency band more heavily than frequency bands with smaller changes as taught by Osorio (Paragraphs 0107 and 0144: the focus on maximally different frequency bands) may help improve seizure detection speed and accuracy since the most discriminative frequency bands are being more heavily considered than the less discriminative frequency bands.
Higgins in view of Osorio fails to further teach the device configured to: generate virtual normal cranial nerve signals and virtual convulsive cranial nerve signals having the specific frequency band and the user-customized convulsion detection and prediction algorithm also being trained using virtual normal cranial nerve signals and virtual convulsive cranial nerve signals
Echauz teaches an epileptiform activity patient-specific template creation system which permits a user to efficiently develop an optimized set of patient-specific parameters for epileptiform activity detection algorithms. The epileptiform activity patient template creation system is primarily directed for use with an implantable neurostimulator system having EEG storage capability, in conjunction with a computer software program operating within a computer workstation having a processor, disk storage and input/output facilities for storing, processing and displaying patient EEG signals. The implantable neurostimulator is operative to store records of EEG data when neurological events are detected, when it receives external commands to record, or at preset or arbitrary times. The computer workstation operates on stored and uploaded records of EEG data to derive the patient-specific templates via a single local minimum variant of a multidimensional greedy line search process and a feature overlay process (Abstract), Thus Echauz falls within the same field of endeavor as Applicant’s invention.
Echauz teaches that additional data may be generated from the EEG data uploaded from an implanted neurostimulator by slightly modifying the collected data to create new datasets. The modifications may include adjustments to amplitude, noise, playback speed, and similar modifications. The modifications result in a much larger dataset which includes actual recorded data and the generated data surrogates which facilitates the generation of a set of detection parameters that maintains high sensitivity but is more specific than a set of detection parameters generated from only real recorded data (Page 11 lines 11-29).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to implement the generation of virtual training data to aid in training a seizure detection algorithm as taught by Echauz into the device of Higgins in view of Osorio such that the generated data maintains the changes in PSD of the respective frequency bands because Echauz teaches that generating virtual data produces a larger dataset that allows the model to be trained to be more specific than a model trained only using real data while still maintaining high sensitivity (Echauz: Page 11 lines 11-29) and ensuring that the generated data maintains the same PSD relationships in the most discriminative frequency bands would ensure that the virtual data maintains similar characteristics to true data and thus be more useful in training an accurate model by not introducing false relationships in the PSD analysis.
Regarding claim 2 Higgins in view of Osorio further in view of Echauz teaches the epilepsy monitoring device of claim 1. Modified Higgins further discloses the device wherein the determination unit is configured to, generate an alarm signal when it is determined that the user has convulsions (Paragraphs 0152 and 0167: the system detects the user is at risk of a seizure or detects a seizure and issues seizure warning; Paragraph 0162; the implanted circuitry may generate a warning signal to the outside device).
Regarding claim 3 Higgins in view of Osorio further in view of Echauz teaches the epilepsy monitoring device of claim 2. Modified Higgins further discloses the device wherein the determination unit is configured to generate the brain stimulation signal to apply the cranial nerve stimulation treatment corresponding to the brain stimulation signal to the brain of the user, and provide the brain stimulation signal to the stimulation unit (Paragraph 0170-0172: upon detection or prediction of a seizure event the therapy delivery assembly of the implantable assembly is configure to provide electrical stimulation. The stimulation may be to the cranial nerves through the electrode array implanted in the user’s head).
Regarding claim 15 Higgins in view of Osorio further in view of Echauz teaches the epilepsy monitoring device of claim 1. Modified Higgins fails to further disclose the device, wherein the specific frequency band comprises a frequency band having a certain range based on the frequency having the greatest separation width.
Osorio teaches the device, wherein the specific frequency band comprises a frequency band having a certain range based on the frequency having the greatest separation width (Paragraph 0107: the frequencies with the greatest separation between ictal and interictal PSD are weighted more heavily; Paragraph 0144: the frequency band with the greatest difference in PSD between ictal and interictal is focused on; Paragraph 0209: the frequency bands are a range of frequencies).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to configure the device of modified Higgins to select the frequency band based on the frequency with the greatest separation as taught by Osorio because Osorio teaches that the frequencies and frequency band with the greatest differentiation between ictal and interictal states should be focused on and weighted more heavily (Osorio: Paragraphs 0107 and 0144). Focusing on these bands may help improve the accuracy and detection speed of the algorithm by focusing it on the most discriminative features.
Regarding claim 8, Higgins discloses an epilepsy monitoring system (Abstract) comprising:
a first unit (Paragraph 0152: the electrode array; Fig. 13 reference 12) comprising:
a first body portion configured to be placed on or implanted within a head of a user (Paragraph 0152; fig. 13 reference 12 depicts the electrode arrays including a surface and depth electrode array. These electrode arrays have individual connecting wires which join together into a single wire (reference 16) that leads back to the implantable assembly (reference 14). The point where the wires connect is considered to be “a first unit” and “a first body portion” because the connecting point of these two wires satisfies all requirements set for by the claim),
a sensor unit connected to the first body portion and including a first sensor configured to measure actual normal cranial nerve signals and actual convulsive cranial nerve signals (Paragraphs 0059-0060: the electrode arrays may include cranial nerve electrodes; Paragraphs 0053 and 0055: the systems monitors EEG signals during normal periods and seizure events), and
a stimulation unit connected to the first body portion and configured to apply cranial nerve stimulation treatment to a brain of the user based on a provided brain stimulation signal generated and provided by a determination unit (Paragraph 0063: the stimulation cuff may provide stimulation to cranial nerves; Paragraph 0066: the implanted assembly provides the electrical stimulation signal; Paragraph 0152: the electrodes implanted in the brain may be used for stimulation; Paragraphs 0161-0162, 0166, and 0170: the processing sub-assembly located within the implantable assembly may execute the detection algorithms and provide the stimulation signal);
a second unit electrically connected to the first unit (Paragraphs 0063-0064: the implanted assembly is in wired or wireless communication with the electrode arrays) and comprising:
a second body portion configured to be placed on or implanted within a body of the user other than the head of the user (Paragraph 0064: the implanted assembly may be implanted in a sub-clavicular pocket or abdomen),
a battery unit housed within the second body portion and configured to supply power to the first unit (Paragraph 0071: the implanted assembly includes a power source; Paragraphs 0066: the implanted assembly may provide the electrical stimulation and thus supplies power to the first unit), and
a communication unit configured for wireless communication with an external device (Paragraphs 0054 and 0071-0073: the implanted assembly wirelessly communicates with an external assembly);
the second unit configured to:
receive the actual normal cranial nerve signals and the actual convulsive cranial nerve signals in time series from the first sensor of the first unit (Paragraphs 0059-0060: the electrode arrays may include cranial nerve electrodes; Paragraphs 0053 and 0055: the systems monitors EEG signals during normal periods and seizure events; Paragraph 0068-0069: the second unit receives signals from the electrodes of the first unit.)
a learning unit configured to machine learn a user-customized convulsion detection and prediction algorithm, the user-customized convulsion detection and prediction algorithm having been pre-trained by the actual normal cranial nerve signals and the actual convulsive cranial nerve signals (Paragraphs 0157-0159: the feature extraction and classification models are customized to the user to characterize a subject’s condition; Paragraphs 0164-0165: the seizure detection algorithms may be trained on the annotated EEG data collected from the patient); and
the determination unit including or operatively connected to a storage unit that stores the trained user-customized convulsion detection and prediction algorithm (Paragraphs 0069 and 0072: the memory sub-assembly and processing sub-assembly; Paragraphs 0161-0162 and 0166: the processing sub-assembly located within the implantable assembly may execute the detection algorithm), the determination configured to execute the trained user-customized convulsion detection and prediction algorithm to determine whether the user has convulsions (Paragraphs 0161-0162 and 0166: the processing sub-assembly located within the implantable assembly may execute the detection algorithms; Paragraphs 0157-0159: the feature extraction and classification models are customized to the user to characterize a subject’s condition)
Higgins fails to further disclose the system wherein the second unit is configured to: analyze the power spectrum density to detect a specific frequency band including a frequency having the greatest separation width between the actual normal cranial nerve signals and the actual convulsive cranial nerve signals, and generate virtual normal cranial nerve signals and virtual convulsive cranial nerve signals having the specific frequency band and the user-customized convulsion detection and prediction algorithm also being trained using virtual normal cranial nerve signals and virtual convulsive cranial nerve signals
Osorio teaches the analysis of brainwave signals by performing power spectral density (PSD) analysis and matching the PSD signal to patterns representative of interictal, or normal, and ictal, or seizure, states. Osorio teaches that the system focuses on the PSD of frequency bands of past seizures that are maximally different from their respective normal segments. These bands of greatest difference are weighted more heavily in the determination of ictal states. (Paragraphs 0107 and 0144). Thus, Osorio teaches the calculation of PSD values for the normal and convulsive signal and the analysis of the PSD values to determine the frequency band with the greatest difference between normal and convulsive signals.
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to implement the PSD analysis and focus on the frequency bands with the greatest difference as taught by Osorio into the system of Higgins because weighting these frequency band more heavily than frequency bands with smaller changes as taught by Osorio (Paragraphs 0107 and 0144: the focus on maximally different frequency bands) may help improve seizure detection speed and accuracy since the most discriminative frequency bands are being more heavily considered than the less discriminative frequency bands.
Higgins in view of Osorio fails to further teach the system configured to: generate virtual normal cranial nerve signals and virtual convulsive cranial nerve signals having the specific frequency band and the user-customized convulsion detection and prediction algorithm also being trained using virtual normal cranial nerve signals and virtual convulsive cranial nerve signals
Echauz teaches that additional data may be generated from the EEG data uploaded from an implanted neurostimulator by slightly modifying the collected data to create new datasets. The modifications result in a much larger dataset which includes actual recorded data and the generated data surrogates which facilitates the generation of a set of detection parameters that maintains high sensitivity but is more specific than a set of detection parameters generated from only real recorded data (Page 11 lines 11-29).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to implement the generation of virtual training data to aid in training a seizure detection algorithm as taught by Echauz into the system of Higgins in view of Osorio such that the generated data maintains the changes in PSD of the respective frequency bands because Echauz teaches that generating virtual data produces a larger dataset that allows the model to be trained to be more specific than a model trained only using real data while still maintaining high sensitivity (Echauz: Page 11 lines 11-29) and ensuring that the generated data maintains the same PSD relationships in the most discriminative frequency bands would ensure that the virtual data maintains similar characteristics to true data and thus be more useful in training an accurate model by not introducing false relationships in the PSD analysis..
Regarding claim 9 Higgins in view of Osorio further in view of Echauz teaches the epilepsy monitoring system of claim 8. Modified Higgins further discloses the system wherein the determination unit is arranged in the second unit, and configured to generate an alarm signal when it is determined that the user has convulsions (Paragraph 0162; the implanted circuitry may generate a warning signal to the outside device).
Regarding claim 10 Higgins in view of Osorio further in view of Echauz teaches the epilepsy monitoring system of claim 9. Modified Higgins further discloses the system wherein the determination unit is configured to generate the brain stimulation signal to apply the cranial nerve stimulation treatment corresponding to the brain stimulation signal to the brain of the user, and provide the brain stimulation signal to the stimulation unit (Paragraph 0170-0172: upon detection or prediction of a seizure event the therapy delivery assembly of the implantable assembly is configure to provide electrical stimulation. The stimulation may be to the cranial nerves through the electrode array implanted in the user’s head).
Claims 4-5 and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Higgins US Patent Application Publication Number US 2011/0201944 A1 hereinafter Higgins in view of Osorio US Patent Application Publication Number US 2005/0197590 A1 hereinafter Osorio and further in view of Echauz international Patent Application Publication Number WO 03/030734 A2 hereinafter Echauz as applied to claims 3 and 10 above and further in view of Fried US Patent Application Publication Number US 2017/0113046 A1 hereinafter Fried.
Regarding claim 4 Higgins in view of Osorio further in view of Echauz teaches the epilepsy monitoring device of claim 3. Modified Higgins further discloses the device wherein the communication unit comprises: first communication means configured to communicate with the external device by a remote active communication method (Paragraph 0089: the telemetry transmitter). Higgins further discloses that the communication sub-assembly may include a magnetic reed switch (Paragraph 0085) which is a passive component operated by a proximity of a magnetic field, but such a switch does not reasonably “communicate with” the external device as it merely reacts to its presence and does not transmit or receive data itself.
Thus, modified Higgins fails to further disclose the device comprising a second communication means configured to communicate with the external device by a proximity passive communication method.
Fried teaches systems and methods for restoring cognitive function are disclosed. In some implementations, a method includes, at a computing device, separately stimulating one or more of lateral and medial entorhinal afferents and other structures connecting to a hippocampus of an animal subject in accordance with a plurality of predefined stimulation patterns, thereby attempting to restore object-specific memories and location-specific memories; collecting a plurality of one or more of macro- and micro-recordings of the stimulation of hippocampal entorhinal cortical (HEC) system; and refining the computational model for restoring individual memories in accordance with a portion of the plurality of one or more of macro- and micro-recordings (Abstract). Thus, Fried is reasonably pertinent to the problem at hand.
Fried teaches an implantable system for recording and stimulating the brain which includes radiofrequency coils (Paragraph 0160). Fried teaches that the device may include a low-power wireless transceiver within the implantable device for near-field communication (Paragraph 0154 It is noted that near field communication is considered a proximity passive communication method).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to combine the near field communication device of Fried with the device of modified Higgins because Fried teaches that the near field communication module has low power requirements (Fried paragraph 0154: low-power radio blocks) and would thus be advantageous to implement into the implantable device of modified Higgins to preserve battery power during wireless communication for times where the shorter range and lower data transfer rate of near-field communication is acceptable.
Regarding claim 5 Higgins in view of Osorio in view of Echauz and further in view of Fried teaches the epilepsy monitoring device of claim 4. Modified Higgins further discloses the device wherein: the first communication means is configured to transmit and receive the alarm signal to the external device (Paragraphs 0152 and 0167: the system detects the user is at risk of a seizure or detects a seizure and issues seizure warning. Paragraph 0162: the implanted circuitry may send the alarm signal to the external device) by the remote active communication method (Paragraph 0089: the telemetry transmitter).
Modified Higgins fails to further disclose the device wherein the second communication means is configured to transmit and receive the cranial nerve signals or the brain stimulation signal to the external device by the proximity passive communication method.
Fried teaches an implantable system for recording and stimulating the brain which includes radiofrequency coils (Paragraph 0160). Fried teaches that the device may include a low-power wireless transceiver within the implantable device for near-field communication (Paragraph 0154 It is noted that near field communication is considered a proximity passive communication method).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to combine the near field communication device of Fried with the device of Higgins in view of Osorio in view of Echauz further in view of Fried because Fried teaches that the near field communication module has low power requirements (Fried paragraph 0154: low-power radio blocks) and would thus be advantageous to implement into the implantable device of modified Higgins to preserve battery power during wireless communication for times where the shorter range and lower data transfer rate of near-field communication is acceptable.
An obvious variation of Higgins in view of Osorio in view of Echauz further in view of Fried as presented above would be to transmit and receive the cranial nerve signal or the brain stimulation signal to an external device by a proximity passive communication method. Higgins discloses that the implanted circuitry may communicate EEG data to an external assembly within a given range and at a variety of transfer speeds (Paragraph 0086). Since the embodiment of Higgins utilized for the above rejections is an embodiment where the EEG data is processed within the implantable device (Paragraph 0162), the transmission of EEG data may be considered low priority but still desirable to obtain for the patient medical records, or for review by a clinician. As such, the low power proximity passive communication method of Higgins in view of Echauz further in view of Fried would be an ideal method of communicating this low-priority data given its lower range and transmission speed for the benefit of reduced power consumption. Higher priority data such as extracted features or the alarm signals may be transmitted using the more power intensive system to ensure fast communication even at increased range.
Regarding claim 11 Higgins in view of Osorio further in view of Echauz teaches the epilepsy monitoring system of claim 10. Modified Higgins further discloses the system wherein the communication unit comprises: first communication means configured to communicate with the external device (Paragraphs 0152 and 0167: the system detects the user is at risk of a seizure or detects a seizure and issues seizure warning. Paragraph 0162: the implanted circuitry may send the alarm signal to the external device) by a remote active communication method (Paragraph 0089: the telemetry transmitter). Higgins further discloses that the communication sub-assembly may include a magnetic reed switch (Paragraph 0085) which is a passive component operated by a proximity of a magnetic field, but such a switch does not reasonably “communicate with” the external device as it merely reacts to its presence and does not transmit or receive data itself.
Thus, modified Higgins fails to further disclose the system comprising second communication means configured to communicate with the external device by a proximity passive communication method.
Fried teaches an implantable system for recording and stimulating the brain which includes radiofrequency coils (Paragraph 0160). Fried teaches that the device may include a low-power wireless transceiver within the implantable device for near-field communication (Paragraph 0154 It is noted that near field communication is considered a proximity passive communication method).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to combine the near field communication device of Fried with the system of Higgins in view of Osorio further in view of Echauz because Fried teaches that the near field communication module has low power requirements (Fried paragraph 0154: low-power radio blocks) and would thus be advantageous to implement into the implantable device of modified Higgins to preserve battery power during wireless communication for times where the shorter range and lower data transfer rate of near-field communication is acceptable.
Regarding claim 12 Higgins in view of Osorio in view of Echauz further in view of Fried teaches the epilepsy monitoring system of claim 11. Modified Higgins further discloses the system wherein: the first communication means is configured to transmit and receive the alarm signal to the external device (Paragraphs 0152 and 0167: the system detects the user is at risk of a seizure or detects a seizure and issues seizure warning. Paragraph 0162: the implanted circuitry may send the alarm signal to the external device) by the remote active communication method (Paragraph 0089: the telemetry transmitter), and
Modified Higgins fails to further disclose the system wherein the second communication means is configured to transmit and receive the cranial nerve signals or the brain stimulation signal to the external device by the proximity passive communication method.
Fried teaches an implantable system for recording and stimulating the brain which includes radiofrequency coils (Paragraph 0160). Fried teaches that the device may include a low-power wireless transceiver within the implantable device for near-field communication (Paragraph 0154 It is noted that near field communication is considered a proximity passive communication method).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to combine the near field communication device of Fried with the system of Higgins in view of Osorio in view of Echauz further in view of Fried because Fried teaches that the near field communication module has low power requirements (Fried paragraph 0154: low-power radio blocks) and would thus be advantageous to implement into the implantable system of modified Higgins to preserve battery power during wireless communication for times where the shorter range and lower data transfer rate of near-field communication is acceptable.
An obvious variation of Higgins in view of Osorio in view of Echauz in view of Fried as presented above would be to transmit and receive the cranial nerve signal or the brain stimulation signal to an external device by a proximity passive communication method. Higgins discloses that the implanted circuitry may communicate EEG data to an external assembly within a given range and at a variety of transfer speeds (Paragraph 0086). Since the embodiment of Higgins utilized for the above rejections is an embodiment where the EEG data is processed within the implantable device (Paragraph 0162), the transmission of EEG data may be considered low priority but still desirable to obtain for the patient medical records, or for review by a clinician. As such, the low power proximity passive communication method of Higgins in view of Fried would be an ideal method of communicating this low-priority data given its lower range and transmission speed for the benefit of reduced power consumption. Higher priority data such as extracted features or the alarm signals may be transmitted using the more power intensive system to ensure fast communication even at increased range.
Claims 6-7 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Higgins US Patent Application Publication Number US 2011/0201944 A1 hereinafter Higgins in view of Osorio US Patent Application Publication Number US 2005/0197590 A1 hereinafter Osorio and further in view of Echauz international Patent Application Publication Number WO 03/030734 A2 hereinafter Echauz as applied to claims 1 and 8 above and further in view of Giftakis US Patent Application Publication Number US 2010/0121213 A1 hereinafter Giftakis.
Regarding claim 6 Higgins in view of Osorio further in view of Echauz teaches the epilepsy monitoring device of claim 1. Modified Higgins further discloses the device configured to detect a biological signal different from the cranial nerve signals (Paragraph 0181)
Modified Higgins fails to further disclose the location of such a sensor and thus fails to disclose the sensor unit further comprises a second sensor.
Giftakis teaches systems and methods for monitoring trends in the intracranial pressure over time, e.g., to detect changes to the patient's condition. In addition, in some examples, a seizure metric may be generated for a detected seizure based on sensed intracranial pressures. The seizure metric may indicate, for example, an average, median, or highest relative intracranial pressure value observed during a seizure, a percent change from a baseline value during the seizure, or the time for the intracranial pressure to return to a baseline state after the occurrence of a seizure. In addition to or instead of intracranial pressure, patient motion or posture may be monitored in order to assess the patient's seizure disorder. For example, a seizure type or severity may be determined based on patient motion sensed during a seizure (Abstract). Thus, Giftakis falls within the same field of endeavor as Applicant’s invention.
Giftakis teaches a therapy system which includes an implantable motion sensor which may be located on one of the electrode lead implanted within the patient’s brain. The motion signals are analyzed to determine current patient activity levels and patient posture which may be used to evaluate detected seizures and determine typical patient movement activity during seizure events (Paragraphs 0058-0059; fig. 1 references 20A and 20B).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to incorporate the motion sensor located on an implanted electrode lead as described by Giftakis into the device of Higgins in view of Osorio further in view of Echauz because Higgins already considers the utilization of motion signals to help detect seizure events (Higgins: Paragraph 0181) but fails to disclose where such a sensor would be implemented. The device of Giftakis teaches that the electrodes in the user’s brain are acceptable locations for such a sensor and that the resultant data can be used to help characterize what movements are typical for a particular patient during a seizure event (Giftakis: paragraph 0059) which may help the user better plan and react for predicted seizure events
Regarding claim 7 Higgins in view of Osorio in view of Echauz further in view of Giftakis teaches the epilepsy monitoring device of claim 6. Modified Higgins further discloses the device wherein the second sensor is configured to detect a motion signal of the user (Paragraph 0181: accelerometer or movement recordings)
Regarding claim 13 Higgins in view of Osorio further in view of Echauz teaches the epilepsy monitoring system of claim 8. Modified Higgins further discloses the system comprising a second sensor configured to detect a biological signal different from the cranial nerve signals (Paragraph 0181).
Modified Higgins fails to further disclose the location of such a sensor and thus fails to disclose the sensor unit further comprises a second sensor.
Giftakis teaches a therapy system which includes an implantable motion sensor which may be located on one of the electrode lead implanted within the patient’s brain. The motion signals are analyzed to determine current patient activity levels and patient posture which may be used to evaluate detected seizures and determine typical patient movement activity during seizure events (Paragraphs 0058-0059; fig. 1 references 20A and 20B).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to incorporate the motion sensor located on an implanted electrode lead as described by Giftakis into the system of Higgins in view of Osorio further in view of Echauz because Higgins already considers the utilization of motion signals to help detect seizure events (Higgins: Paragraph 0181) but fails to disclose where such a sensor would be implemented. The device of Giftakis teaches that the electrodes in the user’s brain are acceptable locations for such a sensor and that the resultant data can be used to help characterize what movements are typical for a particular patient during a seizure event (Giftakis: paragraph 0059) which may help the user better plan and react for predicted seizure events
Regarding claim 14 Higgins in view of Osorio in view of Echauz further in view of Giftakis teaches the epilepsy monitoring system of claim 13. Modified Higgins further discloses the system wherein the second sensor is configured to detect a motion signal of the user (Paragraph 0181: accelerometer or movement recordings).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Higgins US Patent Application Publication Number US 2011/0201944 A1 hereinafter Higgins in view of Osorio US Patent Application Publication Number US 2005/0197590 A1 hereinafter Osorio and further in view of Echauz international Patent Application Publication Number WO 03/030734 A2 hereinafter Echauz as applied to claim 1 above and further in view of Kidmose US Patent Application Publication Number US 2013/0296731 A1 hereinafter Kidmose.
Regarding claim 16 Higgins in view of Osorio further in view of Echauz teaches the epilepsy monitoring device of claim 1. Modified Higgins fails to further disclose the device,, wherein the specific frequency band is configured to be detected by a Welch method.
Kidmose teaches a personal wearable EEG monitor is adapted to be carried at the head of a person. The EEG monitor comprises an EEG sensor part having skin surface electrodes for measuring EEG signals from said person. The EEG monitor comprises an EEG signal analyzer adapted for monitoring and analyzing the EEG signal. The EEG monitor performs at least one of the following: providing a stimulus to the person, requesting the person to perform a stimuli creating act, or identifying a stimuli creating ambient sound. The EEG monitor comprises means for identifying an induced response from the EEG signal caused by the stimuli, and a classifier for deciding whether the skin surface electrodes receive EEG signals. The invention further provides a method of monitoring EEG signals of a person (Abstract). Thus, Kidmose falls within the same field of endeavor as Applicant’s invention.
Kidmose teaches that power spectral density may be determined using a Welch method (Paragraph 0069).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to configure the device of modified Higgins to determine the PSD and thus the frequency and frequency band having the greatest difference between the ictal and interictal states by using a Welch method because Kidmose teaches that a Welch method may be used to determine the PSD of EEG signal (Paragraph 0069) and the use of such a method is a simple substitution of one known method (the method used by Osorio) for another known method (a Welch method as taught by Kidmose) with no surprising technical effect (the PSD is calculated).
Response to Arguments
Applicant’s arguments with respect to claims 1 and 8 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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/MATTHEW ERIC OGLES/Examiner, Art Unit 3791
/JASON M SIMS/Supervisory Patent Examiner, Art Unit 3791