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 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.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
A control unit in claim 1.
A visual or auditive means of alarm in claim 1
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
A control unit is required to be “adapted to: configure…, read…, filter…, segment…, input…, apply…, configure…, and trigger..” as recited in lines 7-25. The control unit is this interpreted as the particular algorithm which carried out each of the recited functions of the control unit. The specification does not explicitly describe the steps carried out to perform each of the function of the control unit. For example, the function of filtering and down-sampling the EEG data is described in purely functional language in paragraphs 0043-0044, 0051-0052, and 0056-0057. The particular steps required to filter and/or down-sample the data in the manners recited by the specification are not disclosed. Similarly, the particular steps taken by the algorithm to carry out each of the recited function of the control unit are not fully disclosed.
A visual or auditive means of alarm of claim 1 lines 4-5 is described in the specification as being a flashing LED in paragraph 0044. The specification does not describe an auditive means of alarm.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-22 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 limitations “a control unit”, and “visual or auditive means of alarm” of claim 1 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. 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. As described in the above presented claim interpretation section, the control unit is interpreted as the particular algorithm for carrying out the recited functions of the control unit. The specification does not describe the specific steps taken to carry out each of the recited functions of the control unit as described above. Furthermore, the specification does not describe and auditive means of alarm 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. For the purposes of this examination, the control unit will be interpreted as a processor and the visual or auditive means of alarm will be interpreted as an LED and its equivalents or a speaker and its equivalents.
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.
Claim 1 recites “input the plurality of sequential epochs of EEG data to the convolutional neural network, the convolutional neural network is adapted to output the probability of occurrence of a seizure in the inputted plurality of sequential epochs of EEG data” but it is unclear how the input signals are transformed by the CNN to produce the recited output. For the purposes of this examination, this limitation will be interpreted as any method utilizing a CNN to produce the recited output from EEG signals.
Claim 1 recites “configure the analog front-end integrated circuit to receive a plurality of channels of EEG data from a plurality of EEG acquisition electrodes, amplify and digitize the received plurality of channels of EEG data, and transmit the EEG data from the plurality of EEG channels to the control unit; read the EEG data from the plurality of EEG channels transmitted from the analog front-end integrated circuit; filter and down sample the EEG data in a pre-processing routine” but it is unclear if the “EEG data” being transmitted at the end of the “configure” step is the raw data received from the EEG acquisition electrodes, or the conditioned data that has undergone the “amplify and digitize” process. Similarly, it is unclear if the “EEG data” being red and subsequently pre-processed is the raw EEG data, or the EEG data which has undergone the “amplify and digitize” process. For the purposes of this examination, “the EEG data” will be interpreted as the data which has undergone the “amplify and digitize” process. This rejection is similarly applied to claim 18 steps b) and c) and claim 19.
Claim 1 recites “ segment the plurality of channels of EEG data into a plurality of sequential epochs of EEG data; input the plurality of sequential epochs of EEG data to the convolutional neural network, the convolutional neural network is adapted to output the probability of occurrence of a seizure in the inputted plurality of sequential epochs of EEG data” but it is unclear if the EEG data being segmented into epochs is the raw EEG data, the amplified and digitized EEG, and/or the pre-processed data. It is unclear which data type is being segmented and subsequently fed to the CNN. For the purposes of this examination, the limitation will be interpreted as segmenting the pre-processed EEG data for each of the plurality of channels. This rejection is similarly applied to claim 18 steps d) and e)
Claim 1 recites “input the plurality of sequential epochs of EEG data to the convolutional neural network, the convolutional neural network is adapted to output the probability of occurrence of a seizure in the inputted plurality of sequential epochs of EEG data” but it is unclear if a single probability value is being output which encompasses all the input epochs, or if the output is a probability of occurrence of a seizure for each of the inputted epochs. For the purposes of this examination, this limitation will be interpreted as a probability for each of the inputted epochs. This rejection is similarly applied to claim 18 step e).
Claim 1 recites “apply a filter to smooth the outputs in a post-processing routine” but it is unclear what element “the outputs” is referring to since it would seem a number of previous steps produce outputs. For the purposes of this examination, the limitation will be interpreted as “the outputs of the convolutional neural network”.
Claim 1 recites “configure the communication integrated circuit to communicate in real time the plurality of channels of EEG data and the output of the convolutional neural network, to a server or cloud based platform” but it is unclear if the smoothed outputs of the CNN are being transmitted or the raw outputs of the CNN are being transmitted. For the purposes of this examination, the limitation will be interpreted as the smoothed outputs being transmitted.
Claim 1 recites “ trigger the visual or auditive means of alarm if the output of the convolutional neural network exceeds a predetermined threshold probability value” but it is unclear if the raw output of the CNN is being compared to the threshold or the smoothed output is being compared to the threshold. For the purposes of this examination, the limitation will be interpreted as the smoothed output being compared to the threshold.
Claims 2-17 are rejected by virtue of their dependance on claim 1.
Claims 19-22 are rejected by virtue of their dependance on claim 18.
Claim 11 recites “wherein the control unit, the analog front-end integrated circuit, communication integrated circuit, the power management circuit, and the visual or auditive means of alarm, are integrated to a printed circuit board” but it is unclear what particular structure “integrated to” is meant to convey. It is unclear if all of these elements are meant to be located on a single PCB, located on multiple different PCBs, or in electrical communication with a single or multiple PCBS. For the purposes of this examination, the limitation will be interpreted as at least in communication with one or more PCBs.
Claim 13 recites “wherein the moving average filter includes a binarization step configured to smooth the output and improve the classification accuracy” but it is unclear what this step entails, how it relates to the moving average filter, and how it results in improved classification accuracy. In particular, it is unclear what element is undergoing binarization. It is unclear how this binarization is implemented with the moving average filter and what results are considered “improvements” to the classification accuracy. For the purposes of this examination, any type of binarization step implemented in a post-processing procedure will be considered sufficient to anticipate this limitation.
Claim 15 recites “wherein the post-processing routine comprises a bandpass filtering and a down-sampling step” but it is unclear how these steps result in a smoothed output as required by the post-processing routine of claim 1. In particular, it would seem that the CNN outputs a probability of a given epoch being a seizure. It is unclear how these probabilities are filtered through a bandpass filter. It is further unclear how down-sampling the output of the CNN results in “smoothing” of the data. For the purposes of this examination, any type of smoothing operation will be considered sufficient to render this limitation obvious.
Claim 16 recites “wherein the convolutional neural network comprises a classification structure having non connected layers” but it is unclear what “non connected layers” comprise. It would seem from Applicant’s specification that this limitation is meant to refer to non fully-connected layers and will be interpreted as such.
Claim 17 recites “wherein the classification comprises one or more of the following neonatal seizure detection; neonatal neurological health; onset of abnormal neurological events” but it is unclear what “neonatal neurological health” comprises and what a classification of such an event comprises. For the purposes of this examination, the limitation will be interpreted as classifying any event related to neurological health.
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, 13, and 15-18 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 limitations “a control unit”, and “visual or auditive means of alarm” of claim 1 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. 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. As described in the above presented claim interpretation section, the control unit is interpreted as the particular algorithm for carrying out the recited functions of the control unit. The specification does not describe the specific steps taken to carry out each of the recited functions of the control unit as described above. Furthermore, the specification does not describe and auditive means of alarm Therefore, the claim lacks sufficient written description.
Claim 1 recites “input the plurality of sequential epochs of EEG data to the convolutional neural network, the convolutional neural network is adapted to output the probability of occurrence of a seizure in the inputted plurality of sequential epochs of EEG data” but the specification does not appear to detail how the recited inputs are converted into the recited outputs. Furthermore, the specification does not appear to fully describe the architecture of the CNN used to perform the recited operation. In particular, the specification appears to describe the CNN as having 10 layers and does not include a fully connected layer in paragraphs 0019, 0031, 0044, and 0051. In particular, paragraphs 0048-0051 describe how the CNN of the claimed system is intended to be different from standard CNN architecture. However, the description provided in paragraph 0050-0051 are insufficient to fully describe a non-conventional CNN architecture capable of carrying out the recited function. The recitations that the fully connected layers are replaced with simple convolutional filters is considered insufficient to support the claimed CNN, because no explanation of how these layers carry out the same function as a conventional CNN is provided. In particular, the specification does not clearly describe how the 10 non fully-connected layers of the lightweight algorithm achieve the claimed classification result. The specification states that the use of minimally pre-processed data is noisier and higher-dimensional than traditional extracted features then provides a mere statement of functionality that the CNN can still process this data using a lightweight algorithm that does not sacrifice classification accuracy (Paragraphs 0050-0051) The CNN is considered to be described as a “black box” algorithm where the desired inputs are entered and the desired outputs are produced. The specification describes the CNN in such a manner in paragraphs 0023, 0044, and 0057. This rejection is further applied to claim 18 step e).
Claim 13 recites “wherein the moving average filter includes a binarization step configured to smooth the output and improve the classification accuracy” but the specification does not appear to detail what this binarization step entails or how it results in improved classification accuracy. In particular, the binarization step is mentioned only in paragraph 0029 which provides a generic statement of functionality that such a step is applied and results in improved classification accuracy. No detailed explanation of how this step is applied and how it results in improved classification accuracy is provided.
Claim 15 recites “wherein the post-processing routine comprises a bandpass filtering and a down-sampling step” but the specification does not describe how these steps are applied to the output of the CNN and how such a process results in smoothed data. In particular, this post-processing method is mentioned only in paragraph 0031 which provides only a generic statement of functionality. The specification does not appear to further describe the implementation or effect of this post-processing routine.
Claim 16 recites “wherein the convolutional neural network comprises a classification structure having non connected layers” but the specification does not define a “non connected layer” or describe their use. In particular, the specification appears to only detail that the CNN may include or only be comprised by non fully-connected layers in paragraphs 0019, 0031, and 0048-0051.
Claim 17 recites “wherein the classification comprises one or more of the following neonatal seizure detection; neonatal neurological health; onset of abnormal neurological events” but the specification does not appear to describe what “neonatal neurological health” comprises or how it is detected by the CNN. In particular, “neonatal neurological health” is only referenced in paragraph 0032 as a generic statement of functionality with description of what such a category entails or how it is detected. Furthermore, the specification does not appear to fully support the scope of “onset of abnormal neurological events” in particular, paragraph 0026 describes that the algorithm is capable of detecting abnormal EEG for the purpose of detecting neonatal seizures. Paragraphs 0032 and 0049 provide mere generic statements of functionality of detecting abnormalities in EEG signals. These statements are considered insufficient to support the full scope of “onset of abnormal neurological events”.
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 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-22 are directed to a method of processing EEG signals using a computational algorithm, which is an abstract idea. Claims 1-22 do not include additional elements that integrate the exception into a practical application or that are sufficient to amount to significantly more than the judicial exception for the reasons provided below which are in line with the 2014 Interim Guidance on Patent Subject Matter Eligibility (Federal Register, Vol. 79, No. 241, p 74618, December 16, 2014), the July 2015 Update on Subject Matter Eligibility (Federal Register, Vol. 80, No. 146, p. 45429, July 30, 2015), the May 2016 Subject Matter Eligibility Update (Federal Register, Vol. 81, No. 88, p. 27381, May 6, 2016), and the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 4, page 50, January 7, 2019).
The analysis of claim 1 is as follows:
Step 1: Claim 1 is drawn to a machine.
Step 2A – Prong One: Claim 1 recites an abstract idea. In particular, claim 1 recites the following limitations:
[A1] receive a plurality of channels of EEG data
[B1] amplify the received plurality of channels of EEG data
[C1] transmit the EEG data from a plurality of EEG channels
[D1] read the EEG data
[E1] filter and down sample the EEG data in a pre-processing routine
[F1] segment the plurality of channels of EEG data into a plurality of sequential epochs of EEG data
[G1] input the plurality of sequential epochs of EEG data
[H1] output the probability of occurrence of a seizure in the inputted plurality of sequential epochs of EEG data
[I1] apply a filter to smooth the outputs in a post-processing routine
[J1] communicate in real time the plurality of channels of EEG data and the output
[K1] trigger an alarm if the output of the convolutional neural network exceeds a predetermined threshold probability value
These elements [A1]-[K1] of claim 1 are drawn to an abstract idea since (1) they involve mathematical concepts in the form of mathematical relationships, mathematical formulas or equations, and/or mathematical calculations; and (2) they involve a mental process that can be practically performed in the human mind including observation, evaluation, judgment, and opinion and using pen and paper.
Step 2A – Prong Two: Claim 1 recites the following limitations that are beyond the judicial exception:
[A2] a control unit
[B2] a convolutional neural network
[C2] an analog front-end integrated circuit
[D2] a visual or auditive means of alarm
[E2] a communication integrated circuit
[F2] a plurality of EEG acquisition electrodes
[G2] a server or cloud based platform
[H2] digitize the received plurality of channels of EEG data
[I2] transmit the EEG data from the plurality of EEG channels to the control unit
These elements [A2]-[I2] of claim 1 do not integrate the exception into a practical application of the exception. In particular, the element [F2] is merely adding insignificant extra-solution activity to the judicial exception, i.e., mere data gathering at a higher level of generality - see MPEP 2106.04(d) and MPEP 2106.05(g). Furthermore, the elements [A2], [C2]-[E2], and [G2]-[I2] are merely an instruction to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f). Additionally, the element [B2] is nothing more than the computer implementation/automation of an abstract mental process of screening a patient, which is what a physician typically does with a patient in a diagnostic setting
Step 2B: Claim 1 does not recite additional elements that amount to significantly more than the judicial exception itself. In particular, the recitation “configure the analog front-end integrated circuit to receive a plurality of channels of EEG data from a plurality of EEG acquisition electrodes” does not qualify as significantly more because this limitation merely describes the nature of the received data and does not incorporate the EEG electrodes as part of the claimed invention. Also, the recitation is merely insignificant extrasolution activity to the judicial exception, e.g., mere data gathering in conjunction with the abstract idea that uses conventional, routine, and well known elements or simply displaying the results of the algorithm that uses conventional, routine, and well known elements. In particular, the data acquirer is nothing more than conventional EEG electrodes. Such electrodes are conventional as evidenced by:
U.S. Patent Application Publication No. US 2006/0173510 A1 (Besio) discloses that EEG electrodes are conventional (paragraph 0013 of Besio);
U.S. Patent No. US 3993046 A (Fernandez) discloses that EEG signals are conventionally derived from electrodes (Col 1 lines 31-56 of Fernandez);
U.S. Patent No. US 3859988 A (Lencioni) discloses that EEG electrodes are conventional (Col 2 lines 35-49 of Lencioni); and
U.S. Patent Application Publication No. US 2015/0313498 A1 (Coleman) discloses that EEG electrodes and EEG electrode caps in various configurations are conventional (paragraphs 0108 of Coleman).
Further, the elements [A2], [C2]-[E2], and [G2]-[I2] do not qualify as significantly more because these limitations are simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)). The element [A2], [C2]-[E2], and [G2] are well-known computer elements as evidenced by Applicant’s lack of particular description as to the structure, operation, and function of each of these elements. Furthermore, the elements [H2]-[I2] are nothing more than the computer implementation/automation of an abstract mental process. In particular the element [H2] merely recites that the gathered data is converted into a computer-processable form. The element [I2] is merely a recitation that the computer elements may communicate data between each other.
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Claims 2-17 depend from claim 1, and recite the same abstract idea as claim 1. Furthermore, these claims only contain recitations that further limit the abstract idea (that is, the claims only recite limitations that further limit the algorithm), with the following exceptions:
Claim 2: the analog front-end integrated circuit is an eight channel twenty four bit programmable gain amplifier and analog to digital converter
Claim 5: a Serial Peripheral Interface; and
Claim 6: the communication module is a Bluetooth module.
Claim 7: the communication module is a Wireless Fidelity module.
Claim 8: a power management circuit.
Claim 9: the power management circuit includes a lithium polymer battery.
Claim 11: wherein the control unit, the analog front-end integrated circuit, communication integrated circuit, the power management circuit, and the visual or auditive means of alarm, are integrated to a printed circuit board.
Each of these claim limitations does not integrate the exception into a practical application. In particular, the elements of each of the above claims are simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions (that is, one of data communication, and transmission as well as power management) that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)). In particular, each of the elements of the above claims are not explicitly described in the specification. The lack of particular description of each of these elements indicates they are routine and conventional computational elements that are well-known to one of ordinary skill in the art. It is noted that an assertion that such elements are not well-known may necessitate a rejection under 35 USC 112(a) as the descriptions within the specification are insufficient to support non-conventional componentry. In particular, the analog front end is described as being commercially available in paragraph 0043. the Serial Peripheral Interface (SPI) is mentioned by name only and its mode of operation is not described in paragraphs 0015, 0043, and 0055. The communication modules are described only generically in paragraphs 0041, 0045, and 0055. The power management circuitry and battery are described using only functional language and generic structure in paragraphs 0046 and 0055. The printed circuit board integrating the variety of elements of claim 11 is described generically in paragraphs 0015, 0041 and 0055 with statements of functionality. None of the above elements are described in a manner to provide sufficient written description support for non-conventional or well-known componentry and thus each element is being interpreted as a generic well-known, routine, and/or conventional computational element which may be present within or integrated into a generic computer.
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations of each claim as an ordered combination in conjunction with the claims from which they depend (that is, 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, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
The below analysis of claim 18 is done in light of the above presented analysis of claim 1 and may be abridged where limitations are similar.
The analysis of claim 18 is as follows:
Step 1: Claim 18 is drawn to a process.
Step 2A – Prong One: Claim 18 recites an abstract idea. In particular, claim 18 recites the following limitations:
[A1] receiving a plurality of channels of EEG data
[B1] amplifying and digitizing the received plurality of channels of EEG data
[C1] transmitting the EEG data from a plurality of EEG channels
[D1] segmenting the received plurality of channels of EEG data into a plurality of sequential epochs of EEG data
[E1] inputting the plurality of sequential epochs of EEG data
[F1] output the probability of occurrence of a seizure in the inputted plurality of sequential epochs of EEG data
[G1] communicating in real time the plurality of channels of EEG data received in step (a) and the output of the convolutional neural network,
[H1] trigger an alarm if the output of the convolutional neural network exceeds a predetermined threshold probability value
These elements [A1]-[H1] of claim 18 are drawn to an abstract idea since (1) they involve mathematical concepts in the form of mathematical relationships, mathematical formulas or equations, and/or mathematical calculations; and (2) they involve a mental process that can be practically performed in the human mind including observation, evaluation, judgment, and opinion and using pen and paper.
Step 2A – Prong Two: Claim 18 recites the following limitations that are beyond the judicial exception:
[A2] a control unit
[B2] a convolutional neural network
[D2] a visual or auditive means of alarm
[F2] a plurality of EEG acquisition electrodes
[G2] a server or cloud based platform
[H2] digitize the received plurality of channels of EEG data
[I2] transmitting the EEG data from a plurality of EEG channels to the control unit
These elements [A2]-[B2], [D2], [F2]-[I2] of claim 18 do not integrate the exception into a practical application of the exception. For the reasons described in the above rejection of claim 1
Step 2B: Claim 1 does not recite additional elements that amount to significantly more than the judicial exception itself. In particular, the additional elements do not qualify as significantly more for the same reasons provided in the above rejection of claim 1.
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Claims 19-22 depend from claim 18, and recite the same abstract idea as claim 18. Furthermore, these claims only contain recitations that further limit the abstract idea (that is, the claims only recite limitations that further limit the algorithm).
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations of each claim as an ordered combination in conjunction with the claims from which they depend (that is, 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, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
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, 6-8, 10, and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Kabrams US Patent Application Publication Number US 2020/0188697 A1 hereinafter Kabrams in view of Uvaydov US Patent Application Publication Number US 2021/0259639 A1 hereinafter Uvaydov and further in view of Litt US Patent Number US 6658287 B1 hereinafter Litt.
Regarding claim 1, Kabrams discloses an integrated system for neonatal Electroencephalogram (EEG) acquisition and interpretation (Abstract; Paragraphs 0003-0004 and 0049: receive and analyze an EEG signal of a person. A person is considered to include a neonatal person), the system comprising:
a control unit (Paragraph 0141 and 0181-0182: the processor, or local processing device, of the device ) having embedded on it a convolutional neural network (Paragraphs 0181-0182: the statistical model implemented on the processor; Paragraphs 0190-0191: the statistical model may include a Deep Convolutional Neural Network (DCNN). A DCNN algorithm is considered a type of CNN ), the control unit operably interfaced to an analog front-end integrated circuit (Paragraph 0151: the analog front-end) and a means of alarm (Paragraphs 0146-0150: the machine learning algorithm may generate an alert. The system may display real-time seizure risk.) and
a communication integrated circuit operably interfaced to the control unit (Paragraphs 0141-0143 and 0212: the radio and/or physical connector for transmitting data and the output of the algorithm; Paragraph 0151: data transmission circuitry); and
characterised in that the control unit is adapted to:
configure the analog front-end integrated circuit to receive a plurality of channels of EEG data from a plurality of EEG acquisition electrodes (Paragraph 0141: the electrodes for receiving EEG data), amplify and digitize the received plurality of channels of EEG data, and transmit the EEG data from the plurality of EEG channels to the control unit (Paragraph 0151-0152: the analog front-end conditions, amplifies and digitizes the signals acquired by the one or more sensors which are transmitted to the mobile device and/or cloud server; Paragraph 0191: the received EEG data may be from multiple channels);
read the EEG data from the plurality of EEG channels transmitted from the analog front-end integrated circuit (Paragraphs 0151-0152 and 0157: the data from the analog front-end and/or digital back-end are later processed. Thus the limitation of reading the EEG data is considered to be implicitly disclosed since the disclosed algorithm and processing methods are performed by a computer which must “read” the data to perform the recited steps);
filter the EEG data in a pre-processing routine (Paragraph 0151: the digital back-end for buffering, pre-processing, and/or packetizing; Paragraph 0167: various filters may be applied to the input data to extract useful representations of the data. The filtered data is what the classifiers are trained on);
segment the plurality of channels of EEG data into a plurality of sequential epochs of EEG data (Paragraphs 0178-0180, 0191, and 0197: the algorithm may process the input data in windows, or sequential epochs, and label each window. The window durations may vary);
input the plurality of sequential epochs of EEG data to the convolutional neural network, the convolutional neural network is adapted to output the probability of occurrence of a seizure in the inputted plurality of sequential epochs of EEG data (Paragraphs 0138-0140: the algorithm detects the probability of a seizure occurring; Paragraph 0149: real-time seizure risk; Paragraphs 0178-0180: the algorithm may output values between 0 and 1.);
apply a filter to smooth the outputs in a post-processing routine (Paragraph 0178: a filter may be applied to create a smooth final output of the algorithm);
configure the communication integrated circuit to communicate in real time the plurality of channels of EEG data and the output of the convolutional neural network, to a server or cloud based platform (Paragraphs 0141-0143: the local processing device may be configured to transmit incoming EEG data to a server; Paragraph 0191: the incoming EEG data may be from a plurality of channels. Paragraph 0153: various relationships between the device and its mobile application and the cloud server are contemplated.; Paragraphs 0156-0157: the wearable device itself may run the machine learning seizure detection algorithm. Kabrams is considered to at least suggest transmitting the output of the CNN to the server because Kabrams explicitly discloses embodiments where the data processing is performed on the device itself as well as embodiments where the data is transmitted to the server for processing. Kabrams further explicitly contemplates different relationships between the device and server including the transmission of data for storage and the use of stored data for training the machine learning algorithm (Fig. 3B reference 383 Paragraphs 0152 and 0170). Thus an obvious variation of Kabrams is an embodiment where the machine learning algorithm is executed on the device and where both the data and algorithm output are transmitted to the server because such a transmission configuration would be useful for training subsequent algorithms. Furthermore, paragraph 0212 discloses a separate embodiment where data and an output of a classification algorithm are transmitted to the server.); and
trigger the means of alarm based on the output of the convolutional neural network (Paragraph 0137: seizure risk is compared to a predetermined threshold to begin stimulation; Paragraph 0150: the algorithm may send alerts when seizures are detected or are likely to occur).
Kabrams further discloses a step of down-sampling (Paragraph 0203) however this step is carried out by the DCNN and thus is not part of the pre-processing routine as required by the present claim language.
Kabrams fails to further disclose the alarm means being a visual or auditive means of alarm, the pre-processing including down-sampling, or triggering the means of alarm if the output of the convolutional neural network exceeds a predetermined threshold probability value.
Uvaydov teaches a deep learning module including a neural network trained to process the input samples, received from the sensing and actuation unit, through a plurality of layers to classify physiological parameters and provide classification results. A communication interface in communication with the deep learning module receives the classification results for ultrasonic transmission through biological tissue. Methods of sensing and classifying physiological parameters of a body and methods of embedding deep learning into an implantable medical device (Abstract). Thus Uvaydov is reasonably pertinent to the problem at hand.
Uvaydov teaches that pre-processing EEG data may include down-sampling the data to decrease the amount of block random access memory (BRAM) used by the processor (Paragraph 0074).
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 down-sampling as part of pre-processing as taught by Uvaydov into the system of Kabrams because Uvaydov teaches that down-sampling reduces the memory requirements of the processor and Kabra