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 .
Information Disclosure Statement
The listing of references in the specification is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered.
Claim Objections
Claim 13 is objected to because of the following informalities:
in step A: “wherein the client device comprises wherein the client device comprises a processing unit” should be “wherein the client device comprises a processing unit”
Appropriate correction is required.
Claim Warning
Applicant is advised that should claims 6-9 and 11 be found allowable, claims 16-20 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-20 are all within at least one of the four categories.
The independent claim 1 recites:
providing a client device managed by at least one remote server;
providing at least one machine learning unit managed by the at least one remote server;
providing an EEG device, wherein the EEG device is adapted to acquire scalp EEG data of a candidate;
collecting behaviorally measured metadata from the candidate through external means;
collecting EEG data from the candidate through the EEG device;
preprocessing, segmenting, and performing data augmentation on the EEG data through the at least one machine learning unit;
performing model interference and prediction aggregation based on the behaviorally measured metadata, through the at least one machine learning unit;
The independent claim 13 recites:
providing a client device managed by at least one remote server, wherein the client device comprises wherein the client device comprises a processing unit, an EEG data reception unit, a data transmission unit, and a data storage unit;
providing at least one machine learning unit managed by the at least one remote server;
providing an EEG device, wherein the EEG device is adapted to acquire scalp EEG data of a candidate;
collecting behaviorally measured metadata from the candidate through external means, wherein external means comprises at least one of digital means and physical means;
collecting EEG data from the candidate through the EEG device;
preprocessing, segmenting, and performing data augmentation on the EEG data through the at least one machine learning unit;
performing model interference and prediction aggregation based on the behaviorally measured metadata, through the at least one machine learning unit;
The above claim limitations constitute an abstract idea that is part of the Mathematical Concepts and/or Mental Processes group identified in the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019. See footnotes 14 and 15.
“A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words ….” October 2019 Update: Subject Matter Eligibility, II. A. i. “[T]here are instances where a formula or equation is written in text format that should also be considered as falling within this grouping.” Id. at II. A. ii. “[A] claim does not have to recite the word “calculating” in order to be considered a mathematical calculation.” Id. at II. A. iii. See for example, SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65 (Fed. Cir. 2018) (performing a resampled statistical analysis to generate a resampled distribution).
The claimed steps of providing; collecting; preprocessing; segmenting and performing can be practically performed in the human mind using mental steps or basic critical thinking, which are types of activities that have been found by the courts to represent abstract ideas.
Examples of ineligible claims that recite mental processes include:
a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group, LLC v. Alstom, S.A.;
claims to “comparing BRCA sequences and determining the existence of alterations,” where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind, University of Utah Research Foundation v. Ambry Genetics Corp.
a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC.
See p. 7-8 of October 2019 Update: Subject Matter Eligibility.
With respect to the pending claims, for example, an experienced clinician and can perform the claimed step of providing by mentally noting that a client device, a machine learning unit and a EEG device are all present within a room. The experienced clinician can then collect data from those devices by mentally noting the output Thus, the claims can be readily interpreted as being a mere application of a mental process on a computer.
Regarding the dependent claims, the dependent claims are directed to either 1) steps that are also abstract or 2) additional data output that is well-understood, routine and previously known to the industry. For example, dependent claims 2-12 and 14-20 recite steps (e.g. demeaning; bandpass-filtering; removing; and flagging) that can be performed in the mind. Although the dependent claims are further limiting, they do not recite significantly more than the abstract idea. A narrow abstract idea is still an abstract idea and an abstract idea with additional well-known equipment/functions is not significantly more than the abstract idea.
This judicial exception (abstract idea) in claims 1-20 is not integrated into a practical application because:
The abstract idea amounts to simply implementing the abstract idea on a computer. For example, the recitations regarding the generic computing components for providing; collecting; uploading; transmitting; preprocessing; segmenting; performing; outputting; transmitting; taking; storing; demeaning; bandpass-filtering; removing and flagging merely invoke a computer as a tool.
The data-gathering step (collecting; and uploading) and the data-output step (transmitting and storing) do not add a meaningful limitation to the method as they are insignificant extra-solution activity.
There is no improvement to a computer or other technology. “The McRO court indicated that it was the incorporation of the particular claimed rules in computer animation that "improved [the] existing technological process", unlike cases such as Alice where a computer was merely used as a tool to perform an existing process.” MPEP 2106.05(a) II. The claims recite a computer that is used as a tool for providing; collecting; uploading; transmitting; preprocessing; segmenting; performing; outputting; transmitting; taking; storing; demeaning; bandpass-filtering; removing and flagging.
The claims do not apply the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition. Rather, the abstract idea is utilized to determine a relationship among data to provide information about EEG data.
The claims do not apply the abstract idea to a particular machine. “Integral use of a machine to achieve performance of a method may provide significantly more, in contrast to where the machine is merely an object on which the method operates, which does not provide significantly more.” MPEP 2106.05(b). II. “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more.” MPEP 2106.05(b) III. The pending claims utilize a computer for providing; collecting; uploading; transmitting; preprocessing; segmenting; performing; outputting; transmitting; taking; storing; demeaning; bandpass-filtering; removing and flagging . The claims do not apply the obtained data to a particular machine. Rather, the data is merely output in an post-solution step.
The additional elements are identified as follows: a client device with a processing unit, an EEG data reception unit, a data transmission unit and a data storage unit; at least one remote server; at least one machine learning unit; an EEG device; and electrodes
Those in the relevant field of art would recognize the above-identified additional elements as being well-understood, routine, and conventional means for data-gathering and computing, as demonstrated by
Applicant' s specification (page 9 lines 27-31) which discloses that EEG data is processed using known automatic preprocessing routines that are well-understood, routine, and conventional activities previously known to the pertinent industry; and
the non-patent literature cited herewith:
Oakley, Thomas, et al. "EEG biomarkers to predict response to sertraline and placebo treatment in major depressive disorder." IEEE Transactions on Biomedical Engineering 70.3 (2022): 909-919.
Thus, the claimed additional elements “are so well-known that they do not need to be described in detail in a patent application to satisfy 35 U.S.C. § 112(a).” Berkheimer Memorandum, III. A. 3.
Furthermore, the court decisions discussed in MPEP § 2106.05(d)(lI) note the well-understood, routine and conventional nature of such additional elements as those claimed. See option III. A. 2. in the Berkheimer memorandum.
When considered in combination, the additional elements (i.e. the generic computer functions and conventional equipment/steps) do not amount to significantly more than the abstract idea. Looking at the claim limitations as a whole adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 5, 9-10, 13-15 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over De Bruin (US 20110119212 A1) in view of Mishanin (Applicant Admitted Prior Art (“APA”) – Application 18/777325).
With respect to claim 1, De Bruin discloses a method, the method comprising:
providing a client device managed by at least one remote server (see paragraph 0069: data is sent electronically #103 to a remote central processing site);
providing at least one machine learning unit managed by the at least one remote server (see paragraph 0045-0046: machine learning methodology managed by the remote central processing site);
providing an EEG device, wherein the EEG device is adapted to acquire scalp EEG data of a candidate (see paragraph 0045-0046, EEG data is collected by the many sensors placed on the scalp);
collecting behaviorally measured metadata from the candidate through external means (see paragraph 0071: system collects demographic and other clinical data by using machine learning; and see paragraph 0204);
collecting EEG data from the candidate through the EEG device (see paragraph 0045-0046, EEG data is collected by the many sensors placed on the scalp; pre-treatment EEG signals and clinical attributes are collected);
uploading the EEG data and the behaviorally measured metadata to the client device (see paragraph 0069-0071: system collects demographic and other clinical data by using machine learning and is sent electronically to remote central processing site; and see paragraph 0204);
transmitting the EEG data and the behaviorally measured metadata of the candidate to the remote server through the client device (see paragraph 0069-0071: data is processed on site using a computer algorithm pre-loaded onto the user’s computer or sent electronically to a remote central processing site);
preprocessing, segmenting, and performing data augmentation on the EEG data through the at least one machine learning unit (see paragraph 0113: data preprocessing, feature extraction and feature selection is done via machine interface and computational learning);
performing model interference and prediction aggregation based on the behaviorally measured metadata, through the at least one machine learning unit (see paragraph 0129: relevant information may be enhanced and while at the same time the influence of unwanted and random interference/noise may be reduced using machine learning);
transmitting a result to the remote server through the at least one machine learning unit (see paragraph 0079: the prediction results are sent back to the physician treating the patient).
De Bruin does not disclose a method of predicting response of individuals to placebo during clinical trials and outputting a result for placebo response prediction of the candidate through the at least one machine learning unit.
However, Mishanin teaches that the probability that an individual will respond to a placebo could be predicted from their EEG resting state (see Background section of Applicant’s specification).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified De Bruin with the teachings from Mishanin because it would have resulted in the predictable result of uniting known elements according to their established functions to yield the predictable results of an automated placebo-response prediction.
With respect to claim 2, all limitations of claim 1 apply in which De Bruin further discloses wherein external means of collecting behaviorally measured metadata comprises at least one of digital means and physical means (see paragraph 0094: medical assessment or diagnosis training is done digitally or physically).
With respect to claim 3, all limitations of claim 1 apply in which De Bruin further discloses wherein the client device comprises wherein the client device comprises a processing unit (see paragraph 0086-0091, remote processing site), an EEG data reception unit (see paragraph 0086-0091, measured data is sent to remote station where it is received), a data transmission unit (see paragraph 0086-0091, remote processing site as communication means), and a data storage unit (see paragraph 0086-0091, remote station has storage).
With respect to claim 5, all limitations of claim 1 apply in which Mishanin further teaches wherein preprocessing the EEG data further comprising: demeaning the EEG data; bandpass-filtering the EEG data; removing notch-frequencies from the EEG data; and flagging artifacts from the EEG data (see Page 9 lines 27-31 – page 10 lines 1-8).
With respect to claims 9 and 19, all limitations of claim 1 apply in which the combination of De Bruin and Mishanin further teaches wherein the result of placebo response prediction is at least one of 0 and 1 (De Bruin: predication outcome is either responder or non-responder which can be translated to binary numbers of 0 and 1).
With respect to claim 10, all limitations of claim 9 apply in which the combination of De Bruin and Mishanin further teaches wherein: a result of 0 indicates no placebo response for the candidate; and a result of 1 indicates a placebo response for the candidate (De Bruin: predication outcome is either responder or non-responder which can be translated to binary numbers of 0 and 1).
With respect to claim 13, De Bruin discloses a method of predicting response of individuals to placebo during clinical trials, the method comprising:
providing a client device managed by at least one remote server (see paragraph 0069: data is sent electronically #103 to a remote central processing site), wherein client device comprises a processing unit (see paragraph 0086-0091, remote processing site), an EEG data reception unit (see paragraph 0086-0091, measured data is sent to remote station where it is received), a data transmission unit (see paragraph 0086-0091, remote processing site as communication means), and a data storage unit (see paragraph 0086-0091, remote station has storage);
providing at least one machine learning unit managed by the at least one remote server (see paragraph 0045-0046: machine learning methodology managed by the remote central processing site);
providing an EEG device, wherein the EEG device is adapted to acquire scalp EEG data of a candidate (see paragraph 0045-0046, EEG data is collected by the many sensors placed on the scalp);
collecting behaviorally measured metadata from the candidate through external means (see paragraph 0071: system collects demographic and other clinical data by using machine learning; and see paragraph 0204), wherein external means comprises at least one of digital means and physical means (see paragraph 0094: medical assessment or diagnosis training is done digitally or physically);
collecting EEG data from the candidate through the EEG device (see paragraph 0045-0046, EEG data is collected by the many sensors placed on the scalp; pre-treatment EEG signals and clinical attributes are collected);
uploading the EEG data and the behaviorally measured metadata to the client device (see paragraph 0069-0071: system collects demographic and other clinical data by using machine learning and is sent electronically to remote central processing site; and see paragraph 0204);
transmitting the EEG data and the behaviorally measured metadata of the candidate to the remote server through the client device (see paragraph 0069-0071: data is processed on site using a computer algorithm pre-loaded onto the user’s computer or sent electronically to a remote central processing site);
preprocessing, segmenting, and performing data augmentation on the EEG data through the at least one machine learning unit (see paragraph 0113: data preprocessing, feature extraction and feature selection is done via machine interface and computational learning);
performing model interference and prediction aggregation based on the behaviorally measured metadata, through the at least one machine learning unit (see paragraph 0129: relevant information may be enhanced and while at the same time the influence of unwanted and random interference/noise may be reduced using machine learning);
transmitting the result to the remote server through the at least one machine learning unit (see paragraph 0079: the prediction results are sent back to the physician treating the patient).
De Bruin does not disclose a method of predicting response of individuals to placebo during clinical trials and outputting a result for placebo response prediction of the candidate through the at least one machine learning unit.
However, Mishanin teaches that the probability that an individual will respond to a placebo could be predicted from their EEG resting state (see Background section of Applicant’s specification).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified De Bruin with the teachings from Mishanin because it would have resulted in the predictable result of uniting known elements according to their established functions to yield the predictable results of an automated placebo-response prediction.
With respect to claim 15, all limitations of claim 13 apply in which Mishanin further teaches wherein preprocessing the EEG data further comprising: demeaning the EEG data; bandpass-filtering the EEG data; removing notch-frequencies from the EEG data; and flagging artifacts from the EEG data (see Page 9 lines 27-31 – page 10 lines 1-8).
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over De Bruin in view of Mishanin as applied to claim 1 and 13 respectively, and further in view of Elwood (US 20210307672 A1).
With respect to claims 4 and 14, all limitations of claim 1 and 13 apply respectively in which De Bruin and Mishanin do not specifically teach wherein collecting EEG data from the candidate further comprising: attaching electrodes to the candidate’s scalp; attaching electrodes around the candidates’ eyes; taking EEG measurements using resting state eyes closed condition for the candidate; taking EEG measurements using resting state eyes open conditions for the candidate; and storing the EEG data in the client device.
Elwood teaches attaching electrodes to the candidate’s scalp (see paragraph 0022, attaching sensor to scalp); attaching electrodes around the candidates’ eyes (see paragraph 0032, attaching sensor over the eyes); taking EEG measurements using resting state eyes closed condition for the candidate (see paragraph 0032, EEG measurements taken); taking EEG measurements using resting state eyes open conditions for the candidate (see paragraph 0032, EEG measurements taken); and storing the EEG data in the client device (see paragraph 0058: storing EEG data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified De Bruin and Mishanin with the teachings of Elwood to have attached electrodes to both a patients scalp and eyes and take EEG measurements because it is conventional to place electrodes on scalp and peri-ocular (Elwood: see [0031]) to diagnose seizures.
Claims 6-8 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over De Bruin in view of Mishanin as applied to claim 1, and further in view of Laszlo (US 20200205712 A1).
With respect to claim 6 and 16, all limitations of claim 1 apply in which De Bruin and Mishanin do not specifically disclose wherein the at least one machine learning unit employes a deep convolutional neural network for performing data augmentation and channel rolling on the EEG data.
Laszlo teaches a deep convolutional neural network (see paragraph 0070, deep neural network with added convolutional layers) to perform data augmentation and channel rolling for EEG data (see paragraph 0077).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified De Bruin and Mishanin with the teachings of Laszlo to have a deep convolutional neural network because it would have resulted in the predictable result of adding filters so that EEG signals can be cleaned and processed by the same machine learning model (Laszlo: see [0070]).
With respect to claims 7 and 17, all limitations of claims 6 and claim 16 apply respectively in which Laszlo further teaches wherein data augmentation employs the following algorithm (see paragraph 0077, time series EEG data augmentation operations):
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With respect to claims 8 and 18, all limitations of claims 6 and 16 apply respectively in which Lazslo further teaches wherein channel rolling employs the following algorithm (see paragraph 0069-0070).
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Claims 11-12 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over De Bruin in view of Mishanin as applied to claim 1, and further in view of Venkata (US 20100324936 A1).
With respect to claims 11 and 20, all limitations of claim 1 apply in which De Bruin and Mishanin do not specifically teach wherein a Software as a Service (SaaS) framework is employed for performing step (H) through step (K).
Venkata teaches employing a Software as a Service (SaaS) framework (see paragraph 0346: SaaS baed architecture runs the data collection).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified De Bruin with the teachings of Venkata to employ a “SaaS” framework because it would have resulted in the predictable result of allowing the sharing of information collected between users with access credentials (Venkata: see [0346]).
With respect to claim 12, all limitations of claim 11 apply in which Venkata further teaches wherein the SaaS framework comprises a preprocessing module, a run-time module, a training solution, and client third party systems (see paragraph 0346).
Conclusion
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/N.N.P./Examiner, Art Unit 3791
/JENNIFER ROBERTSON/Supervisory Patent Examiner, Art Unit 3791
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