DETAILED ACTION
Acknowledgements
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-6, 9-11 are pending.
This action is Final.
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 1-6, 9-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. The claims are directed to process and apparatus and recite features which are judicial exceptions. The claim(s) recite(s):
Claim 1:
extracting clustering features using frequency powers for each of first, second, and third bands of brain signals measured from learning subjects, the feature extractor being configured to compute an average power spectral density (PSD) value for each said frequency band and to perform principal component analysis (PCA) on the PSD values to determine two principal component features for clustering (mathematical concepts)
generate clustering models based on the extracted clustering features, by performing a k-means clustering analysis on the principal component features; generate the clustering models such that inter-subject variability (ISV) of each of the clustering models is determined to be less than inter-subject variability (ISV) of all learning subjects without cluster division, and determine the ISV of each of the clustering models based on a standard deviation of a frequency power measured for left and right hemispheres of a brain region in the first, second, and third bands, and wherein the standard deviation of the frequency power measured for each frequency band for the ISV of each of the clustering models is less than a standard deviation of the frequency power measured for each frequency band for the ISV of all learning subjects without cluster division (mathematical concepts)
construct an intention determination model by performing a machine learning process on brain signals for each of the clustering models, the machine learning process including applying a common spatial pattern (CSP) filter to the brain signals to extract features and training a support vector machine (SVM) classifier as the intention determination model (mathematical concepts)
determine a newly measured brain signal of a measurement subject as one of the clustering models (mathematical concept, mental process); and
determine an intention of the measurement subject by using the constructed intention determination model corresponding to the determined clustering model thereby omitting the machine learning process for the newly measured brain signal of the measurement subject and a process of measuring the brain signal of the measurement subject multiple times (mathematical concept, mental process)
Claim 9:
extracting clustering features using frequency powers for each band of brain signals measured from learning subjects, wherein extracting the clustering features comprises averaging power spectral density (PSD) values for each of first, second, and third bands of the brain signals and performing principal component analysis (PCA) on the PSD values to determine two principal component features (mathematical concepts);
generating clustering models based on the extracted clustering features, by performing a k-means cluster analysis on the principal component features; wherein the generating of the clustering models based on the extracted clustering features comprises: generating the clustering models such that inter-subject variability (ISV) of each of the clustering models is determined to be less than inter-subject variability (ISV) of all learning subjects without cluster division, and determining the ISV of each of the clustering models based on a standard deviation of a frequency power measured for left and right hemispheres of a brain region in the first, second, and third bands, and wherein the standard deviation of the frequency power measured for each frequency band for the ISV of each of the clustering models is less than a standard deviation of the frequency power measured for each frequency band for the ISV of all learning subjects without cluster division (mathematical concepts)
constructing an intention determination model by performing a machine learning process on brain signals for each of the generated clustering models, the machine learning process including applying a common spatial pattern (CSP) filter to the brain signals to extract features and training a support vector machine (SVM) classifier as the intention determination model (mathematical concepts)
determining a newly measured brain signal of a measurement subject as any one of the clustering models (mathematical concept, mental process)
determining an intention of the measurement subject by using the constructed intention determination model corresponding to the determined clustering model, thereby omitting the machine learning process for the newly measured brain signal of the measurement subject and a process of measuring the brain signal of the measurement subject multiple times (mathematical concept, mental process)
These claim limitations fall within the identified groupings of abstract ideas:
Mathematical Concepts:
mathematical relationships
mathematical formulas or equations
mathematical calculations
Mental Processes
concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
This judicial exception is not integrated into a practical application because:
Under the step 2B prong 1, analysis is conducted on the additional features of the claim. Under this analysis, the additional features beyond the judicial exception are:
Claim 1:
hardware and software
a feature extractor for, a clustering model generator configured to, a brain wave processor configured to
Claim 9:
hardware and software
by a feature extractor, by a clustering model generator, by a brain wave processor
These features recite lexicographic terms for general computer structures to perform the identified exceptions and are merely used as a tool. These features in the claim do not integrate the exception into a practical application of the exception as the additional elements in the claim do not apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is no more than a drafting effort designed to monopolize the exception.
Limitation concepts that are indicative of integration into a practical application:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
Limitation concepts that are not indicative of integration into a practical application:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)
Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
Under Step 2B prong 2, the claim limitations are evaluated for an inventive concept. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and in combination, they do not add significantly more to the exception. Analyzing the additional claim limitations individually, the additional limitation that is not directed to the abstract idea are the same as those identified above in prong 1. The computer structures cited above are claimed as performing generic computer functions routinely used in computer applications. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. The additional limitations recited in the dependent claims are merely directed to insignificant data gathering using convention EEG electrodes (OFFICIAL NOTICE, see also art of record) and further judicial exceptions in data manipulations/processing (A more specific abstraction is still an abstraction). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Therefore, analyzing the claims as an ordered combination under the Mayo/Alice analysis the features claimed are directed to patent ineligible limitations.
Response to Arguments
The Examiner acknowledges applicant’s submission of amendments to the claims filed 9/15/2025.
Applicant’s arguments regarding the rejections under 35 U.S.C. 101 have been fully considered and are partially persuasive due to the amendments to claim 3 no longer being directed to or reciting human organisms. However, the remaining arguments are not persuasive. The most compelling additional argument made by applicant is “While not a physical transformation of an object, it is a transformation of real physiological measurements into a machine-interpretable command or classification result, which is integral to the operation of a BCI system (e.g., enabling control of an external device based on the user's intent). This kind of signal processing and interpretation is fundamental to the device's function, far from a mere abstraction.”. However, these aspects of further post-solution activities or functions based on the algorithmic intention determinations appear to be lacking explicit recitation in both the disclosure and the claims. The disclosure merely refers to the data gathering and processing of signals, but there is no direct recitation of what the intentions classified are used for to be analogous with Diehr. For example, should the intentions be used to implement a robotic arm to move, or a cursor on a screen, or some other “physical” process in response to the intention, then these arguments would be persuasive. Simply, the disclosure as filed is merely directed to the black box processing of the gathered signals without any practical application or significantly more than the claimed exception as the disclosure, and claims, stop prior to any post-solution activities being performed, and is broadly to the classification of intention. Which is unlike Thales, as the claims do not include any sensor to make any argument similar to Thales. Again to reiterate the Office position of arguments by Applicant, which argue that the claims as amended capture an improvement to technology or computer. Based on the Examiner’s understanding of the disclosure, amendments, technology, and arguments, the purported improvement appears to be that the inventors have improved upon algorithms by using machine learning techniques in forming a model from learning subjects, and then using this derived model in a trained mechanism to feed data from a measurement subject into the model to generate a result. However, the broad form of the overarching concept here seems to be a simple concept in modeling itself. i.e. a model is generated, and desired data used is fed into the model which generates an output. More specifically, involving machine learning itself appears to be contemplated in at least: US 2018/0177619 Figure 10 and [0073]-[0074] which teaches it is known to use responses of other users to form models to then decode the results from later measurements by applying the prior generated models; and US 2019/0108191 [0005], [0058], [0212], [0224], [0284] which teaches that application of data on models can be group or crowd derived as alternatively to a personized model, and later data is processed through such derived models. If the improvement itself is simply using the clustering techniques with the BCI technology, then this aspect is known in the art as evidenced by the non-final rejections mailed 9/21/2023 with specific citations to Lotte and Dennison; as well as Krauledat (appears to be cited in/by Lotte). How are the required features of clustering an improvement to technology outside of the result verification processes related to ISV? It is noted that the additional functional features of the ISV data verification of the generated models alone are the reasons the prior art rejections were removed (see application history), but novel data verification steps does not equate to a showing of improvement to technology for using clustering when that clustering technology is already used/suggested in the prior art for applications in BCI.
However, even if giving the benefit of the doubt that the recited features of algorithm amount to an improvement to technology, such improvements are to the exception(s) itself. The examiner reiterates the arguments from 9/13/2024 and 1/15/2025. Additionally, the MPEP provides guidance:
“It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018)). Thus, it is important for examiners to analyze the claim as a whole when determining whether the claim provides an improvement to the functioning of computers or an improvement to other technology or technical field.
At best it appears that the improvement is to the judicial exception itself, i.e. better math in mathematical concepts to capture such information or by using the clustering technology with computers as a tool algorithmically. The claims do nothing with the judicial exception information, as mere matching of profiles is the overarching result, but nothing of classification is used in any meaningful way, such that the invention is directed to the recited exceptions themselves, and the data gathered is merely conventional and extra-solution to the claimed exceptions.
The arguments for transformation are not persuasive as the M or T test, while a useful tool, has been replaced by the Mayo/Alice analysis. In this case, the transformation argued by applicant (math) is not typically considered a physical transformation (For data, mere “manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’” has not been deemed a transformation. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360, 31 USPQ2d 1754, 1755, 1759 (Fed. Cir. 1994)).). The remaining arguments have all been considered but will not each be individually addressed as the Office response would be repeating the same points over again.
The amendments and arguments are not persuasive in showing that the claims amount to a practical application or significantly more than the claimed judicial exception(s) as mere instructions to apply an exception using a generic computer hardware/software cannot provide an inventive concept. The rejections are respectfully maintained as presented above to account for the amendments to the claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL R BLOCH whose telephone number is (571)270-3252. The examiner can normally be reached M-F 11-8 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert (Tse) Chen can be reached at (571)272-3672. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL R BLOCH/Primary Examiner, Art Unit 3791