DETAILED ACTION
Election/Restrictions
Claims 13-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 5/21/2026. Withdrawn claims 13-20 were cancelled in the response filed 5/21/2026.
Drawings
The drawings are objected to because the text in the figures is pixelated and hard to read in many instances, the greyscale and black shading in several of these images makes the text and figure illegible, and the text specifically in FIG. 1 is incomplete. The text should be updated to be clear and unpixellated and greyscale should be removed. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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 1-12 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.
Each of Claims 1-12 has been analyzed to determine whether it is directed to any judicial exceptions.
Step 2A, Prong 1
Each of Claims 1-12 recites at least one step or instruction for using a person’s resting brain signals to give them a working motor imagery decoded by matching them to a group of similar past users which is grouped as a mental process under the 2019 PEG or a certain method of organizing human activity under the 2019 PEG. Accordingly, each of Claims 1-12 recites an abstract idea.
Specifically, Claims 1 and 7 recites extracting from a data source based on a plurality of users, a motor imagery (MI) task EEG signals dictionary and rest EEG signals, generating a confusion matrix that represents a contract between the EEG signals associated with different MI tasks, receive short segments of rest EEG associated with a target user and output an indication of a target MI task based on applying that representative confusion matrix to the short segments of rest EEG (observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG);
Further, dependent Claims 2-6 and 8-12 merely include limitations that either further define the abstract idea (and thus don’t make the abstract idea any less abstract) or amount to no more than generally linking the use of the abstract idea to a particular technological environment or field of use because they’re merely incidental or token additions to the claims that do not alter or affect how the process steps are performed.
Accordingly, as indicated above, each of the above-identified claims recites an abstract idea.
Step 2A, Prong 2
The above-identified abstract idea in each of independent Claims 1 and 7 (and their respective dependent Claims 2-6 and 8-12) is not integrated into a practical application under 2019 PEG because the additional elements (identified above in independent Claims 1 and 7), either alone or in combination, generally link the use of the above-identified abstract idea to a particular technological environment or field of use. More specifically, there are no claimed additional elements.
Moreover, the above-identified abstract idea is not integrated into a practical application under 2019 PEG because the claimed method and system merely implements the above-identified abstract idea (e.g., mental process and certain method of organizing human activity) using abstract rules without any additional elements.
Accordingly, independent Claims 1 and 7 (and their respective dependent claims) are each directed to an abstract idea under 2019 PEG.
Step 2B
None of Claims 1-12 include additional elements. Therefore, none of the Claims 1-12 amounts to significantly more than the abstract idea itself. Accordingly, Claims 1-12 are not patent eligible and rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 112
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 1-12 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.
Regarding Claims 1 and 7, the limitation “the representative confusion matrix” renders the claim indefinite. Claim 1 previously recites both “a confusion matrix” and “a representative matrix” and Claim 7 previously recites just “a confusion matrix”, but never “the representative confusion matrix”. Therefore, the limitation “the representative confusion matrix” lacks proper antecedent basis. For purposes of examination the indefinite limitation has been deemed to claim where the representative matrix is a representative confusion matrix that is selected based on clustering.
Regarding Claims 5, 6, 11 and 12 are indefinite because the variables in the equations are undefined – the CI bounds are never defined or given any values. Also, Claims 12 recites x3 values when reciting x4 and Claims 6 and 12 the limitation “right hemisphere channels exhibit x3%$ higher coherence than left hemisphere channels, but coherence is a measure between two sites and “coherence of right-hemisphere channels” doesn’t say between which two pairs. Additionally, the term “specific” and “rule” in claims 5, 6, 11 and 12 is a relative term which renders the claim indefinite. The term “specific” and “rule” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. For purposes of examination the indefinite limitations have been deemed to claim that both right and left states are considered by the confusion matrix.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Domain Adaptation with Source Selection for Motor-Imagery based BCI to Jeon et al. (hereinafter, Jeon) in view of Learning a common dictionary for subject-transfer decoding with resting calibration to Morioka et al. (hereinafter, Morioka).
Regarding Claims 1 and 7, Jeon discloses a method comprising (Abstract “…we devise a novel framework of training a deep network by adapting samples of other subjects as a means of domain adaptation.”) inter alia:
extracting, from a data source based on a plurality of users, a motor imagery (MI) task- EEG signals dictionary and rest-EEG signals (Abstract “Assuming that there are EEG trials of motor imagery tasks from multiple subjects available, we first select a subject whose EEG signal characteristics are similar to the target subject based on their power spectral density in resting-state EEG signals.”) (II. Methods “…a few EEG sample sets, called ‘supplementary sets,’ each of which is acquired from a different subject…”) (III. Experiments & Results, A. Dataset & Preprocessing “…dataset consists of EEG signals of four different motor imagery tasks (left hand, right hand, feet, and tongue) acquired from nine subjects.”);
generating, from at least the MI task-EEG signals dictionary and the rest-EEG signals, a confusion matrix that represents at least a contrast between the EEG signals associated with different MI tasks for one or more of the plurality of users (Abstract “…the upper layers branch out into… (2) label prediction optimized for a source subject, and (3) label prediction optimized for a target subject” from a feature extractor that [II. Experiments & Results, C. Network Structure and Training] “...extracts temporal, spectral, and spatial features”) (Built from the MI-plus-rest [Abstract] “We then use EEG signals of both the selected subject (called a source subject) and the target subject jointly in training a deep network” where the source is selected [Abstract] “…based on their power spectral density in resting-state EEG signals…”);
clustering the confusion matrix based at least on one or more values of the rest-EEG signals (III. Experiments & Results, B. Source Subject Selection “We calculated the cosine distance between all subjects and performed a hierarchical clustering based an average linkage method” on rest PSD – [III. Experiments & Results, B. Source Subject Selection] “We transformed the temporal resting-state signals into a frequency domain. Then, we calculated the PSD using welch’s method in the frequency domain per subject.”);
selecting a representative matrix based on the clustering (III. Experiments & Results, B. Source Subject Selection “Based on the clustering results shown in Fig. 2, we selected a source subject for each target subject as in TABLE I”);
receiving short segments of rest-EEG associated with a target user (FIG. 1 “(a) Calculate a similarity between a target and source subjects using power spectral density in resting-state EEG signals and find the most similar subject (i.e., source) with a target subject.”);
identifying a subcategory based on the received short segments of rest-EEG (FIG. 1 “… find the most similar subject (i.e., source) with a target subject…” i.e., Jeon selects a source subject for each target subject from the rest-based clustering – the source/cluster is the identified subcategory); and
outputting an indication of a target MI task (Jeon outputs the target subject’s MI class [Abstract] “…(3) label prediction optimized for a target subject” and the class predictor emits on of the [III. Experiments & Results, A. Dataset & Preprocessing] “…four different motor imagery tasks (left hand, right hand, feet, and tongue)” so an indication of a target MI task is output) based on the identified subcategory (The output is produced using the source selected from the target from the rest-based clustering, [III. Experiments & Results, B. Source Subject Selection] “…we selected a source subject for each target subject as in TABLE I” and [Abstract] “We then use EEG signals of both the selected subject (called a source subject) and the target subject jointly in training a deep network”, the MI-task output is thus a function of the identified subcategory (the selected source/cluster).
Jeon discloses the claimed invention except for expressly disclosing where the outputting the indication of a target MI task are based on applying the representative confusion matrix to the short segments of rest-EEG based on the identified subcategory (i.e., Jeon identifies the target’s subcategory from the target’s resting state EEG and selects a representative for it, but applies that representative to the target’s motor-imagery trial rather than to the target’s rest). However, Morioka teaches a shared “dictionary” (Abstract “…a method for extracting spatial bases (or a dictionary) shared by multiple subjects…) where D is (Method and materials, Basic dictionary learning) “a dictionary matrix whose column vectors dk are called atoms”. Morioka fields their dictionary on a new user from the user’s resting-state segment (Methods and materials, Application to subject-transfer decoding “The basic idea of our subject-transfer decoding is to transfer the subject/ session independent dictionary D into the subject–session dependent dictionary ZN0D, by using the spatial transform calibrated with the resting-state activities, ZN0.”). One having an ordinary skill in the art at the time the invention was filed would have found it obvious to modify the target-decoding step of Jeon (which applies the selected representative to the target’s motor-imagery trials) to apply that representative to the target’s resting-state EEG as taught by Morioka, because Morioka teaches that sound so removes the need for the target’s task data, since “resting-state data are easy to collect, and the proposed scheme does not require expensive task based calibration, which would be beneficial for subjects to easily use BMI” (pg. 168, col. 1 of Morioka) and Jeon explicitly states that resting state bears “high relations to motor-imagery tasks” (under A. Source Subject Selection of Jeon).
Regarding Claims 2 and 8, Jeon in view of Morioka teach wherein the data source comprises EEG data collected while the plurality of users are at rest (Jeon II. Methods, A. Source Subject Selection “…we transform the resting-state of the EEG signals into a frequency domain and calculate the PSD using welch’s method.”).
Regarding Claims 3 and 9, Jeon in view of Morioka teach where the data source comprises EEG data collected while the plurality of users perform one or more tasks (Jeon III. Experiments, A. Dataset & Preprocessing “This dataset consists of EEG signals of four different motor imagery tasks (left hand, right hand, feet, and tongue)…”).
Regarding Claims 4 and 10, Jeon in view of Morioka teach wherein the data source comprises EEG data collected while the plurality of users are at rest, and wherein the data source comprises EEG data collected while the plurality of users perform one or more tasks (Jeon Abstract “Assuming that there are EEG trials of motor imagery tasks from multiple subjects available, we first select a subject whose EEG signal characteristics are similar to the target subject based on their power spectral density in resting-state EEG signals.”).
Regarding Claims 5, 6, 11 and 12 wherein the confusion matrix is modeled on at least one rule that provides that a distinction between a rest state and a RH-MI state is that MI signals exhibit x1% (CI: x1 % - xh %) higher power in a specific frequency range in left hemisphere channels and x2% (CI: x1 % - xh %) lesser power in right hemisphere channels and wherein the at least one rule provides that in a LH-MI state, right hemisphere channels exhibit x3 % higher coherence than left hemisphere channels and x4% (CI: x1 % - xh3%) higher than a rest state (III. Experiments & Results, A. Dataset & Preprocessing “(left hand, right hand…”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SEAN PATRICK DOUGHERTY whose telephone number is (571)270-5044. The examiner can normally be reached 8am-5pm (Pacific Time).
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/SEAN P DOUGHERTY/ Primary Examiner, Art Unit 3791
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