DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 9/22/2025 has been entered.
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 1-14 are pending.
This action is Non-Final.
Drawings
The drawings filed 9/22/2025 are not in compliance with 1.121, and are thus objected to as it appears applicant merely refiled the original drawings without any changes to address the objections; all objections will remain.
(d) Drawings. One or more application drawings shall be amended in the following manner: Any changes to an application drawing must be in compliance with § 1.84 or, for a nonprovisional international design application, in compliance with §§ 1.84(c) and 1.1026 and must be submitted on a replacement sheet of drawings which shall be an attachment to the amendment document and, in the top margin, labeled “Replacement Sheet.” Any replacement sheet of drawings shall include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is amended. Any new sheet of drawings containing an additional figure must be labeled in the top margin as “New Sheet.” All changes to the drawings shall be explained, in detail, in either the drawing amendment or remarks section of the amendment paper.
(1) A marked-up copy of any amended drawing figure, including annotations indicating the changes made, may be included. The marked-up copy must be clearly labeled as “Annotated Sheet” and must be presented in the amendment or remarks section that explains the change to the drawings.
(2) A marked-up copy of any amended drawing figure, including annotations indicating the changes made, must be provided when required by the examiner.
Figure 1 should be designated by a legend such as --Prior Art-- because only that which is old is illustrated. See MPEP § 608.02(g). Corrected drawings in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. The replacement sheet(s) should be labeled “Replacement Sheet” in the page header (as per 37 CFR 1.84(c)) so as not to obstruct any portion of the drawing figures. 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.
The drawings are objected to because Figures 1-6, 8D, 9-22 use improper underlining.
(p)(3) Numbers, letters, and reference characters must measure at least .32 cm. (1/8 inch) in height. They should not be placed in the drawing so as to interfere with its comprehension. Therefore, they should not cross or mingle with the lines. They should not be placed upon hatched or shaded surfaces. When necessary, such as indicating a surface or cross section, a reference character may be underlined and a blank space may be left in the hatching or shading where the character occurs so that it appears distinct.
(q) Lead lines. Lead lines are those lines between the reference characters and the details referred to. Such lines may be straight or curved and should be as short as possible. They must originate in the immediate proximity of the reference character and extend to the feature indicated. Lead lines must not cross each other. Lead lines are required for each reference character except for those which indicate the surface or cross section on which they are placed. Such a reference character must be underlined to make it clear that a lead line has not been left out by mistake. Lead lines must be executed in the same way as lines in the drawing. See paragraph (l) of this section.
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.
The drawings are objected to because Figure 8C is a poor quality photo/photocopy, the examiner is requiring a drawing in place of the photograph/photocopy (1.84(b). 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 § 112
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 1-14 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.
Regarding independent claims 1, 6, and 11, the claims have been amended similarly to recite the features:
“1. (Currently Amended) A computer-implemented method for detecting a brain profile, comprising:
(a) presenting a calibration stimulus or instruction to a user, to receive electroencephalogram (EEG) data collected from sensors attached to or near the user's head;
(b) inputting the EEG data and a calibration label describing the calibration stimulus or instruction into a first deep learning model, the first deep learning model trained using a training data set of additional EEG data from data collection participants to capture information about a user's neural activity to enable decoding of multiple brain profiles;
(c) constraining, based on the calibration label, an evaluation associated with the first deep learning model to determine a general representation related to neural activity, wherein the general representation is a vector comprising a plurality of dimensions representing the user's neural activity; and
(d) in response to the constraining, inputting the determined general representation outputted from the first deep learning model based on the constrained evaluation into a second deep learning model to decode the brain profile of the user, the second deep learning model trained with additional general representations and a training label that is associated with the additional general representations indicating brain profiles of the respective data collection participants, wherein the brain profile specifies a condition of the user's brain, and wherein the additional general representations are outputted based on inputting the training data set of additional EEG data into the first deep learning model.” (bolded added for emphasis)
which includes features which are not adequately described and/or are new matter for the embodiment originally claimed. Applicant states that support for the amendments to the claims can be found in paragraphs 47-49, 52-53, 55, 58, 111, 160, 164, 165. However, these passages appear to be directed to embodiments which are different than the embodiments of the instant claims as originally presented, although some processing is similar as stated in the specification, which correspond to the embodiment of Figure 20. It is not apparent on the face how paragraphs 160-167 provide basis for the amendments as claimed which are bolded. These passages state:
[0160] Figure 20 is a flowchart illustrating a method 2000 of determining a brain profile using EEG data. A brain profile may be thought of as clustering of identities based on biometric templates that are related to some property of the users. For the purpose of brain profiling, a machine learning model may learn a general representation for each user, which is conceptually similar to the biometric templates described above. These general representations aim to capture as much information about the user’s neural activity that will enable decoding of multiple profiles. The property that defines a brain profile may be a condition relating to the subject’s health, such as a brain disorder or a psychiatric disease. Such abnormalities may include major depression, schizophrenia, ADHD or autism. In this way, decoding a brain disorder can be useful in the context of drug discovery or as a tool for clinicians. In the healthcare context, this is sometimes referred to as a digital biomarker. A brain profile may also signify a subject’s propensity for learning a particular subject matter or skill, such as successful completion of flight school. This may be useful for screening people for their job roles in a military or academic setting.
[0161] At 2002, EEG data collected from sensors attached to or near a user’s head is received. In one embodiment, the EEG data may be collected using the consumer devices illustrated in FIGs. 7A-D. In another example, the EEG data may be collected using research grade EEG headsets discussed above with respect to step 602 in the training method in FIG. 6.
[0162] As described above with respect to FIGs. 4 and 5, the user can be exposed to one or more stimuli as described with respect to user calibration modules 410 and 510A-C. In an example, the stimuli may include complex, rich games. It can use visual and auditory stimuli to evoke diverse neural activity that is expected to differ between users having different brain profiles. The stimulus may be repeating and the EEG data may be segmented, aggregated (optionally), and labeled, as described above with respect to Figures 10-12.
[0163] In addition to EEG data, behavior data may be collected. The behavior data may include, for example, eye trackers or hand trackers. As described above with respect to Figure 5, EEG data, along with labels, behavior data, and subject metadata may be input into a machine learning algorithm in step 2004.
[0164] At 2004, the EEG and other data received at 2002 is input into a first machine learning model to determine a general representation related to neural activity. The first machine learning model was previously trained using a training data set of additional EEG data from data collection participants. The first machine learning model may be a deep learning neural network whose architecture resembles that used for calibration. And, as a result, the general representation is conceptually similar to f7(XN, ZN) of figure 5.
[0165] At 2006, the general representation determined at 2004 is input into a second machine learning model. The second machine learning model was previously trained with general representations and a label indicating the brain profiles of the respective data collection participants. The second machine learning model outputs whether the user has the brain profile or not, or to what extent the user has the brain profile. The second machine learning model uses a classification algorithm trained from the plurality of subjects. Such subjects from the plurality of subjects are classified according to their brain profiles. The second machine learning model may be a deep learning neural network. In this way, the general representation can be used to determine whether the user has a specific brain profile.
[0166] Method 2000 need not be executed in real time. All available data collected from a user during one or more entire recording sessions (e.g., 1 hr) could be used, and the user’s brain profile based on features extracted from all of this given data. This is in contrast to the menu selection user-case where the decoding, relies on calibration data, but makes the prediction based on a given and single EEG segment.
[0167] Additionally or alternatively, different models may be used in step 2006 to assess, using the general representation, whether a user has a specific brain profile. For example, one model may be trained to determine, based on the general representation, whether a user has major depression, while another model may be trained to determine, based on the general representation, whether a user has autism.
However, these passages do not expressly state or directly tie the results of the wherein the additional general representations are outputted based on inputting the training data set of additional EEG data into the first deep learning model and must be considered new matter. As such, one of skill in the art would not have recognized applicant was in possession of the claimed invention at the time the application was filed. Applicant is invited to further explain the support for such features. The dependent claims are rejected for depending on a rejected claim.
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.
Claim 11 is 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 11 recites the limitation " the training data set of additional EEG data " in the last line. There is insufficient antecedent basis for this limitation in the claim.
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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. The claim(s) recite(s):
Claim 1
(b) inputting the EEG data and a calibration label describing the calibration stimulus or instruction into a first deep learning model, the first deep learning model trained using a training data set of additional EEG data from data collection participants to capture information about a user's neural activity to enable decoding of multiple brain profiles (mathematical concepts);
(c) constraining, based on the calibration label, an evaluation associated with the first deep learning model to determine a general representation related to neural activity, wherein the general representation is a vector comprising a plurality of dimensions representing the user's neural activity (mathematical concepts); and
(d) in response to the constraining, inputting the determined general representation outputted from the first deep learning model based on the constrained evaluation into a second deep learning model to decode the brain profile of the user, the second deep learning model trained with additional general representations and a training label that is associated with the additional general representations indicating brain profiles of the respective data collection participants, wherein the brain profile specifies a condition of the user's brain, and wherein the additional general representations are outputted based on inputting the training data set of additional EEG data into the first deep learning model (mathematical concepts)
Claim 6
(b) inputting the EEG data and a calibration label describing the calibration stimulus or instruction into a first deep learning model, the first deep learning model trained using a training data set of additional EEG data from data collection participants to capture information about a user's neural activity to enable decoding of multiple brain profiles (mathematical concepts);
(c) constraining, based on the calibration label, an evaluation associated with the first deep learning model to determine a general representation related to neural activity, wherein the general representation is a vector comprising a plurality of dimensions representing the user's neural activity (mathematical concepts); and
(d) in response to the constraining, inputting the determined general representation outputted from the first deep learning model based on the constrained evaluation into a second deep learning model to decode the brain profile of the user, the second deep learning model trained with additional general representations and a training label that is associated with the additional general representations indicating brain profiles of the respective data collection participants, wherein the brain profile specifies a condition of the user's brain, and wherein the additional general representations are outputted based on inputting the training data set of additional EEG data into the first deep learning model (mathematical concepts)
Claim 11
(b) inputting the neural data and a calibration label describing the calibration stimulus or instruction into a first deep learning model to determine a general representation related to neural activity, the first deep learning model trained using a training data set of additional neural data from data collection participants to capture information about a user's neural activity to enable decoding of multiple brain profiles (mathematical concepts);
(c) constraining, based on the calibration label, an evaluation associated with the first deep learning model to determine a general representation related to neural activity, wherein the general representation is a vector comprising a plurality of dimensions representing the user's neural activity (mathematical concepts); and
(d) in response to the constraining, inputting the determined general representation outputted from the first deep learning model based on the constrained evaluation into a second deep learning model to decode the brain profile of the user, the second deep learning model trained with additional general representations and a training label that is associated with the additional general representations indicating brain profiles of the respective data collection participants, wherein the brain profile specifies a condition of the user's brain, and wherein the additional general representations are outputted based on inputting the training data set of additional EEG data into the first deep learning model (mathematical concepts)
These claim limitations fall within the identified groupings of abstract ideas:
Mathematical Concepts:
mathematical relationships
mathematical formulas or equations
mathematical calculations
This judicial exception is not integrated into a practical application because:
Under the step 2A, analysis is conducted on the additional features of the claim. Under this analysis, the additional features beyond the judicial exception are:
Claim 1:
computer-implemented method for detecting a brain profile (field of use, computer used as tool)
(a) presenting a calibration stimulus or instruction to a user, to receive electroencephalogram (EEG) data collected from sensors attached to or near the user's head (insignificant data gathering)
Claim 6:
a non-transitory, tangible computer-readable device having instructions stored thereon that, when executed by at least one computing device, causes the at least one computing device to perform operations for detecting a brain profile (field of use, computer features used as tool)
(a) presenting a calibration stimulus or instruction to a user, to receive electroencephalogram (EEG) data collected from sensors attached to or near the user's head (insignificant data gathering)
Claim 11:
a computer-implemented method for detecting a brain profile (field of use, computer used as tool)
(a) presenting a calibration stimulus or instruction being presented to a user, receiving to receive neural data collected from sensors (insignificant data gathering)
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, 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 in step 2A above. Such limitations related to the sensors are recognized by the courts as routine data gathering in order to input data to the mathematical algorithm, and thus, do not add a meaningful limitation to the method as it would be routinely used by those of ordinary skill in the art in order to apply the mathematical algorithm. In addition, these sensor structures are known from US 2019/0160287, US 2020/0107766, US 2020/0187841, US 2020/0337625 and in general are generic sensors in generic locations producing the expected brain related data signals. 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 further details of mathematical concepts of machine learning modeling (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 and drawings filed 9/22/2025; and IDS filed 9/30/2025 and 1/28/2026.
Applicant’s arguments regarding drawing objections have been fully considered but are not sufficient. The rules for amendment have not been filed and it looks like changes were not even completed that were stated to have been completed but merely refiling of the original drawings. Drawing objections cannot be held in abeyance and resubmitting original Figures in the future will not be considered a bona fide response.
Applicant’s arguments regarding the rejections of the claims under 35 U.S.C. 101 have been fully considered but are not persuasive. Applicant argues that the claimed invention amounts to a practical application by being an improvement to technology in brain profiling of EEG data. The examiner respectfully disagrees. The passages cited in the arguments are directed to a different embodiment (i.e. not Figure 20) than that of claimed invention, and are stated as mere conclusory that the methods of decoding present allow for more accurate results, but this does not tie into anything specific and that is why it is considered conclusory. Additionally, the claimed invention is a different way to determine brain profiles using machine learning on recorded evoked data, but such ability to match known profiles is not a new concept in EEG diagnostics as is evident from US 2019/0160287, US 2020/0107766, US 2020/0187841, US 2020/0337625. To clarify, the additional feature of EEG and/or EEG evoked data to profile a brain state is not an improvement and thus does not provide basis for a practical application of the claimed exception by its presence, nor significantly more than the claimed exception. Applicant’s arguments are directed to the algorithmic processes in operating two different machine learning algorithms using the collected data and applicant argues that this amounts to an improvement to technology. Again, while the steps themselves may be different to the prior art, novelty does not equate to patentability of a judicial exception without significantly more than the claimed exception. Here, the alleged improvement lies within the exception itself. MPEP 2106 cautions examiners that “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.” Here, the alleged improvement lies within the claimed algorithmic processing of data in a black box, but the black box does nothing with the results of such data processing and the data input is insignificant to the exception. Again, the only additional features outside the exception are the data gathering steps which are insignificant extra solution activities to the claimed algorithm which inputs data and runs machine learning processes to generate a result within the black box. As such, the features do not amount to a practical application of the claimed exception, nor to significantly more than the claimed exception. The rejections are respectfully maintained as presented above.
Applicant’s arguments regarding the rejections of the claims in view of prior art have been fully considered and are persuasive due to the amendments to the claimed scope and the remarks about the distinguishing differences between the claimed algorithm and the algorithm of the prior art, in that while they both use machine learning to process data, the data presented to the machine learning in each instance is different. The rejections have been withdrawn.
Conclusion
No prior art rejections have been applied, but the claims are not in condition for allowance due to the rejections of the claims under 35 U.S.C. 101 and 112.
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
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
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