DETAILED ACTION
Status of Claims
This action is in reply to the amendment filed on 01/14/2026.
Claims 1, 4 and 8-9 have been amended.
Claims 1-6 and 8-9 are currently pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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.
Claims 1-6 are rejected under 35 U.S.C. 103 as being unpatentable over Aimone (US 2020/0337625 A1) in view of Forsland (US 2021/0223864 A1).
Claim 1:
Aimone discloses an automatic evolution method used in brain training for a brainwave database which collects physiological information of brainwave signals about healthy and clinical groups, the automatic evolution method (See Fig. 1, 150 sensor data store 150, Fig. 4, a data store 480 and a brain model store 490 as brainwave databases mentioned in P0060, P0115. See bio-signal and non-bio-signal data collected in cloud or cloud server in P0096, P0101. See population of patients with suspected sleep apnea, seizures, individuals with depressive symptoms, etc. in P0212-P0213, and P0093 where updated brainwave patterns serve as an automatic evolution.) comprising:
using a training device to collect brainwave signals of a subject, the training device to collect the brainwave signals, and software and hardware equipment programmed with machine learning (With the training device used for classifying the physiological information of brainwaves collected, see diagnostic classification, classifying brainwave patterns, parameters and collected features to build the base brain model in P0025-P0026, P0093, P0218, classifying stages of sleep in P0234-P0239 and machine learning in P0104-P0105, Fig. 4 the brain model store 490 in the computing platform 120 mentioned in P0113-P0115. Also, see [P0077-P0078] analyze collected bio-signal and non-bio-signal data. Processing and analysis of data can be performed on computing platform 120 by hardware, software, or any combination, Fig. 4 the organization of software at the computing device of FIG. 3 mentioned in P0115-P0019.) and a neural network architecture to analyze and classify the physiological information of the brainwave signals according to data characteristics of the physiological information so as to establish a feedback algorithm model and incorporate the physiological information into the brainwave database (See Fig. 7, items 702 bio-signal/non-bio-signal data input into neural network, sensor data stored in data store 480 and brain model data stored in brain model store 490 mentioned in P0119, P0235-P0238.);
using the brainwave database to store the brainwave signals and the feedback algorithm model and generate an output signal based on the feedback algorithm model (See sensor data stored in data store 480 and brain model data stored in brain model store 490 mentioned in P0119, P0125, P0180-P0183.);
providing an estimation and prediction device coupled to the brainwave database to receive the output signal, calculate subsequent performance data according to the feedback algorithm model and measure accuracy of the subsequent performance data to verify an evaluation index of the feedback algorithm model (See Fig .12, collected user data in P0207-P0208, P0223-P0229 where Validated Model and Input Expert Scores serve as the feedback algorithm model and measure accuracy of the subsequent performance data to verify an evaluation index of the feedback algorithm model.); and
providing a user terminal having a conversion device to evaluate the subsequent performance data based on the feedback algorithm model to generate a feedback signal on the user terminal for the subject under the brain training (With converted input data as brainwave pattern and characteristics, see brainwave patterns (P0087) of different classes of users and specific set of characteristics when using machine learning (P0092-P0093, P0128, P0135).);
wherein the method is used by the subject in the brain training with new brainwave signals of the subject being collected and incorporated into the brainwave database, the software and hardware equipment is programmed (Besides stored data and utilized algorithms in P0082-P0084, see Fig. 4 the brain model store 490 in the computing platform 120 mentioned in P0113-P0115.) to automatically adjust the feedback algorithm model to establish an updated feedback algorithm model based on increased brainwave data, the machine learning and an updated neural network architecture, and the conversion device feeds an updated feedback signal generated based on the updated feedback algorithm model to the subject (Taught as adaptive brain state change notification (ABCN) neurofeedabck, also called neurotherapy or neurobiofeedback in P0069, tune aggregated data analysis in P0094-P0096, developing a model based on convolutional neural networks in P0016-P0019, P0120, P0176 and adjusting intervention along a progression path in P0313-P0315.); and
wherein the updated feedback algorithm model is updated and evolved automatically with the increased brainwave data by real-time processing in the training device, real-time feedback is provided by the conversion device to the subject, and each updated feedback algorithm model evolves automatically as frequency of use by the subject increases (See brainwave patterns (P0087) of different classes of users and specific set of characteristics when using machine learning (P0092-P0093, P0128, P0135). Also, see collecting brainwaves with sensors in P0062-P0063 with neurofeedback, pattern analysis (P0087), neuro-physiological state (P0134-P0135, P0295-P0296) and specific physical or emotional states in [P0092-P0093] These patterns can then be used to update the algorithms so that these patterns are used in the analysis of data collected in the future. There may be correlations found between specific groups of users with a specific set of characteristics. These correlations may be used to improve the performance of the analyzer, algorithm collector, or algorithm improver, or other aspects of the systems or methods. Also, see brain state predictions in Abstract, Fig. 14, P0118-P0120, P0178.), and
wherein the feedback algorithm model evolves from an initial model version to a subsequent model version iteratively, and the automatic evolution from a current model version to a next model version is triggered after a plurality of new brainwave signal inputs are collected and processed, allowing the feedback algorithm model to continuously operate to perform estimation while evolving (Taught as incremental building of a model and the process of automatically building a data set including raw data fed for optimization mentioned in P00083-P0086, P0095, P0218-0219 and P0321-P0322.).
Although Aimone discloses the automatic evolution method and system used for a brainwave database which collects physiological information of brainwaves about healthy clinical groups, with a feedback algorithm model mentioned above and head-mounted sensors mentioned in Aimone’s P0063, Aimone does not explicitly teach using a brainwave cap. Forsland teaches having a brainwave cap worn by the subject (See Fig. 17 an EEG-based brain-computer interface headset containing electrodes that are contacting the scalp mentioned in P0208-P0209.).
Therefore, it would have been obvious to one of ordinary skill in the art of brain computer interfaces before the effective filing date of the claimed invention to modify the method and system of Aimone to using a brainwave cap as taught by Forsland for creating and strengthening neural pathways mentioned in Forsland’s P0006.
Claim 4:
Aimone discloses an automatic evolution brainwave detection system used in brain training (See Fig. 1 a brain model system 100 mentioned in P0058-P0063.) comprising:
a training device including a brainwave, software and hardware equipment, to collect brainwave signals of the subject, the software and hardware equipment being programmed with machine learning (With the training device used for classifying the physiological information of brainwaves collected, see diagnostic classification, classifying brainwave patterns, parameters and collected features to build the base brain model in P0025-P0026, P0093, P0218, classifying stages of sleep in P0234-P0239 and machine learning in P0104-P0105, Fig. 4 the brain model store 490 in the computing platform 120 mentioned in P0113-P0115. Also, see [P0077-P0078] analyze collected bio-signal and non-bio-signal data. Processing and analysis of data can be performed on computing platform 120 by hardware, software, or any combination, Fig. 4 the organization of software at the computing device of FIG. 3 mentioned in P0115-P0019.) and a neural network architecture to analyze and classify physiological information of the brainwave signals according to data characteristics of the physiological information so as to establish a feedback algorithm model (See Fig. 7, items 702 bio-signal/non-bio-signal data input into neural network, sensor data stored in data store 480 and brain model data stored in brain model store 490 mentioned in P0119, P0235-P0238.);
a brainwave database coupled to the training device and storing the brainwave signals and the feedback algorithm model, the physiological information being incorporated in the brainwave database, and the brainwave database generating an output signal based on the feedback algorithm model (See sensor data stored in data store 480 and brain model data stored in brain model store 490 mentioned in P0119, P0125, P0180-P0183.);
an estimation and prediction device coupled to the brainwave database, receiving the output signal, calculating subsequent performance data according to the feedback algorithm model and measuring accuracy of the subsequent performance data to verify an evaluation index of the feedback algorithm model (See Fig .12, collected user data in P0207-P0208, P0223-P0229 where Validated Model and Input Expert Scores serve as the feedback algorithm model and measure accuracy of the subsequent performance data to verify an evaluation index of the feedback algorithm model.); and
a user terminal having a conversion device, the conversion device evaluating the subsequent performance data based on the feedback algorithm model to generate a feedback signal on the user terminal for the subject under the brain training (With converted input data as brainwave pattern and characteristics, see brainwave patterns (P0087) of different classes of users and specific set of characteristics when using machine learning (P0092-P0093, P0128, P0135).);
wherein the system is used by the subject in the brain training with new brainwave signals of the subject being collected and incorporated into the brainwave database, the software and hardware equipment is programmed (Besides stored data and utilized algorithms in P0082-P0084, see Fig. 4 the brain model store 490 in the computing platform 120 mentioned in P0113-P0115.) to automatically adjust the feedback algorithm model to establish an updated feedback algorithm model based on increased brainwave data, the machine learning and an updated neural network architecture, and the conversion device feeds an updated feedback signal generated based on the updated feedback algorithm model to the subject (Taught as adaptive brain state change notification (ABCN) neurofeedabck, also called neurotherapy or neurobiofeedback in P0069, tune aggregated data analysis in P0094-P0096, developing a model based on convolutional neural networks in P0016-P0019, P0120, P0176 and adjusting intervention along a progression path in P0313-P0315.); and
wherein the updated feedback algorithm model is updated and evolved automatically with the increased brainwave data by real-time processing in the training device, real-time feedback is provided by the conversion device to the subject, and each updated feedback algorithm model evolves automatically as frequency of use by the subject increases (See brainwave patterns (P0087) of different classes of users and specific set of characteristics when using machine learning (P0092-P0093, P0128, P0135). Also, see collecting brainwaves with sensors in P0062-P0063 with neurofeedback, pattern analysis (P0087), neuro-physiological state (P0134-P0135, P0295-P0296) and specific physical or emotional states in [P0092-P0093] These patterns can then be used to update the algorithms so that these patterns are used in the analysis of data collected in the future. There may be correlations found between specific groups of users with a specific set of characteristics. These correlations may be used to improve the performance of the analyzer, algorithm collector, or algorithm improver, or other aspects of the systems or methods. Also, see brain state predictions in Abstract, Fig. 14, P0118-P0120, P0178.), and
wherein the feedback algorithm model evolves from an initial model version to a subsequent model version iteratively, and the automatic evolution from a current model version to a next model version is triggered after a plurality of new brainwave signal inputs are collected and processed, allowing the feedback algorithm model to continuously operate to perform estimation while evolving (Taught as incremental building of a model and the process of automatically building a data set including raw data fed for optimization mentioned in P00083-P0086, P0095, P0218 and P0321-P0322.).
Although Aimone discloses the automatic evolution method and system used for a brainwave database which collects physiological information of brainwaves about healthy clinical groups, with a feedback algorithm model mentioned above and head-mounted sensors mentioned in Aimone’s P0063, Aimone does not explicitly teach using a brainwave cap. Forsland teaches a brainwave cap, the brainwave cap being worn by a subject (See Fig. 17 an EEG-based brain-computer interface headset containing electrodes that are contacting the scalp mentioned in P0208-P0209.).
Therefore, it would have been obvious to one of ordinary skill in the art of brain computer interfaces before the effective filing date of the claimed invention to modify the method and system of Aimone to using a brainwave cap as taught by Forsland for creating and strengthening neural pathways mentioned in Forsland’s P0006.
Regarding claim 2, Aimone discloses the automatic evolution brainwave detection method of claim 1, wherein the physiological information of brainwave signals includes gender, age, education level, mental state and behavioral feature (See [P0178-P0180] Features can be derived from any measure or variable that is available to system 100, such as time of day, EEG signal, heart rate, person's mood, age, gender, height, weight education, income, etc.).
Regarding claim 3, Aimone discloses the automatic evolution brainwave detection method of claim 1, wherein the physiological information of brainwave signals corresponds to a behavioral performance and a mental process of the subject (See [P0024] the method further comprises a treatment algorithm that provides state estimates of the user's brain and behavior states through inputs received from the brain model, resulting in a treatment protocol stimulus displayed to the user resulting in a brain and behavioral response in the user. Also, see brainwave measured activities in P0068-P0070, P0087 and brainwave patterns correlate with specific physical or emotional states in P0093.).
Regarding claim 5, Aimone discloses the automatic evolution brainwave detection system of claim 4, wherein the physiological information of brainwave signals includes gender, age, education level, mental state and behavioral feature (See [P0178-P0180] Features can be derived from any measure or variable that is available to system 100, such as time of day, EEG signal, heart rate, person's mood, age, gender, height, weight education, income, etc.).
Regarding claim 6, Aimone discloses the automatic evolution brainwave detection system of claim 4, wherein the physiological information of brainwave signals corresponds to a behavioral performance and a mental process of the subject (See [P0024] the method further comprises a treatment algorithm that provides state estimates of the user's brain and behavior states through inputs received from the brain model, resulting in a treatment protocol stimulus displayed to the user resulting in a brain and behavioral response in the user. Also, see brainwave measured activities in P0068-P0070, P0087 and brainwave patterns correlate with specific physical or emotional states in P0093.).
Claims 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Aimone (US 2020/0337625 A1) in view of Forsland (US 2021/0223864 A1) further in view of Chen (US 2018/0289919 A1).
Regarding claim 8, although Aimone and Forsland teach the automatic evolution method of claim 1 mentioned above, Aimone and Forsland do not explicitly teach feeding a brainwave signal to a subject within one minute. Chen teaches wherein the updated feedback signal is fed to the subject within 1 minute after the new brainwave signals of the subject are collected (See Fig. 6-7, exemplary one minute time signal in P0040- P0041 for brainwave frequency to drive an ear bone oscillator to oscillate.).
Therefore, it would have been obvious to one of ordinary skill in the art of brainwave regulation management before the effective filing date of the claimed invention to modify the method and system of Aimone to include feeding a brainwave signal to a subject within one minute as taught by Chen when using music and songs to make a user comfortable during brainwave analysis as mentioned in Chen’s P0003-P0005.
Regarding claim 9, although Aimone and Forsland teach the automatic evolution brainwave detection system of claim 4 mentioned above, Aimone and Forsland do not explicitly teach feeding a brainwave signal to a subject within one minute. Chen teaches wherein the updated feedback signal is fed to the subject within 1 minute after the new brainwave signal of the subject are collected (See Fig. 6-7, exemplary one minute time signal in P0040- P0041 for brainwave frequency to drive an ear bone oscillator to oscillate.).
Therefore, it would have been obvious to one of ordinary skill in the art of brainwave regulation management before the effective filing date of the claimed invention to modify the method and system of Aimone to include feeding a brainwave signal to a subject within one minute as taught by Chen when using music and songs to make a user comfortable during brainwave analysis as mentioned in Chen’s P0003-P0005.
Response to Arguments
In response to applicant's amendment, arguments and under the Alice/Mayo analysis the claimed invention, the subject matter integrates the claim limitation into a practical (Step 2A – Prong Two). Limitations underlined with “the brain training with new brainwave signals of the subject”, “the software and hardware equipment is programmed to automatically adjust the feedback algorithm model to establish an updated feedback algorithm model based on increased brainwave data”, “the conversion device feeds an updated feedback signal generated based on the updated feedback algorithm model to the subject”, and “a current model version to a next model version is triggered after a plurality of new brainwave signal inputs are collected and processed, allowing the feedback algorithm model to continuously operate to perform estimation while evolving” integrate the claim limitation into a practical application. Therefore, no “abstract idea” judicial exception is applicable, and the 101 rejections have been withdrawn.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “an end-to-end feedback limit triggered by the collection of new brainwaves", "the closed-loop processing time within a single feedback cycle” or “closed-loop feedback retraining”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
The revised amendments recited with “wherein the feedback algorithm model evolves from an initial model version to a subsequent model version iteratively, and the automatic evolution from a current model version to a next model version is triggered after a plurality of new brainwave signal inputs are collected and processed, allowing the feedback algorithm model to continuously operate to perform estimation while evolving” do not sufficiently overcome the art rejection. Aimone teaches ways of incrementally building a model and automatically building a data set including raw data fed, for optimization mentioned as in P00083-P0086, P0095, P0218 and P0321-P0322.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. (See Aimone (US 2023/0309887 A1), Nenadovic (US 2022/0160287 A1), Meharwade (US 2019/0050771 A1) and Ce Colman (US 10,606,353 B2).
THIS ACTION IS MADE FINAL. 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.
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/T.S.W./Examiner, Art Unit 3687 05/14/2026
/ALAAELDIN M. ELSHAER/Primary Examiner, Art Unit 3687
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