Detailed Notice
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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 03/30/2026 has been entered.
Status of Claims
Claims 1-16 and 18-20 are currently pending.
Claims 1-16 and 18-20 are rejected.
Claims 1, 18, and 19 are amended.
Claim 17 is canceled.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-3, 12-16, and 19-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 8-1012, 14, 17, and 19-20 of U.S. Patent No. 11,696,714 B2 in view of the prior art in the Non-Final Office Action (mailed on 08/01/2025), Barrett et al. (US 20190102522 A1), hereinafter Barrett. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-3, 12-16, and 19-20 recite similar features to claims 1-3, 8-10, 12, 14, 17, and 19-20 of U.S. Patent No. 11,696,714 B2 as shown in the table below:
Application No. 18/201,502
U.S. Patent No. 11,696,714 B2
1. (Currently Amended) A computer-implemented method for generating a treatment protocol, the method comprising:
receiving time-coded bio-signal data associated with a user from a bio-signal sensor; projecting the time-coded bio-signal data into a lower dimensioned feature space;
extracting, with a machine learning system, features from the lower dimensioned feature space to identify a brain response;
generating a training data set for the brain response using the features;
generating a brain model by transfer learning from one or more similar users by:
comparing at least one of the features to at least one feature of users to identify the one or more similar users, wherein the at least one feature of the users has been extracted using the machine learning system;
pooling data of the one or more similar users stored in a memory; and
training the brain model from a base brain model using the pooled data using a processor that modifies parameters of the base brain model, wherein the brain model is a neural network; and
training the brain model using the training set using a processor that modifies the parameters of the brain model stored on the memory, the trained brain model unique to the user;
generating a treatment protocol for the user based on a treatment model that incorporates the trained brain model, using a processor that accesses the trained brain model stored in the memory.
1. A computer-implemented method for brain modelling, the method comprising:
receiving time-coded bio-signal data associated with a user from a bio-signal sensor;
receiving time-coded stimulus event data;
projecting the time-coded bio-signal data into a lower dimensioned feature space;
extracting features from the lower dimensioned feature space that correspond to time codes of the time-coded stimulus event data to identify a brain response;
generating a training data set for the brain response using the features;
generating a brain model by transfer learning from one or more similar users by:
comparing attributes of users to identify the one or more similar users;
pooling data of the one or more similar users stored in a memory; and
training the brain model from a base brain model using the pooled data using a processor that modifies parameters of the base brain model; and
training the brain model using the training set using a processor that modifies the parameters of the brain model, the trained brain model unique to the user;
generating a brain state prediction for the user output from the trained brain model, using a processor that accesses the trained brain model stored in the memory;
inputting the brain state prediction to a feedback model to determine a feedback stimulus for the user, wherein the feedback model is associated with a target brain state; and
causing the feedback stimulus to be provided to the user using a user effector.
10. The method of claim 1, wherein the brain model is a neural network.
2. (Original) The computer-implemented method of claim 1, further comprising: receiving time-coded stimulus event data; and wherein the features correspond to time codes of the time-coded stimulus event data.
1. A computer-implemented method for brain modelling, the method comprising…
receiving time-coded stimulus event data…
extracting features from the lower dimensioned feature space that correspond to time codes of the time-coded stimulus event data to identify a brain response;
3. (Original) The computer-implemented method of claim 1, wherein the treatment protocol is generated by: generating a brain state prediction for the user output from the trained brain model, using a processor that accesses the trained brain model stored in the memory; and inputting the brain state prediction to a feedback model to determine the treatment protocol for the user, wherein the feedback model is associated with a target brain state; and wherein the method further comprises causing the treatment protocol to be provided to the user using a user effector.
1. A computer-implemented method for brain modelling, the method comprising…
generating a brain state prediction for the user output from the trained brain model, using a processor that accesses the trained brain model stored in the memory;
inputting the brain state prediction to a feedback model to determine a feedback stimulus for the user, wherein the feedback model is associated with a target brain state; and
causing the feedback stimulus to be provided to the user using a user effector.
4. (Original) The computer-implemented method of claim 1, wherein the treatment protocol comprises at least one of administering an intervention, sensory inputs, dosing inputs, duration of therapeutic setting, frequency and intensity of interaction, set, setting, and intensification of experience.
X
5. (Original) The computer-implemented method of claim 1, further comprising: receiving additional time-coded bio-signal data associated with the user after an interval of time; updating the trained brain model based on the additional time-coded bio-signal data; and updating the treatment protocol based on the updated trained brain model.
X
6. (Original) The computer-implemented method of claim 5, wherein the updated treatment protocol comprises continuation, alteration, deviation, cessation, adjustment, titration, modulation, or variation of a given treatment.
X
7. (Original) The computer-implemented method of claim 5, wherein the additional time-coded bio-signal data is received in real-time or over a longitudinal progression of time.
X
8. (Original) The computer-implemented method of claim 1, wherein the one or more similar users are selected based on at least one of time of day, food and chemical consumptions, medical conditions, emotional states, sleepiness, age, gender, and geographical location.
X
9. (Original) The computer-implemented method of claim 1, wherein the treatment protocol is configured to account for at least one of default mode network and functional connectivity changes.
X
10. (Original) The computer-implemented method of claim 9, wherein the changes arise from sedatives or antidepressants.
X
11. (Original) The computer-implemented method of claim 1, further comprising determining a response to a therapeutic or medical parameter based on the trained brain model.
X
12. (Original) The method of claim 1, further comprising comparing attributes of users to determine covariance, and based on the covariance, identifying the one or more similar users.
2. The method of claim 1, further comprising comparing attributes of users to determine covariance, and based on the covariance, identifying the one or more similar users.
13. (Original) The method of claim 1, wherein the time-coded bio-signal data includes time-coded EEG data generated by repeated measures.
3. The method of claim 1, wherein the time-coded bio-signal data includes time-coded EEG data generated by repeated measures.
14. (Original) The method of claim 1, wherein the training of the brain model includes at least one of supervised, semi-supervised, unsupervised learning, or reinforcement learning.
8. The method of claim 1, wherein the training of the brain model includes at least one of supervised, semi-supervised, unsupervised learning, or reinforcement learning.
15. (Original) The method of claim 1, wherein the brain model is at least one of a logistic regression, linear discriminant analysis, a random forest, gradient boosted trees, support vector machines, or ensemble learning.
9. The method of claim 1, wherein the brain model is at least one of a logistic regression, linear discriminant analysis, a random forest, gradient boosted trees, support vector machines, or ensemble learning.
16. (Original) The method of claim 1, wherein the brain model comprises one or more models selected from the group of linear model, logistic regression, linear discriminant, Gaussian naive Bayes classifier, linear support vector machines, nonlinear classifiers, nonlinear support vector machines, random forests, gradient boosted trees, k-nearest neighbours classifier, neural network, fully connected neural network, convolutional neural network, recurrent neural network, long short term memory neural network, residual neural network, autoencoder, restricted Boltzmann machines, generative adversarial network, capsule network, histogram, standard parametrized distribution, multivariate Gaussian, Wishart, expert system, including combinations thereof.
9. The method of claim 1, wherein the brain model is at least one of a logistic regression, linear discriminant analysis, a random forest, gradient boosted trees, support vector machines, or ensemble learning.
10. The method of claim 1, wherein the brain model is a neural network.
12. The method of claim 10, wherein the neural network is at least one of or a combination of a convolutional neural network, recurrent neural network or long short-term memory network.
14. The method of claim 1, wherein the brain model is a generator and the feedback model is a discriminator in a generative adversarial network (GAN) framework.
17. (Cancelled)
X
18. (Currently Amended) The method of claim 1, wherein the neural network is at least one of or a combination of a convolutional neural network, recurrent neural network or long short-term memory network.
X
19. (Currently Amended) A system for generating a treatment protocol, the system comprising:
a client computing device;
a bio-signal sensor, wherein the bio-signal sensor is in communication with the client computing device;
a server comprising a memory storing a plurality of user brain models, wherein the server is in communication with the client computing device;
the client computing device configured to:
generate time-coded bio-signal data associated with a user using the bio-signal sensor;
transmit the time-coded bio-signal data to the server;
the server configured to:
receive the time-coded bio-signal data;
project the time-coded bio-signal data into a lower dimensioned feature space;
extract features from the lower dimensioned feature space to identify a brain response with a machine learning system;
generate a training data set for the brain response using the features;
generate a brain model by transfer learning from one or more similar users by:
comparing at least one of the features to at least one feature of users to identify the one or more similar users, wherein the at least one feature of the users has been extracted using the machine learning system;
pooling data of the one or more similar users stored in the memory; and
training the brain model from a base brain model using the pooled data by modifying the parameters of the base brain model, wherein the brain model is a neural network; and
train the brain model using the training set by modifying the parameters of the brain model, the trained brain model unique to the user;
and at least one of the client computing device and the server configured to:
generate a treatment protocol for the user based on a treatment model that incorporates the trained brain model.
17. The method of claim 1, further comprising determining a treatment protocol for the user based at least in part on the trained brain model and the feedback model.
20. A system for brain modelling, the system comprising:
a client computing device;
a bio-signal sensor, wherein the bio-signal sensor is in communication with the client computing device;
a user effector, wherein the user effector is in communication with the client computing device;
a server comprising a memory storing a plurality of user brain models, wherein the server is in communication with the client computing device;
the client computing device configured to:
generate time-coded bio-signal data associated with a user using the bio-signal sensor;
generate time-coded stimulus event data;
transmit the time-coded bio-signal data and the time-coded stimulus event data to the server;
the server configured to:
receive the time-coded bio-signal data;
receive the time-coded stimulus event data;
project the time-coded bio-signal data into a lower dimensioned feature space;
extract features from the lower dimensioned feature space that correspond to time codes of the time-coded stimulus event data to identify a brain response;
generate a training data set for the brain response using the features;
generate a brain model by transfer learning from one or more similar users by:
comparing attributes of users to identify the one or more similar users;
pooling data of the one or more similar users stored in the memory; and
training the brain model from a base brain model using the pooled data by modifying the parameters of the base brain model; and
train the brain model using the training set by modifying the parameters of the brain model, the trained brain model unique to the user;
at least one of the client computing device and the server configured to:
generate a brain state prediction for the user output using the trained brain model; and
determine a feedback stimulus for the user from the brain state prediction using a feedback model, wherein the feedback model is associated with a target brain state; and
the user effector configured to:
provide the feedback stimulus to the user.
10. The method of claim 1, wherein the brain model is a neural network.
20. (Original) A non-transitory computer readable medium comprising a computer readable memory storing computer executable instructions thereon that when executed by a computer cause the computer to perform the method of claim 1.
19. A non-transitory computer readable medium comprising a computer readable memory storing computer executable instructions thereon that when executed by a computer cause the computer to perform the method of claim 1.
U.S. Patent No. 11,696,714 B2 does not explicitly teach “with a machine learning system” or “using a machine learning model, and even though there is no prior art rejection, one of ordinary skill in the art would know to combine U.S. Patent No. 11,696,714 B2 to perform “extract features from the lower dimensioned feature space to identify a brain response with a machine learning system” and “the at least one feature of the users has been extracted using the machine learning system”, by combining U.S. Patent No. 11,696,714 B2 and Barrett.
Barrett discloses at paragraph [0007]: “The risk score is determined using a combination of parameters including a patient's medical history, a patient's current situation on a day-to-day basis, and environmental conditions relating to atmospheric and weather conditions. The relationship between these parameters and risk assessment generated for the patient is embodied in a machine learned model. The model, and system more generally, is capable of receiving input values for the parameters and categorizing a patient's risk score to provide a risk assessment with accurate and medically relevant treatment options to mitigate the risk” and [0130]: “the model 640 is trained using some a function (B) or another more complex logical structure. In one embodiment, the model 640 is trained using a machine learning technique, examples of which include but are not limited to linear, logistic, and other forms of regression (e.g., elastic net), decision trees (e.g., random forest, gradient boosting), support vector machines, classifiers (e.g. Naïve Bayes classifier), fuzzy matching. An example model 640 trained using gradient boosting is described in Section IV.E below”.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify U.S. Patent No. 11,696,714 B2 of to incorporate the teachings of Barrett and account for asthma risk notifications in advance of predicted rescue usage events in order to help effect behavior changes in a patient to prevent those events from occurring. Rescue medication events, changes in environmental conditions, and other contextually relevant information are detected by sensors associated with the patient's medicament device/s and are collected from other sources, respectively, to provide a basis to determine a patient's risk score. This data is analyzed to determine the severity of the patient's risk for an asthma event (Barrett, Abstract and [0002]-[0005]).
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 1-16 and 18-20 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.
Step 1:
In the instant case, claims 1-16 and 18 are directed toward a method (i.e. a process), claim 19 is directed toward a system (i.e., machine), and claim 20 is directed toward a non-transitory computer readable medium (i.e., manufacture). Thus, each of the claims falls within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A—Prong 1:
Independent claims 1 and 19 recites steps that, under their broadest reasonable interpretations, cover performance of the limitations of a certain method of organizing human activity but for the recitation of generic computer components.
Claim 1 recites: “A computer-implemented method for generating a treatment protocol, the method comprising: receiving time-coded bio-signal data associated with a user from a bio-signal sensor; projecting the time-coded bio-signal data into a lower dimensioned feature space; extracting, with a machine learning system, features from the lower dimensioned feature space to identify a brain response; generating a training data set for the brain response using the features; generating a brain model by transfer learning from one or more similar users by: comparing at least one of the features to at least one feature of users to identify the one or more similar users, wherein the at least one feature of the users has been extracted using the machine learning system; pooling data of the one or more similar users stored in a memory; and training the brain model from a base brain model using the pooled data using a processor that modifies parameters of the base brain model, wherein the brain model is a neural network; and training the brain model using the training set using a processor that modifies the parameters of the brain model stored on the memory, the trained brain model unique to the user; generating a treatment protocol for the user based on a treatment model that incorporates the trained brain model, using a processor that accesses the trained brain model stored in the memory”.
The limitations of receiving time-coded bio-signal data associated with a user; projecting the time-coded bio-signal data into a lower dimensioned feature space; generating a training data set for the brain response using the features; comparing at least one of the features to at least one feature of users to identify the one or more similar users, wherein the at least one feature of the users has been extracted; pooling data of the one or more similar users; and generating a treatment protocol for the user based on a treatment model that incorporates the trained brain model, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions—in this case the aforementioned steps recite a process of receiving, generating, a training data set, comparing, and generating a treatment protocol, which is properly interpreted as a “personal behavior”), but instead automates the process via a computer model, e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements”, and will be discussed in further detail below.
Additionally, claim 19 recites: “A system for generating a treatment protocol, the system comprising: a client computing device; a bio-signal sensor, wherein the bio-signal sensor is in communication with the client computing device; a server comprising a memory storing a plurality of user brain models, wherein the server is in communication with the client computing device; the client computing device configured to: generate time-coded bio-signal data associated with a user using the bio-signal sensor; transmit the time-coded bio-signal data to the server; the server configured to: receive the time-coded bio-signal data; project the time-coded bio-signal data into a lower dimensioned feature space; extract features from the lower dimensioned feature space to identify a brain response with a machine learning system; generate a training data set for the brain response using the features; generate a brain model by transfer learning from one or more similar users by: comparing at least one of the features to at least one feature of users to identify the one or more similar users, wherein the at least one feature of the users has been extracted using the machine learning system; pooling data of the one or more similar users stored in the memory; and training the brain model from a base brain model using the pooled data by modifying the parameters of the base brain model, wherein the brain model is a neural network; and train the brain model using the training set by modifying the parameters of the brain model, the trained brain model unique to the user; and at least one of the client computing device and the server configured to: generate a treatment protocol for the user based on a treatment model that incorporates the trained brain model”.
The limitations of generate time-coded bio-signal data associated with a user; transmit the time-coded bio-signal data; receive the time-coded bio-signal data; generate a training data set for the brain response using the features; comparing at least one of the features to at least one feature of users to identify the one or more similar users, wherein the at least one feature of the users has been extracted; pooling data of the one or more similar users; generate a treatment protocol for the user, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions—in this case the aforementioned steps recite a process of generate a training data set, transmit, receive, comparing, and generate a treatment protocol, which is properly interpreted as a “personal behavior”), but instead automates the process via a computer model or machine learning, e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements”, and will be discussed in further detail below.
Dependent claims 2-16, 18, and 20 include other limitations, as well as specific step of data to be processed, received, and applied, but these only serve to further limit the abstract idea and do not add and additional elements, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1 and 19. However, recitation of an abstract idea is not the end of the 35 U.S.C. 101 analysis. Each of the claims must be analyzed for additional elements that indicate the abstract idea is integrated into a practical application to determine whether the claim is considered to be “directed to” an abstract idea.
Step 2A—Prong 2:
Claims 1-16 and 18-20 are not integrated into a practical application because the additional elements (i.e. any limitations that are not identified as part of the abstract idea) amount to no more than limitations which:
Amount to mere instructions to apply an exception—for example, the recitation of “bio-signal sensor”, “machine learning system”, “brain model”, “memory”, “processor”, “neural network”, “client computing device”, “server”, and “non-transitory computer readable medium”, which amount to merely invoking a computer as a tool to perform the abstract idea, e.g. see FIG. 1-2, [0055], [0080]-[0081], [0096], and [0109]-[0112], of the present specification, and see further MPEP 2106.05(f);
Generally linking the abstract idea to a particular technological environment or field of use, for example, “from a bio-signal sensor”, “with a machine learning system, features from the lower dimensioned feature space to identify a brain response”, “generating a brain model by transfer learning from one or more similar users by”, “using the machine learning system”, “stored in a memory”, “ training the brain model from a base brain model using the pooled data using a processor that modifies parameters of the base brain model, wherein the brain model is a neural network; and training the brain model using the training set using a processor that modifies the parameters of the brain model stored on the memory, the trained brain model unique to the user”, “using a processor that accesses the trained brain model stored in the memory”, “a client computing device; a bio-signal sensor, wherein the bio-signal sensor is in communication with the client computing device; a server comprising a memory storing a plurality of user brain models, wherein the server is in communication with the client computing device; the client computing device configured to”, “using the bio-signal sensor”, “to the server; the server configured to”, “project the time-coded bio-signal data into a lower dimensioned feature space; extract features from the lower dimensioned feature space to identify a brain response with a machine learning system”, “generate a brain model by transfer learning from one or more similar users by”, “using the machine learning system”, “stored in the memory”, “training the brain model from a base brain model using the pooled data by modifying the parameters of the base brain model, wherein the brain model is a neural network; and train the brain model using the training set by modifying the parameters of the brain model, the trained brain model unique to the user; and at least one of the client computing device and the server configured to”, and “based on a treatment model that incorporates the trained brain model”, which amounts to limiting the abstract idea to the field of technology/the environment of computers, see MPEP 2106.05(h); and/or
Merely acquiring information for further analysis by the system and the particular manner of acquisition is not described or shown to be important, for example, “receiving time-coded bio-signal data associated with a user from a bio-signal sensor” and “receive the time-coded bio-signal data”, which amounts to insignificant extra-solution activity in the form of mere data gathering because it merely functions tangentially to the main idea of the invention and serves only to bring in the data necessary for the inventions main analysis, see MPEP 2106.05(g).
Additionally, dependent claims 2-16, 18, and 20 include other limitations, for example:
Claim 9 recites additional elements of “default mode network”;
Claim 14 recites additional elements of “the training of the brain model includes at least one of supervised, semi-supervised, unsupervised learning, or reinforcement learning”;
Claim 15 recites additional elements of “the brain model is at least one of a logistic regression, linear discriminant analysis, a random forest, gradient boosted trees, support vector machines, or ensemble learning”;
Claim 16 recites additional elements of “the brain model comprises one or more models selected from the group of linear model, logistic regression, linear discriminant, Gaussian naive Bayes classifier, linear support vector machines, nonlinear classifiers, nonlinear support vector machines, random forests, gradient boosted trees, k-nearest neighbours classifier, neural network, fully connected neural network, convolutional neural network, recurrent neural network, long short term memory neural network, residual neural network, autoencoder, restricted Boltzmann machines, generative adversarial network, capsule network, histogram, standard parametrized distribution, multivariate Gaussian, Wishart, expert system, including combinations thereof”;
Claim 18 recites additional elements of “the neural network is at least one of or a combination of a convolutional neural network, recurrent neural network or long short-term memory network”;
but as stated above, the limitations recited by these claims also do not integrate the aforementioned abstract idea into a practical application.
Step 2B:
The claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the elements other than the abstract idea), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, and/or generally link the abstract idea to a particular technological environment or field of use, which even when reevaluated under the considerations of Step 2B of the analysis, do not amount to “significantly more” than the abstract idea.
Dependent claims 2-16 and 18 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because, as stated above, the aforementioned dependent claims do not recite any additional elements not already recited in independent claims 1 and 19, and hence do not amount to “significantly more” than the abstract idea.
Additionally, the additional elements (i.e., “receiving time-coded bio-signal data associated with a user from a bio-signal sensor” and “receive the time-coded bio-signal data”), add extra solution activity, which comprises limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in a particular field as demonstrated by:
Relevant court decisions (See MPEP 2106.05(d)(II)):
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) (“Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink.” (emphasis added)).
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-16 and 18-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed 03/30/2026 have been fully considered but they are not persuasive. Regarding the Non-Statutory Double Patenting Rejection, the rejection is held in abeyance, however the rejection has been updated with the amendments.
Regarding the 35 U.S.C. 101 Rejection, Applicant argues the claims do not recite an abstract idea, that the limitations of “extracting, with a machine learning system”, “comparing… wherein the at least one feature of the users has been extracted using the machine learning system”, “training the brain model from a base brain model”, “training the brain model using the training set”, and “generating a treatment protocol for the user based on a treatment model”, do not recite an abstract idea. Examiner respectfully disagrees. The machine learning system and brain model are not part of the abstract idea, but are additional elements. The additional elements are recited at a high level of generality that they amount to generic computer tools, and the steps of extracting data using the machine learning system and training the model amount to merely “apply it” (see MPEP 2106.05(f): “adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984”). It is also noted that the use of a computer tool does not mean there no abstract idea, see MPEP 2106.04(a)(2)(II) states “the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the “certain methods of organizing human activity” grouping”.
Applicant further argues the claims integrate any alleged abstract idea into a practical application because they recite an improvement of brain model that is capable of learning the relationships between an external or internal stimulus event and detect brain responses, develop a brain model using supervised learning to process hundreds to millions of labeled examples, the brain model uses transfer learning to find-tune the model, which all increase accuracy and lower costs. Examiner respectfully disagrees. A brain model being used to learn or detect responses via internal or external inputs is not an improvement, but an obvious function of a model (i.e., input data to a model, model processes data, and then outputs a response). Pertaining to Applicant’s argument of processing large quantities of data, MPEP 2106.04(a)(2) states “The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures “can be carried out in existing computers long in use, no new machinery being necessary.” 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699” and in MPEP 2106.05(f) states “The Court found that the recitation of the computer in the claim amounted to mere instructions to apply the abstract idea on a generic computer. 573 U.S. at 225-26, 110 USPQ2d at 1984. The Supreme Court also discussed this concept in an earlier case, Gottschalk v. Benson, 409 U.S. 63, 70, 175 USPQ 673, 676 (1972), where the claim recited a process for converting binary-coded-decimal (BCD) numerals into pure binary numbers. The Court found that the claimed process had no meaningful practical application except in connection with a computer. Benson, 409 U.S. at 71-72, 175 USPQ at 676. The claim simply stated a judicial exception (e.g., law of nature or abstract idea) while effectively adding words that “apply it” in a computer”. It is also unclear how the brain model is able to increase accuracy and lower costs, and that this improvement appears to be a business practice improvement and not an improvement to the technology itself.
Applicant further argues the claims amount to significantly more because the art rejection was withdrawn and the claims recite an improvement of a more accurate and robust brain model. Examiner respectfully disagrees. MPEP 2106.05 states “Specifically, lack of novelty under 35 U.S.C. 102 or obviousness under 35 U.S.C. 103 of a claimed invention does not necessarily indicate that additional elements are well-understood, routine, conventional elements. Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101”. Examiner has also included citations to the MPEP as well as relevant court decisions in the previous office action as needed by MPEP 2106.07(III). And as stated above, the claims do not reflect a technological improvement, and amounts to merely applying the abstract idea to the additional elements. Therefore, the 35 U.S.C. 101 Rejection is maintained.
Conclusion
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/R.S.S./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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