DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-24 have been presented for examination based on the application filed on 3/1/2022.
Claims 1-24 are rejected under 35 U.S.C. 101.
Claims 1-24 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
Claim(s) 1, 4-6, 10, 16, 21-22 and 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over US PGPUB No. US 20200238074 A1 by Song. et al., in view of US 20190321583 A1 by Poltorak; Alexander.
Claim(s) 2-3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Song, in view of Poltorak, further in view of US 20200397330 A1by KOBAYASHI; KEN et al.
This action is made Non-Final.
Examiner Note
Claims 7-9, 11-15, 17-20 and 23 are not rejected with prior art.
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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 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mental process without any additional elements that provide a practical application or amount to significantly more than the abstract idea.
Claims 1 & 24:
Step 1: the claims are drawn to a method and system respectively, falling under one of the four statutory categories of invention.
Step 2A, Prong 1: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. The limitations are bolded for abstract idea/judicial exception identification.
Claim 1
Mapping Under Step 2A Prong 1
1. (Original) A method for nonlinear modeling, decoding, and control of neural dynamics, the method comprising: identifying, based on neural time-series samples, a type of a manifold as a base for a neural model;
learning, based on a covering space, a dynamic model that is fit on the manifold to create the neural model; and
creating, using the neural model, a geometric decoder and a geometric controller.
Abstract Idea/Mathematical Concept/Mental Process: Identifying is a mental step to form an opinion (identify a type of manifold) based on observation (neural time-series samples). See Fig.4 step 400. The manifold may be mathematically defined (See Fig.4 model identification). See MPEP 2106.04(a)(2)(III).Abstract Idea/Mathematical Concept/Mental Process: The learning a dynamic model recites mathematical calculations (as in MPEP 2106.04(a)(2)(I)(C)) as shown in Fig.4 Step 402 which shows the dynamic model is combination of mathematical relationships based on mental step of identifying which manifold to choose and then modify. Also see Fig.9 element 606 showing dynamic model as mathematical model.
Abstract Idea/Mathematical Concept/Mental Process: The creating a geometric decoder is also considered as a mathematical concept. (as in MPEP 2106.04(a)(2)(I)(C)) as shown in specification [0066]-[0068]1, & [0071]2
Under its broadest reasonable interpretation, these covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. That is, nothing in the claim element precludes the step from practically being performed in the mind or with the aid of pencil and paper but for the recitation of generic computer components (In this case processor is implied at best in the learning step, see specification [0034]-[0035]). The mathematical concepts/abstract idea are identified in the mapping above.
Step 2A, Prong 2: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). As per (1) the additional elements are identified as bolded parts of the limitations in column 1 of the table below, and as per (2) the evaluation is shown in the mapping section of the table.
In accordance with this step, the judicial exception is not integrated into a practical application.
Claim 1
Mapping Under Step 2A Prong 2
1. (Original) A method for nonlinear modeling, decoding, and control of neural dynamics, the method comprising: identifying, based on neural time-series samples, a type of a manifold as a base for a neural model;
learning, based on a covering space, a dynamic model that is fit on the manifold to create the neural model; and
creating, using the neural model, a geometric decoder and a geometric controller.
Under MPEP 2106.05(g) determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. In this case data gathering is implied in the “based on neural time-series samples”.
See Step 2A Prong 1.
Under MPEP 2106.05(f)(1) the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution (creating a geometric controller ) to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result (Specifically not disclosing how the geometric decoder decodes using the dynamic model and how it controls the geometric controller to provide stimulation as in Fig.4), does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Specification [0078]-[0080]3
Further under MPEP 2106.05(h) the use of mathematical construct (dynamic model) to decode the time-series data and geometric controller is field of use at best. There is no nexus on how the stimulations are generated based on the dynamic model. See specification [0066]4.
Further under MPEP 2106.05(g), geometric controller is claimed as extra (post) solution (creating dynamic model) activity, generically using the dynamic model.
In particular the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(f).
As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer/processor to perform the claimed steps amounts to no more than mere instructions to apply the exception (with implied generic computer/processing component). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (see MPEP 2106.05(f)).
Claim 24 recites a system with additional elements of sensor (Under MPEP 2106.05(g) are considered as generic components for data gathering), and a generic processor performing the limitations similar to claim 1. The claims 24 is therefore rejected with similar rationale. The claims 1 & 24 are therefore considered to be patent ineligible.
Claims 2 & 3 respectively recite "... wherein identifying the type of manifold includes counting a quantity of persistent holes or loops in the neural time-series samples using topological data analysis (TDA)..." and "... wherein counting the quantity of the persistent holes or loops using the TDA includes computing Betti numbers...", and are considered as abstract idea/mathematical concept (MPEP 2106.04)(2)(I)(C)) for reciting use of TDA and mental step for forming an opinion on number of holes on observation of the TDA results. See specification [0057]. The claims do not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B).
Claim 4 recites "...wherein learning the dynamic model includes learning a covering map, learning a function indicating how the manifold is embedded in a space of neural activity, and finding parameters of the dynamic model on the covering space...". The learning a covering map & a function for embedding & finding parameters of dynamic model are mathematical processes as described in specification [0055]5, [0057]6 & [0058] respectively. The claims do not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B).
Claim 5 recites"... wherein learning the covering map includes learning the covering map based on the type of manifold....", further adding to the abstract idea that selects the manifold. The claims do not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B).
Claim 6 recites "... wherein finding the parameters of the dynamic model includes a new unsupervised expectation-maximization (EM) method...", recites mathematical concept (MPEP 2106.04(a)(2)(I)(C)). The generic recitation and limited disclosure (See specification [0058]7) without details how the EM method is used in current application to find parameters, this is further considered as idea of solution under MPEP 2106.05(f)(2). The claims do not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B).
Claim 7-9 recites "... wherein learning the function includes learning a first portion of the function that maps undistorted coordinates to neural data, and a second portion of the function that maps a manifold state of the type of manifold to an embedding space...." and "... wherein learning the second portion of the function includes learning the second portion based on the type of manifold...." and "... wherein learning the first portion of the function includes using nonlinear dimensionality reduction (NDR) and combining the NDR with support vector methods of functional approximation of various kernels....". The learning two portions are considered as mathematical concept being applied based on mental step (opinion based on observation), where there is no disclosure how the learning itself is performed, other than use of various mathematical techniques like PCA, NDR, TDS (loop counting) and See Fig.6-8 and [0059]-[0064]. The claim does not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B).
Claim 10-15 disclose various mathematical techniques applied to learn a function, however nowhere it is apparent how the learning itself is performed and how the parameters are determined from the learning. These steps are considered a mathematical concepts and abstract idea under MPEP 2106.04(a)(2)(I)(C) and steo 2A Prong 1. The claims do not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B).
Claim 16 recites "... wherein creating the geometric decoder and the geometric controller includes creating the geometric decoder to decode a brain state based on neural activity in real time....". This is considered an idea of solution (creating the geometric decoder) and field of use of mathematical concept (use of dynamic model / neural model) using the input. The hard part is determining the parameters of the dynamic model so that it can be used to model the neural activity. These are provided externally8. This is simply using the dynamic model (mathematical formulas, where the parameters are manually entered, not learned as in claims 6-9) with real time input to output the result. Hence creating and using the decoder is akin to In re Flook to raise alarm. The claim does not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B).
Claim 17-20 further detail use of mathematical concepts related to geometric decoder and are considered as abstract idea. The claim does not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B).
Claim 21 recites "... wherein creating the geometric decoder and the geometric controller includes creating the geometric controller by taking the decoded brain state as feedback and using the dynamic model....". This step specifies the input to geometric controller (decoded brain state as feedback and using the dynamic model). There are no implementation details claimed/provided for the geometric controller and therefore it remains an idea of solution under Step2A Prong 2 & step 2B. The claim does not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B).
Claim 22 recites "... wherein the dynamic model is configured to predict a change in the brain state in response to a given stimulation input level at a current time....". This is merely field of use (of the mathematical model/dynamic model) under step 2A Prong 2. This can also considered a mathematical concept to evaluate a function (dynamic model) based on the input, under Step 2A Prong 1. The claim does not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B).
Claim 23 recites "... wherein the geometric controller includes at least one of an optimal linear quadratic regulator, a linear quadratic Gaussian controller, or a model- predictive controller on the covering space built using the dynamic model....". This is considered an idea of solution under MPEP 2106.5(f)(1) and Step2A Prong 2 and Step 2B. Neither the claim nor the disclosure shows how these mathematical concepts are implemented as controllers for this specific application and how are the stimulations generated based on use of decoder output (as input) to the geometric controller. This can also be considered as field of use of these various controllers in the current application. See specification [0067] & [0080]. The claim does not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B).
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Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1-24 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1& 24 recites the limitation:
Claim 1: …. creating, using the neural model, a geometric decoder and a geometric controller.
Claim 24: … create a geometric decoder and a geometric controller using the neural model.
It is unclear how the geometric controller is created using the neural model. Limited Support for the creation of geometric controller is shown in [0065]-[0067]; however it is unclear how the such is achieved.
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The dependent claims 2-23, dependent on claim 1 do not cure this deficiency and are rejected for same reasons.
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Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 4-6, 10, 16, 21-22 and 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over US PGPUB No. US 20200238074 A1 by Song. et al., in view of US 20190321583 A1 by Poltorak; Alexander.
Regarding Claim 1
Song teaches (Claim 1) A method for nonlinear modeling, decoding, and control of neural dynamics (Song: Abstract, Fig.7 and 17) , the method comprising: identifying, based on neural time-series samples, a type of a (Song: [0126]-[0128] manifold may be interpreted as a polynomial, trigonometric, or geometric function) ; learning, based on a covering space, a dynamic model that is fit on the manifold to create the neural model (Song:[0126]-[0128] "... In some embodiments, fitting a B-spline model to the input signal may include varying a number of interior knot points of the B-spline basis function. In other embodiments, a different curve fitting approach may be used. For example, a polynomial, trigonometric, or geometric function may be fitted to the input signal....") ; and creating, using the neural model, a geometric decoder (Song: [0094] decoding model; [0128]-[0131]; Fig.17 process) and a geometric controller (Song: [0076]-[0081]; Fig. 7 for stimulation (geometric controller as the decoder model providing stimulation as shown in Fig.7) & Fig. 17 process) .
If Song is not understood as teaching the a type of a manifold as a base for a neural model, Poltorak teaches said limitation.
Poltorak teaches identifying, based on neural time-series samples, a type of a manifold as a base for a neural model (Poltorak: [0137]-[0141]).
It would have been obvious to one (e.g. a designer) of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Poltorak to Song to reduce dimensionality of the data (Poltorak: [0137]-[0141] ) such as in Song (Song: [0106] "... the MISO classification model may suffer from serious overfitting problems due to the high dimensional input (typically with hundreds of features) and the relatively small number of data points (80 trials in this study)....") . Additional motivation to combine would have been that Poltorak and Song are analogous art to the instant claim in the field of replicating mental state (Song: Fig.7, 17 & Abstract; Poltorak: Abstract).
Regarding Claim 24
Song teaches A system (Song : Fig.5) for nonlinear modeling, decoding, and control of neural dynamics (Song: Abstract, Fig.7 and 17), the system comprising:
at least one of an input device or a sensor configured to receive neural time-series samples (Song: [0041] ) ; and a processor coupled to the at least one of the input device or the sensor (Song : Fig.5 element 509) and configured to: receive or determine a type of (Song: [0126]-[0128] manifold may be interpreted as a polynomial, trigonometric, or geometric function), learn a dynamic model that is fit on the manifold to create the neural model based on a covering space (Song:[0126]-[0128] "... In some embodiments, fitting a B-spline model to the input signal may include varying a number of interior knot points of the B-spline basis function. In other embodiments, a different curve fitting approach may be used. For example, a polynomial, trigonometric, or geometric function may be fitted to the input signal...."), and create a geometric decoder (Song: [0094] decoding model; [0128]-[0131]; Fig.17 process) and a geometric controller using the neural model (Song: [0076]-[0081]; Fig. 7 for stimulation (geometric controller as the decoder model providing stimulation as shown in Fig.7) & Fig. 17 process).
If Song is not understood as teaching the receive or determine a type of manifold to use as a base for a neural model.
Poltorak teaches receive or determine a type of manifold to use as a base for a neural model (Poltorak: [0137]-[0141]).
It would have been obvious to one (e.g. a designer) of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Poltorak to Song to reduce dimensionality of the data (Poltorak: [0137]-[0141] ) such as in Song (Song: [0106] "... the MISO classification model may suffer from serious overfitting problems due to the high dimensional input (typically with hundreds of features) and the relatively small number of data points (80 trials in this study)....") . Additional motivation to combine would have been that Poltorak and Song are analogous art to the instant claim in the field of replicating mental state (Song: Fig.7, 17 & Abstract; Poltorak: Abstract).
Regarding Claim 4
Song & Poltorak teaches the method of claim 1 wherein learning the dynamic model includes learning a covering map (Song: [0046] [0050]-[0060], Fig.3A-3C) , learning a function indicating how the manifold is embedded in a space of neural activity (Poltorak: [0140]-[0147] discussing how the manifold is mapped to neural activity, Song: [0126]-[0128] manifold is taught as a polynomial, trigonometric, or geometric function) , and finding parameters of the dynamic model on the covering space (Song: [0050]-[0060] as determining the sparce coefficients/parameters) .
Regarding Claim 5
Song & Poltarek teaches the method of claim 4 wherein learning the covering map includes learning the covering map based on the type of manifold (Song: [0126]-[0128] manifold is taught as a polynomial, trigonometric, or geometric function, further details in [0050]-[0060]; Poltorak: [0139]-[0147]; [0139] "... The objective function includes a quality of data approximation and some penalty terms for the bending of the manifold. The popular initial approximations are generated by linear PCA, Kohonen's SOM or autoencoders. The elastic map method provides the expectation-maximization algorithm for principal manifold learning with minimization of quadratic energy functional at the “maximization” step....") .
Regarding Claim 6
Poltarek teaches the method of claim 4 wherein finding the parameters of the dynamic model includes a new unsupervised expectation-maximization (EM) method (Poltarek teaches: [0139] "... The objective function includes a quality of data approximation and some penalty terms for the bending of the manifold. The popular initial approximations are generated by linear PCA, Kohonen's SOM or autoencoders. The elastic map method provides the expectation-maximization algorithm for principal manifold learning with minimization of quadratic energy functional at the “maximization” step....") .
Regarding Claim 10
Song teaches the method of claim 4 wherein learning the function includes learning a composition of the function and the covering map (Song: [0049]-[0060]).
Regarding Claim 16
Poltrack teaches the method of claim 1 wherein creating the geometric decoder and the geometric controller includes creating the geometric decoder to decode a brain state based on neural activity in real time (Poltrack: [0164]-[0171]) .
Regarding Claim 21
Song teaches the method of claim 16 wherein creating the geometric decoder and the geometric controller includes creating the geometric controller by taking the decoded brain state as feedback and using the dynamic model (Song: Fig.8 & [0019]; [0046], [0062][, [0084]-[0085]) .
Regarding Claim 22
Song teaches the method of claim 21 wherein the dynamic model is configured to predict a change in the brain state in response to a given stimulation input level at a current time (Song: Fig.10 & [0090], [0137]) .
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Claim(s) 2-3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Song, in view of Poltorak, further in view of US 20200397330 A1by KOBAYASHI; KEN et al.
Regarding Claim 2
Teachings of Song & Poltorak are shown in the parent claim 1.
Song does not explicitly teach topological data analysis (TDA).
Kobayashi teaches the method of claim 1 wherein identifying the type of manifold includes counting a quantity of persistent holes or loops in the neural time-series samples using topological data analysis (TDA) (Kobayashi: [0024] "... the detection device 10 detects an abnormality in brain data by performing an analysis method using topological data analysis (TAD) on brainwave data of a patient, which is chronological data. For example, the detection device 10 receives brainwave data input thereto, generates a pseudo-attractor that is a limited number of attractor from the brainwave data, and subjects the pseudo-attractor to persistent homology transform (PH transform), to calculate a Betti number. .."; [0032] [0040]-[0044]) .
It would have been obvious to one (e.g. a designer) of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Kobayashi to Song as Kobayashi improves in data detection of the time-series/chronological brainwave data (Kobayashi: Fig.1; [0052] "... subjecting the attractor to persistent homology transform, and extracting a first order component of a Betti sequence. As a result, the detection device 10 enables delirium detection by converting waveforms characterizing delirium into a numerical form, not affected by fluctuations in width of waveforms in brainwave data and, therefore, can improve the detection accuracy...."). The motivation to combine would have been that Song and Kobayashi are analogous art to instant claim in the field of detection of certain condition related to the time-series/chronological brainwave data (Kobayashi: Fig.1) using transform/model (Kobayashi:
Regarding Claim 3
Kobayashi teaches The method of claim 2 wherein counting the quantity of the persistent holes or loops using the TDA includes computing Betti numbers (Kobayashi: [0024][0032][0040]-[0044]) .
Conclusion
All claims are rejected.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Examiner’s Note: Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
In the case of amending the claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
Communication
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AKASH SAXENA whose telephone number is (571)272-8351. The examiner can normally be reached Mon-Fri, 7AM-3:30PM.
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, RYAN PITARO can be reached on (571) 272-4071. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
AKASH SAXENA
Primary Examiner
Art Unit 2188
/AKASH SAXENA/Primary Examiner, Art Unit 2188 Thursday, February 5, 2026
1 Specification [0068] "... (2) Transform the nonlinear dynamic model of x.sub.t (no z.sub.t here) on the low-D manifold to a linear dynamic model in a high-D space by combining TDA with the Koopman operator method just introduced in control theory, which exploits user-selected transform functions of state to get the high-D space. Then control may be performed with this linear model. TDA may be used to select the transfer functions (e.g., if there is a loop, use cos θ and sin θ). (3) Build novel linear but adaptive controllers on a low-D hyperplane, the controllers designed to capture nonlinearities through time-adaptation. (4) Incorporating generic nonlinearities, e.g., with spline bases, to build the dynamic model on low-D hyperplanes and build nonlinear decoders and controllers...."
2 Specification [0071] "...The inventors built a geometric model and decoder to estimate joint angles from neural activity...."
3 See Specification [0080] "... The controlled state can be compared with the target and the difference or error used to update the stimulation parameters continuously and optimally using the geometric controller...." – Notice There is no mechanism recited there to show how the stimulation parameters are updated based on difference/error.
4 See specification [0066] "... To decode, z.sub.t will be estimated from y.sub.1:t by deriving a recursive Bayesian filter consisting of for example either an unscented Kalman filter (UKF) or a particle filter (PF) for Equations 1-3 and 7 to denoise the data as also depicted in FIG. 12 as an example. Also, model-based controllers of network activity (currently lacking) may be derived by taking the decoded brain state as feedback and using the nonlinear dynamic model of Equations 1-3 and 7 (FIG. 9). The dynamic model will predict how the brain state will respond to a given stimulation level at current time, thus guiding the controller 608 to adjust the stimulation...."
5 See Specification [0055] "... [0055] In Equations 1-3, y.sub.t is the n-D activity at time t (FIG. 7, D; firing rates, ECoG features). x.sub.t is the d-D brain state on the manifold (FIG. 7, B). Map ƒ describes how the manifold is embedded in custom-character.sup.n (FIG. 7, C). z.sub.t is the equivalent d-D state on the covering space with a covering map g (FIG. 7, A), which describes the operation of putting all bounded planes on each other. u.sub.t is input (e.g., electrical stimulation). A and B are parameters. r.sub.t and w.sub.t are white Gaussian noises with covariances R and W...."
6 See Specification [0057] "... This manifold type identification is a major departure from current methods on dynamic modeling, which assume that the brain state evolves linearly on linear hyperplanes whose embedding function ƒ.sup.−1 is calculated by principal component analysis (PCA),..."
7 See Specification [0058] "... then parameters A, B, W, R are found by deriving new unsupervised expectation-maximization (EM) methods, for example...."
8 See specification [0078] "... The input will be the known stimulation parameters here and can be given to the machine learning processor 104 through the input device 108 in FIG. 1...."