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 .
Information Disclosure Statement
Acknowledgement is made of the Information Disclosure Statement dated 05/09/2022 and 03/10/2025. All of the cited references have been considered.
Drawings
The drawings have been received on 03/08/2022. These drawings are accepted.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3, 8, 18, 23, 27 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth 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.
Claims 3, 18 and 23 substantially recite “wherein the scaled predicted first neural response is defined as r̂α1=wαvαp̂αi”. It is unclear what applicant means by p̂ as it needs to be defined in the claims.
Claims 8 and 27 recite wherein the representation similarity metric is Sijmodel=êi·êj for the first image and the second image, where ei=(ri−r)/(∥ri−r∥) where r̄=Ei[ri] and ej=(rj−r)/(∥rj−r∥) where r̄ =Ej[rj]. It is unclear what applicant means as the variables aren’t defined in the claim. For purposes of examination, Examiner is interpreting the representation similarity metric as a similarity or distance metric between two images.
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 1-8 and 16-27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1,
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a method, i.e., process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating, [by the neural predictive model,] a prediction of a first neural response of a biological system to the first stimulus;”
As drafted, under their broadest reasonable interpretation, cover concepts performed in human mind (including an observation, evaluation, judgement, or opinion, e.g., generating). The above limitations in the context of this claim encompass, inter alia, generating a prediction (corresponding to mental processes which can be done mentally or by pen and paper).
“scaling, [by the neural predictive model,] the predicted first neural response with a signal-to-noise weight to generate a denoised predicted first neural response; and”
As drafted, under their broadest reasonable interpretation, cover mathematical concepts (including mathematical relationships, mathematical formulas or equations, or mathematical calculations, e.g., scaling). The above limitations in the context of this claim encompass, inter alia, scaling is a mathematical concept in view of how scaling is described in paragraph [0074] of the Specification, where scaling is shown by performing this mathematical equation of wa = σa / ηa (corresponding to mathematical concepts).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application.
The limitations:
“by a computing system”
“by the neural predictive model”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). Specifically, they amount to mere instructions to apply the exception using a neural predictive model (e.g., by using these elements as tools).
The limitations:
“accessing, by a computing system, a plurality of stimuli for a stimulus scheme;”
“inputting, by the computing system, a first stimulus of the plurality of stimuli into a neural predictive model;”
“providing, by the computing system, the denoised predicted first neural response.”
As drafted, amount to insignificant extra-solution activities, which do not integrate a judicial exception into a practical application. For example, the additional elements of
"accessing stimuli", “inputting a first stimulus” and “providing the denoised predicted first neural response” amount to mere data gathering and data storage, respectively, which are insignificant extra-solution activities that do not integrate a judicial exception into a
practical application. See MPEP 2106.05(g).
Step 2B Analysis: The claim does not include additional elements that are sufficient to
amount to significantly more than the judicial exception. As discussed above with respect to
integration of the abstract idea into a practical application, all of the additional elements are
insignificant extra-solution activities or mere instructions to apply an exception. (i.e., the
additional element describes a unit for applying the abstract ideas). Insignificant extra-solution
activities and mere instructions to apply an exception cannot provide an inventive concept.
Moreover, receiving, communicating, and storing data are insignificant extra-solution activities
that are well-understood, routine, and conventional. See MPEP 2106.05(d)(II) ("The courts have
recognized the following computer functions as well-understood, routine, and conventional
functions ... i. Receiving or transmitting data over a network") (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)).
The claim is not patent eligible.
Regarding Claim 2,
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to a method, i.e., process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein the signal-to-noise weight (wα) = (signal strength σ2α) / (noise strength η2α), where α is a given neuron of the biological system.”
As drafted, under their broadest reasonable interpretation, cover mathematical concepts (including mathematical relationships, mathematical formulas or equations, or mathematical calculations, e.g., signal-to-noise weight). The above limitations in the context of this claim encompass, inter alia, signal-to-noise weight (wα) = (signal strength σ2α) / (noise strength η2α) (corresponding to mathematical concepts).
Step 2A Prong Two Analysis: Please see the corresponding analysis of Claim 1.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 3,
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to a method, i.e., process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein the scaled predicted first neural response is defined as r̂αi = wαvαp̂αi , where (wα) = (signal strength σ2α) / (noise strength η2α), α is a given neuron of the biological system and i is the first stimulus, and vα is a correlation between an actual neural response of the biological system to the first stimulus and the predicted first neural response of the biological system.”
As drafted, under their broadest reasonable interpretation, cover mathematical concepts (including mathematical relationships, mathematical formulas or equations, or mathematical calculations, e.g., scaled predicted first neural response). The above limitations in the context of this claim encompass, inter alia, scaling is a mathematical concept in view of the scaled predicted first neural response formula (corresponding to mathematical concepts).
Step 2A Prong Two Analysis: Please see the corresponding analysis of Claim 1.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 4,
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 4 is directed to a method, i.e., process, one of the statutory categories.
Step 2A Prong One Analysis: Please see the corresponding analysis of Claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application.
The limitations:
“wherein the neural predictive model is a convolutional neural network,”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). Specifically, they amount to mere instructions to apply the exception using a convolutional neural network (e.g., by using these elements as tools).
The limitations:
“the plurality of stimuli are a plurality of images, and”
“the first stimulus is a first image.”
As drafted, amount to insignificant extra-solution activities, which do not integrate a judicial exception into a practical application. For example, the additional elements of
"accessing stimuli" and “inputting a first stimulus” amount to mere data gathering and data storage, respectively, which are insignificant extra-solution activities that do not integrate a judicial exception into a practical application. See MPEP 2106.05(g).
Step 2B Analysis: The claim does not include additional elements that are sufficient to
amount to significantly more than the judicial exception. As discussed above with respect to
integration of the abstract idea into a practical application, all of the additional elements are
insignificant extra-solution activities or mere instructions to apply an exception. (i.e., the
additional element describes a unit for applying the abstract ideas). Insignificant extra-solution
activities and mere instructions to apply an exception cannot provide an inventive concept.
Moreover, receiving, communicating, and storing data are insignificant extra-solution activities
that are well-understood, routine, and conventional. See MPEP 2106.05(d)(II) ("The courts have
recognized the following computer functions as well-understood, routine, and conventional
functions ... i. Receiving or transmitting data over a network") (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)).
The claim is not patent eligible.
Regarding Claim 5,
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to a method, i.e., process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating, [by the neural predictive model,] a denoised population first neural response based on the plurality of denoised predicted first neural responses, wherein the denoised population first neural response is a vector of the plurality of denoised predicted first neural responses for the first stimulus.”
As drafted, under their broadest reasonable interpretation, cover concepts performed in human mind (including an observation, evaluation, judgement, or opinion, e.g., generating). The above limitations in the context of this claim encompass, inter alia, generating a denoised population first neural response (corresponding to mental processes which can be done mentally or by pen and paper).
“[repeating the inputting of the first stimulus] to generate, [by the neural predictive model,] a plurality of denoised predicted first neural responses for the first stimulus; and”
As drafted, under their broadest reasonable interpretation, cover mathematical concepts (including mathematical relationships, mathematical formulas or equations, or mathematical calculations, e.g., scaled predicted first neural response). The above limitations in the context of this claim encompass, inter alia, scaling is a mathematical concept in view of the scaled predicted first neural response formula to generate a denoised first neural response (corresponding to mathematical concepts).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application.
The limitations:
“by the neural predictive model”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). Specifically, they amount to mere instructions to apply the exception using a neural predictive model (e.g., by using these elements as tools).
The limitations:
“repeating the inputting of the first stimulus”
As drafted, amount to insignificant extra-solution activities, which do not integrate a judicial exception into a practical application. For example, the additional elements of
"inputting of the first stimulus” amount to mere data gathering and data storage, respectively, which are insignificant extra-solution activities that do not integrate a judicial exception into a practical application. See MPEP 2106.05(g).
Step 2B Analysis: The claim does not include additional elements that are sufficient to
amount to significantly more than the judicial exception. As discussed above with respect to
integration of the abstract idea into a practical application, all of the additional elements are
insignificant extra-solution activities or mere instructions to apply an exception. (i.e., the
additional element describes a unit for applying the abstract ideas). Insignificant extra-solution
activities and mere instructions to apply an exception cannot provide an inventive concept.
Moreover, receiving, communicating, and storing data are insignificant extra-solution activities
that are well-understood, routine, and conventional. See MPEP 2106.05(d)(II) ("The courts have
recognized the following computer functions as well-understood, routine, and conventional
functions ... i. Receiving or transmitting data over a network") (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)).
The claim is not patent eligible.
Regarding Claim 6,
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to a method, i.e., process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating, [by the neural predictive model,] a prediction of a second neural response of a biological system to the second stimulus;”
“generating, [by the neural predictive model,] a denoised population second neural response based on the plurality of denoised predicted second neural responses, wherein the denoised population second neural response is a vector of the plurality of denoised predicted second neural responses for the second stimulus.”
As drafted, under their broadest reasonable interpretation, cover concepts performed in human mind (including an observation, evaluation, judgement, or opinion, e.g., generating). The above limitations in the context of this claim encompass, inter alia, generating a prediction (corresponding to mental processes which can be done mentally or by pen and paper).
“scaling, [by the neural predictive model,] the predicted second neural response with a signal-to-noise weight to generate a denoised predicted second neural response; and”
“[repeating the inputting of the second stimulus] to generate, [by the neural predictive model,] a plurality of denoised predicted second neural responses for the first stimulus; and”
As drafted, under their broadest reasonable interpretation, cover mathematical concepts (including mathematical relationships, mathematical formulas or equations, or mathematical calculations, e.g., scaling). The above limitations in the context of this claim encompass, inter alia, scaling is a mathematical concept in view of how scaling is described in paragraph [0074] of the Specification, where scaling is shown by performing this mathematical equation of wa = σa / ηa and in view of the scaled predicted second neural response formula to generate a denoised second neural response (corresponding to mathematical concepts).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application.
The limitations:
“by a computing system”
“by the neural predictive model”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). Specifically, they amount to mere instructions to apply the exception using a neural predictive model (e.g., by using these elements as tools).
The limitations:
“inputting, by the computing system, a second stimulus of the plurality of stimuli into a neural predictive model;”
“repeating the inputting of the second stimulus”
As drafted, amount to insignificant extra-solution activities, which do not integrate a judicial exception into a practical application. For example, the additional elements of
"accessing stimuli", “inputting a first stimulus” and “providing the denoised predicted first neural response” amount to mere data gathering and data storage, respectively, which are insignificant extra-solution activities that do not integrate a judicial exception into a
practical application. See MPEP 2106.05(g).
Step 2B Analysis: The claim does not include additional elements that are sufficient to
amount to significantly more than the judicial exception. As discussed above with respect to
integration of the abstract idea into a practical application, all of the additional elements are
insignificant extra-solution activities or mere instructions to apply an exception. (i.e., the
additional element describes a unit for applying the abstract ideas). Insignificant extra-solution
activities and mere instructions to apply an exception cannot provide an inventive concept.
Moreover, receiving, communicating, and storing data are insignificant extra-solution activities
that are well-understood, routine, and conventional. See MPEP 2106.05(d)(II) ("The courts have
recognized the following computer functions as well-understood, routine, and conventional
functions ... i. Receiving or transmitting data over a network") (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)).
The claim is not patent eligible.
Regarding Claim 7,
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 7 is directed to a method, i.e., process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“shifting and normalizing, [by the neural predictive model,] the denoised population first neural response and the denoised population second neural response to create a centered unit vector for each of the denoised population first neural response and the denoised population second neural response; and”
“constructing a similarity matrix using the centered unit vector for each of the denoised population first neural response and the denoised population second neural response based on a representation similarity metric.”
As drafted, under their broadest reasonable interpretation, cover concepts performed in human mind (including an observation, evaluation, judgement, or opinion, e.g., shifting, normalizing, constructing). The above limitations in the context of this claim encompass, inter alia, shifting and normalizing responses and constructing a similarity matrix (corresponding to mental processes which can be done mentally or by pen and paper).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application.
The limitations:
“by the neural predictive model”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). Specifically, they amount to mere instructions to apply the exception using a neural predictive model (e.g., by using these elements as tools).
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 8,
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 8 is directed to a method, i.e., process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein the representation similarity metric is Sijmodel = êi·êj for the first image and the second image, where ei=(ri−r)/(∥ri−r∥) where r=Ei[ri] and ej=(rj−r)/(∥rj−r∥) where r=Ej[rj].”
As drafted, under their broadest reasonable interpretation, cover mathematical concepts (including mathematical relationships, mathematical formulas or equations, or mathematical calculations, e.g., similarity metric formula). The above limitations in the context of this claim encompass, inter alia, the similarity metric formula (corresponding to mathematical concepts).
Step 2A Prong Two Analysis: Please see the corresponding analysis of Claim 1.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 16,
Claim 16 recites a method for performing steps substantially similar to those of claim 1 and is rejected with the same rationale, mutatis mutandis.
Regarding Claim 17,
Claim 17 recites a method for performing steps substantially similar to those of claim 2 and is rejected with the same rationale, mutatis mutandis.
Regarding Claim 18,
Claim 18 recites a method for performing steps substantially similar to those of claim 3 and is rejected with the same rationale, mutatis mutandis.
Regarding Claim 19,
Claim 19 recites a method for performing steps substantially similar to those of claim 4 and is rejected with the same rationale, mutatis mutandis.
Regarding Claim 20,
Claim 20 recites a method for performing steps substantially similar to those of claim 5 and is rejected with the same rationale, mutatis mutandis.
Regarding Claim 21,
Claim 21 recites a system for performing steps similar of claim 1 and is rejected with the same rationale, mutatis mutandis, in view of the following additional elements, considered individually and as an ordered combination with the additional elements identified above, failing to integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
“one or more processors; and”
“a memory coupled to the one or more processors, the memory storing a plurality of instructions executable by the one or more processors, the plurality of instructions comprising instructions that when executed by the one or more processors cause the one or more processors to perform the following operations:”
This is a recitation of generic computer components to be used in performing the abstract idea, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).
Regarding Claim 22,
Claim 22 recites a method for performing steps substantially similar to those of claim 2 and is rejected with the same rationale, mutatis mutandis.
Regarding Claim 23,
Claim 23 recites a method for performing steps substantially similar to those of claim 3 and is rejected with the same rationale, mutatis mutandis.
Regarding Claim 24,
Claim 24 recites a method for performing steps substantially similar to those of claim 4 and is rejected with the same rationale, mutatis mutandis.
Regarding Claim 25,
Claim 25 recites a method for performing steps substantially similar to those of claim 5 and is rejected with the same rationale, mutatis mutandis.
Regarding Claim 26,
Claim 26 recites a method for performing steps substantially similar to those of claim 6 and is rejected with the same rationale, mutatis mutandis.
Regarding Claim 27,
Claim 27 recites a method for performing steps substantially similar to those of claims 7 and 8 and is rejected with the same rationale, mutatis mutandis.
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.
Claims 1, 4, 5, 16, 19, 20, 21, 24 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Khaligh-Razavi et al. (Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation); hereinafter Khaligh-Razavi in view of Malik et al., (US20120093376A1); hereinafter Malik
Claim 1 is rejected over Khaligh-Razavi and Malik.
Regarding claim 1, Khaligh-Razavi teaches a method comprising:
accessing, by a computing system, a plurality of stimuli for a stimulus scheme; (“We used the experimental stimuli from Kriegeskorte et al. [7]. The stimuli were 96 images which half were animates and the other half were inanimates. The animate cluster consisted of faces and bodies, and the inanimate cluster consisted of natural and artificial inanimates.”; page 23, Stimuli and response measurements)
inputting, by the computing system, a first stimulus of the plurality of stimuli into a neural predictive model; (“we tested a deep supervised convolutional neural network [41], trained by supervision with over a million category-labeled images from ImageNet [49].”; page 2, paragraph 4)
generating, by the neural predictive model, a prediction of a first neural response of a biological system to the first stimulus; (Evaluating a computational model requires a framework for relating brain representations and model representations. One approach is to directly predict the brain responses to a set of stimuli by means of the computational models.”; page 2; Note: The set of stimuli is the first stimulus and the computational models are the neural predictive model.)
Khaligh-Razavi does not teach scaling, [by the neural predictive model,] the [predicted] first neural response with a signal-to-noise weight to generate a denoised [predicted] first neural response; and
providing, by the computing system, the denoised [predicted] first neural response.
However, Malik teaches scaling, [by the neural predictive model,] the [predicted] first neural response with a signal-to-noise weight to generate a denoised [predicted] first neural response; and (“To use the signal plus colored noise (SCN) model in Eqs. 1-3 to reduce noise in calcium imaging data, estimate its parameters β=(μ,a1,b1,…,ah,bh), α=(α1,…,αp) and σ2 by maximum likelihood using a cyclic descent algorithm. The cyclic descent algorithm provides an efficient approach for solving this nonlinear estimation problem by iterating between computing the solutions to two highly tractable linear estimation problems. That is, at iteration l, given Wˆ-1(l-1) the estimate of the inverse of the covariance matrix of vk from iteration l-1, the algorithm computes β^(l), the weighted least-squares estimate of β. The cyclic descent algorithm for joint estimation of harmonic and autoregressive coefficient vectors, β^ and α^, from data vector f, is as follows.”; [0033]; Note: See [0036] to see that the formula uses the inverse noise weighting W-1 to help contribute to reducing the influence of noise in calcium imaging data.
providing, by the computing system, the denoised [predicted] first neural response. (“Denoised images can be constructed using the signal component estimate, ŝk, at each pixel. A comparison of the fluorescence response estimates of pixels around a cell obtained with conventional across-trial averaging and with the SCN model (FIG. 4 a) demonstrates the enhanced image contrast and clarity provided by the model. This denoising method delineates the stimulus response within the cell soma and allows improved observation of calcium dynamics around the cell associated with excitation.“; [0064]’ The processor then formulates 218 the selected parameters (filter) to be applied to the noise. The processor then applies 220 this to the data to remove the noise from the image data which can then be displayed 222. From this denoised image additional details can be generated a metric 224 characterizing the quality of the “; [0070], Figures 7 and 8, 218-222)
It would have been obvious before the effective filing date to combine the predicted neural response to stimuli of Khaligh-Razavi with the neural activity denoising of image data of Malik to efficiently separate neural activity from background activity and noise (Malik, [0006]). Khaligh-Razavi and Malik are analogous art because they concern analyzing neural responses to stimuli.
Claim 4 is rejected over Khaligh-Razavi and Malik with the incorporation of claim 1.
Regarding claim 4, Khaligh-Razavi teaches wherein the neural predictive model is a convolutional neural network, the plurality of stimuli are a plurality of images, and the first stimulus is a first image. (“we tested a deep supervised convolutional neural network [41], trained by supervision with over a million category-labeled images from ImageNet [49].”; page 2, col. 2, paragraph 2)
Claim 5 is rejected over Khaligh-Razavi and Malik with the incorporation of claim 1.
Regarding claim 5, Khaligh-Razavi does not teach repeating the inputting of the first stimulus to generate, by the neural predictive model, a plurality of denoised predicted first neural responses for the first stimulus; and
generating, by the neural predictive model, a denoised population first neural response based on the plurality of denoised predicted first neural responses, wherein the denoised population first neural response is a vector of the plurality of denoised predicted first neural responses for the first stimulus.
However, Malik teaches repeating the inputting of the first stimulus to generate, by the neural predictive model, a plurality of denoised predicted first neural responses for the first stimulus; and (“The stimulation method used square-wave gratings with 100% contrast which drifted at 3 Hz orthogonally to the orientation and rotated by 10° every second (each data frame). That is, the stimulus rotated 360° in 36 sec. The time series of the response of a neuron to this stimulus approximated a full orientation tuning curve. This stimulus was repeated three times in this particular embodiment.”; [0029])
generating, by the neural predictive model, a denoised population first neural response based on the plurality of denoised predicted first neural responses, wherein the denoised population first neural response is a vector of the plurality of denoised predicted first neural responses for the first stimulus. (“The SCN model can be used to characterize the relative fluorescence response to the stimulus at a single pixel. The close fit between the data and the signal estimate establishes the validity of this model (FIG. 3Ba). The signal component, ŝk, provides a denoised estimate of the response for three trials of stimulus presentation (FIG. 3Bb).”; [0061]; and “Denoised images can be constructed using the signal component estimate, ŝk, at each pixel.”; [0064]; “Third, the time-series of neural responses in each pixel can be modelled separately, and does not consider inter-pixel dependencies.”; [0027]; Note: Each pixel has a time-series vector of neural activity.)
It would have been obvious before the effective filing date to combine the predicted neural response to stimuli of Khaligh-Razavi with the neural activity denoising of image data of Malik to efficiently separate neural activity from background activity and noise (Malik, [0006]). Khaligh-Razavi and Malik are analogous art because they concern analyzing neural responses to stimuli.
Claim 16 recites a method for performing steps substantially similar to those of claim 1 and is rejected with the same rationale, mutatis mutandis.
Claim 19 recites a method for performing steps substantially similar to those of claim 4 and is rejected with the same rationale, mutatis mutandis.
Claim 20 recites a method for performing steps substantially similar to those of claim 5 and is rejected with the same rationale, mutatis mutandis.
Claim 21 recites a system for performing steps substantially similar to those of claim 1 and is rejected with the same rationale, mutatis mutandis.
Claim 24 recites a system for performing steps substantially similar to those of claim 4 and is rejected with the same rationale, mutatis mutandis.
Claim 25 recites a system for performing steps substantially similar to those of claim 5 and is rejected with the same rationale, mutatis mutandis.
Claims 2, 17 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Khaligh-Razavi and Malik in view of Czanner et al. (Measuring the signal-to-noise ratio of a neuron); hereinafter Czanner
Claim 2 is rejected over Khaligh-Razavi, Malik and Czanner with the incorporation of claim 1.
Regarding claim 2, Khaligh-Razavi does not teach wherein the signal-to-noise weight (wα)=(signal strength σ2α)/(noise strength η2α), where α is a given neuron of the biological system.
However, Czanner teaches wherein the signal-to-noise weight (wα)=(signal strength σ2α)/(noise strength η2α), where α is a given neuron of the biological system. (See page 7142 of Czannera to see that SNR is defined as SNR = (σ2signal)/(σ2noise), and α is interpreted as a time step = each neuron.)
It would have been obvious before the effective filing date to combine the predicted neural response to stimuli of Khaligh-Razavi with the signal-to-noise ratio of Czanner to effectively analyze neural responses to stimuli (Czanner, 7145). Khaligh-Razavi and Czanner are analogous art because they both concern analyzing neural responses to stimuli.
Claim 17 recites a method for performing steps substantially similar to those of claim 2 and is rejected with the same rationale, mutatis mutandis.
Claim 22 recites a system for performing steps substantially similar to those of claim 2 and is rejected with the same rationale, mutatis mutandis.
Claims 3, 18 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Khaligh-Razavi, Malik and Czanner in view of Hsu et al. (Quantifying variability in neural responses and its application for the validation of model predictions); hereinafter Hsu
Claim 3 is rejected over Khaligh-Razavi, Malik, Czanner and Hsu with the incorporation of claim 1.
Regarding claim 3, Khaligh-Razavi does not teach wherein the scaled predicted first neural response is defined as r̂α1=wαvαp̂αi, where (wα)=(signal strength σ2α)/(noise strength η2α), α is a given neuron of the biological system and i is the first stimulus, and
However, Czanner teaches wherein the scaled predicted first neural response is defined as r̂α1=wαvαp̂αi, where (wα)=(signal strength σ2α)/(noise strength η2α),
α is a given neuron of the biological system and i is the first stimulus, and (“Hence, the definition of SNR must account for the extent to which a neuron’s spiking responses are due to the applied stimulus or signal and to these intrinsic biophysical properties “See page 7142 of Czannera to see that SNR is defined as SNR = (σ2signal)/(σ2noise), and α is interpreted as a time step = each neuron.)
It would have been obvious before the effective filing date to combine the predicted neural response to stimuli of Khaligh-Razavi with the signal-to-noise ratio of Czanner to effectively analyze neural responses to stimuli (Czanner, 7145). Khaligh-Razavi and Czanner are analogous art because they both concern analyzing neural responses to stimuli.
Khaligh-Razavi does not teach vα is a correlation between an actual neural response of the biological system to the first stimulus and the predicted first neural response of the biological system.
However, Hsu teaches vα is a correlation between an actual neural response of the biological system to the first stimulus and the predicted first neural response of the biological system. (“To validate these models of stimulus–response functions, the predicted response must be compared with the actual response, the time-varying mean firing rate being estimated from real spike data. In previous studies, this comparison was often quantified using the correlation coefficient or the coherence between the predicted response and an estimation of the actual response obtained by averaging the spike data”; page 92, paragraph 3)
It would have been obvious before the effective filing date to combine the predicted neural response to stimuli of Khaligh-Razavi with the predicted neural response to stimuli of Khaligh-Razavi with the correlation coefficient or the coherence between the predicted response and an estimation of the actual response of Hsu to effectively quantify the comparison of the predicted response and the actual response (Hsu, page 92). Khaligh-Razavi and Hsu are analogous art because they both concern analysis of neural responses to stimuli.
Claim 18 recites a method for performing steps substantially similar to those of claim 3 and is rejected with the same rationale, mutatis mutandis.
Claim 23 recites a system for performing steps substantially similar to those of claim 3 and is rejected with the same rationale, mutatis mutandis.
Claims 6, 7, 8, 26 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Khaligh-Razavi and Malik in view of Watson et al. (Patterns of response to visual scenes are linked to the low-level properties of the image); hereinafter Watson
Claim 6 is rejected over Khaligh-Razavi, Malik and Watson with the incorporation of claim 1.
Regarding claim 6, Khaligh-Razavi does not teach inputting, by the computing system, a second stimulus of the plurality of stimuli [into the neural predictive model;]
generating, by the neural predictive model, a prediction of a second neural response of the biological system to the second stimulus;
However, Watson teaches by the computing system, a second stimulus of the plurality of stimuli [into the neural predictive model;] (“To determine whether this variation in the categorical response to scenes could reflect variation in the low-level image properties, we measured the similarity of images from each category of scene. Although we found that the low-level properties of images from each category were more similar to each other than to other categories of scenes, we also found that the magnitude of the within-category similarity varied across different scenes.”; Abstract)
generating, by the neural predictive model, a prediction of a second neural response of the biological system to the second stimulus; (“Finally, we compared variation in the neural response to different categories of scenes with corresponding variation in the low-level image properties.”; Abstract; Note: The first image can correspond to city scenes and the second image can correspond to indoor scenes as shown in Figure 5 of Watson)
It would have been obvious before the effective filing date to combine the predicted neural response to stimuli of Khaligh-Razavi with the predicted neural response to stimuli of Khaligh-Razavi with the multiple images of scenery of Watson for effective comparison of neural activity to multiple images (Watson, page 404, column 2). Khaligh-Razavi and Watson are analogous art because they both concern neural response to stimuli.
Khaligh-Razavi does not teach scaling, by the neural predictive model, the predicted second neural response with the signal-to-noise weight to generate a denoised predicted second neural response;
repeating the inputting of the second stimulus to generate, by the neural predictive model, a plurality of denoised predicted second neural responses for the second stimulus; and
generating, by the neural predictive model, a denoised population second neural response based on the plurality of denoised predicted second neural responses, wherein the denoised population second neural response is a vector of plurality of denoised predicted second neural responses for the second stimulus.
However, Malik teaches scaling, by the neural predictive model, the predicted second neural response with the signal-to-noise weight to generate a denoised predicted second neural response; (“To use the signal plus colored noise (SCN) model in Eqs. 1-3 to reduce noise in calcium imaging data, estimate its parameters β=(μ,a1,b1,…,ah,bh), α=(α1,…,αp) and σ2 by maximum likelihood using a cyclic descent algorithm. The cyclic descent algorithm provides an efficient approach for solving this nonlinear estimation problem by iterating between computing the solutions to two highly tractable linear estimation problems. That is, at iteration l, given Wˆ-1(l-1) the estimate of the inverse of the covariance matrix of vk from iteration l-1, the algorithm computes β^(l), the weighted least-squares estimate of β. The cyclic descent algorithm for joint estimation of harmonic and autoregressive coefficient vectors, β^ and α^, from data vector f, is as follows.”; [0033]; Note: See [0036] to see that the formula uses the inverse noise weighting W-1 to help contribute to reducing the influence of noise in calcium imaging data.
repeating the inputting of the second stimulus to generate, by the neural predictive model, a plurality of denoised predicted second neural responses for the second stimulus; and (“The stimulation method used square-wave gratings with 100% contrast which drifted at 3 Hz orthogonally to the orientation and rotated by 10° every second (each data frame). That is, the stimulus rotated 360° in 36 sec. The time series of the response of a neuron to this stimulus approximated a full orientation tuning curve. This stimulus was repeated three times in this particular embodiment.”; [0029])
generating, by the neural predictive model, a denoised population second neural response based on the plurality of denoised predicted second neural responses, wherein the denoised population second neural response is a vector of plurality of denoised predicted second neural responses for the second stimulus. (“The SCN model can be used to characterize the relative fluorescence response to the stimulus at a single pixel. The close fit between the data and the signal estimate establishes the validity of this model (FIG. 3Ba). The signal component, ŝk, provides a denoised estimate of the response for three trials of stimulus presentation (FIG. 3Bb).”; [0061]; and “Denoised images can be constructed using the signal component estimate, ŝk, at each pixel.”; [0064]; “Third, the time-series of neural responses in each pixel can be modelled separately, and does not consider inter-pixel dependencies.”; [0027]; Note: Each pixel has a time-series vector of neural activity.)
It would have been obvious before the effective filing date to combine the predicted neural response to stimuli of Khaligh-Razavi with the neural activity denoising of image data of Malik to efficiently separate neural activity from background activity and noise (Malik, [0006]). Khaligh-Razavi and Malik are analogous art because they concern analyzing neural responses to stimuli.
Claim 7 is rejected over Khaligh-Razavi, Malik and Watson with the incorporation of claim 1.
Regarding claim 7, Khaligh-Razavi does not teach shifting and normalizing, by the neural predictive model, the denoised population first neural response and the denoised population second neural response to create a centered unit vector for each of the [denoised population] first neural response and the denoised population second neural response; and
constructing a similarity matrix using the centered unit vector for each of the denoised population first neural response and the denoised population second neural response based on a representation similarity metric.
However, Watson teaches shifting and normalizing, by the neural predictive model, the denoised population first neural response and the denoised population second neural response to create a centered unit vector for each of the [denoised population] first neural response and the denoised population second neural response; and (“Parameter estimates from the univariate analysis were normalized by subtracting the response to the mixed condition. Pattern analyses were then performed using the PyMVPA toolbox (Hanke et al., 2009). Fig. 2 illustrates the method for determining the reliability of these neural patterns within and across subjects. To determine the reliability of the data within individual participants, the parameter estimates for each scene condition were correlated across odd (1, 3, 5, 7) and even (2, 4, 6, 8) blocks across all voxels in the scene-selective region (Haxby et al., 2001).”; page 404, fMRI Analysis, column 1; and “A Fisher's z-transformation was applied to the within-category and between category correlations prior to further statistical analyses. For each category, the within-category and the average of the between-category correlations were calculated. These were entered into 3 × 2 repeated ANOVAs with the scene category (Experiment 1: city, indoor, natural; Experiment 2: coast, forest, mountain) and comparison (within, between) as the main factors.”; page 404, column 2)
constructing a similarity matrix using the centered unit vector for each of the denoised population first neural response and the denoised population second neural response based on a representation similarity metric. (“To determine the reliability of the data within individual participants, the parameter estimates for each scene condition were correlated across odd (1, 3, 5, 7) and even (2, 4, 6, 8) blocks across all voxels in the scene-selective region (Haxby et al., 2001)”; page 404, fMRI Analysis, column 1; Note: See Figure 5 of Watson to see the similarity matrices of neural response patterns to scene categories from fMRI data)
It would have been obvious before the effective filing date to combine the predicted neural response to stimuli of Khaligh-Razavi with the predicted neural response to stimuli of Khaligh-Razavi with the similarity matrix consisting of multiple images of scenery of Watson for effective comparison of neural activity to multiple images (Watson, page 404, column 2). Khaligh-Razavi and Watson are analogous art because they both concern neural response to stimuli.
Claim 8 is rejected over Khaligh-Razavi, Malik and Watson with the incorporation of claim 1.
Regarding claim 8, Khaligh-Razavi does not teach wherein the representation similarity metric is Sijmodel=êi·êj for the first image and the second image, where ei=(ri−r)/(∥ri−r∥) where r̄=Ei[ri] and ej=(rj−r)/(∥rj−r∥) where r̄ =Ej[rj].
However, Watson teaches wherein the representation similarity metric is Sijmodel=êi·êj for the first image and the second image, where ei=(ri−r)/(∥ri−r∥) where r̄=Ei[ri] and ej=(rj−r)/(∥rj−r∥) where r̄ =Ej[rj]. (“To determine the reliability of the data within individual participants, the parameter estimates for each scene condition were correlated across odd (1, 3, 5, 7) and even (2, 4, 6, 8) blocks across all voxels in the scene-selective region (Haxby et al., 2001)”; page 404, fMRI Analysis, column 1; Note: The first image can correspond to city scenes and the second image can correspond to indoor scenes as shown Figure 5 of Watson)
It would have been obvious before the effective filing date to combine the predicted neural response to stimuli of Khaligh-Razavi with the predicted neural response to stimuli of Khaligh-Razavi with the similarity matrix consisting of multiple images of scenery of Watson for effective comparison of neural activity to multiple images (Watson, page 404, column 2). Khaligh-Razavi and Watson are analogous art because they both concern neural response to stimuli.
Claim 26 recites a system for performing steps substantially similar to those of claim 6 and is rejected with the same rationale, mutatis mutandis.
Claim 27 recites a system for performing steps substantially similar to those of claim 7 and 8 and is rejected with the same rationale, mutatis mutandis.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID H TRAN whose telephone number is (703)756-1525. The examiner can normally be reached M-F 9:30 am - 5:30 pm.
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, Viker Lamardo can be reached at (571) 270-5871. The fax phone number for the organization where this application or pro